0\",\n \"def process_text(self):\\n prp1 = preprocessor.Preprocess()\\n processed_text = prp1.clean_data(self.text)\\n self.vec1 = self.vec.transform(pd.Series(processed_text))\",\n \"def feat():\\n pass\",\n \"def TransformData(text):\\n global COUNT_VECTORIZER\\n if COUNT_VECTORIZER is None:\\n COUNT_VECTORIZER = CountVectorizer(analyzer = 'word', lowercase = True)\\n COUNT_VECTORIZER.fit(text)\\n features = COUNT_VECTORIZER.transform(text)\\n features_nd = features.toarray() # for easy usage\\n global TFIDF\\n if TFIDF is None:\\n TFIDF = TfidfTransformer(use_idf=False)\\n TFIDF.fit(features_nd)\\n text_tfidf = TFIDF.transform(features_nd)\\n return text_tfidf\",\n \"def analyze(self, word_count_thresh):\",\n \"def evaluate(self, features, labels):\\n raise NotImplementedError('Not implemented')\",\n \"def classify(model, featuresFile='tmp/features.txt'):\\n\\n # Use external svm_classify to classify audio using the given features\\n subprocess.call(['svm_classify', featuresFile, model, 'tmp/result.txt'])\\n\\n # Read results\\n results = []\\n with open('tmp/result.txt', 'r') as f:\\n results = f.readlines()\\n for i in range(0, len(results)):\\n results[i] = 'Music' if float(results[i]) > 0 else 'Speech'\\n\\n return results\",\n \"def main(args):\\n bad_words_file = codecs.open(args.language + \\\"/feature_files/bad_words\\\", \\\"r\\\", \\\"utf-8\\\").readlines()\\n bad_words = read_known_words(bad_words_file)\\n \\n good_words_file = codecs.open(args.language + \\\"/feature_files/good_words\\\", \\\"r\\\", \\\"utf-8\\\").readlines()\\n good_words = read_known_words(good_words_file)\\n\\n curse_words_file = codecs.open(args.language + \\\"/feature_files/curse_words\\\", \\\"r\\\", \\\"utf-8\\\").readlines()\\n curse_words = read_known_words(curse_words_file)\\n\\n prepositions_file = codecs.open(args.language + \\\"/feature_files/prepositions\\\", \\\"r\\\", \\\"utf-8\\\").readlines()\\n prepositions = read_known_words(prepositions_file)\\n\\n determiners_file = codecs.open(args.language + \\\"/feature_files/determiners\\\", \\\"r\\\", \\\"utf-8\\\").readlines()\\n determiners = read_known_words(determiners_file)\\n\\n syllables_file = codecs.open(args.language + \\\"/feature_files/syllables\\\", \\\"r\\\", \\\"utf-8\\\").readlines()\\n syllable_structure = read_syllables_file(syllables_file)\\n\\n other_feature_files = glob.glob(args.language + \\\"/feature_files/*.txt\\\")\\n other_features = set_features_from_files(other_feature_files)\\n \\n ermaObj = ConllToErma(args, bad_words, good_words, curse_words, prepositions, \\\\\\n determiners, syllable_structure, other_features)\\n\\n if not args.just_test:\\n # Input training file.\\n train_id = open(args.train, \\\"r\\\")\\n train = train_id.readlines()\\n train_id.close()\\n sys.stdout.write(\\\"Reading training file...\\\\n\\\")\\n (train_features, train_skip_chains) = ermaObj.read_conll_file(train)\\n sys.stdout.write(\\\"Building model...\\\\n\\\")\\n train_hash = ermaObj.make_nodes(train_features)\\n # Freeze the known features based on what's seen in the training data\\n ermaObj.cutoff_features()\\n else:\\n train_hash = {}\\n train_skip_chains = {}\\n # Input testing file.\\n test_id = open(args.test, \\\"r\\\")\\n test = test_id.readlines()\\n test_id.close()\\n sys.stdout.write(\\\"Reading test file...\\\\n\\\")\\n (test_features, test_skip_chains) = ermaObj.read_conll_file(test)\\n sys.stdout.write(\\\"Building model...\\\\n\\\")\\n test_hash = ermaObj.make_nodes(test_features, test=True)\\n ermaObj.write_out(train_hash, train_skip_chains, test_hash, test_skip_chains)\",\n \"def analyze_text(self):\\n\\n # Read stylesheet.\\n found_stylesheet = False\\n for filename in self.project.namelist():\\n if os.path.basename(filename) == 'styles.xml':\\n found_stylesheet = True\\n style = self.project.open(filename, 'r')\\n self._read_stylesheet(style)\\n if not found_stylesheet:\\n raise IOError('stylesheet not found')\\n\\n # Process text data.\\n for filename in self.project.namelist():\\n if filename.endswith('.usx'):\\n usx = self.project.open(filename, 'r')\\n for text in self._process_usx_file(usx):\\n yield text\\n # self.exemplars.process(text)\\n # self.corpus.write(text + '\\\\n')\",\n \"def preprocess_training_text(text, accented_chars=True, \\n convert_num=False, extra_whitespace=True, \\n lemmatization=True, lowercase=True, punctuations=True,\\n remove_html=True, remove_num=True, special_chars=True, \\n stop_words=True):\\n \\n\\n \\\"\\\"\\\"preprocess text with default option set to true for all steps\\\"\\\"\\\"\\n if remove_html == True: #remove html tags\\n text = strip_html_tags(text)\\n if extra_whitespace == True: #remove extra whitespaces\\n text = remove_whitespace(text)\\n if accented_chars == True: #remove accented characters\\n text = remove_accented_chars(text)\\n if lowercase == True: #convert all characters to lowercase\\n text = text.lower()\\n \\n \\n doc = nlp(text) #tokenise text\\n\\n\\n clean_text = []\\n for token in doc:\\n flag = True\\n edit = token.text\\n # print(\\\"Word: \\\", edit, \\\" Type: \\\", token.pos_)\\n # remove stop words\\n if stop_words == True and token.is_stop and token.pos_ != 'NUM': \\n flag = False\\n # remove punctuations\\n if punctuations == True and (token.pos_ == 'PUNCT') and flag == True: \\n flag = False\\n \\n # remove 'X' characters:\\n if token.pos_ == 'X':\\n flag = False\\n # remove special characters\\n if special_chars == True and token.pos_ == 'SYM' and flag == True: \\n flag = False\\n # remove numbers\\n if remove_num == True and (token.pos_ == 'NUM' or token.text.isnumeric()) \\\\\\n and flag == True:\\n flag = False\\n # convert number words to numeric numbers\\n if convert_num == True and token.pos_ == 'NUM' and flag == True:\\n edit = w2n.word_to_num(token.text)\\n # convert tokens to base form\\n elif lemmatization == True and token.lemma_ != \\\"-PRON-\\\" and flag == True:\\n edit = token.lemma_\\n # append tokens edited and not removed to list \\n if edit != \\\"\\\" and flag == True:\\n clean_text.append(edit)\\n \\n # Convert back to string:\\n new_text = ' '.join(clean_text)\\n regex = re.compile('[^a-zA-Z]')\\n new_text = regex.sub(' ', new_text)\\n words = re.findall(r'\\\\w+.', new_text)\\n return ' '.join(words)\",\n \"def findFeatures(self):\\n\\t\\tpass\",\n \"def enhance_metadata(metadata, features='all'):\\n\\n # available options\\n ortographic_features = ['w_length','n_vowels','n_consonants']\\n lexical_features = ['uni_freq', 'bi_freq', 'func_word','count']\\n position_features = ['position','position_end','is_first_word','is_last_word']\\n\\n # make list of features\\n if features == 'all': features = ortographic_features +lexical_features + position_features \\n\\n # use ws clean to lower case\\n words = [word.lower() for word in metadata['word'].values]\\n\\n # itereate features and fill metadata\\n for feature in features:\\n # ORTOGRAPHIC ##############################\\n if feature == 'w_length': \\n metadata[feature] = w_length(words)\\n if feature == 'n_consonants':\\n metadata[feature] = n_consonants(words)\\n if feature == 'n_vowels':\\n metadata[feature] = n_vowels(words)\\n\\n # LEXICAL ###################################\\n if feature == 'uni_freq':\\n metadata[feature] = unigram(words)\\n if feature == 'bi_freq':\\n metadata[feature] = bigram(words)\\n if feature == 'func_word':\\n metadata[feature] = function_word(words)\\n if feature == 'count':\\n metadata[feature] = count(words)\\n\\n # POSITION ###################################\\n if feature == 'position':\\n metadata[feature] = position(words)\\n if feature == 'position_end':\\n metadata[feature] = position_end(words)\\n if feature == 'is_first_word':\\n metadata[feature] = first_word(words)\\n if feature == 'is_last_word':\\n metadata[feature] = last_word(words)\\n\\n return metadata\",\n \"def analyze(self, texts, analyses, external_entity_data=None,\\n single_document=False):\\n url = urljoin(self.base_url, '/analyze/')\\n data = {'texts': texts, 'analyses': analyses}\\n if external_entity_data is not None:\\n data['absa'] = {'external_entity_data': external_entity_data}\\n if single_document:\\n data['single_document'] = True\\n res = _analysis(url, self.auth, **data)\\n return res.json()\",\n \"def main(args: List[str]):\\n argv = {a.split('=')[0]: a.split('=')[1] for a in args[1:]}\\n\\n load_path = argv.get('load_path', None)\\n assert load_path, \\\"No load_path specified\\\"\\n\\n batch_size = int(argv.get('batch_size', 128))\\n\\n device = argv.get('device', None)\\n\\n text_path = argv.get('text_path', None)\\n\\n hashtag_analysis = argv.get('hashtag', 'true').lower()\\n assert hashtag_analysis in ['true', 'false']\\n hashtag_analysis = False if 'f' in hashtag_analysis else True\\n\\n fast_tokenizer = argv.get('fast_tokenizer', 'false').lower()\\n assert fast_tokenizer in ['true', 'false']\\n fast_tokenizer = False if 'f' in fast_tokenizer else True\\n\\n if text_path is None:\\n data_path = get_data_path()\\n _text_path = Path(data_path, 'test_data.txt')\\n if _text_path.is_file():\\n text_path = _text_path\\n else:\\n logger.error(\\\"No text_path specified\\\")\\n exit(0)\\n\\n logger.info(f\\\"Predicting sentiment from data inside {text_path}\\\")\\n\\n if not hashtag_analysis:\\n trans_predict = TransformersPredict(load_path=load_path, text_path=text_path, device=device,\\n fast_tokenizer=fast_tokenizer)\\n else:\\n freq_threshold = int(argv.get('hashtag_freq', 500))\\n prob_threshold = float(argv.get('hashtag_prob', 0.7))\\n trans_predict = TransformersPredictWithHashtag(load_path=load_path, text_path=text_path, device=device,\\n fast_tokenizer=fast_tokenizer,\\n freq_threshold=freq_threshold,\\n prob_threshold=prob_threshold)\\n trans_predict.predict(batch_size=batch_size)\\n trans_predict.submissionToFile()\",\n \"def train_routine(training_file, output_folder):\\n if output_folder[-1] != '/':\\n output_folder += '/'\\n\\n svm_file = output_folder + 'svm.txt'\\n centroid_file = output_folder + 'centroids.txt'\\n ids_file = output_folder + 'ids.txt'\\n\\n surf = cv2.SURF(250, extended=False)\\n categories = dict()\\n ids = dict()\\n id = 1\\n features = list()\\n\\n print \\\"Extracting features\\\"\\n for line in open(training_file):\\n try:\\n category, path = line.split(';')\\n except:\\n print \\\"Error: File not in proper format. Ensure: <category/class name>; <path to image of said category>\\\"\\n sys.exit(0)\\n path = path.strip()\\n\\n try:\\n img = cv2.imread(path)\\n #img = cv2.resize(img, (500, 500))\\n except Exception as e:\\n print e\\n continue\\n\\n keypoints, descriptors = surf.detectAndCompute(img, None)\\n\\n if not category in categories:\\n categories[category] = Category(label=category)\\n ids[category] = id\\n id += 1\\n categories[category].add_feature(descriptors)\\n\\n #for category in categories:\\n #f = categories[category].yield_features()\\n ##features.extend(f)\\n #for i in f:\\n #features.extend(i)\\n\\n print \\\"Calculating centroids\\\"\\n #np_features = numpy.array(features)\\n #print \\\"Features: \\\", np_features.shape\\n #centroids, labels = kmeans2(np_features, FEATURE_TYPES)\\n centroids = helpers.loadObject(output_folder + 'centroids.txt')\\n print centroids.shape\\n\\n print \\\"Forming bag of words\\\"\\n X, Y = [], []\\n for category in categories:\\n categories[category].calc_bagofwords(centroids)\\n for bow in categories[category].bagofwords:\\n X.append(bow)\\n Y.append(ids[category])\\n print \\\"Fitting linear SVMs onto the bag of words\\\"\\n lin_clf = svm.LinearSVC()\\n lin_clf.fit(X, Y)\\n\\n helpers.saveObject(lin_clf, svm_file)\\n helpers.saveObject(centroids, centroid_file)\\n helpers.saveObject(ids, ids_file)\",\n \"def test_text_classifier_get_testing_samples(self):\\n pass\",\n \"def features(self, tokens, index, history):\\n\\t\\t# print history\\n\\t\\t# print tokens\\n\\n\\t\\t# Pad the sequence with placeholders\\n\\t\\ttokens = [('[START2]', '[START2]'), ('[START1]', '[START1]')] + list(tokens) + [('[END1]', '[END1]'), ('[END2]', '[END2]')]\\n\\t\\thistory = ['[START2]', '[START1]'] + list(history)\\n\\n\\t\\t# shift the index with 2, to accommodate the padding\\n\\t\\tindex += 2\\n\\n\\t\\tword, pos = tokens[index]\\n\\t\\tprevword, prevpos = tokens[index - 1]\\n\\t\\tprevprevword, prevprevpos = tokens[index - 2]\\n\\t\\tnextword, nextpos = tokens[index + 1]\\n\\t\\tnextnextword, nextnextpos = tokens[index + 2]\\n\\t\\tpreviob = history[index - 1]\\n\\t\\tcontains_dash = '-' in word\\n\\t\\tcontains_dot = '.' in word\\n\\t\\tallascii = all([True for c in word if c in string.ascii_lowercase])\\n\\n\\t\\tallcaps = word == word.capitalize()\\n\\t\\tcapitalized = word[0] in string.ascii_uppercase\\n\\n\\t\\tprevallcaps = prevword == prevword.capitalize()\\n\\t\\tprevcapitalized = prevword[0] in string.ascii_uppercase\\n\\n\\t\\tnextallcaps = prevword == prevword.capitalize()\\n\\t\\tnextcapitalized = prevword[0] in string.ascii_uppercase\\n\\n\\t\\treturn [word, str(self.stemmer.stem(word)), str(pos), str(allascii), str(nextword), str(self.stemmer.stem(nextword)), str(nextpos), str(nextnextword), str(nextnextpos), str(prevword), str(self.stemmer.stem(prevword)), str(prevpos), str(prevprevword), str(prevprevpos), str(previob), str(contains_dash), str(contains_dot), str(allcaps), str(capitalized), str(prevallcaps), str(prevcapitalized), str(nextallcaps), str(nextcapitalized)]\",\n \"def analyze_text (self, testing_string): \\n self.length = len(self.testing_string)\\n self.total_words = (self.testing_string).split()\\n self.total_unique_words = set(self.total_words)\\n\\n self.total_characters = (int)(0)\\n for ch in self.testing_string :\\n if(ch.isspace() != True):\\n self.total_characters = self.total_characters + 1 \\n\\n self.total_unique_characters = set(self.testing_string)\\n \\n Linguist.about_given_string[\\\"Length\\\"] = self.length\\n Linguist.about_given_string[\\\"Total_words\\\"] = len(self.total_words)\\n Linguist.about_given_string[\\\"Total_unique_words\\\"] = len(self.total_unique_words)\\n Linguist.about_given_string[\\\"Total_characters\\\"] = self.total_characters\\n Linguist.about_given_string[\\\"Total_unique_characters\\\"] = len(self.total_unique_characters)\",\n \"def lexicon_features(tokens, feats):\\n# feats['neg_words'] = 0;\\n# feats['pos_words'] = 0;\\n# tokens = list([token.lower() for token in tokens])\\n# feats['neg_words'] , feats['pos_words'] = np.count_nonzero(np.in1d(tokens, list(neg_words))), np.count_nonzero(np.in1d(tokens, list(pos_words)))\\n neg_count=0\\n pos_count=0\\n for i in tokens:\\n if i.lower() in neg_words:\\n neg_count+=1\\n if i.lower() in pos_words:\\n pos_count+=1\\n feats[\\\"neg_words\\\"]=neg_count\\n feats[\\\"pos_words\\\"]=pos_count\",\n \"def run_feature_extraction(word2vec_model=None, fasttext_model=None, ptlkb64_model=None, glove300_model=None, numberbatch_model=None):\\n\\n\\tparser = argparse.ArgumentParser(description=__doc__)\\n\\tparser.add_argument('test', help='XML file with test data')\\n\\tparser.add_argument('output', help='Output tagged XML file')\\n\\targs = parser.parse_args()\\n\\n\\t\\\"\\\"\\\"\\n\\tsystem_mode = 0 -> uses the variant questions with the system\\n\\tsystem_mode = 1 -> uses the PTPT and PTBR train ASSIN collection datasets with the system\\n\\tsystem_mode = 2 -> uses the PTPT and PTBR train and test ASSIN collection datasets with the system\\n\\tsystem_mode = 3 -> uses the Whoosh collection with the system\\n\\tsystem_mode = 4 -> uses ASSIN 1 and ASSIN 2 training collection datasets with the system\\n\\tsystem_mode = 5 -> uses ASSIN 1 training and testing collection and ASSIN 2 training collection datasets with the system\\n\\n\\trun_pipeline = 0 -> uses the pre-computed files with the components needed to extract some features\\n\\trun_pipeline = 1 -> uses NLPyPort pipeline which avoids having to pre-compute certain components to extract features\\n\\t\\\"\\\"\\\"\\n\\n\\tsystem_mode = 5\\n\\trun_pipeline = 1\\n\\n\\t# Flag to indicate if the extracted features should be written to a file (1) or not (0)\\n\\tfeatures_to_file_flag = 0\\n\\n\\t# extract labels\\n\\ttrain_pairs = []\\n\\ttrain_pairs.extend(read_xml(ROOT_PATH + \\\"/datasets/assin/assin1/assin-ptpt-train.xml\\\", need_labels=True))\\n\\ttrain_pairs.extend(read_xml(ROOT_PATH + \\\"/datasets/assin/assin1/assin-ptbr-train.xml\\\", need_labels=True))\\n\\n\\tif system_mode == 2 or system_mode == 5:\\n\\t\\ttrain_pairs.extend(read_xml(ROOT_PATH + \\\"/datasets/assin/assin1/assin-ptpt-test.xml\\\", need_labels=True))\\n\\t\\ttrain_pairs.extend(read_xml(ROOT_PATH + \\\"/datasets/assin/assin1/assin-ptbr-test.xml\\\", need_labels=True))\\n\\tif system_mode == 4 or system_mode == 5:\\n\\t\\ttrain_pairs.extend(read_xml(ROOT_PATH + \\\"/datasets/assin/assin2/assin2-train-only.xml\\\", need_labels=True))\\n\\n\\ttrain_similarity_target = np.array([pair.similarity for pair in train_pairs])\\n\\n\\t# extract training features\\n\\ttrain_corpus = read_corpus(train_pairs)\\n\\n\\t# debug_data(train_corpus, \\\"finetune.train.raw\\\")\\n\\t# print(\\\"Wrote training corpus\\\")\\n\\n\\t# preprocessing(text, tokenization=0, rm_stopwords=0, numbers_to_text=0, to_tfidf=0)\\n\\tpreprocessed_train_corpus = preprocessing(train_corpus, 0, 0, 0, 0)\\n\\ttrain_features, used_train_features = extract_features(run_pipeline, train_corpus, preprocessed_train_corpus, word2vec_mdl=word2vec_model, fasttext_mdl=fasttext_model, ptlkb64_mdl=ptlkb64_model, glove300_mdl=glove300_model, numberbatch_mdl=numberbatch_model)\\n\\n\\t# write train features to a .csv file\\n\\tif features_to_file_flag == 1:\\n\\t\\twrite_features_to_csv(train_pairs, train_features, \\\"assin1-train-test-assin2-train-ftrain.csv\\\")\\n\\n\\t#############################################################\\n\\ttest_pairs_dev = read_xml('datasets/assin/assin2/assin2-dev.xml', need_labels=False)\\n\\n\\ttest_corpus_dev = read_corpus(test_pairs_dev)\\n\\t# preprocessing(text, tokenization=0, rm_stopwords=0, numbers_to_text=0, to_tfidf=0)\\n\\tpreprocessed_test_corpus_dev = preprocessing(test_corpus_dev, 0, 0, 0, 0)\\n\\ttest_features_dev, used_test_features_dev = extract_features(run_pipeline, test_corpus_dev, preprocessed_test_corpus_dev, word2vec_mdl=word2vec_model, fasttext_mdl=fasttext_model, ptlkb64_mdl=ptlkb64_model, glove300_mdl=glove300_model, numberbatch_mdl=numberbatch_model)\\n\\n\\ttest_pairs_selection = read_xml('datasets/assin/assin2/assin2-dev.xml', need_labels=True)\\n\\ttest_similarity_target = np.array([pair.similarity for pair in test_pairs_selection])\\n\\t#############################################################\\n\\n\\t# extract test features\\n\\t# test_pairs = read_xml(args.test, need_labels=False)\\n\\n\\t# uncomment next line and comment previous one to compute ASSIN 2 submission results\\n\\ttest_pairs = read_xml_no_attributes(args.test)\\n\\n\\ttest_corpus = read_corpus(test_pairs)\\n\\t# preprocessing(text, tokenization=0, rm_stopwords=0, numbers_to_text=0, to_tfidf=0)\\n\\tpreprocessed_test_corpus = preprocessing(test_corpus, 0, 0, 0, 0)\\n\\ttest_features, used_test_features = extract_features(run_pipeline, test_corpus, preprocessed_test_corpus, word2vec_mdl=word2vec_model, fasttext_mdl=fasttext_model, ptlkb64_mdl=ptlkb64_model, glove300_mdl=glove300_model, numberbatch_mdl=numberbatch_model)\\n\\n\\t# write test features to a .csv file\\n\\tif features_to_file_flag == 1:\\n\\t\\twrite_features_to_csv(test_pairs, test_features, \\\"assin1-train-test-assin2-train-ftest.csv\\\")\\n\\n\\t# extract test features for feature selection (labels needed in order to perform evaluation)\\n\\t# test_pairs_selection = read_xml(args.test, need_labels=True)\\n\\t# test_similarity_target = np.array([pair.similarity for pair in test_pairs_selection])\\n\\n\\t'''\\n\\tSelect one type of regressor from scikit-learn. Here is a list with some examples: \\n\\t\\t- GaussianProcessRegressor()\\n\\t\\t- DecisionTreeRegressor()\\n\\t\\t- LinearRegression()\\n\\t\\t- BaggingRegressor(n_estimators=100)\\n\\t\\t- AdaBoostRegressor(n_estimators=100)\\n\\t\\t- GradientBoostingRegressor()\\n\\t\\t- RandomForestRegressor(n_estimators=100)\\n\\t'''\\n\\n\\tregressor = SVR(gamma='scale', C=10.0, kernel='rbf')\\n\\n\\t# ensemble = VotingRegressor(estimators=[('svr', regressor_1), ('gb', regressor_2), ('rf', regressor_3)])\\n\\n\\t# params = {'svr__C': [1.0, 10.0, 100.0], 'svr__kernel': ['linear', 'poly', 'rbf', 'sigmoid'], 'rf__n_estimators': [10, 20, 100, 200]}\\n\\n\\t# params = {'kernel':('linear', 'poly', 'rbf', 'sigmoid')}\\n\\n\\t# regressor = GridSearchCV(regressor_1, params, cv=5)\\n\\n\\tuse_feature_selection = 0\\n\\n\\tif use_feature_selection:\\n\\t\\t# selected_selector, selected_train_features, selected_test_features = feature_selection(train_features, test_features_dev, train_similarity_target, test_similarity_target, regressor, used_train_features)\\n\\t\\t# selected_train_features, selected_test_features = rfe_cross_validation(train_features, train_similarity_target, test_features)\\n\\t\\tselected_train_features, selected_test_features, percentile_selector = aux_best_percentile_selector(train_features, test_features_dev, train_similarity_target, test_similarity_target, regressor, used_train_features)\\n\\n\\t\\ttest_features_selected = percentile_selector.transform(test_features)\\n\\t\\t# test_features_selected = selected_selector.transform(test_features)\\n\\n\\t\\tregressor.fit(selected_train_features, train_similarity_target)\\n\\n\\t\\t# save model to disk\\n\\t\\tmodel_save_path = os.path.join('trained_models', 'SVR_FS.joblib')\\n\\t\\tdump(regressor, model_save_path)\\n\\n\\t\\t# apply model to the test dataset\\n\\t\\t## this needs to be fixed in order to take advantage of the manual feature selection\\n\\t\\tpredicted_similarity = regressor.predict(test_features_selected)\\n\\t\\t# predicted_similarity = regressor.predict(test_features_selected)\\n\\telse:\\n\\t\\tregressor.fit(train_features, train_similarity_target)\\n\\n\\t\\t# save model to disk\\n\\t\\tmodel_save_path = os.path.join('trained_models', 'SVR_NFS.joblib')\\n\\t\\tdump(regressor, model_save_path)\\n\\n\\t\\t# apply model to the test dataset\\n\\t\\tpredicted_similarity = regressor.predict(test_features)\\n\\n\\t# write output\\n\\ttree = ET.parse(args.test)\\n\\troot = tree.getroot()\\n\\tfor i in range(len(test_pairs)):\\n\\t\\tpairs = root[i]\\n\\t\\tpairs.set('entailment', \\\"None\\\")\\n\\t\\tpairs.set('similarity', str(predicted_similarity[i]))\\n\\n\\ttree.write(args.output, 'utf-8')\"\n]","isConversational":false},"negative_scores":{"kind":"list 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\"0.65689343\",\n \"0.6477436\",\n \"0.64146715\",\n \"0.6368037\",\n \"0.6330938\",\n \"0.6260085\",\n \"0.6223099\",\n \"0.6171409\",\n \"0.616159\",\n \"0.61302584\",\n \"0.61197525\",\n \"0.6115434\",\n \"0.6103395\",\n \"0.6098626\",\n \"0.60901034\",\n \"0.60735404\",\n \"0.60727555\",\n \"0.6036561\",\n \"0.6018226\",\n \"0.6005281\",\n \"0.6003854\",\n \"0.6003626\",\n \"0.5992649\",\n \"0.5969228\",\n \"0.59652513\",\n \"0.5959914\",\n \"0.5951894\",\n \"0.59510285\",\n \"0.59442264\",\n \"0.5927903\",\n \"0.59136367\",\n \"0.5912452\",\n \"0.5912208\",\n \"0.5899267\",\n \"0.58983225\",\n \"0.58577955\",\n \"0.58550507\",\n \"0.58550507\",\n \"0.5854623\",\n \"0.58508277\",\n \"0.58488876\",\n \"0.5846177\",\n \"0.58393604\",\n \"0.58316016\",\n \"0.58223504\",\n \"0.5820204\",\n \"0.5818826\",\n \"0.5811205\",\n \"0.5808082\",\n \"0.580282\",\n \"0.57760113\",\n \"0.5770582\",\n \"0.57690966\",\n \"0.5768823\",\n \"0.57596517\",\n \"0.5757636\",\n \"0.5750189\",\n 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environments."},"document":{"kind":"string","value":"def __init__(self, env: gym.Env, info_obs_weights: dict):\n super().__init__(env)\n self.info_obs_weights = info_obs_weights"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def __init__(self, env):\n gym.RewardWrapper.__init__(self, env)","def reward(self, env):\n del env\n return 1","def add_env_args(parser):\n # sawyer\n parser.add_argument(\n \"--reward_type\",\n type=str,\n default=\"dense\",\n choices=[\"dense\", \"sparse\"],\n help=\"reward type\",\n )\n parser.add_argument(\n \"--distance_threshold\",\n type=float,\n default=0.06,\n help=\"distance threshold for termination\",\n )\n parser.add_argument(\n \"--max_episode_steps\",\n type=int,\n default=70,\n help=\"maximum timesteps in an episode\",\n )\n parser.add_argument(\n \"--camera_name\",\n type=str,\n default=\"visview\",\n help=\"camera name in an environment\",\n )\n\n # observations\n parser.add_argument(\n \"--frame_skip\", type=int, default=1, help=\"Numer of skip frames\"\n )\n parser.add_argument(\n \"--action_repeat\", type=int, default=1, help=\"number of action repeats\"\n )\n parser.add_argument(\n \"--ctrl_reward_coef\", type=float, default=0, help=\"control reward coefficient\"\n )\n\n parser.add_argument(\n \"--kp\", type=float, default=40.0, help=\"p term for a PID controller\"\n ) # 150.)\n parser.add_argument(\n \"--kd\", type=float, default=8.0, help=\"d term for a PID controller\"\n ) # 20.)\n parser.add_argument(\n \"--ki\", type=float, default=0.0, help=\"i term for a PID controller\"\n )\n parser.add_argument(\n \"--frame_dt\", type=float, default=0.15, help=\"delta t between each frame\"\n ) # 0.1)\n parser.add_argument(\n \"--use_robot_indicator\",\n type=eval,\n default=False,\n help=\"enable visualization of robot indicator for motion planner\",\n )\n parser.add_argument(\n \"--use_target_robot_indicator\",\n type=eval,\n default=False,\n help=\"enable visualization of robot indicator for target position of motion planner\",\n )\n parser.add_argument(\n \"--success_reward\", type=float, default=150.0, help=\"completion reward\"\n )\n parser.add_argument(\n \"--contact_threshold\",\n type=float,\n default=-0.002,\n help=\"depth thredhold for contact\",\n )\n parser.add_argument(\n \"--joint_margin\", type=float, default=0.001, help=\"marin of each joint\"\n )\n parser.add_argument(\"--task_level\", type=str, default=\"easy\")\n parser.add_argument(\n \"--step_size\",\n type=float,\n default=0.02,\n help=\"step size for invalid target handling\",\n )\n # puck\n parser.add_argument(\"--puck_friction\", type=float, default=2.0)\n parser.add_argument(\"--puck_mass\", type=float, default=0.01)\n parser.add_argument(\"--source_env_puck_friction\", type=float, default=2.0)\n parser.add_argument(\"--source_env_puck_mass\", type=float, default=0.01)\n parser.add_argument(\"--target_env_puck_friction\", type=float, default=2.0)\n parser.add_argument(\"--target_env_puck_mass\", type=float, default=0.01)\n\n parser.add_argument(\"--env_ob_source\", type=str2bool, default=False)\n parser.add_argument(\"--end_effector\", type=str2bool, default=True)\n parser.add_argument(\"--ik_target\", type=str, default=\"grip_site\")\n parser.add_argument(\n \"--action_range\", type=float, default=0.1, help=\"range of radian\"\n )\n parser.add_argument(\"--dr\", type=str2bool, default=False)\n parser.add_argument(\"--dr_params_set\", type=str, default=\"IP_large_range\")\n\n parser.add_argument(\"--mod_env_params\", type=str2bool, default=False)\n parser.add_argument(\"--param_mod_instructions\", type=eval, default=[])\n\n parser.add_argument(\"--unity\", type=str2bool, default=False)\n parser.add_argument(\"--unity_editor\", type=str2bool, default=False)\n parser.add_argument(\"--virtual_display\", type=str, default=\":1\")\n parser.add_argument(\"--port\", type=int, default=4000)\n\n # FetchReach action\n parser.add_argument(\"--action_rotation_degrees\", type=float, default=0.0)\n parser.add_argument(\"--action_z_bias\", type=float, default=0.0)","def update(self, arm, reward, context):","def __generate_reward_function(self):\n K = -3\n self.reward = np.array([[10, 0, K],\n [0, 2, 0],\n [K, 0, 10]])","def reward_spec(self):\n task_reward_spec = self._task.get_reward_spec()\n if task_reward_spec is not None:\n return task_reward_spec\n else:\n return super(Environment, self).reward_spec()","def reward(self, history_id, reward):\n pass","def reward(self) -> Reward:\n return self.__reward","def reward(self, value):\n self._custom_setter('reward', value)","def update_reward(state, reward, max_reward, alpha=1, c=100, gamma=0.9):\n\t\t# update number of actions done so far to this state\n\t\tactions[state] = actions.get(state, 0.0) + 1.0\n\t\t# compute learning rate\n\t\talpha *= c / (c + actions[state])\n\t\trewards[state] = rewards.get(state, 0.0) + alpha*(reward+gamma*max_reward-rewards.get(state, 0.0))","def get_reward(self):\n\t\t# returns the reward for current state\n\n\t\t#temporary line for testing:\n\t\t#return self.reward_idea() # also not working yet\n\n\t\tcost = self.cost_function()\n\t\tconstraints_violation = self.get_constraints_violation()\n\t\t#old_aug_cost = self.augmented_cost\n\t\t#new_aug_cost = self.get_augmented_cost(cost,constraints_violation)\n\t\t#reward = old_aug_cost - new_aug_cost # reward formula\n\t\t#self.augmented_cost = new_aug_cost # update augmented_cost\n\t\treward = -self.get_augmented_cost(cost,constraints_violation)\n\t\t#print(\"***\\nDEBUG cost: \" +str(cost)+\" constraints_violation: \"+str(constraints_violation))\n\t\t#print(\"DEBUG reward: \"+str(reward))\n\t\t'''\n\t\t#old idea that is probably bad and not necessary:\n\t\tif(self.step_count == 0):\n\t\t\t# old_aug_cost doesn't exist in first step... ACTUALLY IT DOES!\n\t\t\tprint(\"DEBUG step_count == 0, reward would be \"+str(reward))\n\t\t\tprint(\"DEBUG old_aug_cost: \"+str(old_aug_cost) + \" new_aug_cost: \"+str(new_aug_cost) )\n\t\t\treturn 0\n\t\t'''\n\n\t\treturn reward","def experiment(config):\n with tf.Session() as sess:\n\n seed = config.pop('seed')\n\n if seed:\n seed = int(seed)\n random.seed(seed)\n tf.set_random_seed(seed)\n np.random.seed(seed)\n\n env_id = config.pop('env_id')\n LOGGER.info('using {} env'.format(env_id))\n\n env = gym.make(env_id)\n\n global_rewards = []\n global_step, episode = 0, 0\n\n config['env'] = env\n config['env_repr'] = repr(env)\n config['sess'] = sess\n\n render = int(config.pop('render'))\n\n agent = Agent(**config)\n\n rl_writer = tf.summary.FileWriter('./results/rl')\n save_args(config, 'results/args.txt')\n\n while global_step < config['total_steps']:\n episode += 1\n done = False\n rewards, actions = [], []\n observation = env.reset()\n\n while not done:\n global_step += 1\n\n # if episode % 1 == render:\n env.render()\n action = agent.act(observation)\n next_observation, reward, done, info = env.step(action)\n agent.remember(observation, action, reward, next_observation, done)\n train_info = agent.learn()\n\n rewards.append(reward)\n actions.append(action)\n observation = next_observation\n\n ep_rew = sum(rewards)\n global_rewards.append(ep_rew)\n avg_reward = sum(global_rewards[-100:]) / len(global_rewards[-100:])\n\n if episode % 10 == 0:\n log_str =' step {:.0f} ep {:.0f} reward {:.1f} avg {:.1f}'\n logging.info(log_str.format(global_step,\n episode,\n ep_rew,\n avg_reward))\n\n summary = tf.Summary(value=[tf.Summary.Value(tag='episode_reward',\n simple_value=ep_rew)])\n rl_writer.add_summary(summary, episode)\n avg_sum = tf.Summary(value=[tf.Summary.Value(tag='avg_last_100_ep',\n simple_value=avg_reward)])\n rl_writer.add_summary(avg_sum, episode)\n rl_writer.flush()\n \n return config","def update(self, reward):\n raise NotImplementedError","def reward_function(self):\r\n def R(state, decision, nodes):\r\n return -1.0/1000*nodes['G'].get_preds_value(state)*(decision['G:R_1']+decision['G:L'])\r\n \r\n return R","def addReward(self):\n # by default, the cumulative reward is just the sum over the episode\n if self.discount:\n self.cumreward += power(self.discount, self.samples) * self.getReward()\n else:\n self.cumreward += self.getReward()","def set_reward(self, reward):\n self.reward = reward","def nchain_extras(env, gamma=0.99):\n\n # How to handle <TimeLimit<______>> and other Wrappers?\n # assert isinstance(env, gym.envs.toy_text.nchain.NChainEnv)\n\n # Action constants\n A_FORWARD = 0\n A_BACKWARD = 1\n\n states = np.arange(env.observation_space.n)\n actions = np.arange(env.action_space.n)\n\n p0s = np.zeros(env.observation_space.n)\n p0s[0] = 1.0\n\n # Populate dynamics\n t_mat = np.zeros(\n (env.observation_space.n, env.action_space.n, env.observation_space.n)\n )\n\n # Backward action moves to 0th state if it doesn't fail, forward if it does\n t_mat[:, A_BACKWARD, 0] = 1.0 - env.slip\n for s1 in states:\n t_mat[s1, A_BACKWARD, min(s1 + 1, env.observation_space.n - 1)] = env.slip\n\n # Forward action moves to next state if it doesn't fail, 0th if it does\n for s1 in states:\n t_mat[s1, A_FORWARD, min(s1 + 1, env.observation_space.n - 1)] = 1.0 - env.slip\n t_mat[:, A_FORWARD, 0] = env.slip\n\n terminal_state_mask = np.zeros(env.observation_space.n)\n\n xtr = DiscreteExplicitExtras(\n states, actions, p0s, t_mat, terminal_state_mask, gamma=gamma,\n )\n\n phi = Indicator(Indicator.Type.OBSERVATION_ACTION, xtr)\n\n state_action_rewards = np.zeros((env.observation_space.n, env.action_space.n))\n state_action_rewards[:, A_BACKWARD] = env.small\n state_action_rewards[env.observation_space.n - 1, A_FORWARD] = env.large\n reward = Linear(state_action_rewards.flatten())\n\n return (xtr, phi, reward)","def gae(done, rewards, values, n_envs, steps_per_env, gamma, gae_lambda, device):\n advantages = torch.zeros((n_envs, steps_per_env, 1), dtype=torch.float, device=device)\n last_advantage = 0\n for state in reversed(range(steps_per_env)):\n error = rewards[:, state] + gamma * values[:, state + 1] * (~done[:, state]) - values[:, state]\n last_advantage = (error + gamma * gae_lambda * last_advantage * (~done[:, state]))\n\n advantages[:, state] = last_advantage\n\n return advantages","def reward(self, observation, action, reward):\n self.iter[action] += 1\n self.expvalue[action] += 1/self.iter[action]*(reward - self.expvalue[action])","def _reward(self, action):\n raise NotImplementedError","def run_one_step(self):\n # Get the current state, action and initialise the reward\n state = copy.copy(self.env.get_state())\n action = self.agent.get_action(state)\n reward = 0.0\n # Check if the environment has reached a terminal state\n if self.env.check_terminal() is False:\n # Save the initial state and action to an 'experience'\n latest_experience = Experience(copy.copy(state), copy.copy(action))\n # Update the environment using the chosne action\n self.env.update(action)\n # Get the reward to attribute to the agent and save to the experience to save\n reward = self.env.get_reward()\n latest_experience.reward = copy.copy(reward)\n # Get the updated state\n state = self.env.get_state()\n if self.env.check_terminal() is False:\n # If the new state isn't terminal, save the next action and the 'done' flag to the experience\n action = self.agent.get_action(state)\n latest_experience.done = False\n else:\n # If the new state is terminal, save a dummy action and the 'done' flag to the experience\n action = 0.0\n latest_experience.done = True\n latest_experience.next_state = copy.copy(state)\n latest_experience.next_action = copy.copy(action)\n # Update the history with the latest experience\n self.agent.update_history(copy.copy(latest_experience))\n # Update the agents policy using a batch of experiences chosen from the history\n self.agent.update_policy_batch(max(1, self.batch_size))\n self.count += 1\n # Update the target network if appropriate\n if self.update_target_rate is not None:\n if self.count % self.update_target_rate == 0:\n self.agent.policy.learner.update_target_network()\n else:\n # If the environment is in a terminal state, record this and perform a policy update\n latest_experience = Experience(copy.copy(state), copy.copy(action))\n latest_experience.reward = 0.0\n latest_experience.next_state = copy.copy(state)\n latest_experience.next_action = 0.0\n latest_experience.done = True\n self.agent.update_history(copy.copy(latest_experience))\n self.agent.update_policy_batch(max(1, self.batch_size))\n self.count = 0\n return reward","def update(self, arm, reward):\n raise NotImplementedError","def update_rewards(self, arm: int, reward: float):\r\n if self.rewards[arm][0] == self.optim_c:\r\n self.rewards[arm] = np.array(reward)\r\n else:\r\n self.rewards[arm] = np.append(self.rewards[arm], reward)","def add_rewards(self, step_reward, goal_reward, bad_state_reward=None, restart_state_reward = None):\n self.r_step = step_reward\n self.r_goal = goal_reward\n self.r_bad = bad_state_reward\n self.r_restart = restart_state_reward","def add_reward(self, r):\n self.reward += r","def compute_reward(self, state, rl_actions, **kwargs):\n raise NotImplementedError","def set_reward(self, rewards, trader):\n\n NAV_chg = float(trader.acc.nav - trader.acc.prev_nav)\n\n # maximize NAV\n #rewards[trader.ID] = NAV_chg\n\n # maximize NAV, minimize num of trades (more trades gets penalized).\n if NAV_chg >= 0:\n rewards[trader.ID] = NAV_chg / (trader.acc.num_trades + 1)\n else:\n rewards[trader.ID] = NAV_chg * (trader.acc.num_trades + 1)\n\n trader.acc.reward = rewards[trader.ID]\n\n return rewards","def reward(self) -> float:\n\n if not self._reward_provided:\n self._reward_provided = True\n return self._reward\n\n return 0","def immediate_reward(state_node):\n #DANIEL: edited the reward function to get rid of the parent argument and will just be a function of the current state\n return state_node.state.reward()","def update(self, arm, context, reward):\n raise NotImplementedError","def update(self, arm, context, reward):\n raise NotImplementedError","def _get_reward(self):\n if self.status():\n return self.current_step/self.ep_length # the reward is proportional to the duration \n else:\n return 0","def setReward(self, reward):\n assert isinstance(reward, (float, int)), ' Stochy veut un nombre!'\n \n self.__reward.append(reward)\n self.__total += reward","def eval_policy_on_env(self, eval_gym_env, eval_episodes=10, seed=None):\n if not seed:\n eval_gym_env.seed(seed)\n else:\n eval_gym_env.seed(int(time.time()))\n avg_reward = 0.\n for i in range(eval_episodes):\n state, done = eval_gym_env.reset(), False\n obs = self.observer(state)\n step = 0\n #while not done and step < max_steps:\n while not done:\n action = self.plan(np.array(obs))\n state, reward, done, _ = eval_gym_env.step(action)\n obs = self.observer(state)\n avg_reward += reward\n step += 1\n\n avg_reward /= eval_episodes\n return avg_reward","def agent_reward(self, func):\n if len(self.agents_reward) is 2:\n raise Exception(\"You cannot register 3 or more agents_reward.\")\n\n self.agents_reward.append(func)\n return func","def modify_reward(reward):\n # CASE 1 ----------------\n # Nominal reward - return the same reward\n new_reward = reward\n \n # CASE 2 ----------------\n # Aggressive striker - remove penalty for being scored on\n if reward < -0.1:\n new_reward = reward + 0.1\n \n # CASE 3 ----------------\n # Aggressive striker - add more penalty for being scored on\n# if reward < -0.1:\n# new_reward = reward - 0.5\n \n # ============================= DEBUG ONLY =============================\n# print(\"reward = \", reward)\n# print(\"new_reward = \", new_reward)\n # ============================= DEBUG ONLY =============================\n \n return new_reward","def _reward(self, action: Action) -> float:\n raise NotImplementedError","def reinforce(env, estimator_policy, estimator_value, num_episodes, discount_factor=1.0):\n\n # Keeps track of useful statistics\n stats = plotting.EpisodeStats(\n episode_lengths=np.zeros(num_episodes),\n episode_rewards=np.zeros(num_episodes)) \n \n Transition = collections.namedtuple(\"Transition\", [\"state\", \"action\", \"reward\", \"next_state\", \"done\"])\n \n for i_episode in range(num_episodes):\n # Reset the environment and pick the fisrst action\n state = env.reset()\n \n episode = []\n \n # One step in the environment\n for t in itertools.count():\n \n # Take a step\n #action_means = np.ndarray.flatten(estimator_policy.predict(state))\n #action = np.random.multivariate_normal(mean=action_means, cov=full_var)\n\n action = estimator_policy.predict(state)\n\n '''\n max_idx = np.argmax(np.abs(action))\n a_max = action[max_idx]\n\n if a_max > high_threshold or a_max < low_threshold: \n action_clipped = action / (10*np.abs(a_max))\n '''\n\n\n '''\n action_clipped = [np.max([np.min([action[0], high_threshold]), low_threshold]), \n np.max([np.min([action[1], high_threshold]),\n low_threshold])]\n '''\n\n next_state, reward, done, _ = env.step(action)\n\n '''\n if t > 50:\n done = True\n '''\n \n # Keep track of the transition\n episode.append(Transition(\n state=state, action=action, reward=reward, next_state=next_state, done=done))\n \n # Update statistics \n stats.episode_rewards[i_episode] += reward\n stats.episode_lengths[i_episode] = t\n \n '''\n # Print out which step we're on, useful for debugging.\n print(\"\\rStep {} @ Episode {}/{} ({})\".format(\n t, i_episode + 1, num_episodes, stats.episode_rewards[i_episode - 1]), end=\"\")\n # sys.stdout.flush()\n '''\n\n if done:\n break\n \n state = next_state\n \n monitor_epoch = 50\n if i_episode % monitor_epoch == 0 and i_episode > 0: \n print(\"avg reward : %f\" %(np.mean(stats.episode_rewards[i_episode-monitor_epoch:i_episode])))\n\n\n baseline_value = np.mean([cur_trans.reward for cur_trans in episode]) \n\n # Go through the episode and make policy updates\n for t, transition in enumerate(episode):\n # The return after this timestep\n total_return = sum(discount_factor**i * t.reward for i, t in enumerate(episode[t:]))\n \n advantage = total_return - baseline_value\n #advantage += np.max([0, baseline_value - v_prev])\n\n # Update our policy estimator\n estimator_policy.update(transition.state, advantage, transition.action)\n\n #print(p_action)\n\n if i_episode % 200 == 0 and i_episode > 0:\n plt.figure(1)\n plt.plot(transition.state[0], transition.state[1], 'bo')\n if t == len(episode) - 1:\n plt.show()\n \n \n return stats","def run_episode(env, policy, gamma=1.0, render = False):\n obs = env.reset()\n total_reward = 0\n step_idx = 0\n while True:\n if render:\n env.render()\n obs, reward, done , _ = env.step(int(policy[obs]))\n total_reward += (gamma ** step_idx * reward)\n step_idx += 1\n if done:\n break\n #print(total_reward)\n return total_reward","def train(self, reward: str):\r\n pass","def update(self, next_state, reward):\n pass","def get_reward(self, state, action, next_state, absorbing):\n raise NotImplementedError","def reward(self, observation, action, reward):\n self.counts[action] = self.counts[action] + 1\n n = self.counts[action]\n value = self.values[action]\n \n new_value = ((n - 1) / float(n)) * value + (1 / float(n)) * reward\n self.values[action] = new_value\n self.minmax = max(self.values) - min(self.values)\n \n \n pass","def reward(self, **kwargs):\n # rewards = self.get_mos_from_aps()\n rewards = self.get_mos_from_localhost()\n self.log.debug(\"Collect MOS: {}\".format(rewards))\n\n # obtain the global reward\n avgs = []\n for i in rewards:\n m = np.nanmean(rewards[i])\n # trunc to bounds\n if m < 1:\n m = 1\n elif m > 5:\n m = 5\n avgs.append(m)\n self.log.debug(\"Avgs: {}\".format(avgs))\n if np.any(np.isnan(avgs)):\n # if found a nan, that means an error (e.g. a disconnected station)\n r = [np.nan for _ in rewards]\n else:\n avgs = np.array(avgs)\n C = self.DEFAULT_C if 'C' not in kwargs else kwargs['C']\n r = reward_hossfeld(avgs, C=C)\n self.log.info(\"Hossfeld reward: {} C: {}\".format(r, C))\n\n self.log.info(\"Rewards: {}\".format(r))\n return r","def reward_calc(self, reward_traj,V,V_end):\n r_all = np.concatenate((reward_traj,[V_end]),-1)\n V_all = V #np.concatenate((V,[V_end]),-1)\n delta = r_all[:-1] + self.gamma * V_all[1:] - V_all[:-1]\n \n adv = Generalized_Adv_Estimator.discounted_sum(delta,self.gamma*self.lam)\n rtg = adv + V_all[:-1]\n\n adv = adv.astype('float32')\n rtg = rtg.astype('float32')\n\n return adv, rtg","def _compute_reward(self):\n last_score = self.episode_qualities[-2]\n new_score = self.episode_qualities[-1]\n reward = new_score - last_score\n return reward","def reward(self,\n state: float) -> float:\n raise NotImplementedError","def run(self):\n\t\tep_rewards = [0.0]\n\t\tavg_rewards = []\n\t\tobs = self.env.reset()\n\t\tstep_counter = 0\n\n\t\tself.mylogger.info('Task: {}, epochs: {}, batch size: {}'.format(self.env.unwrapped.spec.id, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.epochs,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.batch_size\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t ))\n\n\t\tfor epoch in range(self.epochs):\n\t\t\tfor step in range(self.batch_size):\n\t\t\t\tstep_counter += 1\n\n\t\t\t\tself.observations[step] = obs.copy()\n\t\t\t\tself.actions[step], self.values[step] = self.model.action_value(obs[None, :])\n\t\t\t\tobs, self.rewards[step], self.dones[step], _ = self.env.step(self.actions[step])\n\t\t\t\tep_rewards[-1] += self.rewards[step]\n\n\t\t\t\tif step_counter % self.log_step == 0:\n\t\t\t\t\tlog_msg = 'global_step: {}, obs: {}, act: {}, reward: {}'.format(step_counter,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t obs, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.actions[step], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.rewards[step]\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t )\n\t\t\t\t\tself.mylogger.info(log_msg)\n\t\t\t\t\tself.mylogger.info(\"prev episode reward: {}\".format(ep_rewards[-2]))\n\n\t\t\t\tif self.dones[step]:\n\t\t\t\t\twith self.summary_writer.as_default():\n\t\t\t\t\t\ttf.summary.scalar('episode reward', ep_rewards[-1], step=step_counter)\n\t\t\t\t\tep_rewards.append(0.0)\n\t\t\t\t\tobs = self.env.reset()\n\n\t\t\t_, next_value = self.model.action_value(obs[None, :])\n\t\t\treturns, advs = self._returns_advantages(self.rewards, self.dones, self.values, next_value)\n\t\t\t# A trick to input actions and advantages through same API.\n\t\t\tacts_and_advs = np.concatenate([self.actions[:, None], advs[:, None]], axis=-1)\n\n\t\t\t# update weights \n\t\t\tlosses = self.model.train_on_batch(self.observations, [acts_and_advs, returns])\n\n\t\t\twith self.summary_writer.as_default():\n\t\t\t\ttf.summary.scalar('policy loss', losses[1], step=step_counter)\n\t\t\t\ttf.summary.scalar('value loss', losses[2], step=step_counter)","def __init__(self, init_pose=None, init_velocities=None, \n init_angle_velocities=None, runtime=5., target_pos=None, reward_function=None, angle_penalty=10.):\n # Simulation\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \n self.action_repeat = 3\n\n self.state_size = self.action_repeat * 6\n self.action_low = 0\n self.action_high = 900\n self.action_size = 4\n self.angle_penalty = angle_penalty\n self.max_v_length = 0.\n\n # Goal\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.]) \n \n self.distance_to_target = self.vector_length(self.target_pos - self.sim.pose[:3])\n \n if( reward_function == 6 ):\n self.reward_function_str = 'Using Reward Function 6'\n self.reward_function = self.reward_function_6\n elif( reward_function == 5 ):\n self.reward_function_str = 'Using Reward Function 5'\n self.reward_function = self.reward_function_5\n elif( reward_function == 4 ):\n self.reward_function_str = 'Using Reward Function 4'\n self.reward_function = self.reward_function_4\n elif( reward_function == 3 ):\n self.reward_function_str = 'Using Reward Function 3'\n self.reward_function = self.reward_function_3\n elif( reward_function == 2 ):\n self.reward_function_str = 'Using Reward Function 2'\n self.reward_function = self.reward_function_2\n elif( reward_function == 1 ):\n self.reward_function_str = 'Using Reward Function 1'\n self.reward_function = self.reward_function_1\n else:\n self.reward_function_str = 'Using Deafault Reward Function'\n self.reward_function = self.default_reward","def _compute_reward(self): \n reward = -1\n return reward","def q_learning(env, learning, discount, epsilon, min_eps, episodes):\n # [18.00000072 14.00000006]\n num_states = (env.observation_space.high - env.observation_space.low) * \\\n np.array([10, 100]) # >> [18.00000072 14.00000006]\n num_states = np.round(num_states, 0).astype(int) + 1 # >> [19 15]\n\n # Initialize Q table\n # env.action_space.n return the number of action that our agent can make (here 3, left, cease, right)\n Q = np.random.uniform(low=-1, high=1, size=(num_states[0], num_states[1], env.action_space.n))\n\n # Initialize variable to track rewards\n reward_list = []\n ave_reward_list = []\n\n # Calculate episodic reduction in epsilon\n reduction = (epsilon - min_eps) / (episodes / 2)\n\n for i in range(episodes):\n # Initialize parameters\n done = False\n tot_reward, reward = 0, 0\n state = env.reset()\n\n # Discretize state\n state_adj = adjust_state(state)\n\n while done != True:\n # Render env for last five eps\n if i >= (episodes - 20):\n env.render()\n\n # Determine next action - epsilon greedy strategy\n if np.random.random() < 1 - epsilon:\n action = np.argmax(Q[state_adj[0], state_adj[1]])\n else:\n action = np.random.randint(0, env.action_space.n)\n\n # Get next state and reward\n state2, reward, done, info = env.step(action)\n\n # Discretize state2\n state2_adj = adjust_state(state2)\n\n # Allow for terminal states // .5 on env_space[0] represent the flag position\n if done and state2[0] >= .5:\n Q[state_adj[0], state_adj[1], action] = reward\n\n # adjust Q value for current state\n else:\n '''work on this, it's complicated but far from non-understandable'''\n delta = learning*(reward + discount*np.max(Q[state2_adj[0], state2_adj[1]]) -\n Q[state_adj[0], state_adj[1], action])\n Q[state_adj[0], state_adj[1], action] += delta\n\n tot_reward += reward\n state_adj = state2_adj\n\n # Decay epsilon\n if epsilon > min_eps:\n epsilon -= reduction\n\n # Track rewards\n reward_list.append(tot_reward)\n\n if (i+1) % 100 == 0:\n ave_reward = np.mean(reward_list)\n ave_reward_list.append(ave_reward)\n reward_list = []\n print(f'Episode {i+1} Average Reward: {ave_reward}')\n\n env.close()\n\n return ave_reward_list","def update_Q(self, reward):\n old_estimate = self.q_estimates[self.prev_action]\n self.q_estimates[self.prev_action] = old_estimate + 1/self.N[self.prev_action] * (reward - old_estimate)","def run_one_step(self):\n state = copy.copy(self.env.get_state())\n action = self.agent.get_action(state)\n reward = 0.0\n if self.env.check_terminal() is False:\n latest_experience = Experience(copy.copy(state), copy.copy(action))\n self.env.update(action)\n reward = self.env.get_reward()\n latest_experience.reward = copy.copy(reward)\n state = self.env.get_state()\n if self.env.check_terminal() is False:\n action = self.agent.get_action(state)\n latest_experience.done = False\n else:\n action = 0.0\n latest_experience.done = True\n latest_experience.next_state = copy.copy(state)\n latest_experience.next_action = copy.copy(action)\n self.agent.update_history(copy.copy(latest_experience))\n self.count += 1\n # If the latest history has a large enough batch, perform an update\n # CHECK IF THIS IS THE RIGHT METHOD\n if self.count % self.batch_size == 0:\n self.agent.update_policy_ordered(max(1, self.batch_size))\n if self.update_target_rate is not None:\n if self.count % self.update_target_rate == 0:\n self.count = 0\n self.agent.policy.learner.update_target_network()\n else:\n latest_experience = Experience(copy.copy(state), copy.copy(action))\n latest_experience.reward = 0.0\n latest_experience.next_state = copy.copy(state)\n latest_experience.next_action = 0.0\n latest_experience.done = True\n self.agent.update_history(copy.copy(latest_experience))\n # Perform an update on all of the previous experiences that haven't been updated\n if self.count % self.batch_size > 0:\n self.agent.update_policy_ordered((self.count % self.batch_size) + 1)\n self.count = 0\n return reward","def evaluate(env, model, num_env, iter_step):\n episode_rewards = []\n episode_reward = np.zeros((num_env))\n obs = env.reset()\n for _ in tqdm(range(iter_step)):\n action, _states = model.predict(obs)\n obs, reward, done, info = env.step(action)\n episode_reward += reward\n for i in range(num_env):\n if done[i]:\n episode_rewards.append(episode_reward[i])\n episode_reward[i] = 0\n return episode_rewards","def __init__(self,\n sess,\n num_actions,\n observation_shape=base_dqn_agent.NATURE_DQN_OBSERVATION_SHAPE,\n gamma=0.99,\n update_horizon=1,\n min_replay_history=20000,\n update_period=4,\n target_update_period=8000,\n epsilon_fn=linearly_decaying_epsilon,\n epsilon_train=0.01,\n epsilon_eval=0.001,\n epsilon_decay_period=250000,\n tf_device='/cpu:*',\n use_staging=True,\n max_tf_checkpoints_to_keep=3,\n optimizer=tf.train.RMSPropOptimizer(\n learning_rate=0.00025,\n decay=0.95,\n momentum=0.0,\n epsilon=0.00001,\n centered=True),\n summary_writer=None,\n summary_writing_frequency=500,\n clip_reward=False):\n self._clip_reward = clip_reward\n self.intrinsic_model = intrinsic_rewards.RNDIntrinsicReward(\n sess=sess,\n tf_device=tf_device,\n summary_writer=summary_writer)\n super(RNDDQNAgent, self).__init__(\n sess=sess,\n num_actions=num_actions,\n observation_shape=observation_shape,\n gamma=gamma,\n update_horizon=update_horizon,\n min_replay_history=min_replay_history,\n update_period=update_period,\n target_update_period=target_update_period,\n epsilon_fn=epsilon_fn,\n epsilon_train=epsilon_train,\n epsilon_eval=epsilon_eval,\n epsilon_decay_period=epsilon_decay_period,\n tf_device=tf_device,\n use_staging=use_staging,\n optimizer=optimizer,\n summary_writer=summary_writer,\n summary_writing_frequency=summary_writing_frequency)","def reward(input):\n state = np.array([input[0], input[1]])\n action = input[2]\n action = np.clip(action, -2.0, 2.0)\n costs = angle_normalize(state[0])**2 + .1 * state[1]**2 + .001 * (action**2)\n\n return - costs","def apply_reinforcement_learning(self, reward: int, p) -> None:\n for cl in self:\n cl.update_reward(reward + self.cfg.gamma * p)\n cl.update_intermediate_reward(reward)","def __init__(self, environment):\n self.env = environment\n self.cumreward = 0 # tracking cumulative reward\n self.samples = 0 # tracking the number of samples\n\n self.sensor_limits = None\n self.actor_limits = None\n self.clipping = True\n\n self.current_action = 0 # Saving current action\n self.prev_action = -1 # Saving previous action","def AttendanceRewardExcelAddRewardAmount(builder, RewardAmount):\n return AddRewardAmount(builder, RewardAmount)","def _reward(self):\n # Clock reward -----------------------------------------------------------------\n A, B = self.get_von_mises(0.0, self.ratio, self.kappa)\n phi = self.phase / self.cycle_len\n #print('Cycles completed = ', self.cycle_complete)\n\n #print('A, B = ', (A,B))\n\n phi_FL = self.wrap(phi + self.theta_FL)\n phi_FR = self.wrap(phi + self.theta_FR)\n phi_RL = self.wrap(phi + self.theta_RL)\n phi_RR = self.wrap(phi + self.theta_RR)\n\n #print(phi_FL)\n #print(phi_FR)\n #print(phi_RL)\n #print(phi_RR)\n\n FL_swing = self.in_swing(A, B, phi_FL)\n FR_swing = self.in_swing(A, B, phi_FR)\n RL_swing = self.in_swing(A, B, phi_RL)\n RR_swing = self.in_swing(A, B, phi_RR)\n\n #print('Time since reset = ', self.rex.GetTimeSinceReset())\n #print('phase phi = ', phi)\n #print('FL swing = ', FL_swing)\n #print('FR swing = ', FR_swing)\n #print('RL swing = ', RL_swing)\n #print('RR swing = ', RR_swing)\n\n if FL_swing:\n c_swing_frc_FL = 1\n c_swing_spd_FL = 0\n else:\n c_swing_frc_FL = 0\n c_swing_spd_FL = 1\n\n if FR_swing:\n c_swing_frc_FR = 1\n c_swing_spd_FR = 0\n else:\n c_swing_frc_FR = 0\n c_swing_spd_FR = 1\n\n if RL_swing:\n c_swing_frc_RL = 1\n c_swing_spd_RL = 0\n else:\n c_swing_frc_RL = 0\n c_swing_spd_RL = 1\n\n if RR_swing:\n c_swing_frc_RR = 1\n c_swing_spd_RR = 0\n else:\n c_swing_frc_RR = 0\n c_swing_spd_RR = 1\n\n FL_foot_force, FR_foot_force, RL_foot_force, RR_foot_force = self.get_contact_forces()\n FL_vel, FR_vel, RL_vel, RR_vel = self.get_foot_velocities()\n\n FL_penalty = c_swing_frc_FL*FL_foot_force + c_swing_spd_FL*FL_vel\n FR_penalty = c_swing_frc_FR*FR_foot_force + c_swing_spd_FR*FR_vel\n RL_penalty = c_swing_frc_RL*RL_foot_force + c_swing_spd_RL*RL_vel\n RR_penalty = c_swing_frc_RR*RR_foot_force + c_swing_spd_RR*RR_vel\n\n foot_penalties = FL_penalty + FR_penalty + RL_penalty + RR_penalty\n \n # Deviation Penalties ----------------------------------------------------------\n # Base height\n base_height = self.rex.GetBasePosition()[-1]\n height_err = np.abs(base_height - self.height_des)\n \n if height_err < 0.02:\n height_err = 0\n\n # Speed \n vx, vy, _ = p.getBaseVelocity(bodyUniqueId=self.rex.quadruped)[0]\n vx = -vx # in rex, forward is the negative x direction\n x_vel_err = 4*np.abs(vx - self.speed) # higher emphasis on x velocity error\n y_vel_err = np.abs(vy - self.side_speed)\n\n # Orientation\n orient_curr = self.rex.GetBaseOrientation()\n orient_des = [0, 0, 0, 1] # not exact, but shouldn't be too far from this\n orient_err = 6 * (1 - np.inner(orient_curr, orient_des)**2 )\n\n shoulder_orient_des = [0, 0, 0, 1]\n FL_sh, FR_sh, RL_sh, RR_sh = self.get_shoulder_orientation()\n\n # quaternion similarity: 1 - <q1, q2>**2 == 0 when 100% similar\n # good when error < 0.01 (individually)\n # put HUGE penalty on this\n shoulder_err = 20 * ((1 - np.inner(shoulder_orient_des, FL_sh)**2) + \n (1 - np.inner(shoulder_orient_des, FR_sh)**2) +\n (1 - np.inner(shoulder_orient_des, RL_sh)**2) + \n (1 - np.inner(shoulder_orient_des, RR_sh)**2))\n\n # Energy Penalties --------------------------------------------------------------\n energy_penalty = np.abs(np.dot(self.rex.GetMotorTorques(),\n self.rex.GetMotorVelocities())) * self._time_step\n\n # Acceleration\n a_trans, a_rot = self.get_base_accelerations()\n accel_penalty = 0.15 * np.abs(a_trans.sum() + a_rot.sum())\n\n # need to encourage exploration: current issue --> Rex is stuck at origin\n # because positive rewards all the time\n # need lim error --> 0, reward > 0 \n\n beta = -0.75\n\n reward = beta + \\\n 0.200 * np.exp(-orient_err - shoulder_err) + \\\n 0.275 * np.exp(-foot_penalties) + \\\n 0.075 * np.exp(-height_err) + \\\n 0.250 * np.exp(-x_vel_err) + \\\n 0.100 * np.exp(-y_vel_err) + \\\n 0.075 * np.exp(-accel_penalty) + \\\n 0.025 * np.exp(-energy_penalty)\n\n\n return reward","def update_Q(env, episode, Q, alpha, gamma):\n states, actions, rewards = zip(*episode)\n # prepare for discounting\n discounts = np.array([gamma**i for i in range(len(rewards)+1)])\n for i, state in enumerate(states):\n old_Q = Q[state][actions[i]] \n Q[state][actions[i]] = old_Q + alpha*(sum(rewards[i:]*discounts[:-(1+i)]) - old_Q)\n return Q","def phase(self, phase, **kwargs):\n ## Rewards\n if phase == 'set_default_behavior':\n rewards = {\n 'swim': +0.1, # reward given for anything else\n 'skin': +0.5, # reward given when breathing through the skin\n 'pneumostome': +1. # reward given when breathing through the pneumostome\n #'miss': # to force some type of trial duration limit if implementing trials in this task ?\n }\n elif phase == 'training_session':\n rewards = {\n 'swim': +0.1,\n 'skin': +1.,\n 'pneumostome': -1.\n }\n elif phase == 'testing_session':\n rewards = {\n 'swim': 0.,\n 'skin': 0.,\n 'pneumostome': 0.\n }\n return rewards","def test_prop_reward(self):\n tmax = 10.0\n dt = 1.0\n\n reward_scale = 5.0\n\n ini_rate = 80.0\n\n tutor = SimpleNeurons(1, out_fct=lambda _: ini_rate+20.0)\n reward = MockReward(lambda t: 1.0 if t < tmax/2 else -1)\n tutor_rule = ReinforcementTutorRule(tutor, reward, tau=0,\n constrain_rates=False, ini_rate=ini_rate, learning_rate=1.0,\n use_tutor_baseline=False)\n\n sim1 = simulation.Simulation(tutor, reward, tutor_rule, dt=dt)\n sim1.run(tmax)\n\n drates1 = tutor_rule.rates - ini_rate\n\n tutor_rule.reset_rates()\n reward.reward_fct = lambda t: reward_scale if t < tmax/2 else -reward_scale\n\n sim2 = simulation.Simulation(tutor, reward, tutor_rule, dt=dt)\n sim2.run(tmax)\n\n drates2 = tutor_rule.rates - ini_rate\n\n self.assertLess(np.max(np.abs(reward_scale*drates1 - drates2)), 1e-6)","def reward(self, observation, action, reward):\n self.counts[action] = self.counts[action] + 1\n n = self.counts[action]\n value = self.values[action]\n \n new_value = ((n - 1) / float(n)) * value + (1 / float(n)) * reward\n self.values[action] = new_value\n \n pass","def reward(self, observation, action, reward):\n self.counts[action] = self.counts[action] + 1\n n = self.counts[action]\n value = self.values[action]\n \n new_value = ((n - 1) / float(n)) * value + (1 / float(n)) * reward\n self.values[action] = new_value\n \n pass","def reward(self, observation, action, reward):\n self.counts[action] = self.counts[action] + 1\n n = self.counts[action]\n value = self.values[action]\n \n new_value = ((n - 1) / float(n)) * value + (1 / float(n)) * reward\n self.values[action] = new_value\n \n pass","def evaluate(self, env, num_episode, epsilon):\n num_environment = env.num_process\n env.reset()\n reward_of_each_environment = np.zeros(num_environment)\n rewards_list = []\n\n num_finished_episode = 0\n\n while num_finished_episode < num_episode:\n old_state, action, reward, new_state, is_terminal = env.get_state()\n action = self.get_action(new_state, epsilon)\n env.take_action(action)\n for i, r, is_t in zip(range(num_environment), reward, is_terminal):\n if not is_t:\n reward_of_each_environment[i] += r\n else:\n rewards_list.append(reward_of_each_environment[i])\n reward_of_each_environment[i] = 0\n num_finished_episode += 1\n return np.mean(rewards_list), np.std(rewards_list), self.epsilon","def reward_exchanges(self):\n exchange_reward_per_euro = get_exchange_reward_per_euro(self.model)\n #payout to agents according to the number of hours they contributed\n for agent_id, agent in self.model.schedule.agents_by_type['Customer'].items():\n agent.teo_wallet += agent.exchanged_euros * exchange_reward_per_euro\n agent.exchange_surplus += agent.exchanged_euros * exchange_reward_per_euro","def __init__(self,\n debug=False,\n urdf_version=None,\n control_time_step=0.005,\n action_repeat=5,\n control_latency=0,\n pd_latency=0,\n on_rack=False,\n motor_kp=1.0,\n motor_kd=0.02,\n render=False,\n num_steps_to_log=2000,\n env_randomizer=None,\n log_path=None,\n signal_type='ik',\n target_position=None,\n backwards=None,\n gait_type='trot',\n terrain_type='plane',\n terrain_id='plane',\n mark='base',\n ):\n self.phase = 0\n\n self._gait_type = gait_type \n # for observation space bounding \n self.max_speed = 1.0\n self.min_speed = 0.5 # change back to 0.2 for OLD TD3 model evaluation\n \n self.min_side_speed = 0.0\n self.max_side_speed = 0.0\n\n self.speed = np.random.uniform(self.min_speed, self.max_speed)\n self.side_speed = np.random.uniform(self.min_side_speed, self.max_side_speed)\n self.speed_des = [self.speed, self.side_speed]\n\n # Initialization variables for periodic reward sum composition\n self.theta_FL = phase_constants.PHASE_VALS[self._gait_type]['front_left']\n self.theta_FR = phase_constants.PHASE_VALS[self._gait_type]['front_right']\n self.theta_RL = phase_constants.PHASE_VALS[self._gait_type]['rear_left']\n self.theta_RR = phase_constants.PHASE_VALS[self._gait_type]['rear_right']\n\n self.min_swing_ratio = 0.6\n self.max_swing_ratio = 0.8\n self.ratio = np.random.uniform(self.min_swing_ratio, self.max_swing_ratio)\n\n super(rexPeriodicRewardEnv,\n self).__init__(urdf_version=urdf_version,\n accurate_motor_model_enabled=True,\n motor_overheat_protection=False,\n motor_kp=motor_kp,\n motor_kd=motor_kd,\n remove_default_joint_damping=False,\n control_latency=control_latency,\n pd_latency=pd_latency,\n on_rack=on_rack,\n render=render,\n num_steps_to_log=num_steps_to_log,\n env_randomizer=env_randomizer,\n log_path=log_path,\n control_time_step=control_time_step,\n action_repeat=action_repeat,\n target_position=target_position,\n signal_type=signal_type,\n backwards=backwards,\n debug=debug,\n terrain_id=terrain_id,\n terrain_type=terrain_type,\n mark=mark,\n ratio=self.ratio,\n forward_reward_cap=5\n )\n\n self.height_des = 0.206 # this is init standing height for rex\n\n self.cycle_complete = 0\n self.cycle_len = 1000 # this is L\n \n # vonmises variables\n self.kappa = phase_constants.VON_MISES_KAPPA\n\n rex_joints = p.getNumJoints(bodyUniqueId=self.rex.quadruped)\n link_name_to_ID = {}\n for i in range(rex_joints):\n name = p.getJointInfo(self.rex.quadruped, i)[12].decode('UTF-8')\n link_name_to_ID[name] = i\n\n self.link_name_to_ID = link_name_to_ID\n self.toe_pos_last = { 'front_left_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['front_left_toe_link'])[0],\n 'front_right_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['front_right_toe_link'])[0],\n 'rear_left_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['rear_left_toe_link'])[0],\n 'rear_right_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['rear_right_toe_link'])[0]\n\n } \n\n print('Using Periodic Reward Composition Rex Environment')","def apply_reinforcement_learning(self, reward: int, p: float) -> None:\n for cl in self:\n cl.update_reward(reward + cl.cfg.gamma * p)\n cl.update_intermediate_reward(reward)","def learn(self, state, action, reward, next_state):\r\n\r\n \"\"\"Please Fill Your Code Here.\r\n \"\"\"\r\n self.Q[state][action] = self.Q[state][action] + self.alpha * (reward + self.gamma * max(self.Q[next_state]) - self.Q[state][action])\r\n\r\n return 0","def step_reward(self) -> Tuple[float, dict]:\n unserved_reward = -self.unserved_penalty * self.state[\"real_power_unserved\"]**2\n peak_reward = -self.peak_penalty * \\\n max(0, self.state[\"real_power_consumed\"] - self.peak_threshold)**2\n reward = unserved_reward + peak_reward\n reward /= self.reward_scale\n return reward, {\"real_power_unserved\": unserved_reward, \"peak_reward\": peak_reward}","def add_reward(self, reward):\n self.quest_node['reward'] = reward\n self.reward = reward\n graph.push(self.quest_node)","def __init__(self, env=None, tilesEnv=False):\n super(MarioEnv, self).__init__(env)\n self.resetCount = -1\n # reward is distance travelled. So normalize it with total distance\n # https://github.com/ppaquette/gym-super-mario/blob/master/ppaquette_gym_super_mario/lua/super-mario-bros.lua\n # However, we will not use this reward at all. It is only for completion.\n self.maxDistance = 3000.0\n self.tilesEnv = tilesEnv","def __init__(self):\n self.last_reward_pos = 0\n super().__init__()\n self.TERRAIN_VARIANCE = 0.0\n self.stump_spacing = 4.0\n self.stump_height = 1.0\n self.my_init({'leg_length': 35, 'walker_type': 'default'})","def reward_spec(self) -> types.NestedArraySpec:\n return array_spec.ArraySpec(shape=(), dtype=np.float32, name='reward')","def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n Q_Value = self.Q #calling constructor\n\n learning_rate = self.alpha #gives us the learning rate\n\n temporary_QValue = self.getQValue(state,action) #to get the Q value of the state\n\n nextState_QValue = self.getValue(nextState) #to get the Q value of the landing state when taken action a and state s\n\n discount_factor = self.discount #to get the gamma/ discount factor\n\n\n Q_Value[(state,action)] = ((1-learning_rate) * temporary_QValue) + (learning_rate * (reward + discount_factor * nextState_QValue)) #for formula go to README_Reinforcement.txt at line 8\n\n #util.raiseNotDefined()","def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n self.qValues[(state, action)] = ((1 - self.alpha) * self.getQValue(state, action)) + self.alpha \\\n * (reward + self.discount * self.computeValueFromQValues(nextState))","def get_reward(self, is_terminal_state, result, reward, is_agent_move):\n if is_terminal_state:\n reward += 0 if result == \"Tie\" else 10 if is_agent_move else -10\n #elif (not is_terminal_state and is_agent_move):\n else:\n reward += -1\n\n return reward","def genetic_algorithm(env: RailEnv):\r\n # Determine number of agent and number of combinations of randomized order\r\n n_agents = len(env.agents)\r\n n_combination = math.factorial(n_agents)\r\n agent_ids= list(range(n_agents))\r\n # Compute number of iteration for genetic algo\r\n # Run ten iterations\r\n n_iterations = 10\r\n\r\n # create an initial population. The population contains three combinations of randomized order\r\n population = []\r\n final_reward = dict()\r\n for i in range(3):\r\n randomized_order = random.sample(agent_ids, len(agent_ids))\r\n if not population.__contains__(randomized_order):\r\n population.append(randomized_order)\r\n\r\n highest_reward_of_each_iteration=[]\r\n action_plan = dict()\r\n # Run the genetic algorithm in n number of iteration\r\n for iteration in range(n_iterations):\r\n # Pass each randomized order to the modified a_star\r\n i=0\r\n reward = dict()\r\n print(\"*******************************************************New Population***********************************\")\r\n print(\"Iteration \",iteration)\r\n # For each planning order in population we run it to get the cumulative reward\r\n for randomized_order in population:\r\n schedules = search_a_for_genetic(env,randomized_order)\r\n action_plan[i] = schedules\r\n test_env = deepcopy(env)\r\n success = False\r\n sumreward = 0\r\n # apply to the copied environment to calculate the sumreward for fitness score\r\n for action in schedules:\r\n _, _reward_dict, _done, _ = test_env.step(action)\r\n success = all(_done.values())\r\n sumreward = sumreward + sum(_reward_dict.values())\r\n reward[i] = sumreward\r\n #print(\"Total Time Step: \",test_env._elapsed_steps)\r\n #print(\"Randomize order: \", randomized_order)\r\n #print(\"Sum reward: \",sumreward)\r\n i += 1\r\n final_reward = reward.copy()\r\n highest_reward_of_each_iteration.append(final_reward)\r\n # find the randomized order give us the min reward\r\n key_min = min(reward.keys(), key=(lambda k: reward[k]))\r\n min_randomized_order = population[key_min]\r\n del reward[key_min]\r\n\r\n # Crossover\r\n # Select the parent\r\n # Find two combinations of randomized order that give us highest reward\r\n max_key_a = max(reward.keys(), key=(lambda k: reward[k]))\r\n del reward[max_key_a]\r\n randomized_order_a = population[max_key_a]\r\n\r\n max_key_b = max(reward.keys(), key=(lambda k: reward[k]))\r\n del reward[max_key_b]\r\n randomized_order_b = population[max_key_b]\r\n\r\n # Crossover\r\n # Generate a random crossover\r\n cross_point = random.randrange(1,n_agents-1)\r\n first_part_order_a = randomized_order_a[:cross_point]\r\n second_part_order_a = randomized_order_a[cross_point:]\r\n\r\n first_part_order_b = randomized_order_b[:cross_point]\r\n second_part_order_b = randomized_order_b[cross_point:]\r\n\r\n # Create two children based on the genes of parent\r\n child_a = first_part_order_a + second_part_order_b\r\n child_b = first_part_order_b + second_part_order_a\r\n\r\n # Mutation\r\n mutation(child_a)\r\n mutation(child_b)\r\n\r\n # remove the two combinations in the population that have lower reward\r\n population.remove(min_randomized_order)\r\n population.remove(randomized_order_b)\r\n\r\n # add two new children to the population\r\n\r\n population.append(child_a)\r\n population.append(child_b)\r\n\r\n\r\n # Find the best schedule\r\n max_key_reward = max(final_reward.keys(), key=(lambda k: final_reward[k]))\r\n best_order = population[max_key_reward]\r\n\r\n print(\"--------------------------------Result-------------------------------------\")\r\n print(\"Best Order: \", best_order)\r\n print(\"Reward: \",highest_reward_of_each_iteration)\r\n return best_order,action_plan[max_key_reward]","def adjust_lr_exp(optimizer, base_lr, ep, total_ep, start_decay_at_ep):\n assert ep >= 1, \"Current epoch number should be >= 1\"\n\n if ep < start_decay_at_ep:\n return\n\n for g in optimizer.param_groups:\n g['lr'] = (base_lr * (0.001 ** (float(ep + 1 - start_decay_at_ep)\n / (total_ep + 1 - start_decay_at_ep))))\n print('=====> lr adjusted to {:.10f}'.format(g['lr']).rstrip('0'))","def get_reward(self, observations, actions):\n\n #initialize and reshape as needed, for batch mode\n self.reward_dict = {}\n if(len(observations.shape)==1):\n observations = np.expand_dims(observations, axis = 0)\n actions = np.expand_dims(actions, axis = 0)\n batch_mode = False\n else:\n batch_mode = True\n\n #get vars\n xvel = observations[:, 9].copy()\n body_angle = observations[:, 2].copy()\n front_leg = observations[:, 6].copy()\n front_shin = observations[:, 7].copy()\n front_foot = observations[:, 8].copy()\n zeros = np.zeros((observations.shape[0],)).copy()\n\n # ranges\n leg_range = 0.2\n shin_range = 0\n foot_range = 0\n penalty_factor = 10\n\n #calc rew\n self.reward_dict['run'] = xvel\n\n front_leg_rew = zeros.copy()\n front_leg_rew[front_leg>leg_range] = -penalty_factor\n self.reward_dict['leg'] = front_leg_rew\n\n front_shin_rew = zeros.copy()\n front_shin_rew[front_shin>shin_range] = -penalty_factor\n self.reward_dict['shin'] = front_shin_rew\n\n front_foot_rew = zeros.copy()\n front_foot_rew[front_foot>foot_range] = -penalty_factor\n self.reward_dict['foot'] = front_foot_rew\n\n # total reward\n self.reward_dict['r_total'] = self.reward_dict['run'] + self.reward_dict['leg'] + self.reward_dict['shin'] + self.reward_dict['foot']\n\n #return\n dones = zeros.copy()\n if(not batch_mode):\n return self.reward_dict['r_total'][0], dones[0]\n return self.reward_dict['r_total'], dones","def update(self, state, action, nextState, reward):\n \"\"\"Description:\n Use Q-Learning algoritm in slide 58 of MDP\n \"\"\"\n \"\"\" YOUR CODE HERE \"\"\"\n maxQns = self.getValue(nextState) # get max q-value of next state\n if maxQns == None:\n maxQns = 0\n Qsa = self.getQValue(state, action) #self.qValues[(state, action)]\n difference = reward + self.discountRate * maxQns - Qsa\n self.qValues[(state, action)] += self.alpha * difference\n \n self.vitCount[(state, action)] += 1\n \"\"\" END CODE \"\"\"","def update_reward(reward, done, info):\n if done and info['ale.lives'] == 0:\n return reward - 100\n\n return reward","def update(self, state, action, nextState, reward):\n \"*** YOUR CODE HERE ***\"\n features = self.featExtractor.getFeatures(state,action)\n\n learning_rate = self.alpha #gives us the learning rate\n\n temporary_QValue = self.getQValue(state,action) #to get the Q value of the state,action pair\n\n nextState_QValue = self.getValue(nextState) #to get the Q value of the landing state when taken action a and state s\n\n discount_factor = self.discount #to get the gamma/ discount factor\n\n weight = self.weights\n\n Q_Value = 0\n\n difference = (reward + discount_factor * nextState_QValue ) - (temporary_QValue) #refer to README_Reinforcement.txt for the formula\n\n for each_feature in features:\n\n #refer to README_Reinforcement.txt for the formula at line 20\n weight[each_feature] = weight[each_feature] + learning_rate * difference * features[each_feature]\n\n #util.raiseNotDefined()","def _compute_reward(self):\n reward = 0.0\n return reward","def reward_func(self, state):\n if abs(state['theta']) < 1:\n return 2.0\n\n elif abs(state['theta']) < 3:\n return 0.0\n\n elif abs(state['theta']) > 30:\n return -100.0\n return -2.0","def observe_reward(self,state,action,statePrime,reward):\n pass","def get_gae(rewards, value_estimates, value_next=0.0, gamma=0.99, lambd=0.95):\n value_estimates = np.append(value_estimates, value_next)\n delta_t = rewards + gamma * value_estimates[1:] - value_estimates[:-1]\n advantage = discount_rewards(r=delta_t, gamma=gamma * lambd)\n return advantage","def _action_rewards(self, context) -> ActionRewards:\n pass","def update_sample_reward(self, r):\n self.r_exp[-1] += r * self.gamma ** (len(self.u_seq[-1])-1)","def reward(self):\n return self._reward","def finish_episode(tribes, learners, optimizers, gamma, cuda): \n \n num_learners = len(learners)\n total_norms = [0 for i in range(num_learners)]\n policy_losses = [[] for i in range(num_learners)]\n losses = [[] for i in range(num_learners)]\n T_reward = []\n\n \n for i in range(num_learners):\n\n R = 0\n saved_actions = learners[i].saved_actions\n \n for t in tribes:\n if t.name is learners[i].tribe:\n \n # Based on team culture, calculate the team reward for the agent \n culture = t.culture['name']\n \n if culture is 'cooperative':\n T_reward = t.tribal_awards()\n elif culture is 'individualist':\n T_reward = t.tribal_awards()\n elif culture is 'no_fragging':\n T_reward = t.tribal_awards(US_hits = learners[i].US_hits)\n elif culture is 'pacifist':\n T_reward = t.tribal_awards(tag_hist = learners[i].tag_hist)\n elif culture is 'pacifist_exile':\n T_reward = t.tribal_awards(tag_hist = learners[i].tag_hist, \\\n in_banned_hist=learners[i].in_banned_hist)\n elif culture is 'pacifist_follower':\n T_reward = t.tribal_awards(tag_hist = learners[i].tag_hist, \\\n in_target_hist=learners[i].in_target_hist)\n elif culture is 'warlike':\n T_reward = t.tribal_awards(US_hits = learners[i].US_hits,THEM_hits = learners[i].THEM_hits)\n else:\n T_reward = t.tribal_awards()\n \n # For debug only\n # print('Agent{} receives tribal award from Tribe{}'.format(i,t.name))\n # print (T_reward)\n # print (learners[i].rewards)\n \n # Do not implement actor-critic for now\n # value_losses = []\n \n rewards = deque()\n\n for r,T in zip(learners[i].rewards[::-1],T_reward[::-1]):\n # The agent is incentivized to cooperate by an award of 30% of what the tribe takes\n # in by all its members\n R = r + T + gamma * R\n rewards.appendleft(R)\n \n rewards = list(rewards)\n rewards = torch.Tensor(rewards)\n if cuda:\n rewards = rewards.cuda()\n\n # z-score rewards\n rewards = (rewards - rewards.mean()) / (1.1e-7+rewards.std())\n \n #Debug \n #print (rewards) \n \n \"\"\"\n Do not implement actor-critic for now!!!\n for (log_prob, state_value), r in zip(saved_actions, rewards):\n reward = r - state_value.data[0]\n policy_losses.append(-log_prob * Variable(reward))\n r = torch.Tensor([r])\n if cuda:\n r = r.cuda()\n value_losses.append(torch.nn.functional.smooth_l1_loss(state_value,\n Variable(r)))\n\n optimizer.zero_grad()\n loss = torch.stack(policy_losses).sum() + torch.stack(value_losses).sum()\n loss.backward() \n \n \n \"\"\"\n for log_prob, r in zip(saved_actions, rewards):\n r = torch.Tensor([r])\n if cuda:\n r = r.cuda()\n policy_losses[i].append(-log_prob * Variable(r))\n\n optimizers[i].zero_grad()\n losses[i] = torch.stack(policy_losses[i]).sum()\n losses[i].backward()\n \n # Gradient Clipping Update: prevent exploding gradient\n total_norms[i] = torch.nn.utils.clip_grad_norm_(learners[i].parameters(), 8000)\n \n optimizers[i].step()\n learners[i].clear_history() # clear an agent's history at the end of episode\n\n\n return total_norms","def discount_rewards(self, r, epEnd):\n res = np.zeros_like(r)\n running_add = 0.0\n assert (len(r)-1) in epEnd, 'simple sanity check: the last reward must be the episode end'\n assert isinstance(r, list)\n for t in reversed(xrange(len(r))):\n if t in epEnd: running_add = 0.0 # reset \n running_add = GAMMA * running_add + r[t]\n res[t] = running_add\n return res","def intrinsic_reward(\n self,\n state: Any,\n algo_step: OrderedDict[str, Any],\n reward: Any,\n terminal: Any,\n next_state: Any\n ) -> Any:\n with torch.no_grad():\n with evaluate(self.normalization_layer):\n return self._get_loss(next_state).item()","def update_policy(self, minibatch_size):\n \n steps = self.rewards.shape[0]\n batch_size = self.rewards.shape[0] * self.rewards.shape[1]\n #steps = 500\n #batch_size = 500\n #print(steps)\n #print(batch_size)\n \n # Compute advantages\n '''\n with torch.no_grad():\n if self.gae:\n advantages = torch.zeros_like(self.rewards).to(self.training_device)\n lastgaelam = 0\n for t in reversed(range(steps)):\n if t == steps - 1:\n nextnonterminal = 1.0 - self.dones[t]\n nextvalues = self.state_values[t]\n else:\n nextnonterminal = 1.0 - self.dones[t + 1]\n nextvalues = self.state_values[t + 1]\n delta = self.rewards[t] + self.gamma * nextvalues * nextnonterminal - self.state_values[t]\n advantages[t] = lastgaelam = delta + self.gamma * self.gae_lambda * nextnonterminal * lastgaelam\n returns = advantages + self.state_values\n else:\n returns = torch.zeros_like(self.rewards).to(self.training_device)\n for t in reversed(range(steps)):\n if t == steps - 1:\n nextnonterminal = 1.0 - self.dones[t]\n next_return = self.state_values[t]\n else:\n nextnonterminal = 1.0 - self.dones[t+1]\n next_return = returns[t+1]\n returns[t] = self.rewards[t] + self.gamma * nextnonterminal * next_return\n advantages = returns - self.state_values\n ''' \n returns = torch.zeros_like(self.rewards).to(self.training_device)\n for t in reversed(range(steps)):\n if t == steps - 1:\n nextnonterminal = 1.0 - self.dones[t]\n next_return = self.state_values[t]\n else:\n nextnonterminal = 1.0 - self.dones[t+1]\n next_return = returns[t+1]\n returns[t] = self.rewards[t] + self.gamma * nextnonterminal * next_return\n advantages = returns - self.state_values\n \n\n # flatten the batch\n #b_obs = self.states.reshape((-1,) + self.state_space)\n #print(self.states.shape)\n b_obs = self.states.reshape((-1,4)).detach()\n b_logprobs = self.action_probs.reshape(-1,1).detach()\n b_actions = self.actions.reshape((-1,)).detach()\n b_advantages = advantages.reshape(-1,1)\n b_returns = returns.reshape(-1,1)\n b_values = self.state_values.reshape(-1,1)\n \n # Optimize policy and value network for K epochs, run optimization in minibatches\n \n inds = np.arange(batch_size)\n for i_epoch_pi in range(self.epochs):\n np.random.shuffle(inds)\n for start in range(0, batch_size, minibatch_size):\n end = start + minibatch_size\n minibatch_ind = inds[start:end]\n mb_advantages = b_advantages[minibatch_ind]\n if self.norm_adv:\n mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)\n \n #_, newlogproba, entropy = self.get_action(b_obs[minibatch_ind], b_actions[minibatch_ind])\n newlogproba, entropy = self.evaluate(b_obs[minibatch_ind], b_actions[minibatch_ind])\n #ratio = (newlogproba - b_logprobs[minibatch_ind]).exp()\n ratio = torch.exp((newlogproba - b_logprobs[minibatch_ind].detach()))\n \n # Stats\n approx_kl = (b_logprobs[minibatch_ind] - newlogproba).mean()\n\n # Policy loss\n pg_loss1 = -mb_advantages * ratio\n pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - self.clip_epsilon, 1 + self.clip_epsilon)\n pg_loss = torch.max(pg_loss1, pg_loss2).mean()\n entropy_loss = entropy.mean()\n\n # Value loss\n _, new_values = self.policy.forward(b_obs[minibatch_ind])\n if self.clip_vloss:\n \n v_loss_unclipped = self.MseLoss(new_values,b_returns[minibatch_ind])\n #v_loss_unclipped = ((new_values - b_returns[minibatch_ind]) ** 2)\n v_clipped = b_values[minibatch_ind] + torch.clamp(new_values - b_values[minibatch_ind],\n -self.clip_epsilon, self.clip_epsilon)\n #v_loss_clipped = (v_clipped - b_returns[minibatch_ind]) ** 2\n v_loss_clipped = self.MseLoss(v_clipped,b_returns[minibatch_ind])\n v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)\n #v_loss = 0.5 * v_loss_max.mean()\n v_loss = 0.5 * v_loss_max\n else:\n #v_loss = 0.5 * ((new_values - b_returns[minibatch_ind]) ** 2).mean()\n v_loss = self.MseLoss(new_values,b_returns[minibatch_ind])\n\n loss = pg_loss + v_loss * self.vf_coeff - self.ent_coeff * entropy_loss\n\n self.optimizer.zero_grad()\n loss.backward()\n torch.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)\n self.optimizer.step()\n # Copy new weights into old policy:\n self.old_policy.load_state_dict(self.policy.state_dict())","def EventContentMissionExcelAddMissionRewardAmount(builder, MissionRewardAmount):\n return AddMissionRewardAmount(builder, MissionRewardAmount)","def reward_values(self):\n return {\"positive\": 100.0, \"tick\": 1.0, \"loss\": -1000.0}","def reset(self):\n\n # initialize gym env variables\n self.finish = False\n self.curr_step = -1\n self.curr_episode += 1\n\n # initialize target position\n self.target = np.random.uniform(-10.0,10.0,size=(2))\n\n # initialize sheep positions\n if self.fixed_reset:\n init_sheep_pose = np.array([75.0, 75.0])\n self.sheep_poses = (np.random.uniform(-50.0, 50.0, \n size=(self.num_sheep,2))) + init_sheep_pose[None,:]\n else:\n init_sheep_pose = np.random.uniform(-self.init_sheep_root, \n self.init_sheep_root, size=(2))\n self.sheep_poses = (np.random.uniform(-self.init_sheep_range, \n self.init_sheep_range, size=(self.num_sheep,2))) \\\n + init_sheep_pose[None,:]\n self.sheep_com = self.sheep_poses.mean(axis=0)\n\n # get the farthest sheep and radius of the sheep\n dist_to_com = np.linalg.norm((self.sheep_poses - self.sheep_com[None,:]), axis=1)\n self.farthest_sheep = self.sheep_poses[np.argmax(dist_to_com),:]\n self.radius_sheep = np.array([np.max(dist_to_com)])\n\n # update distance to target\n self.target_distance = np.linalg.norm(self.target - self.sheep_com)\n\n # initialize values for reward estimation\n self.init_radius_sheep = self.radius_sheep\n self.init_target_distance = self.target_distance\n\n # initialize dog position\n if self.fixed_reset:\n init_dog_pose = np.array([0.0,75.0])\n else:\n init_theta = np.random.uniform(-np.pi,np.pi)\n init_dog_pose = init_sheep_pose + self.init_dog_distance*np.array([np.cos(init_theta), \n np.sin(init_theta)])\n self.dog_pose = init_dog_pose\n\n # initialize inertia\n self.inertia = np.ones((self.num_sheep, 2))\n\n # initialize episode reward and length\n self.episode_reward = 0\n self.episode_length = 0\n\n # get the state, reward, finish, info\n state = self._get_state()\n \n return state","def reward(self, history_id, reward):\n reward_action = self._historystorage.unrewarded_histories[history_id].action\n reward_action_idx = self._actions.index(reward_action)\n context = self._historystorage.unrewarded_histories[history_id].context[reward_action_idx]\n context = np.matrix(context)\n\n # Update the model\n matrix_a = self._modelstorage.get_model()['matrix_a']\n matrix_ainv = self._modelstorage.get_model()['matrix_ainv']\n b = self._modelstorage.get_model()['b']\n theta = self._modelstorage.get_model()['theta']\n matrix_a[reward_action] += np.dot(context.T, context)\n matrix_ainv[reward_action] = np.linalg.solve(matrix_a[reward_action], np.identity(self.d))\n b[reward_action] += reward * context.T\n theta[reward_action] = np.dot(matrix_ainv[reward_action], b[reward_action])\n self._modelstorage.save_model({'matrix_a': matrix_a, 'matrix_ainv': matrix_ainv, 'b': b, 'theta': theta})\n\n # Update the history\n self._historystorage.add_reward(history_id, reward)","def _update_arms(self, arm, reward):\n # Update selected arm statistics\n self.emar[arm] = (1. - self.gamma) * self.emar[arm] + self.gamma * reward\n self.success[arm] += self.emar[arm]\n self.failure[arm] += 1. - self.emar[arm]\n\n # Forget past rewards for unselected arms\n for i in chain(range(arm), range(arm + 1, len(self.emar))):\n self.emar[i] = (1. - self.epsilon) * self.emar[i] + self.epsilon * 0.5\n self.success[i] *= (1.0 - self.epsilon)\n self.failure[i] *= (1.0 - self.epsilon)"],"string":"[\n \"def __init__(self, env):\\n gym.RewardWrapper.__init__(self, env)\",\n \"def reward(self, env):\\n del env\\n return 1\",\n \"def add_env_args(parser):\\n # sawyer\\n parser.add_argument(\\n \\\"--reward_type\\\",\\n type=str,\\n default=\\\"dense\\\",\\n choices=[\\\"dense\\\", \\\"sparse\\\"],\\n help=\\\"reward type\\\",\\n )\\n parser.add_argument(\\n \\\"--distance_threshold\\\",\\n type=float,\\n default=0.06,\\n help=\\\"distance threshold for termination\\\",\\n )\\n parser.add_argument(\\n \\\"--max_episode_steps\\\",\\n type=int,\\n default=70,\\n help=\\\"maximum timesteps in an episode\\\",\\n )\\n parser.add_argument(\\n \\\"--camera_name\\\",\\n type=str,\\n default=\\\"visview\\\",\\n help=\\\"camera name in an environment\\\",\\n )\\n\\n # observations\\n parser.add_argument(\\n \\\"--frame_skip\\\", type=int, default=1, help=\\\"Numer of skip frames\\\"\\n )\\n parser.add_argument(\\n \\\"--action_repeat\\\", type=int, default=1, help=\\\"number of action repeats\\\"\\n )\\n parser.add_argument(\\n \\\"--ctrl_reward_coef\\\", type=float, default=0, help=\\\"control reward coefficient\\\"\\n )\\n\\n parser.add_argument(\\n \\\"--kp\\\", type=float, default=40.0, help=\\\"p term for a PID controller\\\"\\n ) # 150.)\\n parser.add_argument(\\n \\\"--kd\\\", type=float, default=8.0, help=\\\"d term for a PID controller\\\"\\n ) # 20.)\\n parser.add_argument(\\n \\\"--ki\\\", type=float, default=0.0, help=\\\"i term for a PID controller\\\"\\n )\\n parser.add_argument(\\n \\\"--frame_dt\\\", type=float, default=0.15, help=\\\"delta t between each frame\\\"\\n ) # 0.1)\\n parser.add_argument(\\n \\\"--use_robot_indicator\\\",\\n type=eval,\\n default=False,\\n help=\\\"enable visualization of robot indicator for motion planner\\\",\\n )\\n parser.add_argument(\\n \\\"--use_target_robot_indicator\\\",\\n type=eval,\\n default=False,\\n help=\\\"enable visualization of robot indicator for target position of motion planner\\\",\\n )\\n parser.add_argument(\\n \\\"--success_reward\\\", type=float, default=150.0, help=\\\"completion reward\\\"\\n )\\n parser.add_argument(\\n \\\"--contact_threshold\\\",\\n type=float,\\n default=-0.002,\\n help=\\\"depth thredhold for contact\\\",\\n )\\n parser.add_argument(\\n \\\"--joint_margin\\\", type=float, default=0.001, help=\\\"marin of each joint\\\"\\n )\\n parser.add_argument(\\\"--task_level\\\", type=str, default=\\\"easy\\\")\\n parser.add_argument(\\n \\\"--step_size\\\",\\n type=float,\\n default=0.02,\\n help=\\\"step size for invalid target handling\\\",\\n )\\n # puck\\n parser.add_argument(\\\"--puck_friction\\\", type=float, default=2.0)\\n parser.add_argument(\\\"--puck_mass\\\", type=float, default=0.01)\\n parser.add_argument(\\\"--source_env_puck_friction\\\", type=float, default=2.0)\\n parser.add_argument(\\\"--source_env_puck_mass\\\", type=float, default=0.01)\\n parser.add_argument(\\\"--target_env_puck_friction\\\", type=float, default=2.0)\\n parser.add_argument(\\\"--target_env_puck_mass\\\", type=float, default=0.01)\\n\\n parser.add_argument(\\\"--env_ob_source\\\", type=str2bool, default=False)\\n parser.add_argument(\\\"--end_effector\\\", type=str2bool, default=True)\\n parser.add_argument(\\\"--ik_target\\\", type=str, default=\\\"grip_site\\\")\\n parser.add_argument(\\n \\\"--action_range\\\", type=float, default=0.1, help=\\\"range of radian\\\"\\n )\\n parser.add_argument(\\\"--dr\\\", type=str2bool, default=False)\\n parser.add_argument(\\\"--dr_params_set\\\", type=str, default=\\\"IP_large_range\\\")\\n\\n parser.add_argument(\\\"--mod_env_params\\\", type=str2bool, default=False)\\n parser.add_argument(\\\"--param_mod_instructions\\\", type=eval, default=[])\\n\\n parser.add_argument(\\\"--unity\\\", type=str2bool, default=False)\\n parser.add_argument(\\\"--unity_editor\\\", type=str2bool, default=False)\\n parser.add_argument(\\\"--virtual_display\\\", type=str, default=\\\":1\\\")\\n parser.add_argument(\\\"--port\\\", type=int, default=4000)\\n\\n # FetchReach action\\n parser.add_argument(\\\"--action_rotation_degrees\\\", type=float, default=0.0)\\n parser.add_argument(\\\"--action_z_bias\\\", type=float, default=0.0)\",\n \"def update(self, arm, reward, context):\",\n \"def __generate_reward_function(self):\\n K = -3\\n self.reward = np.array([[10, 0, K],\\n [0, 2, 0],\\n [K, 0, 10]])\",\n \"def reward_spec(self):\\n task_reward_spec = self._task.get_reward_spec()\\n if task_reward_spec is not None:\\n return task_reward_spec\\n else:\\n return super(Environment, self).reward_spec()\",\n \"def reward(self, history_id, reward):\\n pass\",\n \"def reward(self) -> Reward:\\n return self.__reward\",\n \"def reward(self, value):\\n self._custom_setter('reward', value)\",\n \"def update_reward(state, reward, max_reward, alpha=1, c=100, gamma=0.9):\\n\\t\\t# update number of actions done so far to this state\\n\\t\\tactions[state] = actions.get(state, 0.0) + 1.0\\n\\t\\t# compute learning rate\\n\\t\\talpha *= c / (c + actions[state])\\n\\t\\trewards[state] = rewards.get(state, 0.0) + alpha*(reward+gamma*max_reward-rewards.get(state, 0.0))\",\n \"def get_reward(self):\\n\\t\\t# returns the reward for current state\\n\\n\\t\\t#temporary line for testing:\\n\\t\\t#return self.reward_idea() # also not working yet\\n\\n\\t\\tcost = self.cost_function()\\n\\t\\tconstraints_violation = self.get_constraints_violation()\\n\\t\\t#old_aug_cost = self.augmented_cost\\n\\t\\t#new_aug_cost = self.get_augmented_cost(cost,constraints_violation)\\n\\t\\t#reward = old_aug_cost - new_aug_cost # reward formula\\n\\t\\t#self.augmented_cost = new_aug_cost # update augmented_cost\\n\\t\\treward = -self.get_augmented_cost(cost,constraints_violation)\\n\\t\\t#print(\\\"***\\\\nDEBUG cost: \\\" +str(cost)+\\\" constraints_violation: \\\"+str(constraints_violation))\\n\\t\\t#print(\\\"DEBUG reward: \\\"+str(reward))\\n\\t\\t'''\\n\\t\\t#old idea that is probably bad and not necessary:\\n\\t\\tif(self.step_count == 0):\\n\\t\\t\\t# old_aug_cost doesn't exist in first step... ACTUALLY IT DOES!\\n\\t\\t\\tprint(\\\"DEBUG step_count == 0, reward would be \\\"+str(reward))\\n\\t\\t\\tprint(\\\"DEBUG old_aug_cost: \\\"+str(old_aug_cost) + \\\" new_aug_cost: \\\"+str(new_aug_cost) )\\n\\t\\t\\treturn 0\\n\\t\\t'''\\n\\n\\t\\treturn reward\",\n \"def experiment(config):\\n with tf.Session() as sess:\\n\\n seed = config.pop('seed')\\n\\n if seed:\\n seed = int(seed)\\n random.seed(seed)\\n tf.set_random_seed(seed)\\n np.random.seed(seed)\\n\\n env_id = config.pop('env_id')\\n LOGGER.info('using {} env'.format(env_id))\\n\\n env = gym.make(env_id)\\n\\n global_rewards = []\\n global_step, episode = 0, 0\\n\\n config['env'] = env\\n config['env_repr'] = repr(env)\\n config['sess'] = sess\\n\\n render = int(config.pop('render'))\\n\\n agent = Agent(**config)\\n\\n rl_writer = tf.summary.FileWriter('./results/rl')\\n save_args(config, 'results/args.txt')\\n\\n while global_step < config['total_steps']:\\n episode += 1\\n done = False\\n rewards, actions = [], []\\n observation = env.reset()\\n\\n while not done:\\n global_step += 1\\n\\n # if episode % 1 == render:\\n env.render()\\n action = agent.act(observation)\\n next_observation, reward, done, info = env.step(action)\\n agent.remember(observation, action, reward, next_observation, done)\\n train_info = agent.learn()\\n\\n rewards.append(reward)\\n actions.append(action)\\n observation = next_observation\\n\\n ep_rew = sum(rewards)\\n global_rewards.append(ep_rew)\\n avg_reward = sum(global_rewards[-100:]) / len(global_rewards[-100:])\\n\\n if episode % 10 == 0:\\n log_str =' step {:.0f} ep {:.0f} reward {:.1f} avg {:.1f}'\\n logging.info(log_str.format(global_step,\\n episode,\\n ep_rew,\\n avg_reward))\\n\\n summary = tf.Summary(value=[tf.Summary.Value(tag='episode_reward',\\n simple_value=ep_rew)])\\n rl_writer.add_summary(summary, episode)\\n avg_sum = tf.Summary(value=[tf.Summary.Value(tag='avg_last_100_ep',\\n simple_value=avg_reward)])\\n rl_writer.add_summary(avg_sum, episode)\\n rl_writer.flush()\\n \\n return config\",\n \"def update(self, reward):\\n raise NotImplementedError\",\n \"def reward_function(self):\\r\\n def R(state, decision, nodes):\\r\\n return -1.0/1000*nodes['G'].get_preds_value(state)*(decision['G:R_1']+decision['G:L'])\\r\\n \\r\\n return R\",\n \"def addReward(self):\\n # by default, the cumulative reward is just the sum over the episode\\n if self.discount:\\n self.cumreward += power(self.discount, self.samples) * self.getReward()\\n else:\\n self.cumreward += self.getReward()\",\n \"def set_reward(self, reward):\\n self.reward = reward\",\n \"def nchain_extras(env, gamma=0.99):\\n\\n # How to handle <TimeLimit<______>> and other Wrappers?\\n # assert isinstance(env, gym.envs.toy_text.nchain.NChainEnv)\\n\\n # Action constants\\n A_FORWARD = 0\\n A_BACKWARD = 1\\n\\n states = np.arange(env.observation_space.n)\\n actions = np.arange(env.action_space.n)\\n\\n p0s = np.zeros(env.observation_space.n)\\n p0s[0] = 1.0\\n\\n # Populate dynamics\\n t_mat = np.zeros(\\n (env.observation_space.n, env.action_space.n, env.observation_space.n)\\n )\\n\\n # Backward action moves to 0th state if it doesn't fail, forward if it does\\n t_mat[:, A_BACKWARD, 0] = 1.0 - env.slip\\n for s1 in states:\\n t_mat[s1, A_BACKWARD, min(s1 + 1, env.observation_space.n - 1)] = env.slip\\n\\n # Forward action moves to next state if it doesn't fail, 0th if it does\\n for s1 in states:\\n t_mat[s1, A_FORWARD, min(s1 + 1, env.observation_space.n - 1)] = 1.0 - env.slip\\n t_mat[:, A_FORWARD, 0] = env.slip\\n\\n terminal_state_mask = np.zeros(env.observation_space.n)\\n\\n xtr = DiscreteExplicitExtras(\\n states, actions, p0s, t_mat, terminal_state_mask, gamma=gamma,\\n )\\n\\n phi = Indicator(Indicator.Type.OBSERVATION_ACTION, xtr)\\n\\n state_action_rewards = np.zeros((env.observation_space.n, env.action_space.n))\\n state_action_rewards[:, A_BACKWARD] = env.small\\n state_action_rewards[env.observation_space.n - 1, A_FORWARD] = env.large\\n reward = Linear(state_action_rewards.flatten())\\n\\n return (xtr, phi, reward)\",\n \"def gae(done, rewards, values, n_envs, steps_per_env, gamma, gae_lambda, device):\\n advantages = torch.zeros((n_envs, steps_per_env, 1), dtype=torch.float, device=device)\\n last_advantage = 0\\n for state in reversed(range(steps_per_env)):\\n error = rewards[:, state] + gamma * values[:, state + 1] * (~done[:, state]) - values[:, state]\\n last_advantage = (error + gamma * gae_lambda * last_advantage * (~done[:, state]))\\n\\n advantages[:, state] = last_advantage\\n\\n return advantages\",\n \"def reward(self, observation, action, reward):\\n self.iter[action] += 1\\n self.expvalue[action] += 1/self.iter[action]*(reward - self.expvalue[action])\",\n \"def _reward(self, action):\\n raise NotImplementedError\",\n \"def run_one_step(self):\\n # Get the current state, action and initialise the reward\\n state = copy.copy(self.env.get_state())\\n action = self.agent.get_action(state)\\n reward = 0.0\\n # Check if the environment has reached a terminal state\\n if self.env.check_terminal() is False:\\n # Save the initial state and action to an 'experience'\\n latest_experience = Experience(copy.copy(state), copy.copy(action))\\n # Update the environment using the chosne action\\n self.env.update(action)\\n # Get the reward to attribute to the agent and save to the experience to save\\n reward = self.env.get_reward()\\n latest_experience.reward = copy.copy(reward)\\n # Get the updated state\\n state = self.env.get_state()\\n if self.env.check_terminal() is False:\\n # If the new state isn't terminal, save the next action and the 'done' flag to the experience\\n action = self.agent.get_action(state)\\n latest_experience.done = False\\n else:\\n # If the new state is terminal, save a dummy action and the 'done' flag to the experience\\n action = 0.0\\n latest_experience.done = True\\n latest_experience.next_state = copy.copy(state)\\n latest_experience.next_action = copy.copy(action)\\n # Update the history with the latest experience\\n self.agent.update_history(copy.copy(latest_experience))\\n # Update the agents policy using a batch of experiences chosen from the history\\n self.agent.update_policy_batch(max(1, self.batch_size))\\n self.count += 1\\n # Update the target network if appropriate\\n if self.update_target_rate is not None:\\n if self.count % self.update_target_rate == 0:\\n self.agent.policy.learner.update_target_network()\\n else:\\n # If the environment is in a terminal state, record this and perform a policy update\\n latest_experience = Experience(copy.copy(state), copy.copy(action))\\n latest_experience.reward = 0.0\\n latest_experience.next_state = copy.copy(state)\\n latest_experience.next_action = 0.0\\n latest_experience.done = True\\n self.agent.update_history(copy.copy(latest_experience))\\n self.agent.update_policy_batch(max(1, self.batch_size))\\n self.count = 0\\n return reward\",\n \"def update(self, arm, reward):\\n raise NotImplementedError\",\n \"def update_rewards(self, arm: int, reward: float):\\r\\n if self.rewards[arm][0] == self.optim_c:\\r\\n self.rewards[arm] = np.array(reward)\\r\\n else:\\r\\n self.rewards[arm] = np.append(self.rewards[arm], reward)\",\n \"def add_rewards(self, step_reward, goal_reward, bad_state_reward=None, restart_state_reward = None):\\n self.r_step = step_reward\\n self.r_goal = goal_reward\\n self.r_bad = bad_state_reward\\n self.r_restart = restart_state_reward\",\n \"def add_reward(self, r):\\n self.reward += r\",\n \"def compute_reward(self, state, rl_actions, **kwargs):\\n raise NotImplementedError\",\n \"def set_reward(self, rewards, trader):\\n\\n NAV_chg = float(trader.acc.nav - trader.acc.prev_nav)\\n\\n # maximize NAV\\n #rewards[trader.ID] = NAV_chg\\n\\n # maximize NAV, minimize num of trades (more trades gets penalized).\\n if NAV_chg >= 0:\\n rewards[trader.ID] = NAV_chg / (trader.acc.num_trades + 1)\\n else:\\n rewards[trader.ID] = NAV_chg * (trader.acc.num_trades + 1)\\n\\n trader.acc.reward = rewards[trader.ID]\\n\\n return rewards\",\n \"def reward(self) -> float:\\n\\n if not self._reward_provided:\\n self._reward_provided = True\\n return self._reward\\n\\n return 0\",\n \"def immediate_reward(state_node):\\n #DANIEL: edited the reward function to get rid of the parent argument and will just be a function of the current state\\n return state_node.state.reward()\",\n \"def update(self, arm, context, reward):\\n raise NotImplementedError\",\n \"def update(self, arm, context, reward):\\n raise NotImplementedError\",\n \"def _get_reward(self):\\n if self.status():\\n return self.current_step/self.ep_length # the reward is proportional to the duration \\n else:\\n return 0\",\n \"def setReward(self, reward):\\n assert isinstance(reward, (float, int)), ' Stochy veut un nombre!'\\n \\n self.__reward.append(reward)\\n self.__total += reward\",\n \"def eval_policy_on_env(self, eval_gym_env, eval_episodes=10, seed=None):\\n if not seed:\\n eval_gym_env.seed(seed)\\n else:\\n eval_gym_env.seed(int(time.time()))\\n avg_reward = 0.\\n for i in range(eval_episodes):\\n state, done = eval_gym_env.reset(), False\\n obs = self.observer(state)\\n step = 0\\n #while not done and step < max_steps:\\n while not done:\\n action = self.plan(np.array(obs))\\n state, reward, done, _ = eval_gym_env.step(action)\\n obs = self.observer(state)\\n avg_reward += reward\\n step += 1\\n\\n avg_reward /= eval_episodes\\n return avg_reward\",\n \"def agent_reward(self, func):\\n if len(self.agents_reward) is 2:\\n raise Exception(\\\"You cannot register 3 or more agents_reward.\\\")\\n\\n self.agents_reward.append(func)\\n return func\",\n \"def modify_reward(reward):\\n # CASE 1 ----------------\\n # Nominal reward - return the same reward\\n new_reward = reward\\n \\n # CASE 2 ----------------\\n # Aggressive striker - remove penalty for being scored on\\n if reward < -0.1:\\n new_reward = reward + 0.1\\n \\n # CASE 3 ----------------\\n # Aggressive striker - add more penalty for being scored on\\n# if reward < -0.1:\\n# new_reward = reward - 0.5\\n \\n # ============================= DEBUG ONLY =============================\\n# print(\\\"reward = \\\", reward)\\n# print(\\\"new_reward = \\\", new_reward)\\n # ============================= DEBUG ONLY =============================\\n \\n return new_reward\",\n \"def _reward(self, action: Action) -> float:\\n raise NotImplementedError\",\n \"def reinforce(env, estimator_policy, estimator_value, num_episodes, discount_factor=1.0):\\n\\n # Keeps track of useful statistics\\n stats = plotting.EpisodeStats(\\n episode_lengths=np.zeros(num_episodes),\\n episode_rewards=np.zeros(num_episodes)) \\n \\n Transition = collections.namedtuple(\\\"Transition\\\", [\\\"state\\\", \\\"action\\\", \\\"reward\\\", \\\"next_state\\\", \\\"done\\\"])\\n \\n for i_episode in range(num_episodes):\\n # Reset the environment and pick the fisrst action\\n state = env.reset()\\n \\n episode = []\\n \\n # One step in the environment\\n for t in itertools.count():\\n \\n # Take a step\\n #action_means = np.ndarray.flatten(estimator_policy.predict(state))\\n #action = np.random.multivariate_normal(mean=action_means, cov=full_var)\\n\\n action = estimator_policy.predict(state)\\n\\n '''\\n max_idx = np.argmax(np.abs(action))\\n a_max = action[max_idx]\\n\\n if a_max > high_threshold or a_max < low_threshold: \\n action_clipped = action / (10*np.abs(a_max))\\n '''\\n\\n\\n '''\\n action_clipped = [np.max([np.min([action[0], high_threshold]), low_threshold]), \\n np.max([np.min([action[1], high_threshold]),\\n low_threshold])]\\n '''\\n\\n next_state, reward, done, _ = env.step(action)\\n\\n '''\\n if t > 50:\\n done = True\\n '''\\n \\n # Keep track of the transition\\n episode.append(Transition(\\n state=state, action=action, reward=reward, next_state=next_state, done=done))\\n \\n # Update statistics \\n stats.episode_rewards[i_episode] += reward\\n stats.episode_lengths[i_episode] = t\\n \\n '''\\n # Print out which step we're on, useful for debugging.\\n print(\\\"\\\\rStep {} @ Episode {}/{} ({})\\\".format(\\n t, i_episode + 1, num_episodes, stats.episode_rewards[i_episode - 1]), end=\\\"\\\")\\n # sys.stdout.flush()\\n '''\\n\\n if done:\\n break\\n \\n state = next_state\\n \\n monitor_epoch = 50\\n if i_episode % monitor_epoch == 0 and i_episode > 0: \\n print(\\\"avg reward : %f\\\" %(np.mean(stats.episode_rewards[i_episode-monitor_epoch:i_episode])))\\n\\n\\n baseline_value = np.mean([cur_trans.reward for cur_trans in episode]) \\n\\n # Go through the episode and make policy updates\\n for t, transition in enumerate(episode):\\n # The return after this timestep\\n total_return = sum(discount_factor**i * t.reward for i, t in enumerate(episode[t:]))\\n \\n advantage = total_return - baseline_value\\n #advantage += np.max([0, baseline_value - v_prev])\\n\\n # Update our policy estimator\\n estimator_policy.update(transition.state, advantage, transition.action)\\n\\n #print(p_action)\\n\\n if i_episode % 200 == 0 and i_episode > 0:\\n plt.figure(1)\\n plt.plot(transition.state[0], transition.state[1], 'bo')\\n if t == len(episode) - 1:\\n plt.show()\\n \\n \\n return stats\",\n \"def run_episode(env, policy, gamma=1.0, render = False):\\n obs = env.reset()\\n total_reward = 0\\n step_idx = 0\\n while True:\\n if render:\\n env.render()\\n obs, reward, done , _ = env.step(int(policy[obs]))\\n total_reward += (gamma ** step_idx * reward)\\n step_idx += 1\\n if done:\\n break\\n #print(total_reward)\\n return total_reward\",\n \"def train(self, reward: str):\\r\\n pass\",\n \"def update(self, next_state, reward):\\n pass\",\n \"def get_reward(self, state, action, next_state, absorbing):\\n raise NotImplementedError\",\n \"def reward(self, observation, action, reward):\\n self.counts[action] = self.counts[action] + 1\\n n = self.counts[action]\\n value = self.values[action]\\n \\n new_value = ((n - 1) / float(n)) * value + (1 / float(n)) * reward\\n self.values[action] = new_value\\n self.minmax = max(self.values) - min(self.values)\\n \\n \\n pass\",\n \"def reward(self, **kwargs):\\n # rewards = self.get_mos_from_aps()\\n rewards = self.get_mos_from_localhost()\\n self.log.debug(\\\"Collect MOS: {}\\\".format(rewards))\\n\\n # obtain the global reward\\n avgs = []\\n for i in rewards:\\n m = np.nanmean(rewards[i])\\n # trunc to bounds\\n if m < 1:\\n m = 1\\n elif m > 5:\\n m = 5\\n avgs.append(m)\\n self.log.debug(\\\"Avgs: {}\\\".format(avgs))\\n if np.any(np.isnan(avgs)):\\n # if found a nan, that means an error (e.g. a disconnected station)\\n r = [np.nan for _ in rewards]\\n else:\\n avgs = np.array(avgs)\\n C = self.DEFAULT_C if 'C' not in kwargs else kwargs['C']\\n r = reward_hossfeld(avgs, C=C)\\n self.log.info(\\\"Hossfeld reward: {} C: {}\\\".format(r, C))\\n\\n self.log.info(\\\"Rewards: {}\\\".format(r))\\n return r\",\n \"def reward_calc(self, reward_traj,V,V_end):\\n r_all = np.concatenate((reward_traj,[V_end]),-1)\\n V_all = V #np.concatenate((V,[V_end]),-1)\\n delta = r_all[:-1] + self.gamma * V_all[1:] - V_all[:-1]\\n \\n adv = Generalized_Adv_Estimator.discounted_sum(delta,self.gamma*self.lam)\\n rtg = adv + V_all[:-1]\\n\\n adv = adv.astype('float32')\\n rtg = rtg.astype('float32')\\n\\n return adv, rtg\",\n \"def _compute_reward(self):\\n last_score = self.episode_qualities[-2]\\n new_score = self.episode_qualities[-1]\\n reward = new_score - last_score\\n return reward\",\n \"def reward(self,\\n state: float) -> float:\\n raise NotImplementedError\",\n \"def run(self):\\n\\t\\tep_rewards = [0.0]\\n\\t\\tavg_rewards = []\\n\\t\\tobs = self.env.reset()\\n\\t\\tstep_counter = 0\\n\\n\\t\\tself.mylogger.info('Task: {}, epochs: {}, batch size: {}'.format(self.env.unwrapped.spec.id, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t self.epochs,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t self.batch_size\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t ))\\n\\n\\t\\tfor epoch in range(self.epochs):\\n\\t\\t\\tfor step in range(self.batch_size):\\n\\t\\t\\t\\tstep_counter += 1\\n\\n\\t\\t\\t\\tself.observations[step] = obs.copy()\\n\\t\\t\\t\\tself.actions[step], self.values[step] = self.model.action_value(obs[None, :])\\n\\t\\t\\t\\tobs, self.rewards[step], self.dones[step], _ = self.env.step(self.actions[step])\\n\\t\\t\\t\\tep_rewards[-1] += self.rewards[step]\\n\\n\\t\\t\\t\\tif step_counter % self.log_step == 0:\\n\\t\\t\\t\\t\\tlog_msg = 'global_step: {}, obs: {}, act: {}, reward: {}'.format(step_counter,\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t obs, \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t self.actions[step], \\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t self.rewards[step]\\n\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t\\t )\\n\\t\\t\\t\\t\\tself.mylogger.info(log_msg)\\n\\t\\t\\t\\t\\tself.mylogger.info(\\\"prev episode reward: {}\\\".format(ep_rewards[-2]))\\n\\n\\t\\t\\t\\tif self.dones[step]:\\n\\t\\t\\t\\t\\twith self.summary_writer.as_default():\\n\\t\\t\\t\\t\\t\\ttf.summary.scalar('episode reward', ep_rewards[-1], step=step_counter)\\n\\t\\t\\t\\t\\tep_rewards.append(0.0)\\n\\t\\t\\t\\t\\tobs = self.env.reset()\\n\\n\\t\\t\\t_, next_value = self.model.action_value(obs[None, :])\\n\\t\\t\\treturns, advs = self._returns_advantages(self.rewards, self.dones, self.values, next_value)\\n\\t\\t\\t# A trick to input actions and advantages through same API.\\n\\t\\t\\tacts_and_advs = np.concatenate([self.actions[:, None], advs[:, None]], axis=-1)\\n\\n\\t\\t\\t# update weights \\n\\t\\t\\tlosses = self.model.train_on_batch(self.observations, [acts_and_advs, returns])\\n\\n\\t\\t\\twith self.summary_writer.as_default():\\n\\t\\t\\t\\ttf.summary.scalar('policy loss', losses[1], step=step_counter)\\n\\t\\t\\t\\ttf.summary.scalar('value loss', losses[2], step=step_counter)\",\n \"def __init__(self, init_pose=None, init_velocities=None, \\n init_angle_velocities=None, runtime=5., target_pos=None, reward_function=None, angle_penalty=10.):\\n # Simulation\\n self.sim = PhysicsSim(init_pose, init_velocities, init_angle_velocities, runtime) \\n self.action_repeat = 3\\n\\n self.state_size = self.action_repeat * 6\\n self.action_low = 0\\n self.action_high = 900\\n self.action_size = 4\\n self.angle_penalty = angle_penalty\\n self.max_v_length = 0.\\n\\n # Goal\\n self.target_pos = target_pos if target_pos is not None else np.array([0., 0., 10.]) \\n \\n self.distance_to_target = self.vector_length(self.target_pos - self.sim.pose[:3])\\n \\n if( reward_function == 6 ):\\n self.reward_function_str = 'Using Reward Function 6'\\n self.reward_function = self.reward_function_6\\n elif( reward_function == 5 ):\\n self.reward_function_str = 'Using Reward Function 5'\\n self.reward_function = self.reward_function_5\\n elif( reward_function == 4 ):\\n self.reward_function_str = 'Using Reward Function 4'\\n self.reward_function = self.reward_function_4\\n elif( reward_function == 3 ):\\n self.reward_function_str = 'Using Reward Function 3'\\n self.reward_function = self.reward_function_3\\n elif( reward_function == 2 ):\\n self.reward_function_str = 'Using Reward Function 2'\\n self.reward_function = self.reward_function_2\\n elif( reward_function == 1 ):\\n self.reward_function_str = 'Using Reward Function 1'\\n self.reward_function = self.reward_function_1\\n else:\\n self.reward_function_str = 'Using Deafault Reward Function'\\n self.reward_function = self.default_reward\",\n \"def _compute_reward(self): \\n reward = -1\\n return reward\",\n \"def q_learning(env, learning, discount, epsilon, min_eps, episodes):\\n # [18.00000072 14.00000006]\\n num_states = (env.observation_space.high - env.observation_space.low) * \\\\\\n np.array([10, 100]) # >> [18.00000072 14.00000006]\\n num_states = np.round(num_states, 0).astype(int) + 1 # >> [19 15]\\n\\n # Initialize Q table\\n # env.action_space.n return the number of action that our agent can make (here 3, left, cease, right)\\n Q = np.random.uniform(low=-1, high=1, size=(num_states[0], num_states[1], env.action_space.n))\\n\\n # Initialize variable to track rewards\\n reward_list = []\\n ave_reward_list = []\\n\\n # Calculate episodic reduction in epsilon\\n reduction = (epsilon - min_eps) / (episodes / 2)\\n\\n for i in range(episodes):\\n # Initialize parameters\\n done = False\\n tot_reward, reward = 0, 0\\n state = env.reset()\\n\\n # Discretize state\\n state_adj = adjust_state(state)\\n\\n while done != True:\\n # Render env for last five eps\\n if i >= (episodes - 20):\\n env.render()\\n\\n # Determine next action - epsilon greedy strategy\\n if np.random.random() < 1 - epsilon:\\n action = np.argmax(Q[state_adj[0], state_adj[1]])\\n else:\\n action = np.random.randint(0, env.action_space.n)\\n\\n # Get next state and reward\\n state2, reward, done, info = env.step(action)\\n\\n # Discretize state2\\n state2_adj = adjust_state(state2)\\n\\n # Allow for terminal states // .5 on env_space[0] represent the flag position\\n if done and state2[0] >= .5:\\n Q[state_adj[0], state_adj[1], action] = reward\\n\\n # adjust Q value for current state\\n else:\\n '''work on this, it's complicated but far from non-understandable'''\\n delta = learning*(reward + discount*np.max(Q[state2_adj[0], state2_adj[1]]) -\\n Q[state_adj[0], state_adj[1], action])\\n Q[state_adj[0], state_adj[1], action] += delta\\n\\n tot_reward += reward\\n state_adj = state2_adj\\n\\n # Decay epsilon\\n if epsilon > min_eps:\\n epsilon -= reduction\\n\\n # Track rewards\\n reward_list.append(tot_reward)\\n\\n if (i+1) % 100 == 0:\\n ave_reward = np.mean(reward_list)\\n ave_reward_list.append(ave_reward)\\n reward_list = []\\n print(f'Episode {i+1} Average Reward: {ave_reward}')\\n\\n env.close()\\n\\n return ave_reward_list\",\n \"def update_Q(self, reward):\\n old_estimate = self.q_estimates[self.prev_action]\\n self.q_estimates[self.prev_action] = old_estimate + 1/self.N[self.prev_action] * (reward - old_estimate)\",\n \"def run_one_step(self):\\n state = copy.copy(self.env.get_state())\\n action = self.agent.get_action(state)\\n reward = 0.0\\n if self.env.check_terminal() is False:\\n latest_experience = Experience(copy.copy(state), copy.copy(action))\\n self.env.update(action)\\n reward = self.env.get_reward()\\n latest_experience.reward = copy.copy(reward)\\n state = self.env.get_state()\\n if self.env.check_terminal() is False:\\n action = self.agent.get_action(state)\\n latest_experience.done = False\\n else:\\n action = 0.0\\n latest_experience.done = True\\n latest_experience.next_state = copy.copy(state)\\n latest_experience.next_action = copy.copy(action)\\n self.agent.update_history(copy.copy(latest_experience))\\n self.count += 1\\n # If the latest history has a large enough batch, perform an update\\n # CHECK IF THIS IS THE RIGHT METHOD\\n if self.count % self.batch_size == 0:\\n self.agent.update_policy_ordered(max(1, self.batch_size))\\n if self.update_target_rate is not None:\\n if self.count % self.update_target_rate == 0:\\n self.count = 0\\n self.agent.policy.learner.update_target_network()\\n else:\\n latest_experience = Experience(copy.copy(state), copy.copy(action))\\n latest_experience.reward = 0.0\\n latest_experience.next_state = copy.copy(state)\\n latest_experience.next_action = 0.0\\n latest_experience.done = True\\n self.agent.update_history(copy.copy(latest_experience))\\n # Perform an update on all of the previous experiences that haven't been updated\\n if self.count % self.batch_size > 0:\\n self.agent.update_policy_ordered((self.count % self.batch_size) + 1)\\n self.count = 0\\n return reward\",\n \"def evaluate(env, model, num_env, iter_step):\\n episode_rewards = []\\n episode_reward = np.zeros((num_env))\\n obs = env.reset()\\n for _ in tqdm(range(iter_step)):\\n action, _states = model.predict(obs)\\n obs, reward, done, info = env.step(action)\\n episode_reward += reward\\n for i in range(num_env):\\n if done[i]:\\n episode_rewards.append(episode_reward[i])\\n episode_reward[i] = 0\\n return episode_rewards\",\n \"def __init__(self,\\n sess,\\n num_actions,\\n observation_shape=base_dqn_agent.NATURE_DQN_OBSERVATION_SHAPE,\\n gamma=0.99,\\n update_horizon=1,\\n min_replay_history=20000,\\n update_period=4,\\n target_update_period=8000,\\n epsilon_fn=linearly_decaying_epsilon,\\n epsilon_train=0.01,\\n epsilon_eval=0.001,\\n epsilon_decay_period=250000,\\n tf_device='/cpu:*',\\n use_staging=True,\\n max_tf_checkpoints_to_keep=3,\\n optimizer=tf.train.RMSPropOptimizer(\\n learning_rate=0.00025,\\n decay=0.95,\\n momentum=0.0,\\n epsilon=0.00001,\\n centered=True),\\n summary_writer=None,\\n summary_writing_frequency=500,\\n clip_reward=False):\\n self._clip_reward = clip_reward\\n self.intrinsic_model = intrinsic_rewards.RNDIntrinsicReward(\\n sess=sess,\\n tf_device=tf_device,\\n summary_writer=summary_writer)\\n super(RNDDQNAgent, self).__init__(\\n sess=sess,\\n num_actions=num_actions,\\n observation_shape=observation_shape,\\n gamma=gamma,\\n update_horizon=update_horizon,\\n min_replay_history=min_replay_history,\\n update_period=update_period,\\n target_update_period=target_update_period,\\n epsilon_fn=epsilon_fn,\\n epsilon_train=epsilon_train,\\n epsilon_eval=epsilon_eval,\\n epsilon_decay_period=epsilon_decay_period,\\n tf_device=tf_device,\\n use_staging=use_staging,\\n optimizer=optimizer,\\n summary_writer=summary_writer,\\n summary_writing_frequency=summary_writing_frequency)\",\n \"def reward(input):\\n state = np.array([input[0], input[1]])\\n action = input[2]\\n action = np.clip(action, -2.0, 2.0)\\n costs = angle_normalize(state[0])**2 + .1 * state[1]**2 + .001 * (action**2)\\n\\n return - costs\",\n \"def apply_reinforcement_learning(self, reward: int, p) -> None:\\n for cl in self:\\n cl.update_reward(reward + self.cfg.gamma * p)\\n cl.update_intermediate_reward(reward)\",\n \"def __init__(self, environment):\\n self.env = environment\\n self.cumreward = 0 # tracking cumulative reward\\n self.samples = 0 # tracking the number of samples\\n\\n self.sensor_limits = None\\n self.actor_limits = None\\n self.clipping = True\\n\\n self.current_action = 0 # Saving current action\\n self.prev_action = -1 # Saving previous action\",\n \"def AttendanceRewardExcelAddRewardAmount(builder, RewardAmount):\\n return AddRewardAmount(builder, RewardAmount)\",\n \"def _reward(self):\\n # Clock reward -----------------------------------------------------------------\\n A, B = self.get_von_mises(0.0, self.ratio, self.kappa)\\n phi = self.phase / self.cycle_len\\n #print('Cycles completed = ', self.cycle_complete)\\n\\n #print('A, B = ', (A,B))\\n\\n phi_FL = self.wrap(phi + self.theta_FL)\\n phi_FR = self.wrap(phi + self.theta_FR)\\n phi_RL = self.wrap(phi + self.theta_RL)\\n phi_RR = self.wrap(phi + self.theta_RR)\\n\\n #print(phi_FL)\\n #print(phi_FR)\\n #print(phi_RL)\\n #print(phi_RR)\\n\\n FL_swing = self.in_swing(A, B, phi_FL)\\n FR_swing = self.in_swing(A, B, phi_FR)\\n RL_swing = self.in_swing(A, B, phi_RL)\\n RR_swing = self.in_swing(A, B, phi_RR)\\n\\n #print('Time since reset = ', self.rex.GetTimeSinceReset())\\n #print('phase phi = ', phi)\\n #print('FL swing = ', FL_swing)\\n #print('FR swing = ', FR_swing)\\n #print('RL swing = ', RL_swing)\\n #print('RR swing = ', RR_swing)\\n\\n if FL_swing:\\n c_swing_frc_FL = 1\\n c_swing_spd_FL = 0\\n else:\\n c_swing_frc_FL = 0\\n c_swing_spd_FL = 1\\n\\n if FR_swing:\\n c_swing_frc_FR = 1\\n c_swing_spd_FR = 0\\n else:\\n c_swing_frc_FR = 0\\n c_swing_spd_FR = 1\\n\\n if RL_swing:\\n c_swing_frc_RL = 1\\n c_swing_spd_RL = 0\\n else:\\n c_swing_frc_RL = 0\\n c_swing_spd_RL = 1\\n\\n if RR_swing:\\n c_swing_frc_RR = 1\\n c_swing_spd_RR = 0\\n else:\\n c_swing_frc_RR = 0\\n c_swing_spd_RR = 1\\n\\n FL_foot_force, FR_foot_force, RL_foot_force, RR_foot_force = self.get_contact_forces()\\n FL_vel, FR_vel, RL_vel, RR_vel = self.get_foot_velocities()\\n\\n FL_penalty = c_swing_frc_FL*FL_foot_force + c_swing_spd_FL*FL_vel\\n FR_penalty = c_swing_frc_FR*FR_foot_force + c_swing_spd_FR*FR_vel\\n RL_penalty = c_swing_frc_RL*RL_foot_force + c_swing_spd_RL*RL_vel\\n RR_penalty = c_swing_frc_RR*RR_foot_force + c_swing_spd_RR*RR_vel\\n\\n foot_penalties = FL_penalty + FR_penalty + RL_penalty + RR_penalty\\n \\n # Deviation Penalties ----------------------------------------------------------\\n # Base height\\n base_height = self.rex.GetBasePosition()[-1]\\n height_err = np.abs(base_height - self.height_des)\\n \\n if height_err < 0.02:\\n height_err = 0\\n\\n # Speed \\n vx, vy, _ = p.getBaseVelocity(bodyUniqueId=self.rex.quadruped)[0]\\n vx = -vx # in rex, forward is the negative x direction\\n x_vel_err = 4*np.abs(vx - self.speed) # higher emphasis on x velocity error\\n y_vel_err = np.abs(vy - self.side_speed)\\n\\n # Orientation\\n orient_curr = self.rex.GetBaseOrientation()\\n orient_des = [0, 0, 0, 1] # not exact, but shouldn't be too far from this\\n orient_err = 6 * (1 - np.inner(orient_curr, orient_des)**2 )\\n\\n shoulder_orient_des = [0, 0, 0, 1]\\n FL_sh, FR_sh, RL_sh, RR_sh = self.get_shoulder_orientation()\\n\\n # quaternion similarity: 1 - <q1, q2>**2 == 0 when 100% similar\\n # good when error < 0.01 (individually)\\n # put HUGE penalty on this\\n shoulder_err = 20 * ((1 - np.inner(shoulder_orient_des, FL_sh)**2) + \\n (1 - np.inner(shoulder_orient_des, FR_sh)**2) +\\n (1 - np.inner(shoulder_orient_des, RL_sh)**2) + \\n (1 - np.inner(shoulder_orient_des, RR_sh)**2))\\n\\n # Energy Penalties --------------------------------------------------------------\\n energy_penalty = np.abs(np.dot(self.rex.GetMotorTorques(),\\n self.rex.GetMotorVelocities())) * self._time_step\\n\\n # Acceleration\\n a_trans, a_rot = self.get_base_accelerations()\\n accel_penalty = 0.15 * np.abs(a_trans.sum() + a_rot.sum())\\n\\n # need to encourage exploration: current issue --> Rex is stuck at origin\\n # because positive rewards all the time\\n # need lim error --> 0, reward > 0 \\n\\n beta = -0.75\\n\\n reward = beta + \\\\\\n 0.200 * np.exp(-orient_err - shoulder_err) + \\\\\\n 0.275 * np.exp(-foot_penalties) + \\\\\\n 0.075 * np.exp(-height_err) + \\\\\\n 0.250 * np.exp(-x_vel_err) + \\\\\\n 0.100 * np.exp(-y_vel_err) + \\\\\\n 0.075 * np.exp(-accel_penalty) + \\\\\\n 0.025 * np.exp(-energy_penalty)\\n\\n\\n return reward\",\n \"def update_Q(env, episode, Q, alpha, gamma):\\n states, actions, rewards = zip(*episode)\\n # prepare for discounting\\n discounts = np.array([gamma**i for i in range(len(rewards)+1)])\\n for i, state in enumerate(states):\\n old_Q = Q[state][actions[i]] \\n Q[state][actions[i]] = old_Q + alpha*(sum(rewards[i:]*discounts[:-(1+i)]) - old_Q)\\n return Q\",\n \"def phase(self, phase, **kwargs):\\n ## Rewards\\n if phase == 'set_default_behavior':\\n rewards = {\\n 'swim': +0.1, # reward given for anything else\\n 'skin': +0.5, # reward given when breathing through the skin\\n 'pneumostome': +1. # reward given when breathing through the pneumostome\\n #'miss': # to force some type of trial duration limit if implementing trials in this task ?\\n }\\n elif phase == 'training_session':\\n rewards = {\\n 'swim': +0.1,\\n 'skin': +1.,\\n 'pneumostome': -1.\\n }\\n elif phase == 'testing_session':\\n rewards = {\\n 'swim': 0.,\\n 'skin': 0.,\\n 'pneumostome': 0.\\n }\\n return rewards\",\n \"def test_prop_reward(self):\\n tmax = 10.0\\n dt = 1.0\\n\\n reward_scale = 5.0\\n\\n ini_rate = 80.0\\n\\n tutor = SimpleNeurons(1, out_fct=lambda _: ini_rate+20.0)\\n reward = MockReward(lambda t: 1.0 if t < tmax/2 else -1)\\n tutor_rule = ReinforcementTutorRule(tutor, reward, tau=0,\\n constrain_rates=False, ini_rate=ini_rate, learning_rate=1.0,\\n use_tutor_baseline=False)\\n\\n sim1 = simulation.Simulation(tutor, reward, tutor_rule, dt=dt)\\n sim1.run(tmax)\\n\\n drates1 = tutor_rule.rates - ini_rate\\n\\n tutor_rule.reset_rates()\\n reward.reward_fct = lambda t: reward_scale if t < tmax/2 else -reward_scale\\n\\n sim2 = simulation.Simulation(tutor, reward, tutor_rule, dt=dt)\\n sim2.run(tmax)\\n\\n drates2 = tutor_rule.rates - ini_rate\\n\\n self.assertLess(np.max(np.abs(reward_scale*drates1 - drates2)), 1e-6)\",\n \"def reward(self, observation, action, reward):\\n self.counts[action] = self.counts[action] + 1\\n n = self.counts[action]\\n value = self.values[action]\\n \\n new_value = ((n - 1) / float(n)) * value + (1 / float(n)) * reward\\n self.values[action] = new_value\\n \\n pass\",\n \"def reward(self, observation, action, reward):\\n self.counts[action] = self.counts[action] + 1\\n n = self.counts[action]\\n value = self.values[action]\\n \\n new_value = ((n - 1) / float(n)) * value + (1 / float(n)) * reward\\n self.values[action] = new_value\\n \\n pass\",\n \"def reward(self, observation, action, reward):\\n self.counts[action] = self.counts[action] + 1\\n n = self.counts[action]\\n value = self.values[action]\\n \\n new_value = ((n - 1) / float(n)) * value + (1 / float(n)) * reward\\n self.values[action] = new_value\\n \\n pass\",\n \"def evaluate(self, env, num_episode, epsilon):\\n num_environment = env.num_process\\n env.reset()\\n reward_of_each_environment = np.zeros(num_environment)\\n rewards_list = []\\n\\n num_finished_episode = 0\\n\\n while num_finished_episode < num_episode:\\n old_state, action, reward, new_state, is_terminal = env.get_state()\\n action = self.get_action(new_state, epsilon)\\n env.take_action(action)\\n for i, r, is_t in zip(range(num_environment), reward, is_terminal):\\n if not is_t:\\n reward_of_each_environment[i] += r\\n else:\\n rewards_list.append(reward_of_each_environment[i])\\n reward_of_each_environment[i] = 0\\n num_finished_episode += 1\\n return np.mean(rewards_list), np.std(rewards_list), self.epsilon\",\n \"def reward_exchanges(self):\\n exchange_reward_per_euro = get_exchange_reward_per_euro(self.model)\\n #payout to agents according to the number of hours they contributed\\n for agent_id, agent in self.model.schedule.agents_by_type['Customer'].items():\\n agent.teo_wallet += agent.exchanged_euros * exchange_reward_per_euro\\n agent.exchange_surplus += agent.exchanged_euros * exchange_reward_per_euro\",\n \"def __init__(self,\\n debug=False,\\n urdf_version=None,\\n control_time_step=0.005,\\n action_repeat=5,\\n control_latency=0,\\n pd_latency=0,\\n on_rack=False,\\n motor_kp=1.0,\\n motor_kd=0.02,\\n render=False,\\n num_steps_to_log=2000,\\n env_randomizer=None,\\n log_path=None,\\n signal_type='ik',\\n target_position=None,\\n backwards=None,\\n gait_type='trot',\\n terrain_type='plane',\\n terrain_id='plane',\\n mark='base',\\n ):\\n self.phase = 0\\n\\n self._gait_type = gait_type \\n # for observation space bounding \\n self.max_speed = 1.0\\n self.min_speed = 0.5 # change back to 0.2 for OLD TD3 model evaluation\\n \\n self.min_side_speed = 0.0\\n self.max_side_speed = 0.0\\n\\n self.speed = np.random.uniform(self.min_speed, self.max_speed)\\n self.side_speed = np.random.uniform(self.min_side_speed, self.max_side_speed)\\n self.speed_des = [self.speed, self.side_speed]\\n\\n # Initialization variables for periodic reward sum composition\\n self.theta_FL = phase_constants.PHASE_VALS[self._gait_type]['front_left']\\n self.theta_FR = phase_constants.PHASE_VALS[self._gait_type]['front_right']\\n self.theta_RL = phase_constants.PHASE_VALS[self._gait_type]['rear_left']\\n self.theta_RR = phase_constants.PHASE_VALS[self._gait_type]['rear_right']\\n\\n self.min_swing_ratio = 0.6\\n self.max_swing_ratio = 0.8\\n self.ratio = np.random.uniform(self.min_swing_ratio, self.max_swing_ratio)\\n\\n super(rexPeriodicRewardEnv,\\n self).__init__(urdf_version=urdf_version,\\n accurate_motor_model_enabled=True,\\n motor_overheat_protection=False,\\n motor_kp=motor_kp,\\n motor_kd=motor_kd,\\n remove_default_joint_damping=False,\\n control_latency=control_latency,\\n pd_latency=pd_latency,\\n on_rack=on_rack,\\n render=render,\\n num_steps_to_log=num_steps_to_log,\\n env_randomizer=env_randomizer,\\n log_path=log_path,\\n control_time_step=control_time_step,\\n action_repeat=action_repeat,\\n target_position=target_position,\\n signal_type=signal_type,\\n backwards=backwards,\\n debug=debug,\\n terrain_id=terrain_id,\\n terrain_type=terrain_type,\\n mark=mark,\\n ratio=self.ratio,\\n forward_reward_cap=5\\n )\\n\\n self.height_des = 0.206 # this is init standing height for rex\\n\\n self.cycle_complete = 0\\n self.cycle_len = 1000 # this is L\\n \\n # vonmises variables\\n self.kappa = phase_constants.VON_MISES_KAPPA\\n\\n rex_joints = p.getNumJoints(bodyUniqueId=self.rex.quadruped)\\n link_name_to_ID = {}\\n for i in range(rex_joints):\\n name = p.getJointInfo(self.rex.quadruped, i)[12].decode('UTF-8')\\n link_name_to_ID[name] = i\\n\\n self.link_name_to_ID = link_name_to_ID\\n self.toe_pos_last = { 'front_left_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['front_left_toe_link'])[0],\\n 'front_right_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['front_right_toe_link'])[0],\\n 'rear_left_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['rear_left_toe_link'])[0],\\n 'rear_right_toe_pos' : p.getLinkState(self.rex.quadruped, self.link_name_to_ID['rear_right_toe_link'])[0]\\n\\n } \\n\\n print('Using Periodic Reward Composition Rex Environment')\",\n \"def apply_reinforcement_learning(self, reward: int, p: float) -> None:\\n for cl in self:\\n cl.update_reward(reward + cl.cfg.gamma * p)\\n cl.update_intermediate_reward(reward)\",\n \"def learn(self, state, action, reward, next_state):\\r\\n\\r\\n \\\"\\\"\\\"Please Fill Your Code Here.\\r\\n \\\"\\\"\\\"\\r\\n self.Q[state][action] = self.Q[state][action] + self.alpha * (reward + self.gamma * max(self.Q[next_state]) - self.Q[state][action])\\r\\n\\r\\n return 0\",\n \"def step_reward(self) -> Tuple[float, dict]:\\n unserved_reward = -self.unserved_penalty * self.state[\\\"real_power_unserved\\\"]**2\\n peak_reward = -self.peak_penalty * \\\\\\n max(0, self.state[\\\"real_power_consumed\\\"] - self.peak_threshold)**2\\n reward = unserved_reward + peak_reward\\n reward /= self.reward_scale\\n return reward, {\\\"real_power_unserved\\\": unserved_reward, \\\"peak_reward\\\": peak_reward}\",\n \"def add_reward(self, reward):\\n self.quest_node['reward'] = reward\\n self.reward = reward\\n graph.push(self.quest_node)\",\n \"def __init__(self, env=None, tilesEnv=False):\\n super(MarioEnv, self).__init__(env)\\n self.resetCount = -1\\n # reward is distance travelled. So normalize it with total distance\\n # https://github.com/ppaquette/gym-super-mario/blob/master/ppaquette_gym_super_mario/lua/super-mario-bros.lua\\n # However, we will not use this reward at all. It is only for completion.\\n self.maxDistance = 3000.0\\n self.tilesEnv = tilesEnv\",\n \"def __init__(self):\\n self.last_reward_pos = 0\\n super().__init__()\\n self.TERRAIN_VARIANCE = 0.0\\n self.stump_spacing = 4.0\\n self.stump_height = 1.0\\n self.my_init({'leg_length': 35, 'walker_type': 'default'})\",\n \"def reward_spec(self) -> types.NestedArraySpec:\\n return array_spec.ArraySpec(shape=(), dtype=np.float32, name='reward')\",\n \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n Q_Value = self.Q #calling constructor\\n\\n learning_rate = self.alpha #gives us the learning rate\\n\\n temporary_QValue = self.getQValue(state,action) #to get the Q value of the state\\n\\n nextState_QValue = self.getValue(nextState) #to get the Q value of the landing state when taken action a and state s\\n\\n discount_factor = self.discount #to get the gamma/ discount factor\\n\\n\\n Q_Value[(state,action)] = ((1-learning_rate) * temporary_QValue) + (learning_rate * (reward + discount_factor * nextState_QValue)) #for formula go to README_Reinforcement.txt at line 8\\n\\n #util.raiseNotDefined()\",\n \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n self.qValues[(state, action)] = ((1 - self.alpha) * self.getQValue(state, action)) + self.alpha \\\\\\n * (reward + self.discount * self.computeValueFromQValues(nextState))\",\n \"def get_reward(self, is_terminal_state, result, reward, is_agent_move):\\n if is_terminal_state:\\n reward += 0 if result == \\\"Tie\\\" else 10 if is_agent_move else -10\\n #elif (not is_terminal_state and is_agent_move):\\n else:\\n reward += -1\\n\\n return reward\",\n \"def genetic_algorithm(env: RailEnv):\\r\\n # Determine number of agent and number of combinations of randomized order\\r\\n n_agents = len(env.agents)\\r\\n n_combination = math.factorial(n_agents)\\r\\n agent_ids= list(range(n_agents))\\r\\n # Compute number of iteration for genetic algo\\r\\n # Run ten iterations\\r\\n n_iterations = 10\\r\\n\\r\\n # create an initial population. The population contains three combinations of randomized order\\r\\n population = []\\r\\n final_reward = dict()\\r\\n for i in range(3):\\r\\n randomized_order = random.sample(agent_ids, len(agent_ids))\\r\\n if not population.__contains__(randomized_order):\\r\\n population.append(randomized_order)\\r\\n\\r\\n highest_reward_of_each_iteration=[]\\r\\n action_plan = dict()\\r\\n # Run the genetic algorithm in n number of iteration\\r\\n for iteration in range(n_iterations):\\r\\n # Pass each randomized order to the modified a_star\\r\\n i=0\\r\\n reward = dict()\\r\\n print(\\\"*******************************************************New Population***********************************\\\")\\r\\n print(\\\"Iteration \\\",iteration)\\r\\n # For each planning order in population we run it to get the cumulative reward\\r\\n for randomized_order in population:\\r\\n schedules = search_a_for_genetic(env,randomized_order)\\r\\n action_plan[i] = schedules\\r\\n test_env = deepcopy(env)\\r\\n success = False\\r\\n sumreward = 0\\r\\n # apply to the copied environment to calculate the sumreward for fitness score\\r\\n for action in schedules:\\r\\n _, _reward_dict, _done, _ = test_env.step(action)\\r\\n success = all(_done.values())\\r\\n sumreward = sumreward + sum(_reward_dict.values())\\r\\n reward[i] = sumreward\\r\\n #print(\\\"Total Time Step: \\\",test_env._elapsed_steps)\\r\\n #print(\\\"Randomize order: \\\", randomized_order)\\r\\n #print(\\\"Sum reward: \\\",sumreward)\\r\\n i += 1\\r\\n final_reward = reward.copy()\\r\\n highest_reward_of_each_iteration.append(final_reward)\\r\\n # find the randomized order give us the min reward\\r\\n key_min = min(reward.keys(), key=(lambda k: reward[k]))\\r\\n min_randomized_order = population[key_min]\\r\\n del reward[key_min]\\r\\n\\r\\n # Crossover\\r\\n # Select the parent\\r\\n # Find two combinations of randomized order that give us highest reward\\r\\n max_key_a = max(reward.keys(), key=(lambda k: reward[k]))\\r\\n del reward[max_key_a]\\r\\n randomized_order_a = population[max_key_a]\\r\\n\\r\\n max_key_b = max(reward.keys(), key=(lambda k: reward[k]))\\r\\n del reward[max_key_b]\\r\\n randomized_order_b = population[max_key_b]\\r\\n\\r\\n # Crossover\\r\\n # Generate a random crossover\\r\\n cross_point = random.randrange(1,n_agents-1)\\r\\n first_part_order_a = randomized_order_a[:cross_point]\\r\\n second_part_order_a = randomized_order_a[cross_point:]\\r\\n\\r\\n first_part_order_b = randomized_order_b[:cross_point]\\r\\n second_part_order_b = randomized_order_b[cross_point:]\\r\\n\\r\\n # Create two children based on the genes of parent\\r\\n child_a = first_part_order_a + second_part_order_b\\r\\n child_b = first_part_order_b + second_part_order_a\\r\\n\\r\\n # Mutation\\r\\n mutation(child_a)\\r\\n mutation(child_b)\\r\\n\\r\\n # remove the two combinations in the population that have lower reward\\r\\n population.remove(min_randomized_order)\\r\\n population.remove(randomized_order_b)\\r\\n\\r\\n # add two new children to the population\\r\\n\\r\\n population.append(child_a)\\r\\n population.append(child_b)\\r\\n\\r\\n\\r\\n # Find the best schedule\\r\\n max_key_reward = max(final_reward.keys(), key=(lambda k: final_reward[k]))\\r\\n best_order = population[max_key_reward]\\r\\n\\r\\n print(\\\"--------------------------------Result-------------------------------------\\\")\\r\\n print(\\\"Best Order: \\\", best_order)\\r\\n print(\\\"Reward: \\\",highest_reward_of_each_iteration)\\r\\n return best_order,action_plan[max_key_reward]\",\n \"def adjust_lr_exp(optimizer, base_lr, ep, total_ep, start_decay_at_ep):\\n assert ep >= 1, \\\"Current epoch number should be >= 1\\\"\\n\\n if ep < start_decay_at_ep:\\n return\\n\\n for g in optimizer.param_groups:\\n g['lr'] = (base_lr * (0.001 ** (float(ep + 1 - start_decay_at_ep)\\n / (total_ep + 1 - start_decay_at_ep))))\\n print('=====> lr adjusted to {:.10f}'.format(g['lr']).rstrip('0'))\",\n \"def get_reward(self, observations, actions):\\n\\n #initialize and reshape as needed, for batch mode\\n self.reward_dict = {}\\n if(len(observations.shape)==1):\\n observations = np.expand_dims(observations, axis = 0)\\n actions = np.expand_dims(actions, axis = 0)\\n batch_mode = False\\n else:\\n batch_mode = True\\n\\n #get vars\\n xvel = observations[:, 9].copy()\\n body_angle = observations[:, 2].copy()\\n front_leg = observations[:, 6].copy()\\n front_shin = observations[:, 7].copy()\\n front_foot = observations[:, 8].copy()\\n zeros = np.zeros((observations.shape[0],)).copy()\\n\\n # ranges\\n leg_range = 0.2\\n shin_range = 0\\n foot_range = 0\\n penalty_factor = 10\\n\\n #calc rew\\n self.reward_dict['run'] = xvel\\n\\n front_leg_rew = zeros.copy()\\n front_leg_rew[front_leg>leg_range] = -penalty_factor\\n self.reward_dict['leg'] = front_leg_rew\\n\\n front_shin_rew = zeros.copy()\\n front_shin_rew[front_shin>shin_range] = -penalty_factor\\n self.reward_dict['shin'] = front_shin_rew\\n\\n front_foot_rew = zeros.copy()\\n front_foot_rew[front_foot>foot_range] = -penalty_factor\\n self.reward_dict['foot'] = front_foot_rew\\n\\n # total reward\\n self.reward_dict['r_total'] = self.reward_dict['run'] + self.reward_dict['leg'] + self.reward_dict['shin'] + self.reward_dict['foot']\\n\\n #return\\n dones = zeros.copy()\\n if(not batch_mode):\\n return self.reward_dict['r_total'][0], dones[0]\\n return self.reward_dict['r_total'], dones\",\n \"def update(self, state, action, nextState, reward):\\n \\\"\\\"\\\"Description:\\n Use Q-Learning algoritm in slide 58 of MDP\\n \\\"\\\"\\\"\\n \\\"\\\"\\\" YOUR CODE HERE \\\"\\\"\\\"\\n maxQns = self.getValue(nextState) # get max q-value of next state\\n if maxQns == None:\\n maxQns = 0\\n Qsa = self.getQValue(state, action) #self.qValues[(state, action)]\\n difference = reward + self.discountRate * maxQns - Qsa\\n self.qValues[(state, action)] += self.alpha * difference\\n \\n self.vitCount[(state, action)] += 1\\n \\\"\\\"\\\" END CODE \\\"\\\"\\\"\",\n \"def update_reward(reward, done, info):\\n if done and info['ale.lives'] == 0:\\n return reward - 100\\n\\n return reward\",\n \"def update(self, state, action, nextState, reward):\\n \\\"*** YOUR CODE HERE ***\\\"\\n features = self.featExtractor.getFeatures(state,action)\\n\\n learning_rate = self.alpha #gives us the learning rate\\n\\n temporary_QValue = self.getQValue(state,action) #to get the Q value of the state,action pair\\n\\n nextState_QValue = self.getValue(nextState) #to get the Q value of the landing state when taken action a and state s\\n\\n discount_factor = self.discount #to get the gamma/ discount factor\\n\\n weight = self.weights\\n\\n Q_Value = 0\\n\\n difference = (reward + discount_factor * nextState_QValue ) - (temporary_QValue) #refer to README_Reinforcement.txt for the formula\\n\\n for each_feature in features:\\n\\n #refer to README_Reinforcement.txt for the formula at line 20\\n weight[each_feature] = weight[each_feature] + learning_rate * difference * features[each_feature]\\n\\n #util.raiseNotDefined()\",\n \"def _compute_reward(self):\\n reward = 0.0\\n return reward\",\n \"def reward_func(self, state):\\n if abs(state['theta']) < 1:\\n return 2.0\\n\\n elif abs(state['theta']) < 3:\\n return 0.0\\n\\n elif abs(state['theta']) > 30:\\n return -100.0\\n return -2.0\",\n \"def observe_reward(self,state,action,statePrime,reward):\\n pass\",\n \"def get_gae(rewards, value_estimates, value_next=0.0, gamma=0.99, lambd=0.95):\\n value_estimates = np.append(value_estimates, value_next)\\n delta_t = rewards + gamma * value_estimates[1:] - value_estimates[:-1]\\n advantage = discount_rewards(r=delta_t, gamma=gamma * lambd)\\n return advantage\",\n \"def _action_rewards(self, context) -> ActionRewards:\\n pass\",\n \"def update_sample_reward(self, r):\\n self.r_exp[-1] += r * self.gamma ** (len(self.u_seq[-1])-1)\",\n \"def reward(self):\\n return self._reward\",\n \"def finish_episode(tribes, learners, optimizers, gamma, cuda): \\n \\n num_learners = len(learners)\\n total_norms = [0 for i in range(num_learners)]\\n policy_losses = [[] for i in range(num_learners)]\\n losses = [[] for i in range(num_learners)]\\n T_reward = []\\n\\n \\n for i in range(num_learners):\\n\\n R = 0\\n saved_actions = learners[i].saved_actions\\n \\n for t in tribes:\\n if t.name is learners[i].tribe:\\n \\n # Based on team culture, calculate the team reward for the agent \\n culture = t.culture['name']\\n \\n if culture is 'cooperative':\\n T_reward = t.tribal_awards()\\n elif culture is 'individualist':\\n T_reward = t.tribal_awards()\\n elif culture is 'no_fragging':\\n T_reward = t.tribal_awards(US_hits = learners[i].US_hits)\\n elif culture is 'pacifist':\\n T_reward = t.tribal_awards(tag_hist = learners[i].tag_hist)\\n elif culture is 'pacifist_exile':\\n T_reward = t.tribal_awards(tag_hist = learners[i].tag_hist, \\\\\\n in_banned_hist=learners[i].in_banned_hist)\\n elif culture is 'pacifist_follower':\\n T_reward = t.tribal_awards(tag_hist = learners[i].tag_hist, \\\\\\n in_target_hist=learners[i].in_target_hist)\\n elif culture is 'warlike':\\n T_reward = t.tribal_awards(US_hits = learners[i].US_hits,THEM_hits = learners[i].THEM_hits)\\n else:\\n T_reward = t.tribal_awards()\\n \\n # For debug only\\n # print('Agent{} receives tribal award from Tribe{}'.format(i,t.name))\\n # print (T_reward)\\n # print (learners[i].rewards)\\n \\n # Do not implement actor-critic for now\\n # value_losses = []\\n \\n rewards = deque()\\n\\n for r,T in zip(learners[i].rewards[::-1],T_reward[::-1]):\\n # The agent is incentivized to cooperate by an award of 30% of what the tribe takes\\n # in by all its members\\n R = r + T + gamma * R\\n rewards.appendleft(R)\\n \\n rewards = list(rewards)\\n rewards = torch.Tensor(rewards)\\n if cuda:\\n rewards = rewards.cuda()\\n\\n # z-score rewards\\n rewards = (rewards - rewards.mean()) / (1.1e-7+rewards.std())\\n \\n #Debug \\n #print (rewards) \\n \\n \\\"\\\"\\\"\\n Do not implement actor-critic for now!!!\\n for (log_prob, state_value), r in zip(saved_actions, rewards):\\n reward = r - state_value.data[0]\\n policy_losses.append(-log_prob * Variable(reward))\\n r = torch.Tensor([r])\\n if cuda:\\n r = r.cuda()\\n value_losses.append(torch.nn.functional.smooth_l1_loss(state_value,\\n Variable(r)))\\n\\n optimizer.zero_grad()\\n loss = torch.stack(policy_losses).sum() + torch.stack(value_losses).sum()\\n loss.backward() \\n \\n \\n \\\"\\\"\\\"\\n for log_prob, r in zip(saved_actions, rewards):\\n r = torch.Tensor([r])\\n if cuda:\\n r = r.cuda()\\n policy_losses[i].append(-log_prob * Variable(r))\\n\\n optimizers[i].zero_grad()\\n losses[i] = torch.stack(policy_losses[i]).sum()\\n losses[i].backward()\\n \\n # Gradient Clipping Update: prevent exploding gradient\\n total_norms[i] = torch.nn.utils.clip_grad_norm_(learners[i].parameters(), 8000)\\n \\n optimizers[i].step()\\n learners[i].clear_history() # clear an agent's history at the end of episode\\n\\n\\n return total_norms\",\n \"def discount_rewards(self, r, epEnd):\\n res = np.zeros_like(r)\\n running_add = 0.0\\n assert (len(r)-1) in epEnd, 'simple sanity check: the last reward must be the episode end'\\n assert isinstance(r, list)\\n for t in reversed(xrange(len(r))):\\n if t in epEnd: running_add = 0.0 # reset \\n running_add = GAMMA * running_add + r[t]\\n res[t] = running_add\\n return res\",\n \"def intrinsic_reward(\\n self,\\n state: Any,\\n algo_step: OrderedDict[str, Any],\\n reward: Any,\\n terminal: Any,\\n next_state: Any\\n ) -> Any:\\n with torch.no_grad():\\n with evaluate(self.normalization_layer):\\n return self._get_loss(next_state).item()\",\n \"def update_policy(self, minibatch_size):\\n \\n steps = self.rewards.shape[0]\\n batch_size = self.rewards.shape[0] * self.rewards.shape[1]\\n #steps = 500\\n #batch_size = 500\\n #print(steps)\\n #print(batch_size)\\n \\n # Compute advantages\\n '''\\n with torch.no_grad():\\n if self.gae:\\n advantages = torch.zeros_like(self.rewards).to(self.training_device)\\n lastgaelam = 0\\n for t in reversed(range(steps)):\\n if t == steps - 1:\\n nextnonterminal = 1.0 - self.dones[t]\\n nextvalues = self.state_values[t]\\n else:\\n nextnonterminal = 1.0 - self.dones[t + 1]\\n nextvalues = self.state_values[t + 1]\\n delta = self.rewards[t] + self.gamma * nextvalues * nextnonterminal - self.state_values[t]\\n advantages[t] = lastgaelam = delta + self.gamma * self.gae_lambda * nextnonterminal * lastgaelam\\n returns = advantages + self.state_values\\n else:\\n returns = torch.zeros_like(self.rewards).to(self.training_device)\\n for t in reversed(range(steps)):\\n if t == steps - 1:\\n nextnonterminal = 1.0 - self.dones[t]\\n next_return = self.state_values[t]\\n else:\\n nextnonterminal = 1.0 - self.dones[t+1]\\n next_return = returns[t+1]\\n returns[t] = self.rewards[t] + self.gamma * nextnonterminal * next_return\\n advantages = returns - self.state_values\\n ''' \\n returns = torch.zeros_like(self.rewards).to(self.training_device)\\n for t in reversed(range(steps)):\\n if t == steps - 1:\\n nextnonterminal = 1.0 - self.dones[t]\\n next_return = self.state_values[t]\\n else:\\n nextnonterminal = 1.0 - self.dones[t+1]\\n next_return = returns[t+1]\\n returns[t] = self.rewards[t] + self.gamma * nextnonterminal * next_return\\n advantages = returns - self.state_values\\n \\n\\n # flatten the batch\\n #b_obs = self.states.reshape((-1,) + self.state_space)\\n #print(self.states.shape)\\n b_obs = self.states.reshape((-1,4)).detach()\\n b_logprobs = self.action_probs.reshape(-1,1).detach()\\n b_actions = self.actions.reshape((-1,)).detach()\\n b_advantages = advantages.reshape(-1,1)\\n b_returns = returns.reshape(-1,1)\\n b_values = self.state_values.reshape(-1,1)\\n \\n # Optimize policy and value network for K epochs, run optimization in minibatches\\n \\n inds = np.arange(batch_size)\\n for i_epoch_pi in range(self.epochs):\\n np.random.shuffle(inds)\\n for start in range(0, batch_size, minibatch_size):\\n end = start + minibatch_size\\n minibatch_ind = inds[start:end]\\n mb_advantages = b_advantages[minibatch_ind]\\n if self.norm_adv:\\n mb_advantages = (mb_advantages - mb_advantages.mean()) / (mb_advantages.std() + 1e-8)\\n \\n #_, newlogproba, entropy = self.get_action(b_obs[minibatch_ind], b_actions[minibatch_ind])\\n newlogproba, entropy = self.evaluate(b_obs[minibatch_ind], b_actions[minibatch_ind])\\n #ratio = (newlogproba - b_logprobs[minibatch_ind]).exp()\\n ratio = torch.exp((newlogproba - b_logprobs[minibatch_ind].detach()))\\n \\n # Stats\\n approx_kl = (b_logprobs[minibatch_ind] - newlogproba).mean()\\n\\n # Policy loss\\n pg_loss1 = -mb_advantages * ratio\\n pg_loss2 = -mb_advantages * torch.clamp(ratio, 1 - self.clip_epsilon, 1 + self.clip_epsilon)\\n pg_loss = torch.max(pg_loss1, pg_loss2).mean()\\n entropy_loss = entropy.mean()\\n\\n # Value loss\\n _, new_values = self.policy.forward(b_obs[minibatch_ind])\\n if self.clip_vloss:\\n \\n v_loss_unclipped = self.MseLoss(new_values,b_returns[minibatch_ind])\\n #v_loss_unclipped = ((new_values - b_returns[minibatch_ind]) ** 2)\\n v_clipped = b_values[minibatch_ind] + torch.clamp(new_values - b_values[minibatch_ind],\\n -self.clip_epsilon, self.clip_epsilon)\\n #v_loss_clipped = (v_clipped - b_returns[minibatch_ind]) ** 2\\n v_loss_clipped = self.MseLoss(v_clipped,b_returns[minibatch_ind])\\n v_loss_max = torch.max(v_loss_unclipped, v_loss_clipped)\\n #v_loss = 0.5 * v_loss_max.mean()\\n v_loss = 0.5 * v_loss_max\\n else:\\n #v_loss = 0.5 * ((new_values - b_returns[minibatch_ind]) ** 2).mean()\\n v_loss = self.MseLoss(new_values,b_returns[minibatch_ind])\\n\\n loss = pg_loss + v_loss * self.vf_coeff - self.ent_coeff * entropy_loss\\n\\n self.optimizer.zero_grad()\\n loss.backward()\\n torch.nn.utils.clip_grad_norm_(self.policy.parameters(), self.max_grad_norm)\\n self.optimizer.step()\\n # Copy new weights into old policy:\\n self.old_policy.load_state_dict(self.policy.state_dict())\",\n \"def EventContentMissionExcelAddMissionRewardAmount(builder, MissionRewardAmount):\\n return AddMissionRewardAmount(builder, MissionRewardAmount)\",\n \"def reward_values(self):\\n return {\\\"positive\\\": 100.0, \\\"tick\\\": 1.0, \\\"loss\\\": -1000.0}\",\n \"def reset(self):\\n\\n # initialize gym env variables\\n self.finish = False\\n self.curr_step = -1\\n self.curr_episode += 1\\n\\n # initialize target position\\n self.target = np.random.uniform(-10.0,10.0,size=(2))\\n\\n # initialize sheep positions\\n if self.fixed_reset:\\n init_sheep_pose = np.array([75.0, 75.0])\\n self.sheep_poses = (np.random.uniform(-50.0, 50.0, \\n size=(self.num_sheep,2))) + init_sheep_pose[None,:]\\n else:\\n init_sheep_pose = np.random.uniform(-self.init_sheep_root, \\n self.init_sheep_root, size=(2))\\n self.sheep_poses = (np.random.uniform(-self.init_sheep_range, \\n self.init_sheep_range, size=(self.num_sheep,2))) \\\\\\n + init_sheep_pose[None,:]\\n self.sheep_com = self.sheep_poses.mean(axis=0)\\n\\n # get the farthest sheep and radius of the sheep\\n dist_to_com = np.linalg.norm((self.sheep_poses - self.sheep_com[None,:]), axis=1)\\n self.farthest_sheep = self.sheep_poses[np.argmax(dist_to_com),:]\\n self.radius_sheep = np.array([np.max(dist_to_com)])\\n\\n # update distance to target\\n self.target_distance = np.linalg.norm(self.target - self.sheep_com)\\n\\n # initialize values for reward estimation\\n self.init_radius_sheep = self.radius_sheep\\n self.init_target_distance = self.target_distance\\n\\n # initialize dog position\\n if self.fixed_reset:\\n init_dog_pose = np.array([0.0,75.0])\\n else:\\n init_theta = np.random.uniform(-np.pi,np.pi)\\n init_dog_pose = init_sheep_pose + self.init_dog_distance*np.array([np.cos(init_theta), \\n np.sin(init_theta)])\\n self.dog_pose = init_dog_pose\\n\\n # initialize inertia\\n self.inertia = np.ones((self.num_sheep, 2))\\n\\n # initialize episode reward and length\\n self.episode_reward = 0\\n self.episode_length = 0\\n\\n # get the state, reward, finish, info\\n state = self._get_state()\\n \\n return state\",\n \"def reward(self, history_id, reward):\\n reward_action = self._historystorage.unrewarded_histories[history_id].action\\n reward_action_idx = self._actions.index(reward_action)\\n context = self._historystorage.unrewarded_histories[history_id].context[reward_action_idx]\\n context = np.matrix(context)\\n\\n # Update the model\\n matrix_a = self._modelstorage.get_model()['matrix_a']\\n matrix_ainv = self._modelstorage.get_model()['matrix_ainv']\\n b = self._modelstorage.get_model()['b']\\n theta = self._modelstorage.get_model()['theta']\\n matrix_a[reward_action] += np.dot(context.T, context)\\n matrix_ainv[reward_action] = np.linalg.solve(matrix_a[reward_action], np.identity(self.d))\\n b[reward_action] += reward * context.T\\n theta[reward_action] = np.dot(matrix_ainv[reward_action], b[reward_action])\\n self._modelstorage.save_model({'matrix_a': matrix_a, 'matrix_ainv': matrix_ainv, 'b': b, 'theta': theta})\\n\\n # Update the history\\n self._historystorage.add_reward(history_id, reward)\",\n \"def _update_arms(self, arm, reward):\\n # Update selected arm statistics\\n self.emar[arm] = (1. - self.gamma) * self.emar[arm] + self.gamma * reward\\n self.success[arm] += self.emar[arm]\\n self.failure[arm] += 1. - self.emar[arm]\\n\\n # Forget past rewards for unselected arms\\n for i in chain(range(arm), range(arm + 1, len(self.emar))):\\n self.emar[i] = (1. - self.epsilon) * self.emar[i] + self.epsilon * 0.5\\n self.success[i] *= (1.0 - self.epsilon)\\n self.failure[i] *= (1.0 - self.epsilon)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64872587","0.6484783","0.64239675","0.62843674","0.6223589","0.6176895","0.6125481","0.59527415","0.59006923","0.58524704","0.5797344","0.576553","0.57535696","0.5753127","0.57492626","0.5742453","0.5741872","0.57114106","0.56750464","0.5674529","0.5666838","0.5654328","0.56174636","0.5602399","0.5599374","0.55982965","0.5593049","0.55869675","0.55824775","0.5577863","0.5577863","0.5574889","0.5544518","0.554386","0.55351055","0.5531421","0.5531301","0.5525509","0.551121","0.55106026","0.5497965","0.5496947","0.5493163","0.54845","0.54785675","0.5477403","0.5472649","0.5472569","0.5466258","0.5462245","0.5458762","0.5455678","0.54438084","0.54387784","0.54375213","0.54325545","0.5422783","0.5420322","0.5419624","0.5400096","0.53988355","0.53934026","0.53933257","0.5393077","0.5393077","0.5393077","0.53860176","0.5385872","0.538434","0.53791946","0.53786266","0.53727615","0.53665745","0.5359219","0.5358834","0.5355937","0.53533745","0.53513324","0.5346712","0.53447384","0.53435606","0.53412247","0.5340194","0.53333604","0.5326065","0.53259885","0.5325685","0.5323591","0.5317245","0.53170276","0.53162503","0.53159106","0.53139704","0.52989465","0.5295076","0.52916235","0.52861303","0.5276861","0.5274021","0.5266756","0.52647"],"string":"[\n \"0.64872587\",\n \"0.6484783\",\n \"0.64239675\",\n \"0.62843674\",\n \"0.6223589\",\n \"0.6176895\",\n \"0.6125481\",\n \"0.59527415\",\n \"0.59006923\",\n \"0.58524704\",\n \"0.5797344\",\n \"0.576553\",\n \"0.57535696\",\n \"0.5753127\",\n \"0.57492626\",\n \"0.5742453\",\n \"0.5741872\",\n \"0.57114106\",\n \"0.56750464\",\n \"0.5674529\",\n \"0.5666838\",\n \"0.5654328\",\n \"0.56174636\",\n \"0.5602399\",\n \"0.5599374\",\n \"0.55982965\",\n \"0.5593049\",\n \"0.55869675\",\n \"0.55824775\",\n \"0.5577863\",\n \"0.5577863\",\n \"0.5574889\",\n \"0.5544518\",\n \"0.554386\",\n \"0.55351055\",\n \"0.5531421\",\n \"0.5531301\",\n \"0.5525509\",\n \"0.551121\",\n \"0.55106026\",\n \"0.5497965\",\n \"0.5496947\",\n \"0.5493163\",\n \"0.54845\",\n \"0.54785675\",\n \"0.5477403\",\n \"0.5472649\",\n \"0.5472569\",\n \"0.5466258\",\n \"0.5462245\",\n \"0.5458762\",\n \"0.5455678\",\n \"0.54438084\",\n \"0.54387784\",\n \"0.54375213\",\n \"0.54325545\",\n \"0.5422783\",\n \"0.5420322\",\n \"0.5419624\",\n \"0.5400096\",\n \"0.53988355\",\n \"0.53934026\",\n \"0.53933257\",\n \"0.5393077\",\n \"0.5393077\",\n \"0.5393077\",\n \"0.53860176\",\n \"0.5385872\",\n \"0.538434\",\n \"0.53791946\",\n \"0.53786266\",\n \"0.53727615\",\n \"0.53665745\",\n \"0.5359219\",\n \"0.5358834\",\n \"0.5355937\",\n \"0.53533745\",\n \"0.53513324\",\n \"0.5346712\",\n \"0.53447384\",\n \"0.53435606\",\n \"0.53412247\",\n \"0.5340194\",\n \"0.53333604\",\n \"0.5326065\",\n \"0.53259885\",\n \"0.5325685\",\n \"0.5323591\",\n \"0.5317245\",\n \"0.53170276\",\n \"0.53162503\",\n \"0.53159106\",\n \"0.53139704\",\n \"0.52989465\",\n \"0.5295076\",\n \"0.52916235\",\n \"0.52861303\",\n \"0.5276861\",\n \"0.5274021\",\n \"0.5266756\",\n \"0.52647\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17761,"cells":{"query":{"kind":"string","value":"Check if all files are available before going deeper"},"document":{"kind":"string","value":"def _check_before_run(self):\n if not osp.exists(self.dataset_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.dataset_dir))\n if not osp.exists(self.train_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.train_dir))\n if not osp.exists(self.query_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.query_dir))\n if not osp.exists(self.gallery_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.gallery_dir))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def init_check(self):\n for required_file in self._required_files:\n # Check if required files are there\n # FIXME Sometimes it doesn't work :?\n if required_file not in self.files:\n self.valid = False","def check_for_missing_files(self, path):\n return None","def contains_files(self):\n if self.file_list is None:\n self._set_file_list()\n for individual_file in self.file_list:\n if not os.path.exists(os.path.join(self.base_dir, individual_file)):\n return False\n return True","def _check_file_not_used(self):\n module_files = set(self._get_module_files())\n referenced_files = set(self._get_manifest_referenced_files()).union(\n set(self._get_xml_referenced_files())\n )\n excluded_dirs = ['static', 'test', 'tests', 'migrations']\n no_referenced_files = [\n f for f in (module_files - referenced_files)\n if f.split(os.path.sep)[0] not in excluded_dirs\n ]\n self.msg_args = no_referenced_files\n return not no_referenced_files","def check_all():\n for name, module in sorted(sys.modules.items()): # module files\n filepath = getattr(module, '__file__', None)\n if filepath is None:\n # we land here when a module is an attribute of another module\n # i.e., it exists twice in the sys.modules table, once as its\n # canonical representation, and again having been imported\n # within another module\n continue\n filepath = filepath.endswith(\".pyc\") and filepath[:-1] or filepath\n check_one(filepath)\n\n for filepath in extras: # additional files\n check_one(filepath)","def data_available(dataset_name=None):\r\n for file_list in data_resources[dataset_name]['files']:\r\n for file in file_list:\r\n if not os.path.exists(os.path.join(data_path, dataset_name, file)):\r\n return False\r\n return True","def output_files_exist(self):\n return all([split.exists() for split in self.split_files])","def test_scan_dir_files(self):\n self.run_scan(self.subdir, self.nest_fcount + 1)","def test_check():\n for f in cfg.required_files:\n assert os.path.isfile(f)","def check_init_files_and_folders():\n\t#['cascade_wimb_bus_front_100_stages_1000_pos_3000_neg.xml', 'cascade_wimb_bus_front_33_stages_1000_pos_3000_neg_wrong.xml', 'color_detect_2.py', 'dedupe.py', 'detect_image_group_ku.py', 'detect_shape_5.py', 'get_cam_id_2.py', 'get_image_8.py', 'gui_hsv.py', 'knaps.py', 'knapsack_2.py', 'maps.html', 'program_detect_rectangle.zip', 'start_capture.py']\n\tfile_list=[\n\t#'cascade_wimb_bus_front_100_stages_1000_pos_3000_neg.xml', \n\t'models/cascade_wimb_bus_front_33_stages_1000_pos_3000_neg_wrong.xml', \n\t#'color_detect_2.py', \n\t#'dedupe.py', \n\t'detect_bus_haar_group.py', \n\t#'detect_shape_5.py', \n\t'get_cam_detail.py', \n\t'get_image.py', \n\t#'gui_hsv.py', \n\t#'knaps.py', \n\t#'knapsack_2.py', \n\t#'maps.html', \n\t#'program_detect_rectangle.zip', \n\t'start_wimb.py',\n\t'g.php',\n\t]\n\tdirectory_list=[\n\t'images',\n\t'images_bgs',\n\t'images_bgs_mask',\n\t#'images_bgs_result',\n\t'images_color',\n\t'images_haar',\n\t'images_haar_result',\n\t'images_number',\n\t'images_number_result',\n\t'models',\n\t'images_old',\n\t'text_number',\n\t]\n\t\n\tfor file_name in file_list: print 'file '+file_name+' existed: '+str(os.path.isfile(file_name))\n\tfor directory_name in directory_list: \n\t\tprint 'directory '+directory_name+' existed: '+str(os.path.isdir(directory_name))\n\t\tif not os.path.isdir(directory_name): \n\t\t\tos.makedirs(directory_name)\n\t\tif \"images\" in directory_name: shutil.copy(path+'/g.php',path+'/'+directory_name+'/g.php')","def check_out_files_exist(self):\n for filetype in self.filetypes:\n filename = self.out_filename(filetype)\n if not filename.is_file():\n log.error('MISSING: {}'.format(filename))\n return False\n\n return True","def check_out_files_exist(self):\n for filetype in self.filetypes:\n filename = self.out_filename(filetype)\n if not filename.is_file():\n log.error('MISSING: {}'.format(filename))\n return False\n\n return True","def check_out_files_exist(self):\n for filetype in self.filetypes:\n filename = self.out_filename(filetype)\n if not filename.is_file():\n log.error('MISSING: {}'.format(filename))\n return False\n\n return True","def check_comps(root, comps):\n for key, comp in comps.items():\n\n filename = os.path.join(root, comp['filename'])\n if not os.path.isfile(filename):\n warnings.warn(\n 'The file {0} could not be found'.format(filename))","def check_for_file(self):\n if self.task.file_name in os.listdir(self.task.file_storage):\n return True\n return False","def files_exist(self):\n\n passed = []\n warned = []\n failed = []\n ignored = []\n\n # NB: Should all be files, not directories\n # List of lists. Passes if any of the files in the sublist are found.\n #: test autodoc\n try:\n _, short_name = self.nf_config[\"manifest.name\"].strip(\"\\\"'\").split(\"/\")\n except ValueError:\n log.warning(\"Expected manifest.name to be in the format '<repo>/<pipeline>'. Will assume it is '<pipeline>'.\")\n short_name = self.nf_config[\"manifest.name\"].strip(\"\\\"'\").split(\"/\")\n\n files_fail = [\n [\".gitattributes\"],\n [\".gitignore\"],\n [\".nf-core.yml\"],\n [\".editorconfig\"],\n [\".prettierignore\"],\n [\".prettierrc.yml\"],\n [\"CHANGELOG.md\"],\n [\"CITATIONS.md\"],\n [\"CODE_OF_CONDUCT.md\"],\n [\"CODE_OF_CONDUCT.md\"],\n [\"LICENSE\", \"LICENSE.md\", \"LICENCE\", \"LICENCE.md\"], # NB: British / American spelling\n [\"nextflow_schema.json\"],\n [\"nextflow.config\"],\n [\"README.md\"],\n [os.path.join(\".github\", \".dockstore.yml\")],\n [os.path.join(\".github\", \"CONTRIBUTING.md\")],\n [os.path.join(\".github\", \"ISSUE_TEMPLATE\", \"bug_report.yml\")],\n [os.path.join(\".github\", \"ISSUE_TEMPLATE\", \"config.yml\")],\n [os.path.join(\".github\", \"ISSUE_TEMPLATE\", \"feature_request.yml\")],\n [os.path.join(\".github\", \"PULL_REQUEST_TEMPLATE.md\")],\n [os.path.join(\".github\", \"workflows\", \"branch.yml\")],\n [os.path.join(\".github\", \"workflows\", \"ci.yml\")],\n [os.path.join(\".github\", \"workflows\", \"linting_comment.yml\")],\n [os.path.join(\".github\", \"workflows\", \"linting.yml\")],\n [os.path.join(\"assets\", \"email_template.html\")],\n [os.path.join(\"assets\", \"email_template.txt\")],\n [os.path.join(\"assets\", \"sendmail_template.txt\")],\n [os.path.join(\"assets\", f\"nf-core-{short_name}_logo_light.png\")],\n [os.path.join(\"conf\", \"modules.config\")],\n [os.path.join(\"conf\", \"test.config\")],\n [os.path.join(\"conf\", \"test_full.config\")],\n [os.path.join(\"docs\", \"images\", f\"nf-core-{short_name}_logo_light.png\")],\n [os.path.join(\"docs\", \"images\", f\"nf-core-{short_name}_logo_dark.png\")],\n [os.path.join(\"docs\", \"output.md\")],\n [os.path.join(\"docs\", \"README.md\")],\n [os.path.join(\"docs\", \"README.md\")],\n [os.path.join(\"docs\", \"usage.md\")],\n [os.path.join(\"lib\", \"nfcore_external_java_deps.jar\")],\n [os.path.join(\"lib\", \"NfcoreTemplate.groovy\")],\n [os.path.join(\"lib\", \"Utils.groovy\")],\n [os.path.join(\"lib\", \"WorkflowMain.groovy\")],\n ]\n\n files_warn = [\n [\"main.nf\"],\n [os.path.join(\"assets\", \"multiqc_config.yml\")],\n [os.path.join(\"conf\", \"base.config\")],\n [os.path.join(\"conf\", \"igenomes.config\")],\n [os.path.join(\".github\", \"workflows\", \"awstest.yml\")],\n [os.path.join(\".github\", \"workflows\", \"awsfulltest.yml\")],\n [os.path.join(\"lib\", f\"Workflow{short_name[0].upper()}{short_name[1:]}.groovy\")],\n [\"modules.json\"],\n [\"pyproject.toml\"],\n ]\n\n # List of strings. Fails / warns if any of the strings exist.\n files_fail_ifexists = [\n \"Singularity\",\n \"parameters.settings.json\",\n \".nf-core.yaml\", # yml not yaml\n os.path.join(\"bin\", \"markdown_to_html.r\"),\n os.path.join(\"conf\", \"aws.config\"),\n os.path.join(\".github\", \"workflows\", \"push_dockerhub.yml\"),\n os.path.join(\".github\", \"ISSUE_TEMPLATE\", \"bug_report.md\"),\n os.path.join(\".github\", \"ISSUE_TEMPLATE\", \"feature_request.md\"),\n os.path.join(\"docs\", \"images\", f\"nf-core-{short_name}_logo.png\"),\n \".markdownlint.yml\",\n \".yamllint.yml\",\n os.path.join(\"lib\", \"Checks.groovy\"),\n os.path.join(\"lib\", \"Completion.groovy\"),\n os.path.join(\"lib\", \"Workflow.groovy\"),\n ]\n files_warn_ifexists = [\".travis.yml\"]\n\n # Remove files that should be ignored according to the linting config\n ignore_files = self.lint_config.get(\"files_exist\", [])\n\n def pf(file_path):\n return os.path.join(self.wf_path, file_path)\n\n # First - critical files. Check that this is actually a Nextflow pipeline\n if not os.path.isfile(pf(\"nextflow.config\")) and not os.path.isfile(pf(\"main.nf\")):\n failed.append(\"File not found: nextflow.config or main.nf\")\n raise AssertionError(\"Neither nextflow.config or main.nf found! Is this a Nextflow pipeline?\")\n\n # Files that cause an error if they don't exist\n for files in files_fail:\n if any([f in ignore_files for f in files]):\n continue\n if any([os.path.isfile(pf(f)) for f in files]):\n passed.append(f\"File found: {self._wrap_quotes(files)}\")\n else:\n failed.append(f\"File not found: {self._wrap_quotes(files)}\")\n\n # Files that cause a warning if they don't exist\n for files in files_warn:\n if any([f in ignore_files for f in files]):\n continue\n if any([os.path.isfile(pf(f)) for f in files]):\n passed.append(f\"File found: {self._wrap_quotes(files)}\")\n else:\n warned.append(f\"File not found: {self._wrap_quotes(files)}\")\n\n # Files that cause an error if they exist\n for file in files_fail_ifexists:\n if file in ignore_files:\n continue\n if os.path.isfile(pf(file)):\n failed.append(f\"File must be removed: {self._wrap_quotes(file)}\")\n else:\n passed.append(f\"File not found check: {self._wrap_quotes(file)}\")\n\n # Files that cause a warning if they exist\n for file in files_warn_ifexists:\n if file in ignore_files:\n continue\n if os.path.isfile(pf(file)):\n warned.append(f\"File should be removed: {self._wrap_quotes(file)}\")\n else:\n passed.append(f\"File not found check: {self._wrap_quotes(file)}\")\n\n # Files that are ignoed\n for file in ignore_files:\n ignored.append(f\"File is ignored: {self._wrap_quotes(file)}\")\n\n return {\"passed\": passed, \"warned\": warned, \"failed\": failed, \"ignored\": ignored}","def resources():\n check_resources()","async def check_files(hass):\n # Verify that the user downloaded all files.\n base = f\"{hass.config.path()}/custom_components/{DOMAIN}/\"\n missing = []\n for file in REQUIRED_FILES:\n fullpath = \"{}{}\".format(base, file)\n if not os.path.exists(fullpath):\n missing.append(file)\n\n if missing:\n _LOGGER.critical(\"The following files are missing: %s\", str(missing))\n returnvalue = False\n else:\n returnvalue = True\n\n return returnvalue","def check_helpers(self):\n paths = self.get_helper_out_paths()\n\n for p in paths:\n full_path = p + \".data-00000-of-00001\"\n file = Path(full_path)\n if not file.exists():\n return False\n\n return True","def get_all_files(self, custom=None):\n super().get_all_files(custom)\n return True","def file_list_emptydirs(load):\n # TODO - implement this\n _init()\n\n return []","def status(name=None):\n name = \"freezer\" if not name else name\n return all(os.path.isfile(i) for i in _paths(name))","def scan ( self, **kw ):\n stats = self.STATS\n stats.scan_time.begin ( self.name )\n for subdir in os.listdir ( self.physical_location ):\n if self.has_dir ( subdir ):\n pkgdir = self._get_package_dir ( subdir )\n try:\n pkgdir.scan ( stats=stats, **kw )\n finally:\n if pkgdir.empty():\n del self._subdirs [subdir]\n\n stats.scan_time.end ( self.name )","def exists(self):\n basedir = os.path.dirname(self.path)\n\n for filename in self.files:\n path = os.path.join(basedir, filename)\n if not os.path.exists(path):\n return False\n\n return True","def valid(self):\r\n if self.dir_exists and self.files_exist:\r\n return True\r\n else:\r\n return False","def self_check(self):\n out = \"Loaded components\\n\"\n for package_name, package in sorted(self.packages.items()):\n out += \"\\t%s:\\n\" % package_name\n for c, fd in sorted(package.components.iteritems()):\n out += \"\\t\\t%s (%s)\\n\" % (c, fd.filename)\n\n LOG.info(out)\n\n for p in self.packages.values():\n for f in p.files:\n for id in f.requires:\n # This throws if it doesn't find something.\n try:\n self.get(id)\n except:\n LOG.exception(\"Error in: \" + f.filename)\n raise","def santityCheckInitialization(self):\r\n\r\n for obj in self.config[\"repos\"]:\r\n if not isdir(obj[\"path\"]):\r\n print(\"ERROR : Initialization Failed missing {} at path {}\".format(obj[\"name\"], obj[\"path\"]))","def objectsReady(self, n):\n return len(self.files) >= n","def check(self):\n json = JsonBackend(\"../src/builder/projects.json\")\n json.load()\n\n TM_ITSELF = 1\n expected_files = TM_ITSELF + sum(p.downloadable is True\n for p in json.projects)\n self.downloads_for_project('tots', expected_files)\n\n expected_files = TM_ITSELF + sum(p.softcatala is True and\n p.downloadable is True\n for p in json.projects)\n\n self.downloads_for_project('softcatala', expected_files)\n\n expected_files = 1\n for project_dto in json.projects:\n if not project_dto.downloadable:\n continue\n\n self.downloads_for_project(project_dto.name, expected_files)\n self.check_project_link(project_dto.projectweb)","def __runChecks(self):\n runnedChecks = []\n runnedChecks.append(Checks.checksFilesInstalled(self.__targetPath, verbose=True))\n return all(runnedChecks)","def _haveReadLocks(self): \n readLockFileName = ReadLock.fileName\n for name in os.listdir(self.dir):\n if name.startswith(readLockFileName):\n return True\n return False","def checkMissingFiles(inDir, jsonUrl):\n\n file_list = []\n remote = False\n try:\n file_list = os.listdir(inDir)\n except OSError:\n remote = True\n file_list = eos_ls(inDir)\n\n if file_list == []:\n print \"Directory does not exist or is empty!\"\n return []\n\n total_expected = 0\n missing_files = []\n suspicious_files = []\n recovered_files = []\n\n print 'Found %d files in input directory' % len(file_list)\n print 20*'-'\n\n jsonFile = open(jsonUrl,'r')\n procList = json.load(jsonFile,encoding = 'utf-8').items()\n\n for proc in procList:\n for desc in proc[1]:\n data = desc['data']\n isData = desc.get('isdata',False)\n mctruthmode = desc.get('mctruthmode')\n for d in data:\n dtag = d.get('dtag','')\n split = d.get('split',1)\n\n for segment in range(0,split):\n eventsFile = dtag\n if split > 1:\n eventsFile = dtag + '_' + str(segment)\n if mctruthmode:\n eventsFile += '_filt%d' % mctruthmode\n filename = eventsFile+'.root'\n\n sys.stdout.write('... checking %s' % filename)\n sys.stdout.flush()\n\n total_expected += 1\n\n if not filename in file_list:\n missing_files.append(filename)\n sys.stdout.write('\\033[91m MISSING \\033[0m \\n')\n # sys.stdout.flush()\n continue\n\n rootFileUrl = os.path.join(inDir, filename)\n if remote:\n rootFileUrl = ('root://eoscms//eos/cms/store' +\n rootFileUrl.split('store',1)[1])\n\n recovered, suspicious = False, False\n tfile = TFile.Open(rootFileUrl)\n try:\n if tfile.TestBit(TFile.kRecovered):\n recovered = True\n if tfile.IsZombie():\n suspicious = True\n tfile.Close()\n except AttributeError, ReferenceError:\n suspicious = True\n\n if recovered:\n sys.stdout.write('\\033[93m Recovered \\033[0m \\n')\n recovered_files.append(filename)\n if suspicious:\n sys.stdout.write('\\033[93m Failed to open \\033[0m \\n')\n suspicious_files.append(filename)\n\n sys.stdout.write('\\033[92m OK \\033[0m \\n')\n sys.stdout.flush()\n\n print 20*'-'\n if len(missing_files):\n print \"Missing the following files:\"\n print \"(%d out of %d expected)\"% (len(missing_files), total_expected)\n for filename in missing_files:\n print filename\n else:\n print \"NO MISSING FILES!\"\n print 20*'-'\n if len(suspicious_files):\n print \"Failed to open the following files:\"\n print \"(%d out of %d expected)\"% (len(suspicious_files), total_expected)\n for filename in suspicious_files:\n print filename\n print 20*'-'\n if len(recovered_files):\n print \"The following files are recovered:\"\n print \"(%d out of %d expected)\"% (len(recovered_files), total_expected)\n for filename in recovered_files:\n print filename\n print 20*'-'\n\n return missing_files+suspicious_files+recovered_files","def mark_local_files_as_available(checksums):\n mark_local_files_availability(checksums, True)","def _check_integrity(self):\n root = self.root\n for scene_name in self.scene_list:\n if not(os.path.isdir(os.path.join(root,scene_name)) and \n os.path.isdir(os.path.join(root,scene_name, images_dir)) and\n os.path.isfile(os.path.join(root,scene_name,annotation_filename))):\n return False\n return True","def _check_integrity(self):\n root = self.root\n for scene_name in self.scene_list:\n if not(os.path.isdir(os.path.join(root,scene_name)) and \n os.path.isdir(os.path.join(root,scene_name, images_dir)) and\n os.path.isfile(os.path.join(root,scene_name,annotation_filename))):\n return False\n return True","def _check_before_run(self):\n\t\tif not osp.exists(self.dataset_dir):\n\t\t\traise RuntimeError(\"'{}' is not available\".format(self.dataset_dir))\n\t\tif not osp.exists(self.train_dir):\n\t\t\traise RuntimeError(\"'{}' is not available\".format(self.train_dir))\n\t\tif not osp.exists(self.query_dir):\n\t\t\traise RuntimeError(\"'{}' is not available\".format(self.query_dir))\n\t\tif not osp.exists(self.gallery_dir):\n\t\t\traise RuntimeError(\"'{}' is not available\".format(self.gallery_dir))","def _check_before_run(self):\n if not osp.exists(self.dataset_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.dataset_dir))\n if not osp.exists(self.train_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.train_dir))\n if not osp.exists(self.list_query_path):\n raise RuntimeError(\"'{}' is not available\".format(self.list_query_path))\n if not osp.exists(self.list_gallery_path):\n raise RuntimeError(\"'{}' is not available\".format(self.list_gallery_path))","def find_files(base_dir, consumer_q):\n for dirpath, dirs, files in os.walk(base_dir, topdown=False):\n for f in files:\n fullpath = os.path.join(dirpath, f)\n if os.path.isfile(fullpath):\n consumer_q.put(fullpath)","def _check_before_run(self):\n if not osp.exists(self.dataset_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.dataset_dir))\n if not osp.exists(self.data_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.data_dir))\n if not osp.exists(self.split_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.split_dir))","def check_files(self, data_path):\n files = os.listdir(data_path)\n\n if 'test_batch' not in files:\n return False\n\n if 'batches.meta' not in files:\n return False\n\n for i in range(1, 6):\n if 'data_batch_{}'.format(i) not in files:\n return False\n\n return True","def check_file_existence(self, files):\n if len(files) == 0:\n if self._nonfile_error is True:\n raise FileNotFound(\"No files are found.\")\n else:\n self._logger.info(\"No files are found. Nothing to do.\")\n return\n self._logger.info(\"Files found %s\" % files)","def check_files(self) -> None:\n notfound = False\n give_neuro_data_hint = False\n fullpaths = [f for f, _ in self.input_sources]\n if self.target_sources is not None:\n fullpaths.extend([f for f, _ in self.target_sources])\n for p in fullpaths:\n if not os.path.exists(p):\n print('{} not found.'.format(p))\n notfound = True\n if 'neuro_data_cdhw' in p:\n give_neuro_data_hint = True\n if give_neuro_data_hint:\n print('\\nIt looks like you are referencing the neuro_data_cdhw dataset.\\n'\n 'To install the neuro_data_xzy dataset to the default location, run:\\n'\n ' $ wget https://github.com/ELEKTRONN/elektronn.github.io/releases/download/neuro_data_cdhw/neuro_data_cdhw.zip\\n'\n ' $ unzip neuro_data_cdhw.zip -d ~/neuro_data_cdhw')\n if notfound:\n print('\\nPlease fetch the necessary dataset and/or '\n 'change the relevant file paths in the network config.')\n sys.stdout.flush()\n sys.exit(1)","def _check_before_run(self):\n if not osp.exists(self.dataset_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.dataset_dir))\n if not osp.exists(self.train_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.train_dir))\n if not osp.exists(self.probe_gallery_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.probe_gallery_dir))","def testAvailableFiles(self):\n assert self.dummySubscription.availableFiles() == \\\n self.dummySubscription.available.getFiles(type='set'), \\\n 'Method availableFiles does not return available files Set'","def test_scan_empty_dir(self):\n self.run_scan(self.emptydir, 0)","def check_all_files_and_dirs(self):\n err = 0\n err_m = ''\n warning = 0\n warning_m = ''\n # Check the pdb file for refinement\n if self.refine_pdb_in == None:\n err = 1\n err_m += '\\nPdb file should be supplied'\n else:\n if self.check_single_file(self.refine_pdb_in):\n self.refine_pdb_in = os.path.abspath(self.refine_pdb_in)\n else:\n err = 1\n err_m += '\\nFile not found: %s' %(self.refine_pdb_in)\n\n # Check the pdb file for distance analysis\n if self.check_single_file(self.X8_pdb_in):\n self.X8_pdb_in = os.path.abspath(self.X8_pdb_in)\n else:\n self.X8_pdb_in != None\n warning = 1\n warning_m += '\\nXtrapol8 pdb_in not found. No additional analysis will be applied'\n\n # Check additional files and append them to a string\n additional = \"\"\n for fle in self.additional:\n if len(fle)>0:\n if self.check_single_file(fle):\n new_add = os.path.abspath(fle)\n additional = additional + \"%s \" % (new_add)\n else:\n err = 1\n err_m += '\\nFile not found: %s' %(fle)\n self.additional = additional\n\n #Check the output directory\n if os.path.isdir(self.outdir):\n self.outdir = os.path.abspath(self.outdir)\n else:\n err = 1\n err_m += \"\\nXtrapol8 output directory cannot be found.\" \\\n \"Please run this from the same directory from which you ran Xtrapol8.\"\n\n #Check the phil file for reciprocal space refinement\n if self.check_single_file(self.reciprocal_space_phil):\n self.reciprocal_space_phil = os.path.abspath(self.reciprocal_space_phil)\n else:\n self.reciprocal_space_phil = ''\n warning = 1\n warning_m += '\\nPhil for reciprocal space refinement not found. Refinement will use default parameters.'\n\n\n #Check the phil file for real space refinement\n if self.check_single_file(self.real_space_phil):\n self.real_space_phil = os.path.abspath(self.real_space_phil)\n else:\n self.real_space_phil = ''\n warning = 1\n warning_m += '\\nPhil for real space refinement not found. Refinement will use default parameters.'\n\n #Check the residue list for distance analysis\n if self.check_single_file(self.residue_list):\n self.residue_list = os.path.abspath(self.residue_list)\n else:\n self.residue_list = None\n warning = 1\n warning_m += '\\nResidue list not found. Distance analysis (if required) will be performed without residue list.'\n\n return err, err_m, warning, warning_m","def _dir_empty(path):\n try:\n next(os.scandir(str(path)))\n except StopIteration:\n return True\n return False","def _find_files(directory, dirs_to_look_in, files_to_search_for, \n current_dir, see_files):\n full_name = True\n if see_files:\n full_name = False\n files_to_load = search_directory(directory, \n look_in=dirs_to_look_in,\n search_for=files_to_search_for,\n file_type='files',\n current_dir=current_dir,\n full_name=full_name)\n if not files_to_load:\n raise UserWarning('No files were found matching the search for %s'\\\n ' in the directory(s) %s%s' \\\n % (files_to_search_for, directory, \n dirs_to_look_in))\n return files_to_load","def _verify(self) -> None:\n # Check if the files already exist\n if os.path.exists(os.path.join(self.root, self.image_root)):\n return\n\n # Check if .zip files already exists (if so extract)\n exists = []\n for filename, md5 in zip(self.filenames, self.md5s):\n filepath = os.path.join(self.root, filename)\n if os.path.isfile(filepath):\n if self.checksum and not check_integrity(filepath, md5):\n raise RuntimeError(\"Dataset found, but corrupted.\")\n exists.append(True)\n extract_archive(filepath)\n else:\n exists.append(False)\n\n if all(exists):\n return\n\n # Check if the user requested to download the dataset\n raise RuntimeError(\n \"Dataset not found in `root` directory, either specify a different\"\n + \" `root` directory or manually download the dataset to this directory.\"\n )","def buffers_exist(self):\n for buff in self.buffers:\n if not buff.is_separate_file:\n continue\n\n path = self.path.parent / buff.uri\n if not path.exists():\n raise FileNotFoundError(\n \"Buffer {} referenced in {} not found\".format(path, self.path)\n )","def missing_in_gn_by_file(self):\n return self._missing_gn_files","def check_paths( self ):\n check_a = utility_code.checkDirectoryExistence( self.PATH_TO_SOURCE_FILE_DIRECTORY )\n check_b = utility_code.checkDirectoryExistence( self.PATH_TO_ARCHIVES_ORIGINALS_DIRECTORY )\n check_c = utility_code.checkDirectoryExistence( self.PATH_TO_ARCHIVES_PARSED_DIRECTORY )\n check_d = utility_code.checkDirectoryExistence( self.PATH_TO_PARSED_ANNEX_DATA_DIRECTORY )\n check_e = utility_code.checkDirectoryExistence( self.PATH_TO_PARSED_ANNEX_COUNT_DIRECTORY )\n if check_a == 'exists' and check_b == 'exists' and check_c == 'exists' and check_d == 'exists' and check_e == 'exists':\n log.debug( 'path check passed' )\n else:\n message='path check failed; quitting'\n log.error( message )\n sys.exit( message )\n return","def check_for_setup_error(self):\n if self.share2nms:\n for nfs_share in self.share2nms:\n nms = self.share2nms[nfs_share]\n volume_name, dataset = self._get_share_datasets(nfs_share)\n if not nms.volume.object_exists(volume_name):\n raise LookupError(_(\"Volume %s does not exist in Nexenta \"\n \"Store appliance\"), volume_name)\n folder = '%s/%s' % (volume_name, dataset)\n if not nms.folder.object_exists(folder):\n raise LookupError(_(\"Folder %s does not exist in Nexenta \"\n \"Store appliance\"), folder)\n if (folder not in nms.netstorsvc.get_shared_folders(\n 'svc:/network/nfs/server:default', '')):\n self._share_folder(nms, volume_name, dataset)\n self._get_capacity_info(nfs_share)","def are_readable_files(self, fnames):\n for fname in fnames:\n if not os.access(fname, os.R_OK):\n self.cli_parser.error(\"%s doesn't exist or you do \"\n \"not have read permissions to it.\" % fname)","def list_existing_files(files):\n\n result = []\n for f in flatten(files):\n if file_exists(f): result.append(f)\n return None if len(result) == 0 else result","def check_dir(self):\n if not Path(self.src_dir).exists():\n print('No such directory found:', self.src_dir)\n return\n\n nc_all = self.src_dir + \"/*.nc*\"\n if len(glob.glob(nc_all)) == 0:\n print('No NetCDF files found in:', self.src_dir)\n return\n\n return nc_all","def _checkForInefficientDirectories(self, warn=True):\n self._populatedLevels = len([v for v in self._tiffDirectories if v is not None])\n missing = [v is None for v in self._tiffDirectories]\n maxMissing = max(0 if not v else missing.index(False, idx) - idx\n for idx, v in enumerate(missing))\n self._skippedLevels = maxMissing\n if maxMissing >= self._maxSkippedLevels:\n if warn:\n config.getConfig('logger').warning(\n 'Tiff image is missing many lower resolution levels (%d). '\n 'It will be inefficient to read lower resolution tiles.', maxMissing)\n self._inefficientWarning = True","def file_checker():\n\n PATH_RELEASE1_IDEN = os.getcwd()+'/archive_all_2014-10/'\n PATH_RELEASE1_UNIDE = None\n #PATH_RELEASE1_UNIDE = os.getcwd()+'/archive_all_2014-10/'\n\n PATH_RELEASE2_IDEN = os.getcwd()+'/archive_all_2016-10/archive_identified_2016-10/'\n PATH_RELEASE2_UNIDE = os.getcwd() + '/archive_all_2016-10/archive_unidentified_2016-10/'\n\n\n #From here don't change anything.\n #This global function finds the .mgf files in paths\n list_of_files_release1_ide = glob.glob(PATH_RELEASE1_IDEN+'*.mgf')\n list_of_files_release1_unide = None #REMOVE THIS PART AND UNCOMMENT NEXT LINE IN NEXT RELEASES.\n\n #list_of_files_release1_unid = glob.glob(PATH_RELEASE1_UNID'+*.mgf')\n\n list_of_files_release2_ide = glob.glob(PATH_RELEASE2_IDEN+'*.mgf')\n list_of_files_release2_unide = glob.glob(PATH_RELEASE2_UNIDE+'*.mgf')\n\n\n #Check if exist cache folder. If not will make it. \n #RELEASE 1 \n if not os.path.exists(PATH_RELEASE1_IDEN+'cache'):\n os.makedirs(PATH_RELEASE1_IDEN+'cache')\n\n # if not os.path.exists(PATH_RELEASE1_UNIDE'+cache'):\n # os.makedirs(PATH_RELEASE1_UNIDE'+cache')\n\n #RELEASE2\n if not os.path.exists(PATH_RELEASE2_IDEN+'cache'):\n os.makedirs(PATH_RELEASE2_IDEN+'cache')\n\n if not os.path.exists(PATH_RELEASE2_UNIDE+'cache'):\n os.makedirs(PATH_RELEASE2_UNIDE+'cache')\n \n\n return PATH_RELEASE1_IDEN, \\\n PATH_RELEASE2_IDEN, \\\n PATH_RELEASE2_UNIDE, \\\n list_of_files_release1_ide, \\\n list_of_files_release2_ide, \\\n list_of_files_release2_unide","def _check_all_systems_ready(self):\n raise NotImplementedError()","def check_files(self):\n print('checking files')\n for f in tqdm(self.filenames):\n img = cv2.imread(f, int(self.color))\n if img is None:\n os.remove(f)","def has_file(self, doc):\n return len(doc.package.files) != 0","def find_all_files(self):\n look4files = [ f for f in listdir(self.file_location) if isfile(join(self.file_location,f)) ]\n return look4files","def walk_files():\n\n # TODO: not check twice the same dir or file\n for path in config.targets:\n abs_path = os.path.join(cwd, path)\n\n if not os.path.islink(abs_path) and os.path.isfile(abs_path):\n walked.append(abs_path)\n yield abs_path\n #process_file(abs_path)\n\n if os.path.isdir(abs_path):\n walked.append(abs_path)\n for root, dirs, files in os.walk(abs_path):\n for fname in files:\n if isbackup(fname):\n continue\n abs_path = os.path.join(root, fname)\n walked.append(abs_path)\n if not os.path.islink(abs_path) and\\\n os.path.isfile(abs_path):\n base, name = os.path.split(abs_path)\n XXX, ext = os.path.splitext(name)\n\n ignored = False\n for pattern in IGNORE_FILES:\n if pattern.search(fname):\n ignored = True\n break\n\n # maybe should be merged with IGNORE_FILES?\n for regexp in config.exclude_list:\n if regexp.search(fname):\n ignored = True\n break\n\n if not ignored:\n for test_ext in config.disallow_exts:\n if test_ext == ext:\n ignored = True\n break\n\n if not ignored:\n if config.allow_exts:\n ignored = True\n for test_ext in config.allow_exts:\n if test_ext == ext:\n ignored = False\n break\n\n if not ignored:\n yield abs_path\n #process_file(abs_path)\n\n for dir in dirs[:]:\n if dir in IGNORE_DIRS:\n dirs.remove(dir)\n if dir in dirs:\n dirs.remove(dir)\n # mayb be should be merged with IGNORE_DIRS?\n else:\n for regexp in config.exclude_list:\n if regexp.search(dir):\n # This check is required\n # because several different patterns\n # could match one file name\n if dir in dirs:\n dirs.remove(dir)\n\n for dir in dirs:\n abs_path = os.path.join(root, dir)\n walked.append(abs_path)","def scan ( self ):\n root = self.root\n try:\n filenames = sorted ( os.listdir ( root ) )\n except OSError as oserr:\n if oserr.errno != errno.ENOENT:\n raise\n else:\n HOOK_CLS = self.HOOK_SCRIPT_CLS\n for fname in filenames:\n if self.filename_filter ( fname ):\n fspath = root + os.sep + fname\n if os.path.isfile ( fspath ):\n script_obj = HOOK_CLS ( fspath, filename=fname )\n self.scripts [script_obj.name] = script_obj","def collect_local(self, path, req_tag=True):\n for f in [os.path.join(dp, f) for dp, dn, filenames in os.walk(path) for f in filenames]:\n if not os.path.isfile(f):\n continue\n self.collect_single(f, req_tag)","def check_corrupted_files(self):\r\n for store in STORES:\r\n path = f\"{self.system.config_path}/.storage/{STORES[store]}\"\r\n if os.path.exists(path):\r\n if os.stat(path).st_size == 0:\r\n # File is empty (corrupted)\r\n return True\r\n return False","def __init__(self, root):\n FileHelper.ALL_PATHS = [os.path.join(dp, f) for dp, dn, filenames in os.walk(root) for f in filenames if os.path.splitext(f)[1] in Enums.App.VALID_FILE_TYPES]","def check_tree(cls, root, require_init_py=True):\n if os.path.basename(root) == '__pycache__':\n return\n\n if not os.path.isfile(os.path.join(root, '__init__.py')):\n # Not a Python package directory\n if require_init_py:\n raise util.UserError(\n f\"No __init__.py file in '{root}'\")\n else:\n return\n\n # First, check for the case where a .py file and a directory\n # have the same name (without the extension). This can't be\n # handled, so just raise an exception\n found = set()\n for filename in os.listdir(root):\n path = os.path.join(root, filename)\n if os.path.isfile(path):\n filename, ext = os.path.splitext(filename)\n if ext == '.py':\n found.add(filename)\n\n for dirname in os.listdir(root):\n path = os.path.join(root, dirname)\n if os.path.isdir(path):\n if dirname in found:\n raise util.UserError(\n \"Found a directory and python file with same name in \"\n \"benchmark tree: '{0}'\".format(path))\n cls.check_tree(path, require_init_py=False)","def test_walk_not_full(self):\n dummy_folder = TestOspaListDir.get_dummy_folder()\n need_result = []\n for i in range(1, 4):\n need_result.append('meme{}.jpg'.format(i))\n need_result.extend(['meme1.jpg',\n 'meme2.png',\n 'meme4.jpg',\n 'meme4.png',\n '1.txt',\n '2.txt',\n '3.txt',\n 'not_txt.not_txt',\n 'antigravity.png',\n 'egg.png',\n 'empty.txt',\n 'holy_grenade.png',\n 'spam.jpg',\n ])\n\n result = listdir(dummy_folder, full_path=False, only_files=True, walk=True)\n self.assertEqual(sorted(result), sorted(need_result))","def check_config(outconfig):\n self.log.info(\"Checking if all the necessary files exist.\")\n\n # Perform necessary checks\n\n log.info(\"All necessary files exist for {} configuration.\".format(outconfig[\"Flavor\"]))\n\n return","def checkAllFilesIncluded(self):\n # TODO: config objects: this import can go away\n from exhale.configs import unabridgedOrphanKinds\n\n # build path to unabridged api document that adds all toctree directives\n root = get_exhale_root(self)\n containmentFolder = self.getAbsContainmentFolder()\n unabridged_api_path = os.path.join(containmentFolder, \"unabridged_api.rst.include\")\n unabridged_orphan_path = os.path.join(containmentFolder, \"unabridged_orphan.rst\")\n\n # gather lines that match the indented part of the toctree\n #\n # .. toctree::\n # :maxdepth: {toctreeMaxDepth}\n #\n # some_node.file_name.rst\n #\n # so just lazily look for the leading three spaces and ending with .rst\n full_api_toctrees = []\n under_toctree_re = re.compile(r\"^ .+\\.rst$\")\n with open(unabridged_api_path) as unabridged_api:\n for line in unabridged_api:\n if under_toctree_re.match(line):\n full_api_toctrees.append(line.strip())\n\n orphan_toctrees = []\n if os.path.isfile(unabridged_orphan_path):\n with open(unabridged_orphan_path) as unabridged_orphan:\n for line in unabridged_orphan:\n if under_toctree_re.match(line):\n orphan_toctrees.append(line.strip())\n\n # Scan all nodes and make sure they were found in the toctrees above.\n doxygen_mainpage_was_used = False\n for node in root.all_nodes:\n if node.kind in {\"enumvalue\", \"group\"}:\n continue\n\n # When doxygen \\mainpage command is used, the .. doxygenpage:: index\n # is .. include::'ed in the root file document. Those checks come\n # after this loop.\n if node.kind == \"page\" and node.refid == \"indexpage\":\n doxygen_mainpage_was_used = True\n continue\n\n if node.kind in unabridgedOrphanKinds or \\\n (node.kind == \"class\" and \"struct\" in unabridgedOrphanKinds) or \\\n (node.kind == \"struct\" and \"class\" in unabridgedOrphanKinds):\n toctrees = orphan_toctrees\n doc = unabridged_orphan_path\n else:\n toctrees = full_api_toctrees\n doc = unabridged_api_path\n\n self.assertTrue(\n node.file_name in toctrees,\n \"Node refid=[{refid}] and basename=[{file_name}] not found in [{doc}]!\".format(\n refid=node.refid, file_name=node.file_name, doc=doc\n )\n )\n\n # Make sure every document expected to be .. include::'ed in the library root\n # has actually been included.\n full_root_file_path = root.full_root_file_path\n root_file_includes = []\n with open(full_root_file_path, \"r\") as full_root_f:\n for line in full_root_f:\n include_mark = \".. include::\"\n if line.startswith(include_mark):\n root_file_includes.append(line.split(include_mark)[-1].strip())\n\n index_include = False # page_index.rst comes from doxygen \\mainpage\n page_hierarchy_include = False # page_view_hierarchy.rst\n class_hierarchy_include = False # class_view_hierarchy.rst\n file_hierarchy_include = False # file_view_hierarchy.rst\n unabridged_api_include = True # unabridged_api.rst. Always included.\n orphan_api_include = False # unabridged_orphan.rst. Never included.\n for node in root.all_nodes:\n if node.kind == \"page\":\n if node.refid == \"indexpage\":\n index_include = True\n else:\n page_hierarchy_include = True\n elif node.kind in exhale.utils.CLASS_LIKE_KINDS:\n class_hierarchy_include = True\n elif node.kind in {\"dir\", \"file\"}:\n file_hierarchy_include = True\n\n self.assertTrue(\n doxygen_mainpage_was_used == index_include,\n \"Mismatch: doxygen_mainpage_was_used != index_include!\")\n\n include_map = {\n \"page_index.rst.include\": index_include,\n os.path.basename(root.page_hierarchy_file): page_hierarchy_include,\n os.path.basename(root.class_hierarchy_file): class_hierarchy_include,\n os.path.basename(root.file_hierarchy_file): file_hierarchy_include,\n os.path.basename(root.unabridged_api_file): unabridged_api_include,\n os.path.basename(root.unabridged_orphan_file): orphan_api_include\n }\n root_file_base = os.path.basename(full_root_file_path)\n for key, val in include_map.items():\n if val:\n check = getattr(self, \"assertTrue\")\n msg = \"*WAS* expected in {root_file_base}, but was *NOT* found!\".format(\n root_file_base=root_file_base\n )\n else:\n check = getattr(self, \"assertFalse\")\n msg = \"was *NOT* expected in {root_file_base}, but *WAS* found!\".format(\n root_file_base=root_file_base\n )\n check(\n key in root_file_includes,\n \"Page '{key}' {msg}\".format(key=key, msg=msg))\n\n # Some tests may want the toctree names afterward.\n return full_api_toctrees, orphan_toctrees","def check():\n dist_path = Path(DIST_PATH)\n if not dist_path.exists() or not list(dist_path.glob('*')):\n print(\"No distribution files found. Please run 'build' command first\")\n return\n\n subprocess.check_call(['twine', 'check', 'dist/*'])","def site_directory_contains_stale_files(site_directory):\n\n return True if os.path.exists(site_directory) and os.listdir(site_directory) else False","def list_missing_files(files):\n\n result = []\n for f in flatten(files):\n if not file_exists(f): result.append(f)\n return None if len(result) == 0 else result","def check_missing_files(self):\n files = [getattr(self, attr) for attr in self._required]\n try:\n utilities.check_missing_files(files)\n except utilities.MissingConstraintError as err:\n err.message += \"\\nSkipping {}\\n\".format(self.__class__.__name__)\n raise err","def checkfiles(args):\n\n from .query import Database\n db = Database()\n\n r = db.objects(\n protocol=args.protocol,\n support=args.support,\n groups=args.group,\n purposes=args.purposes,\n gender=args.gender,\n clients=args.client,\n )\n\n # go through all files, check if they are available on the filesystem\n good = []\n bad = []\n for f in r:\n if os.path.exists(f.make_path(args.directory, args.extension)):\n good.append(f)\n else:\n bad.append(f)\n\n # report\n output = sys.stdout\n if args.selftest:\n from bob.db.base.utils import null\n output = null()\n\n if bad:\n for f in bad:\n output.write('Cannot find file \"%s\"\\n' % (f.make_path(args.directory, args.extension),))\n output.write('%d files (out of %d) were not found at \"%s\"\\n' % \\\n (len(bad), len(r), args.directory))\n\n return 0","def checkAllFilesGenerated(self):\n root = get_exhale_root(self)\n containmentFolder = self.getAbsContainmentFolder()\n for node in root.all_nodes:\n if node.kind in [\"enumvalue\", \"group\"]:\n continue\n gen_file_path = os.path.join(containmentFolder, node.file_name)\n self.assertTrue(\n os.path.isfile(gen_file_path),\n \"File for {kind} node with refid=[{refid}] not generated to [{gen_file_path}]!\".format(\n kind=node.kind, refid=node.refid, gen_file_path=gen_file_path\n )\n )","def in_folder(self):\n return len(os.path.split(self.file_path)) > 1","def test_GetFilesInDirectory_empty_dir(tempdir: pathlib.Path):\n assert not dpack.GetFilesInDirectory(tempdir, [])","def read_all(self, prog:progress=None):\t\t\t\n\t\tself.__output_status(\"Read & compare all files\")\n\t\tself.__read_files('all', prog)","def check_configfiles():\n return (all(os.path.isdir(x) for x in CONFIG_DIRS) and\n os.path.isfile(CONFIG_FILE) and os.path.isfile(LOG_CONFIG_FILE))","def avail(self):\n\n return os.listdir(self.datadir)","def fileListCheck(self, key = None, verbose = True, errorCheck = True):\n\n # if key is not None:\n\n fileListTest = self.nbDetails[key]['pkgFileList']\n\n # Count file suffixes\n suffixList = [Path(item).suffix for item in fileListTest]\n c = Counter(suffixList)\n\n # Check for dirs & log\n dirList = []\n fileList = []\n for n, item in enumerate(suffixList):\n # if Path(item).is_dir(): # Checking with Path OK for local files, but not for a list from another machine.\n if item == '':\n dirList.append(fileListTest[n])\n else:\n fileList.append(fileListTest[n])\n\n if fileList:\n self.nbDetails[key]['pkgRootDir'] = os.path.commonpath(fileListTest)\n self.nbDetails[key]['pkgDirList'] = dirList\n self.nbDetails[key]['pkgSuffixCount'] = c\n else:\n print(f\"***Warning, job {key}, title: {self.nbDetails[key]['title']}\\nNo files found.\")\n self.nbDetails[key]['pkgRootDir'] = []\n self.nbDetails[key]['pkgDirList'] = []\n self.nbDetails[key]['pkgSuffixCount'] = []\n\n # Print details\n if verbose:\n print('\\n***Pkg job summary')\n print(f\"Job {key}, title: {self.nbDetails[key]['title']}\")\n print(f\"Notebook file: {self.nbDetails[key]['file']}\")\n print(*self.nbDetails[key]['pkgInfo'][2:], sep='\\n')\n print(f\"Root dir: {self.nbDetails[key]['pkgRootDir']}\")\n print(f\"Found {len(fileListTest)} items, with file types:\")\n # Throw out full dictionary here, may want to switch to formatted list.\n # Set small width to force vertical format\n pprint.pprint(c, width=50)\n\n if dirList:\n print(\"SubDirs:\")\n print(*dirList, sep='\\n')\n\n\n # Check if wavefunction job, and if files are present.\n # TODO: propagate this issue!!! For init self.nbDetails[key]['pkg'] is not yet set... may need to move this check to later?\n self.nbDetails[key]['wf'] = any(['waveFn' in item for item in self.nbDetails[key]['pkgDirList']])|any(['wf' in item for item in self.nbDetails[key]['pkgDirList']])\n # if self.nbDetails[key]['wf']:\n # print(f\"Wavefunction job, with {c['.dat']} dat files, expected {self.nbDetails[key]['E'][2]*5}\")\n\n # Error checking based on file types\n # Set pass/fail in self.nbDetails[key]['fileListCheck']\n if errorCheck:\n self.nbDetails[key]['fileListCheck'] = True\n\n if c['.out'] > 1:\n print(f\"***Warning: found {c['.out']} ePS output files.\")\n self.nbDetails[key]['fileListCheck'] = False\n\n if c['.idy'] != 3:\n # TODO: change to checking by number of symmetries\n print(f\"***Warning: found {c['.idy']} ePS idy files.\")\n self.nbDetails[key]['fileListCheck'] = False\n\n if self.nbDetails[key]['wf']:\n # if (c['.dat'] is None):\n # print(\"***Warning: No .dat files found\")\n # elif (c['.dat'] != self.nbDetails[key]['E'][2]*5):\n # print(f\"***Warning: found {c['.dat']} dat files, expected {self.nbDetails[key]['E'][2]*5} for wavefunction job.\")\n fPerE = 5 # Set files per energy point for numerical checks\n if self.nbDetails[key]['E'] is None:\n En = 0\n else:\n En = int(self.nbDetails[key]['E'][3])*fPerE\n\n if En == 0:\n print(f\"***Warning: found {c['.dat']} dat files, missing job E details for wavefunction job.\")\n self.nbDetails[key]['fileListCheck'] = False\n elif (c['.dat'] is None) or (c['.dat'] < En):\n print(f\"***Warning: found {c['.dat']} dat files, expected {En}*syms for wavefunction job.\")\n self.nbDetails[key]['fileListCheck'] = False\n else:\n # Check symmetries...\n # SnTest = [En%Sn for Sn in range(1,6)]\n Sn = c['.dat']/En\n if c['.dat']%En == 0:\n print(f\"Wavefunction job, with {c['.dat']} dat files OK (for {Sn} syms)\")\n else:\n print(f\"***Warning: Wavefunction job, with {c['.dat']} dat files indeterminate symmetries {Sn}\")\n self.nbDetails[key]['fileListCheck'] = False\n\n if c['.nc'] != 2:\n # TODO: change to checking by number of symmetries\n print(f\"***Warning: found {c['.nc']} ePSproc nc files, expected 2.\")\n self.nbDetails[key]['fileListCheck'] = False","def _check_before_run(self):\n if not osp.exists(self.dataset_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.dataset_dir))\n if not osp.exists(self.data_dir):\n raise RuntimeError(\"'{}' is not available\".format(self.data_dir))\n if not osp.exists(self.raw_mat_path):\n raise RuntimeError(\"'{}' is not available\".format(self.raw_mat_path))\n if not osp.exists(self.split_new_det_mat_path):\n raise RuntimeError(\"'{}' is not available\".format(self.split_new_det_mat_path))\n if not osp.exists(self.split_new_lab_mat_path):\n raise RuntimeError(\"'{}' is not available\".format(self.split_new_lab_mat_path))","def checkForFiles(quickLogger, fileList):\n \n for filename in fileList:\n if(not os.path.exists(filename)):\n quickLogger.critical(filename+\" doesn't exist.\")\n raise FileNotFound","def verify_directories_empty(self):\n if self.install_type == \"rpm\":\n return # For RPM install we don't want to clean anything\n\n instance_dir = \"frontend_%(service)s-%(instance)s\" % \\\n { \"service\": self.service_name(), \n \"instance\": self.glidein.instance_name(), }\n #-- directories to check ---\n dirs = {}\n dirs[\"logs........\"] = os.path.join(self.logs_dir(), instance_dir)\n dirs[\"install.....\"] = os.path.join(self.install_location(), instance_dir) \n dirs[\"config......\"] = self.config_dir()\n for subdir in [\"monitor\", \"stage\"]:\n dirs[\"web %s \" % subdir] = os.path.join(self.glidein.web_location(), subdir, instance_dir)\n #--- check them --\n for type in dirs.keys():\n if os.path.isdir(dirs[type]): \n if len(os.listdir(dirs[type])) == 0:\n os.rmdir(dirs[type])\n del dirs[type] # remove from dict\n else:\n del dirs[type] # it does not exist, remove from dict\n\n #--- if all are empty, return \n if len(dirs) == 0:\n time.sleep(3)\n return # all directories are empty\n\n #--- See if we can remove them --- \n common.logit(\"\"\"The following directories must be empty for the install to succeed: \"\"\")\n types = sorted(dirs.keys())\n for type in types:\n common.logit(\"\"\" %(type)s: %(dir)s\"\"\" % \\\n { \"type\" : type, \"dir\" : dirs[type] })\n common.ask_continue(\"... can we remove their contents\")\n for type in dirs.keys():\n common.remove_dir_contents(dirs[type])\n os.rmdir(dirs[type])\n time.sleep(3)\n return","def exists():\n check50.include(\"data\")\n check50.exists(\"adventure.py\")\n check50.exists(\"room.py\")","def _check_available_irfs(self):\n\n available_caldb = [path.split('/')[-1] for path in glob.glob(self.caldb_path + '/data/cta/*')]\n\n if self.verbose:\n print('-- Available CALDBs -- ')\n print(\" {}\".format(available_caldb))\n\n for caldb in available_caldb:\n available_irf = [path.split('/')[-1] for path in glob.glob(self.caldb_path + '/data/cta/' + caldb + '/bcf/*')]\n\n if self.verbose:\n print(\"-- Available IRFs for '{:s}' --\".format(caldb))\n print(\" {}\".format(available_irf))\n\n if self.caldb_name not in available_caldb:\n print(\"ERROR: provided CALDB name '{:s}' is not found!\".format(self.caldb_name))\n print('Available options are:')\n print(\" {}\".format(available_caldb))\n self.am_ok = False\n\n if self.irf not in available_irf:\n print(\"ERROR: provided IRF name '{:s}' is not found!\".format(self.irf))\n print('Available options are:')\n print(\" {}\".format(available_irf))\n self.am_ok = False","def autodetect_files(self):\n if self._is_valid_requirements_file('requirements.txt'):\n self.filenames.append('requirements.txt')\n\n if self._is_valid_requirements_file('requirements.pip'): # pragma: nocover\n self.filenames.append('requirements.pip')\n\n if os.path.isdir('requirements'):\n for filename in os.listdir('requirements'):\n file_path = os.path.join('requirements', filename)\n if self._is_valid_requirements_file(file_path):\n self.filenames.append(file_path)\n self._check_inclusions_recursively()","def _check_before_run(self):\n if not osp.exists(self.root):\n raise RuntimeError(\"'{}' is not available\".format(self.root))\n if not osp.exists(self.train_name_path):\n raise RuntimeError(\"'{}' is not available\".format(self.train_name_path))\n if not osp.exists(self.test_name_path):\n raise RuntimeError(\"'{}' is not available\".format(self.test_name_path))\n if not osp.exists(self.track_train_info_path):\n raise RuntimeError(\"'{}' is not available\".format(self.track_train_info_path))\n if not osp.exists(self.track_test_info_path):\n raise RuntimeError(\"'{}' is not available\".format(self.track_test_info_path))\n if not osp.exists(self.query_IDX_path):\n raise RuntimeError(\"'{}' is not available\".format(self.query_IDX_path))","def missing_in_gyp_by_file(self):\n return self._missing_gyp_files","def blocks(self, dirs):\n return any([d in self.directories for d in dirs])","def test_only_files(self):\n dummy_folder = TestOspaListDir.get_dummy_folder()\n need_result = ['meme1.jpg',\n 'meme2.png',\n 'meme4.jpg',\n 'meme4.png',\n 'meme monty python',\n ]\n need_result_new = [os.path.join(dummy_folder, 'memes', x) for x in need_result[:-1]]\n result = listdir(os.path.join(dummy_folder, 'memes'), only_files=True)\n self.assertEqual(sorted(result), sorted(need_result_new))\n\n need_result_new = [os.path.join(dummy_folder, 'memes', x) for x in need_result]\n result = listdir(os.path.join(dummy_folder, 'memes'), only_files=False)\n self.assertEqual(sorted(result), sorted(need_result_new))","def checkFiles( filenames ):\n \n missing = []\n for filename in filenames:\n if not os.path.exists( filename ):\n missing.append( filename )\n\n if missing:\n raise ValueError( \"missing files: %s\" % \",\".join(missing) )"],"string":"[\n \"def init_check(self):\\n for required_file in self._required_files:\\n # Check if required files are there\\n # FIXME Sometimes it doesn't work :?\\n if required_file not in self.files:\\n self.valid = False\",\n \"def check_for_missing_files(self, path):\\n return None\",\n \"def contains_files(self):\\n if self.file_list is None:\\n self._set_file_list()\\n for individual_file in self.file_list:\\n if not os.path.exists(os.path.join(self.base_dir, individual_file)):\\n return False\\n return True\",\n \"def _check_file_not_used(self):\\n module_files = set(self._get_module_files())\\n referenced_files = set(self._get_manifest_referenced_files()).union(\\n set(self._get_xml_referenced_files())\\n )\\n excluded_dirs = ['static', 'test', 'tests', 'migrations']\\n no_referenced_files = [\\n f for f in (module_files - referenced_files)\\n if f.split(os.path.sep)[0] not in excluded_dirs\\n ]\\n self.msg_args = no_referenced_files\\n return not no_referenced_files\",\n \"def check_all():\\n for name, module in sorted(sys.modules.items()): # module files\\n filepath = getattr(module, '__file__', None)\\n if filepath is None:\\n # we land here when a module is an attribute of another module\\n # i.e., it exists twice in the sys.modules table, once as its\\n # canonical representation, and again having been imported\\n # within another module\\n continue\\n filepath = filepath.endswith(\\\".pyc\\\") and filepath[:-1] or filepath\\n check_one(filepath)\\n\\n for filepath in extras: # additional files\\n check_one(filepath)\",\n \"def data_available(dataset_name=None):\\r\\n for file_list in data_resources[dataset_name]['files']:\\r\\n for file in file_list:\\r\\n if not os.path.exists(os.path.join(data_path, dataset_name, file)):\\r\\n return False\\r\\n return True\",\n \"def output_files_exist(self):\\n return all([split.exists() for split in self.split_files])\",\n \"def test_scan_dir_files(self):\\n self.run_scan(self.subdir, self.nest_fcount + 1)\",\n \"def test_check():\\n for f in cfg.required_files:\\n assert os.path.isfile(f)\",\n \"def check_init_files_and_folders():\\n\\t#['cascade_wimb_bus_front_100_stages_1000_pos_3000_neg.xml', 'cascade_wimb_bus_front_33_stages_1000_pos_3000_neg_wrong.xml', 'color_detect_2.py', 'dedupe.py', 'detect_image_group_ku.py', 'detect_shape_5.py', 'get_cam_id_2.py', 'get_image_8.py', 'gui_hsv.py', 'knaps.py', 'knapsack_2.py', 'maps.html', 'program_detect_rectangle.zip', 'start_capture.py']\\n\\tfile_list=[\\n\\t#'cascade_wimb_bus_front_100_stages_1000_pos_3000_neg.xml', \\n\\t'models/cascade_wimb_bus_front_33_stages_1000_pos_3000_neg_wrong.xml', \\n\\t#'color_detect_2.py', \\n\\t#'dedupe.py', \\n\\t'detect_bus_haar_group.py', \\n\\t#'detect_shape_5.py', \\n\\t'get_cam_detail.py', \\n\\t'get_image.py', \\n\\t#'gui_hsv.py', \\n\\t#'knaps.py', \\n\\t#'knapsack_2.py', \\n\\t#'maps.html', \\n\\t#'program_detect_rectangle.zip', \\n\\t'start_wimb.py',\\n\\t'g.php',\\n\\t]\\n\\tdirectory_list=[\\n\\t'images',\\n\\t'images_bgs',\\n\\t'images_bgs_mask',\\n\\t#'images_bgs_result',\\n\\t'images_color',\\n\\t'images_haar',\\n\\t'images_haar_result',\\n\\t'images_number',\\n\\t'images_number_result',\\n\\t'models',\\n\\t'images_old',\\n\\t'text_number',\\n\\t]\\n\\t\\n\\tfor file_name in file_list: print 'file '+file_name+' existed: '+str(os.path.isfile(file_name))\\n\\tfor directory_name in directory_list: \\n\\t\\tprint 'directory '+directory_name+' existed: '+str(os.path.isdir(directory_name))\\n\\t\\tif not os.path.isdir(directory_name): \\n\\t\\t\\tos.makedirs(directory_name)\\n\\t\\tif \\\"images\\\" in directory_name: shutil.copy(path+'/g.php',path+'/'+directory_name+'/g.php')\",\n \"def check_out_files_exist(self):\\n for filetype in self.filetypes:\\n filename = self.out_filename(filetype)\\n if not filename.is_file():\\n log.error('MISSING: {}'.format(filename))\\n return False\\n\\n return True\",\n \"def check_out_files_exist(self):\\n for filetype in self.filetypes:\\n filename = self.out_filename(filetype)\\n if not filename.is_file():\\n log.error('MISSING: {}'.format(filename))\\n return False\\n\\n return True\",\n \"def check_out_files_exist(self):\\n for filetype in self.filetypes:\\n filename = self.out_filename(filetype)\\n if not filename.is_file():\\n log.error('MISSING: {}'.format(filename))\\n return False\\n\\n return True\",\n \"def check_comps(root, comps):\\n for key, comp in comps.items():\\n\\n filename = os.path.join(root, comp['filename'])\\n if not os.path.isfile(filename):\\n warnings.warn(\\n 'The file {0} could not be found'.format(filename))\",\n \"def check_for_file(self):\\n if self.task.file_name in os.listdir(self.task.file_storage):\\n return True\\n return False\",\n \"def files_exist(self):\\n\\n passed = []\\n warned = []\\n failed = []\\n ignored = []\\n\\n # NB: Should all be files, not directories\\n # List of lists. Passes if any of the files in the sublist are found.\\n #: test autodoc\\n try:\\n _, short_name = self.nf_config[\\\"manifest.name\\\"].strip(\\\"\\\\\\\"'\\\").split(\\\"/\\\")\\n except ValueError:\\n log.warning(\\\"Expected manifest.name to be in the format '<repo>/<pipeline>'. Will assume it is '<pipeline>'.\\\")\\n short_name = self.nf_config[\\\"manifest.name\\\"].strip(\\\"\\\\\\\"'\\\").split(\\\"/\\\")\\n\\n files_fail = [\\n [\\\".gitattributes\\\"],\\n [\\\".gitignore\\\"],\\n [\\\".nf-core.yml\\\"],\\n [\\\".editorconfig\\\"],\\n [\\\".prettierignore\\\"],\\n [\\\".prettierrc.yml\\\"],\\n [\\\"CHANGELOG.md\\\"],\\n [\\\"CITATIONS.md\\\"],\\n [\\\"CODE_OF_CONDUCT.md\\\"],\\n [\\\"CODE_OF_CONDUCT.md\\\"],\\n [\\\"LICENSE\\\", \\\"LICENSE.md\\\", \\\"LICENCE\\\", \\\"LICENCE.md\\\"], # NB: British / American spelling\\n [\\\"nextflow_schema.json\\\"],\\n [\\\"nextflow.config\\\"],\\n [\\\"README.md\\\"],\\n [os.path.join(\\\".github\\\", \\\".dockstore.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"CONTRIBUTING.md\\\")],\\n [os.path.join(\\\".github\\\", \\\"ISSUE_TEMPLATE\\\", \\\"bug_report.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"ISSUE_TEMPLATE\\\", \\\"config.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"ISSUE_TEMPLATE\\\", \\\"feature_request.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"PULL_REQUEST_TEMPLATE.md\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"branch.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"ci.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"linting_comment.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"linting.yml\\\")],\\n [os.path.join(\\\"assets\\\", \\\"email_template.html\\\")],\\n [os.path.join(\\\"assets\\\", \\\"email_template.txt\\\")],\\n [os.path.join(\\\"assets\\\", \\\"sendmail_template.txt\\\")],\\n [os.path.join(\\\"assets\\\", f\\\"nf-core-{short_name}_logo_light.png\\\")],\\n [os.path.join(\\\"conf\\\", \\\"modules.config\\\")],\\n [os.path.join(\\\"conf\\\", \\\"test.config\\\")],\\n [os.path.join(\\\"conf\\\", \\\"test_full.config\\\")],\\n [os.path.join(\\\"docs\\\", \\\"images\\\", f\\\"nf-core-{short_name}_logo_light.png\\\")],\\n [os.path.join(\\\"docs\\\", \\\"images\\\", f\\\"nf-core-{short_name}_logo_dark.png\\\")],\\n [os.path.join(\\\"docs\\\", \\\"output.md\\\")],\\n [os.path.join(\\\"docs\\\", \\\"README.md\\\")],\\n [os.path.join(\\\"docs\\\", \\\"README.md\\\")],\\n [os.path.join(\\\"docs\\\", \\\"usage.md\\\")],\\n [os.path.join(\\\"lib\\\", \\\"nfcore_external_java_deps.jar\\\")],\\n [os.path.join(\\\"lib\\\", \\\"NfcoreTemplate.groovy\\\")],\\n [os.path.join(\\\"lib\\\", \\\"Utils.groovy\\\")],\\n [os.path.join(\\\"lib\\\", \\\"WorkflowMain.groovy\\\")],\\n ]\\n\\n files_warn = [\\n [\\\"main.nf\\\"],\\n [os.path.join(\\\"assets\\\", \\\"multiqc_config.yml\\\")],\\n [os.path.join(\\\"conf\\\", \\\"base.config\\\")],\\n [os.path.join(\\\"conf\\\", \\\"igenomes.config\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"awstest.yml\\\")],\\n [os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"awsfulltest.yml\\\")],\\n [os.path.join(\\\"lib\\\", f\\\"Workflow{short_name[0].upper()}{short_name[1:]}.groovy\\\")],\\n [\\\"modules.json\\\"],\\n [\\\"pyproject.toml\\\"],\\n ]\\n\\n # List of strings. Fails / warns if any of the strings exist.\\n files_fail_ifexists = [\\n \\\"Singularity\\\",\\n \\\"parameters.settings.json\\\",\\n \\\".nf-core.yaml\\\", # yml not yaml\\n os.path.join(\\\"bin\\\", \\\"markdown_to_html.r\\\"),\\n os.path.join(\\\"conf\\\", \\\"aws.config\\\"),\\n os.path.join(\\\".github\\\", \\\"workflows\\\", \\\"push_dockerhub.yml\\\"),\\n os.path.join(\\\".github\\\", \\\"ISSUE_TEMPLATE\\\", \\\"bug_report.md\\\"),\\n os.path.join(\\\".github\\\", \\\"ISSUE_TEMPLATE\\\", \\\"feature_request.md\\\"),\\n os.path.join(\\\"docs\\\", \\\"images\\\", f\\\"nf-core-{short_name}_logo.png\\\"),\\n \\\".markdownlint.yml\\\",\\n \\\".yamllint.yml\\\",\\n os.path.join(\\\"lib\\\", \\\"Checks.groovy\\\"),\\n os.path.join(\\\"lib\\\", \\\"Completion.groovy\\\"),\\n os.path.join(\\\"lib\\\", \\\"Workflow.groovy\\\"),\\n ]\\n files_warn_ifexists = [\\\".travis.yml\\\"]\\n\\n # Remove files that should be ignored according to the linting config\\n ignore_files = self.lint_config.get(\\\"files_exist\\\", [])\\n\\n def pf(file_path):\\n return os.path.join(self.wf_path, file_path)\\n\\n # First - critical files. Check that this is actually a Nextflow pipeline\\n if not os.path.isfile(pf(\\\"nextflow.config\\\")) and not os.path.isfile(pf(\\\"main.nf\\\")):\\n failed.append(\\\"File not found: nextflow.config or main.nf\\\")\\n raise AssertionError(\\\"Neither nextflow.config or main.nf found! Is this a Nextflow pipeline?\\\")\\n\\n # Files that cause an error if they don't exist\\n for files in files_fail:\\n if any([f in ignore_files for f in files]):\\n continue\\n if any([os.path.isfile(pf(f)) for f in files]):\\n passed.append(f\\\"File found: {self._wrap_quotes(files)}\\\")\\n else:\\n failed.append(f\\\"File not found: {self._wrap_quotes(files)}\\\")\\n\\n # Files that cause a warning if they don't exist\\n for files in files_warn:\\n if any([f in ignore_files for f in files]):\\n continue\\n if any([os.path.isfile(pf(f)) for f in files]):\\n passed.append(f\\\"File found: {self._wrap_quotes(files)}\\\")\\n else:\\n warned.append(f\\\"File not found: {self._wrap_quotes(files)}\\\")\\n\\n # Files that cause an error if they exist\\n for file in files_fail_ifexists:\\n if file in ignore_files:\\n continue\\n if os.path.isfile(pf(file)):\\n failed.append(f\\\"File must be removed: {self._wrap_quotes(file)}\\\")\\n else:\\n passed.append(f\\\"File not found check: {self._wrap_quotes(file)}\\\")\\n\\n # Files that cause a warning if they exist\\n for file in files_warn_ifexists:\\n if file in ignore_files:\\n continue\\n if os.path.isfile(pf(file)):\\n warned.append(f\\\"File should be removed: {self._wrap_quotes(file)}\\\")\\n else:\\n passed.append(f\\\"File not found check: {self._wrap_quotes(file)}\\\")\\n\\n # Files that are ignoed\\n for file in ignore_files:\\n ignored.append(f\\\"File is ignored: {self._wrap_quotes(file)}\\\")\\n\\n return {\\\"passed\\\": passed, \\\"warned\\\": warned, \\\"failed\\\": failed, \\\"ignored\\\": ignored}\",\n \"def resources():\\n check_resources()\",\n \"async def check_files(hass):\\n # Verify that the user downloaded all files.\\n base = f\\\"{hass.config.path()}/custom_components/{DOMAIN}/\\\"\\n missing = []\\n for file in REQUIRED_FILES:\\n fullpath = \\\"{}{}\\\".format(base, file)\\n if not os.path.exists(fullpath):\\n missing.append(file)\\n\\n if missing:\\n _LOGGER.critical(\\\"The following files are missing: %s\\\", str(missing))\\n returnvalue = False\\n else:\\n returnvalue = True\\n\\n return returnvalue\",\n \"def check_helpers(self):\\n paths = self.get_helper_out_paths()\\n\\n for p in paths:\\n full_path = p + \\\".data-00000-of-00001\\\"\\n file = Path(full_path)\\n if not file.exists():\\n return False\\n\\n return True\",\n \"def get_all_files(self, custom=None):\\n super().get_all_files(custom)\\n return True\",\n \"def file_list_emptydirs(load):\\n # TODO - implement this\\n _init()\\n\\n return []\",\n \"def status(name=None):\\n name = \\\"freezer\\\" if not name else name\\n return all(os.path.isfile(i) for i in _paths(name))\",\n \"def scan ( self, **kw ):\\n stats = self.STATS\\n stats.scan_time.begin ( self.name )\\n for subdir in os.listdir ( self.physical_location ):\\n if self.has_dir ( subdir ):\\n pkgdir = self._get_package_dir ( subdir )\\n try:\\n pkgdir.scan ( stats=stats, **kw )\\n finally:\\n if pkgdir.empty():\\n del self._subdirs [subdir]\\n\\n stats.scan_time.end ( self.name )\",\n \"def exists(self):\\n basedir = os.path.dirname(self.path)\\n\\n for filename in self.files:\\n path = os.path.join(basedir, filename)\\n if not os.path.exists(path):\\n return False\\n\\n return True\",\n \"def valid(self):\\r\\n if self.dir_exists and self.files_exist:\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def self_check(self):\\n out = \\\"Loaded components\\\\n\\\"\\n for package_name, package in sorted(self.packages.items()):\\n out += \\\"\\\\t%s:\\\\n\\\" % package_name\\n for c, fd in sorted(package.components.iteritems()):\\n out += \\\"\\\\t\\\\t%s (%s)\\\\n\\\" % (c, fd.filename)\\n\\n LOG.info(out)\\n\\n for p in self.packages.values():\\n for f in p.files:\\n for id in f.requires:\\n # This throws if it doesn't find something.\\n try:\\n self.get(id)\\n except:\\n LOG.exception(\\\"Error in: \\\" + f.filename)\\n raise\",\n \"def santityCheckInitialization(self):\\r\\n\\r\\n for obj in self.config[\\\"repos\\\"]:\\r\\n if not isdir(obj[\\\"path\\\"]):\\r\\n print(\\\"ERROR : Initialization Failed missing {} at path {}\\\".format(obj[\\\"name\\\"], obj[\\\"path\\\"]))\",\n \"def objectsReady(self, n):\\n return len(self.files) >= n\",\n \"def check(self):\\n json = JsonBackend(\\\"../src/builder/projects.json\\\")\\n json.load()\\n\\n TM_ITSELF = 1\\n expected_files = TM_ITSELF + sum(p.downloadable is True\\n for p in json.projects)\\n self.downloads_for_project('tots', expected_files)\\n\\n expected_files = TM_ITSELF + sum(p.softcatala is True and\\n p.downloadable is True\\n for p in json.projects)\\n\\n self.downloads_for_project('softcatala', expected_files)\\n\\n expected_files = 1\\n for project_dto in json.projects:\\n if not project_dto.downloadable:\\n continue\\n\\n self.downloads_for_project(project_dto.name, expected_files)\\n self.check_project_link(project_dto.projectweb)\",\n \"def __runChecks(self):\\n runnedChecks = []\\n runnedChecks.append(Checks.checksFilesInstalled(self.__targetPath, verbose=True))\\n return all(runnedChecks)\",\n \"def _haveReadLocks(self): \\n readLockFileName = ReadLock.fileName\\n for name in os.listdir(self.dir):\\n if name.startswith(readLockFileName):\\n return True\\n return False\",\n \"def checkMissingFiles(inDir, jsonUrl):\\n\\n file_list = []\\n remote = False\\n try:\\n file_list = os.listdir(inDir)\\n except OSError:\\n remote = True\\n file_list = eos_ls(inDir)\\n\\n if file_list == []:\\n print \\\"Directory does not exist or is empty!\\\"\\n return []\\n\\n total_expected = 0\\n missing_files = []\\n suspicious_files = []\\n recovered_files = []\\n\\n print 'Found %d files in input directory' % len(file_list)\\n print 20*'-'\\n\\n jsonFile = open(jsonUrl,'r')\\n procList = json.load(jsonFile,encoding = 'utf-8').items()\\n\\n for proc in procList:\\n for desc in proc[1]:\\n data = desc['data']\\n isData = desc.get('isdata',False)\\n mctruthmode = desc.get('mctruthmode')\\n for d in data:\\n dtag = d.get('dtag','')\\n split = d.get('split',1)\\n\\n for segment in range(0,split):\\n eventsFile = dtag\\n if split > 1:\\n eventsFile = dtag + '_' + str(segment)\\n if mctruthmode:\\n eventsFile += '_filt%d' % mctruthmode\\n filename = eventsFile+'.root'\\n\\n sys.stdout.write('... checking %s' % filename)\\n sys.stdout.flush()\\n\\n total_expected += 1\\n\\n if not filename in file_list:\\n missing_files.append(filename)\\n sys.stdout.write('\\\\033[91m MISSING \\\\033[0m \\\\n')\\n # sys.stdout.flush()\\n continue\\n\\n rootFileUrl = os.path.join(inDir, filename)\\n if remote:\\n rootFileUrl = ('root://eoscms//eos/cms/store' +\\n rootFileUrl.split('store',1)[1])\\n\\n recovered, suspicious = False, False\\n tfile = TFile.Open(rootFileUrl)\\n try:\\n if tfile.TestBit(TFile.kRecovered):\\n recovered = True\\n if tfile.IsZombie():\\n suspicious = True\\n tfile.Close()\\n except AttributeError, ReferenceError:\\n suspicious = True\\n\\n if recovered:\\n sys.stdout.write('\\\\033[93m Recovered \\\\033[0m \\\\n')\\n recovered_files.append(filename)\\n if suspicious:\\n sys.stdout.write('\\\\033[93m Failed to open \\\\033[0m \\\\n')\\n suspicious_files.append(filename)\\n\\n sys.stdout.write('\\\\033[92m OK \\\\033[0m \\\\n')\\n sys.stdout.flush()\\n\\n print 20*'-'\\n if len(missing_files):\\n print \\\"Missing the following files:\\\"\\n print \\\"(%d out of %d expected)\\\"% (len(missing_files), total_expected)\\n for filename in missing_files:\\n print filename\\n else:\\n print \\\"NO MISSING FILES!\\\"\\n print 20*'-'\\n if len(suspicious_files):\\n print \\\"Failed to open the following files:\\\"\\n print \\\"(%d out of %d expected)\\\"% (len(suspicious_files), total_expected)\\n for filename in suspicious_files:\\n print filename\\n print 20*'-'\\n if len(recovered_files):\\n print \\\"The following files are recovered:\\\"\\n print \\\"(%d out of %d expected)\\\"% (len(recovered_files), total_expected)\\n for filename in recovered_files:\\n print filename\\n print 20*'-'\\n\\n return missing_files+suspicious_files+recovered_files\",\n \"def mark_local_files_as_available(checksums):\\n mark_local_files_availability(checksums, True)\",\n \"def _check_integrity(self):\\n root = self.root\\n for scene_name in self.scene_list:\\n if not(os.path.isdir(os.path.join(root,scene_name)) and \\n os.path.isdir(os.path.join(root,scene_name, images_dir)) and\\n os.path.isfile(os.path.join(root,scene_name,annotation_filename))):\\n return False\\n return True\",\n \"def _check_integrity(self):\\n root = self.root\\n for scene_name in self.scene_list:\\n if not(os.path.isdir(os.path.join(root,scene_name)) and \\n os.path.isdir(os.path.join(root,scene_name, images_dir)) and\\n os.path.isfile(os.path.join(root,scene_name,annotation_filename))):\\n return False\\n return True\",\n \"def _check_before_run(self):\\n\\t\\tif not osp.exists(self.dataset_dir):\\n\\t\\t\\traise RuntimeError(\\\"'{}' is not available\\\".format(self.dataset_dir))\\n\\t\\tif not osp.exists(self.train_dir):\\n\\t\\t\\traise RuntimeError(\\\"'{}' is not available\\\".format(self.train_dir))\\n\\t\\tif not osp.exists(self.query_dir):\\n\\t\\t\\traise RuntimeError(\\\"'{}' is not available\\\".format(self.query_dir))\\n\\t\\tif not osp.exists(self.gallery_dir):\\n\\t\\t\\traise RuntimeError(\\\"'{}' is not available\\\".format(self.gallery_dir))\",\n \"def _check_before_run(self):\\n if not osp.exists(self.dataset_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.dataset_dir))\\n if not osp.exists(self.train_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.train_dir))\\n if not osp.exists(self.list_query_path):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.list_query_path))\\n if not osp.exists(self.list_gallery_path):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.list_gallery_path))\",\n \"def find_files(base_dir, consumer_q):\\n for dirpath, dirs, files in os.walk(base_dir, topdown=False):\\n for f in files:\\n fullpath = os.path.join(dirpath, f)\\n if os.path.isfile(fullpath):\\n consumer_q.put(fullpath)\",\n \"def _check_before_run(self):\\n if not osp.exists(self.dataset_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.dataset_dir))\\n if not osp.exists(self.data_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.data_dir))\\n if not osp.exists(self.split_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.split_dir))\",\n \"def check_files(self, data_path):\\n files = os.listdir(data_path)\\n\\n if 'test_batch' not in files:\\n return False\\n\\n if 'batches.meta' not in files:\\n return False\\n\\n for i in range(1, 6):\\n if 'data_batch_{}'.format(i) not in files:\\n return False\\n\\n return True\",\n \"def check_file_existence(self, files):\\n if len(files) == 0:\\n if self._nonfile_error is True:\\n raise FileNotFound(\\\"No files are found.\\\")\\n else:\\n self._logger.info(\\\"No files are found. Nothing to do.\\\")\\n return\\n self._logger.info(\\\"Files found %s\\\" % files)\",\n \"def check_files(self) -> None:\\n notfound = False\\n give_neuro_data_hint = False\\n fullpaths = [f for f, _ in self.input_sources]\\n if self.target_sources is not None:\\n fullpaths.extend([f for f, _ in self.target_sources])\\n for p in fullpaths:\\n if not os.path.exists(p):\\n print('{} not found.'.format(p))\\n notfound = True\\n if 'neuro_data_cdhw' in p:\\n give_neuro_data_hint = True\\n if give_neuro_data_hint:\\n print('\\\\nIt looks like you are referencing the neuro_data_cdhw dataset.\\\\n'\\n 'To install the neuro_data_xzy dataset to the default location, run:\\\\n'\\n ' $ wget https://github.com/ELEKTRONN/elektronn.github.io/releases/download/neuro_data_cdhw/neuro_data_cdhw.zip\\\\n'\\n ' $ unzip neuro_data_cdhw.zip -d ~/neuro_data_cdhw')\\n if notfound:\\n print('\\\\nPlease fetch the necessary dataset and/or '\\n 'change the relevant file paths in the network config.')\\n sys.stdout.flush()\\n sys.exit(1)\",\n \"def _check_before_run(self):\\n if not osp.exists(self.dataset_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.dataset_dir))\\n if not osp.exists(self.train_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.train_dir))\\n if not osp.exists(self.probe_gallery_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.probe_gallery_dir))\",\n \"def testAvailableFiles(self):\\n assert self.dummySubscription.availableFiles() == \\\\\\n self.dummySubscription.available.getFiles(type='set'), \\\\\\n 'Method availableFiles does not return available files Set'\",\n \"def test_scan_empty_dir(self):\\n self.run_scan(self.emptydir, 0)\",\n \"def check_all_files_and_dirs(self):\\n err = 0\\n err_m = ''\\n warning = 0\\n warning_m = ''\\n # Check the pdb file for refinement\\n if self.refine_pdb_in == None:\\n err = 1\\n err_m += '\\\\nPdb file should be supplied'\\n else:\\n if self.check_single_file(self.refine_pdb_in):\\n self.refine_pdb_in = os.path.abspath(self.refine_pdb_in)\\n else:\\n err = 1\\n err_m += '\\\\nFile not found: %s' %(self.refine_pdb_in)\\n\\n # Check the pdb file for distance analysis\\n if self.check_single_file(self.X8_pdb_in):\\n self.X8_pdb_in = os.path.abspath(self.X8_pdb_in)\\n else:\\n self.X8_pdb_in != None\\n warning = 1\\n warning_m += '\\\\nXtrapol8 pdb_in not found. No additional analysis will be applied'\\n\\n # Check additional files and append them to a string\\n additional = \\\"\\\"\\n for fle in self.additional:\\n if len(fle)>0:\\n if self.check_single_file(fle):\\n new_add = os.path.abspath(fle)\\n additional = additional + \\\"%s \\\" % (new_add)\\n else:\\n err = 1\\n err_m += '\\\\nFile not found: %s' %(fle)\\n self.additional = additional\\n\\n #Check the output directory\\n if os.path.isdir(self.outdir):\\n self.outdir = os.path.abspath(self.outdir)\\n else:\\n err = 1\\n err_m += \\\"\\\\nXtrapol8 output directory cannot be found.\\\" \\\\\\n \\\"Please run this from the same directory from which you ran Xtrapol8.\\\"\\n\\n #Check the phil file for reciprocal space refinement\\n if self.check_single_file(self.reciprocal_space_phil):\\n self.reciprocal_space_phil = os.path.abspath(self.reciprocal_space_phil)\\n else:\\n self.reciprocal_space_phil = ''\\n warning = 1\\n warning_m += '\\\\nPhil for reciprocal space refinement not found. Refinement will use default parameters.'\\n\\n\\n #Check the phil file for real space refinement\\n if self.check_single_file(self.real_space_phil):\\n self.real_space_phil = os.path.abspath(self.real_space_phil)\\n else:\\n self.real_space_phil = ''\\n warning = 1\\n warning_m += '\\\\nPhil for real space refinement not found. Refinement will use default parameters.'\\n\\n #Check the residue list for distance analysis\\n if self.check_single_file(self.residue_list):\\n self.residue_list = os.path.abspath(self.residue_list)\\n else:\\n self.residue_list = None\\n warning = 1\\n warning_m += '\\\\nResidue list not found. Distance analysis (if required) will be performed without residue list.'\\n\\n return err, err_m, warning, warning_m\",\n \"def _dir_empty(path):\\n try:\\n next(os.scandir(str(path)))\\n except StopIteration:\\n return True\\n return False\",\n \"def _find_files(directory, dirs_to_look_in, files_to_search_for, \\n current_dir, see_files):\\n full_name = True\\n if see_files:\\n full_name = False\\n files_to_load = search_directory(directory, \\n look_in=dirs_to_look_in,\\n search_for=files_to_search_for,\\n file_type='files',\\n current_dir=current_dir,\\n full_name=full_name)\\n if not files_to_load:\\n raise UserWarning('No files were found matching the search for %s'\\\\\\n ' in the directory(s) %s%s' \\\\\\n % (files_to_search_for, directory, \\n dirs_to_look_in))\\n return files_to_load\",\n \"def _verify(self) -> None:\\n # Check if the files already exist\\n if os.path.exists(os.path.join(self.root, self.image_root)):\\n return\\n\\n # Check if .zip files already exists (if so extract)\\n exists = []\\n for filename, md5 in zip(self.filenames, self.md5s):\\n filepath = os.path.join(self.root, filename)\\n if os.path.isfile(filepath):\\n if self.checksum and not check_integrity(filepath, md5):\\n raise RuntimeError(\\\"Dataset found, but corrupted.\\\")\\n exists.append(True)\\n extract_archive(filepath)\\n else:\\n exists.append(False)\\n\\n if all(exists):\\n return\\n\\n # Check if the user requested to download the dataset\\n raise RuntimeError(\\n \\\"Dataset not found in `root` directory, either specify a different\\\"\\n + \\\" `root` directory or manually download the dataset to this directory.\\\"\\n )\",\n \"def buffers_exist(self):\\n for buff in self.buffers:\\n if not buff.is_separate_file:\\n continue\\n\\n path = self.path.parent / buff.uri\\n if not path.exists():\\n raise FileNotFoundError(\\n \\\"Buffer {} referenced in {} not found\\\".format(path, self.path)\\n )\",\n \"def missing_in_gn_by_file(self):\\n return self._missing_gn_files\",\n \"def check_paths( self ):\\n check_a = utility_code.checkDirectoryExistence( self.PATH_TO_SOURCE_FILE_DIRECTORY )\\n check_b = utility_code.checkDirectoryExistence( self.PATH_TO_ARCHIVES_ORIGINALS_DIRECTORY )\\n check_c = utility_code.checkDirectoryExistence( self.PATH_TO_ARCHIVES_PARSED_DIRECTORY )\\n check_d = utility_code.checkDirectoryExistence( self.PATH_TO_PARSED_ANNEX_DATA_DIRECTORY )\\n check_e = utility_code.checkDirectoryExistence( self.PATH_TO_PARSED_ANNEX_COUNT_DIRECTORY )\\n if check_a == 'exists' and check_b == 'exists' and check_c == 'exists' and check_d == 'exists' and check_e == 'exists':\\n log.debug( 'path check passed' )\\n else:\\n message='path check failed; quitting'\\n log.error( message )\\n sys.exit( message )\\n return\",\n \"def check_for_setup_error(self):\\n if self.share2nms:\\n for nfs_share in self.share2nms:\\n nms = self.share2nms[nfs_share]\\n volume_name, dataset = self._get_share_datasets(nfs_share)\\n if not nms.volume.object_exists(volume_name):\\n raise LookupError(_(\\\"Volume %s does not exist in Nexenta \\\"\\n \\\"Store appliance\\\"), volume_name)\\n folder = '%s/%s' % (volume_name, dataset)\\n if not nms.folder.object_exists(folder):\\n raise LookupError(_(\\\"Folder %s does not exist in Nexenta \\\"\\n \\\"Store appliance\\\"), folder)\\n if (folder not in nms.netstorsvc.get_shared_folders(\\n 'svc:/network/nfs/server:default', '')):\\n self._share_folder(nms, volume_name, dataset)\\n self._get_capacity_info(nfs_share)\",\n \"def are_readable_files(self, fnames):\\n for fname in fnames:\\n if not os.access(fname, os.R_OK):\\n self.cli_parser.error(\\\"%s doesn't exist or you do \\\"\\n \\\"not have read permissions to it.\\\" % fname)\",\n \"def list_existing_files(files):\\n\\n result = []\\n for f in flatten(files):\\n if file_exists(f): result.append(f)\\n return None if len(result) == 0 else result\",\n \"def check_dir(self):\\n if not Path(self.src_dir).exists():\\n print('No such directory found:', self.src_dir)\\n return\\n\\n nc_all = self.src_dir + \\\"/*.nc*\\\"\\n if len(glob.glob(nc_all)) == 0:\\n print('No NetCDF files found in:', self.src_dir)\\n return\\n\\n return nc_all\",\n \"def _checkForInefficientDirectories(self, warn=True):\\n self._populatedLevels = len([v for v in self._tiffDirectories if v is not None])\\n missing = [v is None for v in self._tiffDirectories]\\n maxMissing = max(0 if not v else missing.index(False, idx) - idx\\n for idx, v in enumerate(missing))\\n self._skippedLevels = maxMissing\\n if maxMissing >= self._maxSkippedLevels:\\n if warn:\\n config.getConfig('logger').warning(\\n 'Tiff image is missing many lower resolution levels (%d). '\\n 'It will be inefficient to read lower resolution tiles.', maxMissing)\\n self._inefficientWarning = True\",\n \"def file_checker():\\n\\n PATH_RELEASE1_IDEN = os.getcwd()+'/archive_all_2014-10/'\\n PATH_RELEASE1_UNIDE = None\\n #PATH_RELEASE1_UNIDE = os.getcwd()+'/archive_all_2014-10/'\\n\\n PATH_RELEASE2_IDEN = os.getcwd()+'/archive_all_2016-10/archive_identified_2016-10/'\\n PATH_RELEASE2_UNIDE = os.getcwd() + '/archive_all_2016-10/archive_unidentified_2016-10/'\\n\\n\\n #From here don't change anything.\\n #This global function finds the .mgf files in paths\\n list_of_files_release1_ide = glob.glob(PATH_RELEASE1_IDEN+'*.mgf')\\n list_of_files_release1_unide = None #REMOVE THIS PART AND UNCOMMENT NEXT LINE IN NEXT RELEASES.\\n\\n #list_of_files_release1_unid = glob.glob(PATH_RELEASE1_UNID'+*.mgf')\\n\\n list_of_files_release2_ide = glob.glob(PATH_RELEASE2_IDEN+'*.mgf')\\n list_of_files_release2_unide = glob.glob(PATH_RELEASE2_UNIDE+'*.mgf')\\n\\n\\n #Check if exist cache folder. If not will make it. \\n #RELEASE 1 \\n if not os.path.exists(PATH_RELEASE1_IDEN+'cache'):\\n os.makedirs(PATH_RELEASE1_IDEN+'cache')\\n\\n # if not os.path.exists(PATH_RELEASE1_UNIDE'+cache'):\\n # os.makedirs(PATH_RELEASE1_UNIDE'+cache')\\n\\n #RELEASE2\\n if not os.path.exists(PATH_RELEASE2_IDEN+'cache'):\\n os.makedirs(PATH_RELEASE2_IDEN+'cache')\\n\\n if not os.path.exists(PATH_RELEASE2_UNIDE+'cache'):\\n os.makedirs(PATH_RELEASE2_UNIDE+'cache')\\n \\n\\n return PATH_RELEASE1_IDEN, \\\\\\n PATH_RELEASE2_IDEN, \\\\\\n PATH_RELEASE2_UNIDE, \\\\\\n list_of_files_release1_ide, \\\\\\n list_of_files_release2_ide, \\\\\\n list_of_files_release2_unide\",\n \"def _check_all_systems_ready(self):\\n raise NotImplementedError()\",\n \"def check_files(self):\\n print('checking files')\\n for f in tqdm(self.filenames):\\n img = cv2.imread(f, int(self.color))\\n if img is None:\\n os.remove(f)\",\n \"def has_file(self, doc):\\n return len(doc.package.files) != 0\",\n \"def find_all_files(self):\\n look4files = [ f for f in listdir(self.file_location) if isfile(join(self.file_location,f)) ]\\n return look4files\",\n \"def walk_files():\\n\\n # TODO: not check twice the same dir or file\\n for path in config.targets:\\n abs_path = os.path.join(cwd, path)\\n\\n if not os.path.islink(abs_path) and os.path.isfile(abs_path):\\n walked.append(abs_path)\\n yield abs_path\\n #process_file(abs_path)\\n\\n if os.path.isdir(abs_path):\\n walked.append(abs_path)\\n for root, dirs, files in os.walk(abs_path):\\n for fname in files:\\n if isbackup(fname):\\n continue\\n abs_path = os.path.join(root, fname)\\n walked.append(abs_path)\\n if not os.path.islink(abs_path) and\\\\\\n os.path.isfile(abs_path):\\n base, name = os.path.split(abs_path)\\n XXX, ext = os.path.splitext(name)\\n\\n ignored = False\\n for pattern in IGNORE_FILES:\\n if pattern.search(fname):\\n ignored = True\\n break\\n\\n # maybe should be merged with IGNORE_FILES?\\n for regexp in config.exclude_list:\\n if regexp.search(fname):\\n ignored = True\\n break\\n\\n if not ignored:\\n for test_ext in config.disallow_exts:\\n if test_ext == ext:\\n ignored = True\\n break\\n\\n if not ignored:\\n if config.allow_exts:\\n ignored = True\\n for test_ext in config.allow_exts:\\n if test_ext == ext:\\n ignored = False\\n break\\n\\n if not ignored:\\n yield abs_path\\n #process_file(abs_path)\\n\\n for dir in dirs[:]:\\n if dir in IGNORE_DIRS:\\n dirs.remove(dir)\\n if dir in dirs:\\n dirs.remove(dir)\\n # mayb be should be merged with IGNORE_DIRS?\\n else:\\n for regexp in config.exclude_list:\\n if regexp.search(dir):\\n # This check is required\\n # because several different patterns\\n # could match one file name\\n if dir in dirs:\\n dirs.remove(dir)\\n\\n for dir in dirs:\\n abs_path = os.path.join(root, dir)\\n walked.append(abs_path)\",\n \"def scan ( self ):\\n root = self.root\\n try:\\n filenames = sorted ( os.listdir ( root ) )\\n except OSError as oserr:\\n if oserr.errno != errno.ENOENT:\\n raise\\n else:\\n HOOK_CLS = self.HOOK_SCRIPT_CLS\\n for fname in filenames:\\n if self.filename_filter ( fname ):\\n fspath = root + os.sep + fname\\n if os.path.isfile ( fspath ):\\n script_obj = HOOK_CLS ( fspath, filename=fname )\\n self.scripts [script_obj.name] = script_obj\",\n \"def collect_local(self, path, req_tag=True):\\n for f in [os.path.join(dp, f) for dp, dn, filenames in os.walk(path) for f in filenames]:\\n if not os.path.isfile(f):\\n continue\\n self.collect_single(f, req_tag)\",\n \"def check_corrupted_files(self):\\r\\n for store in STORES:\\r\\n path = f\\\"{self.system.config_path}/.storage/{STORES[store]}\\\"\\r\\n if os.path.exists(path):\\r\\n if os.stat(path).st_size == 0:\\r\\n # File is empty (corrupted)\\r\\n return True\\r\\n return False\",\n \"def __init__(self, root):\\n FileHelper.ALL_PATHS = [os.path.join(dp, f) for dp, dn, filenames in os.walk(root) for f in filenames if os.path.splitext(f)[1] in Enums.App.VALID_FILE_TYPES]\",\n \"def check_tree(cls, root, require_init_py=True):\\n if os.path.basename(root) == '__pycache__':\\n return\\n\\n if not os.path.isfile(os.path.join(root, '__init__.py')):\\n # Not a Python package directory\\n if require_init_py:\\n raise util.UserError(\\n f\\\"No __init__.py file in '{root}'\\\")\\n else:\\n return\\n\\n # First, check for the case where a .py file and a directory\\n # have the same name (without the extension). This can't be\\n # handled, so just raise an exception\\n found = set()\\n for filename in os.listdir(root):\\n path = os.path.join(root, filename)\\n if os.path.isfile(path):\\n filename, ext = os.path.splitext(filename)\\n if ext == '.py':\\n found.add(filename)\\n\\n for dirname in os.listdir(root):\\n path = os.path.join(root, dirname)\\n if os.path.isdir(path):\\n if dirname in found:\\n raise util.UserError(\\n \\\"Found a directory and python file with same name in \\\"\\n \\\"benchmark tree: '{0}'\\\".format(path))\\n cls.check_tree(path, require_init_py=False)\",\n \"def test_walk_not_full(self):\\n dummy_folder = TestOspaListDir.get_dummy_folder()\\n need_result = []\\n for i in range(1, 4):\\n need_result.append('meme{}.jpg'.format(i))\\n need_result.extend(['meme1.jpg',\\n 'meme2.png',\\n 'meme4.jpg',\\n 'meme4.png',\\n '1.txt',\\n '2.txt',\\n '3.txt',\\n 'not_txt.not_txt',\\n 'antigravity.png',\\n 'egg.png',\\n 'empty.txt',\\n 'holy_grenade.png',\\n 'spam.jpg',\\n ])\\n\\n result = listdir(dummy_folder, full_path=False, only_files=True, walk=True)\\n self.assertEqual(sorted(result), sorted(need_result))\",\n \"def check_config(outconfig):\\n self.log.info(\\\"Checking if all the necessary files exist.\\\")\\n\\n # Perform necessary checks\\n\\n log.info(\\\"All necessary files exist for {} configuration.\\\".format(outconfig[\\\"Flavor\\\"]))\\n\\n return\",\n \"def checkAllFilesIncluded(self):\\n # TODO: config objects: this import can go away\\n from exhale.configs import unabridgedOrphanKinds\\n\\n # build path to unabridged api document that adds all toctree directives\\n root = get_exhale_root(self)\\n containmentFolder = self.getAbsContainmentFolder()\\n unabridged_api_path = os.path.join(containmentFolder, \\\"unabridged_api.rst.include\\\")\\n unabridged_orphan_path = os.path.join(containmentFolder, \\\"unabridged_orphan.rst\\\")\\n\\n # gather lines that match the indented part of the toctree\\n #\\n # .. toctree::\\n # :maxdepth: {toctreeMaxDepth}\\n #\\n # some_node.file_name.rst\\n #\\n # so just lazily look for the leading three spaces and ending with .rst\\n full_api_toctrees = []\\n under_toctree_re = re.compile(r\\\"^ .+\\\\.rst$\\\")\\n with open(unabridged_api_path) as unabridged_api:\\n for line in unabridged_api:\\n if under_toctree_re.match(line):\\n full_api_toctrees.append(line.strip())\\n\\n orphan_toctrees = []\\n if os.path.isfile(unabridged_orphan_path):\\n with open(unabridged_orphan_path) as unabridged_orphan:\\n for line in unabridged_orphan:\\n if under_toctree_re.match(line):\\n orphan_toctrees.append(line.strip())\\n\\n # Scan all nodes and make sure they were found in the toctrees above.\\n doxygen_mainpage_was_used = False\\n for node in root.all_nodes:\\n if node.kind in {\\\"enumvalue\\\", \\\"group\\\"}:\\n continue\\n\\n # When doxygen \\\\mainpage command is used, the .. doxygenpage:: index\\n # is .. include::'ed in the root file document. Those checks come\\n # after this loop.\\n if node.kind == \\\"page\\\" and node.refid == \\\"indexpage\\\":\\n doxygen_mainpage_was_used = True\\n continue\\n\\n if node.kind in unabridgedOrphanKinds or \\\\\\n (node.kind == \\\"class\\\" and \\\"struct\\\" in unabridgedOrphanKinds) or \\\\\\n (node.kind == \\\"struct\\\" and \\\"class\\\" in unabridgedOrphanKinds):\\n toctrees = orphan_toctrees\\n doc = unabridged_orphan_path\\n else:\\n toctrees = full_api_toctrees\\n doc = unabridged_api_path\\n\\n self.assertTrue(\\n node.file_name in toctrees,\\n \\\"Node refid=[{refid}] and basename=[{file_name}] not found in [{doc}]!\\\".format(\\n refid=node.refid, file_name=node.file_name, doc=doc\\n )\\n )\\n\\n # Make sure every document expected to be .. include::'ed in the library root\\n # has actually been included.\\n full_root_file_path = root.full_root_file_path\\n root_file_includes = []\\n with open(full_root_file_path, \\\"r\\\") as full_root_f:\\n for line in full_root_f:\\n include_mark = \\\".. include::\\\"\\n if line.startswith(include_mark):\\n root_file_includes.append(line.split(include_mark)[-1].strip())\\n\\n index_include = False # page_index.rst comes from doxygen \\\\mainpage\\n page_hierarchy_include = False # page_view_hierarchy.rst\\n class_hierarchy_include = False # class_view_hierarchy.rst\\n file_hierarchy_include = False # file_view_hierarchy.rst\\n unabridged_api_include = True # unabridged_api.rst. Always included.\\n orphan_api_include = False # unabridged_orphan.rst. Never included.\\n for node in root.all_nodes:\\n if node.kind == \\\"page\\\":\\n if node.refid == \\\"indexpage\\\":\\n index_include = True\\n else:\\n page_hierarchy_include = True\\n elif node.kind in exhale.utils.CLASS_LIKE_KINDS:\\n class_hierarchy_include = True\\n elif node.kind in {\\\"dir\\\", \\\"file\\\"}:\\n file_hierarchy_include = True\\n\\n self.assertTrue(\\n doxygen_mainpage_was_used == index_include,\\n \\\"Mismatch: doxygen_mainpage_was_used != index_include!\\\")\\n\\n include_map = {\\n \\\"page_index.rst.include\\\": index_include,\\n os.path.basename(root.page_hierarchy_file): page_hierarchy_include,\\n os.path.basename(root.class_hierarchy_file): class_hierarchy_include,\\n os.path.basename(root.file_hierarchy_file): file_hierarchy_include,\\n os.path.basename(root.unabridged_api_file): unabridged_api_include,\\n os.path.basename(root.unabridged_orphan_file): orphan_api_include\\n }\\n root_file_base = os.path.basename(full_root_file_path)\\n for key, val in include_map.items():\\n if val:\\n check = getattr(self, \\\"assertTrue\\\")\\n msg = \\\"*WAS* expected in {root_file_base}, but was *NOT* found!\\\".format(\\n root_file_base=root_file_base\\n )\\n else:\\n check = getattr(self, \\\"assertFalse\\\")\\n msg = \\\"was *NOT* expected in {root_file_base}, but *WAS* found!\\\".format(\\n root_file_base=root_file_base\\n )\\n check(\\n key in root_file_includes,\\n \\\"Page '{key}' {msg}\\\".format(key=key, msg=msg))\\n\\n # Some tests may want the toctree names afterward.\\n return full_api_toctrees, orphan_toctrees\",\n \"def check():\\n dist_path = Path(DIST_PATH)\\n if not dist_path.exists() or not list(dist_path.glob('*')):\\n print(\\\"No distribution files found. Please run 'build' command first\\\")\\n return\\n\\n subprocess.check_call(['twine', 'check', 'dist/*'])\",\n \"def site_directory_contains_stale_files(site_directory):\\n\\n return True if os.path.exists(site_directory) and os.listdir(site_directory) else False\",\n \"def list_missing_files(files):\\n\\n result = []\\n for f in flatten(files):\\n if not file_exists(f): result.append(f)\\n return None if len(result) == 0 else result\",\n \"def check_missing_files(self):\\n files = [getattr(self, attr) for attr in self._required]\\n try:\\n utilities.check_missing_files(files)\\n except utilities.MissingConstraintError as err:\\n err.message += \\\"\\\\nSkipping {}\\\\n\\\".format(self.__class__.__name__)\\n raise err\",\n \"def checkfiles(args):\\n\\n from .query import Database\\n db = Database()\\n\\n r = db.objects(\\n protocol=args.protocol,\\n support=args.support,\\n groups=args.group,\\n purposes=args.purposes,\\n gender=args.gender,\\n clients=args.client,\\n )\\n\\n # go through all files, check if they are available on the filesystem\\n good = []\\n bad = []\\n for f in r:\\n if os.path.exists(f.make_path(args.directory, args.extension)):\\n good.append(f)\\n else:\\n bad.append(f)\\n\\n # report\\n output = sys.stdout\\n if args.selftest:\\n from bob.db.base.utils import null\\n output = null()\\n\\n if bad:\\n for f in bad:\\n output.write('Cannot find file \\\"%s\\\"\\\\n' % (f.make_path(args.directory, args.extension),))\\n output.write('%d files (out of %d) were not found at \\\"%s\\\"\\\\n' % \\\\\\n (len(bad), len(r), args.directory))\\n\\n return 0\",\n \"def checkAllFilesGenerated(self):\\n root = get_exhale_root(self)\\n containmentFolder = self.getAbsContainmentFolder()\\n for node in root.all_nodes:\\n if node.kind in [\\\"enumvalue\\\", \\\"group\\\"]:\\n continue\\n gen_file_path = os.path.join(containmentFolder, node.file_name)\\n self.assertTrue(\\n os.path.isfile(gen_file_path),\\n \\\"File for {kind} node with refid=[{refid}] not generated to [{gen_file_path}]!\\\".format(\\n kind=node.kind, refid=node.refid, gen_file_path=gen_file_path\\n )\\n )\",\n \"def in_folder(self):\\n return len(os.path.split(self.file_path)) > 1\",\n \"def test_GetFilesInDirectory_empty_dir(tempdir: pathlib.Path):\\n assert not dpack.GetFilesInDirectory(tempdir, [])\",\n \"def read_all(self, prog:progress=None):\\t\\t\\t\\n\\t\\tself.__output_status(\\\"Read & compare all files\\\")\\n\\t\\tself.__read_files('all', prog)\",\n \"def check_configfiles():\\n return (all(os.path.isdir(x) for x in CONFIG_DIRS) and\\n os.path.isfile(CONFIG_FILE) and os.path.isfile(LOG_CONFIG_FILE))\",\n \"def avail(self):\\n\\n return os.listdir(self.datadir)\",\n \"def fileListCheck(self, key = None, verbose = True, errorCheck = True):\\n\\n # if key is not None:\\n\\n fileListTest = self.nbDetails[key]['pkgFileList']\\n\\n # Count file suffixes\\n suffixList = [Path(item).suffix for item in fileListTest]\\n c = Counter(suffixList)\\n\\n # Check for dirs & log\\n dirList = []\\n fileList = []\\n for n, item in enumerate(suffixList):\\n # if Path(item).is_dir(): # Checking with Path OK for local files, but not for a list from another machine.\\n if item == '':\\n dirList.append(fileListTest[n])\\n else:\\n fileList.append(fileListTest[n])\\n\\n if fileList:\\n self.nbDetails[key]['pkgRootDir'] = os.path.commonpath(fileListTest)\\n self.nbDetails[key]['pkgDirList'] = dirList\\n self.nbDetails[key]['pkgSuffixCount'] = c\\n else:\\n print(f\\\"***Warning, job {key}, title: {self.nbDetails[key]['title']}\\\\nNo files found.\\\")\\n self.nbDetails[key]['pkgRootDir'] = []\\n self.nbDetails[key]['pkgDirList'] = []\\n self.nbDetails[key]['pkgSuffixCount'] = []\\n\\n # Print details\\n if verbose:\\n print('\\\\n***Pkg job summary')\\n print(f\\\"Job {key}, title: {self.nbDetails[key]['title']}\\\")\\n print(f\\\"Notebook file: {self.nbDetails[key]['file']}\\\")\\n print(*self.nbDetails[key]['pkgInfo'][2:], sep='\\\\n')\\n print(f\\\"Root dir: {self.nbDetails[key]['pkgRootDir']}\\\")\\n print(f\\\"Found {len(fileListTest)} items, with file types:\\\")\\n # Throw out full dictionary here, may want to switch to formatted list.\\n # Set small width to force vertical format\\n pprint.pprint(c, width=50)\\n\\n if dirList:\\n print(\\\"SubDirs:\\\")\\n print(*dirList, sep='\\\\n')\\n\\n\\n # Check if wavefunction job, and if files are present.\\n # TODO: propagate this issue!!! For init self.nbDetails[key]['pkg'] is not yet set... may need to move this check to later?\\n self.nbDetails[key]['wf'] = any(['waveFn' in item for item in self.nbDetails[key]['pkgDirList']])|any(['wf' in item for item in self.nbDetails[key]['pkgDirList']])\\n # if self.nbDetails[key]['wf']:\\n # print(f\\\"Wavefunction job, with {c['.dat']} dat files, expected {self.nbDetails[key]['E'][2]*5}\\\")\\n\\n # Error checking based on file types\\n # Set pass/fail in self.nbDetails[key]['fileListCheck']\\n if errorCheck:\\n self.nbDetails[key]['fileListCheck'] = True\\n\\n if c['.out'] > 1:\\n print(f\\\"***Warning: found {c['.out']} ePS output files.\\\")\\n self.nbDetails[key]['fileListCheck'] = False\\n\\n if c['.idy'] != 3:\\n # TODO: change to checking by number of symmetries\\n print(f\\\"***Warning: found {c['.idy']} ePS idy files.\\\")\\n self.nbDetails[key]['fileListCheck'] = False\\n\\n if self.nbDetails[key]['wf']:\\n # if (c['.dat'] is None):\\n # print(\\\"***Warning: No .dat files found\\\")\\n # elif (c['.dat'] != self.nbDetails[key]['E'][2]*5):\\n # print(f\\\"***Warning: found {c['.dat']} dat files, expected {self.nbDetails[key]['E'][2]*5} for wavefunction job.\\\")\\n fPerE = 5 # Set files per energy point for numerical checks\\n if self.nbDetails[key]['E'] is None:\\n En = 0\\n else:\\n En = int(self.nbDetails[key]['E'][3])*fPerE\\n\\n if En == 0:\\n print(f\\\"***Warning: found {c['.dat']} dat files, missing job E details for wavefunction job.\\\")\\n self.nbDetails[key]['fileListCheck'] = False\\n elif (c['.dat'] is None) or (c['.dat'] < En):\\n print(f\\\"***Warning: found {c['.dat']} dat files, expected {En}*syms for wavefunction job.\\\")\\n self.nbDetails[key]['fileListCheck'] = False\\n else:\\n # Check symmetries...\\n # SnTest = [En%Sn for Sn in range(1,6)]\\n Sn = c['.dat']/En\\n if c['.dat']%En == 0:\\n print(f\\\"Wavefunction job, with {c['.dat']} dat files OK (for {Sn} syms)\\\")\\n else:\\n print(f\\\"***Warning: Wavefunction job, with {c['.dat']} dat files indeterminate symmetries {Sn}\\\")\\n self.nbDetails[key]['fileListCheck'] = False\\n\\n if c['.nc'] != 2:\\n # TODO: change to checking by number of symmetries\\n print(f\\\"***Warning: found {c['.nc']} ePSproc nc files, expected 2.\\\")\\n self.nbDetails[key]['fileListCheck'] = False\",\n \"def _check_before_run(self):\\n if not osp.exists(self.dataset_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.dataset_dir))\\n if not osp.exists(self.data_dir):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.data_dir))\\n if not osp.exists(self.raw_mat_path):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.raw_mat_path))\\n if not osp.exists(self.split_new_det_mat_path):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.split_new_det_mat_path))\\n if not osp.exists(self.split_new_lab_mat_path):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.split_new_lab_mat_path))\",\n \"def checkForFiles(quickLogger, fileList):\\n \\n for filename in fileList:\\n if(not os.path.exists(filename)):\\n quickLogger.critical(filename+\\\" doesn't exist.\\\")\\n raise FileNotFound\",\n \"def verify_directories_empty(self):\\n if self.install_type == \\\"rpm\\\":\\n return # For RPM install we don't want to clean anything\\n\\n instance_dir = \\\"frontend_%(service)s-%(instance)s\\\" % \\\\\\n { \\\"service\\\": self.service_name(), \\n \\\"instance\\\": self.glidein.instance_name(), }\\n #-- directories to check ---\\n dirs = {}\\n dirs[\\\"logs........\\\"] = os.path.join(self.logs_dir(), instance_dir)\\n dirs[\\\"install.....\\\"] = os.path.join(self.install_location(), instance_dir) \\n dirs[\\\"config......\\\"] = self.config_dir()\\n for subdir in [\\\"monitor\\\", \\\"stage\\\"]:\\n dirs[\\\"web %s \\\" % subdir] = os.path.join(self.glidein.web_location(), subdir, instance_dir)\\n #--- check them --\\n for type in dirs.keys():\\n if os.path.isdir(dirs[type]): \\n if len(os.listdir(dirs[type])) == 0:\\n os.rmdir(dirs[type])\\n del dirs[type] # remove from dict\\n else:\\n del dirs[type] # it does not exist, remove from dict\\n\\n #--- if all are empty, return \\n if len(dirs) == 0:\\n time.sleep(3)\\n return # all directories are empty\\n\\n #--- See if we can remove them --- \\n common.logit(\\\"\\\"\\\"The following directories must be empty for the install to succeed: \\\"\\\"\\\")\\n types = sorted(dirs.keys())\\n for type in types:\\n common.logit(\\\"\\\"\\\" %(type)s: %(dir)s\\\"\\\"\\\" % \\\\\\n { \\\"type\\\" : type, \\\"dir\\\" : dirs[type] })\\n common.ask_continue(\\\"... can we remove their contents\\\")\\n for type in dirs.keys():\\n common.remove_dir_contents(dirs[type])\\n os.rmdir(dirs[type])\\n time.sleep(3)\\n return\",\n \"def exists():\\n check50.include(\\\"data\\\")\\n check50.exists(\\\"adventure.py\\\")\\n check50.exists(\\\"room.py\\\")\",\n \"def _check_available_irfs(self):\\n\\n available_caldb = [path.split('/')[-1] for path in glob.glob(self.caldb_path + '/data/cta/*')]\\n\\n if self.verbose:\\n print('-- Available CALDBs -- ')\\n print(\\\" {}\\\".format(available_caldb))\\n\\n for caldb in available_caldb:\\n available_irf = [path.split('/')[-1] for path in glob.glob(self.caldb_path + '/data/cta/' + caldb + '/bcf/*')]\\n\\n if self.verbose:\\n print(\\\"-- Available IRFs for '{:s}' --\\\".format(caldb))\\n print(\\\" {}\\\".format(available_irf))\\n\\n if self.caldb_name not in available_caldb:\\n print(\\\"ERROR: provided CALDB name '{:s}' is not found!\\\".format(self.caldb_name))\\n print('Available options are:')\\n print(\\\" {}\\\".format(available_caldb))\\n self.am_ok = False\\n\\n if self.irf not in available_irf:\\n print(\\\"ERROR: provided IRF name '{:s}' is not found!\\\".format(self.irf))\\n print('Available options are:')\\n print(\\\" {}\\\".format(available_irf))\\n self.am_ok = False\",\n \"def autodetect_files(self):\\n if self._is_valid_requirements_file('requirements.txt'):\\n self.filenames.append('requirements.txt')\\n\\n if self._is_valid_requirements_file('requirements.pip'): # pragma: nocover\\n self.filenames.append('requirements.pip')\\n\\n if os.path.isdir('requirements'):\\n for filename in os.listdir('requirements'):\\n file_path = os.path.join('requirements', filename)\\n if self._is_valid_requirements_file(file_path):\\n self.filenames.append(file_path)\\n self._check_inclusions_recursively()\",\n \"def _check_before_run(self):\\n if not osp.exists(self.root):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.root))\\n if not osp.exists(self.train_name_path):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.train_name_path))\\n if not osp.exists(self.test_name_path):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.test_name_path))\\n if not osp.exists(self.track_train_info_path):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.track_train_info_path))\\n if not osp.exists(self.track_test_info_path):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.track_test_info_path))\\n if not osp.exists(self.query_IDX_path):\\n raise RuntimeError(\\\"'{}' is not available\\\".format(self.query_IDX_path))\",\n \"def missing_in_gyp_by_file(self):\\n return self._missing_gyp_files\",\n \"def blocks(self, dirs):\\n return any([d in self.directories for d in dirs])\",\n \"def test_only_files(self):\\n dummy_folder = TestOspaListDir.get_dummy_folder()\\n need_result = ['meme1.jpg',\\n 'meme2.png',\\n 'meme4.jpg',\\n 'meme4.png',\\n 'meme monty python',\\n ]\\n need_result_new = [os.path.join(dummy_folder, 'memes', x) for x in need_result[:-1]]\\n result = listdir(os.path.join(dummy_folder, 'memes'), only_files=True)\\n self.assertEqual(sorted(result), sorted(need_result_new))\\n\\n need_result_new = [os.path.join(dummy_folder, 'memes', x) for x in need_result]\\n result = listdir(os.path.join(dummy_folder, 'memes'), only_files=False)\\n self.assertEqual(sorted(result), sorted(need_result_new))\",\n \"def checkFiles( filenames ):\\n \\n missing = []\\n for filename in filenames:\\n if not os.path.exists( filename ):\\n missing.append( filename )\\n\\n if missing:\\n raise ValueError( \\\"missing files: %s\\\" % \\\",\\\".join(missing) )\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6751535","0.67471784","0.67164296","0.6647079","0.6547783","0.6459677","0.639774","0.63332593","0.6278475","0.6277023","0.62649715","0.62649715","0.62649715","0.62176186","0.61934626","0.61883324","0.6181167","0.6169776","0.6145234","0.6128254","0.60981965","0.60980296","0.6079424","0.6077777","0.60777056","0.6076375","0.60747576","0.60729295","0.60688835","0.6065264","0.6040686","0.6030061","0.6016243","0.6013212","0.6013212","0.6004223","0.59958076","0.59867984","0.5960751","0.5958312","0.59555805","0.5949845","0.5936961","0.5897695","0.5897495","0.5865667","0.58632773","0.5848225","0.58475256","0.5843291","0.58390695","0.5837625","0.5834589","0.5834193","0.5831649","0.58300346","0.58283675","0.58240885","0.5814248","0.5799996","0.57963616","0.5794411","0.5793565","0.5792523","0.5788037","0.57713854","0.5766312","0.5761813","0.5761441","0.5753726","0.57529527","0.5750368","0.5748786","0.5748249","0.57411003","0.57371515","0.57365125","0.57286084","0.57285863","0.5717199","0.57137334","0.5705961","0.570145","0.56923157","0.56921184","0.5683761","0.5683672","0.56806964","0.56800073","0.567592","0.56710213","0.5669945","0.5669924","0.56639093"],"string":"[\n \"0.6751535\",\n \"0.67471784\",\n \"0.67164296\",\n \"0.6647079\",\n \"0.6547783\",\n \"0.6459677\",\n \"0.639774\",\n \"0.63332593\",\n \"0.6278475\",\n \"0.6277023\",\n \"0.62649715\",\n \"0.62649715\",\n \"0.62649715\",\n \"0.62176186\",\n \"0.61934626\",\n \"0.61883324\",\n \"0.6181167\",\n \"0.6169776\",\n \"0.6145234\",\n \"0.6128254\",\n \"0.60981965\",\n \"0.60980296\",\n \"0.6079424\",\n \"0.6077777\",\n \"0.60777056\",\n \"0.6076375\",\n \"0.60747576\",\n \"0.60729295\",\n \"0.60688835\",\n \"0.6065264\",\n \"0.6040686\",\n \"0.6030061\",\n \"0.6016243\",\n \"0.6013212\",\n \"0.6013212\",\n \"0.6004223\",\n \"0.59958076\",\n \"0.59867984\",\n \"0.5960751\",\n \"0.5958312\",\n \"0.59555805\",\n \"0.5949845\",\n \"0.5936961\",\n \"0.5897695\",\n \"0.5897495\",\n \"0.5865667\",\n \"0.58632773\",\n \"0.5848225\",\n \"0.58475256\",\n \"0.5843291\",\n \"0.58390695\",\n \"0.5837625\",\n \"0.5834589\",\n \"0.5834193\",\n \"0.5831649\",\n \"0.58300346\",\n \"0.58283675\",\n \"0.58240885\",\n \"0.5814248\",\n \"0.5799996\",\n \"0.57963616\",\n \"0.5794411\",\n \"0.5793565\",\n \"0.5792523\",\n \"0.5788037\",\n \"0.57713854\",\n \"0.5766312\",\n \"0.5761813\",\n \"0.5761441\",\n \"0.5753726\",\n \"0.57529527\",\n \"0.5750368\",\n \"0.5748786\",\n \"0.5748249\",\n \"0.57411003\",\n \"0.57371515\",\n \"0.57365125\",\n \"0.57286084\",\n \"0.57285863\",\n \"0.5717199\",\n \"0.57137334\",\n \"0.5705961\",\n \"0.570145\",\n \"0.56923157\",\n \"0.56921184\",\n \"0.5683761\",\n \"0.5683672\",\n \"0.56806964\",\n \"0.56800073\",\n \"0.567592\",\n \"0.56710213\",\n \"0.5669945\",\n \"0.5669924\",\n \"0.56639093\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6022399"},"document_rank":{"kind":"string","value":"38"}}},{"rowIdx":17762,"cells":{"query":{"kind":"string","value":"Called if no route is entered"},"document":{"kind":"string","value":"def main():\n return redirect('chat')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def pre_runroute_callable(self, route, request):\n return None","def route(self):\n pass","def post_runroute_callable(self, request):\n return None","def test_abort_route_when_id_route_not_exist(self):\n\n pass","def route(self, env):\n return None","def route(self):\n # TODO: wenn keine url, herausfinden, welche ????\n # TODO: wenn url = hostname (fqdn), dann -> google.ch\n if not (self.META.has_key('REMOTE_ADDR') and \n self.GET.has_key('provider')):\n #self.GET.has_key('url')):\n #return HttpResponseRedirect('/index.php')\n # TODO: Auf die Fehlerseite Link zu back.php\n return render_to_response('error.htm', {\n 'error': \"Falsche Parameter auf route.php\",\n })\n src_ip = self.META['REMOTE_ADDR']\n prov = self.GET['provider']\n url = \"http://www.google.ch\"\n if self.GET.has_key('url'):\n url = self.GET['url']\n # Add and save new route\n add_active_route(src_ip = src_ip, prov = prov)\n return HttpResponseRedirect(url)","def requires_route(self) -> bool:\n return self.goal.is_specific()","def post_runroute_callable(self, request):\n\n if (False):\n request.logevent(EWarning(\"This is a test warning called POST run route: \" + request.get_path()))\n return None","def pre_runroute_callable(self, route, request):\n\n #request.logevent(EInfo(\"pre_runroute_callable Request URL: {0} from {1}.\".format(request.get_full_path(), request.get_remote_addr())))\n # ATTN: test, let's trigger a signal\n if (False):\n id = 'signal.site.pre_runroute'\n message = {'route':route}\n source = None\n flag_collectresults = True\n signalresults = self.comp('signalmanager').broadcast(id, message, request, source, flag_collectresults)\n return None","def test_no_routes(self):\n response = self.client.get(reverse('routes_app:index'))\n self.assertEqual(response.status_code, 200)\n self.assertContains(response, \"No routes are available.\")","def apply(self, callback, route):","def handleNoneFound(message):\n return reply(\"Sorry, there were no restrooms found. Make sure the address was entered correctly.\")","def node_no_route_found_for_packet(self, node, packet):\n for subscriber in self.subscribers:\n subscriber.node_no_route_found_for_packet(node, packet)","def on_route_canceled(self, strategy) -> None:\n pass","def route_accepted(self, prefix, next_hop, as_path):","def route_rejected(self, prefix, next_hop, as_path):","def index_route(self, request, *args, **kwargs):\n raise Http404()","def route( request, c ):","def __loop_detection(self, route: Route) -> bool:\n if self.node_id in route.path:\n return True\n return False","def __loop_detection(self, route: Route) -> bool:\n if self.node_id in route.path:\n return True\n return False","def add_routes(self):\n pass","def test_handle_no_url_match(self):\n with self.assertRaises(Resolver404):\n with mock.patch(self.view) as view:\n self.handler.handle(self.request, '/no/match/')\n\n self.assertFalse(view.called)","def routes(self, body):\n pass","def _handle_first_request(self):\n pass","def get(self):\n self.response.out.write(\"There's nothing to see here. How 'bout a \"\n \"<a href='/'>puzzle</a>?\")","def _route_get(self):\n if self.path == '/status':\n self._create_status()\n else:\n self._create_method_not_allowed()","def has_route(self, network):\n try:\n distance = self.networks[network]\n if distance == 0:\n print('Router is an edge router for the network.')\n else:\n print(f'Network {network} is {distance} hops away')\n except KeyError:\n print('Route to the network is unknown.')","def test_raise_on_invalid(self):\n assert \"raise_on_invalid\" not in self.route.route\n assert self.route.raise_on_invalid().route[\"raise_on_invalid\"]","def setDefaultRoute( self, intf ):\n self.cmd( 'ip route flush root 0/0' )\n return self.cmd( 'route add default ' + intf )","def func(self):\n if (not self.switches or \"online\" in self.switches) and not self.args:\n self.display_lists()\n return\n if \"claim\" in self.switches or (not self.switches and self.args):\n self.claim_scene()\n return\n if \"validate\" in self.switches:\n self.validate_scene()\n return\n if \"viewrequests\" in self.switches:\n self.view_requests()\n return\n self.msg(\"Invalid switch.\")","def __init__(self, root_handler, path_not_found):\n if self.__test_path(root_handler) and self.__test_path(path_not_found):\n self.route_trie = RouteTrie(root_handler) # Passes root handler to the initialised trie\n self.path_not_found = path_not_found # Stores 404 not found response","def test_no_action(self):\n self.request.log(\"Hello World\")\n self.request.end()\n entry = self.get_entry()\n assert entry['action'] is None","def onGoal(self):\n return self.index == len(self.path)","async def test_route_message_with_no_route(base_central_system, heartbeat_call):\n # Empty the route map\n base_central_system.route_map = {}\n\n await base_central_system.route_message(heartbeat_call)\n base_central_system._connection.send.assert_called_once_with(\n json.dumps(\n [\n 4,\n 1,\n \"NotSupported\",\n \"Request Action is recognized but not supported by the receiver\",\n {\"cause\": \"No handler for Heartbeat registered.\"},\n ],\n separators=(\",\", \":\"),\n )\n )","def RoutingInterfaceInitialize(self, request, context):\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)","def filter(self, route: Route) -> bool:\n if self.__loop_detection(route):\n return True\n return False","def filter(self, route: Route) -> bool:\n if self.__loop_detection(route):\n return True\n return False","def view(self, url):\r\n abort(404)","def no_response(self):\n raise NotImplementedError","def route(self, method, pattern, handler):\n pass","def use_routes(self) -> Optional[pulumi.Input[bool]]:\n return pulumi.get(self, \"use_routes\")","def handle_context_missing(self):","def validate_route(self, route):\n\n for router in ROUTER:\n if router.value == route:\n return True\n return False","def handle_event(event, context):\n print(\"Executing...\")\n router = Router(ROUTE_MAP)\n return router.route_request(event, context)","def test_get_not_found(self):\n url = reverse('route', kwargs={'way_id': 999, 'route_id': 100})\n response = self.client.get(url)\n self.assertEqual(response.status_code, 400)\n\n url = reverse('route', kwargs={'way_id': 100, 'route_id': 999})\n response = self.client.get(url)\n self.assertEqual(response.status_code, 400)","def handler(self, *args, **kwargs):\n return True","def match_route(self, host, uri):\n raise NotImplementedError(\"route matching method not implimented\")","def match_request(self):\n try:\n url_rule, self.request.view_args = \\\n self.url_adapter.match(return_rule=True)\n self.request.url_rule = url_rule\n except HTTPException as e:\n self.request.routing_exception = e","def incoming_call_route(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"incoming_call_route\")","def incoming_call_route(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"incoming_call_route\")","def route(self, rule: str, **option: Any) -> None:\n pass","def pre_bgp_router_read(self, resource_id):\n pass","def register_route(self, route, app):\n assert route not in self.routes\n self.routes[route] = app","def view(self, url):\n abort(404)","def sentinel(request):\n return request.param","def route_removed(self, prefix, next_hop, as_path):","def is_router(self):\n # @todo: Rewrite\n return self.address_set.count() > 1","def do_GET(self):\n if not self.path or self.path == \"/\":\n self.redirect()\n elif self.is_viewvc():\n try:\n self.run_viewvc()\n except IOError:\n # ignore IOError: [Errno 32] Broken pipe\n pass\n else:\n self.send_error(404)","def pre_virtual_router_read(self, resource_id):\n pass","def do_print_routes(self, line=''):\n self.fibbing.print_routes()","def reset_route(self):\r\n\t\tself.shift = 0\r\n\t\tself.start_pos = None\r\n\t\tself.goal_pos = None","def pre_logical_router_read(self, resource_id):\n pass","def RoutingInterfaceInitialize(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def _handle_empty_feed(self, empty_feed_code, region, request, args,\n kwargs):\n if empty_feed_code == 0:\n return self._get(request, rest_of_world=True,\n original_region=region, *args, **kwargs)\n return response.Response(status=status.HTTP_404_NOT_FOUND)","def help(route):\n help_text = getRouteHelp(route.split('/') if route else [])\n\n if help_text is None:\n err('Can\\'t help :(')\n\n else:\n print '\\n%s' % help_text","def receive_advertisement(self, a_route: Route, event: Event = None) -> None:\n # Add the route to the common knowledge\n self.__add_to_knowledge(a_route)\n # Register the route\n route, substitute = self.adj_rib_in.insert(a_route)\n # If the route has been registered then check for substitutes and\n # log the event\n if route is not None:\n if substitute is not None:\n self.local_state.remove(self.rib_knowledge[hash(substitute)])\n self.local_state.add(self.rib_knowledge[hash(a_route)])\n self.rib_event_register(event)\n if route is None and substitute is not None:\n if len(self.adj_rib_in[a_route]) == 0:\n del self.adj_rib_in[a_route]\n self.local_state.remove(self.rib_knowledge[hash(substitute)])","def handle_notfound(self, message):\n cmd = self._popMatchingCmd(message)\n if cmd is not None:\n cmd.success(None)","def chooseAction(self):\n print \"nothing\"\n pass","def default_handler(self, _: argparse.Namespace) -> int:\n print(\"No command specified!\")\n self.parser.print_usage()\n return 1","def pre_physical_router_read(self, resource_id):\n pass","def serve(self, request, *args, **kwargs):\n raise Http404","def serve(self, request, *args, **kwargs):\n raise Http404","def serve(self, request, *args, **kwargs):\n raise Http404","def serve(self, request, *args, **kwargs):\n raise Http404","def serve(self, request, *args, **kwargs):\n raise Http404","def _pre_dispatch(self, request, *args, **kwargs):\n pass","def test_no_tour(self):\n mock_request = Mock(user=self.test_user, path='mock2', method='get', GET={})\n mock_view = MockView(request=mock_request)\n response = mock_view.dispatch(mock_request)\n self.assertEqual(200, response.status_code)","def route_layout(self):\n self.route_pins()\n self.route_internal()\n self.route_supplies()","def after(self, after: Route.Decorator):\n pass","def _broadcast(self, msg: str) -> None:\n from jesse.routes import router\n\n for r in router.routes:\n # skip self\n if r.strategy.id == self.id:\n continue\n\n if msg == 'route-open-position':\n r.strategy.on_route_open_position(self)\n elif msg == 'route-close-position':\n r.strategy.on_route_close_position(self)\n elif msg == 'route-increased-position':\n r.strategy.on_route_increased_position(self)\n elif msg == 'route-reduced-position':\n r.strategy.on_route_reduced_position(self)\n elif msg == 'route-canceled':\n r.strategy.on_route_canceled(self)\n\n r.strategy._detect_and_handle_entry_and_exit_modifications()","def robot_is_wanting_default(giver, object, receiver, ctxt) :\n if receiver==\"compliant robot\" :\n raise ActionHandled()","def goblin(request, path):\n print \"This endpoint is throwing a 404 error!\"","def start(self):\n try:\n pass\n except:\n pass","def at_empty_target(self, start_direction, end_direction):\n return None","def invalid_route(e):\n return render_template(\"404.html\")","def route(self):\n self.route_gnd()\n self.route_vdd()\n self.route_access_tx()","def route(self) -> Optional[RoadMap.Route]:\n return self._route","def on_route_open_position(self, strategy) -> None:\n pass","def no_route(arg=None):\n def wrapper(fn):\n setattr(fn, NO_ROUTES_ATTR, True)\n return fn\n\n if callable(arg):\n return wrapper(arg)\n return wrapper","def _do_run(self, path, args):\n try:\n self.router.route(path, args)\n except TypeError, e:\n # To catch the follow errors\n # TypeError: xxxx got an unexpected keyword argument 'k'\n # TypeError: 'print_my_good() takes at least 1 argument (0 given)'\n print \"run job %s with arg < %s > error:\" % (path, \", \".join(args))\n print \"%s\" % e","def test_init(self):\n assert self.route.route[\"transform\"] == \"transform\"\n assert self.route.route[\"output\"] == \"output\"\n assert \"api\" not in self.route.route","def callbackPath(self, path):\n\t\tif path == None:\n\t\t\tprint \"Start- and endpoint not at the same island. No move possible.\"\n\t\tif (\"go\",\"on\") in path:\t# current path is only a part of the whole one\n\t\t\tpass\n\t\telif (\"end\",\"point\") in path:\t# now this is the last part of the whole path\n\t\t\tpass","def pre_route_table_read(self, resource_id):\n pass","def create_route(self):\n try:\n route = input(\"Enter Route/Route Description: \")\n route = RouteModel(route=route)\n self.admin_repository.create_route(route)\n print(\"Route added successfully!\")\n return True\n except Exception as e:\n print(\"Some Error occurred.Please try again\")\n return False","def process_IN_MOVED_TO(self, event):","def _order_not_found():\n pecan.abort(404, u._('Order not found.'))","def test_on_invalid(self):\n assert self.route.on_invalid(str).route[\"on_invalid\"] == str","def show_all_routes(self):\n try:\n routes = self.admin_repository.show_all_routes()\n if routes:\n for route in routes:\n print(\"Route Id: {}\".format(route[0]))\n print(\"Route : {}\".format(route[1]))\n print(\"----------------------------\")\n return True\n else:\n print(\"No records found.\")\n return False\n except Exception as e:\n print(\"Some Error occurred.Please try again\")\n return False","def _dispatch_empty():\n return _dispatch(None)","def _create_new_route(self, tick):\n if self.target_node_id is None:\n self.source_node_id = random.choice(Network.nodes).getID()\n else:\n self.source_node_id = self.target_node_id # We start where we stopped\n # random target\n self.target_node_id = random.choice(Network.nodes).getID()\n self.current_route_id = self.id + \"-\" + str(self.rounds)\n self.current_router_result = CustomRouter.minimal_route(self.source_node_id, self.target_node_id)\n \n if len(self.current_router_result.edges) > 0:\n traci.route.add(self.current_route_id, self.current_router_result.edges)\n return self.current_route_id\n else:\n # try again\n return self._create_new_route(tick)","def _find_route(self, route_path_or_name):\n for route in self.router.routes:\n if route.path == route_path_or_name or route.name == route_path_or_name:\n return route"],"string":"[\n \"def pre_runroute_callable(self, route, request):\\n return None\",\n \"def route(self):\\n pass\",\n \"def post_runroute_callable(self, request):\\n return None\",\n \"def test_abort_route_when_id_route_not_exist(self):\\n\\n pass\",\n \"def route(self, env):\\n return None\",\n \"def route(self):\\n # TODO: wenn keine url, herausfinden, welche ????\\n # TODO: wenn url = hostname (fqdn), dann -> google.ch\\n if not (self.META.has_key('REMOTE_ADDR') and \\n self.GET.has_key('provider')):\\n #self.GET.has_key('url')):\\n #return HttpResponseRedirect('/index.php')\\n # TODO: Auf die Fehlerseite Link zu back.php\\n return render_to_response('error.htm', {\\n 'error': \\\"Falsche Parameter auf route.php\\\",\\n })\\n src_ip = self.META['REMOTE_ADDR']\\n prov = self.GET['provider']\\n url = \\\"http://www.google.ch\\\"\\n if self.GET.has_key('url'):\\n url = self.GET['url']\\n # Add and save new route\\n add_active_route(src_ip = src_ip, prov = prov)\\n return HttpResponseRedirect(url)\",\n \"def requires_route(self) -> bool:\\n return self.goal.is_specific()\",\n \"def post_runroute_callable(self, request):\\n\\n if (False):\\n request.logevent(EWarning(\\\"This is a test warning called POST run route: \\\" + request.get_path()))\\n return None\",\n \"def pre_runroute_callable(self, route, request):\\n\\n #request.logevent(EInfo(\\\"pre_runroute_callable Request URL: {0} from {1}.\\\".format(request.get_full_path(), request.get_remote_addr())))\\n # ATTN: test, let's trigger a signal\\n if (False):\\n id = 'signal.site.pre_runroute'\\n message = {'route':route}\\n source = None\\n flag_collectresults = True\\n signalresults = self.comp('signalmanager').broadcast(id, message, request, source, flag_collectresults)\\n return None\",\n \"def test_no_routes(self):\\n response = self.client.get(reverse('routes_app:index'))\\n self.assertEqual(response.status_code, 200)\\n self.assertContains(response, \\\"No routes are available.\\\")\",\n \"def apply(self, callback, route):\",\n \"def handleNoneFound(message):\\n return reply(\\\"Sorry, there were no restrooms found. Make sure the address was entered correctly.\\\")\",\n \"def node_no_route_found_for_packet(self, node, packet):\\n for subscriber in self.subscribers:\\n subscriber.node_no_route_found_for_packet(node, packet)\",\n \"def on_route_canceled(self, strategy) -> None:\\n pass\",\n \"def route_accepted(self, prefix, next_hop, as_path):\",\n \"def route_rejected(self, prefix, next_hop, as_path):\",\n \"def index_route(self, request, *args, **kwargs):\\n raise Http404()\",\n \"def route( request, c ):\",\n \"def __loop_detection(self, route: Route) -> bool:\\n if self.node_id in route.path:\\n return True\\n return False\",\n \"def __loop_detection(self, route: Route) -> bool:\\n if self.node_id in route.path:\\n return True\\n return False\",\n \"def add_routes(self):\\n pass\",\n \"def test_handle_no_url_match(self):\\n with self.assertRaises(Resolver404):\\n with mock.patch(self.view) as view:\\n self.handler.handle(self.request, '/no/match/')\\n\\n self.assertFalse(view.called)\",\n \"def routes(self, body):\\n pass\",\n \"def _handle_first_request(self):\\n pass\",\n \"def get(self):\\n self.response.out.write(\\\"There's nothing to see here. How 'bout a \\\"\\n \\\"<a href='/'>puzzle</a>?\\\")\",\n \"def _route_get(self):\\n if self.path == '/status':\\n self._create_status()\\n else:\\n self._create_method_not_allowed()\",\n \"def has_route(self, network):\\n try:\\n distance = self.networks[network]\\n if distance == 0:\\n print('Router is an edge router for the network.')\\n else:\\n print(f'Network {network} is {distance} hops away')\\n except KeyError:\\n print('Route to the network is unknown.')\",\n \"def test_raise_on_invalid(self):\\n assert \\\"raise_on_invalid\\\" not in self.route.route\\n assert self.route.raise_on_invalid().route[\\\"raise_on_invalid\\\"]\",\n \"def setDefaultRoute( self, intf ):\\n self.cmd( 'ip route flush root 0/0' )\\n return self.cmd( 'route add default ' + intf )\",\n \"def func(self):\\n if (not self.switches or \\\"online\\\" in self.switches) and not self.args:\\n self.display_lists()\\n return\\n if \\\"claim\\\" in self.switches or (not self.switches and self.args):\\n self.claim_scene()\\n return\\n if \\\"validate\\\" in self.switches:\\n self.validate_scene()\\n return\\n if \\\"viewrequests\\\" in self.switches:\\n self.view_requests()\\n return\\n self.msg(\\\"Invalid switch.\\\")\",\n \"def __init__(self, root_handler, path_not_found):\\n if self.__test_path(root_handler) and self.__test_path(path_not_found):\\n self.route_trie = RouteTrie(root_handler) # Passes root handler to the initialised trie\\n self.path_not_found = path_not_found # Stores 404 not found response\",\n \"def test_no_action(self):\\n self.request.log(\\\"Hello World\\\")\\n self.request.end()\\n entry = self.get_entry()\\n assert entry['action'] is None\",\n \"def onGoal(self):\\n return self.index == len(self.path)\",\n \"async def test_route_message_with_no_route(base_central_system, heartbeat_call):\\n # Empty the route map\\n base_central_system.route_map = {}\\n\\n await base_central_system.route_message(heartbeat_call)\\n base_central_system._connection.send.assert_called_once_with(\\n json.dumps(\\n [\\n 4,\\n 1,\\n \\\"NotSupported\\\",\\n \\\"Request Action is recognized but not supported by the receiver\\\",\\n {\\\"cause\\\": \\\"No handler for Heartbeat registered.\\\"},\\n ],\\n separators=(\\\",\\\", \\\":\\\"),\\n )\\n )\",\n \"def RoutingInterfaceInitialize(self, request, context):\\n context.code(beta_interfaces.StatusCode.UNIMPLEMENTED)\",\n \"def filter(self, route: Route) -> bool:\\n if self.__loop_detection(route):\\n return True\\n return False\",\n \"def filter(self, route: Route) -> bool:\\n if self.__loop_detection(route):\\n return True\\n return False\",\n \"def view(self, url):\\r\\n abort(404)\",\n \"def no_response(self):\\n raise NotImplementedError\",\n \"def route(self, method, pattern, handler):\\n pass\",\n \"def use_routes(self) -> Optional[pulumi.Input[bool]]:\\n return pulumi.get(self, \\\"use_routes\\\")\",\n \"def handle_context_missing(self):\",\n \"def validate_route(self, route):\\n\\n for router in ROUTER:\\n if router.value == route:\\n return True\\n return False\",\n \"def handle_event(event, context):\\n print(\\\"Executing...\\\")\\n router = Router(ROUTE_MAP)\\n return router.route_request(event, context)\",\n \"def test_get_not_found(self):\\n url = reverse('route', kwargs={'way_id': 999, 'route_id': 100})\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 400)\\n\\n url = reverse('route', kwargs={'way_id': 100, 'route_id': 999})\\n response = self.client.get(url)\\n self.assertEqual(response.status_code, 400)\",\n \"def handler(self, *args, **kwargs):\\n return True\",\n \"def match_route(self, host, uri):\\n raise NotImplementedError(\\\"route matching method not implimented\\\")\",\n \"def match_request(self):\\n try:\\n url_rule, self.request.view_args = \\\\\\n self.url_adapter.match(return_rule=True)\\n self.request.url_rule = url_rule\\n except HTTPException as e:\\n self.request.routing_exception = e\",\n \"def incoming_call_route(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"incoming_call_route\\\")\",\n \"def incoming_call_route(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"incoming_call_route\\\")\",\n \"def route(self, rule: str, **option: Any) -> None:\\n pass\",\n \"def pre_bgp_router_read(self, resource_id):\\n pass\",\n \"def register_route(self, route, app):\\n assert route not in self.routes\\n self.routes[route] = app\",\n \"def view(self, url):\\n abort(404)\",\n \"def sentinel(request):\\n return request.param\",\n \"def route_removed(self, prefix, next_hop, as_path):\",\n \"def is_router(self):\\n # @todo: Rewrite\\n return self.address_set.count() > 1\",\n \"def do_GET(self):\\n if not self.path or self.path == \\\"/\\\":\\n self.redirect()\\n elif self.is_viewvc():\\n try:\\n self.run_viewvc()\\n except IOError:\\n # ignore IOError: [Errno 32] Broken pipe\\n pass\\n else:\\n self.send_error(404)\",\n \"def pre_virtual_router_read(self, resource_id):\\n pass\",\n \"def do_print_routes(self, line=''):\\n self.fibbing.print_routes()\",\n \"def reset_route(self):\\r\\n\\t\\tself.shift = 0\\r\\n\\t\\tself.start_pos = None\\r\\n\\t\\tself.goal_pos = None\",\n \"def pre_logical_router_read(self, resource_id):\\n pass\",\n \"def RoutingInterfaceInitialize(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def _handle_empty_feed(self, empty_feed_code, region, request, args,\\n kwargs):\\n if empty_feed_code == 0:\\n return self._get(request, rest_of_world=True,\\n original_region=region, *args, **kwargs)\\n return response.Response(status=status.HTTP_404_NOT_FOUND)\",\n \"def help(route):\\n help_text = getRouteHelp(route.split('/') if route else [])\\n\\n if help_text is None:\\n err('Can\\\\'t help :(')\\n\\n else:\\n print '\\\\n%s' % help_text\",\n \"def receive_advertisement(self, a_route: Route, event: Event = None) -> None:\\n # Add the route to the common knowledge\\n self.__add_to_knowledge(a_route)\\n # Register the route\\n route, substitute = self.adj_rib_in.insert(a_route)\\n # If the route has been registered then check for substitutes and\\n # log the event\\n if route is not None:\\n if substitute is not None:\\n self.local_state.remove(self.rib_knowledge[hash(substitute)])\\n self.local_state.add(self.rib_knowledge[hash(a_route)])\\n self.rib_event_register(event)\\n if route is None and substitute is not None:\\n if len(self.adj_rib_in[a_route]) == 0:\\n del self.adj_rib_in[a_route]\\n self.local_state.remove(self.rib_knowledge[hash(substitute)])\",\n \"def handle_notfound(self, message):\\n cmd = self._popMatchingCmd(message)\\n if cmd is not None:\\n cmd.success(None)\",\n \"def chooseAction(self):\\n print \\\"nothing\\\"\\n pass\",\n \"def default_handler(self, _: argparse.Namespace) -> int:\\n print(\\\"No command specified!\\\")\\n self.parser.print_usage()\\n return 1\",\n \"def pre_physical_router_read(self, resource_id):\\n pass\",\n \"def serve(self, request, *args, **kwargs):\\n raise Http404\",\n \"def serve(self, request, *args, **kwargs):\\n raise Http404\",\n \"def serve(self, request, *args, **kwargs):\\n raise Http404\",\n \"def serve(self, request, *args, **kwargs):\\n raise Http404\",\n \"def serve(self, request, *args, **kwargs):\\n raise Http404\",\n \"def _pre_dispatch(self, request, *args, **kwargs):\\n pass\",\n \"def test_no_tour(self):\\n mock_request = Mock(user=self.test_user, path='mock2', method='get', GET={})\\n mock_view = MockView(request=mock_request)\\n response = mock_view.dispatch(mock_request)\\n self.assertEqual(200, response.status_code)\",\n \"def route_layout(self):\\n self.route_pins()\\n self.route_internal()\\n self.route_supplies()\",\n \"def after(self, after: Route.Decorator):\\n pass\",\n \"def _broadcast(self, msg: str) -> None:\\n from jesse.routes import router\\n\\n for r in router.routes:\\n # skip self\\n if r.strategy.id == self.id:\\n continue\\n\\n if msg == 'route-open-position':\\n r.strategy.on_route_open_position(self)\\n elif msg == 'route-close-position':\\n r.strategy.on_route_close_position(self)\\n elif msg == 'route-increased-position':\\n r.strategy.on_route_increased_position(self)\\n elif msg == 'route-reduced-position':\\n r.strategy.on_route_reduced_position(self)\\n elif msg == 'route-canceled':\\n r.strategy.on_route_canceled(self)\\n\\n r.strategy._detect_and_handle_entry_and_exit_modifications()\",\n \"def robot_is_wanting_default(giver, object, receiver, ctxt) :\\n if receiver==\\\"compliant robot\\\" :\\n raise ActionHandled()\",\n \"def goblin(request, path):\\n print \\\"This endpoint is throwing a 404 error!\\\"\",\n \"def start(self):\\n try:\\n pass\\n except:\\n pass\",\n \"def at_empty_target(self, start_direction, end_direction):\\n return None\",\n \"def invalid_route(e):\\n return render_template(\\\"404.html\\\")\",\n \"def route(self):\\n self.route_gnd()\\n self.route_vdd()\\n self.route_access_tx()\",\n \"def route(self) -> Optional[RoadMap.Route]:\\n return self._route\",\n \"def on_route_open_position(self, strategy) -> None:\\n pass\",\n \"def no_route(arg=None):\\n def wrapper(fn):\\n setattr(fn, NO_ROUTES_ATTR, True)\\n return fn\\n\\n if callable(arg):\\n return wrapper(arg)\\n return wrapper\",\n \"def _do_run(self, path, args):\\n try:\\n self.router.route(path, args)\\n except TypeError, e:\\n # To catch the follow errors\\n # TypeError: xxxx got an unexpected keyword argument 'k'\\n # TypeError: 'print_my_good() takes at least 1 argument (0 given)'\\n print \\\"run job %s with arg < %s > error:\\\" % (path, \\\", \\\".join(args))\\n print \\\"%s\\\" % e\",\n \"def test_init(self):\\n assert self.route.route[\\\"transform\\\"] == \\\"transform\\\"\\n assert self.route.route[\\\"output\\\"] == \\\"output\\\"\\n assert \\\"api\\\" not in self.route.route\",\n \"def callbackPath(self, path):\\n\\t\\tif path == None:\\n\\t\\t\\tprint \\\"Start- and endpoint not at the same island. No move possible.\\\"\\n\\t\\tif (\\\"go\\\",\\\"on\\\") in path:\\t# current path is only a part of the whole one\\n\\t\\t\\tpass\\n\\t\\telif (\\\"end\\\",\\\"point\\\") in path:\\t# now this is the last part of the whole path\\n\\t\\t\\tpass\",\n \"def pre_route_table_read(self, resource_id):\\n pass\",\n \"def create_route(self):\\n try:\\n route = input(\\\"Enter Route/Route Description: \\\")\\n route = RouteModel(route=route)\\n self.admin_repository.create_route(route)\\n print(\\\"Route added successfully!\\\")\\n return True\\n except Exception as e:\\n print(\\\"Some Error occurred.Please try again\\\")\\n return False\",\n \"def process_IN_MOVED_TO(self, event):\",\n \"def _order_not_found():\\n pecan.abort(404, u._('Order not found.'))\",\n \"def test_on_invalid(self):\\n assert self.route.on_invalid(str).route[\\\"on_invalid\\\"] == str\",\n \"def show_all_routes(self):\\n try:\\n routes = self.admin_repository.show_all_routes()\\n if routes:\\n for route in routes:\\n print(\\\"Route Id: {}\\\".format(route[0]))\\n print(\\\"Route : {}\\\".format(route[1]))\\n print(\\\"----------------------------\\\")\\n return True\\n else:\\n print(\\\"No records found.\\\")\\n return False\\n except Exception as e:\\n print(\\\"Some Error occurred.Please try again\\\")\\n return False\",\n \"def _dispatch_empty():\\n return _dispatch(None)\",\n \"def _create_new_route(self, tick):\\n if self.target_node_id is None:\\n self.source_node_id = random.choice(Network.nodes).getID()\\n else:\\n self.source_node_id = self.target_node_id # We start where we stopped\\n # random target\\n self.target_node_id = random.choice(Network.nodes).getID()\\n self.current_route_id = self.id + \\\"-\\\" + str(self.rounds)\\n self.current_router_result = CustomRouter.minimal_route(self.source_node_id, self.target_node_id)\\n \\n if len(self.current_router_result.edges) > 0:\\n traci.route.add(self.current_route_id, self.current_router_result.edges)\\n return self.current_route_id\\n else:\\n # try again\\n return self._create_new_route(tick)\",\n \"def _find_route(self, route_path_or_name):\\n for route in self.router.routes:\\n if route.path == route_path_or_name or route.name == route_path_or_name:\\n return route\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.713702","0.6929891","0.6734935","0.632262","0.62484354","0.62403715","0.6215604","0.6121625","0.6100147","0.60336834","0.5908486","0.5858942","0.5817849","0.57772964","0.5679842","0.5653751","0.56365013","0.5586848","0.5540685","0.5540685","0.5502926","0.5484791","0.54349583","0.5430079","0.5407861","0.53994215","0.53950846","0.5381429","0.53600305","0.5340868","0.5340787","0.531767","0.53108966","0.5297598","0.52916026","0.52842253","0.52842253","0.52791584","0.525828","0.5256358","0.5240572","0.5236254","0.5181642","0.5179907","0.5165063","0.51598316","0.5151092","0.5146287","0.51400876","0.51400876","0.513949","0.5139402","0.5135164","0.5134686","0.5134019","0.5124125","0.5116053","0.51091206","0.51065814","0.51004785","0.5090508","0.50808847","0.5073523","0.50670654","0.5055762","0.5053801","0.50452","0.5019568","0.50193036","0.5014914","0.5011986","0.5011986","0.5011986","0.5011986","0.5011986","0.50037336","0.5002828","0.49991345","0.4994776","0.49887282","0.49817786","0.49785435","0.4978086","0.49757507","0.496835","0.49653295","0.49646518","0.49637613","0.49627513","0.4961384","0.49598476","0.49555013","0.49544588","0.49515083","0.4947625","0.4946912","0.49441925","0.49405557","0.49402875","0.49394256","0.493836"],"string":"[\n \"0.713702\",\n \"0.6929891\",\n \"0.6734935\",\n \"0.632262\",\n \"0.62484354\",\n \"0.62403715\",\n \"0.6215604\",\n \"0.6121625\",\n \"0.6100147\",\n \"0.60336834\",\n \"0.5908486\",\n \"0.5858942\",\n \"0.5817849\",\n \"0.57772964\",\n \"0.5679842\",\n \"0.5653751\",\n \"0.56365013\",\n \"0.5586848\",\n \"0.5540685\",\n \"0.5540685\",\n \"0.5502926\",\n \"0.5484791\",\n \"0.54349583\",\n \"0.5430079\",\n \"0.5407861\",\n \"0.53994215\",\n \"0.53950846\",\n \"0.5381429\",\n \"0.53600305\",\n \"0.5340868\",\n \"0.5340787\",\n \"0.531767\",\n \"0.53108966\",\n \"0.5297598\",\n \"0.52916026\",\n \"0.52842253\",\n \"0.52842253\",\n \"0.52791584\",\n \"0.525828\",\n \"0.5256358\",\n \"0.5240572\",\n \"0.5236254\",\n \"0.5181642\",\n \"0.5179907\",\n \"0.5165063\",\n \"0.51598316\",\n \"0.5151092\",\n \"0.5146287\",\n \"0.51400876\",\n \"0.51400876\",\n \"0.513949\",\n \"0.5139402\",\n \"0.5135164\",\n \"0.5134686\",\n \"0.5134019\",\n \"0.5124125\",\n \"0.5116053\",\n \"0.51091206\",\n \"0.51065814\",\n \"0.51004785\",\n \"0.5090508\",\n \"0.50808847\",\n \"0.5073523\",\n \"0.50670654\",\n \"0.5055762\",\n \"0.5053801\",\n \"0.50452\",\n \"0.5019568\",\n \"0.50193036\",\n \"0.5014914\",\n \"0.5011986\",\n \"0.5011986\",\n \"0.5011986\",\n \"0.5011986\",\n \"0.5011986\",\n \"0.50037336\",\n \"0.5002828\",\n \"0.49991345\",\n \"0.4994776\",\n \"0.49887282\",\n \"0.49817786\",\n \"0.49785435\",\n \"0.4978086\",\n \"0.49757507\",\n \"0.496835\",\n \"0.49653295\",\n \"0.49646518\",\n \"0.49637613\",\n \"0.49627513\",\n \"0.4961384\",\n \"0.49598476\",\n \"0.49555013\",\n \"0.49544588\",\n \"0.49515083\",\n \"0.4947625\",\n \"0.4946912\",\n \"0.49441925\",\n \"0.49405557\",\n \"0.49402875\",\n \"0.49394256\",\n \"0.493836\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17763,"cells":{"query":{"kind":"string","value":"Main page, for chatting"},"document":{"kind":"string","value":"def chat():\n username = request.cookies.get('username')\n\n if username != None and username != \"\":\n return r.renderContent('chat.html', name=username)\n return redirect('/login')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def chat():\n kwargs = {\"title\": u\"chat channel\", \"entries\": log.getLogEntries()}\n return render_template(\"chat.html\", **kwargs)","def view_contact_chat(self):\n if self._user.chats == {}:\n print(\"No chats to be viewed yet\")\n self.homepage()\n \n print('-=' * 30)\n chats = self._user.list_chats()\n user_choice = self._int_input_in_range(\"Pick whose contact chat to be viewed: \"\n ,range_ = (1, len(chats)))\n if not user_choice:\n return self.homepage()\n \n chat, contact = chats[user_choice - 1]\n chat_content = chat.get_content(self._user)\n print('-=' * 12 + \" Chat Window \" + '-=' * 12)\n if chat_content != []:\n for line in chat_content:\n print(line.rstrip()) \n else:\n print('This chat is empty, send your first msg now')\n \n user_choice = self._int_input_in_range(' (1) Send new msg \\n (2) Back to homepage \\n Your choice: '\n , range_ = (1,2))\n if user_choice == 1:\n print('HINT: send (0) to exist the chat window')\n return self._send_msg(contact)\n else:\n return self.homepage()","def main():\n return redirect('chat')","def chat(request):\n return jingo.render(request, 'chat/chat.html')","def home():\n data = read_data()\n return render_template(\n 'index.html',\n title='Raft Message Implementation: Client',\n content=data\n )","def send_messages(self):\r\n self.clear_screen()\r\n user_label = Label(self.root, text=\"Hello \" + self.username,\r\n font=self.title_font, bg=self.bg_color, height=2)\r\n user_label.pack(pady=10, padx=50)\r\n messages_frame = Frame(self.root)\r\n messages_frame.pack(padx=30, pady=10)\r\n scrollbar_msg = Scrollbar(messages_frame)\r\n scrollbar_msg.pack(side=RIGHT, fill=Y)\r\n write_message = Text(messages_frame, width=50, height=15, font=self.text_font,\r\n yscrollcommand=scrollbar_msg.set)\r\n write_message.pack()\r\n scrollbar_msg.config(command=write_message.yview)\r\n button_speech_rec = Button(self.root, text=\"listen\\nto speech\", font=self.text_font,\r\n height=2, width=20,\r\n command=lambda: self.create_speech_thread(write_message))\r\n button_speech_rec.pack(pady=10)\r\n button_send = Button(self.root, text=\"send\", font=self.text_font,\r\n height=2, width=20, command=lambda: self.send(write_message))\r\n button_send.pack(pady=10)\r\n button_send = Button(self.root, text=\"go back\", font=self.text_font,\r\n height=2, width=20, command=self.choose_path)\r\n button_send.pack(pady=10)","def run_chat_client():\r\n while must_run:\r\n print_menu()\r\n action = select_user_action()\r\n perform_user_action(action)\r\n print(\"Thanks for watching. Like and subscribe! 👍\")","def main():\n\n bus_controller = BusController()\n steve = TelegramController(\"990223452:AAHrln4bCzwGpkR2w-5pqesPHpuMjGKuJUI\")\n message_sender = MessagesSender()\n db = DBManager()\n gui = GUI()\n\n message_sender.connect(bus_controller=bus_controller)\n bus_controller.connect(telegram_bot=steve, message_sender=message_sender)\n steve.connect(bus_controller=bus_controller, gui=gui, message_sender=message_sender, data_base=db)\n gui.connect(bus_controller=bus_controller, telegram_controller=steve, message_sender=message_sender, data_base=db)\n\n message_sender.start()\n bus_controller.start()\n steve.start()\n gui.start()","def chat():\n name = session.get('name', '')\n room = session.get('room', '')\n if name == '' or room == '':\n return redirect(url_for('.index'))\n return render_template('chat.html', name=name, room=room)","def chat():\n name = session.get('name', '')\n room = session.get('room', '')\n if name == '' or room == '':\n return redirect(url_for('.index'))\n return render_template('chat.html', name=name, room=room)","def chat():\n name = session.get('name', '')\n room = session.get('room', '')\n if name == '' or room == '':\n return redirect(url_for('.index'))\n return render_template('chat.html', name=name, room=room)","def main():\n global discuss_bot_id, discussion_chat_id\n r = requests.get('https://slack.com/api/rtm.connect', {'token': bot_token})\n discuss_bot_id = r.json()['self']['id']\n url = r.json()['url']\n r = requests.get('https://slack.com/api/conversations.list',\n {'token': bot_token})\n for channel in r.json()['channels']:\n if channel['name'] == 'discussion':\n discussion_chat_id = channel['id']\n print(discussion_chat_id)\n ws = websocket.WebSocketApp(\n url=url, on_message=on_message, on_error=on_error, on_close=on_close)\n ws.on_open = on_open\n ws.run_forever()","def OnB(self, event):\n bp = event.GetEventObject()\n friend = bp.parameterVal\n app1 = wx.PySimpleApp(0)\n wx.InitAllImageHandlers()\n frame_1 = ChatScreen(self.user, self.passw,friend, None, -1, \"\")\n TextThread()\n app1.SetTopWindow(frame_1)\n frame_1.Show()\n app1.MainLoop()","def chat(request):\n message = '{}: {}'.format(request.form['user'], request.form['message'])\n if message:\n ChatNamespace.broadcast('message', message)\n return Response()","def app():\n # Add balloons animation when page opens\n st.balloons()\n\n # Add title\n st.title(\"Welcome to the about me page\")\n \n # Add Contact Details\n st.header('Contact Us')\n\n # Add email\n st.markdown('''### Name:\n Khushal Mehrotra''')\n\n # Add name\n st.markdown('''### Email:\n kmehrotra07@gmail.com''')\n\n # Add github\n st.markdown('''### GitHub: [Khushal Mehrotra](https://github.com/km-07)''')\n\n # Add linkedin\n st.markdown('''### Linkedin: [Khushal Mehrotra](https://www.linkedin.com/in/khushal-mehrotra-5276091a9/)''')","def index(self):\n print \"Starting Wall\"\n print \"Retrieving name\"\n print session['user_id']\n full_name = self.models['Wall_m'].full_name(session['user_id']) # Grab user'sname\n print full_name\n full_name = full_name[0]['full_name']\n print full_name \n\n messages = self.models['Wall_m'].load_messages(session['user_id'])\n print \"MESSAGE: \"+str(messages)\n\n if messages != []:\n comments = self.models['Wall_m'].load_comments(messages)\n else: \n comments = []\n\n print \"COMMENT: \"+str(comments)\n return self.load_view('wall.html', Welcome = full_name, mess = messages, comm = comments)","def get(self):\n self.write(\n '{\"error\": \"cryptochat-server main page, '\n 'please refer to /api/message/new or /api/message/updates\"}')","def index(self):\n return \"\"\"\n<html><body>\n<pre>\n Hello World!\n\n To send a message, please point to:\n http://server:port/msg?msgType=YOURTYPE&payload=YOURPAYLOAD\n</pre>\n</body></html>\n \"\"\"","def main(self):\n\n dp = self.dispatcher\n\n dp.add_handler(MessageHandler(Filters.text, self.__msg_handler))\n dp.add_handler(MessageHandler(Filters.command, self.__msg_handler))\n dp.add_handler(MessageHandler(Filters.photo, self.__photo_handler))\n dp.add_handler(MessageHandler(\n Filters.location, self.__location_handler))\n dp.add_handler(CallbackQueryHandler(callback=self.__clb_handler))\n\n self.updater.start_polling()\n self.updater.idle()","def chat(self) -> \"api.Chat\":\n raise NotImplementedError","def index():\n #Have to create LOGIN\n #serverxmpp.init()\n return dict()","def main(args):\n\n cocos.director.director.init(resizable=True)\n\n Conversation = wit_handler.Conversation()\n TextBox = text_box.TextBox(\n enter_function=Conversation.send_message\n )\n\n # Run a scene with our event displayers:\n cocos.director.director.run(cocos.scene.Scene(TextBox))","def __sendMessage(self):\n # TODO: Switch to this when implemented\n \n msg = self.ui.inputWidget.toPlainText()\n self.ui.inputWidget.clear()\n strv = StringView()\n strv.appendText(unicode(msg))\n self._amsn_conversation.sendMessage(strv)\n self.ui.textEdit.append(\"<b>/me says:</b><br>\"+unicode(msg)+\"\")","def about_screen(self):\r\n self.clear_screen()\r\n user_label = Label(self.root, text=\"Hello \" + self.username,\r\n font=self.title_font, bg=self.bg_color, height=2)\r\n user_label.pack(pady=10, padx=50)\r\n about_text = \"\"\"My name is Jasmin, I am 17 years old and this is my final project\r\nfor 12th grade in the Cyber Bagrut.\r\nThe project contains a multi client-server connection.\r\nThe project includes Enigma simulator and explanations about the encryption.\r\nIt allows chatting between all the connected users.\r\nLogging in and signing in is through the server. \r\nthe client sends the user data to the server with RSA encryption,\r\nand the server responds appropriately.\r\nEncryption key of messaging is changed every 10 minutes according to the\r\ndatabase enigma settings. the user can also send a message through the \r\nsimulator, using whichever settings he wants. the setting key\r\n(time/settings from the simulator) are sent with RSA encryption. \r\nthe encryption of a message is done with the Enigma machine and\r\nMorse code combined with ASCII afterwards.\"\"\"\r\n lbl_about = Label(self.root, text=\"About The Project\",\r\n font=self.title_font, bg=self.bg_color)\r\n lbl_about.pack(pady=5, padx=10)\r\n about_frame = Frame(self.root, width=100, height=300, bg='white')\r\n about_frame.pack(padx=30, pady=20)\r\n text_widget = Text(about_frame)\r\n text_widget.pack()\r\n text_widget.insert(END, about_text)\r\n text_widget.config(state=DISABLED)\r\n button_send = Button(self.root, text=\"go back to choose path\", font=self.text_font,\r\n height=2, width=20, command=self.choose_path)\r\n button_send.pack(pady=20)","def new_window_messages(self, button_see_all_msgs):\r\n # changing the button command to closing the window\r\n button_see_all_msgs.config(command=lambda: self.close_window(button_see_all_msgs))\r\n\r\n # creating the chat Tk object\r\n self.messages_window = Tk()\r\n self.messages_window.resizable(False, False)\r\n self.messages_window.config(bg=self.bg_color)\r\n self.messages_window.protocol(\"WM_DELETE_WINDOW\",\r\n lambda: self.close_window(button_see_all_msgs))\r\n\r\n chat_label = Label(self.messages_window, text=\"Hello \" + self.username +\r\n \"\\nHere are your messages\",\r\n bg=self.bg_color, font=self.title_font)\r\n chat_label.pack(padx=20, pady=10)\r\n chat_frame = Frame(self.messages_window)\r\n chat_frame.pack(padx=15, pady=15)\r\n scrollbar_chat = Scrollbar(chat_frame)\r\n scrollbar_chat.pack(side=RIGHT, fill=Y)\r\n text_chat = Text(chat_frame, width=30, height=15, font=self.text_font,\r\n yscrollcommand=scrollbar_chat.set)\r\n text_chat.pack()\r\n scrollbar_chat.config(command=text_chat.yview)\r\n for msg, encryption_data, sender_user in self.msg_list:\r\n text_chat.insert(END, \"from: \" + sender_user + \"\\n\")\r\n text_chat.insert(END, msg + \"\\n\\n\")\r\n text_chat.config(state=DISABLED)","def chat(request):\n team = Hunt.objects.get(is_current_hunt=True).team_from_user(request.user)\n if request.method == 'POST':\n # There is data in the post request, but we don't need anything but\n # the message because normal users can't send as staff or other teams\n m = Message.objects.create(time=timezone.now(), text=request.POST.get('message'),\n is_response=False, team=team)\n team.num_waiting_messages = 0\n messages = [m]\n else:\n if(team is None):\n return render(request, 'access_error.html', {'reason': \"team\"})\n if(team.hunt.is_locked and not team.is_playtester_team):\n return render(request, 'access_error.html', {'reason': \"hunt\"})\n if request.is_ajax():\n messages = Message.objects.filter(pk__gt=request.GET.get(\"last_pk\"))\n else:\n messages = Message.objects\n messages = messages.filter(team=team).order_by('time')\n\n # The whole message_dict format is for ajax/template uniformity\n rendered_messages = render_to_string('chat_messages.html',\n {'messages': messages, 'team_name': team.team_name})\n message_dict = {team.team_name: {'pk': team.pk, 'messages': rendered_messages}}\n try:\n last_pk = Message.objects.latest('id').id\n except Message.DoesNotExist:\n last_pk = 0\n team.num_waiting_messages = 0\n\n team.save() # Save last_*_message vars\n context = {'message_dict': message_dict, 'last_pk': last_pk}\n if request.is_ajax() or request.method == 'POST':\n return HttpResponse(json.dumps(context))\n else:\n context['team'] = team\n return render(request, 'chat.html', context)","def main(request):\n users = get_channel_users(MEMBER_CHANNEL)\n human_users = filter_bots(users)\n buddy_groups = group_users(human_users)\n if buddy_groups:\n resp = post_message(buddy_groups, POST_CHANNEL)\n ret = str(resp)\n else:\n ret = 'Not enough members to run'\n return ret","def read_messages(self, msg_num):\r\n self.clear_screen()\r\n user_label = Label(self.root, text=\"Hello \" + self.username, font=self.title_font,\r\n bg=self.bg_color, height=2)\r\n user_label.pack(pady=5, padx=50)\r\n lbl_msg = Label(self.root, text=\"Message \" + str(msg_num), font=self.title_font,\r\n bg=self.bg_color)\r\n lbl_msg.pack(pady=5, padx=10)\r\n self.refresh_button = Button(self.root, text=\"Refresh page\", font=self.text_font,\r\n bg=self.bg_color, command=lambda: self.refresh(msg_num))\r\n self.refresh_button.pack(padx=10, pady=10)\r\n messages_frame = Frame(self.root)\r\n messages_frame.pack(padx=30, pady=15)\r\n scrollbar_msg = Scrollbar(messages_frame)\r\n scrollbar_msg.pack(side=RIGHT, fill=Y)\r\n text_widget = Text(messages_frame, width=50, height=15, font=self.text_font,\r\n yscrollcommand=scrollbar_msg.set)\r\n text_widget.pack()\r\n scrollbar_msg.config(command=text_widget.yview)\r\n button_send = Button(self.root, text=\"go back\", font=self.text_font,\r\n height=2, width=20, command=self.go_back_read)\r\n button_send.pack(pady=5, side=BOTTOM)\r\n button_send = Button(self.root, text=\"see/close message\\ncontrol panel\",\r\n font=self.text_font,\r\n height=2, width=20,\r\n command=lambda: self.new_window_messages(button_send))\r\n button_send.pack(pady=5, side=BOTTOM)\r\n if self.msg_list:\r\n if msg_num < len(self.msg_list):\r\n next_msg = Button(self.root, text=\"next message\", font=self.text_font,\r\n height=2, width=20,\r\n command=lambda: self.read_messages(msg_num + 1))\r\n next_msg.pack(pady=5, padx=5, side=RIGHT)\r\n if msg_num > 1:\r\n previous_msg = Button(self.root, text=\"previous message\", font=self.text_font,\r\n height=2, width=20,\r\n command=lambda: self.read_messages(msg_num - 1))\r\n previous_msg.pack(pady=5, padx=5, side=LEFT)\r\n text_widget.insert(END, \"from: \" + self.msg_list[msg_num - 1][2] + \"\\n\")\r\n text_widget.tag_add('sender', '1.0', '1.end')\r\n text_widget.tag_config('sender', font='none 14')\r\n\r\n text_widget.insert(END, self.msg_list[msg_num - 1][0])\r\n text_widget.tag_add('msg', '2.0', END)\r\n text_widget.tag_config('msg', font='none 12')\r\n\r\n text_widget.config(state=DISABLED)","async def messages(self, ctx):\n\n\t\tawait self.message_leaderboard(ctx, \"messages\")","def welcome(request):\n # flag che indica se sono presenti oppure no nuovi messaggi nella casella di posta\n new_messages = False\n if request.user.is_authenticated:\n # controllo le liste di attesa a cui è prenotato l'utente\n controlList(request.user)\n messaggi = Messaggio.objects.filter(userDestinatario=request.user)\n for msg in messaggi:\n if msg.letto == False:\n new_messages = True\n return render(request, 'main_page/main_page.html', {'new_messages':new_messages})","def main():\r\n st.sidebar.title(\"MỤC LỤC\")\r\n selection = st.sidebar.radio(\"Go to\", list(PAGES.keys()))\r\n\r\n page = PAGES[selection]\r\n\r\n with st.spinner(f\"Loading {selection} ...\"):\r\n ast.shared.components.write_page(page)\r\n st.sidebar.title(\"Intro\")\r\n st.sidebar.info(\r\n \"App này được làm ra nhằm mục đích phục vụ cho việc **nghiên cứu và học tập** \"\r\n \"[source code](https://github.com/BienQuangTruong/streamlit_app). \"\r\n )\r\n st.sidebar.title(\"About\")\r\n st.sidebar.info(\r\n \"\"\"\r\n App này được thực hiện bởi Biện Quang Trường - Nguyễn Văn Phước.\r\n\"\"\"\r\n )","def start(self):\n self.show_greeting()\n self.read_frame()","def tornar_main(self):\n pub.sendMessage(\"BACK_TO_MAIN_PAGE\")","def index():\n return render_template(\"main_board/board.html\", url_for_api_messages=url_for('api.main_board'), limit=10,\n message_form=MessageForm())","def run(self):\n self.logger.info(\"Starting messenger.\")\n self.recv()","def start(self, event):\n self.send_presence()\n self.get_roster()\n self.send_message(mto=self.recipient, mbody=self.msg, mtype='chat')\n self.disconnect(wait=True)","def start_message(self, update, context):\n\n user = self.User(update)\n output = \"Greetings, we're happy that you decided to join and use the Bus4U service!\\n\" \\\n \"in order to see all the possible commands you can type /help\\n\" \\\n \"Also we want you to know that every command that you type and the server response will\" \\\n \"be logged and you can access your history with /history.\\n\\n\" \\\n \"we hope you'll enjoy the product and wish you the best.\\n Never Miss a Bus.\"\n user.send_message(output)\n self.data_base.log(user, \"*Showed Greeting Message*\")","def test_im_chat_messages(self):\n pass","def _do_start(self, chat_id, user_id, args, update):\n \n self.tclient.send_message('Hallo! Ich bin ein Bot, um dir zu helfen, dir deine Nasensprüche zu merken!', chat_id)","def display_messages(self, layout):","def connectionMade(self):\n\n login_message = f\"login:{self.factory.login}\" # form a login registration line\n self.send_message(login_message) # send to a server\n\n self.wrap() # enable the input / output mode in the console (to send messages by pressing Enter)","def main():\n if 'username' in session:\n flash(f'Logged in as {session[\"username\"]}')\n else:\n flash('You are not logged in.')\n return render_template(\"main.html\", title=\"Main\")","def start(update, context):\n chats = load_chats()\n chats.append( str( update.message.chat_id ) )\n save_channels(chats)\n update.message.reply_text('Chat registered!')","def single_chat(request, key):\n \n if not Messages.objects.using('msgstore').filter(key_remote_jid=key).count() > 0:\n return render_to_response('whatsapp/errors/404.html', context_instance=RequestContext(request)) \n\n msgs = get_chat_messages(key)\n msgs_list = pagination(request, msgs, MESSAGES_PER_PAGE)\n\n dic = {\n 'peer': key,\n 'chatmessages': msgs_list,\n 'gps': Messages.objects.using('msgstore').exclude((Q(longitude='0.0') | Q(latitude='0.0'))),\n 'media': Messages.objects.using('msgstore').exclude(media_url__isnull=True),\n 'PAG_TITLE': 'Conversation'\n }\n\n return render_to_response('whatsapp/chat.html', dic, context_instance=RequestContext(request))","def chatlist(request):\n\n chats = get_chat_list()\n chat_list = pagination(request, chats, CHATS_PER_PAGE)\n\n dic = {'chatlist': chat_list}\n return render_to_response('whatsapp/chatlist.html', dic, context_instance=RequestContext(request))","def on_chat(self, event, text):\n return None","def __init__(self, master, Client):\n super(Chat, self).__init__(master)\n self.grid()\n self.create_widgets()\n master.grid_propagate(True)\n master.grid_rowconfigure(0, weight = 1)\n master.grid_columnconfigure(0, weight = 1)\n self.Client = Client\n threading.Thread(target = self.listen).start()","def showMessage(self):","def index(request):\n\n chats = Chat.objects.all().order_by('-created_at')\n\n if request.user.is_authenticated():\n chats = chats.filter(friend_groups__in=request.user.get_profile().\\\n friend_groups.all().values_list('id'))\n else:\n chats = chats.filter(friend_groups__isnull=True)\n\n return render_to_response('index.html', {\n 'chats': chats[:10],\n }, context_instance=RequestContext(request))","def setup(self):\n curses.curs_set(1)\n curses.noecho()\n curses.cbreak()\n # Keypad disabled until scrolling properly implemented\n # self.stdscr.keypad(True)\n self.stdscr.clear()\n self.stdscr.addstr(\"SecureChat v{}\".format(__version__))\n self.chat_container.box()\n self.chat_win.addstr(\"Welcome to SecureChat!\")\n self.chat_win.scrollok(True)\n self.chat_win.setscrreg(0, self.max_y - 5)\n self.prompt_win.addstr(\"> \")\n self.refresh_all()","def introduce_chat_interface(self):\n self.send_message(\n message=constants.ONBOARDING_WELCOME,\n onboarding_step=constants.ONBOARDING_STEPS[\"CHAT_INTERFACE\"],\n done=True,\n )","def main(self):\n\n updater = Updater(self.token)\n dp = updater.dispatcher\n\n dp.add_handler(MessageHandler(Filters.text, self.__msg_handler))\n dp.add_handler(MessageHandler(Filters.command, self.__msg_handler))\n dp.add_handler(CallbackQueryHandler(callback=self.__clb_handler))\n\n updater.start_polling()\n updater.idle()","def main():\n # Events from google calendar:\n events = GC.get_events()\n\n message_info = MC.create_message(events, True)\n message_to_post = message_info[0]\n emojis = message_info[1]\n\n # Post message to Slack\n posted_message = SL.send_message(\n sc, channels[\"bot-dev\"], message_to_post, True)\n\n # React the emojis to Slack\n for emoji in emojis:\n SL.emoji_react(sc, emoji, posted_message, False)\n\n correct_message = input(\"Did the message look ok in the #polls channel? If you answer with 'y' it will be posted in the polls channel. If you answer 'gen' it will be posted in general. If you answer with 'n' or anything other than the commands shown before, then it will be stopped and not posted any where else.\\n\")\n\n if \"gen\" in correct_message.lower():\n posted_message = SL.send_message(\n sc, channels[\"general\"], message_to_post, True)\n for emoji in emojis:\n SL.emoji_react(sc, emoji, posted_message, False)\n elif \"y\" in correct_message.lower():\n posted_message = SL.send_message(\n sc, channels[\"polls\"], message_to_post, True)\n for emoji in emojis:\n SL.emoji_react(sc, emoji, posted_message, False)","def test_chat_page(self):\n c_code = 'new-chat'\n rsp = self.client.get('/{}'.format(c_code))\n html = rsp.data\n\n self.assertEqual(200, rsp.status_code)\n # HTML is returned.\n self.assertIn('<html>', html)\n # HTML for chat is retrieved.\n self.assertIn(c_code, html)","async def on_chat_message(self, chat_message):\n pass","def text_e(self, event):\n directory=os.getcwd()+ '/messages'\n filename=str(self.user)+'_'+str(self.friend)\n text = self.text_send.GetValue()\n messages = mf.addMessage(self.user, self.friend, self.passw, text)\n mf.makeTextFile(self.user, self.friend, self.passw, messages)\n \n self.chat_log.LoadFile('/'.join((directory, filename)))\n self.text_send.SetValue(\"\")\n event.Skip()","def listener(messages):\n for m in messages:\n chatid = m.chat.id\n print(str(chatid))\n if m.content_type == 'text':\n text = m.text\n tb.send_message(chatid, text)","def get(self): # pylint:disable-msg=C0103\n user_id = users.get_current_user().user_id()\n token = channel.create_channel(user_id)\n self.RenderResponse('main.html', token=token, table_id=TABLE_ID,\n rows=ROWS, columns=COLUMNS)","def __init__(self,sys_argv,port):\n super(Chat,self).__init__(sys_argv)\n self.cliente=Cliente('0.0.0.0',port)\n self.controlador=Controlador(self.cliente)\n self.igchat=GUIChat(self.controlador,self.cliente)\n self.cliente.seConecto()\n t1=threading.Thread(target=self.cliente.enviado)\n t1.setDaemon(True)\n t1.start()\n time.sleep(0.001)\n self.igchat.show()","def help(chat_lines):\n for line in xrange(9):\n chat_lines[line].setTextColor(\"Blue\")\n chat_lines[0].setText(\"------------------------------------------Welcome to the online chat - Amdur's Chat!--------\"\n \"----------------------------------\")\n chat_lines[1].setText(\"You can use the following commands:\")\n chat_lines[2].setText(\"# 'Kick (Member)' - to kick a member (Only for Managers)\")\n chat_lines[3].setText(\"# 'Mute/UnMute (Member)' - to mute or cancel a mute of member (Only for Managers)\")\n chat_lines[4].setText(\"# 'Private (Member):' - to send message to a specific member \")\n chat_lines[5].setText(\"# 'Add (Member)' - to become a member to manager (Only for Managers)\")\n chat_lines[6].setText(\"# 'Manager list' - for getting the manager list\")\n chat_lines[7].setText(\"# 'Quit' - to leave chat \")\n chat_lines[8].setText(\"--------------------------------------------------------------------------------------------\"\n \"----------------------------------------------------\")","def main():\n #Gmail2TelegramClient(\"1234\") -- a person\n #Gmail2TelegramClient(\"-1234\") -- group chat","def run(self):\n self.user_t = None\n self.users.show()\n self.connect()","def on_message(self, message):\n #print(f\"This message was sent: {message}\") # Writes to the console window (server side)\n self.write_message(f\"This message was sent: {message}\") # Writes message to sender","def chat(self, p: str):\n message = p[1:]\n if message.strip() != '':\n message: str = f\"{self.username} says: {message[:80]}\"\n self.broadcast(packet.construct_log_packet(message), include_self=True)\n self.logger.log(message)","def main():\n database_connection = userdata.init()\n # signed_in = account.sign_in('jordan00', 'Jordan!23', database_connection)\n signed_in = account.sign_in_or_create_user(database_connection)\n timeline.timeline(database_connection, signed_in, 1)\n\n while True:\n action = timeline.prompt_for_timeline_action()\n\n if action.isdigit():\n action = int(action)\n if action:\n timeline.timeline(database_connection, signed_in, action)\n else:\n print('-' * 100)\n print('No posts on page 0. Please go to page 1.')\n print('-' * 100)\n\n elif action == 'ACCOUNT SETTINGS':\n account.account(signed_in, database_connection)\n timeline.timeline(database_connection, signed_in, 1)\n elif action == 'USERS':\n usersmodule.users_main_page(database_connection, signed_in)\n timeline.timeline(database_connection, signed_in, 1)\n elif action == 'POST':\n post = input('Type your post:\\n\\t>')\n userdata.add_post(database_connection, post, signed_in)\n timeline.timeline(database_connection, signed_in, 1)\n elif action == 'SIGN OUT':\n database_connection.close()\n print('Bye!')\n break","def main_menu():\n connection = ClientToServer(ip = \"192.168.1.1\", \n port = \"1234\",\n username = \"Admin\",\n hns_password = \"password\")\n \n game_loop(connection)","def main():\n root = tkinter.Tk()\n delegate = ReceiveMessages()\n mqtt_client = com.MqttClient(delegate)\n mqtt_client.connect_to_ev3()\n\n progress_bar = setup_gui(root, mqtt_client)\n delegate.progress_bar = progress_bar\n\n root.mainloop()","def __init__(self, controller):\n self.controller = controller\n\n self.top = tkinter.Tk()\n self.top.title(\"Chatter\")\n\n self.input = tkinter.StringVar() # For the messages to be sent.\n self.input.set(\"\")\n\n \"\"\"Message box\"\"\"\n messages_frame = tkinter.Frame(self.top)\n scrollbar = tkinter.Scrollbar(messages_frame)\n self.msg_list = tkinter.Listbox(messages_frame, height=15, width=60, yscrollcommand=scrollbar.set)\n scrollbar.pack(side=tkinter.RIGHT, fill=tkinter.Y)\n self.msg_list.pack(side=tkinter.LEFT, fill=tkinter.BOTH)\n self.msg_list.pack()\n messages_frame.pack()\n\n\n\n\n \"\"\"Input box and send button\"\"\"\n entry_field = tkinter.Entry(self.top, textvariable=self.input)\n entry_field.bind(\"<Return>\", self.controller.msg_to_send)\n entry_field.pack()\n send_button = tkinter.Button(self.top, text=\"Send\", command=lambda: self.controller.msg_to_send(self.input))\n send_button.pack()\n \"\"\"Send file button\"\"\"\n file_button = tkinter.Button(self.top, text=\"Send file\", command=lambda: self.controller.find_file())\n file_button.pack()\n\n \"\"\"On closing the window\"\"\"\n self.top.protocol(\"WM_DELETE_WINDOW\", self.controller.close)","def show_messages(self):\n for msg in self.messages:\n print msg['text']","def new_message_from_conn(self, friend, msg):\n print(\"new_msg signal activated with friend\",friend,\"and msg\",msg)\n\n if not self.stack.get_child_by_name(friend):\n new_chat_window = chat_layout.ChatLayout(orientation=Gtk.Orientation.VERTICAL,friend=friend)\n new_chat_window.show_all()\n self.stack.add_titled(new_chat_window, friend, friend)\n\n child = self.move_to_child(friend)\n child.append_friend_text(msg)","def useChat(self):\n # Implements a subprocess to run the Kuri robot simultaneously with the user input loop\n proc_stdin = io.TextIOWrapper(self.proc.stdin, encoding='utf-8', line_buffering=True)\n\n while True:\n txt = input(\"Talk to me! (Type 'q' to quit) \").lower()\n if txt == 'q':\n proc_stdin.write('q\\n')\n quit()\n else:\n sentiment = self.sd.getSentiment(txt)\n proc_stdin.write(sentiment + '\\n')\n print(\"Sentiment: \" + sentiment + '\\n')","def index(request):\n # most of the heavy lifting is handled by Angular, on the front end\n return render(request, 'forum/index.html')","def handle_messages():\n print(\"Handling Messages\")\n payload = request.get_data()\n for sender, incoming_message, payload in messaging_events(payload):\n # The following statements check which options the user selected\n # Response handler contains \"templates\" for the various messages\n user_name = get_full_name(sender, PAT)\n if \"hei\" in incoming_message.lower() or \"hallo\" in incoming_message.lower() or \"yo\" in incoming_message.lower()\\\n or \"hi\" in incoming_message.lower():\n send_message(PAT, send_message(PAT, response_handler.greeting_message(sender, user_name)))\n if user_methods.has_user(user_name):\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n else:\n send_message(PAT, response_handler.no_course(sender))\n\n elif payload == \"change subject\" or \"change subject\" in incoming_message.lower():\n send_message(PAT, response_handler.text_message(sender, \"You can change course at any time simply by \"\n \"writing the course code on the form [TAG][CODE]\\n\"\n \"ex. TDT4120\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n elif \"help\" in incoming_message.lower():\n\n send_message(PAT, response_handler.text_message(sender, \"Are you lost ...? \"))\n send_message(PAT, response_handler.text_message(sender, \"You can change course at any time simply by \"\n \"writing the course code on the form: [TAG][CODE]\\n\"\n \"ex. TDT4120\"))\n send_message(PAT, response_handler.text_message(sender, \"If you want to see your currently selected course \"\n \"and other information type 'Status'.\"))\n send_message(PAT, response_handler.text_message(sender, \"You can also type 'Hei' or 'Hallo' at any time \"\n \"to receive a greeting that shows your options.\"))\n send_message(PAT, response_handler.text_message(sender, \"Here is a list of commands you can use. This is \"\n \"recommended for the experienced user:\\n\"\n \"Change subject\\n\"\n \"Give feedback\\n\"\n \"How did today's lecture go?\\n\"\n \"Get schedule\\n\"\n \"Get info\\n\"\n \"All lectures\\n\"\n \"A specific lecture\\n\"\n \"You can type most of the commands in chat. Just \"\n \"give it a try!\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n elif \"status\" in incoming_message.lower():\n subject = user_methods.get_subject_from_user(user_name)\n year = feedback_methods.get_year()\n week = feedback_methods.get_week()\n day = feedback_methods.get_day()\n user = get_full_name(sender, PAT)\n lecture_id_current = lecture_methods.get_lecture_from_date(year, week, day, subject)\n lecture = feedback_methods.get_lecture_object(lecture_id_current)\n\n if user_methods.has_user(user_name):\n sub = user_methods.get_subject_from_user(user_name) + \" : \" + \\\n subject_info.course_name(user_methods.get_subject_from_user(user_name))\n send_message(PAT, response_handler.user_info(sender, user_name, sub))\n if feedback_methods.user_has_feedback_for_lecture(user, lecture):\n send_message(PAT, response_handler.text_message(sender, \"You have given feedback for \"\n + subject + \"today. Well done! Be proud of \"\n \"yourself and remember to check in \"\n \"tomorrow.\"))\n send_message(PAT,\n response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n else:\n send_message(PAT, response_handler.text_message(sender,\n \"No feedback for the given lecture on this date. \"\n \"Please press 'Give Feedback' or write it in the \"\n \"chat to do so.\"))\n send_message(PAT,\n response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n else:\n send_message(PAT, response_handler.text_message(sender,\n \"We seem to not be able to detect you in the database. \"\n \"Please report this to the staff!\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n # Checks if the subject has lectures in the database, adds them if not.\n\n elif payload == \"give feedback\" or \"give feedback\" in incoming_message.lower():\n send_message(PAT, response_handler.give_feedback_choice(sender))\n\n elif payload == \"lecture speed\" or \"lecture speed\" in incoming_message.lower():\n\n subject = user_methods.get_subject_from_user(user_name)\n\n if lecture_methods.check_lecture_in_db(subject):\n send_message(PAT, response_handler.lec_feed(sender))\n else:\n schedule = subject_info.get_schedule(subject)\n if schedule:\n database_entry = subject_info.gather_lecture_information(schedule)\n lecture_methods.add_lecture_information_db(database_entry)\n send_message(PAT, response_handler.text_message(sender, \"Lectures for the subject \" + subject +\n \" were not in the database. It is now added.\"))\n send_message(PAT, response_handler.lec_feed(sender))\n else:\n send_message(PAT, response_handler.text_message(sender, \"Lectures for the subject \" + subject +\n \" does not exist. Likely due to the subject having \"\n \"no lectures this semester.\"))\n send_message(PAT, response_handler.has_course(sender, subject))\n\n elif payload == \"evaluation_questions\" or \"lecture questions\" in incoming_message.lower():\n # User wants to give feedback for a lecture.\n subject = user_methods.get_subject_from_user(user_name)\n payload = \"evaluation_questions\" # if user typed 'lecture questions' the payload will be None\n\n if lecture_methods.check_lecture_in_db(subject):\n if feedback_methods.user_can_give_feedback_evaluation(user_name,\n user_methods.get_subject_from_user(user_name)):\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\n else:\n send_message(PAT, response_handler.text_message(sender,\n \"Feedback can not be given either because there \"\n \"is no lecture today, or because you have already \"\n \"given feedback for this lecture.\"))\n send_message(PAT, response_handler.has_course(sender, subject))\n else:\n schedule = subject_info.get_schedule(subject)\n if schedule:\n database_entry = subject_info.gather_lecture_information(schedule)\n lecture_methods.add_lecture_information_db(database_entry)\n send_message(PAT, response_handler.text_message(sender, \"Lectures for the subject \" + subject +\n \" were not in the database. It is now added\"))\n if feedback_methods.user_can_give_feedback_evaluation(user_name,\n user_methods.get_subject_from_user(\n user_name)):\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\n else:\n send_message(PAT, response_handler.text_message(sender,\n \"Feedback can not be given either because \"\n \"there is no lecture today, or because you\"\n \" have already given feedback for this lecture.\"\n \"\"))\n send_message(PAT, response_handler.has_course(sender, subject))\n else:\n send_message(PAT, response_handler.text_message(sender, \"Lectures for the subject \" + subject +\n \"does not exist. Likely due to the subject having \"\n \"no \"\n \"lectures this semester.\"))\n send_message(PAT, response_handler.has_course(sender, subject))\n\n elif \"too slow\" in incoming_message.lower():\n # Adds feedback if the subject has a lecture on the given day\n # and if the user has not already given feedback\n payload = '0'\n message_response = \"too slow\"\n if feedback_methods.add_entry(user_name, user_methods.get_subject_from_user(user_name), payload):\n send_message(PAT, response_handler.text_message(sender, \"You chose: \" + \"'\" +\n message_response + \"'\" + \"\\nFeedback Received!\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n else:\n send_message(PAT,\n response_handler.text_message(sender, \"There is either no lecture active in the selected\"\n \" subject, or you have already given feedback\"\n \" to the active lecture.\\nFeedback denied!\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n elif \"it's all right\" in incoming_message.lower() or \"its all right\" in incoming_message.lower():\n # Adds feedback if the subject has a lecture on the given day\n # and if the user has not already given feedback\n payload = '1'\n message_response = \"It's all right\"\n if feedback_methods.add_entry(user_name, user_methods.get_subject_from_user(user_name), payload):\n send_message(PAT, response_handler.text_message(sender, \"You chose: \" + \"'\" +\n message_response + \"'\" + \"\\nFeedback Received!\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n else:\n send_message(PAT,\n response_handler.text_message(sender, \"There is either no lecture active in the selected\"\n \" subject, or you have already given feedback\"\n \" to the active lecture.\\nFeedback denied!\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n elif \"too fast\" in incoming_message.lower():\n # Adds feedback if the subject has a lecture on the given day\n # and if the user has not already given feedback\n payload = '2'\n message_response = \"too fast\"\n if feedback_methods.add_entry(user_name, user_methods.get_subject_from_user(user_name), payload):\n send_message(PAT, response_handler.text_message(sender, \"You chose: \" + \"'\" +\n message_response + \"'\" + \"\\nFeedback Received!\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n else:\n send_message(PAT,\n response_handler.text_message(sender, \"There is either no lecture active in the selected\"\n \" subject, or you have already given feedback\"\n \" to the active lecture.\\nFeedback denied!\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n elif (\"today\" in incoming_message.lower() and \"lecture\" in incoming_message.lower()) or \\\n (\"todays\" in incoming_message.lower() and \"lecture\" in incoming_message.lower()) or \\\n (\"today's\" in incoming_message.lower() and \"lecture\" in incoming_message.lower()):\n # Gathers the correct information about the date.\n year = feedback_methods.get_year()\n week = feedback_methods.get_week()\n day = feedback_methods.get_day()\n subject = user_methods.get_subject_from_user(user_name)\n # Gathers the feedback from today's lecture:\n if lecture_methods.check_lecture_in_db(subject):\n feedback_list = feedback_methods.get_single_lecture_feed(year, week, day, subject)\n if feedback_list[0] is not None:\n send_message(PAT, response_handler.present_single_lecture_feedback(sender, feedback_list))\n send_message(PAT,\n response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n else:\n send_message(PAT, response_handler.text_message(sender,\n \"No feedback for the given lecture on this date. \"\n \"Please try again at a later date.\"))\n send_message(PAT,\n response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n else:\n send_message(PAT, response_handler.text_message(sender, \"No lecture present in the database. \"\n \"Please provide some feedback and try again.\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n elif payload == \"get schedule\" or \"get schedule\" in incoming_message.lower():\n subject = user_methods.get_subject_from_user(user_name)\n schedule = subject_info.printable_schedule(subject_info.get_schedule(subject))\n if len(schedule) > 640:\n msg_list = message_split.message_split(schedule)\n for msg in msg_list:\n print(msg)\n send_message(PAT, response_handler.text_message(sender, msg))\n else:\n send_message(PAT, response_handler.text_message(sender, schedule))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n elif payload == \"get info\" or \"get info\" in incoming_message.lower():\n subject = user_methods.get_subject_from_user(user_name)\n send_message(PAT, response_handler.text_message(sender,\n subject_info.printable_course_info(\n subject_info.get_course_json(subject))))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n elif payload == \"get feedback\" or \"get feedback\" in incoming_message.lower():\n send_message(PAT, response_handler.get_feedback_specific_or_all(sender))\n\n elif payload == \"all_lectures\" or \"all lectures\" in incoming_message.lower():\n # The user wants to see feedback for all lectures in the selected subject\n subject = user_methods.get_subject_from_user(user_name)\n if not lecture_methods.check_lecture_in_db(subject):\n send_message(PAT, response_handler.text_message(sender, \"Course has no feedback.\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n else:\n feedback, feedbackevaluation = feedback_methods.get_all_subject_feed(subject)\n if len(feedback) > 0:\n percent_list = bot_feedback.generate_percent_for_speed(feedback)\n send_message(PAT, response_handler.all_feedback_speed(sender, subject, percent_list))\n else:\n send_message(PAT,\n response_handler.text_message(sender, \"Course has no feedback for lecture speed.\"))\n if len(feedbackevaluation) > 0:\n percent_list_questions = bot_feedback.generate_percent_for_questions(feedbackevaluation)\n\n send_message(PAT, response_handler.all_feedback_questions(sender, subject, percent_list_questions))\n else:\n send_message(PAT,\n response_handler.text_message(sender, \"Course has no feedback for lecture questions.\"))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n elif payload == \"a_specific_lecture\" or \"a specific lecture\" in incoming_message.lower():\n # Let the user choose what year to get feedback from.\n years = lecture_feedback_db_methods.get_year(user_methods.get_subject_from_user(user_name))\n if len(years) > 0:\n send_message(PAT, response_handler.get_feedback_year(sender, years))\n else:\n send_message(PAT, response_handler.text_message(sender, 'No feedback for the selected subject.'))\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n elif payload is not None:\n # Underneath are check that use .split() on the payload.\n if \"evaluation_questions\" in payload.split()[0]:\n payload_split = payload.split()\n if len(payload_split) == 1:\n # 1st question\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\n elif len(payload_split) == 2:\n # 2nd question\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\n elif len(payload_split) == 3:\n # 3rd question\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\n elif len(payload_split) == 4:\n # 4th question\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\n elif len(payload_split) == 5:\n # 5th question\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\n elif len(payload_split) == 6:\n # 6th question\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\n elif len(payload_split) == 7:\n # 7th question\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\n elif len(payload_split) == 8:\n # store feedback.\n subject = user_methods.get_subject_from_user(user_name)\n if feedback_methods.add_feedback_evaluation(user_name, subject, int(payload_split[1]),\n int(payload_split[2]), int(payload_split[3]),\n int(payload_split[4]), int(payload_split[5]),\n int(payload_split[6]), int(payload_split[7])):\n # Storing the feedback succeeded.\n send_message(PAT, response_handler.text_message(sender, 'Feedback received!'))\n send_message(PAT, response_handler.has_course(sender, subject))\n else:\n # Storing the feedback failed.\n send_message(PAT, response_handler.text_message(sender,\n \"There is either no lecture active in the \"\n \"selected subject, or you have already given \"\n \"feedback to the active lecture.\\n Feedback \"\n \"denied!\"))\n send_message(PAT, response_handler.has_course(sender, subject))\n pass\n\n elif \"get_lecture_feedback_year\" in payload.split()[0]:\n # Let the user choose what semester to get feedback from.\n semesters = []\n if lecture_feedback_db_methods.check_lecture_semester(user_methods.get_subject_from_user(user_name),\n 1, 17, int(payload.split()[1])):\n semesters.append('Spring')\n elif lecture_feedback_db_methods.check_lecture_semester(user_methods.get_subject_from_user(user_name),\n 32, 49, int(payload.split()[1])):\n semesters.append('Fall')\n if len(semesters) > 0:\n send_message(PAT, response_handler.get_feedback_semester(sender, payload.split()[1], semesters))\n else:\n # Take the user one step up to choose a different year.\n years = lecture_feedback_db_methods.get_year(user_methods.get_subject_from_user(user_name))\n send_message(PAT, response_handler.get_feedback_year(sender, years))\n\n elif \"get_lecture_feedback_semester\" in payload.split()[0]:\n # Let the user choose what weeks to get feedback from.\n\n week_list = lecture_feedback_db_methods.get_lecture_weeks(user_methods.get_subject_from_user(user_name),\n int(payload.split()[1]), payload.split()[2])\n if len(week_list) > 8:\n send_message(PAT, response_handler.get_feedback_month(sender, payload.split()[1], week_list))\n else:\n send_message(PAT, response_handler.get_feedback_week(sender, payload.split()[1], week_list))\n\n elif \"get_lecture_feedback_month\" in payload.split()[0]:\n # Let the user select week\n week_list = []\n payload_split = payload.split()\n for i in range(2, len(payload_split)):\n week_list.append(int(payload_split[i].rstrip(',')))\n\n send_message(PAT, response_handler.get_feedback_week(sender, payload_split[1], week_list))\n\n elif \"get_lecture_feedback_week\" in payload.split()[0]:\n # Lets the user select day\n lecture_days = lecture_feedback_db_methods.get_day_of_lecture_in_week(\n user_methods.get_subject_from_user(user_name), payload.split()[1], payload.split()[2])\n\n send_message(PAT, response_handler.get_feedback_day(sender, payload.split()[1], lecture_days,\n payload.split()[2]))\n\n elif \"get_lecture_feedback_day\" in payload.split()[0]:\n\n subject = user_methods.get_subject_from_user(user_name)\n # Gives the user feedback from the selected day.\n feedback_list = feedback_methods.get_single_lecture_feed(payload.split()[1],\n payload.split()[2],\n payload.split()[3],\n subject)\n feedback_questions_list = feedback_methods.get_single_lecture_feedback_questions(payload.split()[1],\n payload.split()[2],\n payload.split()[3],\n subject)\n\n if len(feedback_list[1]) > 0: # Checks if there is feedback in the variable.\n send_message(PAT, response_handler.present_single_lecture_feedback(sender, feedback_list))\n else:\n send_message(PAT, response_handler.text_message(sender,\n \"This lecture has no feedback for lecture speed.\"))\n if len(feedback_questions_list) > 0: # Checks if there is feedback in the variable.\n feedback_questions = bot_feedback.generate_percent_for_questions(feedback_questions_list)\n send_message(PAT,\n response_handler.present_single_lecture_feedback_questions(sender, feedback_questions))\n else:\n send_message(PAT, response_handler.text_message(sender,\n \"This lecture has no feedback for lecture \"\n \"questions.\"))\n\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n elif ime_data_fetch.subject_exists_boolean(incoming_message.upper().split()[0]):\n if user_methods.has_user(user_name):\n user_methods.add_subject(user_name, incoming_message.split()[0])\n else:\n user_methods.add_user(user_name, incoming_message.split()[0])\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n\n else:\n send_message(PAT, response_handler.text_message(sender,\n \"Type 'help' to see what you can do with L.I.M.B.O.\\n If \"\n \"you tried to enter a subject-code and got this message,\"\n \" you either misspelled it or the subject you are looking \"\n \"for is not a subject at NTNU.\"))\n if user_methods.has_user(user_name):\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\n else:\n send_message(PAT, response_handler.no_course(sender))\n\n return \"ok\"","def display_message():","async def chat_message(self, event):\n await self.send(\n {'type': \"websocket.send\",\n 'text': event['response_data']}\n )","def updateChat(self, ):\n self.__redrawChat()","def homepage(self):\n print('-=' * 12 + \" Home Page \" + '-=' * 12)\n self._user.list_contacts()\n options = {1: self.add_contact, 2:self.remove_contact ,3: self.view_contact_chat, 4: self.sign_out, 5: self.exit}\n print_out = \"(1) Add new contact \\n (2) Remove Contact \\n (3) View my chats \\n (4) Sign out \\n (5) Exit\"\n return self._take_option(options, print_out)","def handle_chat(self):\n while True:\n if self.chat_breakout:\n return\n\n time.sleep(1)\n messages = \"\"\n for i in range(5):\n try:\n messages += f\"{self.queue.popleft()}\\n\"\n except IndexError:\n # Queue is empty but no worries\n continue\n\n if messages != \"\":\n self.loop.create_task(\n self.ingame_cog.send_chat_to_discord(\n self.bot, self.channel, messages\n )\n )","def have_a_chat():\n \n chat = True\n while chat:\n\n # Get a message from the user\n msg = input('INPUT :\\t')\n out_msg = None\n\n # Check if the input is a question\n question = is_question(msg)\n\n # Prepare the input message\n msg = prepare_text(msg)\n\n # Check for an end msg \n if end_chat(msg):\n out_msg = 'Bye!'\n chat = False \n\n # Check for a selection of topics that we have defined to respond to\n # Here, we will check for a series of topics that we have designed to answer to\n if not out_msg:\n\n # Initialize to collect a list of possible outputs\n outs = []\n # Check if the input looks like a greeting, add a greeting output if so\n outs.append(selector(msg, GREETINGS_IN, GREETINGS_OUT))\n # Check if the input looks like a computer thing, add a computer output if so\n outs.append(selector(msg, COMP_IN, COMP_OUT))\n outs.append(selector(msg, NAMES, RANDOM_LIST))\n # Check if the input mentions a person that is specified, add a person output if so\n if is_in_list(msg, PEOPLE_IN):\n name = find_in_list(msg, PEOPLE_IN)\n outs.append(list_to_string([PEOPLE_NAMES[name], name.capitalize(),\n selector(msg, PEOPLE_IN, PEOPLE_OUT)], ' '))\n\n # Check if the input looks like a joke, add a repeat joke output if so\n outs.append(respond_echo(selector(msg, JOKES_IN, JOKES_OUT), 3, ''))\n \n # Check if the input has some words we don't want to talk about, say that, if so\n if is_in_list(msg, NONO_IN):\n outs.append(list_to_string([selector(msg, NONO_IN, NONO_OUT), find_in_list(msg, NONO_IN)], ' '))\n\n # IF YOU WANTED TO ADD MORE TOPICS TO RESPOND TO, YOU COULD ADD THEM IN HERE\n outs.append(new_selector(msg, PYTHON_TERM_INPUTS, PYTHON_TERM_OUT))\n outs.append(identity(msg, NAMES, RANDOM_LIST))\n outs.append(quiz_trigger(msg))\n outs.append(quizzer(msg))\n # We could have selected multiple outputs from the topic search above (if multiple return possible outputs)\n # We also might have appended None in some cases, meaning we don't have a reply\n # To deal with this, we are going to randomly select an output from the set of outputs that are not None\n options = list(filter(None, outs))\n if options:\n out_msg = random.choice(options)\n\n # If we don't have an output yet, but the input was a question, return msg related to it being a question\n if not out_msg and question:\n out_msg = QUESTION\n\n # Catch-all to say something if msg not caught & processed so far\n if not out_msg:\n out_msg = random.choice(UNKNOWN)\n\n print('OUTPUT:', out_msg)","def on_welcome(self, raw_msg, server, port, nickname, **kwargs):","def index(request):\n messages = SESSION.get_messages_sent_list(request.session)\n feedbacks = SESSION.get_messages_received_list(request.session)\n \n # initially display the first 20 messages/feedback chronologically\n messages.sort(key=lambda r: r.createdAt, reverse=True)\n feedbacks.sort(key=lambda r: r.createdAt, reverse=True)\n \n # prepare our template context variables use in our messages template\n data = {\n 'messages_nav': True,\n 'messages': messages[:PAGINATION_THRESHOLD],\n 'feedback': feedbacks[:PAGINATION_THRESHOLD],\n 'pag_threshold': PAGINATION_THRESHOLD,\n 'pag_page': 1,\n 'sent_count': len(messages),\n 'feedback_count': len(feedbacks),\n 'tab_feedback': request.GET.get('tab_feedback'),\n }\n \n if SESSION.get_patronStore_count(request.session):\n data['has_patrons'] = True\n \n # inserting this success and error message into the template\n # should be done in a cleaner way - this was done by the first guy\n # I just didn't bother changing it.\n if request.GET.get(\"success\"):\n data['success'] = request.GET.get(\"success\")\n if request.GET.get(\"error\"):\n data['error'] = request.GET.get(\"error\")\n \n return render(request, 'manage/messages.djhtml', data)","def _send_msg(self, contact):\n msg_content = input('{} :'.format(self._user.username))\n if msg_content == '0': \n return self.homepage()\n self._user.send_msg(contact, msg_content)\n\n return self._send_msg(contact)","def __init__(self, master=None, *args, **kw):\n Tix.Frame.__init__(self, master, *args, **kw)\n self._init_widgets()\n self._init_bindings()\n self._root().title(\"KaeChat\")\n self._page_serial = 0","def receive():\n\n buttons_frame = tkinter.Frame(top)\n while True:\n\n try:\n msg = client_socket.recv(BUFSIZ).decode(\"utf8\")\n # print(msg)\n if msg == '{quit}':\n break\n elif '{prequest}' in msg[0:12]:\n name = msg[11:]\n handle_connection_request(name)\n elif '{name}' in msg[0:6]:\n print(msg)\n uname.insert(tkinter.END, msg[7:])\n elif '{namelist}' in msg[0:12]:\n nlist = msg.split('_')[1]\n name_list = nlist.split(',')[1:]\n print(name_list)\n\n\n buttons_frame.destroy()\n buttons_frame = tkinter.Frame(top)\n for name in name_list:\n private_button = tkinter.Button(buttons_frame, text=name, command=lambda user=name: create_private(user))\n private_button.pack(side=tkinter.LEFT)\n buttons_frame.pack(side=tkinter.LEFT)\n\n else:\n msg_list.insert(tkinter.END, msg)\n except OSError: # Possibly client has left the chat.\n break","def main(self ):\n print(\"Good morning, I hope you are well today.\")\n print(\"What can I do for you?\")\n while True:\n sentence = input(\"\\n>> \")\n if sentence.upper() == \"QUIT\":\n print(\"Have a nice day!\")\n break\n print(Doctor().reply(sentence))","def run(self):\n self.print_welcome()\n self.handle_inputs()","def start(update, context):\n ch = update.message.chat\n chatID = ch.id\n uname = ch.username\n fname = ch.first_name\n lname = ch.last_name\n if ch.id in CR.ADMINS_id: admin_level = 0\n else: admin_level = 3\n txt = 'Hola!\\n'\n txt += 'Para ver los comandos disponibles: /help\\n'\n # txt += tool.help_txt()\n txt += '\\n\\nActualizaciones y noticias de este bot en el canal:\\n'\n txt += 'https://t.me/parapentebotWiki'\n context.bot.send_message(chat_id=update.message.chat_id, text=txt,\n disable_web_page_preview=True)\n conn,c = admin.connect(RP.DBname)\n try:\n ad_lv = min([usr[-2] for usr in admin.get_user(conn,'chatid',chatID)])\n admin_level = min([admin_level,ad_lv])\n except admin.EntryNotFound: admin_level = 4\n admin.insert_user(conn,chatID,uname,fname,lname,admin_level,0)","def main():\r\n return render_template(\"UI.html\")","async def chat_message(self, event):\n\n print(\"PublicChatConsumer\", \"chat_message from user\", event[\"user_id\"])\n await self.send_json({\n \"msg_type\": MSG_TYPE_MESSAGE,\n \"profile_image\": event[\"profile_image\"],\n \"username\": event[\"username\"],\n \"user_id\": event[\"user_id\"],\n \"message\": event[\"message\"],\n \"natural_timestamp\": humanize_or_normal(timezone.now())\n })","def twitchChat():\n def joinchat():\n \"\"\"\n This function first tries to join the chat by recieving the standard protocol messages that twitch sends to a irc client\n This sends all messages to the laodingComplete function to see if the correct message has confirmed the connection\n :return:\n \"\"\"\n Loading = True\n while Loading:\n readbuffer_join = sock.recv(1024).decode()\n for line in readbuffer_join.split(\"\\n\")[0:-1]:\n print(line)\n Loading = loadingComplete(line)\n\n def loadingComplete(line):\n \"\"\"\n This function takes messages one at a time to see if they include the line End of Names list which confirms the bot is connected to the channel\n Once connceted the bot sends a message to confirm its in the chat room\n\n :param Line is a message by message stream of the twitch chat\n :return:\n \"\"\"\n\n if(\"End of /NAMES list\" in line):\n print(\"Bot has joined \" + channel +\"'s channel\")\n sendMessage(sock, \"Chat room is popping\")\n return False\n else:\n return True\n\n def sendMessage(sock, message):\n messageTemp = \"PRIVMSG \" + channel +\" :\" +message\n sock.send((messageTemp+ \"\\n\").encode())\n\n def getUser(line):\n \"\"\"\n This parses a message that is received by the bot that contains lots of information into just the username\n :param line:\n :return:\n \"\"\"\n seperate = line.split(\":\", 2)\n user = seperate[1].split(\"!\",1)[0]\n return user\n\n def getMessage(line):\n \"\"\"\n This parses the incoming data from line to get just the chat message\n :param Line is a message by message stream of the twitch chat\n :return:\n \"\"\"\n global message\n try:\n message = (line.split(\":\",2))[2]\n except:\n message=\"\"\n return message\n\n def Console(line):\n if \"PRIVMSG\" in line:\n return False\n else:\n return True\n joinchat()\n\n\n while True:\n try:\n readbuffer = sock.recv(1024).decode()\n except:\n readbuffer =\"\"\n\n for line in readbuffer.split(\"\\r\\n\"):\n if line == \"\":\n pass\n elif \"PING\" in line and Console(line):\n pongMSG = \"PONG tmi.chat.twitch.tv\\r\\n\".encode()\n sock.send(pongMSG)\n print(pongMSG)\n continue\n else:\n user = getUser(line)\n messagePrint = getMessage(line)\n\n print(user+\": \" +messagePrint)","def choose_path(self):\r\n if not self.receive_thread.is_alive():\r\n self.receive_thread.start()\r\n self.clear_screen()\r\n self.refresh_button = None\r\n user_label = Label(self.root, text=\"Hello \" + self.username, font=self.title_font,\r\n bg=self.bg_color, height=2)\r\n user_label.pack(pady=10, padx=50)\r\n button_read_msg = Button(self.root, text=\"read messages\", font=self.text_font,\r\n height=2, width=20, command=lambda: self.read_messages(1))\r\n button_read_msg.pack(pady=20, padx=50)\r\n\r\n button_send_msg = Button(self.root, text=\"send message\", font=self.text_font,\r\n height=2, width=20, command=self.send_messages)\r\n button_send_msg.pack(pady=20, padx=50)\r\n\r\n button_simulator = Button(self.root, text=\"simulator\", font=self.text_font,\r\n height=2, width=20, command=self.simulator)\r\n button_simulator.pack(pady=20, padx=50)\r\n\r\n button_read = Button(self.root, text=\"About\", font=self.text_font,\r\n height=2, width=20, command=self.about_screen)\r\n button_read.pack(pady=20, padx=50)","def _send_homepage(self, message=''):\n template_filename = self._get_config_template('homepage')\n text = read_template(\n template_filename,\n title=SERVER_NAME,\n header=SERVER_NAME,\n msg=message)\n self._send_response(text, 200)","def get(self):\r\n self.checkSession(self.request.headers.get('Cookie'))\r\n try:\r\n self.insertMenu()\r\n self.getSentMSG(self.Session['userid'])\r\n except:\r\n self.response.out.write(\"Some error occured\")","def run(self):\n if has_GUI:\n self.GUI(self.buffer)\n else:\n while True:\n message = input(\"Write your command:\\n\")\n # print(message)\n self.buffer.append(message)","def about(self):\n self.main_window.message(\n width=200, aspect=100, justify=tkinter.CENTER,\n text=\"Jeu de Ping\\n\\n\"\n \"(C) Maximin Duvillard, August 2022.\\nLicence = GPL\")","def show_main():\n db = get_db()\n now = datetime.datetime.now()\n rooms = app.config[\"HC_CONFIG\"].values()\n rooms.sort(key=config_lexer.Room.sortkey)\n # Get the three newest counts from the database, sorted by the\n # user-provided time\n newest_counts = db.get_newest_counts(3, hc_db.NewestSort.ENTERED_TIME)\n recent_counts = []\n for count in newest_counts:\n room_rows = db.get_roomdata_for_count_id(count[\"id\"])\n # I couldn't think of a short, descriptive name for this variable.\n some_dict = {\"date\": count[\"entered_time\"], \"counts\": {}}\n for row in room_rows:\n some_dict[\"counts\"][row[\"room\"]] = row[\"people_count\"]\n some_dict[\"counts\"] = OrderedDict(\n sorted(some_dict[\"counts\"].items(), key=sort_count_data)\n )\n recent_counts.append(some_dict)\n if is_admin(session[\"username\"]):\n buttons = [\n NavButton(url_for(\"show_admin\"), \"Administration\"),\n NavButton(url_for(\"show_help\"), \"Help\"),\n NavButton(url_for(\"logout\"), \"Log Out\"),\n ]\n else:\n buttons = [\n NavButton(url_for(\"show_help\"), \"Help\"),\n NavButton(url_for(\"logout\"), \"Log Out\"),\n ]\n return render_template(\n \"main.html\",\n buttons=buttons,\n rooms=rooms,\n recent_counts=recent_counts,\n datewhen=now.strftime(\"%Y-%m-%d\"),\n timewhen=now.strftime(\"%H:%M\"),\n )","def index():\n pages = fbpl().select(fbpl.page.ALL, orderby=fbpl.page.id)\n user = auth.user\n if user:\n response.flash = T('You are %(name)s', dict(name=user['first_name']))\n return dict(message=T('Hello, Facebook is telling that you are %(first_name)s %(last_name)s', dict(first_name=user['first_name'], last_name=user['last_name'])),pages=pages)\n response.flash = T('Welcome to web2py')\n\n return dict(message=T('Hello, please login'),pages=pages)","async def joinChat(self,ctx):\n await ctx.send('Joining the chat kiddo')","def display_messages(self):\n\n\t\twhile self.joined:\n\t\t\tif len(self.messages) != 0:\n\t\t\t\tfor msg in self.messages:\n\t\t\t\t\t#: If the message is empty, ignore it.\n\t\t\t\t\tif msg == \"\":\n\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t#: If the message is close\", then the server has told the client\n\t\t\t\t\t#: to shut down, so it will. This is not an issue, as users\n\t\t\t\t\t#: messages will always have an identifier and : before their\n\t\t\t\t\t#: message, thus,the only messages that don't include an\n\t\t\t\t\t#: identifier will be from the server itself.\n\t\t\t\t\telif msg[:5] == \"close\":\n\n\t\t\t\t\t\treason = msg[6:]\n\n\t\t\t\t\t\tprint(\"This client was closed due to {}.\".format(reason))\n\t\t\t\t\t\tself.quit(True)\n\n\t\t\t\t\t#: Otherwise, print the message to the commandline.\n\t\t\t\t\telif not self.silent:\n\t\t\t\t\t\tprint('\\r' + msg, end='')\n\n\t\t\t\t\t\tprint(\"\\nYou: \", end='')\n\t\t\t\t\t\tself.displayed_you = True\n\n\t\t\t\t\t#: Remove the processed message\n\t\t\t\t\tself.messages.remove(msg)","def message_center(request, url):\n\n if request.POST and url is not 'sent':\n message = Message.objects.get(id=request.POST.get(\"message_id\"))\n if message.read:\n message.read = 0\n message.save()\n else:\n message.read = 1\n message.save()\n\n all_messages = Message.objects.all().order_by(\"-time\")\n user_messages = []\n section = \"all\"\n if url is 'read':\n section = \"read\"\n for m in all_messages:\n if m.user_to.__str__() in request.user.userprofile.get_full_name() and m.read:\n user_messages.append(m)\n\n elif url is 'unread':\n section = \"unread\"\n for m in all_messages:\n if m.user_to.__str__() in request.user.userprofile.get_full_name() and not m.read:\n user_messages.append(m)\n\n elif url is 'sent':\n section = \"sent\"\n for m in all_messages:\n if m.user_from.__str__() in request.user.userprofile.get_full_name():\n user_messages.append(m)\n\n elif url is 'all':\n for m in all_messages:\n if m.user_to.__str__() in request.user.userprofile.get_full_name():\n user_messages.append(m)\n\n elif url is 'compose':\n section = 'compose'\n\n the_users = UserProfile.objects.exclude(user=request.user)\n\n return render(request, 'message_center.html', {'msgs': user_messages, 'the_users': the_users,\n 'display': section})"],"string":"[\n \"def chat():\\n kwargs = {\\\"title\\\": u\\\"chat channel\\\", \\\"entries\\\": log.getLogEntries()}\\n return render_template(\\\"chat.html\\\", **kwargs)\",\n \"def view_contact_chat(self):\\n if self._user.chats == {}:\\n print(\\\"No chats to be viewed yet\\\")\\n self.homepage()\\n \\n print('-=' * 30)\\n chats = self._user.list_chats()\\n user_choice = self._int_input_in_range(\\\"Pick whose contact chat to be viewed: \\\"\\n ,range_ = (1, len(chats)))\\n if not user_choice:\\n return self.homepage()\\n \\n chat, contact = chats[user_choice - 1]\\n chat_content = chat.get_content(self._user)\\n print('-=' * 12 + \\\" Chat Window \\\" + '-=' * 12)\\n if chat_content != []:\\n for line in chat_content:\\n print(line.rstrip()) \\n else:\\n print('This chat is empty, send your first msg now')\\n \\n user_choice = self._int_input_in_range(' (1) Send new msg \\\\n (2) Back to homepage \\\\n Your choice: '\\n , range_ = (1,2))\\n if user_choice == 1:\\n print('HINT: send (0) to exist the chat window')\\n return self._send_msg(contact)\\n else:\\n return self.homepage()\",\n \"def main():\\n return redirect('chat')\",\n \"def chat(request):\\n return jingo.render(request, 'chat/chat.html')\",\n \"def home():\\n data = read_data()\\n return render_template(\\n 'index.html',\\n title='Raft Message Implementation: Client',\\n content=data\\n )\",\n \"def send_messages(self):\\r\\n self.clear_screen()\\r\\n user_label = Label(self.root, text=\\\"Hello \\\" + self.username,\\r\\n font=self.title_font, bg=self.bg_color, height=2)\\r\\n user_label.pack(pady=10, padx=50)\\r\\n messages_frame = Frame(self.root)\\r\\n messages_frame.pack(padx=30, pady=10)\\r\\n scrollbar_msg = Scrollbar(messages_frame)\\r\\n scrollbar_msg.pack(side=RIGHT, fill=Y)\\r\\n write_message = Text(messages_frame, width=50, height=15, font=self.text_font,\\r\\n yscrollcommand=scrollbar_msg.set)\\r\\n write_message.pack()\\r\\n scrollbar_msg.config(command=write_message.yview)\\r\\n button_speech_rec = Button(self.root, text=\\\"listen\\\\nto speech\\\", font=self.text_font,\\r\\n height=2, width=20,\\r\\n command=lambda: self.create_speech_thread(write_message))\\r\\n button_speech_rec.pack(pady=10)\\r\\n button_send = Button(self.root, text=\\\"send\\\", font=self.text_font,\\r\\n height=2, width=20, command=lambda: self.send(write_message))\\r\\n button_send.pack(pady=10)\\r\\n button_send = Button(self.root, text=\\\"go back\\\", font=self.text_font,\\r\\n height=2, width=20, command=self.choose_path)\\r\\n button_send.pack(pady=10)\",\n \"def run_chat_client():\\r\\n while must_run:\\r\\n print_menu()\\r\\n action = select_user_action()\\r\\n perform_user_action(action)\\r\\n print(\\\"Thanks for watching. Like and subscribe! 👍\\\")\",\n \"def main():\\n\\n bus_controller = BusController()\\n steve = TelegramController(\\\"990223452:AAHrln4bCzwGpkR2w-5pqesPHpuMjGKuJUI\\\")\\n message_sender = MessagesSender()\\n db = DBManager()\\n gui = GUI()\\n\\n message_sender.connect(bus_controller=bus_controller)\\n bus_controller.connect(telegram_bot=steve, message_sender=message_sender)\\n steve.connect(bus_controller=bus_controller, gui=gui, message_sender=message_sender, data_base=db)\\n gui.connect(bus_controller=bus_controller, telegram_controller=steve, message_sender=message_sender, data_base=db)\\n\\n message_sender.start()\\n bus_controller.start()\\n steve.start()\\n gui.start()\",\n \"def chat():\\n name = session.get('name', '')\\n room = session.get('room', '')\\n if name == '' or room == '':\\n return redirect(url_for('.index'))\\n return render_template('chat.html', name=name, room=room)\",\n \"def chat():\\n name = session.get('name', '')\\n room = session.get('room', '')\\n if name == '' or room == '':\\n return redirect(url_for('.index'))\\n return render_template('chat.html', name=name, room=room)\",\n \"def chat():\\n name = session.get('name', '')\\n room = session.get('room', '')\\n if name == '' or room == '':\\n return redirect(url_for('.index'))\\n return render_template('chat.html', name=name, room=room)\",\n \"def main():\\n global discuss_bot_id, discussion_chat_id\\n r = requests.get('https://slack.com/api/rtm.connect', {'token': bot_token})\\n discuss_bot_id = r.json()['self']['id']\\n url = r.json()['url']\\n r = requests.get('https://slack.com/api/conversations.list',\\n {'token': bot_token})\\n for channel in r.json()['channels']:\\n if channel['name'] == 'discussion':\\n discussion_chat_id = channel['id']\\n print(discussion_chat_id)\\n ws = websocket.WebSocketApp(\\n url=url, on_message=on_message, on_error=on_error, on_close=on_close)\\n ws.on_open = on_open\\n ws.run_forever()\",\n \"def OnB(self, event):\\n bp = event.GetEventObject()\\n friend = bp.parameterVal\\n app1 = wx.PySimpleApp(0)\\n wx.InitAllImageHandlers()\\n frame_1 = ChatScreen(self.user, self.passw,friend, None, -1, \\\"\\\")\\n TextThread()\\n app1.SetTopWindow(frame_1)\\n frame_1.Show()\\n app1.MainLoop()\",\n \"def chat(request):\\n message = '{}: {}'.format(request.form['user'], request.form['message'])\\n if message:\\n ChatNamespace.broadcast('message', message)\\n return Response()\",\n \"def app():\\n # Add balloons animation when page opens\\n st.balloons()\\n\\n # Add title\\n st.title(\\\"Welcome to the about me page\\\")\\n \\n # Add Contact Details\\n st.header('Contact Us')\\n\\n # Add email\\n st.markdown('''### Name:\\n Khushal Mehrotra''')\\n\\n # Add name\\n st.markdown('''### Email:\\n kmehrotra07@gmail.com''')\\n\\n # Add github\\n st.markdown('''### GitHub: [Khushal Mehrotra](https://github.com/km-07)''')\\n\\n # Add linkedin\\n st.markdown('''### Linkedin: [Khushal Mehrotra](https://www.linkedin.com/in/khushal-mehrotra-5276091a9/)''')\",\n \"def index(self):\\n print \\\"Starting Wall\\\"\\n print \\\"Retrieving name\\\"\\n print session['user_id']\\n full_name = self.models['Wall_m'].full_name(session['user_id']) # Grab user'sname\\n print full_name\\n full_name = full_name[0]['full_name']\\n print full_name \\n\\n messages = self.models['Wall_m'].load_messages(session['user_id'])\\n print \\\"MESSAGE: \\\"+str(messages)\\n\\n if messages != []:\\n comments = self.models['Wall_m'].load_comments(messages)\\n else: \\n comments = []\\n\\n print \\\"COMMENT: \\\"+str(comments)\\n return self.load_view('wall.html', Welcome = full_name, mess = messages, comm = comments)\",\n \"def get(self):\\n self.write(\\n '{\\\"error\\\": \\\"cryptochat-server main page, '\\n 'please refer to /api/message/new or /api/message/updates\\\"}')\",\n \"def index(self):\\n return \\\"\\\"\\\"\\n<html><body>\\n<pre>\\n Hello World!\\n\\n To send a message, please point to:\\n http://server:port/msg?msgType=YOURTYPE&payload=YOURPAYLOAD\\n</pre>\\n</body></html>\\n \\\"\\\"\\\"\",\n \"def main(self):\\n\\n dp = self.dispatcher\\n\\n dp.add_handler(MessageHandler(Filters.text, self.__msg_handler))\\n dp.add_handler(MessageHandler(Filters.command, self.__msg_handler))\\n dp.add_handler(MessageHandler(Filters.photo, self.__photo_handler))\\n dp.add_handler(MessageHandler(\\n Filters.location, self.__location_handler))\\n dp.add_handler(CallbackQueryHandler(callback=self.__clb_handler))\\n\\n self.updater.start_polling()\\n self.updater.idle()\",\n \"def chat(self) -> \\\"api.Chat\\\":\\n raise NotImplementedError\",\n \"def index():\\n #Have to create LOGIN\\n #serverxmpp.init()\\n return dict()\",\n \"def main(args):\\n\\n cocos.director.director.init(resizable=True)\\n\\n Conversation = wit_handler.Conversation()\\n TextBox = text_box.TextBox(\\n enter_function=Conversation.send_message\\n )\\n\\n # Run a scene with our event displayers:\\n cocos.director.director.run(cocos.scene.Scene(TextBox))\",\n \"def __sendMessage(self):\\n # TODO: Switch to this when implemented\\n \\n msg = self.ui.inputWidget.toPlainText()\\n self.ui.inputWidget.clear()\\n strv = StringView()\\n strv.appendText(unicode(msg))\\n self._amsn_conversation.sendMessage(strv)\\n self.ui.textEdit.append(\\\"<b>/me says:</b><br>\\\"+unicode(msg)+\\\"\\\")\",\n \"def about_screen(self):\\r\\n self.clear_screen()\\r\\n user_label = Label(self.root, text=\\\"Hello \\\" + self.username,\\r\\n font=self.title_font, bg=self.bg_color, height=2)\\r\\n user_label.pack(pady=10, padx=50)\\r\\n about_text = \\\"\\\"\\\"My name is Jasmin, I am 17 years old and this is my final project\\r\\nfor 12th grade in the Cyber Bagrut.\\r\\nThe project contains a multi client-server connection.\\r\\nThe project includes Enigma simulator and explanations about the encryption.\\r\\nIt allows chatting between all the connected users.\\r\\nLogging in and signing in is through the server. \\r\\nthe client sends the user data to the server with RSA encryption,\\r\\nand the server responds appropriately.\\r\\nEncryption key of messaging is changed every 10 minutes according to the\\r\\ndatabase enigma settings. the user can also send a message through the \\r\\nsimulator, using whichever settings he wants. the setting key\\r\\n(time/settings from the simulator) are sent with RSA encryption. \\r\\nthe encryption of a message is done with the Enigma machine and\\r\\nMorse code combined with ASCII afterwards.\\\"\\\"\\\"\\r\\n lbl_about = Label(self.root, text=\\\"About The Project\\\",\\r\\n font=self.title_font, bg=self.bg_color)\\r\\n lbl_about.pack(pady=5, padx=10)\\r\\n about_frame = Frame(self.root, width=100, height=300, bg='white')\\r\\n about_frame.pack(padx=30, pady=20)\\r\\n text_widget = Text(about_frame)\\r\\n text_widget.pack()\\r\\n text_widget.insert(END, about_text)\\r\\n text_widget.config(state=DISABLED)\\r\\n button_send = Button(self.root, text=\\\"go back to choose path\\\", font=self.text_font,\\r\\n height=2, width=20, command=self.choose_path)\\r\\n button_send.pack(pady=20)\",\n \"def new_window_messages(self, button_see_all_msgs):\\r\\n # changing the button command to closing the window\\r\\n button_see_all_msgs.config(command=lambda: self.close_window(button_see_all_msgs))\\r\\n\\r\\n # creating the chat Tk object\\r\\n self.messages_window = Tk()\\r\\n self.messages_window.resizable(False, False)\\r\\n self.messages_window.config(bg=self.bg_color)\\r\\n self.messages_window.protocol(\\\"WM_DELETE_WINDOW\\\",\\r\\n lambda: self.close_window(button_see_all_msgs))\\r\\n\\r\\n chat_label = Label(self.messages_window, text=\\\"Hello \\\" + self.username +\\r\\n \\\"\\\\nHere are your messages\\\",\\r\\n bg=self.bg_color, font=self.title_font)\\r\\n chat_label.pack(padx=20, pady=10)\\r\\n chat_frame = Frame(self.messages_window)\\r\\n chat_frame.pack(padx=15, pady=15)\\r\\n scrollbar_chat = Scrollbar(chat_frame)\\r\\n scrollbar_chat.pack(side=RIGHT, fill=Y)\\r\\n text_chat = Text(chat_frame, width=30, height=15, font=self.text_font,\\r\\n yscrollcommand=scrollbar_chat.set)\\r\\n text_chat.pack()\\r\\n scrollbar_chat.config(command=text_chat.yview)\\r\\n for msg, encryption_data, sender_user in self.msg_list:\\r\\n text_chat.insert(END, \\\"from: \\\" + sender_user + \\\"\\\\n\\\")\\r\\n text_chat.insert(END, msg + \\\"\\\\n\\\\n\\\")\\r\\n text_chat.config(state=DISABLED)\",\n \"def chat(request):\\n team = Hunt.objects.get(is_current_hunt=True).team_from_user(request.user)\\n if request.method == 'POST':\\n # There is data in the post request, but we don't need anything but\\n # the message because normal users can't send as staff or other teams\\n m = Message.objects.create(time=timezone.now(), text=request.POST.get('message'),\\n is_response=False, team=team)\\n team.num_waiting_messages = 0\\n messages = [m]\\n else:\\n if(team is None):\\n return render(request, 'access_error.html', {'reason': \\\"team\\\"})\\n if(team.hunt.is_locked and not team.is_playtester_team):\\n return render(request, 'access_error.html', {'reason': \\\"hunt\\\"})\\n if request.is_ajax():\\n messages = Message.objects.filter(pk__gt=request.GET.get(\\\"last_pk\\\"))\\n else:\\n messages = Message.objects\\n messages = messages.filter(team=team).order_by('time')\\n\\n # The whole message_dict format is for ajax/template uniformity\\n rendered_messages = render_to_string('chat_messages.html',\\n {'messages': messages, 'team_name': team.team_name})\\n message_dict = {team.team_name: {'pk': team.pk, 'messages': rendered_messages}}\\n try:\\n last_pk = Message.objects.latest('id').id\\n except Message.DoesNotExist:\\n last_pk = 0\\n team.num_waiting_messages = 0\\n\\n team.save() # Save last_*_message vars\\n context = {'message_dict': message_dict, 'last_pk': last_pk}\\n if request.is_ajax() or request.method == 'POST':\\n return HttpResponse(json.dumps(context))\\n else:\\n context['team'] = team\\n return render(request, 'chat.html', context)\",\n \"def main(request):\\n users = get_channel_users(MEMBER_CHANNEL)\\n human_users = filter_bots(users)\\n buddy_groups = group_users(human_users)\\n if buddy_groups:\\n resp = post_message(buddy_groups, POST_CHANNEL)\\n ret = str(resp)\\n else:\\n ret = 'Not enough members to run'\\n return ret\",\n \"def read_messages(self, msg_num):\\r\\n self.clear_screen()\\r\\n user_label = Label(self.root, text=\\\"Hello \\\" + self.username, font=self.title_font,\\r\\n bg=self.bg_color, height=2)\\r\\n user_label.pack(pady=5, padx=50)\\r\\n lbl_msg = Label(self.root, text=\\\"Message \\\" + str(msg_num), font=self.title_font,\\r\\n bg=self.bg_color)\\r\\n lbl_msg.pack(pady=5, padx=10)\\r\\n self.refresh_button = Button(self.root, text=\\\"Refresh page\\\", font=self.text_font,\\r\\n bg=self.bg_color, command=lambda: self.refresh(msg_num))\\r\\n self.refresh_button.pack(padx=10, pady=10)\\r\\n messages_frame = Frame(self.root)\\r\\n messages_frame.pack(padx=30, pady=15)\\r\\n scrollbar_msg = Scrollbar(messages_frame)\\r\\n scrollbar_msg.pack(side=RIGHT, fill=Y)\\r\\n text_widget = Text(messages_frame, width=50, height=15, font=self.text_font,\\r\\n yscrollcommand=scrollbar_msg.set)\\r\\n text_widget.pack()\\r\\n scrollbar_msg.config(command=text_widget.yview)\\r\\n button_send = Button(self.root, text=\\\"go back\\\", font=self.text_font,\\r\\n height=2, width=20, command=self.go_back_read)\\r\\n button_send.pack(pady=5, side=BOTTOM)\\r\\n button_send = Button(self.root, text=\\\"see/close message\\\\ncontrol panel\\\",\\r\\n font=self.text_font,\\r\\n height=2, width=20,\\r\\n command=lambda: self.new_window_messages(button_send))\\r\\n button_send.pack(pady=5, side=BOTTOM)\\r\\n if self.msg_list:\\r\\n if msg_num < len(self.msg_list):\\r\\n next_msg = Button(self.root, text=\\\"next message\\\", font=self.text_font,\\r\\n height=2, width=20,\\r\\n command=lambda: self.read_messages(msg_num + 1))\\r\\n next_msg.pack(pady=5, padx=5, side=RIGHT)\\r\\n if msg_num > 1:\\r\\n previous_msg = Button(self.root, text=\\\"previous message\\\", font=self.text_font,\\r\\n height=2, width=20,\\r\\n command=lambda: self.read_messages(msg_num - 1))\\r\\n previous_msg.pack(pady=5, padx=5, side=LEFT)\\r\\n text_widget.insert(END, \\\"from: \\\" + self.msg_list[msg_num - 1][2] + \\\"\\\\n\\\")\\r\\n text_widget.tag_add('sender', '1.0', '1.end')\\r\\n text_widget.tag_config('sender', font='none 14')\\r\\n\\r\\n text_widget.insert(END, self.msg_list[msg_num - 1][0])\\r\\n text_widget.tag_add('msg', '2.0', END)\\r\\n text_widget.tag_config('msg', font='none 12')\\r\\n\\r\\n text_widget.config(state=DISABLED)\",\n \"async def messages(self, ctx):\\n\\n\\t\\tawait self.message_leaderboard(ctx, \\\"messages\\\")\",\n \"def welcome(request):\\n # flag che indica se sono presenti oppure no nuovi messaggi nella casella di posta\\n new_messages = False\\n if request.user.is_authenticated:\\n # controllo le liste di attesa a cui è prenotato l'utente\\n controlList(request.user)\\n messaggi = Messaggio.objects.filter(userDestinatario=request.user)\\n for msg in messaggi:\\n if msg.letto == False:\\n new_messages = True\\n return render(request, 'main_page/main_page.html', {'new_messages':new_messages})\",\n \"def main():\\r\\n st.sidebar.title(\\\"MỤC LỤC\\\")\\r\\n selection = st.sidebar.radio(\\\"Go to\\\", list(PAGES.keys()))\\r\\n\\r\\n page = PAGES[selection]\\r\\n\\r\\n with st.spinner(f\\\"Loading {selection} ...\\\"):\\r\\n ast.shared.components.write_page(page)\\r\\n st.sidebar.title(\\\"Intro\\\")\\r\\n st.sidebar.info(\\r\\n \\\"App này được làm ra nhằm mục đích phục vụ cho việc **nghiên cứu và học tập** \\\"\\r\\n \\\"[source code](https://github.com/BienQuangTruong/streamlit_app). \\\"\\r\\n )\\r\\n st.sidebar.title(\\\"About\\\")\\r\\n st.sidebar.info(\\r\\n \\\"\\\"\\\"\\r\\n App này được thực hiện bởi Biện Quang Trường - Nguyễn Văn Phước.\\r\\n\\\"\\\"\\\"\\r\\n )\",\n \"def start(self):\\n self.show_greeting()\\n self.read_frame()\",\n \"def tornar_main(self):\\n pub.sendMessage(\\\"BACK_TO_MAIN_PAGE\\\")\",\n \"def index():\\n return render_template(\\\"main_board/board.html\\\", url_for_api_messages=url_for('api.main_board'), limit=10,\\n message_form=MessageForm())\",\n \"def run(self):\\n self.logger.info(\\\"Starting messenger.\\\")\\n self.recv()\",\n \"def start(self, event):\\n self.send_presence()\\n self.get_roster()\\n self.send_message(mto=self.recipient, mbody=self.msg, mtype='chat')\\n self.disconnect(wait=True)\",\n \"def start_message(self, update, context):\\n\\n user = self.User(update)\\n output = \\\"Greetings, we're happy that you decided to join and use the Bus4U service!\\\\n\\\" \\\\\\n \\\"in order to see all the possible commands you can type /help\\\\n\\\" \\\\\\n \\\"Also we want you to know that every command that you type and the server response will\\\" \\\\\\n \\\"be logged and you can access your history with /history.\\\\n\\\\n\\\" \\\\\\n \\\"we hope you'll enjoy the product and wish you the best.\\\\n Never Miss a Bus.\\\"\\n user.send_message(output)\\n self.data_base.log(user, \\\"*Showed Greeting Message*\\\")\",\n \"def test_im_chat_messages(self):\\n pass\",\n \"def _do_start(self, chat_id, user_id, args, update):\\n \\n self.tclient.send_message('Hallo! Ich bin ein Bot, um dir zu helfen, dir deine Nasensprüche zu merken!', chat_id)\",\n \"def display_messages(self, layout):\",\n \"def connectionMade(self):\\n\\n login_message = f\\\"login:{self.factory.login}\\\" # form a login registration line\\n self.send_message(login_message) # send to a server\\n\\n self.wrap() # enable the input / output mode in the console (to send messages by pressing Enter)\",\n \"def main():\\n if 'username' in session:\\n flash(f'Logged in as {session[\\\"username\\\"]}')\\n else:\\n flash('You are not logged in.')\\n return render_template(\\\"main.html\\\", title=\\\"Main\\\")\",\n \"def start(update, context):\\n chats = load_chats()\\n chats.append( str( update.message.chat_id ) )\\n save_channels(chats)\\n update.message.reply_text('Chat registered!')\",\n \"def single_chat(request, key):\\n \\n if not Messages.objects.using('msgstore').filter(key_remote_jid=key).count() > 0:\\n return render_to_response('whatsapp/errors/404.html', context_instance=RequestContext(request)) \\n\\n msgs = get_chat_messages(key)\\n msgs_list = pagination(request, msgs, MESSAGES_PER_PAGE)\\n\\n dic = {\\n 'peer': key,\\n 'chatmessages': msgs_list,\\n 'gps': Messages.objects.using('msgstore').exclude((Q(longitude='0.0') | Q(latitude='0.0'))),\\n 'media': Messages.objects.using('msgstore').exclude(media_url__isnull=True),\\n 'PAG_TITLE': 'Conversation'\\n }\\n\\n return render_to_response('whatsapp/chat.html', dic, context_instance=RequestContext(request))\",\n \"def chatlist(request):\\n\\n chats = get_chat_list()\\n chat_list = pagination(request, chats, CHATS_PER_PAGE)\\n\\n dic = {'chatlist': chat_list}\\n return render_to_response('whatsapp/chatlist.html', dic, context_instance=RequestContext(request))\",\n \"def on_chat(self, event, text):\\n return None\",\n \"def __init__(self, master, Client):\\n super(Chat, self).__init__(master)\\n self.grid()\\n self.create_widgets()\\n master.grid_propagate(True)\\n master.grid_rowconfigure(0, weight = 1)\\n master.grid_columnconfigure(0, weight = 1)\\n self.Client = Client\\n threading.Thread(target = self.listen).start()\",\n \"def showMessage(self):\",\n \"def index(request):\\n\\n chats = Chat.objects.all().order_by('-created_at')\\n\\n if request.user.is_authenticated():\\n chats = chats.filter(friend_groups__in=request.user.get_profile().\\\\\\n friend_groups.all().values_list('id'))\\n else:\\n chats = chats.filter(friend_groups__isnull=True)\\n\\n return render_to_response('index.html', {\\n 'chats': chats[:10],\\n }, context_instance=RequestContext(request))\",\n \"def setup(self):\\n curses.curs_set(1)\\n curses.noecho()\\n curses.cbreak()\\n # Keypad disabled until scrolling properly implemented\\n # self.stdscr.keypad(True)\\n self.stdscr.clear()\\n self.stdscr.addstr(\\\"SecureChat v{}\\\".format(__version__))\\n self.chat_container.box()\\n self.chat_win.addstr(\\\"Welcome to SecureChat!\\\")\\n self.chat_win.scrollok(True)\\n self.chat_win.setscrreg(0, self.max_y - 5)\\n self.prompt_win.addstr(\\\"> \\\")\\n self.refresh_all()\",\n \"def introduce_chat_interface(self):\\n self.send_message(\\n message=constants.ONBOARDING_WELCOME,\\n onboarding_step=constants.ONBOARDING_STEPS[\\\"CHAT_INTERFACE\\\"],\\n done=True,\\n )\",\n \"def main(self):\\n\\n updater = Updater(self.token)\\n dp = updater.dispatcher\\n\\n dp.add_handler(MessageHandler(Filters.text, self.__msg_handler))\\n dp.add_handler(MessageHandler(Filters.command, self.__msg_handler))\\n dp.add_handler(CallbackQueryHandler(callback=self.__clb_handler))\\n\\n updater.start_polling()\\n updater.idle()\",\n \"def main():\\n # Events from google calendar:\\n events = GC.get_events()\\n\\n message_info = MC.create_message(events, True)\\n message_to_post = message_info[0]\\n emojis = message_info[1]\\n\\n # Post message to Slack\\n posted_message = SL.send_message(\\n sc, channels[\\\"bot-dev\\\"], message_to_post, True)\\n\\n # React the emojis to Slack\\n for emoji in emojis:\\n SL.emoji_react(sc, emoji, posted_message, False)\\n\\n correct_message = input(\\\"Did the message look ok in the #polls channel? If you answer with 'y' it will be posted in the polls channel. If you answer 'gen' it will be posted in general. If you answer with 'n' or anything other than the commands shown before, then it will be stopped and not posted any where else.\\\\n\\\")\\n\\n if \\\"gen\\\" in correct_message.lower():\\n posted_message = SL.send_message(\\n sc, channels[\\\"general\\\"], message_to_post, True)\\n for emoji in emojis:\\n SL.emoji_react(sc, emoji, posted_message, False)\\n elif \\\"y\\\" in correct_message.lower():\\n posted_message = SL.send_message(\\n sc, channels[\\\"polls\\\"], message_to_post, True)\\n for emoji in emojis:\\n SL.emoji_react(sc, emoji, posted_message, False)\",\n \"def test_chat_page(self):\\n c_code = 'new-chat'\\n rsp = self.client.get('/{}'.format(c_code))\\n html = rsp.data\\n\\n self.assertEqual(200, rsp.status_code)\\n # HTML is returned.\\n self.assertIn('<html>', html)\\n # HTML for chat is retrieved.\\n self.assertIn(c_code, html)\",\n \"async def on_chat_message(self, chat_message):\\n pass\",\n \"def text_e(self, event):\\n directory=os.getcwd()+ '/messages'\\n filename=str(self.user)+'_'+str(self.friend)\\n text = self.text_send.GetValue()\\n messages = mf.addMessage(self.user, self.friend, self.passw, text)\\n mf.makeTextFile(self.user, self.friend, self.passw, messages)\\n \\n self.chat_log.LoadFile('/'.join((directory, filename)))\\n self.text_send.SetValue(\\\"\\\")\\n event.Skip()\",\n \"def listener(messages):\\n for m in messages:\\n chatid = m.chat.id\\n print(str(chatid))\\n if m.content_type == 'text':\\n text = m.text\\n tb.send_message(chatid, text)\",\n \"def get(self): # pylint:disable-msg=C0103\\n user_id = users.get_current_user().user_id()\\n token = channel.create_channel(user_id)\\n self.RenderResponse('main.html', token=token, table_id=TABLE_ID,\\n rows=ROWS, columns=COLUMNS)\",\n \"def __init__(self,sys_argv,port):\\n super(Chat,self).__init__(sys_argv)\\n self.cliente=Cliente('0.0.0.0',port)\\n self.controlador=Controlador(self.cliente)\\n self.igchat=GUIChat(self.controlador,self.cliente)\\n self.cliente.seConecto()\\n t1=threading.Thread(target=self.cliente.enviado)\\n t1.setDaemon(True)\\n t1.start()\\n time.sleep(0.001)\\n self.igchat.show()\",\n \"def help(chat_lines):\\n for line in xrange(9):\\n chat_lines[line].setTextColor(\\\"Blue\\\")\\n chat_lines[0].setText(\\\"------------------------------------------Welcome to the online chat - Amdur's Chat!--------\\\"\\n \\\"----------------------------------\\\")\\n chat_lines[1].setText(\\\"You can use the following commands:\\\")\\n chat_lines[2].setText(\\\"# 'Kick (Member)' - to kick a member (Only for Managers)\\\")\\n chat_lines[3].setText(\\\"# 'Mute/UnMute (Member)' - to mute or cancel a mute of member (Only for Managers)\\\")\\n chat_lines[4].setText(\\\"# 'Private (Member):' - to send message to a specific member \\\")\\n chat_lines[5].setText(\\\"# 'Add (Member)' - to become a member to manager (Only for Managers)\\\")\\n chat_lines[6].setText(\\\"# 'Manager list' - for getting the manager list\\\")\\n chat_lines[7].setText(\\\"# 'Quit' - to leave chat \\\")\\n chat_lines[8].setText(\\\"--------------------------------------------------------------------------------------------\\\"\\n \\\"----------------------------------------------------\\\")\",\n \"def main():\\n #Gmail2TelegramClient(\\\"1234\\\") -- a person\\n #Gmail2TelegramClient(\\\"-1234\\\") -- group chat\",\n \"def run(self):\\n self.user_t = None\\n self.users.show()\\n self.connect()\",\n \"def on_message(self, message):\\n #print(f\\\"This message was sent: {message}\\\") # Writes to the console window (server side)\\n self.write_message(f\\\"This message was sent: {message}\\\") # Writes message to sender\",\n \"def chat(self, p: str):\\n message = p[1:]\\n if message.strip() != '':\\n message: str = f\\\"{self.username} says: {message[:80]}\\\"\\n self.broadcast(packet.construct_log_packet(message), include_self=True)\\n self.logger.log(message)\",\n \"def main():\\n database_connection = userdata.init()\\n # signed_in = account.sign_in('jordan00', 'Jordan!23', database_connection)\\n signed_in = account.sign_in_or_create_user(database_connection)\\n timeline.timeline(database_connection, signed_in, 1)\\n\\n while True:\\n action = timeline.prompt_for_timeline_action()\\n\\n if action.isdigit():\\n action = int(action)\\n if action:\\n timeline.timeline(database_connection, signed_in, action)\\n else:\\n print('-' * 100)\\n print('No posts on page 0. Please go to page 1.')\\n print('-' * 100)\\n\\n elif action == 'ACCOUNT SETTINGS':\\n account.account(signed_in, database_connection)\\n timeline.timeline(database_connection, signed_in, 1)\\n elif action == 'USERS':\\n usersmodule.users_main_page(database_connection, signed_in)\\n timeline.timeline(database_connection, signed_in, 1)\\n elif action == 'POST':\\n post = input('Type your post:\\\\n\\\\t>')\\n userdata.add_post(database_connection, post, signed_in)\\n timeline.timeline(database_connection, signed_in, 1)\\n elif action == 'SIGN OUT':\\n database_connection.close()\\n print('Bye!')\\n break\",\n \"def main_menu():\\n connection = ClientToServer(ip = \\\"192.168.1.1\\\", \\n port = \\\"1234\\\",\\n username = \\\"Admin\\\",\\n hns_password = \\\"password\\\")\\n \\n game_loop(connection)\",\n \"def main():\\n root = tkinter.Tk()\\n delegate = ReceiveMessages()\\n mqtt_client = com.MqttClient(delegate)\\n mqtt_client.connect_to_ev3()\\n\\n progress_bar = setup_gui(root, mqtt_client)\\n delegate.progress_bar = progress_bar\\n\\n root.mainloop()\",\n \"def __init__(self, controller):\\n self.controller = controller\\n\\n self.top = tkinter.Tk()\\n self.top.title(\\\"Chatter\\\")\\n\\n self.input = tkinter.StringVar() # For the messages to be sent.\\n self.input.set(\\\"\\\")\\n\\n \\\"\\\"\\\"Message box\\\"\\\"\\\"\\n messages_frame = tkinter.Frame(self.top)\\n scrollbar = tkinter.Scrollbar(messages_frame)\\n self.msg_list = tkinter.Listbox(messages_frame, height=15, width=60, yscrollcommand=scrollbar.set)\\n scrollbar.pack(side=tkinter.RIGHT, fill=tkinter.Y)\\n self.msg_list.pack(side=tkinter.LEFT, fill=tkinter.BOTH)\\n self.msg_list.pack()\\n messages_frame.pack()\\n\\n\\n\\n\\n \\\"\\\"\\\"Input box and send button\\\"\\\"\\\"\\n entry_field = tkinter.Entry(self.top, textvariable=self.input)\\n entry_field.bind(\\\"<Return>\\\", self.controller.msg_to_send)\\n entry_field.pack()\\n send_button = tkinter.Button(self.top, text=\\\"Send\\\", command=lambda: self.controller.msg_to_send(self.input))\\n send_button.pack()\\n \\\"\\\"\\\"Send file button\\\"\\\"\\\"\\n file_button = tkinter.Button(self.top, text=\\\"Send file\\\", command=lambda: self.controller.find_file())\\n file_button.pack()\\n\\n \\\"\\\"\\\"On closing the window\\\"\\\"\\\"\\n self.top.protocol(\\\"WM_DELETE_WINDOW\\\", self.controller.close)\",\n \"def show_messages(self):\\n for msg in self.messages:\\n print msg['text']\",\n \"def new_message_from_conn(self, friend, msg):\\n print(\\\"new_msg signal activated with friend\\\",friend,\\\"and msg\\\",msg)\\n\\n if not self.stack.get_child_by_name(friend):\\n new_chat_window = chat_layout.ChatLayout(orientation=Gtk.Orientation.VERTICAL,friend=friend)\\n new_chat_window.show_all()\\n self.stack.add_titled(new_chat_window, friend, friend)\\n\\n child = self.move_to_child(friend)\\n child.append_friend_text(msg)\",\n \"def useChat(self):\\n # Implements a subprocess to run the Kuri robot simultaneously with the user input loop\\n proc_stdin = io.TextIOWrapper(self.proc.stdin, encoding='utf-8', line_buffering=True)\\n\\n while True:\\n txt = input(\\\"Talk to me! (Type 'q' to quit) \\\").lower()\\n if txt == 'q':\\n proc_stdin.write('q\\\\n')\\n quit()\\n else:\\n sentiment = self.sd.getSentiment(txt)\\n proc_stdin.write(sentiment + '\\\\n')\\n print(\\\"Sentiment: \\\" + sentiment + '\\\\n')\",\n \"def index(request):\\n # most of the heavy lifting is handled by Angular, on the front end\\n return render(request, 'forum/index.html')\",\n \"def handle_messages():\\n print(\\\"Handling Messages\\\")\\n payload = request.get_data()\\n for sender, incoming_message, payload in messaging_events(payload):\\n # The following statements check which options the user selected\\n # Response handler contains \\\"templates\\\" for the various messages\\n user_name = get_full_name(sender, PAT)\\n if \\\"hei\\\" in incoming_message.lower() or \\\"hallo\\\" in incoming_message.lower() or \\\"yo\\\" in incoming_message.lower()\\\\\\n or \\\"hi\\\" in incoming_message.lower():\\n send_message(PAT, send_message(PAT, response_handler.greeting_message(sender, user_name)))\\n if user_methods.has_user(user_name):\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n else:\\n send_message(PAT, response_handler.no_course(sender))\\n\\n elif payload == \\\"change subject\\\" or \\\"change subject\\\" in incoming_message.lower():\\n send_message(PAT, response_handler.text_message(sender, \\\"You can change course at any time simply by \\\"\\n \\\"writing the course code on the form [TAG][CODE]\\\\n\\\"\\n \\\"ex. TDT4120\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n elif \\\"help\\\" in incoming_message.lower():\\n\\n send_message(PAT, response_handler.text_message(sender, \\\"Are you lost ...? \\\"))\\n send_message(PAT, response_handler.text_message(sender, \\\"You can change course at any time simply by \\\"\\n \\\"writing the course code on the form: [TAG][CODE]\\\\n\\\"\\n \\\"ex. TDT4120\\\"))\\n send_message(PAT, response_handler.text_message(sender, \\\"If you want to see your currently selected course \\\"\\n \\\"and other information type 'Status'.\\\"))\\n send_message(PAT, response_handler.text_message(sender, \\\"You can also type 'Hei' or 'Hallo' at any time \\\"\\n \\\"to receive a greeting that shows your options.\\\"))\\n send_message(PAT, response_handler.text_message(sender, \\\"Here is a list of commands you can use. This is \\\"\\n \\\"recommended for the experienced user:\\\\n\\\"\\n \\\"Change subject\\\\n\\\"\\n \\\"Give feedback\\\\n\\\"\\n \\\"How did today's lecture go?\\\\n\\\"\\n \\\"Get schedule\\\\n\\\"\\n \\\"Get info\\\\n\\\"\\n \\\"All lectures\\\\n\\\"\\n \\\"A specific lecture\\\\n\\\"\\n \\\"You can type most of the commands in chat. Just \\\"\\n \\\"give it a try!\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n elif \\\"status\\\" in incoming_message.lower():\\n subject = user_methods.get_subject_from_user(user_name)\\n year = feedback_methods.get_year()\\n week = feedback_methods.get_week()\\n day = feedback_methods.get_day()\\n user = get_full_name(sender, PAT)\\n lecture_id_current = lecture_methods.get_lecture_from_date(year, week, day, subject)\\n lecture = feedback_methods.get_lecture_object(lecture_id_current)\\n\\n if user_methods.has_user(user_name):\\n sub = user_methods.get_subject_from_user(user_name) + \\\" : \\\" + \\\\\\n subject_info.course_name(user_methods.get_subject_from_user(user_name))\\n send_message(PAT, response_handler.user_info(sender, user_name, sub))\\n if feedback_methods.user_has_feedback_for_lecture(user, lecture):\\n send_message(PAT, response_handler.text_message(sender, \\\"You have given feedback for \\\"\\n + subject + \\\"today. Well done! Be proud of \\\"\\n \\\"yourself and remember to check in \\\"\\n \\\"tomorrow.\\\"))\\n send_message(PAT,\\n response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n else:\\n send_message(PAT, response_handler.text_message(sender,\\n \\\"No feedback for the given lecture on this date. \\\"\\n \\\"Please press 'Give Feedback' or write it in the \\\"\\n \\\"chat to do so.\\\"))\\n send_message(PAT,\\n response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n else:\\n send_message(PAT, response_handler.text_message(sender,\\n \\\"We seem to not be able to detect you in the database. \\\"\\n \\\"Please report this to the staff!\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n # Checks if the subject has lectures in the database, adds them if not.\\n\\n elif payload == \\\"give feedback\\\" or \\\"give feedback\\\" in incoming_message.lower():\\n send_message(PAT, response_handler.give_feedback_choice(sender))\\n\\n elif payload == \\\"lecture speed\\\" or \\\"lecture speed\\\" in incoming_message.lower():\\n\\n subject = user_methods.get_subject_from_user(user_name)\\n\\n if lecture_methods.check_lecture_in_db(subject):\\n send_message(PAT, response_handler.lec_feed(sender))\\n else:\\n schedule = subject_info.get_schedule(subject)\\n if schedule:\\n database_entry = subject_info.gather_lecture_information(schedule)\\n lecture_methods.add_lecture_information_db(database_entry)\\n send_message(PAT, response_handler.text_message(sender, \\\"Lectures for the subject \\\" + subject +\\n \\\" were not in the database. It is now added.\\\"))\\n send_message(PAT, response_handler.lec_feed(sender))\\n else:\\n send_message(PAT, response_handler.text_message(sender, \\\"Lectures for the subject \\\" + subject +\\n \\\" does not exist. Likely due to the subject having \\\"\\n \\\"no lectures this semester.\\\"))\\n send_message(PAT, response_handler.has_course(sender, subject))\\n\\n elif payload == \\\"evaluation_questions\\\" or \\\"lecture questions\\\" in incoming_message.lower():\\n # User wants to give feedback for a lecture.\\n subject = user_methods.get_subject_from_user(user_name)\\n payload = \\\"evaluation_questions\\\" # if user typed 'lecture questions' the payload will be None\\n\\n if lecture_methods.check_lecture_in_db(subject):\\n if feedback_methods.user_can_give_feedback_evaluation(user_name,\\n user_methods.get_subject_from_user(user_name)):\\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\\n else:\\n send_message(PAT, response_handler.text_message(sender,\\n \\\"Feedback can not be given either because there \\\"\\n \\\"is no lecture today, or because you have already \\\"\\n \\\"given feedback for this lecture.\\\"))\\n send_message(PAT, response_handler.has_course(sender, subject))\\n else:\\n schedule = subject_info.get_schedule(subject)\\n if schedule:\\n database_entry = subject_info.gather_lecture_information(schedule)\\n lecture_methods.add_lecture_information_db(database_entry)\\n send_message(PAT, response_handler.text_message(sender, \\\"Lectures for the subject \\\" + subject +\\n \\\" were not in the database. It is now added\\\"))\\n if feedback_methods.user_can_give_feedback_evaluation(user_name,\\n user_methods.get_subject_from_user(\\n user_name)):\\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\\n else:\\n send_message(PAT, response_handler.text_message(sender,\\n \\\"Feedback can not be given either because \\\"\\n \\\"there is no lecture today, or because you\\\"\\n \\\" have already given feedback for this lecture.\\\"\\n \\\"\\\"))\\n send_message(PAT, response_handler.has_course(sender, subject))\\n else:\\n send_message(PAT, response_handler.text_message(sender, \\\"Lectures for the subject \\\" + subject +\\n \\\"does not exist. Likely due to the subject having \\\"\\n \\\"no \\\"\\n \\\"lectures this semester.\\\"))\\n send_message(PAT, response_handler.has_course(sender, subject))\\n\\n elif \\\"too slow\\\" in incoming_message.lower():\\n # Adds feedback if the subject has a lecture on the given day\\n # and if the user has not already given feedback\\n payload = '0'\\n message_response = \\\"too slow\\\"\\n if feedback_methods.add_entry(user_name, user_methods.get_subject_from_user(user_name), payload):\\n send_message(PAT, response_handler.text_message(sender, \\\"You chose: \\\" + \\\"'\\\" +\\n message_response + \\\"'\\\" + \\\"\\\\nFeedback Received!\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n else:\\n send_message(PAT,\\n response_handler.text_message(sender, \\\"There is either no lecture active in the selected\\\"\\n \\\" subject, or you have already given feedback\\\"\\n \\\" to the active lecture.\\\\nFeedback denied!\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n elif \\\"it's all right\\\" in incoming_message.lower() or \\\"its all right\\\" in incoming_message.lower():\\n # Adds feedback if the subject has a lecture on the given day\\n # and if the user has not already given feedback\\n payload = '1'\\n message_response = \\\"It's all right\\\"\\n if feedback_methods.add_entry(user_name, user_methods.get_subject_from_user(user_name), payload):\\n send_message(PAT, response_handler.text_message(sender, \\\"You chose: \\\" + \\\"'\\\" +\\n message_response + \\\"'\\\" + \\\"\\\\nFeedback Received!\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n else:\\n send_message(PAT,\\n response_handler.text_message(sender, \\\"There is either no lecture active in the selected\\\"\\n \\\" subject, or you have already given feedback\\\"\\n \\\" to the active lecture.\\\\nFeedback denied!\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n elif \\\"too fast\\\" in incoming_message.lower():\\n # Adds feedback if the subject has a lecture on the given day\\n # and if the user has not already given feedback\\n payload = '2'\\n message_response = \\\"too fast\\\"\\n if feedback_methods.add_entry(user_name, user_methods.get_subject_from_user(user_name), payload):\\n send_message(PAT, response_handler.text_message(sender, \\\"You chose: \\\" + \\\"'\\\" +\\n message_response + \\\"'\\\" + \\\"\\\\nFeedback Received!\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n else:\\n send_message(PAT,\\n response_handler.text_message(sender, \\\"There is either no lecture active in the selected\\\"\\n \\\" subject, or you have already given feedback\\\"\\n \\\" to the active lecture.\\\\nFeedback denied!\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n elif (\\\"today\\\" in incoming_message.lower() and \\\"lecture\\\" in incoming_message.lower()) or \\\\\\n (\\\"todays\\\" in incoming_message.lower() and \\\"lecture\\\" in incoming_message.lower()) or \\\\\\n (\\\"today's\\\" in incoming_message.lower() and \\\"lecture\\\" in incoming_message.lower()):\\n # Gathers the correct information about the date.\\n year = feedback_methods.get_year()\\n week = feedback_methods.get_week()\\n day = feedback_methods.get_day()\\n subject = user_methods.get_subject_from_user(user_name)\\n # Gathers the feedback from today's lecture:\\n if lecture_methods.check_lecture_in_db(subject):\\n feedback_list = feedback_methods.get_single_lecture_feed(year, week, day, subject)\\n if feedback_list[0] is not None:\\n send_message(PAT, response_handler.present_single_lecture_feedback(sender, feedback_list))\\n send_message(PAT,\\n response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n else:\\n send_message(PAT, response_handler.text_message(sender,\\n \\\"No feedback for the given lecture on this date. \\\"\\n \\\"Please try again at a later date.\\\"))\\n send_message(PAT,\\n response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n else:\\n send_message(PAT, response_handler.text_message(sender, \\\"No lecture present in the database. \\\"\\n \\\"Please provide some feedback and try again.\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n elif payload == \\\"get schedule\\\" or \\\"get schedule\\\" in incoming_message.lower():\\n subject = user_methods.get_subject_from_user(user_name)\\n schedule = subject_info.printable_schedule(subject_info.get_schedule(subject))\\n if len(schedule) > 640:\\n msg_list = message_split.message_split(schedule)\\n for msg in msg_list:\\n print(msg)\\n send_message(PAT, response_handler.text_message(sender, msg))\\n else:\\n send_message(PAT, response_handler.text_message(sender, schedule))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n elif payload == \\\"get info\\\" or \\\"get info\\\" in incoming_message.lower():\\n subject = user_methods.get_subject_from_user(user_name)\\n send_message(PAT, response_handler.text_message(sender,\\n subject_info.printable_course_info(\\n subject_info.get_course_json(subject))))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n elif payload == \\\"get feedback\\\" or \\\"get feedback\\\" in incoming_message.lower():\\n send_message(PAT, response_handler.get_feedback_specific_or_all(sender))\\n\\n elif payload == \\\"all_lectures\\\" or \\\"all lectures\\\" in incoming_message.lower():\\n # The user wants to see feedback for all lectures in the selected subject\\n subject = user_methods.get_subject_from_user(user_name)\\n if not lecture_methods.check_lecture_in_db(subject):\\n send_message(PAT, response_handler.text_message(sender, \\\"Course has no feedback.\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n else:\\n feedback, feedbackevaluation = feedback_methods.get_all_subject_feed(subject)\\n if len(feedback) > 0:\\n percent_list = bot_feedback.generate_percent_for_speed(feedback)\\n send_message(PAT, response_handler.all_feedback_speed(sender, subject, percent_list))\\n else:\\n send_message(PAT,\\n response_handler.text_message(sender, \\\"Course has no feedback for lecture speed.\\\"))\\n if len(feedbackevaluation) > 0:\\n percent_list_questions = bot_feedback.generate_percent_for_questions(feedbackevaluation)\\n\\n send_message(PAT, response_handler.all_feedback_questions(sender, subject, percent_list_questions))\\n else:\\n send_message(PAT,\\n response_handler.text_message(sender, \\\"Course has no feedback for lecture questions.\\\"))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n elif payload == \\\"a_specific_lecture\\\" or \\\"a specific lecture\\\" in incoming_message.lower():\\n # Let the user choose what year to get feedback from.\\n years = lecture_feedback_db_methods.get_year(user_methods.get_subject_from_user(user_name))\\n if len(years) > 0:\\n send_message(PAT, response_handler.get_feedback_year(sender, years))\\n else:\\n send_message(PAT, response_handler.text_message(sender, 'No feedback for the selected subject.'))\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n elif payload is not None:\\n # Underneath are check that use .split() on the payload.\\n if \\\"evaluation_questions\\\" in payload.split()[0]:\\n payload_split = payload.split()\\n if len(payload_split) == 1:\\n # 1st question\\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\\n elif len(payload_split) == 2:\\n # 2nd question\\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\\n elif len(payload_split) == 3:\\n # 3rd question\\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\\n elif len(payload_split) == 4:\\n # 4th question\\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\\n elif len(payload_split) == 5:\\n # 5th question\\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\\n elif len(payload_split) == 6:\\n # 6th question\\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\\n elif len(payload_split) == 7:\\n # 7th question\\n send_message(PAT, response_handler.lecture_feedback_questions(sender, payload))\\n elif len(payload_split) == 8:\\n # store feedback.\\n subject = user_methods.get_subject_from_user(user_name)\\n if feedback_methods.add_feedback_evaluation(user_name, subject, int(payload_split[1]),\\n int(payload_split[2]), int(payload_split[3]),\\n int(payload_split[4]), int(payload_split[5]),\\n int(payload_split[6]), int(payload_split[7])):\\n # Storing the feedback succeeded.\\n send_message(PAT, response_handler.text_message(sender, 'Feedback received!'))\\n send_message(PAT, response_handler.has_course(sender, subject))\\n else:\\n # Storing the feedback failed.\\n send_message(PAT, response_handler.text_message(sender,\\n \\\"There is either no lecture active in the \\\"\\n \\\"selected subject, or you have already given \\\"\\n \\\"feedback to the active lecture.\\\\n Feedback \\\"\\n \\\"denied!\\\"))\\n send_message(PAT, response_handler.has_course(sender, subject))\\n pass\\n\\n elif \\\"get_lecture_feedback_year\\\" in payload.split()[0]:\\n # Let the user choose what semester to get feedback from.\\n semesters = []\\n if lecture_feedback_db_methods.check_lecture_semester(user_methods.get_subject_from_user(user_name),\\n 1, 17, int(payload.split()[1])):\\n semesters.append('Spring')\\n elif lecture_feedback_db_methods.check_lecture_semester(user_methods.get_subject_from_user(user_name),\\n 32, 49, int(payload.split()[1])):\\n semesters.append('Fall')\\n if len(semesters) > 0:\\n send_message(PAT, response_handler.get_feedback_semester(sender, payload.split()[1], semesters))\\n else:\\n # Take the user one step up to choose a different year.\\n years = lecture_feedback_db_methods.get_year(user_methods.get_subject_from_user(user_name))\\n send_message(PAT, response_handler.get_feedback_year(sender, years))\\n\\n elif \\\"get_lecture_feedback_semester\\\" in payload.split()[0]:\\n # Let the user choose what weeks to get feedback from.\\n\\n week_list = lecture_feedback_db_methods.get_lecture_weeks(user_methods.get_subject_from_user(user_name),\\n int(payload.split()[1]), payload.split()[2])\\n if len(week_list) > 8:\\n send_message(PAT, response_handler.get_feedback_month(sender, payload.split()[1], week_list))\\n else:\\n send_message(PAT, response_handler.get_feedback_week(sender, payload.split()[1], week_list))\\n\\n elif \\\"get_lecture_feedback_month\\\" in payload.split()[0]:\\n # Let the user select week\\n week_list = []\\n payload_split = payload.split()\\n for i in range(2, len(payload_split)):\\n week_list.append(int(payload_split[i].rstrip(',')))\\n\\n send_message(PAT, response_handler.get_feedback_week(sender, payload_split[1], week_list))\\n\\n elif \\\"get_lecture_feedback_week\\\" in payload.split()[0]:\\n # Lets the user select day\\n lecture_days = lecture_feedback_db_methods.get_day_of_lecture_in_week(\\n user_methods.get_subject_from_user(user_name), payload.split()[1], payload.split()[2])\\n\\n send_message(PAT, response_handler.get_feedback_day(sender, payload.split()[1], lecture_days,\\n payload.split()[2]))\\n\\n elif \\\"get_lecture_feedback_day\\\" in payload.split()[0]:\\n\\n subject = user_methods.get_subject_from_user(user_name)\\n # Gives the user feedback from the selected day.\\n feedback_list = feedback_methods.get_single_lecture_feed(payload.split()[1],\\n payload.split()[2],\\n payload.split()[3],\\n subject)\\n feedback_questions_list = feedback_methods.get_single_lecture_feedback_questions(payload.split()[1],\\n payload.split()[2],\\n payload.split()[3],\\n subject)\\n\\n if len(feedback_list[1]) > 0: # Checks if there is feedback in the variable.\\n send_message(PAT, response_handler.present_single_lecture_feedback(sender, feedback_list))\\n else:\\n send_message(PAT, response_handler.text_message(sender,\\n \\\"This lecture has no feedback for lecture speed.\\\"))\\n if len(feedback_questions_list) > 0: # Checks if there is feedback in the variable.\\n feedback_questions = bot_feedback.generate_percent_for_questions(feedback_questions_list)\\n send_message(PAT,\\n response_handler.present_single_lecture_feedback_questions(sender, feedback_questions))\\n else:\\n send_message(PAT, response_handler.text_message(sender,\\n \\\"This lecture has no feedback for lecture \\\"\\n \\\"questions.\\\"))\\n\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n elif ime_data_fetch.subject_exists_boolean(incoming_message.upper().split()[0]):\\n if user_methods.has_user(user_name):\\n user_methods.add_subject(user_name, incoming_message.split()[0])\\n else:\\n user_methods.add_user(user_name, incoming_message.split()[0])\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n\\n else:\\n send_message(PAT, response_handler.text_message(sender,\\n \\\"Type 'help' to see what you can do with L.I.M.B.O.\\\\n If \\\"\\n \\\"you tried to enter a subject-code and got this message,\\\"\\n \\\" you either misspelled it or the subject you are looking \\\"\\n \\\"for is not a subject at NTNU.\\\"))\\n if user_methods.has_user(user_name):\\n send_message(PAT, response_handler.has_course(sender, user_methods.get_subject_from_user(user_name)))\\n else:\\n send_message(PAT, response_handler.no_course(sender))\\n\\n return \\\"ok\\\"\",\n \"def display_message():\",\n \"async def chat_message(self, event):\\n await self.send(\\n {'type': \\\"websocket.send\\\",\\n 'text': event['response_data']}\\n )\",\n \"def updateChat(self, ):\\n self.__redrawChat()\",\n \"def homepage(self):\\n print('-=' * 12 + \\\" Home Page \\\" + '-=' * 12)\\n self._user.list_contacts()\\n options = {1: self.add_contact, 2:self.remove_contact ,3: self.view_contact_chat, 4: self.sign_out, 5: self.exit}\\n print_out = \\\"(1) Add new contact \\\\n (2) Remove Contact \\\\n (3) View my chats \\\\n (4) Sign out \\\\n (5) Exit\\\"\\n return self._take_option(options, print_out)\",\n \"def handle_chat(self):\\n while True:\\n if self.chat_breakout:\\n return\\n\\n time.sleep(1)\\n messages = \\\"\\\"\\n for i in range(5):\\n try:\\n messages += f\\\"{self.queue.popleft()}\\\\n\\\"\\n except IndexError:\\n # Queue is empty but no worries\\n continue\\n\\n if messages != \\\"\\\":\\n self.loop.create_task(\\n self.ingame_cog.send_chat_to_discord(\\n self.bot, self.channel, messages\\n )\\n )\",\n \"def have_a_chat():\\n \\n chat = True\\n while chat:\\n\\n # Get a message from the user\\n msg = input('INPUT :\\\\t')\\n out_msg = None\\n\\n # Check if the input is a question\\n question = is_question(msg)\\n\\n # Prepare the input message\\n msg = prepare_text(msg)\\n\\n # Check for an end msg \\n if end_chat(msg):\\n out_msg = 'Bye!'\\n chat = False \\n\\n # Check for a selection of topics that we have defined to respond to\\n # Here, we will check for a series of topics that we have designed to answer to\\n if not out_msg:\\n\\n # Initialize to collect a list of possible outputs\\n outs = []\\n # Check if the input looks like a greeting, add a greeting output if so\\n outs.append(selector(msg, GREETINGS_IN, GREETINGS_OUT))\\n # Check if the input looks like a computer thing, add a computer output if so\\n outs.append(selector(msg, COMP_IN, COMP_OUT))\\n outs.append(selector(msg, NAMES, RANDOM_LIST))\\n # Check if the input mentions a person that is specified, add a person output if so\\n if is_in_list(msg, PEOPLE_IN):\\n name = find_in_list(msg, PEOPLE_IN)\\n outs.append(list_to_string([PEOPLE_NAMES[name], name.capitalize(),\\n selector(msg, PEOPLE_IN, PEOPLE_OUT)], ' '))\\n\\n # Check if the input looks like a joke, add a repeat joke output if so\\n outs.append(respond_echo(selector(msg, JOKES_IN, JOKES_OUT), 3, ''))\\n \\n # Check if the input has some words we don't want to talk about, say that, if so\\n if is_in_list(msg, NONO_IN):\\n outs.append(list_to_string([selector(msg, NONO_IN, NONO_OUT), find_in_list(msg, NONO_IN)], ' '))\\n\\n # IF YOU WANTED TO ADD MORE TOPICS TO RESPOND TO, YOU COULD ADD THEM IN HERE\\n outs.append(new_selector(msg, PYTHON_TERM_INPUTS, PYTHON_TERM_OUT))\\n outs.append(identity(msg, NAMES, RANDOM_LIST))\\n outs.append(quiz_trigger(msg))\\n outs.append(quizzer(msg))\\n # We could have selected multiple outputs from the topic search above (if multiple return possible outputs)\\n # We also might have appended None in some cases, meaning we don't have a reply\\n # To deal with this, we are going to randomly select an output from the set of outputs that are not None\\n options = list(filter(None, outs))\\n if options:\\n out_msg = random.choice(options)\\n\\n # If we don't have an output yet, but the input was a question, return msg related to it being a question\\n if not out_msg and question:\\n out_msg = QUESTION\\n\\n # Catch-all to say something if msg not caught & processed so far\\n if not out_msg:\\n out_msg = random.choice(UNKNOWN)\\n\\n print('OUTPUT:', out_msg)\",\n \"def on_welcome(self, raw_msg, server, port, nickname, **kwargs):\",\n \"def index(request):\\n messages = SESSION.get_messages_sent_list(request.session)\\n feedbacks = SESSION.get_messages_received_list(request.session)\\n \\n # initially display the first 20 messages/feedback chronologically\\n messages.sort(key=lambda r: r.createdAt, reverse=True)\\n feedbacks.sort(key=lambda r: r.createdAt, reverse=True)\\n \\n # prepare our template context variables use in our messages template\\n data = {\\n 'messages_nav': True,\\n 'messages': messages[:PAGINATION_THRESHOLD],\\n 'feedback': feedbacks[:PAGINATION_THRESHOLD],\\n 'pag_threshold': PAGINATION_THRESHOLD,\\n 'pag_page': 1,\\n 'sent_count': len(messages),\\n 'feedback_count': len(feedbacks),\\n 'tab_feedback': request.GET.get('tab_feedback'),\\n }\\n \\n if SESSION.get_patronStore_count(request.session):\\n data['has_patrons'] = True\\n \\n # inserting this success and error message into the template\\n # should be done in a cleaner way - this was done by the first guy\\n # I just didn't bother changing it.\\n if request.GET.get(\\\"success\\\"):\\n data['success'] = request.GET.get(\\\"success\\\")\\n if request.GET.get(\\\"error\\\"):\\n data['error'] = request.GET.get(\\\"error\\\")\\n \\n return render(request, 'manage/messages.djhtml', data)\",\n \"def _send_msg(self, contact):\\n msg_content = input('{} :'.format(self._user.username))\\n if msg_content == '0': \\n return self.homepage()\\n self._user.send_msg(contact, msg_content)\\n\\n return self._send_msg(contact)\",\n \"def __init__(self, master=None, *args, **kw):\\n Tix.Frame.__init__(self, master, *args, **kw)\\n self._init_widgets()\\n self._init_bindings()\\n self._root().title(\\\"KaeChat\\\")\\n self._page_serial = 0\",\n \"def receive():\\n\\n buttons_frame = tkinter.Frame(top)\\n while True:\\n\\n try:\\n msg = client_socket.recv(BUFSIZ).decode(\\\"utf8\\\")\\n # print(msg)\\n if msg == '{quit}':\\n break\\n elif '{prequest}' in msg[0:12]:\\n name = msg[11:]\\n handle_connection_request(name)\\n elif '{name}' in msg[0:6]:\\n print(msg)\\n uname.insert(tkinter.END, msg[7:])\\n elif '{namelist}' in msg[0:12]:\\n nlist = msg.split('_')[1]\\n name_list = nlist.split(',')[1:]\\n print(name_list)\\n\\n\\n buttons_frame.destroy()\\n buttons_frame = tkinter.Frame(top)\\n for name in name_list:\\n private_button = tkinter.Button(buttons_frame, text=name, command=lambda user=name: create_private(user))\\n private_button.pack(side=tkinter.LEFT)\\n buttons_frame.pack(side=tkinter.LEFT)\\n\\n else:\\n msg_list.insert(tkinter.END, msg)\\n except OSError: # Possibly client has left the chat.\\n break\",\n \"def main(self ):\\n print(\\\"Good morning, I hope you are well today.\\\")\\n print(\\\"What can I do for you?\\\")\\n while True:\\n sentence = input(\\\"\\\\n>> \\\")\\n if sentence.upper() == \\\"QUIT\\\":\\n print(\\\"Have a nice day!\\\")\\n break\\n print(Doctor().reply(sentence))\",\n \"def run(self):\\n self.print_welcome()\\n self.handle_inputs()\",\n \"def start(update, context):\\n ch = update.message.chat\\n chatID = ch.id\\n uname = ch.username\\n fname = ch.first_name\\n lname = ch.last_name\\n if ch.id in CR.ADMINS_id: admin_level = 0\\n else: admin_level = 3\\n txt = 'Hola!\\\\n'\\n txt += 'Para ver los comandos disponibles: /help\\\\n'\\n # txt += tool.help_txt()\\n txt += '\\\\n\\\\nActualizaciones y noticias de este bot en el canal:\\\\n'\\n txt += 'https://t.me/parapentebotWiki'\\n context.bot.send_message(chat_id=update.message.chat_id, text=txt,\\n disable_web_page_preview=True)\\n conn,c = admin.connect(RP.DBname)\\n try:\\n ad_lv = min([usr[-2] for usr in admin.get_user(conn,'chatid',chatID)])\\n admin_level = min([admin_level,ad_lv])\\n except admin.EntryNotFound: admin_level = 4\\n admin.insert_user(conn,chatID,uname,fname,lname,admin_level,0)\",\n \"def main():\\r\\n return render_template(\\\"UI.html\\\")\",\n \"async def chat_message(self, event):\\n\\n print(\\\"PublicChatConsumer\\\", \\\"chat_message from user\\\", event[\\\"user_id\\\"])\\n await self.send_json({\\n \\\"msg_type\\\": MSG_TYPE_MESSAGE,\\n \\\"profile_image\\\": event[\\\"profile_image\\\"],\\n \\\"username\\\": event[\\\"username\\\"],\\n \\\"user_id\\\": event[\\\"user_id\\\"],\\n \\\"message\\\": event[\\\"message\\\"],\\n \\\"natural_timestamp\\\": humanize_or_normal(timezone.now())\\n })\",\n \"def twitchChat():\\n def joinchat():\\n \\\"\\\"\\\"\\n This function first tries to join the chat by recieving the standard protocol messages that twitch sends to a irc client\\n This sends all messages to the laodingComplete function to see if the correct message has confirmed the connection\\n :return:\\n \\\"\\\"\\\"\\n Loading = True\\n while Loading:\\n readbuffer_join = sock.recv(1024).decode()\\n for line in readbuffer_join.split(\\\"\\\\n\\\")[0:-1]:\\n print(line)\\n Loading = loadingComplete(line)\\n\\n def loadingComplete(line):\\n \\\"\\\"\\\"\\n This function takes messages one at a time to see if they include the line End of Names list which confirms the bot is connected to the channel\\n Once connceted the bot sends a message to confirm its in the chat room\\n\\n :param Line is a message by message stream of the twitch chat\\n :return:\\n \\\"\\\"\\\"\\n\\n if(\\\"End of /NAMES list\\\" in line):\\n print(\\\"Bot has joined \\\" + channel +\\\"'s channel\\\")\\n sendMessage(sock, \\\"Chat room is popping\\\")\\n return False\\n else:\\n return True\\n\\n def sendMessage(sock, message):\\n messageTemp = \\\"PRIVMSG \\\" + channel +\\\" :\\\" +message\\n sock.send((messageTemp+ \\\"\\\\n\\\").encode())\\n\\n def getUser(line):\\n \\\"\\\"\\\"\\n This parses a message that is received by the bot that contains lots of information into just the username\\n :param line:\\n :return:\\n \\\"\\\"\\\"\\n seperate = line.split(\\\":\\\", 2)\\n user = seperate[1].split(\\\"!\\\",1)[0]\\n return user\\n\\n def getMessage(line):\\n \\\"\\\"\\\"\\n This parses the incoming data from line to get just the chat message\\n :param Line is a message by message stream of the twitch chat\\n :return:\\n \\\"\\\"\\\"\\n global message\\n try:\\n message = (line.split(\\\":\\\",2))[2]\\n except:\\n message=\\\"\\\"\\n return message\\n\\n def Console(line):\\n if \\\"PRIVMSG\\\" in line:\\n return False\\n else:\\n return True\\n joinchat()\\n\\n\\n while True:\\n try:\\n readbuffer = sock.recv(1024).decode()\\n except:\\n readbuffer =\\\"\\\"\\n\\n for line in readbuffer.split(\\\"\\\\r\\\\n\\\"):\\n if line == \\\"\\\":\\n pass\\n elif \\\"PING\\\" in line and Console(line):\\n pongMSG = \\\"PONG tmi.chat.twitch.tv\\\\r\\\\n\\\".encode()\\n sock.send(pongMSG)\\n print(pongMSG)\\n continue\\n else:\\n user = getUser(line)\\n messagePrint = getMessage(line)\\n\\n print(user+\\\": \\\" +messagePrint)\",\n \"def choose_path(self):\\r\\n if not self.receive_thread.is_alive():\\r\\n self.receive_thread.start()\\r\\n self.clear_screen()\\r\\n self.refresh_button = None\\r\\n user_label = Label(self.root, text=\\\"Hello \\\" + self.username, font=self.title_font,\\r\\n bg=self.bg_color, height=2)\\r\\n user_label.pack(pady=10, padx=50)\\r\\n button_read_msg = Button(self.root, text=\\\"read messages\\\", font=self.text_font,\\r\\n height=2, width=20, command=lambda: self.read_messages(1))\\r\\n button_read_msg.pack(pady=20, padx=50)\\r\\n\\r\\n button_send_msg = Button(self.root, text=\\\"send message\\\", font=self.text_font,\\r\\n height=2, width=20, command=self.send_messages)\\r\\n button_send_msg.pack(pady=20, padx=50)\\r\\n\\r\\n button_simulator = Button(self.root, text=\\\"simulator\\\", font=self.text_font,\\r\\n height=2, width=20, command=self.simulator)\\r\\n button_simulator.pack(pady=20, padx=50)\\r\\n\\r\\n button_read = Button(self.root, text=\\\"About\\\", font=self.text_font,\\r\\n height=2, width=20, command=self.about_screen)\\r\\n button_read.pack(pady=20, padx=50)\",\n \"def _send_homepage(self, message=''):\\n template_filename = self._get_config_template('homepage')\\n text = read_template(\\n template_filename,\\n title=SERVER_NAME,\\n header=SERVER_NAME,\\n msg=message)\\n self._send_response(text, 200)\",\n \"def get(self):\\r\\n self.checkSession(self.request.headers.get('Cookie'))\\r\\n try:\\r\\n self.insertMenu()\\r\\n self.getSentMSG(self.Session['userid'])\\r\\n except:\\r\\n self.response.out.write(\\\"Some error occured\\\")\",\n \"def run(self):\\n if has_GUI:\\n self.GUI(self.buffer)\\n else:\\n while True:\\n message = input(\\\"Write your command:\\\\n\\\")\\n # print(message)\\n self.buffer.append(message)\",\n \"def about(self):\\n self.main_window.message(\\n width=200, aspect=100, justify=tkinter.CENTER,\\n text=\\\"Jeu de Ping\\\\n\\\\n\\\"\\n \\\"(C) Maximin Duvillard, August 2022.\\\\nLicence = GPL\\\")\",\n \"def show_main():\\n db = get_db()\\n now = datetime.datetime.now()\\n rooms = app.config[\\\"HC_CONFIG\\\"].values()\\n rooms.sort(key=config_lexer.Room.sortkey)\\n # Get the three newest counts from the database, sorted by the\\n # user-provided time\\n newest_counts = db.get_newest_counts(3, hc_db.NewestSort.ENTERED_TIME)\\n recent_counts = []\\n for count in newest_counts:\\n room_rows = db.get_roomdata_for_count_id(count[\\\"id\\\"])\\n # I couldn't think of a short, descriptive name for this variable.\\n some_dict = {\\\"date\\\": count[\\\"entered_time\\\"], \\\"counts\\\": {}}\\n for row in room_rows:\\n some_dict[\\\"counts\\\"][row[\\\"room\\\"]] = row[\\\"people_count\\\"]\\n some_dict[\\\"counts\\\"] = OrderedDict(\\n sorted(some_dict[\\\"counts\\\"].items(), key=sort_count_data)\\n )\\n recent_counts.append(some_dict)\\n if is_admin(session[\\\"username\\\"]):\\n buttons = [\\n NavButton(url_for(\\\"show_admin\\\"), \\\"Administration\\\"),\\n NavButton(url_for(\\\"show_help\\\"), \\\"Help\\\"),\\n NavButton(url_for(\\\"logout\\\"), \\\"Log Out\\\"),\\n ]\\n else:\\n buttons = [\\n NavButton(url_for(\\\"show_help\\\"), \\\"Help\\\"),\\n NavButton(url_for(\\\"logout\\\"), \\\"Log Out\\\"),\\n ]\\n return render_template(\\n \\\"main.html\\\",\\n buttons=buttons,\\n rooms=rooms,\\n recent_counts=recent_counts,\\n datewhen=now.strftime(\\\"%Y-%m-%d\\\"),\\n timewhen=now.strftime(\\\"%H:%M\\\"),\\n )\",\n \"def index():\\n pages = fbpl().select(fbpl.page.ALL, orderby=fbpl.page.id)\\n user = auth.user\\n if user:\\n response.flash = T('You are %(name)s', dict(name=user['first_name']))\\n return dict(message=T('Hello, Facebook is telling that you are %(first_name)s %(last_name)s', dict(first_name=user['first_name'], last_name=user['last_name'])),pages=pages)\\n response.flash = T('Welcome to web2py')\\n\\n return dict(message=T('Hello, please login'),pages=pages)\",\n \"async def joinChat(self,ctx):\\n await ctx.send('Joining the chat kiddo')\",\n \"def display_messages(self):\\n\\n\\t\\twhile self.joined:\\n\\t\\t\\tif len(self.messages) != 0:\\n\\t\\t\\t\\tfor msg in self.messages:\\n\\t\\t\\t\\t\\t#: If the message is empty, ignore it.\\n\\t\\t\\t\\t\\tif msg == \\\"\\\":\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\t\\t#: If the message is close\\\", then the server has told the client\\n\\t\\t\\t\\t\\t#: to shut down, so it will. This is not an issue, as users\\n\\t\\t\\t\\t\\t#: messages will always have an identifier and : before their\\n\\t\\t\\t\\t\\t#: message, thus,the only messages that don't include an\\n\\t\\t\\t\\t\\t#: identifier will be from the server itself.\\n\\t\\t\\t\\t\\telif msg[:5] == \\\"close\\\":\\n\\n\\t\\t\\t\\t\\t\\treason = msg[6:]\\n\\n\\t\\t\\t\\t\\t\\tprint(\\\"This client was closed due to {}.\\\".format(reason))\\n\\t\\t\\t\\t\\t\\tself.quit(True)\\n\\n\\t\\t\\t\\t\\t#: Otherwise, print the message to the commandline.\\n\\t\\t\\t\\t\\telif not self.silent:\\n\\t\\t\\t\\t\\t\\tprint('\\\\r' + msg, end='')\\n\\n\\t\\t\\t\\t\\t\\tprint(\\\"\\\\nYou: \\\", end='')\\n\\t\\t\\t\\t\\t\\tself.displayed_you = True\\n\\n\\t\\t\\t\\t\\t#: Remove the processed message\\n\\t\\t\\t\\t\\tself.messages.remove(msg)\",\n \"def message_center(request, url):\\n\\n if request.POST and url is not 'sent':\\n message = Message.objects.get(id=request.POST.get(\\\"message_id\\\"))\\n if message.read:\\n message.read = 0\\n message.save()\\n else:\\n message.read = 1\\n message.save()\\n\\n all_messages = Message.objects.all().order_by(\\\"-time\\\")\\n user_messages = []\\n section = \\\"all\\\"\\n if url is 'read':\\n section = \\\"read\\\"\\n for m in all_messages:\\n if m.user_to.__str__() in request.user.userprofile.get_full_name() and m.read:\\n user_messages.append(m)\\n\\n elif url is 'unread':\\n section = \\\"unread\\\"\\n for m in all_messages:\\n if m.user_to.__str__() in request.user.userprofile.get_full_name() and not m.read:\\n user_messages.append(m)\\n\\n elif url is 'sent':\\n section = \\\"sent\\\"\\n for m in all_messages:\\n if m.user_from.__str__() in request.user.userprofile.get_full_name():\\n user_messages.append(m)\\n\\n elif url is 'all':\\n for m in all_messages:\\n if m.user_to.__str__() in request.user.userprofile.get_full_name():\\n user_messages.append(m)\\n\\n elif url is 'compose':\\n section = 'compose'\\n\\n the_users = UserProfile.objects.exclude(user=request.user)\\n\\n return render(request, 'message_center.html', {'msgs': user_messages, 'the_users': the_users,\\n 'display': section})\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73176163","0.71010524","0.6999558","0.69722027","0.69469863","0.6837348","0.682718","0.6773148","0.6714431","0.6714431","0.6714431","0.6694911","0.66823596","0.66393375","0.6454156","0.6447436","0.641861","0.6282684","0.62469214","0.62395823","0.6222036","0.6217741","0.62104994","0.618883","0.618798","0.6187217","0.6183843","0.6177985","0.61721265","0.6166228","0.61501616","0.6142551","0.6142253","0.6140829","0.6140573","0.6136645","0.61362404","0.6123005","0.611398","0.6105695","0.60924023","0.6062133","0.6056847","0.6052982","0.6048019","0.6032404","0.6031991","0.601658","0.6014662","0.60066694","0.6004163","0.6003215","0.59885347","0.59704834","0.5968927","0.59606385","0.5958515","0.59573096","0.59348816","0.592411","0.5913747","0.5912888","0.5893336","0.58860797","0.58766145","0.5852584","0.5851894","0.5817907","0.58111775","0.5802875","0.58010364","0.57987225","0.5791947","0.5788023","0.5785719","0.57833266","0.57796","0.57776684","0.5777284","0.57748455","0.57737154","0.576396","0.5760218","0.575983","0.5758403","0.5757097","0.57539344","0.5752175","0.57461596","0.57431936","0.57334393","0.5715343","0.5715178","0.5713728","0.5700076","0.56872493","0.56867814","0.56864744","0.5685658","0.5683458"],"string":"[\n \"0.73176163\",\n \"0.71010524\",\n \"0.6999558\",\n \"0.69722027\",\n \"0.69469863\",\n \"0.6837348\",\n \"0.682718\",\n \"0.6773148\",\n \"0.6714431\",\n \"0.6714431\",\n \"0.6714431\",\n \"0.6694911\",\n \"0.66823596\",\n \"0.66393375\",\n \"0.6454156\",\n \"0.6447436\",\n \"0.641861\",\n \"0.6282684\",\n \"0.62469214\",\n \"0.62395823\",\n \"0.6222036\",\n \"0.6217741\",\n \"0.62104994\",\n \"0.618883\",\n \"0.618798\",\n \"0.6187217\",\n \"0.6183843\",\n \"0.6177985\",\n \"0.61721265\",\n \"0.6166228\",\n \"0.61501616\",\n \"0.6142551\",\n \"0.6142253\",\n \"0.6140829\",\n \"0.6140573\",\n \"0.6136645\",\n \"0.61362404\",\n \"0.6123005\",\n \"0.611398\",\n \"0.6105695\",\n \"0.60924023\",\n \"0.6062133\",\n \"0.6056847\",\n \"0.6052982\",\n \"0.6048019\",\n \"0.6032404\",\n \"0.6031991\",\n \"0.601658\",\n \"0.6014662\",\n \"0.60066694\",\n \"0.6004163\",\n \"0.6003215\",\n \"0.59885347\",\n \"0.59704834\",\n \"0.5968927\",\n \"0.59606385\",\n \"0.5958515\",\n \"0.59573096\",\n \"0.59348816\",\n \"0.592411\",\n \"0.5913747\",\n \"0.5912888\",\n \"0.5893336\",\n \"0.58860797\",\n \"0.58766145\",\n \"0.5852584\",\n \"0.5851894\",\n \"0.5817907\",\n \"0.58111775\",\n \"0.5802875\",\n \"0.58010364\",\n \"0.57987225\",\n \"0.5791947\",\n \"0.5788023\",\n \"0.5785719\",\n \"0.57833266\",\n \"0.57796\",\n \"0.57776684\",\n \"0.5777284\",\n \"0.57748455\",\n \"0.57737154\",\n \"0.576396\",\n \"0.5760218\",\n \"0.575983\",\n \"0.5758403\",\n \"0.5757097\",\n \"0.57539344\",\n \"0.5752175\",\n \"0.57461596\",\n \"0.57431936\",\n \"0.57334393\",\n \"0.5715343\",\n \"0.5715178\",\n \"0.5713728\",\n \"0.5700076\",\n \"0.56872493\",\n \"0.56867814\",\n \"0.56864744\",\n \"0.5685658\",\n \"0.5683458\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.63437665"},"document_rank":{"kind":"string","value":"17"}}},{"rowIdx":17764,"cells":{"query":{"kind":"string","value":"Route for handling logging in"},"document":{"kind":"string","value":"def login():\n error = None\n \n if request.method == 'POST':\n if not db.login(request.form['username'], request.form['password']):\n error = 'Invalid username or password. Please try again!'\n else:\n resp = make_response(redirect(url_for('main')))\n resp.set_cookie('username', request.form['username'])\n resp.set_cookie('password', request.form['password'])\n return resp\n return render_template('login.html', error = error)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def log_login(sender, request, user, **kwargs):\n stracks.user(user).log(\"? has logged in\", action=stracks.login())","def handle_login(self, request):\n self._verify_headers(request)\n self._verify_auth_parameters(request, check_session=False)\n data = self.responses['login']\n mlid = tags.uint32_tag('mlid', data.session)\n mlog = tags.container_tag('mlog', mlid)\n return web.Response(body=mlog, status=data.status)","def logged_in(handler):\n\n def check_login(self, *args, **kwargs):\n jwtToken = self.request.headers.get('Authorization', None)\n\n if jwtToken:\n # validate token\n try:\n userToken = jwt.decode(\n jwtToken,\n defaultConfig.config['jwt_secret'],\n algorithms=[defaultConfig.config['jwt_algorithms']],\n issuer=defaultConfig.config['app_name']\n )\n except (jwt.DecodeError, jwt.ExpiredSignatureError), e:\n self.responseJSON('TOKEN_EXPIRED', **{\n 'data': [str(e)]\n })\n return\n\n # check token created time and changed password time, if < -> token invalid\n try:\n myUser = UserModel.get_by_id(userToken['id'])\n assert myUser is not None\n except:\n self.responseJSON('DATA_NOTFOUND')\n return\n\n if myUser.date_change_password != None:\n if Helper.timestampToDatetime(userToken['iat']) < myUser.date_change_password:\n self.responseJSON('TOKEN_INVALID_TIME')\n return\n\n # check user logged out\n if myUser.status == UserModel.STATUS_LOGOUT:\n self.responseJSON('TOKEN_INVALID')\n return\n\n # authorization system\n else:\n self.abort(403)\n\n return handler(self, userToken, *args, **kwargs)\n return check_login","def _login(self, *args, **kwargs):\n pass","def log_in(self):\n\t\tpass","def log_in():\n if request.method == 'POST':\n username = request.form['username']\n password = request.form['password']\n if PLAN.login_user(username, password):\n session['name'] = username\n flash(\"Login success ...\")\n return redirect(url_for('index'))\n flash(\"Login failed ...\")\n return render_template('login.html')\n return render_template('login.html')","def post_login(self, came_from=lurl('/')):\n if not request.identity:\n login_counter = request.environ.get('repoze.who.logins', 0) + 1\n redirect('/login',\n params=dict(came_from=came_from, __logins=login_counter))\n userid = request.identity['repoze.who.userid']\n flash(_('Welcome back, %s!') % userid)\n\n # Do not use tg.redirect with tg.url as it will add the mountpoint\n # of the application twice.\n return HTTPFound(location=came_from)","def simple_handler(request):\n logger.debug('')\n return Response(200, 'OK', {}, '')","def log_user_logged_in(sender, request, user, **kwargs):\n\n ip = get_client_ip(request)\n date = datetime.now()\n log.info('Login user: %s , IP: %s , Date: %s', user, ip, str(date))","def login(request):\n if request.method == \"GET\":\n return {}\n if request.method == \"POST\":\n if check_credentials(request):\n username = request.POST['username']\n headers = remember(request, username)\n return HTTPFound(location=request.route_url('home_view'), headers=headers) \n return {'error': 'Invalid username or password.'}","def log_route():\n return send_file(path.join('..', 'app.log'), as_attachment=True)","def handle_login(request, login_form):\n if login_form.is_valid():\n user = login_form.login(request)\n if user:\n # Log the user in\n login(request, user)\n # Show a success message and redirect back to the index page\n thumbs_up_symbol = \"<i style='color: #4F844F;' class='fa fa-thumbs-up fa-fw'></i>\"\n messages.success(request, \"Logged In! \" + thumbs_up_symbol)\n return redirect(reverse('spacefinder:index'))\n else:\n # If the login failed then fetch error messages to render to the screen\n return login_form","def login(request):\n\n url = request.route_url('auth_signin_redirect', _app_url=get_app_url(request))\n return HTTPFound(location=url)","def route_request(self,):\n # Let's parse and prepare url, path, query etc..\n up = urlparse(self.path, 'http')\n self.log_data['url'] = path = up.path\n splitpath = path.split('/')\n if len(splitpath) == 1:\n raise HTTPError(404, 'Not found.')\n self.query = parse_qs(up.query)\n\n # Get the route\n route = self.get_route(self.http_method, path.encode('utf-8'))\n # Parse URL path\n urlvars = self.parse_path(path.encode('utf-8'), route)\n post_raw = None\n # Load POST content if any\n if self.http_method == 'POST':\n # TODO: raise an HTTP error if the content-length is\n # too large.\n try:\n post_raw = self.rfile.read(int(self.headers['Content-Length']))\n except Exception as e:\n logger.exception(str(e))\n logger.debug(self.headers)\n logger.error('Unable to read post data')\n raise HTTPError(400, 'Unable to read post data')\n\n username = None\n checked = False\n\n # Authentication checking out\n\n # 1. Try the auth' by key: if this method is available for this API\n # and 'key' arg exists.\n key = self.headers.get('X-TemBoard-Agent-Key')\n if key:\n logger.debug(\"Authentication by key from header.\")\n elif 'key' in self.query:\n # TODO: Remove auth from query in 8.0\n key = self.query['key'][0]\n logger.debug(\"Authentication by key from argument.\")\n\n if route['check_key'] and key:\n if self.app.config.temboard.key is None:\n raise HTTPError(401, \"Authentication key not configured\")\n if key != self.app.config.temboard.key:\n raise HTTPError(401, \"Invalid key\")\n checked = True\n\n # 2. Check session ID if available and not previously auth'd by key\n if not checked and route['check_session']:\n username = check_sessionid(self.headers, self.sessions)\n checked = True\n\n # 3. At this point, if not yet checked out and auth' by key is\n # available then we need to raise an error because 'key' arg hasn't\n # been passed and auth' by key is the only available method.\n if not checked and route['check_key']:\n raise HTTPError(401, \"Missing key\")\n\n try:\n # Load POST content expecting it is in JSON format.\n if self.http_method == 'POST':\n self.post_json = json.loads(post_raw.decode('utf-8'))\n except Exception as e:\n logger.exception(str(e))\n logger.error('Invalid json format')\n raise HTTPError(400, 'Invalid json format')\n\n http_context = dict(\n headers=self.headers,\n query=self.query,\n post=self.post_json,\n urlvars=urlvars,\n username=username,\n )\n\n # Handle the request\n func = getattr(sys.modules[route['module']], route['function'])\n self.log_data['handler'] = route['module'] + '.' + route['function']\n if route['module'] == 'temboardagent.api':\n # some core APIs need to deal with sessions\n return (200, func(http_context, self.app, self.sessions))\n else:\n # plugin\n return (200, func(http_context, self.app))","def create_log_entry_when_user_logs_in(sender, request, user, **kwargs):\n create_user_log(\n request=request,\n user=user,\n type=_account_const.AUTHENTICATION,\n action=_account_const.LOGIN\n )","def log(self):\n\n\t\theader_dict = dict(request.headers)\n\n\t\ttry:\n\t\t\ttracker_id = header_dict[\"tracker_id\"]\n\t\texcept Exception:\n\t\t\ttracker_id = None\n\t\t\n\t\ttry:\n\t\t\tuser_agent = header_dict[\"User-Agent\"]\n\t\texcept Exception:\n\t\t\tuser_agent = None\n\n\t\ttry:\n\t\t\tlanguage = header_dict[\"Accept-Language\"]\n\t\texcept Exception:\n\t\t\tlanguage = None\n\n\t\ttry:\n\t\t\treferer = header_dict[\"Referer\"]\n\t\texcept Exception:\n\t\t\treferer = None\n\n\t\ttry:\n\t\t\torigin = header_dict[\"Origin\"]\n\t\texcept Exception:\n\t\t\torigin = None\n\n\t\ttry:\n\t\t\tjson_data = request.json\n\t\texcept Exception:\n\t\t\tjson_data = None\n\n\t\ttry:\n\t\t\tplatform = request.user_agent.platform.title()\n\t\texcept Exception:\n\t\t\tplatform = None\n\n\t\ttry:\n\t\t\tbrowser = request.user_agent.browser.title()\n\t\texcept Exception:\n\t\t\tbrowser = None\n\n\t\ttry:\n\t\t\tauth_header_token = header_dict[\"Authorization\"].split(\" \")[1]\n\t\texcept Exception:\n\t\t\tauth_header_token = None\n\t\t\n\t\t## If set to run before a request: This is the default setting\n\t\tif self.pre_request:\n\t\t\t@self.app.before_request()\n\t\t\tdef run():\n\t\t\t\t## If the path accessed is in the do_not_log list, it is skipped\n\t\t\t\tif request.path in self.do_not_log:\n\t\t\t\t\treturn\n\t\t\t\t## If the path accessed is not in the do_not_log list, it is posted\n\t\t\t\telse:\n\t\t\t\t\tpost_data = {\n\t\t\t\t\t\t\"error\": None,\n\t\t\t\t\t\t\"stack_trace\": None,\n\t\t\t\t\t\t\"method\": request.method,\n\t\t\t\t\t\t\"source_ip\": request.remote_addr,\n\t\t\t\t\t\t\"url\": request.url,\n\t\t\t\t\t\t\"status_code\": 200, ## Assumed to be 200 due to the nature of the function\n\t\t\t\t\t\t\"headers\": str(header_dict),\n\t\t\t\t\t\t\"user_agent\": user_agent,\n\t\t\t\t\t\t\"language\": language,\n\t\t\t\t\t\t\"platform\": platform,\n\t\t\t\t\t\t\"browser\": browser,\n\t\t\t\t\t\t\"referer\": referer,\n\t\t\t\t\t\t\"origin\": origin,\n\t\t\t\t\t\t\"auth_header\": auth_header_token,\n\t\t\t\t\t\t\"access_time\": datetime.now().strftime(\"%A, %d %B %Y %H:%M:%S\"),\n\t\t\t\t\t\t\"logging_access_key\": self.accessKey,\n\t\t\t\t\t\t\"json\": json_data,\n\t\t\t\t\t\t\"request_params\": str(dict(request.args))\n\t\t\t\t\t}\n\n\t\t\t\t\tself.startPost(post_data)\n\n\t\t\t\t\treturn\n\n\t\t\treturn run\n\t\t\n\t\t## If set to as a wrapper to a function\n\t\telse:\n\t\t\tdef log_decorator(func):\n\n\t\t\t\t@wraps(func)\n\t\t\t\tdef execute(*args, **kwargs):\n\t\t\t\t\ttry:\n\t\t\t\t\t\tresult = func(*args, **kwargs)\n\n\t\t\t\t\t\tresult_response = make_response(result)\n\n\t\t\t\t\t\tpost_data = {\n\t\t\t\t\t\t\t\"error\": None,\n\t\t\t\t\t\t\t\"stack_trace\": None,\n\t\t\t\t\t\t\t\"method\": request.method,\n\t\t\t\t\t\t\t\"source_ip\": request.remote_addr,\n\t\t\t\t\t\t\t\"url\": request.url,\n\t\t\t\t\t\t\t\"status_code\": result_response.status_code,\n\t\t\t\t\t\t\t\"headers\": str(header_dict),\n\t\t\t\t\t\t\t\"user_agent\": user_agent,\n\t\t\t\t\t\t\t\"language\": language,\n\t\t\t\t\t\t\t\"platform\": platform,\n\t\t\t\t\t\t\t\"browser\": browser,\n\t\t\t\t\t\t\t\"referer\": referer,\n\t\t\t\t\t\t\t\"origin\": origin,\n\t\t\t\t\t\t\t\"auth_header\": auth_header_token,\n\t\t\t\t\t\t\t\"access_time\": datetime.now().strftime(\"%A, %d %B %Y %H:%M:%S\"),\n\t\t\t\t\t\t\t\"logging_access_key\": self.accessKey,\n\t\t\t\t\t\t\t\"json\": json_data,\n\t\t\t\t\t\t\t\"request_params\": str(dict(request.args))\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tself.startPost(post_data)\n\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\tresult = func(*args, **kwargs)\n\t\t\t\t\t\t\n\t\t\t\t\t\ttrace = traceback.format_exc()\n\n\t\t\t\t\t\tkwargs = {\n\t\t\t\t\t\t\t\"trace\": trace,\n\t\t\t\t\t\t\t\"exception\": str(e)\n\t\t\t\t\t\t}\n\t\t\t\t\t\t\n\t\t\t\t\t\tpost_data = {\n\t\t\t\t\t\t\t\"error\": str(e),\n\t\t\t\t\t\t\t\"stack_trace\": trace,\n\t\t\t\t\t\t\t\"method\": request.method,\n\t\t\t\t\t\t\t\"source_ip\": request.remote_addr,\n\t\t\t\t\t\t\t\"url\": request.url,\n\t\t\t\t\t\t\t\"status_code\": 500,\n\t\t\t\t\t\t\t\"headers\": str(header_dict),\n\t\t\t\t\t\t\t\"user_agent\": user_agent,\n\t\t\t\t\t\t\t\"language\": language,\n\t\t\t\t\t\t\t\"platform\": platform,\n\t\t\t\t\t\t\t\"browser\": browser,\n\t\t\t\t\t\t\t\"referer\": referer,\n\t\t\t\t\t\t\t\"origin\": origin,\n\t\t\t\t\t\t\t\"auth_header\": auth_header_token,\n\t\t\t\t\t\t\t\"access_time\": datetime.now().strftime(\"%A, %d %B %Y %H:%M:%S\"),\n\t\t\t\t\t\t\t\"logging_access_key\": self.accessKey,\n\t\t\t\t\t\t\t\"json\": json_data,\n\t\t\t\t\t\t\t\"request_params\": str(dict(request.args))\n\t\t\t\t\t\t}\n\n\t\t\t\t\t\tself.startPost(post_data)\n\t\t\t\t\t\n\t\t\t\t\treturn result\n\t\t\t\t\n\t\t\t\treturn execute\n\t\t\t\n\t\t\treturn log_decorator","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return self.login()","def handleLogin(self):\n aVar = self.session.getAttribute(self.settings.authenvar)\n self.loggedin = False\n if not aVar:\n self.currenttemplate = self.settings.logintemplate \n self.logger.debug(\"Not logged in, Login-Mask activated.\")\n return\n\n self.loggedin = True\n self.logger.debug('Loged in as: \"{}\"'.format(aVar))","def post(self):\n username = self.request.get('username')\n password = self.request.get('password')\n\n # User class login function\n u = User.login(username, password)\n if u:\n self.login(u) # BlogHandler login function\n self.redirect('/blog')\n else:\n msg = 'Invalid login'\n self.render('login-form.html', error = msg)","def loginAttempt(request):\n\n userid = request.unauthenticated_userid\n\n if userid:\n\n # TODO: Convert USERS to database object instead of local dictionary. If necessary.\n\n if userid in USERS:\n user = USERS[userid]\n\n else:\n user = _create_user(userid)\n\n if user.check_token(userid):\n headers = remember(request, userid)\n url = request.route_url('home', _app_url=get_app_url(request))\n return HTTPFound(location=url, headers=headers)\n\n url = request.route_url('failed', _app_url=get_app_url(request))\n return HTTPFound(location=url)","def require_logged_in():\n def handler(f, *args, **kwargs):\n if args[0].current_user is not None:\n return f(*args, **kwargs)\n else:\n raise HTTPFound(args[0].route_url('user.login', _query={'redirect': encode_route(args[0])}))\n return decorator(handler)","def authentication_hook(self):\n pass","def test_journal_route_accessible_when_logged_in(self):\n self.client.login(username='mike', password='password')\n response = self.client.get(reverse_lazy('journal'))\n self.assertEqual(response.status_code, 200)","def get(self):\n self.logout()\n self.redirect('/blog/login')","def log():\r\n return render_template(\r\n 'redirect.html',\r\n inform='12345!',\r\n\r\n )","def login(self):\n identity = request.environ.get('repoze.who.identity')\n came_from = str(request.GET.get('came_from', '')) or \\\n url('/')\n if identity:\n redirect(url(came_from))\n else:\n c.came_from = came_from\n c.login_counter = request.environ['repoze.who.logins'] + 1\n return render('/forms/login.mako')","def view_login(self):\n with self.client.get(\"/login\", catch_response=True) as response:\n for r_hist in response.history:\n if r_hist.status_code > 200 and r_hist.status_code < 400:\n response.failure(\"Logged on: Got redirect to /home\")","def index_route(self, request, *args, **kwargs):\n raise Http404()","def log_successful_login(sender, request, user, **kwargs):\r\n if settings.FEATURES['SQUELCH_PII_IN_LOGS']:\r\n AUDIT_LOG.info(u\"Login success - user.id: {0}\".format(user.id))\r\n else:\r\n AUDIT_LOG.info(u\"Login success - {0} ({1})\".format(user.username, user.email))","def process_request(self, request):\n request_type, data = request.request_type, request.data\n if request_type is Request.Type.LOG_IN:\n return self._log_in(data)\n elif request_type is Request.Type.LOG_OUT:\n return self._log_out()\n else:\n return self._parent.handle_request_from(self._user, request)","def route( request, c ):","def route(self, method, pattern, handler):\n pass","def log(self,):\n if self.request.user.is_anonymous():\n self.fail()\n else:\n self.success()\n if conf.LOGIN_GUARD_FREQUENCY_ALERT_ON:\n self.alert()","def post_login(self, came_from='/'):\n if not request.identity:\n login_counter = request.environ['repoze.who.logins'] + 1\n redirect('/login', came_from=came_from, __logins=login_counter)\n userid = request.identity['repoze.who.userid']\n flash(_('Welcome back, %s!') % userid)\n redirect(came_from)","def get(self):\n self.logout()\n self.redirect('/')","def post_login(self, came_from=lurl('/')):\n if not request.identity:\n login_counter = request.environ.get('repoze.who.logins', 0) + 1\n redirect('/login', params=dict(came_from=came_from, __logins=login_counter))\n return\n userid = request.identity['repoze.who.userid']\n flash(_('Welcome back, %s!') % userid)\n redirect(came_from)","def post_login(self, came_from='/'):\n if not request.identity:\n login_counter = request.environ['repoze.who.logins'] + 1\n redirect('/login', came_from=came_from, __logins=login_counter)\n\n userid = request.identity['repoze.who.userid']\n flash(_('Welcome back, %s!') % userid)\n redirect(came_from)","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def log(self, *args, **kwargs):\n self.game_view.log(*args, **kwargs)","def unauthorized_handler(self):\n return flask.redirect(\"/login\")","def log_in():\n form = LoginForm(request.form)\n if request.method == 'POST' and form.validate():\n if form.username.data != current_app.config['USERNAME']:\n flash('Invalid username.')\n elif form.password.data != current_app.config['PASSWORD']:\n flash('Invalid password.')\n else:\n session['logged_in'] = True\n flash('You were logged in.')\n\n return redirect(url_for('blog.show_posts'))\n\n return render_template('auth/log_in.html', form=form)","def log_user_logged_in(sender, request, user, **kwargs):\n ip_address = get_client_ip(request)\n logger.info('User \"%s\" logged in with IP address \"%s\".' % (user, ip_address))","def getLog(request):\n # TODO: GET\n data = {}\n return data","def login_view():\n return url(r'^login/$', login, {'template_name': 'miniuser/login.html'}, name='login')","def GET_login(self):\r\n\r\n # dest is the location to redirect to upon completion\r\n dest = request.get.get('dest','') or request.referer or '/'\r\n return LoginPage(dest = dest).render()","def handle_needs_login():\n flash(\"You must be logged in to access this page.\")\n return redirect(url_for('auth.login', next=request.path))","def login_user():\n pass","def dispatch(self, request, *args, **kwargs):\n dispatch = super().dispatch(request, *args, **kwargs)\n messages.info(request, \"You successfully logged out.\")\n return dispatch","def login_required(view):\n @functools.wraps(view)\n def wrapped_view(**kwargs):\n if g.user is None:\n return redirect(url_for(\"handlers.login\"))\n\n return view(**kwargs)\n\n return wrapped_view","def special(request):\n return HttpResponse(\"You are logged in !\")","def test_create_journal_route_accessible_when_logged_in(self):\n self.client.login(username='mike', password='password')\n response = self.client.get(reverse_lazy('create_journal'))\n self.assertEqual(response.status_code, 200)","def login():","def login():","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def _handle_view(self, name, **kwargs):\n if not self.is_accessible():\n if current_user.is_authenticated:\n # permission denied\n abort(403)\n else:\n # login\n return redirect(url_for('security.login', next=request.url))","def login():\n form = LoginForm()\n if request.method == \"GET\":\n return render_template('login.html', title='Sign In', form=form)\n if request.method == \"POST\":\n if 'loggedin' in session:\n return redirect(url_for('home'))\n if form.validate_on_submit():\n username = form.username.data\n password = form.password.data\n account = db.check_item(\"username\", username)\n if account is None:\n flash('Invalid username or password')\n return redirect(url_for('login'))\n else:\n if check_password_hash(str(account['password_hash']), password):\n session['loggedin'] = True\n session['username'] = account['username']\n session['admin_auth'] = account['admin_auth']\n flash('Login successfully!')\n return redirect(url_for('home'))\n flash('Invalid username or password')\n return redirect(url_for('login'))\n else:\n return redirect(url_for('login'))","def access(self, resp, req, environ, request_time):\n if not (self.cfg.accesslog or self.cfg.logconfig or self.cfg.syslog):\n return\n\n msg = self.make_access_message(resp, req, environ, request_time)\n try:\n self.access_log.info(msg)\n except:\n self.error(traceback.format_exc())","def login(self):","def user_logged_in_callback(sender, request, user, **kwargs):\n logger.info(f'User {user.username} logged in from {get_client_ip(request)}')","def login_required_processor(request):\n if request.user.is_authenticated():\n return {}\n if \"de.thefoundation.DENIED\" in request.META:\n return {}\n if request.path.startswith(u\"/media/\"):\n return {}\n if request.path in ['/login/', '/login/failed/', '/imprint/']:\n return {}\n request.META[\"de.thefoundation.DENIED\"] = True\n raise django.http.Http404(handle_404(request))","def get(self, request):\n\t\tlogout(request)\n\t\treturn redirect('/')","def auth():\n pass","def auth():\n pass","def login():\n if g.user:\n #return redirect(url_for('timeline'))\n return redirect(url_for('index'))\n error = None\n if request.method == 'POST':\n #user = query_db('''select * from user where username = ?''', [request.form['username']], one=True)\n try:\n pid = int(request.form['pid'])\n except:\n abort(404)\n user = User.query.filter_by(pid=pid).first()\n if user is None:\n error = 'Invalid username'\n elif not check_password_hash(user.pw_hash, request.form['password']):\n error = 'Invalid password'\n else:\n #flash(_('You were logged in'))\n session['user_id'] = user.user_id\n return redirect(url_for('index'))\n if error:\n flash(_('Wrong with phone number or password'))\n return render_template('login.html', error=error)","def post(self, request):\n try:\n user = User.objects.all()\n print(user)\n if user.filter(username=request.GET.get(\"username\")).filter(password=request.GET.get(\"password\")):\n return Response({\"message\": \"USER LOGGED IN\"}, status=200)\n print(request.user)\n return Response({\"message\": \"USER LOGGED IN\"}, status=200)\n except ObjectDoesNotExist as e:\n return Response(e)","def log_in():\n\n user = request.form.get(\"username\")\n #password = request.form.get(\"password\")\n\n try:\n\n current_user = User.query.filter_by(email=user).one()\n flash('You were successfully logged in')\n session[\"user_id\"] = current_user.user_id\n\n return render_template('homepage.html')\n\n except sqlalchemy.orm.exc.NoResultFound:\n\n return render_template('/registration_form.html')","def login():\n if request.method == \"POST\":\n # Check that username exists\n existing_username = mongo.db.users.find_one(\n {\"username\": re.compile(\n \"^\" + request.form.get(\"username\") + \"$\", re.IGNORECASE)})\n if existing_username:\n # Ensure hashed password matches input\n if check_password_hash(\n existing_username[\"password\"], request.form.get(\n \"password\")):\n # Check if user is an admin\n is_admin = existing_username.get(\"is_admin\", False)\n if is_admin:\n session[\"admin\"] = True\n session[\"user\"] = existing_username[\"username\"]\n flash(Markup(\"Welcome, \") + session[\"user\"],\n category=\"success\")\n return redirect(url_for(\"get_terms\"))\n else:\n # Invalid password entered\n flash(\"Username and/or password incorrect\", category=\"error\")\n return redirect(url_for(\"login\"))\n else:\n # Username doesn't exist\n flash(\"Username and/or password incorrect\", category=\"error\")\n return redirect(url_for(\"login\"))\n\n try:\n if session[\"user\"]:\n flash(\"You are already logged in\",\n category=\"error\")\n return redirect(url_for(\"get_terms\"))\n except KeyError:\n return render_template(\"login.html\")","def login_form():\n # if request.method == \"GET\":\n return render_template('login.html')","def index(request):\n if request.method == 'GET':\n return render(request, 'welcome/index.html', {})\n elif request.method == 'POST':\n email = request.POST.get('email', None)\n password = request.POST.get('password', None)\n\n login_successful, message, email, full_name = login(email, password)\n\n if login_successful:\n request.session['email'] = email\n request.session['full_name'] = full_name\n messages.add_message(request, messages.INFO, message)\n return redirect('teamsapp:teams') \n else:\n messages.add_message(request, messages.INFO, message)\n return redirect('welcome:index') \n else:\n return Http404('Not allowed')","def user_logged_in(self, sender, request, user, **kwargs):","def login_index():\n try:\n err_id = request.args['err_id']\n except:\n err_id = '0'\n if err_id == '1':\n error_message = 'Wrong username! Please make sure your username is right or you have registered before.'\n elif err_id == '2':\n error_message = 'Wrong password! Please type again with correct one!'\n else:\n error_message = ''\n if check_login():\n did_login = True\n else:\n did_login = False\n if not did_login: \n \"\"\"show login page\"\"\"\n return render_template(\"login/login.html\", message=error_message)\n else:\n \"\"\"jump to manage page\"\"\"\n return redirect(url_for('manage.manage_index'))","def _log_request(self):\n log = self.server.log\n if log:\n if hasattr(log, \"info\"):\n log.info(self.format_request() + '\\n')\n else:\n log.write(self.format_request() + '\\n')","def logger(request):\n log = logging.getLogger()\n hdlr = logging.StreamHandler()\n fmt = '%(asctime)s %(name)s %(levelname)s %(message)s'\n formatter = logging.Formatter(fmt)\n hdlr.setFormatter(formatter)\n log.addHandler(hdlr)\n log.setLevel(logging.DEBUG)\n log.propagate = False\n\n return log","def index(self):\n\n # try and pull the user's data\n user = get_active_user_data()\n\n if not user:\n # they are not logged in give them the login form\n return render('/login_form.html')\n\n # they are logged in, pass them to the home page\n redirect('/')","def log_access():\n # todo use project prefix\n tail('/var/log/nginx/access.log')","def index():\n if 'name' in session:\n return render_template('home.html')\n return redirect(url_for('log_in'))","def login():\n pass","def on_login(self, username):","def on_login(self, username):","def index(self):\n raise cherrypy.HTTPRedirect('/user')","def get(self, request):\n\t\treturn render(request, 'blog/login.html')","def login(request):\r\n login_url = route_url('login', request)\r\n referrer = request.url\r\n if referrer == login_url:\r\n referrer = u'/' # never use the login form itself as came_from\r\n\r\n came_from = request.params.get('came_from', referrer)\r\n\r\n message = u''\r\n login = u''\r\n password = u''\r\n\r\n if 'form.submitted' in request.params:\r\n login = request.params['login'].lower()\r\n password = request.params['password']\r\n\r\n LOG.debug(login)\r\n auth = UserMgr.get(username=login)\r\n LOG.debug(auth)\r\n LOG.debug(UserMgr.get_list())\r\n\r\n if auth and auth.validate_password(password) and auth.activated:\r\n # We use the Primary Key as our identifier once someone has\r\n # authenticated rather than the username. You can change what is\r\n # returned as the userid by altering what is passed to remember.\r\n headers = remember(request, auth.id, max_age=60 * 60 * 24 * 30)\r\n auth.last_login = datetime.utcnow()\r\n\r\n # log the successful login\r\n AuthLog.login(login, True)\r\n\r\n # we're always going to return a user to their own /recent after a\r\n # login\r\n return HTTPFound(\r\n location=request.route_url(\r\n 'user_bmark_recent',\r\n username=auth.username),\r\n headers=headers)\r\n\r\n # log the right level of problem\r\n if auth and not auth.validate_password(password):\r\n message = \"Your login attempt has failed.\"\r\n AuthLog.login(login, False, password=password)\r\n\r\n elif auth and not auth.activated:\r\n message = \"User account deactivated. Please check your email.\"\r\n AuthLog.login(login, False, password=password)\r\n AuthLog.disabled(login)\r\n\r\n elif auth is None:\r\n message = \"Failed login\"\r\n AuthLog.login(login, False, password=password)\r\n\r\n return {\r\n 'message': message,\r\n 'came_from': came_from,\r\n 'login': login,\r\n 'password': password,\r\n }","def log4(request):\n \n if request.method == \"GET\":\n bValid = True\n (usernumber_str, bValid) = getValue(request, u\"uid\", bValid)\n (timelapse_str, bValid) = getValue(request, u\"timelapse\", bValid)\n (figure_str, bValid) = getValue(request, u\"figure\", bValid)\n (term_Q1_str, bValid) = getValue(request, u\"q1\", bValid)\n (term_Q2_str, bValid) = getValue(request, u\"q2\", bValid)\n (term_Q3_str, bValid) = getValue(request, u\"q3\", bValid)\n (correctness_str, bValid) = getValue(request, u\"corr\", bValid)\n (term_S_str, bValid) = getValue(request, u\"S\", bValid)\n (term_M_str, bValid) = getValue(request, u\"M\", bValid)\n (term_P_str, bValid) = getValue(request, u\"P\", bValid)\n (conclusion_truth_value_str, bValid) = getValue(request, u\"conclusion\", bValid)\n (validity_answer, bValid) = getValue(request, u\"validity\", bValid)\n (rule_clicks_str, bValid) = getValue(request, u\"rule_clicks\", bValid)\n\n if not bValid:\n return render(request, 'log.html')\n else:\n if correctness_str == \"true\":\n bCorrect = True\n else:\n bCorrect = False\n\n if conclusion_truth_value_str == \"1\":\n bConclusionTruthValue = True\n else:\n bConclusionTruthValue = False\n\n try:\n rule_clicks = int(rule_clicks_str)\n except:\n rule_clicks = -1\n\n propkind_str = \"verbal\"\n\n newobj4 = Answer4(usernumber=int(usernumber_str),\n timelapse=int(timelapse_str),\n figure=int(figure_str),\n quantor_1=term_Q1_str,\n quantor_2=term_Q2_str,\n quantor_3=term_Q3_str,\n term_S=int(term_S_str),\n term_M=int(term_M_str),\n term_P=int(term_P_str),\n propkind=propkind_str,\n student_answer=validity_answer,\n correctness=bCorrect,\n conclusion_truth_value=bConclusionTruthValue,\n rule_clicks=rule_clicks)\n\n newobj4.save()\n\n return render(request, 'log.html')\n else:\n return render(request, 'log.html')","async def index(request: Request, user: UserInfo) -> HTTPResponse:\n return redirect('home')","def login_post():\n\treturn \"LOGIN\"","def login():\n if request.method == \"POST\":\n # 客户端在login页面发起的POST请求\n username = request.form[\"username\"]\n password = request.form[\"password\"]\n ipaddr = request.form[\"ipaddr\"]\n database = request.form[\"database\"]\n\n db = db_login(username, password, ipaddr, database)\n\n if db == None:\n return render_template(\"login.html\",loginres=0)\n else:\n session['username'] = username\n session['password'] = password\n session['ipaddr'] = ipaddr\n session['database'] = database\n\n return redirect(url_for(\"mainpage\"))\n else :\n # 客户端GET 请求login页面时\n return render_template(\"login.html\")","async def handle_login(\n request: aiohttp.web.Request,\n) -> typing.Union[aiohttp.web.Response, aiohttp.web.FileResponse]:\n response: typing.Union[aiohttp.web.Response, aiohttp.web.FileResponse]\n response = aiohttp.web.Response(status=302, reason=\"Redirection to login\")\n\n # Add a cookie for navigating\n if \"navto\" in request.query.keys():\n response.set_cookie(\"NAV_TO\", request.query[\"navto\"], expires=str(3600))\n\n if setd[\"oidc_enabled\"]:\n session = await aiohttp_session.get_session(request)\n if \"oidc\" in session:\n response = aiohttp.web.FileResponse(\n str(setd[\"static_directory\"]) + \"/login2step.html\"\n )\n else:\n response.headers[\"Location\"] = \"/\"\n\n else:\n response.headers[\"Location\"] = \"/login/front\"\n\n return response","async def on_login(self, login_response):\n pass"],"string":"[\n \"def log_login(sender, request, user, **kwargs):\\n stracks.user(user).log(\\\"? has logged in\\\", action=stracks.login())\",\n \"def handle_login(self, request):\\n self._verify_headers(request)\\n self._verify_auth_parameters(request, check_session=False)\\n data = self.responses['login']\\n mlid = tags.uint32_tag('mlid', data.session)\\n mlog = tags.container_tag('mlog', mlid)\\n return web.Response(body=mlog, status=data.status)\",\n \"def logged_in(handler):\\n\\n def check_login(self, *args, **kwargs):\\n jwtToken = self.request.headers.get('Authorization', None)\\n\\n if jwtToken:\\n # validate token\\n try:\\n userToken = jwt.decode(\\n jwtToken,\\n defaultConfig.config['jwt_secret'],\\n algorithms=[defaultConfig.config['jwt_algorithms']],\\n issuer=defaultConfig.config['app_name']\\n )\\n except (jwt.DecodeError, jwt.ExpiredSignatureError), e:\\n self.responseJSON('TOKEN_EXPIRED', **{\\n 'data': [str(e)]\\n })\\n return\\n\\n # check token created time and changed password time, if < -> token invalid\\n try:\\n myUser = UserModel.get_by_id(userToken['id'])\\n assert myUser is not None\\n except:\\n self.responseJSON('DATA_NOTFOUND')\\n return\\n\\n if myUser.date_change_password != None:\\n if Helper.timestampToDatetime(userToken['iat']) < myUser.date_change_password:\\n self.responseJSON('TOKEN_INVALID_TIME')\\n return\\n\\n # check user logged out\\n if myUser.status == UserModel.STATUS_LOGOUT:\\n self.responseJSON('TOKEN_INVALID')\\n return\\n\\n # authorization system\\n else:\\n self.abort(403)\\n\\n return handler(self, userToken, *args, **kwargs)\\n return check_login\",\n \"def _login(self, *args, **kwargs):\\n pass\",\n \"def log_in(self):\\n\\t\\tpass\",\n \"def log_in():\\n if request.method == 'POST':\\n username = request.form['username']\\n password = request.form['password']\\n if PLAN.login_user(username, password):\\n session['name'] = username\\n flash(\\\"Login success ...\\\")\\n return redirect(url_for('index'))\\n flash(\\\"Login failed ...\\\")\\n return render_template('login.html')\\n return render_template('login.html')\",\n \"def post_login(self, came_from=lurl('/')):\\n if not request.identity:\\n login_counter = request.environ.get('repoze.who.logins', 0) + 1\\n redirect('/login',\\n params=dict(came_from=came_from, __logins=login_counter))\\n userid = request.identity['repoze.who.userid']\\n flash(_('Welcome back, %s!') % userid)\\n\\n # Do not use tg.redirect with tg.url as it will add the mountpoint\\n # of the application twice.\\n return HTTPFound(location=came_from)\",\n \"def simple_handler(request):\\n logger.debug('')\\n return Response(200, 'OK', {}, '')\",\n \"def log_user_logged_in(sender, request, user, **kwargs):\\n\\n ip = get_client_ip(request)\\n date = datetime.now()\\n log.info('Login user: %s , IP: %s , Date: %s', user, ip, str(date))\",\n \"def login(request):\\n if request.method == \\\"GET\\\":\\n return {}\\n if request.method == \\\"POST\\\":\\n if check_credentials(request):\\n username = request.POST['username']\\n headers = remember(request, username)\\n return HTTPFound(location=request.route_url('home_view'), headers=headers) \\n return {'error': 'Invalid username or password.'}\",\n \"def log_route():\\n return send_file(path.join('..', 'app.log'), as_attachment=True)\",\n \"def handle_login(request, login_form):\\n if login_form.is_valid():\\n user = login_form.login(request)\\n if user:\\n # Log the user in\\n login(request, user)\\n # Show a success message and redirect back to the index page\\n thumbs_up_symbol = \\\"<i style='color: #4F844F;' class='fa fa-thumbs-up fa-fw'></i>\\\"\\n messages.success(request, \\\"Logged In! \\\" + thumbs_up_symbol)\\n return redirect(reverse('spacefinder:index'))\\n else:\\n # If the login failed then fetch error messages to render to the screen\\n return login_form\",\n \"def login(request):\\n\\n url = request.route_url('auth_signin_redirect', _app_url=get_app_url(request))\\n return HTTPFound(location=url)\",\n \"def route_request(self,):\\n # Let's parse and prepare url, path, query etc..\\n up = urlparse(self.path, 'http')\\n self.log_data['url'] = path = up.path\\n splitpath = path.split('/')\\n if len(splitpath) == 1:\\n raise HTTPError(404, 'Not found.')\\n self.query = parse_qs(up.query)\\n\\n # Get the route\\n route = self.get_route(self.http_method, path.encode('utf-8'))\\n # Parse URL path\\n urlvars = self.parse_path(path.encode('utf-8'), route)\\n post_raw = None\\n # Load POST content if any\\n if self.http_method == 'POST':\\n # TODO: raise an HTTP error if the content-length is\\n # too large.\\n try:\\n post_raw = self.rfile.read(int(self.headers['Content-Length']))\\n except Exception as e:\\n logger.exception(str(e))\\n logger.debug(self.headers)\\n logger.error('Unable to read post data')\\n raise HTTPError(400, 'Unable to read post data')\\n\\n username = None\\n checked = False\\n\\n # Authentication checking out\\n\\n # 1. Try the auth' by key: if this method is available for this API\\n # and 'key' arg exists.\\n key = self.headers.get('X-TemBoard-Agent-Key')\\n if key:\\n logger.debug(\\\"Authentication by key from header.\\\")\\n elif 'key' in self.query:\\n # TODO: Remove auth from query in 8.0\\n key = self.query['key'][0]\\n logger.debug(\\\"Authentication by key from argument.\\\")\\n\\n if route['check_key'] and key:\\n if self.app.config.temboard.key is None:\\n raise HTTPError(401, \\\"Authentication key not configured\\\")\\n if key != self.app.config.temboard.key:\\n raise HTTPError(401, \\\"Invalid key\\\")\\n checked = True\\n\\n # 2. Check session ID if available and not previously auth'd by key\\n if not checked and route['check_session']:\\n username = check_sessionid(self.headers, self.sessions)\\n checked = True\\n\\n # 3. At this point, if not yet checked out and auth' by key is\\n # available then we need to raise an error because 'key' arg hasn't\\n # been passed and auth' by key is the only available method.\\n if not checked and route['check_key']:\\n raise HTTPError(401, \\\"Missing key\\\")\\n\\n try:\\n # Load POST content expecting it is in JSON format.\\n if self.http_method == 'POST':\\n self.post_json = json.loads(post_raw.decode('utf-8'))\\n except Exception as e:\\n logger.exception(str(e))\\n logger.error('Invalid json format')\\n raise HTTPError(400, 'Invalid json format')\\n\\n http_context = dict(\\n headers=self.headers,\\n query=self.query,\\n post=self.post_json,\\n urlvars=urlvars,\\n username=username,\\n )\\n\\n # Handle the request\\n func = getattr(sys.modules[route['module']], route['function'])\\n self.log_data['handler'] = route['module'] + '.' + route['function']\\n if route['module'] == 'temboardagent.api':\\n # some core APIs need to deal with sessions\\n return (200, func(http_context, self.app, self.sessions))\\n else:\\n # plugin\\n return (200, func(http_context, self.app))\",\n \"def create_log_entry_when_user_logs_in(sender, request, user, **kwargs):\\n create_user_log(\\n request=request,\\n user=user,\\n type=_account_const.AUTHENTICATION,\\n action=_account_const.LOGIN\\n )\",\n \"def log(self):\\n\\n\\t\\theader_dict = dict(request.headers)\\n\\n\\t\\ttry:\\n\\t\\t\\ttracker_id = header_dict[\\\"tracker_id\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\ttracker_id = None\\n\\t\\t\\n\\t\\ttry:\\n\\t\\t\\tuser_agent = header_dict[\\\"User-Agent\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\tuser_agent = None\\n\\n\\t\\ttry:\\n\\t\\t\\tlanguage = header_dict[\\\"Accept-Language\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\tlanguage = None\\n\\n\\t\\ttry:\\n\\t\\t\\treferer = header_dict[\\\"Referer\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\treferer = None\\n\\n\\t\\ttry:\\n\\t\\t\\torigin = header_dict[\\\"Origin\\\"]\\n\\t\\texcept Exception:\\n\\t\\t\\torigin = None\\n\\n\\t\\ttry:\\n\\t\\t\\tjson_data = request.json\\n\\t\\texcept Exception:\\n\\t\\t\\tjson_data = None\\n\\n\\t\\ttry:\\n\\t\\t\\tplatform = request.user_agent.platform.title()\\n\\t\\texcept Exception:\\n\\t\\t\\tplatform = None\\n\\n\\t\\ttry:\\n\\t\\t\\tbrowser = request.user_agent.browser.title()\\n\\t\\texcept Exception:\\n\\t\\t\\tbrowser = None\\n\\n\\t\\ttry:\\n\\t\\t\\tauth_header_token = header_dict[\\\"Authorization\\\"].split(\\\" \\\")[1]\\n\\t\\texcept Exception:\\n\\t\\t\\tauth_header_token = None\\n\\t\\t\\n\\t\\t## If set to run before a request: This is the default setting\\n\\t\\tif self.pre_request:\\n\\t\\t\\t@self.app.before_request()\\n\\t\\t\\tdef run():\\n\\t\\t\\t\\t## If the path accessed is in the do_not_log list, it is skipped\\n\\t\\t\\t\\tif request.path in self.do_not_log:\\n\\t\\t\\t\\t\\treturn\\n\\t\\t\\t\\t## If the path accessed is not in the do_not_log list, it is posted\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tpost_data = {\\n\\t\\t\\t\\t\\t\\t\\\"error\\\": None,\\n\\t\\t\\t\\t\\t\\t\\\"stack_trace\\\": None,\\n\\t\\t\\t\\t\\t\\t\\\"method\\\": request.method,\\n\\t\\t\\t\\t\\t\\t\\\"source_ip\\\": request.remote_addr,\\n\\t\\t\\t\\t\\t\\t\\\"url\\\": request.url,\\n\\t\\t\\t\\t\\t\\t\\\"status_code\\\": 200, ## Assumed to be 200 due to the nature of the function\\n\\t\\t\\t\\t\\t\\t\\\"headers\\\": str(header_dict),\\n\\t\\t\\t\\t\\t\\t\\\"user_agent\\\": user_agent,\\n\\t\\t\\t\\t\\t\\t\\\"language\\\": language,\\n\\t\\t\\t\\t\\t\\t\\\"platform\\\": platform,\\n\\t\\t\\t\\t\\t\\t\\\"browser\\\": browser,\\n\\t\\t\\t\\t\\t\\t\\\"referer\\\": referer,\\n\\t\\t\\t\\t\\t\\t\\\"origin\\\": origin,\\n\\t\\t\\t\\t\\t\\t\\\"auth_header\\\": auth_header_token,\\n\\t\\t\\t\\t\\t\\t\\\"access_time\\\": datetime.now().strftime(\\\"%A, %d %B %Y %H:%M:%S\\\"),\\n\\t\\t\\t\\t\\t\\t\\\"logging_access_key\\\": self.accessKey,\\n\\t\\t\\t\\t\\t\\t\\\"json\\\": json_data,\\n\\t\\t\\t\\t\\t\\t\\\"request_params\\\": str(dict(request.args))\\n\\t\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\t\\tself.startPost(post_data)\\n\\n\\t\\t\\t\\t\\treturn\\n\\n\\t\\t\\treturn run\\n\\t\\t\\n\\t\\t## If set to as a wrapper to a function\\n\\t\\telse:\\n\\t\\t\\tdef log_decorator(func):\\n\\n\\t\\t\\t\\t@wraps(func)\\n\\t\\t\\t\\tdef execute(*args, **kwargs):\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tresult = func(*args, **kwargs)\\n\\n\\t\\t\\t\\t\\t\\tresult_response = make_response(result)\\n\\n\\t\\t\\t\\t\\t\\tpost_data = {\\n\\t\\t\\t\\t\\t\\t\\t\\\"error\\\": None,\\n\\t\\t\\t\\t\\t\\t\\t\\\"stack_trace\\\": None,\\n\\t\\t\\t\\t\\t\\t\\t\\\"method\\\": request.method,\\n\\t\\t\\t\\t\\t\\t\\t\\\"source_ip\\\": request.remote_addr,\\n\\t\\t\\t\\t\\t\\t\\t\\\"url\\\": request.url,\\n\\t\\t\\t\\t\\t\\t\\t\\\"status_code\\\": result_response.status_code,\\n\\t\\t\\t\\t\\t\\t\\t\\\"headers\\\": str(header_dict),\\n\\t\\t\\t\\t\\t\\t\\t\\\"user_agent\\\": user_agent,\\n\\t\\t\\t\\t\\t\\t\\t\\\"language\\\": language,\\n\\t\\t\\t\\t\\t\\t\\t\\\"platform\\\": platform,\\n\\t\\t\\t\\t\\t\\t\\t\\\"browser\\\": browser,\\n\\t\\t\\t\\t\\t\\t\\t\\\"referer\\\": referer,\\n\\t\\t\\t\\t\\t\\t\\t\\\"origin\\\": origin,\\n\\t\\t\\t\\t\\t\\t\\t\\\"auth_header\\\": auth_header_token,\\n\\t\\t\\t\\t\\t\\t\\t\\\"access_time\\\": datetime.now().strftime(\\\"%A, %d %B %Y %H:%M:%S\\\"),\\n\\t\\t\\t\\t\\t\\t\\t\\\"logging_access_key\\\": self.accessKey,\\n\\t\\t\\t\\t\\t\\t\\t\\\"json\\\": json_data,\\n\\t\\t\\t\\t\\t\\t\\t\\\"request_params\\\": str(dict(request.args))\\n\\t\\t\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\t\\t\\tself.startPost(post_data)\\n\\n\\t\\t\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\t\\t\\tresult = func(*args, **kwargs)\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\ttrace = traceback.format_exc()\\n\\n\\t\\t\\t\\t\\t\\tkwargs = {\\n\\t\\t\\t\\t\\t\\t\\t\\\"trace\\\": trace,\\n\\t\\t\\t\\t\\t\\t\\t\\\"exception\\\": str(e)\\n\\t\\t\\t\\t\\t\\t}\\n\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\tpost_data = {\\n\\t\\t\\t\\t\\t\\t\\t\\\"error\\\": str(e),\\n\\t\\t\\t\\t\\t\\t\\t\\\"stack_trace\\\": trace,\\n\\t\\t\\t\\t\\t\\t\\t\\\"method\\\": request.method,\\n\\t\\t\\t\\t\\t\\t\\t\\\"source_ip\\\": request.remote_addr,\\n\\t\\t\\t\\t\\t\\t\\t\\\"url\\\": request.url,\\n\\t\\t\\t\\t\\t\\t\\t\\\"status_code\\\": 500,\\n\\t\\t\\t\\t\\t\\t\\t\\\"headers\\\": str(header_dict),\\n\\t\\t\\t\\t\\t\\t\\t\\\"user_agent\\\": user_agent,\\n\\t\\t\\t\\t\\t\\t\\t\\\"language\\\": language,\\n\\t\\t\\t\\t\\t\\t\\t\\\"platform\\\": platform,\\n\\t\\t\\t\\t\\t\\t\\t\\\"browser\\\": browser,\\n\\t\\t\\t\\t\\t\\t\\t\\\"referer\\\": referer,\\n\\t\\t\\t\\t\\t\\t\\t\\\"origin\\\": origin,\\n\\t\\t\\t\\t\\t\\t\\t\\\"auth_header\\\": auth_header_token,\\n\\t\\t\\t\\t\\t\\t\\t\\\"access_time\\\": datetime.now().strftime(\\\"%A, %d %B %Y %H:%M:%S\\\"),\\n\\t\\t\\t\\t\\t\\t\\t\\\"logging_access_key\\\": self.accessKey,\\n\\t\\t\\t\\t\\t\\t\\t\\\"json\\\": json_data,\\n\\t\\t\\t\\t\\t\\t\\t\\\"request_params\\\": str(dict(request.args))\\n\\t\\t\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\t\\t\\tself.startPost(post_data)\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\treturn result\\n\\t\\t\\t\\t\\n\\t\\t\\t\\treturn execute\\n\\t\\t\\t\\n\\t\\t\\treturn log_decorator\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return self.login()\",\n \"def handleLogin(self):\\n aVar = self.session.getAttribute(self.settings.authenvar)\\n self.loggedin = False\\n if not aVar:\\n self.currenttemplate = self.settings.logintemplate \\n self.logger.debug(\\\"Not logged in, Login-Mask activated.\\\")\\n return\\n\\n self.loggedin = True\\n self.logger.debug('Loged in as: \\\"{}\\\"'.format(aVar))\",\n \"def post(self):\\n username = self.request.get('username')\\n password = self.request.get('password')\\n\\n # User class login function\\n u = User.login(username, password)\\n if u:\\n self.login(u) # BlogHandler login function\\n self.redirect('/blog')\\n else:\\n msg = 'Invalid login'\\n self.render('login-form.html', error = msg)\",\n \"def loginAttempt(request):\\n\\n userid = request.unauthenticated_userid\\n\\n if userid:\\n\\n # TODO: Convert USERS to database object instead of local dictionary. If necessary.\\n\\n if userid in USERS:\\n user = USERS[userid]\\n\\n else:\\n user = _create_user(userid)\\n\\n if user.check_token(userid):\\n headers = remember(request, userid)\\n url = request.route_url('home', _app_url=get_app_url(request))\\n return HTTPFound(location=url, headers=headers)\\n\\n url = request.route_url('failed', _app_url=get_app_url(request))\\n return HTTPFound(location=url)\",\n \"def require_logged_in():\\n def handler(f, *args, **kwargs):\\n if args[0].current_user is not None:\\n return f(*args, **kwargs)\\n else:\\n raise HTTPFound(args[0].route_url('user.login', _query={'redirect': encode_route(args[0])}))\\n return decorator(handler)\",\n \"def authentication_hook(self):\\n pass\",\n \"def test_journal_route_accessible_when_logged_in(self):\\n self.client.login(username='mike', password='password')\\n response = self.client.get(reverse_lazy('journal'))\\n self.assertEqual(response.status_code, 200)\",\n \"def get(self):\\n self.logout()\\n self.redirect('/blog/login')\",\n \"def log():\\r\\n return render_template(\\r\\n 'redirect.html',\\r\\n inform='12345!',\\r\\n\\r\\n )\",\n \"def login(self):\\n identity = request.environ.get('repoze.who.identity')\\n came_from = str(request.GET.get('came_from', '')) or \\\\\\n url('/')\\n if identity:\\n redirect(url(came_from))\\n else:\\n c.came_from = came_from\\n c.login_counter = request.environ['repoze.who.logins'] + 1\\n return render('/forms/login.mako')\",\n \"def view_login(self):\\n with self.client.get(\\\"/login\\\", catch_response=True) as response:\\n for r_hist in response.history:\\n if r_hist.status_code > 200 and r_hist.status_code < 400:\\n response.failure(\\\"Logged on: Got redirect to /home\\\")\",\n \"def index_route(self, request, *args, **kwargs):\\n raise Http404()\",\n \"def log_successful_login(sender, request, user, **kwargs):\\r\\n if settings.FEATURES['SQUELCH_PII_IN_LOGS']:\\r\\n AUDIT_LOG.info(u\\\"Login success - user.id: {0}\\\".format(user.id))\\r\\n else:\\r\\n AUDIT_LOG.info(u\\\"Login success - {0} ({1})\\\".format(user.username, user.email))\",\n \"def process_request(self, request):\\n request_type, data = request.request_type, request.data\\n if request_type is Request.Type.LOG_IN:\\n return self._log_in(data)\\n elif request_type is Request.Type.LOG_OUT:\\n return self._log_out()\\n else:\\n return self._parent.handle_request_from(self._user, request)\",\n \"def route( request, c ):\",\n \"def route(self, method, pattern, handler):\\n pass\",\n \"def log(self,):\\n if self.request.user.is_anonymous():\\n self.fail()\\n else:\\n self.success()\\n if conf.LOGIN_GUARD_FREQUENCY_ALERT_ON:\\n self.alert()\",\n \"def post_login(self, came_from='/'):\\n if not request.identity:\\n login_counter = request.environ['repoze.who.logins'] + 1\\n redirect('/login', came_from=came_from, __logins=login_counter)\\n userid = request.identity['repoze.who.userid']\\n flash(_('Welcome back, %s!') % userid)\\n redirect(came_from)\",\n \"def get(self):\\n self.logout()\\n self.redirect('/')\",\n \"def post_login(self, came_from=lurl('/')):\\n if not request.identity:\\n login_counter = request.environ.get('repoze.who.logins', 0) + 1\\n redirect('/login', params=dict(came_from=came_from, __logins=login_counter))\\n return\\n userid = request.identity['repoze.who.userid']\\n flash(_('Welcome back, %s!') % userid)\\n redirect(came_from)\",\n \"def post_login(self, came_from='/'):\\n if not request.identity:\\n login_counter = request.environ['repoze.who.logins'] + 1\\n redirect('/login', came_from=came_from, __logins=login_counter)\\n\\n userid = request.identity['repoze.who.userid']\\n flash(_('Welcome back, %s!') % userid)\\n redirect(came_from)\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def log(self, *args, **kwargs):\\n self.game_view.log(*args, **kwargs)\",\n \"def unauthorized_handler(self):\\n return flask.redirect(\\\"/login\\\")\",\n \"def log_in():\\n form = LoginForm(request.form)\\n if request.method == 'POST' and form.validate():\\n if form.username.data != current_app.config['USERNAME']:\\n flash('Invalid username.')\\n elif form.password.data != current_app.config['PASSWORD']:\\n flash('Invalid password.')\\n else:\\n session['logged_in'] = True\\n flash('You were logged in.')\\n\\n return redirect(url_for('blog.show_posts'))\\n\\n return render_template('auth/log_in.html', form=form)\",\n \"def log_user_logged_in(sender, request, user, **kwargs):\\n ip_address = get_client_ip(request)\\n logger.info('User \\\"%s\\\" logged in with IP address \\\"%s\\\".' % (user, ip_address))\",\n \"def getLog(request):\\n # TODO: GET\\n data = {}\\n return data\",\n \"def login_view():\\n return url(r'^login/$', login, {'template_name': 'miniuser/login.html'}, name='login')\",\n \"def GET_login(self):\\r\\n\\r\\n # dest is the location to redirect to upon completion\\r\\n dest = request.get.get('dest','') or request.referer or '/'\\r\\n return LoginPage(dest = dest).render()\",\n \"def handle_needs_login():\\n flash(\\\"You must be logged in to access this page.\\\")\\n return redirect(url_for('auth.login', next=request.path))\",\n \"def login_user():\\n pass\",\n \"def dispatch(self, request, *args, **kwargs):\\n dispatch = super().dispatch(request, *args, **kwargs)\\n messages.info(request, \\\"You successfully logged out.\\\")\\n return dispatch\",\n \"def login_required(view):\\n @functools.wraps(view)\\n def wrapped_view(**kwargs):\\n if g.user is None:\\n return redirect(url_for(\\\"handlers.login\\\"))\\n\\n return view(**kwargs)\\n\\n return wrapped_view\",\n \"def special(request):\\n return HttpResponse(\\\"You are logged in !\\\")\",\n \"def test_create_journal_route_accessible_when_logged_in(self):\\n self.client.login(username='mike', password='password')\\n response = self.client.get(reverse_lazy('create_journal'))\\n self.assertEqual(response.status_code, 200)\",\n \"def login():\",\n \"def login():\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def _handle_view(self, name, **kwargs):\\n if not self.is_accessible():\\n if current_user.is_authenticated:\\n # permission denied\\n abort(403)\\n else:\\n # login\\n return redirect(url_for('security.login', next=request.url))\",\n \"def login():\\n form = LoginForm()\\n if request.method == \\\"GET\\\":\\n return render_template('login.html', title='Sign In', form=form)\\n if request.method == \\\"POST\\\":\\n if 'loggedin' in session:\\n return redirect(url_for('home'))\\n if form.validate_on_submit():\\n username = form.username.data\\n password = form.password.data\\n account = db.check_item(\\\"username\\\", username)\\n if account is None:\\n flash('Invalid username or password')\\n return redirect(url_for('login'))\\n else:\\n if check_password_hash(str(account['password_hash']), password):\\n session['loggedin'] = True\\n session['username'] = account['username']\\n session['admin_auth'] = account['admin_auth']\\n flash('Login successfully!')\\n return redirect(url_for('home'))\\n flash('Invalid username or password')\\n return redirect(url_for('login'))\\n else:\\n return redirect(url_for('login'))\",\n \"def access(self, resp, req, environ, request_time):\\n if not (self.cfg.accesslog or self.cfg.logconfig or self.cfg.syslog):\\n return\\n\\n msg = self.make_access_message(resp, req, environ, request_time)\\n try:\\n self.access_log.info(msg)\\n except:\\n self.error(traceback.format_exc())\",\n \"def login(self):\",\n \"def user_logged_in_callback(sender, request, user, **kwargs):\\n logger.info(f'User {user.username} logged in from {get_client_ip(request)}')\",\n \"def login_required_processor(request):\\n if request.user.is_authenticated():\\n return {}\\n if \\\"de.thefoundation.DENIED\\\" in request.META:\\n return {}\\n if request.path.startswith(u\\\"/media/\\\"):\\n return {}\\n if request.path in ['/login/', '/login/failed/', '/imprint/']:\\n return {}\\n request.META[\\\"de.thefoundation.DENIED\\\"] = True\\n raise django.http.Http404(handle_404(request))\",\n \"def get(self, request):\\n\\t\\tlogout(request)\\n\\t\\treturn redirect('/')\",\n \"def auth():\\n pass\",\n \"def auth():\\n pass\",\n \"def login():\\n if g.user:\\n #return redirect(url_for('timeline'))\\n return redirect(url_for('index'))\\n error = None\\n if request.method == 'POST':\\n #user = query_db('''select * from user where username = ?''', [request.form['username']], one=True)\\n try:\\n pid = int(request.form['pid'])\\n except:\\n abort(404)\\n user = User.query.filter_by(pid=pid).first()\\n if user is None:\\n error = 'Invalid username'\\n elif not check_password_hash(user.pw_hash, request.form['password']):\\n error = 'Invalid password'\\n else:\\n #flash(_('You were logged in'))\\n session['user_id'] = user.user_id\\n return redirect(url_for('index'))\\n if error:\\n flash(_('Wrong with phone number or password'))\\n return render_template('login.html', error=error)\",\n \"def post(self, request):\\n try:\\n user = User.objects.all()\\n print(user)\\n if user.filter(username=request.GET.get(\\\"username\\\")).filter(password=request.GET.get(\\\"password\\\")):\\n return Response({\\\"message\\\": \\\"USER LOGGED IN\\\"}, status=200)\\n print(request.user)\\n return Response({\\\"message\\\": \\\"USER LOGGED IN\\\"}, status=200)\\n except ObjectDoesNotExist as e:\\n return Response(e)\",\n \"def log_in():\\n\\n user = request.form.get(\\\"username\\\")\\n #password = request.form.get(\\\"password\\\")\\n\\n try:\\n\\n current_user = User.query.filter_by(email=user).one()\\n flash('You were successfully logged in')\\n session[\\\"user_id\\\"] = current_user.user_id\\n\\n return render_template('homepage.html')\\n\\n except sqlalchemy.orm.exc.NoResultFound:\\n\\n return render_template('/registration_form.html')\",\n \"def login():\\n if request.method == \\\"POST\\\":\\n # Check that username exists\\n existing_username = mongo.db.users.find_one(\\n {\\\"username\\\": re.compile(\\n \\\"^\\\" + request.form.get(\\\"username\\\") + \\\"$\\\", re.IGNORECASE)})\\n if existing_username:\\n # Ensure hashed password matches input\\n if check_password_hash(\\n existing_username[\\\"password\\\"], request.form.get(\\n \\\"password\\\")):\\n # Check if user is an admin\\n is_admin = existing_username.get(\\\"is_admin\\\", False)\\n if is_admin:\\n session[\\\"admin\\\"] = True\\n session[\\\"user\\\"] = existing_username[\\\"username\\\"]\\n flash(Markup(\\\"Welcome, \\\") + session[\\\"user\\\"],\\n category=\\\"success\\\")\\n return redirect(url_for(\\\"get_terms\\\"))\\n else:\\n # Invalid password entered\\n flash(\\\"Username and/or password incorrect\\\", category=\\\"error\\\")\\n return redirect(url_for(\\\"login\\\"))\\n else:\\n # Username doesn't exist\\n flash(\\\"Username and/or password incorrect\\\", category=\\\"error\\\")\\n return redirect(url_for(\\\"login\\\"))\\n\\n try:\\n if session[\\\"user\\\"]:\\n flash(\\\"You are already logged in\\\",\\n category=\\\"error\\\")\\n return redirect(url_for(\\\"get_terms\\\"))\\n except KeyError:\\n return render_template(\\\"login.html\\\")\",\n \"def login_form():\\n # if request.method == \\\"GET\\\":\\n return render_template('login.html')\",\n \"def index(request):\\n if request.method == 'GET':\\n return render(request, 'welcome/index.html', {})\\n elif request.method == 'POST':\\n email = request.POST.get('email', None)\\n password = request.POST.get('password', None)\\n\\n login_successful, message, email, full_name = login(email, password)\\n\\n if login_successful:\\n request.session['email'] = email\\n request.session['full_name'] = full_name\\n messages.add_message(request, messages.INFO, message)\\n return redirect('teamsapp:teams') \\n else:\\n messages.add_message(request, messages.INFO, message)\\n return redirect('welcome:index') \\n else:\\n return Http404('Not allowed')\",\n \"def user_logged_in(self, sender, request, user, **kwargs):\",\n \"def login_index():\\n try:\\n err_id = request.args['err_id']\\n except:\\n err_id = '0'\\n if err_id == '1':\\n error_message = 'Wrong username! Please make sure your username is right or you have registered before.'\\n elif err_id == '2':\\n error_message = 'Wrong password! Please type again with correct one!'\\n else:\\n error_message = ''\\n if check_login():\\n did_login = True\\n else:\\n did_login = False\\n if not did_login: \\n \\\"\\\"\\\"show login page\\\"\\\"\\\"\\n return render_template(\\\"login/login.html\\\", message=error_message)\\n else:\\n \\\"\\\"\\\"jump to manage page\\\"\\\"\\\"\\n return redirect(url_for('manage.manage_index'))\",\n \"def _log_request(self):\\n log = self.server.log\\n if log:\\n if hasattr(log, \\\"info\\\"):\\n log.info(self.format_request() + '\\\\n')\\n else:\\n log.write(self.format_request() + '\\\\n')\",\n \"def logger(request):\\n log = logging.getLogger()\\n hdlr = logging.StreamHandler()\\n fmt = '%(asctime)s %(name)s %(levelname)s %(message)s'\\n formatter = logging.Formatter(fmt)\\n hdlr.setFormatter(formatter)\\n log.addHandler(hdlr)\\n log.setLevel(logging.DEBUG)\\n log.propagate = False\\n\\n return log\",\n \"def index(self):\\n\\n # try and pull the user's data\\n user = get_active_user_data()\\n\\n if not user:\\n # they are not logged in give them the login form\\n return render('/login_form.html')\\n\\n # they are logged in, pass them to the home page\\n redirect('/')\",\n \"def log_access():\\n # todo use project prefix\\n tail('/var/log/nginx/access.log')\",\n \"def index():\\n if 'name' in session:\\n return render_template('home.html')\\n return redirect(url_for('log_in'))\",\n \"def login():\\n pass\",\n \"def on_login(self, username):\",\n \"def on_login(self, username):\",\n \"def index(self):\\n raise cherrypy.HTTPRedirect('/user')\",\n \"def get(self, request):\\n\\t\\treturn render(request, 'blog/login.html')\",\n \"def login(request):\\r\\n login_url = route_url('login', request)\\r\\n referrer = request.url\\r\\n if referrer == login_url:\\r\\n referrer = u'/' # never use the login form itself as came_from\\r\\n\\r\\n came_from = request.params.get('came_from', referrer)\\r\\n\\r\\n message = u''\\r\\n login = u''\\r\\n password = u''\\r\\n\\r\\n if 'form.submitted' in request.params:\\r\\n login = request.params['login'].lower()\\r\\n password = request.params['password']\\r\\n\\r\\n LOG.debug(login)\\r\\n auth = UserMgr.get(username=login)\\r\\n LOG.debug(auth)\\r\\n LOG.debug(UserMgr.get_list())\\r\\n\\r\\n if auth and auth.validate_password(password) and auth.activated:\\r\\n # We use the Primary Key as our identifier once someone has\\r\\n # authenticated rather than the username. You can change what is\\r\\n # returned as the userid by altering what is passed to remember.\\r\\n headers = remember(request, auth.id, max_age=60 * 60 * 24 * 30)\\r\\n auth.last_login = datetime.utcnow()\\r\\n\\r\\n # log the successful login\\r\\n AuthLog.login(login, True)\\r\\n\\r\\n # we're always going to return a user to their own /recent after a\\r\\n # login\\r\\n return HTTPFound(\\r\\n location=request.route_url(\\r\\n 'user_bmark_recent',\\r\\n username=auth.username),\\r\\n headers=headers)\\r\\n\\r\\n # log the right level of problem\\r\\n if auth and not auth.validate_password(password):\\r\\n message = \\\"Your login attempt has failed.\\\"\\r\\n AuthLog.login(login, False, password=password)\\r\\n\\r\\n elif auth and not auth.activated:\\r\\n message = \\\"User account deactivated. Please check your email.\\\"\\r\\n AuthLog.login(login, False, password=password)\\r\\n AuthLog.disabled(login)\\r\\n\\r\\n elif auth is None:\\r\\n message = \\\"Failed login\\\"\\r\\n AuthLog.login(login, False, password=password)\\r\\n\\r\\n return {\\r\\n 'message': message,\\r\\n 'came_from': came_from,\\r\\n 'login': login,\\r\\n 'password': password,\\r\\n }\",\n \"def log4(request):\\n \\n if request.method == \\\"GET\\\":\\n bValid = True\\n (usernumber_str, bValid) = getValue(request, u\\\"uid\\\", bValid)\\n (timelapse_str, bValid) = getValue(request, u\\\"timelapse\\\", bValid)\\n (figure_str, bValid) = getValue(request, u\\\"figure\\\", bValid)\\n (term_Q1_str, bValid) = getValue(request, u\\\"q1\\\", bValid)\\n (term_Q2_str, bValid) = getValue(request, u\\\"q2\\\", bValid)\\n (term_Q3_str, bValid) = getValue(request, u\\\"q3\\\", bValid)\\n (correctness_str, bValid) = getValue(request, u\\\"corr\\\", bValid)\\n (term_S_str, bValid) = getValue(request, u\\\"S\\\", bValid)\\n (term_M_str, bValid) = getValue(request, u\\\"M\\\", bValid)\\n (term_P_str, bValid) = getValue(request, u\\\"P\\\", bValid)\\n (conclusion_truth_value_str, bValid) = getValue(request, u\\\"conclusion\\\", bValid)\\n (validity_answer, bValid) = getValue(request, u\\\"validity\\\", bValid)\\n (rule_clicks_str, bValid) = getValue(request, u\\\"rule_clicks\\\", bValid)\\n\\n if not bValid:\\n return render(request, 'log.html')\\n else:\\n if correctness_str == \\\"true\\\":\\n bCorrect = True\\n else:\\n bCorrect = False\\n\\n if conclusion_truth_value_str == \\\"1\\\":\\n bConclusionTruthValue = True\\n else:\\n bConclusionTruthValue = False\\n\\n try:\\n rule_clicks = int(rule_clicks_str)\\n except:\\n rule_clicks = -1\\n\\n propkind_str = \\\"verbal\\\"\\n\\n newobj4 = Answer4(usernumber=int(usernumber_str),\\n timelapse=int(timelapse_str),\\n figure=int(figure_str),\\n quantor_1=term_Q1_str,\\n quantor_2=term_Q2_str,\\n quantor_3=term_Q3_str,\\n term_S=int(term_S_str),\\n term_M=int(term_M_str),\\n term_P=int(term_P_str),\\n propkind=propkind_str,\\n student_answer=validity_answer,\\n correctness=bCorrect,\\n conclusion_truth_value=bConclusionTruthValue,\\n rule_clicks=rule_clicks)\\n\\n newobj4.save()\\n\\n return render(request, 'log.html')\\n else:\\n return render(request, 'log.html')\",\n \"async def index(request: Request, user: UserInfo) -> HTTPResponse:\\n return redirect('home')\",\n \"def login_post():\\n\\treturn \\\"LOGIN\\\"\",\n \"def login():\\n if request.method == \\\"POST\\\":\\n # 客户端在login页面发起的POST请求\\n username = request.form[\\\"username\\\"]\\n password = request.form[\\\"password\\\"]\\n ipaddr = request.form[\\\"ipaddr\\\"]\\n database = request.form[\\\"database\\\"]\\n\\n db = db_login(username, password, ipaddr, database)\\n\\n if db == None:\\n return render_template(\\\"login.html\\\",loginres=0)\\n else:\\n session['username'] = username\\n session['password'] = password\\n session['ipaddr'] = ipaddr\\n session['database'] = database\\n\\n return redirect(url_for(\\\"mainpage\\\"))\\n else :\\n # 客户端GET 请求login页面时\\n return render_template(\\\"login.html\\\")\",\n \"async def handle_login(\\n request: aiohttp.web.Request,\\n) -> typing.Union[aiohttp.web.Response, aiohttp.web.FileResponse]:\\n response: typing.Union[aiohttp.web.Response, aiohttp.web.FileResponse]\\n response = aiohttp.web.Response(status=302, reason=\\\"Redirection to login\\\")\\n\\n # Add a cookie for navigating\\n if \\\"navto\\\" in request.query.keys():\\n response.set_cookie(\\\"NAV_TO\\\", request.query[\\\"navto\\\"], expires=str(3600))\\n\\n if setd[\\\"oidc_enabled\\\"]:\\n session = await aiohttp_session.get_session(request)\\n if \\\"oidc\\\" in session:\\n response = aiohttp.web.FileResponse(\\n str(setd[\\\"static_directory\\\"]) + \\\"/login2step.html\\\"\\n )\\n else:\\n response.headers[\\\"Location\\\"] = \\\"/\\\"\\n\\n else:\\n response.headers[\\\"Location\\\"] = \\\"/login/front\\\"\\n\\n return response\",\n \"async def on_login(self, login_response):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6088461","0.60372317","0.6023485","0.58360314","0.5830466","0.5750337","0.57417244","0.5734808","0.57280153","0.5713437","0.5711979","0.5710559","0.5706287","0.56738955","0.5670487","0.5635086","0.56344676","0.56225646","0.56205153","0.56148446","0.5611313","0.5605061","0.560199","0.5558547","0.55396074","0.55237824","0.55225366","0.55151933","0.5507501","0.5491306","0.5487988","0.5485861","0.54808336","0.54682344","0.5466603","0.54635805","0.5454077","0.5445899","0.5445899","0.5445899","0.54345125","0.54292625","0.54271525","0.5426079","0.54204506","0.54181033","0.5414075","0.5407101","0.5398465","0.5389056","0.53812677","0.5377745","0.53749794","0.53693795","0.53693795","0.5363416","0.5363416","0.5363416","0.5363416","0.5363416","0.5363416","0.5363416","0.5363416","0.5363416","0.5363416","0.5363416","0.5363416","0.5363416","0.535544","0.5349364","0.53490555","0.53476","0.5339151","0.53286797","0.5323377","0.5323377","0.53230006","0.53208554","0.53205025","0.5318347","0.53118575","0.5311799","0.53106797","0.5300074","0.5296789","0.5292344","0.5290521","0.5274205","0.52713966","0.52690643","0.526719","0.526719","0.5262847","0.5250856","0.52503055","0.52492857","0.5248905","0.52441657","0.5243247","0.5239258","0.5238013"],"string":"[\n \"0.6088461\",\n \"0.60372317\",\n \"0.6023485\",\n \"0.58360314\",\n \"0.5830466\",\n \"0.5750337\",\n \"0.57417244\",\n \"0.5734808\",\n \"0.57280153\",\n \"0.5713437\",\n \"0.5711979\",\n \"0.5710559\",\n \"0.5706287\",\n \"0.56738955\",\n \"0.5670487\",\n \"0.5635086\",\n \"0.56344676\",\n \"0.56225646\",\n \"0.56205153\",\n \"0.56148446\",\n \"0.5611313\",\n \"0.5605061\",\n \"0.560199\",\n \"0.5558547\",\n \"0.55396074\",\n \"0.55237824\",\n \"0.55225366\",\n \"0.55151933\",\n \"0.5507501\",\n \"0.5491306\",\n \"0.5487988\",\n \"0.5485861\",\n \"0.54808336\",\n \"0.54682344\",\n \"0.5466603\",\n \"0.54635805\",\n \"0.5454077\",\n \"0.5445899\",\n \"0.5445899\",\n \"0.5445899\",\n \"0.54345125\",\n \"0.54292625\",\n \"0.54271525\",\n \"0.5426079\",\n \"0.54204506\",\n \"0.54181033\",\n \"0.5414075\",\n \"0.5407101\",\n \"0.5398465\",\n \"0.5389056\",\n \"0.53812677\",\n \"0.5377745\",\n \"0.53749794\",\n \"0.53693795\",\n \"0.53693795\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.5363416\",\n \"0.535544\",\n \"0.5349364\",\n \"0.53490555\",\n \"0.53476\",\n \"0.5339151\",\n \"0.53286797\",\n \"0.5323377\",\n \"0.5323377\",\n \"0.53230006\",\n \"0.53208554\",\n \"0.53205025\",\n \"0.5318347\",\n \"0.53118575\",\n \"0.5311799\",\n \"0.53106797\",\n \"0.5300074\",\n \"0.5296789\",\n \"0.5292344\",\n \"0.5290521\",\n \"0.5274205\",\n \"0.52713966\",\n \"0.52690643\",\n \"0.526719\",\n \"0.526719\",\n \"0.5262847\",\n \"0.5250856\",\n \"0.52503055\",\n \"0.52492857\",\n \"0.5248905\",\n \"0.52441657\",\n \"0.5243247\",\n \"0.5239258\",\n \"0.5238013\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17765,"cells":{"query":{"kind":"string","value":"Route for posting messages or getting the prerendered html"},"document":{"kind":"string","value":"def message():\n if request.method == 'POST':\n db.log_msg(request.form['text'], request.cookies.get('username'))\n return db.get_all_messages()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def render_POST(self, request):\n\t\tprint request.args[\"message\"][0]\n\t\tself.jabberClient.sendMessage(request.args[\"message\"][0])\n\t\treturn ''","def post(self, request, *args, **kwargs):\n return self.render_to_response(self.get_context_data())","def input_post(): #TODO, error handling for privacy checks\n\n message = request.form['message']\n page_token = session['page']['access_token']\n resp = utils.post_message(message, page_token, session['visibility'])\n return render_template('success.html', post_id = resp['id'])","def post(self, request, *args, **kwargs):\n return render(request, self.template_name, self.get_context_data(**kwargs))","def index(self):\n return \"\"\"\n<html><body>\n<pre>\n Hello World!\n\n To send a message, please point to:\n http://server:port/msg?msgType=YOURTYPE&payload=YOURPAYLOAD\n</pre>\n</body></html>\n \"\"\"","def show_message(request):\n return render_to_response('woodstock/messages/view.html', {},\n context_instance = RequestContext(request))","def thanks():\n return render_template('submitted.html')","def get(self):\n self.write(\n '{\"error\": \"cryptochat-server main page, '\n 'please refer to /api/message/new or /api/message/updates\"}')","def render_result():\n\n action = request.form['action']\n session['visibility'] = request.form['vis'] == 'pub'\n if(action == 'list'):\n resp = utils.get_posts(g.graph,session['visibility'],session['page']['id'])\n return render_template('display_posts.html', data = resp, next = resp['next'])\n elif (action == 'add'): \n return render_template('input_post.html')\n else:\n return redirect(url_for('error')) #TODO: add this page","def post(self):\n return write_msg(request.json)","def render():\n html = request.get_data().decode('utf-8')\n sio.emit('render', html)\n return 'OK'","def serve(self, request, *args, **kwargs):\n\n template = self.get_template(request)\n\n if request.method == 'POST':\n\n form = self.get_form(request.POST, page=self, user=request.user)\n\n if form.is_valid():\n self.process_form_submission(form)\n return HttpResponseRedirect(self.url + '?thank=you')\n\n else:\n\n thanks = request.GET.get('thank', False)\n if thanks:\n form = None\n template = self.get_landing_page_template(request)\n if self.thanks_page_title:\n self.title = self.thanks_page_title\n else:\n form = self.get_form(page=self, user=request.user)\n\n context = self.get_context(request)\n context['form'] = form\n if form:\n context['conditional_rules'] = json.dumps(form.conditional_rules)\n\n return render(\n request,\n template,\n context\n )","def receive_message(request):\n message_id = request.GET.get('message_id')\n message = Message.objects.get(pk=message_id)\n return Response({'message': message})","def publish(request):\n issue = request.issue\n if issue.edit_allowed:\n form_class = PublishForm\n else:\n form_class = MiniPublishForm\n draft_message = None\n if not request.POST.get('message_only', None):\n query = models.Message.query(\n models.Message.issue_key == issue.key,\n models.Message.sender == request.user.email(),\n models.Message.draft == True)\n draft_message = query.get()\n if request.method != 'POST':\n reviewers = issue.reviewers[:]\n cc = issue.cc[:]\n if (request.user != issue.owner and\n request.user.email() not in issue.reviewers and\n not issue.is_collaborator(request.user)):\n reviewers.append(request.user.email())\n if request.user.email() in cc:\n cc.remove(request.user.email())\n reviewers = [models.Account.get_nickname_for_email(reviewer,\n default=reviewer)\n for reviewer in reviewers]\n ccs = [models.Account.get_nickname_for_email(cc, default=cc) for cc in cc]\n tbd, comments = _get_draft_comments(request, issue, True)\n preview = _get_draft_details(request, comments)\n if draft_message is None:\n msg = ''\n else:\n msg = draft_message.text\n form = form_class(initial={'subject': issue.subject,\n 'reviewers': ', '.join(reviewers),\n 'cc': ', '.join(ccs),\n 'send_mail': True,\n 'message': msg,\n })\n return respond(request, 'publish.html', {'form': form,\n 'issue': issue,\n 'preview': preview,\n 'draft_message': draft_message,\n })\n\n # Supply subject so that if this is a bare request to /publish, it won't\n # fail out if we've selected PublishForm (which requires a subject).\n augmented_POST = request.POST.copy()\n if issue.subject:\n augmented_POST.setdefault('subject', issue.subject)\n form = form_class(augmented_POST)\n\n # If the user is blocked, intentionally redirects him to the form again to\n # confuse him.\n account = models.Account.get_account_for_user(request.user)\n if account.blocked or not form.is_valid():\n return respond(request, 'publish.html', {'form': form, 'issue': issue})\n if issue.edit_allowed:\n issue.subject = form.cleaned_data['subject']\n if form.is_valid() and not form.cleaned_data.get('message_only', False):\n reviewers = _get_emails(form, 'reviewers')\n else:\n reviewers = issue.reviewers\n if (request.user != issue.owner and\n request.user.email() not in reviewers and\n not issue.is_collaborator(request.user)):\n reviewers.append(db.Email(request.user.email()))\n if form.is_valid() and not form.cleaned_data.get('message_only', False):\n cc = _get_emails(form, 'cc')\n else:\n cc = issue.cc\n # The user is in the reviewer list, remove them from CC if they're there.\n if request.user.email() in cc:\n cc.remove(request.user.email())\n if not form.is_valid():\n return respond(request, 'publish.html', {'form': form, 'issue': issue})\n issue.reviewers = reviewers\n issue.cc = cc\n if not form.cleaned_data.get('message_only', False):\n tbd, comments = _get_draft_comments(request, issue)\n else:\n tbd = []\n comments = []\n issue.update_comment_count(len(comments))\n tbd.append(issue)\n\n if comments:\n logging.warn('Publishing %d comments', len(comments))\n msg = _make_message(request, issue,\n form.cleaned_data['message'],\n comments,\n form.cleaned_data['send_mail'],\n draft=draft_message,\n in_reply_to=form.cleaned_data.get('in_reply_to'))\n tbd.append(msg)\n\n for obj in tbd:\n obj.put()\n\n # There are now no comments here (modulo race conditions)\n models.Account.current_user_account.update_drafts(issue, 0)\n if form.cleaned_data.get('no_redirect', False):\n return HttpTextResponse('OK')\n return HttpResponseRedirect(reverse(show, args=[issue.key.id()]))","def render_POST(self, request):","def get(self):\n text = read_filtered_msgs(request.args)\n return push_to_mattermost(text)","def get(self, request):\n form = PostForm()\n context = {\n 'form': form,\n 'success_message': ''\n }\n return render(request, 'posts/new-post.html', context)","def render(self):\n _ = self.request.getText\n form = self.request.form\n \n if form.has_key('cancel'):\n # User canceled\n return self.page.send_page(self.request)\n\n try:\n if not self.allowed():\n raise ActionError(_('You are not allowed to edit this page.'))\n elif not self.page.exists():\n raise ActionError(_('This page is already deleted or was never created!'))\n \n self.package()\n except ActionError, e:\n return self.page.send_page(self.request, msg=e.args[0])","def get(self):\n\n self.render(\"newpost.html\", user=self.user)","def message_page(request,page_name):\n return HttpResponse(\"This will be the {0} page.\".format(page_name))","def contact(request, contact_id):\n class MessageWrapper(object):\n \"\"\"\n A class which wraps a message and provides additional behavior needed by templates.\n \"\"\"\n def __init__(self, message):\n self.message = message\n self.effects = message.messageeffect_set.all()\n self.wrapped_effects = map(EffectWrapper, self.effects)\n self.needs_action = self.effects.filter(priority__in=MODERATOR_PRIORITIES, \n moderator_dismissed=False).exists()\n self.dismissed = (not self.needs_action \n and self.effects.filter(priority__in=MODERATOR_PRIORITIES).exists())\n self.created_by_moderator = message.connection.backend.name == settings.MODERATOR_BACKEND\n\n class EffectWrapper(object):\n \"\"\"\n A class which wraps a message effect and provides additional behavior needed by templates.\n \"\"\"\n ROW_CLASSES = {\n DEBUG: '',\n INFO: '',\n WARN: '',\n ERROR: 'danger',\n URGENT: '',\n NOTIFY: 'danger',\n }\n LABEL_CLASSES = {\n DEBUG: 'info',\n INFO: 'success',\n WARN: 'warning',\n ERROR: 'danger',\n URGENT: 'warning',\n NOTIFY: 'for moderation',\n }\n def __init__(self, effect):\n self.effect = effect\n self.row_class = self.ROW_CLASSES[effect.priority]\n self.label_class = self.LABEL_CLASSES[effect.priority]\n self.dismissable = (effect.priority in MODERATOR_PRIORITIES \n and not effect.moderator_dismissed)\n self.undismissable = (effect.priority in MODERATOR_PRIORITIES \n and effect.moderator_dismissed)\n\n contact = get_object_or_404(Contact, pk=contact_id)\n\n wrapped_messages = map(MessageWrapper, contact.message_set.order_by('-date'))\n #contact_revisions = reversion.get_for_object(self)\n\n\n return render_to_response(\"contact.html\", \n { 'contact': contact,\n 'wrapped_messages': wrapped_messages,\n },\n context_instance=RequestContext(request))","def render_post(response, post):\n response.out.write('<b>' + post.subject + '</b><br>')\n response.out.write(post.content)","def post(self, request, *args, **kwargs):\n form = self.get_form()\n if form.is_valid():\n self.use_template(self.render_template())\n return self.form_valid(form)\n else:\n return self.form_invalid(form)","def index(request):\n if request.method == 'POST':\n form = SendForm(request.POST)\n if form.is_valid():\n msg = Message()\n result = msg.send(form.cleaned_data['text'], form.cleaned_data['email'])\n form.clean()\n else:\n result = form.errors\n return render(request, 'index.html', {'form': form, 'msg': result})\n else:\n form = SendForm(initial={'email': request.user.email})\n return render(request, 'index.html', {'form': form})","def render_post(response, post):\n\n response.out.write('<b>' + post.subject + '</b><br>')\n response.out.write(post.content)","def chat(request):\n return jingo.render(request, 'chat/chat.html')","def post(self, url_pattern):\n return self.route(url_pattern, methods=['POST'])","def message(request, id, convoId): \n # Query for the appropriate message,\n message = Message.objects.get(id = id)\n\n form = ThoughtForm(request.POST or None)\n if request.method == \"POST\":\n thought = form.save(commit=False)\n thought.Message = message\n thought.timesent = datetime.now()\n thought.save()\n\n # The associated thoughts to the original message. \n thoughts = Thought.objects.filter(Message = message)\n \n return render(\n request, \n \"hoganApp/message.html\", \n {\"form\": form,\n \"thoughts\": thoughts,\n 'text': message.text,\n \"conversationId\": convoId,\n \"id\": id})","def post(self):\n send_slack_log('Entered /slack/get_msg')\n send_slack_log('Request info:')\n send_slack_log(str(request.form))\n msg_id = request.form['text']\n channel_id = request.form['channel_id']\n try:\n id = int(msg_id)\n except ValueError:\n return \"Invalid Msg ID: \" + str(msg_id)\n text = read_msg(id)\n formated_msg = slack_format_msg(text)\n response = send_json_to_slack_channel(formated_msg, channel_id)\n send_slack_log('Response info:')\n send_slack_log(response)\n return \"Msg \" + str(id) + \" fetched\"","def renderHTTP(req):","def display_message(self, message):\n params = {\n 'message': message\n }\n self.render_template('message.html', params)","def chat(request):\n team = Hunt.objects.get(is_current_hunt=True).team_from_user(request.user)\n if request.method == 'POST':\n # There is data in the post request, but we don't need anything but\n # the message because normal users can't send as staff or other teams\n m = Message.objects.create(time=timezone.now(), text=request.POST.get('message'),\n is_response=False, team=team)\n team.num_waiting_messages = 0\n messages = [m]\n else:\n if(team is None):\n return render(request, 'access_error.html', {'reason': \"team\"})\n if(team.hunt.is_locked and not team.is_playtester_team):\n return render(request, 'access_error.html', {'reason': \"hunt\"})\n if request.is_ajax():\n messages = Message.objects.filter(pk__gt=request.GET.get(\"last_pk\"))\n else:\n messages = Message.objects\n messages = messages.filter(team=team).order_by('time')\n\n # The whole message_dict format is for ajax/template uniformity\n rendered_messages = render_to_string('chat_messages.html',\n {'messages': messages, 'team_name': team.team_name})\n message_dict = {team.team_name: {'pk': team.pk, 'messages': rendered_messages}}\n try:\n last_pk = Message.objects.latest('id').id\n except Message.DoesNotExist:\n last_pk = 0\n team.num_waiting_messages = 0\n\n team.save() # Save last_*_message vars\n context = {'message_dict': message_dict, 'last_pk': last_pk}\n if request.is_ajax() or request.method == 'POST':\n return HttpResponse(json.dumps(context))\n else:\n context['team'] = team\n return render(request, 'chat.html', context)","async def render_post(self, request):\n self._hx711.tare()\n return Message(code=CHANGED)","def feedback():\n return render_template(\"feedback.html\")","def contact():\n if request.method == \"POST\":\n flash(\"Thanks {}, we have recived your message!\".format(\n request.form.get(\"name\")))\n return render_template(\"contact.html\", page_title=\"Contact Us\")","def messages_view(request):\n if request.user.is_authenticated:\n user_info = models.UserInfo.objects.get(user=request.user)\n friends=user_info.friends.all()\n\n # TODO Objective 9: query for posts (HINT only return posts needed to be displayed)\n posts = []\n posts = models.Post.objects.all()\n posts=posts[::-1]\n\n post_visits = request.session.get('post_visits', 0)\n allPosts=[]\n for post in posts:\n allPosts.append(post)\n\n\n\n listUpperBound = post_visits + 2\n postSize = len(allPosts)\n\n new_list = []\n if listUpperBound < postSize - 1:\n for i in range(listUpperBound):\n new_list.append(allPosts[i])\n else:\n new_list = allPosts\n\n # TODO Objective 10: check if user has like post, attach as a new attribute to each post\n\n context = { 'user_info':user_info,\n 'posts' : posts,\n 'friends':friends,\n 'new_list':new_list}\n\n return render(request,'messages.djhtml',context)\n\n request.session['failed'] = True\n return redirect('login:login_view')","def api_sendessage():\n message = request.args.get('message')\n send_lyrics(message)\n # stash_lyrics(message)\n return jsonify(status=\"success\")","def requestSubmitted(request):","def view_send_reviewer_message(request):\n success, info_dict = get_request_body_as_json(request)\n if not success:\n return JsonResponse(dict(success=False,\n message=\"No JSON info found in request.\"))\n\n if not KEY_MESSSAGE in info_dict:\n return JsonResponse(\\\n dict(success=False,\n message=\"No '%s' found in request.\" % KEY_MESSSAGE))\n\n msg_to_send = info_dict.get(KEY_MESSSAGE)\n MessageUtil.send_message(msg_to_send)\n\n return JsonResponse(dict(success=True,\n message='message sent: %s' % msg_to_send))","def post(self, pattern, handler):\n return self.route(Router.POST, pattern, handler)","def get(self):\n # Not done yet, cause lazy to do front\n return render_template('ask.html')","def get(self):\n self.post()","def slash_command():\n form_text = request.form[\"text\"]\n \n if len(form_text) > 0:\n data = {\n \"response_type\": \"in_channel\",\n \"text\": \"My response\",\n }\n else:\n \"\"\"\n If the user didn't type a message send a note that only\n they see about typing a message\n \"\"\"\n data = {\n \"response_type\": \"ephemeral\",\n \"text\": \"Error: No status message entered. Please try again.\",\n }\n\n \"\"\"\n Create the response object to send to Mattermost with the\n data object written as json, 200 status, and proper mimetype\n \"\"\"\n response = app.response_class(\n response=json.dumps(data),\n status=200,\n mimetype='application/json'\n )\n return response","def render(request, *args, **kw):","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self, request, *args, **kwargs):\n return render(request, self.template_name)","def get(self):\n\n recipient_form = SimpleRegistrationForm(request.form)\n bitcoin_form = BitcoinForm(request.form)\n\n return render(TEMPLATE, form=recipient_form, registered=False, bitcoin=bitcoin_form)","def post(self):\n subject = self.request.get('subject')\n content = self.request.get('post_content')\n post_id = self.request.get('post_id')\n post = Posts.get_by_id(int(post_id))\n user = self.get_active_user()\n user_id = int(user.key().id())\n\n if post and user and subject and content:\n if post.submitter_id == user_id:\n self.render_editpage(user, post_id, subject, content)\n else:\n self.render_improper_access()\n else:\n self.error(500)","def message_dispatch():\n if not from_twilio(request):\n abort(403)\n resp = twilio.twiml.Response()\n if not session.get(\"pwd\"):\n session['pwd'] = '__META__ROOT__'\n body = request.values.get(\"Body\")\n number = request.values.get(\"From\")\n message = cmd_handler.handle(number,session,body)\n session.modified = True\n resp.message(message)\n # We are probably going to modify the session on every command.\n return str(resp)","def renderHTTP(ctx):","def post():\n pass","def display_message(self, message):\n params = {\n 'message' : message\n }\n self.render_template('/auth/message.html', params)","def preview_handler(self, _, __):\r\n template = self.system.render_template('lti_form.html', self.get_context())\r\n return Response(template, content_type='text/html')","def get(self, request):\n return render(request, self.template, self.context)","def index():\n mog = Mog(request.form)\n if not mog.error:\n update_completed_emoji()\n mog.perform_action()\n\n return format_slack_message(mog.response_message, mog.response_type)","def thanks(request):\n return render(request, 'SNP_Feature_View/thanks.html')","def get(self, request, *args, **kwargs):\n form_class = self.get_form_class()\n form = self.get_form(form_class)\n return self.render_to_response(\n self.get_context_data(form=form, url_end='mechanic')\n )","def index_html(self,REQUEST): \n return self.posting_html(self,REQUEST)","def index(request):\n messages = SESSION.get_messages_sent_list(request.session)\n feedbacks = SESSION.get_messages_received_list(request.session)\n \n # initially display the first 20 messages/feedback chronologically\n messages.sort(key=lambda r: r.createdAt, reverse=True)\n feedbacks.sort(key=lambda r: r.createdAt, reverse=True)\n \n # prepare our template context variables use in our messages template\n data = {\n 'messages_nav': True,\n 'messages': messages[:PAGINATION_THRESHOLD],\n 'feedback': feedbacks[:PAGINATION_THRESHOLD],\n 'pag_threshold': PAGINATION_THRESHOLD,\n 'pag_page': 1,\n 'sent_count': len(messages),\n 'feedback_count': len(feedbacks),\n 'tab_feedback': request.GET.get('tab_feedback'),\n }\n \n if SESSION.get_patronStore_count(request.session):\n data['has_patrons'] = True\n \n # inserting this success and error message into the template\n # should be done in a cleaner way - this was done by the first guy\n # I just didn't bother changing it.\n if request.GET.get(\"success\"):\n data['success'] = request.GET.get(\"success\")\n if request.GET.get(\"error\"):\n data['error'] = request.GET.get(\"error\")\n \n return render(request, 'manage/messages.djhtml', data)","def view_post():\n\n return jsonify(\n get_dict(\"url\", \"args\", \"form\", \"data\", \"origin\", \"headers\", \"files\", \"json\")\n )","def main():\n return redirect('chat')","def get(self):\n self.post()","def get(self):\n self.post()","def post(self):\n subject = self.request.get('subject')\n post_content = self.request.get('post_content')\n submit = self.request.get('submit')\n cancel = self.request.get('cancel')\n user = self.get_active_user()\n created_by = int(user.key().id())\n post_id = self.request.get('post_id')\n\n if not user:\n self.redirect('/login')\n if post_id:\n post = Posts.get_by_id(int(post_id))\n else:\n post = None\n\n if cancel == \"cancel\":\n self.redirect('/%s' % str(post.key().id()))\n return\n if (post and post.submitter_id == user.key().id()) or not post:\n if submit == \"submit\" and subject and post_content:\n if post:\n post.subject = subject\n post.content = post_content\n post.put()\n else:\n post = Posts(subject=subject,\n content=post_content,\n submitter_id=created_by)\n post.put()\n self.redirect('/%s' % str(post.key().id()))\n else:\n self.render_newpage(user=user,\n subject=subject,\n post_content=post_content,\n error=\"\"\"Please provide both a subject and a\n post!\"\"\")\n else:\n self.redirect('/login')","def post(self, request):\n config_name = request.POST.get('config')\n email = request.POST.get('recipient')\n config = MailConfig.objects.get(name=config_name)\n version = TemplateVersion.objects.active(config.template.name)\n message = utils.render(config_name, email, version.test_data)\n pk = utils.send(\n f'[TEST] {message.subject}',\n message.from_email,\n message.to_email,\n message.body\n )\n return JsonResponse({'id': pk})","def post(self):\n send_slack_log('Entered /slack/post_msg')\n send_slack_log('Request info:')\n send_slack_log(str(request.form))\n # unknown request.form\n trigger_id = request.form['trigger_id']\n channel_id = request.form['channel_id']\n response = open_form(channel_id,\n trigger_id,\n config['slack_post_form_path'])\n send_slack_log('Response info:')\n send_slack_log(str(response))\n return 'Please enter the new msg information in the form'","def get(self, user):\n return self.render(\"post-new.html\", user=user)","def display_message(self, message):\n\t\tself.render('message.html', {'message': message})","def get(self):\n response = make_response(render_template_string(self.form_html), 200)\n response.headers['mime-type'] = 'text/html'\n return response","def get(self):\n if self.user:\n self.render(\"blog/addpost.html\")\n else:\n self.redirect(\"/login\")","def echo_handler(request):\n logger.debug('')\n return Response(200, 'OK', request.headers, request.body)","def contact(request):\n if not request.method == 'POST':\n return redirect('/')\n\n data = request.POST\n name = data.get('name')\n\n return render(request, 'contact_sent.html', {'name': name})","def post(self):\n try:\n msgId = int(request.form['text'])\n except ValueError:\n return {\"text\": \"please enter a valid integer as msg Id.\"}\n msg = str(read_msg(msgId))\n return {\"text\": msg}","async def post(self):\n await self.handle_request(self.messages_new_api, 1)","def get(self):\n self.render('view.html')","def msg_send(request):\n template_var = base_template_vals(request)\n template_var[\"allusers\"] = UserProfile.objects.all()\n current_django_user = UserProfile.objects.filter(\n django_user=request.user)[0]\n template_var[\"msg_sent_list\"] = Message.objects.filter(\n msg_from=current_django_user)\n template_var[\"msg_received_list\"] = Message.objects.filter(\n msg_to=current_django_user)\n \n if request.method == \"POST\":\n if request.user.is_authenticated():\n message = Message()\n message.msg_from = UserProfile.objects.filter(\n django_user=request.user)[0]\n message.msg_to = UserProfile.objects.filter(\n id__exact=request.POST[\"msg_to_django_user_id\"])[0]\n message.content = request.POST[\"content\"]\n message.save() \n\n return HttpResponseRedirect(\"/events/msg/\")\n \n return render_to_response(\"event/message_send.html\", template_var,\n context_instance=RequestContext(request))","def sales_rep():\n utils.messageSalesRep(request.form['name'], request.form['email'], request.form['message'])\n return redirect('/')","def post(self):\n subject = self.request.get('subject')\n content = self.request.get('content')\n\n # if user enter good subject and content, redirect them to new post page\n if subject and content:\n p = Post(parent = blog_key(), subject = subject, content = content)\n p.put() # store the post element into database\n self.redirect('/blog/%s' % str(p.key().id()))\n # otherwise, render an error page \n else:\n error = \"subject and content, please!\"\n self.render(\"newpost.html\", subject=subject, content=content, error=error)","def home_page():\n return \"pong\", 201, {'Content-Type': 'text/html'}","def parse(message):\n html = render(message['text'])\n\n return html","def display_messages(self, layout):","def get_destination_and_message(post_request):\n\n #Your code here\n pass","def chat(request):\n message = '{}: {}'.format(request.form['user'], request.form['message'])\n if message:\n ChatNamespace.broadcast('message', message)\n return Response()","def post(request):\n if request.is_ajax() and request.method == \"POST\":\n form = WallForm(request.POST)\n if form.is_valid():\n wall_post = Post(\n user=request.user,\n team=request.user.get_profile().team,\n text=form.cleaned_data[\"post\"]\n )\n wall_post.save()\n\n # Render the post and send it as a response.\n template = render_to_string(\"user_post.html\", {\"post\": wall_post},\n context_instance=RequestContext(request))\n return HttpResponse(json.dumps({\n \"contents\": template,\n }), mimetype=\"application/json\")\n\n # At this point there is a form validation error.\n return HttpResponse(json.dumps({\n \"message\": \"This should not be blank.\"\n }), mimetype=\"application/json\")\n\n raise Http404","def message_resend(request, message_id):\n message = get_object_or_404(Message, pk=message_id)\n contact = message.contact\n\n if request.method == 'POST':\n form = ResendForm(request.POST)\n\n if form.is_valid():\n text = form.cleaned_data['text']\n number = contact.contactprofile.get_phone_number()\n\n connection = lookup_connections(settings.MODERATOR_BACKEND, [number])[0]\n receive(text, connection)\n\n messages.success(request, _(\"The message %(text)s was successfully resent\") % \n {'text': unicode(text)})\n\n return HttpResponseRedirect(reverse(\"moderation.views.contact\", args=(contact.pk,)))\n else:\n form = ResendForm(initial={'text': message.text})\n\n return render_to_response(\"message_resend.html\", \n { 'contact': contact,\n 'message': message,\n 'form': form, \n },\n context_instance=RequestContext(request))","def render(self, request):\n content = request.content.read()\n msg = json.loads(content)\n d = self.engine_bus.handleRequest(self.access_id, msg)\n d.addCallback(self._success, request)\n d.addErrback(self._fail, request)\n return server.NOT_DONE_YET","def post(self):\n return send_email(request.args)","def render():\n\n path = request.args.get('markdown', '')\n raw = request.args.get('raw', False)\n\n username, user_id = g.user.username, g.user.id\n\n tmpl = 'markdown-rendered.html'\n if raw:\n tmpl = 'markdown-raw.html'\n elif request.args.get('presentation'):\n # TODO(dan?) fix presentation post\n # presentation_post = {}\n # presentation_post['authors_string'] = post.author_string\n # presentation_post['tldr'] = post.tldr\n # presentation_post['html'] = html\n # html = create_presentation_text(presentation_post)\n tmpl = \"markdown-presentation.html\"\n\n if not current_app.config.get('REPOSITORY_INDEXING_ENABLED', True):\n return _render_preview(path=path, tmpl=tmpl)\n\n post = (db_session.query(Post)\n .filter(Post.path == path)\n .first())\n if not post:\n knowledge_aliases = current_repo.config.aliases\n if path in knowledge_aliases:\n # TODO: reframe as redirect\n post = (db_session.query(Post)\n .filter(Post.path == knowledge_aliases[path])\n .first())\n if not post:\n raise Exception(\"unable to find post at {}\".format(path))\n\n if post.contains_excluded_tag:\n # It's possible that someone gets a direct link to a post that has an excluded tag\n return render_template(\"error.html\")\n\n if post.private:\n groups = post.groups\n users = set()\n for group in groups:\n user_ids = [user.id for user in group.users]\n users.update(user_ids)\n if user_id not in users and username not in current_repo.config.editors:\n return render_template(\"permission_ask.html\", authors=post.authors_string)\n\n html = render_post(post)\n raw_post = render_post_raw(post) if raw else None\n\n comments = post.comments\n for comment in comments:\n comment.author = db_session.query(User).filter(User.id == comment.user_id).first().username\n if not raw:\n comment.text = render_comment(comment)\n\n user_obj = (db_session.query(User)\n .filter(User.id == user_id)\n .first())\n\n tags_list = [str(t.name) for t in post.tags]\n user_subscriptions = [str(s) for s in user_obj.get_subscriptions]\n\n is_author = user_id in [author.id for author in post.authors]\n\n rendered = render_template(tmpl,\n html=html,\n post_id=post.id,\n post_path=path,\n raw_post=raw_post,\n comments=comments,\n username=username,\n post_author=post.authors_string,\n title=post.title,\n page_views=post.view_count,\n unique_views=post.view_user_count,\n likes=post.vote_counted_for_user(user_id=user_id),\n total_likes=post.vote_count,\n tags_list=tags_list,\n user_subscriptions=user_subscriptions,\n webeditor_buttons=False,\n web_uri=post.kp.web_uri,\n table_id=None,\n is_private=(post.private == 1),\n is_author=is_author)\n return rendered","def get(self, request):\n form = self.form_class()\n return render(request, self.template_name, {\"form\": form})","def post(self):\n try:\n msg_json = json.loads(request.form['text'])\n except json.decoder.JSONDecodeError:\n return {\"text\": \"Failed to send msg. Incorrect json format.\"}\n msgId = write_msg(msg_json)\n responseText = 'successfully created msg ' \\\n 'with id: ' + msgId\n return {\"text\": responseText}","def __serve_message_route(self, update: tg.Update, context: tg_ext.CallbackContext) -> None:\n message = update.message\n\n text = message.text\n\n user = User(message.chat)\n try: # try-except for user start initialization when he is not in db\n user.state = self.__state_manager.get_state(user.id)\n user.roles = self.__role_auth.get_user_roles(user.id)\n except (StateError, RoleError):\n user.state = 'free'\n user.roles = ['user']\n\n found_routes = self.__find_message_routes(text, user.state, user.roles)\n for route in found_routes:\n route.callback(user=user,\n message=text)","def render_form(self, title=\"\", body=\"\", error=\"\"):\n self.render(\"newpost.html\", title=title, body=body, error=error)","def message(self):\r\n url = '{0}/{1}'.format(self.get_url(), 'message')\r\n return http.Request('GET', url), parsers.parse_json","def get(self, request):\n\n # What template should we use?\n template = 'helloworld/default.html'\n\n # What data should we send to the template?\n template_data = {\n 'message': 'Hello World',\n }\n\n # Now render the data and the template.\n return render(request, template, template_data)","def index():\n if request.method == 'GET':\n\n\n return render_template('index.html')\n \n if request.method == 'POST':\n\n message = request.form['text']\n data_vector = text_vector.transform([message])\n data_transform = text_transformer.transform(data_vector)\n prediction = lsvc_model.predict(data_transform)\n # output_prediction = lsvc_model.predict(data_transform)\n \n # return render_template('result.html', output_prediction = prediction)\n return render_template('index.html', output_prediction = prediction)","def index(request):\n # most of the heavy lifting is handled by Angular, on the front end\n return render(request, 'forum/index.html')","def pagemainred():\n return render_template('do_action.html')"],"string":"[\n \"def render_POST(self, request):\\n\\t\\tprint request.args[\\\"message\\\"][0]\\n\\t\\tself.jabberClient.sendMessage(request.args[\\\"message\\\"][0])\\n\\t\\treturn ''\",\n \"def post(self, request, *args, **kwargs):\\n return self.render_to_response(self.get_context_data())\",\n \"def input_post(): #TODO, error handling for privacy checks\\n\\n message = request.form['message']\\n page_token = session['page']['access_token']\\n resp = utils.post_message(message, page_token, session['visibility'])\\n return render_template('success.html', post_id = resp['id'])\",\n \"def post(self, request, *args, **kwargs):\\n return render(request, self.template_name, self.get_context_data(**kwargs))\",\n \"def index(self):\\n return \\\"\\\"\\\"\\n<html><body>\\n<pre>\\n Hello World!\\n\\n To send a message, please point to:\\n http://server:port/msg?msgType=YOURTYPE&payload=YOURPAYLOAD\\n</pre>\\n</body></html>\\n \\\"\\\"\\\"\",\n \"def show_message(request):\\n return render_to_response('woodstock/messages/view.html', {},\\n context_instance = RequestContext(request))\",\n \"def thanks():\\n return render_template('submitted.html')\",\n \"def get(self):\\n self.write(\\n '{\\\"error\\\": \\\"cryptochat-server main page, '\\n 'please refer to /api/message/new or /api/message/updates\\\"}')\",\n \"def render_result():\\n\\n action = request.form['action']\\n session['visibility'] = request.form['vis'] == 'pub'\\n if(action == 'list'):\\n resp = utils.get_posts(g.graph,session['visibility'],session['page']['id'])\\n return render_template('display_posts.html', data = resp, next = resp['next'])\\n elif (action == 'add'): \\n return render_template('input_post.html')\\n else:\\n return redirect(url_for('error')) #TODO: add this page\",\n \"def post(self):\\n return write_msg(request.json)\",\n \"def render():\\n html = request.get_data().decode('utf-8')\\n sio.emit('render', html)\\n return 'OK'\",\n \"def serve(self, request, *args, **kwargs):\\n\\n template = self.get_template(request)\\n\\n if request.method == 'POST':\\n\\n form = self.get_form(request.POST, page=self, user=request.user)\\n\\n if form.is_valid():\\n self.process_form_submission(form)\\n return HttpResponseRedirect(self.url + '?thank=you')\\n\\n else:\\n\\n thanks = request.GET.get('thank', False)\\n if thanks:\\n form = None\\n template = self.get_landing_page_template(request)\\n if self.thanks_page_title:\\n self.title = self.thanks_page_title\\n else:\\n form = self.get_form(page=self, user=request.user)\\n\\n context = self.get_context(request)\\n context['form'] = form\\n if form:\\n context['conditional_rules'] = json.dumps(form.conditional_rules)\\n\\n return render(\\n request,\\n template,\\n context\\n )\",\n \"def receive_message(request):\\n message_id = request.GET.get('message_id')\\n message = Message.objects.get(pk=message_id)\\n return Response({'message': message})\",\n \"def publish(request):\\n issue = request.issue\\n if issue.edit_allowed:\\n form_class = PublishForm\\n else:\\n form_class = MiniPublishForm\\n draft_message = None\\n if not request.POST.get('message_only', None):\\n query = models.Message.query(\\n models.Message.issue_key == issue.key,\\n models.Message.sender == request.user.email(),\\n models.Message.draft == True)\\n draft_message = query.get()\\n if request.method != 'POST':\\n reviewers = issue.reviewers[:]\\n cc = issue.cc[:]\\n if (request.user != issue.owner and\\n request.user.email() not in issue.reviewers and\\n not issue.is_collaborator(request.user)):\\n reviewers.append(request.user.email())\\n if request.user.email() in cc:\\n cc.remove(request.user.email())\\n reviewers = [models.Account.get_nickname_for_email(reviewer,\\n default=reviewer)\\n for reviewer in reviewers]\\n ccs = [models.Account.get_nickname_for_email(cc, default=cc) for cc in cc]\\n tbd, comments = _get_draft_comments(request, issue, True)\\n preview = _get_draft_details(request, comments)\\n if draft_message is None:\\n msg = ''\\n else:\\n msg = draft_message.text\\n form = form_class(initial={'subject': issue.subject,\\n 'reviewers': ', '.join(reviewers),\\n 'cc': ', '.join(ccs),\\n 'send_mail': True,\\n 'message': msg,\\n })\\n return respond(request, 'publish.html', {'form': form,\\n 'issue': issue,\\n 'preview': preview,\\n 'draft_message': draft_message,\\n })\\n\\n # Supply subject so that if this is a bare request to /publish, it won't\\n # fail out if we've selected PublishForm (which requires a subject).\\n augmented_POST = request.POST.copy()\\n if issue.subject:\\n augmented_POST.setdefault('subject', issue.subject)\\n form = form_class(augmented_POST)\\n\\n # If the user is blocked, intentionally redirects him to the form again to\\n # confuse him.\\n account = models.Account.get_account_for_user(request.user)\\n if account.blocked or not form.is_valid():\\n return respond(request, 'publish.html', {'form': form, 'issue': issue})\\n if issue.edit_allowed:\\n issue.subject = form.cleaned_data['subject']\\n if form.is_valid() and not form.cleaned_data.get('message_only', False):\\n reviewers = _get_emails(form, 'reviewers')\\n else:\\n reviewers = issue.reviewers\\n if (request.user != issue.owner and\\n request.user.email() not in reviewers and\\n not issue.is_collaborator(request.user)):\\n reviewers.append(db.Email(request.user.email()))\\n if form.is_valid() and not form.cleaned_data.get('message_only', False):\\n cc = _get_emails(form, 'cc')\\n else:\\n cc = issue.cc\\n # The user is in the reviewer list, remove them from CC if they're there.\\n if request.user.email() in cc:\\n cc.remove(request.user.email())\\n if not form.is_valid():\\n return respond(request, 'publish.html', {'form': form, 'issue': issue})\\n issue.reviewers = reviewers\\n issue.cc = cc\\n if not form.cleaned_data.get('message_only', False):\\n tbd, comments = _get_draft_comments(request, issue)\\n else:\\n tbd = []\\n comments = []\\n issue.update_comment_count(len(comments))\\n tbd.append(issue)\\n\\n if comments:\\n logging.warn('Publishing %d comments', len(comments))\\n msg = _make_message(request, issue,\\n form.cleaned_data['message'],\\n comments,\\n form.cleaned_data['send_mail'],\\n draft=draft_message,\\n in_reply_to=form.cleaned_data.get('in_reply_to'))\\n tbd.append(msg)\\n\\n for obj in tbd:\\n obj.put()\\n\\n # There are now no comments here (modulo race conditions)\\n models.Account.current_user_account.update_drafts(issue, 0)\\n if form.cleaned_data.get('no_redirect', False):\\n return HttpTextResponse('OK')\\n return HttpResponseRedirect(reverse(show, args=[issue.key.id()]))\",\n \"def render_POST(self, request):\",\n \"def get(self):\\n text = read_filtered_msgs(request.args)\\n return push_to_mattermost(text)\",\n \"def get(self, request):\\n form = PostForm()\\n context = {\\n 'form': form,\\n 'success_message': ''\\n }\\n return render(request, 'posts/new-post.html', context)\",\n \"def render(self):\\n _ = self.request.getText\\n form = self.request.form\\n \\n if form.has_key('cancel'):\\n # User canceled\\n return self.page.send_page(self.request)\\n\\n try:\\n if not self.allowed():\\n raise ActionError(_('You are not allowed to edit this page.'))\\n elif not self.page.exists():\\n raise ActionError(_('This page is already deleted or was never created!'))\\n \\n self.package()\\n except ActionError, e:\\n return self.page.send_page(self.request, msg=e.args[0])\",\n \"def get(self):\\n\\n self.render(\\\"newpost.html\\\", user=self.user)\",\n \"def message_page(request,page_name):\\n return HttpResponse(\\\"This will be the {0} page.\\\".format(page_name))\",\n \"def contact(request, contact_id):\\n class MessageWrapper(object):\\n \\\"\\\"\\\"\\n A class which wraps a message and provides additional behavior needed by templates.\\n \\\"\\\"\\\"\\n def __init__(self, message):\\n self.message = message\\n self.effects = message.messageeffect_set.all()\\n self.wrapped_effects = map(EffectWrapper, self.effects)\\n self.needs_action = self.effects.filter(priority__in=MODERATOR_PRIORITIES, \\n moderator_dismissed=False).exists()\\n self.dismissed = (not self.needs_action \\n and self.effects.filter(priority__in=MODERATOR_PRIORITIES).exists())\\n self.created_by_moderator = message.connection.backend.name == settings.MODERATOR_BACKEND\\n\\n class EffectWrapper(object):\\n \\\"\\\"\\\"\\n A class which wraps a message effect and provides additional behavior needed by templates.\\n \\\"\\\"\\\"\\n ROW_CLASSES = {\\n DEBUG: '',\\n INFO: '',\\n WARN: '',\\n ERROR: 'danger',\\n URGENT: '',\\n NOTIFY: 'danger',\\n }\\n LABEL_CLASSES = {\\n DEBUG: 'info',\\n INFO: 'success',\\n WARN: 'warning',\\n ERROR: 'danger',\\n URGENT: 'warning',\\n NOTIFY: 'for moderation',\\n }\\n def __init__(self, effect):\\n self.effect = effect\\n self.row_class = self.ROW_CLASSES[effect.priority]\\n self.label_class = self.LABEL_CLASSES[effect.priority]\\n self.dismissable = (effect.priority in MODERATOR_PRIORITIES \\n and not effect.moderator_dismissed)\\n self.undismissable = (effect.priority in MODERATOR_PRIORITIES \\n and effect.moderator_dismissed)\\n\\n contact = get_object_or_404(Contact, pk=contact_id)\\n\\n wrapped_messages = map(MessageWrapper, contact.message_set.order_by('-date'))\\n #contact_revisions = reversion.get_for_object(self)\\n\\n\\n return render_to_response(\\\"contact.html\\\", \\n { 'contact': contact,\\n 'wrapped_messages': wrapped_messages,\\n },\\n context_instance=RequestContext(request))\",\n \"def render_post(response, post):\\n response.out.write('<b>' + post.subject + '</b><br>')\\n response.out.write(post.content)\",\n \"def post(self, request, *args, **kwargs):\\n form = self.get_form()\\n if form.is_valid():\\n self.use_template(self.render_template())\\n return self.form_valid(form)\\n else:\\n return self.form_invalid(form)\",\n \"def index(request):\\n if request.method == 'POST':\\n form = SendForm(request.POST)\\n if form.is_valid():\\n msg = Message()\\n result = msg.send(form.cleaned_data['text'], form.cleaned_data['email'])\\n form.clean()\\n else:\\n result = form.errors\\n return render(request, 'index.html', {'form': form, 'msg': result})\\n else:\\n form = SendForm(initial={'email': request.user.email})\\n return render(request, 'index.html', {'form': form})\",\n \"def render_post(response, post):\\n\\n response.out.write('<b>' + post.subject + '</b><br>')\\n response.out.write(post.content)\",\n \"def chat(request):\\n return jingo.render(request, 'chat/chat.html')\",\n \"def post(self, url_pattern):\\n return self.route(url_pattern, methods=['POST'])\",\n \"def message(request, id, convoId): \\n # Query for the appropriate message,\\n message = Message.objects.get(id = id)\\n\\n form = ThoughtForm(request.POST or None)\\n if request.method == \\\"POST\\\":\\n thought = form.save(commit=False)\\n thought.Message = message\\n thought.timesent = datetime.now()\\n thought.save()\\n\\n # The associated thoughts to the original message. \\n thoughts = Thought.objects.filter(Message = message)\\n \\n return render(\\n request, \\n \\\"hoganApp/message.html\\\", \\n {\\\"form\\\": form,\\n \\\"thoughts\\\": thoughts,\\n 'text': message.text,\\n \\\"conversationId\\\": convoId,\\n \\\"id\\\": id})\",\n \"def post(self):\\n send_slack_log('Entered /slack/get_msg')\\n send_slack_log('Request info:')\\n send_slack_log(str(request.form))\\n msg_id = request.form['text']\\n channel_id = request.form['channel_id']\\n try:\\n id = int(msg_id)\\n except ValueError:\\n return \\\"Invalid Msg ID: \\\" + str(msg_id)\\n text = read_msg(id)\\n formated_msg = slack_format_msg(text)\\n response = send_json_to_slack_channel(formated_msg, channel_id)\\n send_slack_log('Response info:')\\n send_slack_log(response)\\n return \\\"Msg \\\" + str(id) + \\\" fetched\\\"\",\n \"def renderHTTP(req):\",\n \"def display_message(self, message):\\n params = {\\n 'message': message\\n }\\n self.render_template('message.html', params)\",\n \"def chat(request):\\n team = Hunt.objects.get(is_current_hunt=True).team_from_user(request.user)\\n if request.method == 'POST':\\n # There is data in the post request, but we don't need anything but\\n # the message because normal users can't send as staff or other teams\\n m = Message.objects.create(time=timezone.now(), text=request.POST.get('message'),\\n is_response=False, team=team)\\n team.num_waiting_messages = 0\\n messages = [m]\\n else:\\n if(team is None):\\n return render(request, 'access_error.html', {'reason': \\\"team\\\"})\\n if(team.hunt.is_locked and not team.is_playtester_team):\\n return render(request, 'access_error.html', {'reason': \\\"hunt\\\"})\\n if request.is_ajax():\\n messages = Message.objects.filter(pk__gt=request.GET.get(\\\"last_pk\\\"))\\n else:\\n messages = Message.objects\\n messages = messages.filter(team=team).order_by('time')\\n\\n # The whole message_dict format is for ajax/template uniformity\\n rendered_messages = render_to_string('chat_messages.html',\\n {'messages': messages, 'team_name': team.team_name})\\n message_dict = {team.team_name: {'pk': team.pk, 'messages': rendered_messages}}\\n try:\\n last_pk = Message.objects.latest('id').id\\n except Message.DoesNotExist:\\n last_pk = 0\\n team.num_waiting_messages = 0\\n\\n team.save() # Save last_*_message vars\\n context = {'message_dict': message_dict, 'last_pk': last_pk}\\n if request.is_ajax() or request.method == 'POST':\\n return HttpResponse(json.dumps(context))\\n else:\\n context['team'] = team\\n return render(request, 'chat.html', context)\",\n \"async def render_post(self, request):\\n self._hx711.tare()\\n return Message(code=CHANGED)\",\n \"def feedback():\\n return render_template(\\\"feedback.html\\\")\",\n \"def contact():\\n if request.method == \\\"POST\\\":\\n flash(\\\"Thanks {}, we have recived your message!\\\".format(\\n request.form.get(\\\"name\\\")))\\n return render_template(\\\"contact.html\\\", page_title=\\\"Contact Us\\\")\",\n \"def messages_view(request):\\n if request.user.is_authenticated:\\n user_info = models.UserInfo.objects.get(user=request.user)\\n friends=user_info.friends.all()\\n\\n # TODO Objective 9: query for posts (HINT only return posts needed to be displayed)\\n posts = []\\n posts = models.Post.objects.all()\\n posts=posts[::-1]\\n\\n post_visits = request.session.get('post_visits', 0)\\n allPosts=[]\\n for post in posts:\\n allPosts.append(post)\\n\\n\\n\\n listUpperBound = post_visits + 2\\n postSize = len(allPosts)\\n\\n new_list = []\\n if listUpperBound < postSize - 1:\\n for i in range(listUpperBound):\\n new_list.append(allPosts[i])\\n else:\\n new_list = allPosts\\n\\n # TODO Objective 10: check if user has like post, attach as a new attribute to each post\\n\\n context = { 'user_info':user_info,\\n 'posts' : posts,\\n 'friends':friends,\\n 'new_list':new_list}\\n\\n return render(request,'messages.djhtml',context)\\n\\n request.session['failed'] = True\\n return redirect('login:login_view')\",\n \"def api_sendessage():\\n message = request.args.get('message')\\n send_lyrics(message)\\n # stash_lyrics(message)\\n return jsonify(status=\\\"success\\\")\",\n \"def requestSubmitted(request):\",\n \"def view_send_reviewer_message(request):\\n success, info_dict = get_request_body_as_json(request)\\n if not success:\\n return JsonResponse(dict(success=False,\\n message=\\\"No JSON info found in request.\\\"))\\n\\n if not KEY_MESSSAGE in info_dict:\\n return JsonResponse(\\\\\\n dict(success=False,\\n message=\\\"No '%s' found in request.\\\" % KEY_MESSSAGE))\\n\\n msg_to_send = info_dict.get(KEY_MESSSAGE)\\n MessageUtil.send_message(msg_to_send)\\n\\n return JsonResponse(dict(success=True,\\n message='message sent: %s' % msg_to_send))\",\n \"def post(self, pattern, handler):\\n return self.route(Router.POST, pattern, handler)\",\n \"def get(self):\\n # Not done yet, cause lazy to do front\\n return render_template('ask.html')\",\n \"def get(self):\\n self.post()\",\n \"def slash_command():\\n form_text = request.form[\\\"text\\\"]\\n \\n if len(form_text) > 0:\\n data = {\\n \\\"response_type\\\": \\\"in_channel\\\",\\n \\\"text\\\": \\\"My response\\\",\\n }\\n else:\\n \\\"\\\"\\\"\\n If the user didn't type a message send a note that only\\n they see about typing a message\\n \\\"\\\"\\\"\\n data = {\\n \\\"response_type\\\": \\\"ephemeral\\\",\\n \\\"text\\\": \\\"Error: No status message entered. Please try again.\\\",\\n }\\n\\n \\\"\\\"\\\"\\n Create the response object to send to Mattermost with the\\n data object written as json, 200 status, and proper mimetype\\n \\\"\\\"\\\"\\n response = app.response_class(\\n response=json.dumps(data),\\n status=200,\\n mimetype='application/json'\\n )\\n return response\",\n \"def render(request, *args, **kw):\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self, request, *args, **kwargs):\\n return render(request, self.template_name)\",\n \"def get(self):\\n\\n recipient_form = SimpleRegistrationForm(request.form)\\n bitcoin_form = BitcoinForm(request.form)\\n\\n return render(TEMPLATE, form=recipient_form, registered=False, bitcoin=bitcoin_form)\",\n \"def post(self):\\n subject = self.request.get('subject')\\n content = self.request.get('post_content')\\n post_id = self.request.get('post_id')\\n post = Posts.get_by_id(int(post_id))\\n user = self.get_active_user()\\n user_id = int(user.key().id())\\n\\n if post and user and subject and content:\\n if post.submitter_id == user_id:\\n self.render_editpage(user, post_id, subject, content)\\n else:\\n self.render_improper_access()\\n else:\\n self.error(500)\",\n \"def message_dispatch():\\n if not from_twilio(request):\\n abort(403)\\n resp = twilio.twiml.Response()\\n if not session.get(\\\"pwd\\\"):\\n session['pwd'] = '__META__ROOT__'\\n body = request.values.get(\\\"Body\\\")\\n number = request.values.get(\\\"From\\\")\\n message = cmd_handler.handle(number,session,body)\\n session.modified = True\\n resp.message(message)\\n # We are probably going to modify the session on every command.\\n return str(resp)\",\n \"def renderHTTP(ctx):\",\n \"def post():\\n pass\",\n \"def display_message(self, message):\\n params = {\\n 'message' : message\\n }\\n self.render_template('/auth/message.html', params)\",\n \"def preview_handler(self, _, __):\\r\\n template = self.system.render_template('lti_form.html', self.get_context())\\r\\n return Response(template, content_type='text/html')\",\n \"def get(self, request):\\n return render(request, self.template, self.context)\",\n \"def index():\\n mog = Mog(request.form)\\n if not mog.error:\\n update_completed_emoji()\\n mog.perform_action()\\n\\n return format_slack_message(mog.response_message, mog.response_type)\",\n \"def thanks(request):\\n return render(request, 'SNP_Feature_View/thanks.html')\",\n \"def get(self, request, *args, **kwargs):\\n form_class = self.get_form_class()\\n form = self.get_form(form_class)\\n return self.render_to_response(\\n self.get_context_data(form=form, url_end='mechanic')\\n )\",\n \"def index_html(self,REQUEST): \\n return self.posting_html(self,REQUEST)\",\n \"def index(request):\\n messages = SESSION.get_messages_sent_list(request.session)\\n feedbacks = SESSION.get_messages_received_list(request.session)\\n \\n # initially display the first 20 messages/feedback chronologically\\n messages.sort(key=lambda r: r.createdAt, reverse=True)\\n feedbacks.sort(key=lambda r: r.createdAt, reverse=True)\\n \\n # prepare our template context variables use in our messages template\\n data = {\\n 'messages_nav': True,\\n 'messages': messages[:PAGINATION_THRESHOLD],\\n 'feedback': feedbacks[:PAGINATION_THRESHOLD],\\n 'pag_threshold': PAGINATION_THRESHOLD,\\n 'pag_page': 1,\\n 'sent_count': len(messages),\\n 'feedback_count': len(feedbacks),\\n 'tab_feedback': request.GET.get('tab_feedback'),\\n }\\n \\n if SESSION.get_patronStore_count(request.session):\\n data['has_patrons'] = True\\n \\n # inserting this success and error message into the template\\n # should be done in a cleaner way - this was done by the first guy\\n # I just didn't bother changing it.\\n if request.GET.get(\\\"success\\\"):\\n data['success'] = request.GET.get(\\\"success\\\")\\n if request.GET.get(\\\"error\\\"):\\n data['error'] = request.GET.get(\\\"error\\\")\\n \\n return render(request, 'manage/messages.djhtml', data)\",\n \"def view_post():\\n\\n return jsonify(\\n get_dict(\\\"url\\\", \\\"args\\\", \\\"form\\\", \\\"data\\\", \\\"origin\\\", \\\"headers\\\", \\\"files\\\", \\\"json\\\")\\n )\",\n \"def main():\\n return redirect('chat')\",\n \"def get(self):\\n self.post()\",\n \"def get(self):\\n self.post()\",\n \"def post(self):\\n subject = self.request.get('subject')\\n post_content = self.request.get('post_content')\\n submit = self.request.get('submit')\\n cancel = self.request.get('cancel')\\n user = self.get_active_user()\\n created_by = int(user.key().id())\\n post_id = self.request.get('post_id')\\n\\n if not user:\\n self.redirect('/login')\\n if post_id:\\n post = Posts.get_by_id(int(post_id))\\n else:\\n post = None\\n\\n if cancel == \\\"cancel\\\":\\n self.redirect('/%s' % str(post.key().id()))\\n return\\n if (post and post.submitter_id == user.key().id()) or not post:\\n if submit == \\\"submit\\\" and subject and post_content:\\n if post:\\n post.subject = subject\\n post.content = post_content\\n post.put()\\n else:\\n post = Posts(subject=subject,\\n content=post_content,\\n submitter_id=created_by)\\n post.put()\\n self.redirect('/%s' % str(post.key().id()))\\n else:\\n self.render_newpage(user=user,\\n subject=subject,\\n post_content=post_content,\\n error=\\\"\\\"\\\"Please provide both a subject and a\\n post!\\\"\\\"\\\")\\n else:\\n self.redirect('/login')\",\n \"def post(self, request):\\n config_name = request.POST.get('config')\\n email = request.POST.get('recipient')\\n config = MailConfig.objects.get(name=config_name)\\n version = TemplateVersion.objects.active(config.template.name)\\n message = utils.render(config_name, email, version.test_data)\\n pk = utils.send(\\n f'[TEST] {message.subject}',\\n message.from_email,\\n message.to_email,\\n message.body\\n )\\n return JsonResponse({'id': pk})\",\n \"def post(self):\\n send_slack_log('Entered /slack/post_msg')\\n send_slack_log('Request info:')\\n send_slack_log(str(request.form))\\n # unknown request.form\\n trigger_id = request.form['trigger_id']\\n channel_id = request.form['channel_id']\\n response = open_form(channel_id,\\n trigger_id,\\n config['slack_post_form_path'])\\n send_slack_log('Response info:')\\n send_slack_log(str(response))\\n return 'Please enter the new msg information in the form'\",\n \"def get(self, user):\\n return self.render(\\\"post-new.html\\\", user=user)\",\n \"def display_message(self, message):\\n\\t\\tself.render('message.html', {'message': message})\",\n \"def get(self):\\n response = make_response(render_template_string(self.form_html), 200)\\n response.headers['mime-type'] = 'text/html'\\n return response\",\n \"def get(self):\\n if self.user:\\n self.render(\\\"blog/addpost.html\\\")\\n else:\\n self.redirect(\\\"/login\\\")\",\n \"def echo_handler(request):\\n logger.debug('')\\n return Response(200, 'OK', request.headers, request.body)\",\n \"def contact(request):\\n if not request.method == 'POST':\\n return redirect('/')\\n\\n data = request.POST\\n name = data.get('name')\\n\\n return render(request, 'contact_sent.html', {'name': name})\",\n \"def post(self):\\n try:\\n msgId = int(request.form['text'])\\n except ValueError:\\n return {\\\"text\\\": \\\"please enter a valid integer as msg Id.\\\"}\\n msg = str(read_msg(msgId))\\n return {\\\"text\\\": msg}\",\n \"async def post(self):\\n await self.handle_request(self.messages_new_api, 1)\",\n \"def get(self):\\n self.render('view.html')\",\n \"def msg_send(request):\\n template_var = base_template_vals(request)\\n template_var[\\\"allusers\\\"] = UserProfile.objects.all()\\n current_django_user = UserProfile.objects.filter(\\n django_user=request.user)[0]\\n template_var[\\\"msg_sent_list\\\"] = Message.objects.filter(\\n msg_from=current_django_user)\\n template_var[\\\"msg_received_list\\\"] = Message.objects.filter(\\n msg_to=current_django_user)\\n \\n if request.method == \\\"POST\\\":\\n if request.user.is_authenticated():\\n message = Message()\\n message.msg_from = UserProfile.objects.filter(\\n django_user=request.user)[0]\\n message.msg_to = UserProfile.objects.filter(\\n id__exact=request.POST[\\\"msg_to_django_user_id\\\"])[0]\\n message.content = request.POST[\\\"content\\\"]\\n message.save() \\n\\n return HttpResponseRedirect(\\\"/events/msg/\\\")\\n \\n return render_to_response(\\\"event/message_send.html\\\", template_var,\\n context_instance=RequestContext(request))\",\n \"def sales_rep():\\n utils.messageSalesRep(request.form['name'], request.form['email'], request.form['message'])\\n return redirect('/')\",\n \"def post(self):\\n subject = self.request.get('subject')\\n content = self.request.get('content')\\n\\n # if user enter good subject and content, redirect them to new post page\\n if subject and content:\\n p = Post(parent = blog_key(), subject = subject, content = content)\\n p.put() # store the post element into database\\n self.redirect('/blog/%s' % str(p.key().id()))\\n # otherwise, render an error page \\n else:\\n error = \\\"subject and content, please!\\\"\\n self.render(\\\"newpost.html\\\", subject=subject, content=content, error=error)\",\n \"def home_page():\\n return \\\"pong\\\", 201, {'Content-Type': 'text/html'}\",\n \"def parse(message):\\n html = render(message['text'])\\n\\n return html\",\n \"def display_messages(self, layout):\",\n \"def get_destination_and_message(post_request):\\n\\n #Your code here\\n pass\",\n \"def chat(request):\\n message = '{}: {}'.format(request.form['user'], request.form['message'])\\n if message:\\n ChatNamespace.broadcast('message', message)\\n return Response()\",\n \"def post(request):\\n if request.is_ajax() and request.method == \\\"POST\\\":\\n form = WallForm(request.POST)\\n if form.is_valid():\\n wall_post = Post(\\n user=request.user,\\n team=request.user.get_profile().team,\\n text=form.cleaned_data[\\\"post\\\"]\\n )\\n wall_post.save()\\n\\n # Render the post and send it as a response.\\n template = render_to_string(\\\"user_post.html\\\", {\\\"post\\\": wall_post},\\n context_instance=RequestContext(request))\\n return HttpResponse(json.dumps({\\n \\\"contents\\\": template,\\n }), mimetype=\\\"application/json\\\")\\n\\n # At this point there is a form validation error.\\n return HttpResponse(json.dumps({\\n \\\"message\\\": \\\"This should not be blank.\\\"\\n }), mimetype=\\\"application/json\\\")\\n\\n raise Http404\",\n \"def message_resend(request, message_id):\\n message = get_object_or_404(Message, pk=message_id)\\n contact = message.contact\\n\\n if request.method == 'POST':\\n form = ResendForm(request.POST)\\n\\n if form.is_valid():\\n text = form.cleaned_data['text']\\n number = contact.contactprofile.get_phone_number()\\n\\n connection = lookup_connections(settings.MODERATOR_BACKEND, [number])[0]\\n receive(text, connection)\\n\\n messages.success(request, _(\\\"The message %(text)s was successfully resent\\\") % \\n {'text': unicode(text)})\\n\\n return HttpResponseRedirect(reverse(\\\"moderation.views.contact\\\", args=(contact.pk,)))\\n else:\\n form = ResendForm(initial={'text': message.text})\\n\\n return render_to_response(\\\"message_resend.html\\\", \\n { 'contact': contact,\\n 'message': message,\\n 'form': form, \\n },\\n context_instance=RequestContext(request))\",\n \"def render(self, request):\\n content = request.content.read()\\n msg = json.loads(content)\\n d = self.engine_bus.handleRequest(self.access_id, msg)\\n d.addCallback(self._success, request)\\n d.addErrback(self._fail, request)\\n return server.NOT_DONE_YET\",\n \"def post(self):\\n return send_email(request.args)\",\n \"def render():\\n\\n path = request.args.get('markdown', '')\\n raw = request.args.get('raw', False)\\n\\n username, user_id = g.user.username, g.user.id\\n\\n tmpl = 'markdown-rendered.html'\\n if raw:\\n tmpl = 'markdown-raw.html'\\n elif request.args.get('presentation'):\\n # TODO(dan?) fix presentation post\\n # presentation_post = {}\\n # presentation_post['authors_string'] = post.author_string\\n # presentation_post['tldr'] = post.tldr\\n # presentation_post['html'] = html\\n # html = create_presentation_text(presentation_post)\\n tmpl = \\\"markdown-presentation.html\\\"\\n\\n if not current_app.config.get('REPOSITORY_INDEXING_ENABLED', True):\\n return _render_preview(path=path, tmpl=tmpl)\\n\\n post = (db_session.query(Post)\\n .filter(Post.path == path)\\n .first())\\n if not post:\\n knowledge_aliases = current_repo.config.aliases\\n if path in knowledge_aliases:\\n # TODO: reframe as redirect\\n post = (db_session.query(Post)\\n .filter(Post.path == knowledge_aliases[path])\\n .first())\\n if not post:\\n raise Exception(\\\"unable to find post at {}\\\".format(path))\\n\\n if post.contains_excluded_tag:\\n # It's possible that someone gets a direct link to a post that has an excluded tag\\n return render_template(\\\"error.html\\\")\\n\\n if post.private:\\n groups = post.groups\\n users = set()\\n for group in groups:\\n user_ids = [user.id for user in group.users]\\n users.update(user_ids)\\n if user_id not in users and username not in current_repo.config.editors:\\n return render_template(\\\"permission_ask.html\\\", authors=post.authors_string)\\n\\n html = render_post(post)\\n raw_post = render_post_raw(post) if raw else None\\n\\n comments = post.comments\\n for comment in comments:\\n comment.author = db_session.query(User).filter(User.id == comment.user_id).first().username\\n if not raw:\\n comment.text = render_comment(comment)\\n\\n user_obj = (db_session.query(User)\\n .filter(User.id == user_id)\\n .first())\\n\\n tags_list = [str(t.name) for t in post.tags]\\n user_subscriptions = [str(s) for s in user_obj.get_subscriptions]\\n\\n is_author = user_id in [author.id for author in post.authors]\\n\\n rendered = render_template(tmpl,\\n html=html,\\n post_id=post.id,\\n post_path=path,\\n raw_post=raw_post,\\n comments=comments,\\n username=username,\\n post_author=post.authors_string,\\n title=post.title,\\n page_views=post.view_count,\\n unique_views=post.view_user_count,\\n likes=post.vote_counted_for_user(user_id=user_id),\\n total_likes=post.vote_count,\\n tags_list=tags_list,\\n user_subscriptions=user_subscriptions,\\n webeditor_buttons=False,\\n web_uri=post.kp.web_uri,\\n table_id=None,\\n is_private=(post.private == 1),\\n is_author=is_author)\\n return rendered\",\n \"def get(self, request):\\n form = self.form_class()\\n return render(request, self.template_name, {\\\"form\\\": form})\",\n \"def post(self):\\n try:\\n msg_json = json.loads(request.form['text'])\\n except json.decoder.JSONDecodeError:\\n return {\\\"text\\\": \\\"Failed to send msg. Incorrect json format.\\\"}\\n msgId = write_msg(msg_json)\\n responseText = 'successfully created msg ' \\\\\\n 'with id: ' + msgId\\n return {\\\"text\\\": responseText}\",\n \"def __serve_message_route(self, update: tg.Update, context: tg_ext.CallbackContext) -> None:\\n message = update.message\\n\\n text = message.text\\n\\n user = User(message.chat)\\n try: # try-except for user start initialization when he is not in db\\n user.state = self.__state_manager.get_state(user.id)\\n user.roles = self.__role_auth.get_user_roles(user.id)\\n except (StateError, RoleError):\\n user.state = 'free'\\n user.roles = ['user']\\n\\n found_routes = self.__find_message_routes(text, user.state, user.roles)\\n for route in found_routes:\\n route.callback(user=user,\\n message=text)\",\n \"def render_form(self, title=\\\"\\\", body=\\\"\\\", error=\\\"\\\"):\\n self.render(\\\"newpost.html\\\", title=title, body=body, error=error)\",\n \"def message(self):\\r\\n url = '{0}/{1}'.format(self.get_url(), 'message')\\r\\n return http.Request('GET', url), parsers.parse_json\",\n \"def get(self, request):\\n\\n # What template should we use?\\n template = 'helloworld/default.html'\\n\\n # What data should we send to the template?\\n template_data = {\\n 'message': 'Hello World',\\n }\\n\\n # Now render the data and the template.\\n return render(request, template, template_data)\",\n \"def index():\\n if request.method == 'GET':\\n\\n\\n return render_template('index.html')\\n \\n if request.method == 'POST':\\n\\n message = request.form['text']\\n data_vector = text_vector.transform([message])\\n data_transform = text_transformer.transform(data_vector)\\n prediction = lsvc_model.predict(data_transform)\\n # output_prediction = lsvc_model.predict(data_transform)\\n \\n # return render_template('result.html', output_prediction = prediction)\\n return render_template('index.html', output_prediction = prediction)\",\n \"def index(request):\\n # most of the heavy lifting is handled by Angular, on the front end\\n return render(request, 'forum/index.html')\",\n \"def pagemainred():\\n return render_template('do_action.html')\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6790318","0.67297566","0.66144854","0.6388285","0.63673466","0.6282351","0.6156348","0.6152705","0.61418","0.61377877","0.61028284","0.6077809","0.60488695","0.6045633","0.60317683","0.5997232","0.5917214","0.5899693","0.5897377","0.58870757","0.5816963","0.5789937","0.57815087","0.5772589","0.5771158","0.576391","0.57509893","0.5738518","0.57381654","0.57225597","0.5720082","0.57104605","0.57102144","0.57101524","0.56907475","0.5687513","0.5681393","0.5675477","0.5667421","0.56618404","0.565746","0.5649681","0.56340855","0.5624556","0.56231916","0.56231916","0.56231916","0.56231916","0.56231916","0.5618858","0.5618812","0.56142986","0.5583701","0.5576088","0.55685234","0.5551136","0.55482423","0.5545166","0.5539983","0.55344176","0.5529359","0.5522062","0.551975","0.5519736","0.5518988","0.5518988","0.5512453","0.55123127","0.5503933","0.5499068","0.54862374","0.54836184","0.547534","0.5472008","0.5470952","0.5461812","0.5436872","0.54314435","0.54302603","0.5428569","0.54251033","0.5419763","0.5419299","0.5413747","0.54125637","0.54073936","0.5400695","0.53961396","0.53920275","0.5391018","0.53880066","0.538748","0.5386008","0.53807867","0.5380762","0.5378384","0.5377922","0.53768396","0.5375849","0.53610057"],"string":"[\n \"0.6790318\",\n \"0.67297566\",\n \"0.66144854\",\n \"0.6388285\",\n \"0.63673466\",\n \"0.6282351\",\n \"0.6156348\",\n \"0.6152705\",\n \"0.61418\",\n \"0.61377877\",\n \"0.61028284\",\n \"0.6077809\",\n \"0.60488695\",\n \"0.6045633\",\n \"0.60317683\",\n \"0.5997232\",\n \"0.5917214\",\n \"0.5899693\",\n \"0.5897377\",\n \"0.58870757\",\n \"0.5816963\",\n \"0.5789937\",\n \"0.57815087\",\n \"0.5772589\",\n \"0.5771158\",\n \"0.576391\",\n \"0.57509893\",\n \"0.5738518\",\n \"0.57381654\",\n \"0.57225597\",\n \"0.5720082\",\n \"0.57104605\",\n \"0.57102144\",\n \"0.57101524\",\n \"0.56907475\",\n \"0.5687513\",\n \"0.5681393\",\n \"0.5675477\",\n \"0.5667421\",\n \"0.56618404\",\n \"0.565746\",\n \"0.5649681\",\n \"0.56340855\",\n \"0.5624556\",\n \"0.56231916\",\n \"0.56231916\",\n \"0.56231916\",\n \"0.56231916\",\n \"0.56231916\",\n \"0.5618858\",\n \"0.5618812\",\n \"0.56142986\",\n \"0.5583701\",\n \"0.5576088\",\n \"0.55685234\",\n \"0.5551136\",\n \"0.55482423\",\n \"0.5545166\",\n \"0.5539983\",\n \"0.55344176\",\n \"0.5529359\",\n \"0.5522062\",\n \"0.551975\",\n \"0.5519736\",\n \"0.5518988\",\n \"0.5518988\",\n \"0.5512453\",\n \"0.55123127\",\n \"0.5503933\",\n \"0.5499068\",\n \"0.54862374\",\n \"0.54836184\",\n \"0.547534\",\n \"0.5472008\",\n \"0.5470952\",\n \"0.5461812\",\n \"0.5436872\",\n \"0.54314435\",\n \"0.54302603\",\n \"0.5428569\",\n \"0.54251033\",\n \"0.5419763\",\n \"0.5419299\",\n \"0.5413747\",\n \"0.54125637\",\n \"0.54073936\",\n \"0.5400695\",\n \"0.53961396\",\n \"0.53920275\",\n \"0.5391018\",\n \"0.53880066\",\n \"0.538748\",\n \"0.5386008\",\n \"0.53807867\",\n \"0.5380762\",\n \"0.5378384\",\n \"0.5377922\",\n \"0.53768396\",\n \"0.5375849\",\n \"0.53610057\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.59398806"},"document_rank":{"kind":"string","value":"16"}}},{"rowIdx":17766,"cells":{"query":{"kind":"string","value":"Method for showing kanban board"},"document":{"kind":"string","value":"def kanban():\n if request.method == 'GET':\n (todo, doing, done) = db.get_all_kanban()\n return r.renderContent('kanban.html', \n todo=Markup(todo), doing=Markup(doing), done=Markup(done))\n else:\n db.log_kanban(request.form['status'], request.form['value'])\n return r.renderContent('kanban.html')"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def show_board(self):\n self._game_board.display()","def show_board(self):\n print(self.game_board)","def show_board(self):\n board_vis = f\"\\n{'*' * 22}Board state{'*' * 23}\\n\"\n str_p2_store=\" \"+str(self.p2_store()) if self.p2_store()<10 else str(self.p2_store())\n board_vis += (f\" {str_p2_store} - | \" +\n \" || \".join(\n [i if len(i) == 2 else ' ' + i for i in list(map(str, self.p2_pits()[::-1]))]) + \" | \\n\")\n board_vis += f\"{'-------' * (self.M + 2)}\\n\"\n board_vis += (\" | \" + \" || \".join(\n [i if len(i) == 2 else ' ' + i for i in list(map(str, self.p1_pits()))]) +\n f\" | - {self.p1_store()}\\n\")\n board_vis += f\"{'*' * 56}\\n\"\n print(board_vis)","def display_board(self):\r\n board = self._board\r\n col = \"A\"\r\n row = 1\r\n\r\n print(end=\"|\")\r\n for i in range(9): # Prints column labels of board\r\n print(\" \" + col + \" |\", end=\"\")\r\n col = chr(ord(col) + 1)\r\n print()\r\n\r\n for i in range(10): # Prints the board\r\n print(end=\"|\")\r\n for j in range(9):\r\n if board[i][j].get_piece() is not None:\r\n print(board[i][j].get_piece().get_id(), end =\"|\")\r\n else:\r\n print(\" \", end = \"|\")\r\n print(\" \" + str(row))\r\n if i != 4 and i != 9:\r\n print(\"____\" * 9)\r\n if i == 4:\r\n print(\"~~~~~~~~~~~~~~~~RIVER~~~~~~~~~~~~~~~\")\r\n row += 1","def display_board(self):\n print(self.game_board)","def show_board(self):\n for i in range(self.num_rows):\n print(' ----'*8)\n s = \"\"\n for j in range(self.num_cols):\n s += '| {} '.format(self._show_piece(i, j))\n print(\"{}|\".format(s))\n print(' ----'*8)","def display(self):\n board_dict = dict()\n if self.name == 'two_players_double_board':\n # fill all cells of board_dict with \" \" for empty cells\n letters = [chr(i+97) for i in range(0, 16)]\n for number in range(0, 12):\n for letter in letters:\n position = letter + str(number + 1)\n board_dict[position] = ' '\n\n board_dict = self.set_piece_in_cell(board_dict)\n # first line of board\n board_str = \" |\"\n for i in range(0, 16):\n board_str += chr(i + 97).ljust(2, ' ') + \" | \"\n\n print(board_str)\n\n # print board\n for number in range(0, 12):\n print(\"-\" * 82)\n print(str(number + 1).rjust(2, ' '), end=\"|\")\n for letter in letters:\n position = letter + str(number + 1)\n piece = board_dict[position]\n print(str(piece).ljust(2, ' ') + ' |', end=\" \")\n print()\n print(\"-\" * 82)\n print(\"\\n\")\n print(\"END OF TWO_PLAYERS BOARD\")\n\n elif self.name == \"four_players_board\":\n # fill all cells of board_dict with \" \" for empty cells\n letters = [chr(i+97) for i in range(0, 14)]\n for number in range(0, 14):\n for letter in letters:\n position = letter + str(number + 1)\n board_dict[position] = ' '\n\n board_dict = self.set_piece_in_cell(board_dict)\n # first line of board\n board_str = \" |\"\n for i in range(0, 14):\n board_str += chr(i + 97).ljust(2, ' ') + \" | \"\n\n print(board_str)\n\n empty_letters, empty_numbers = ['a', 'b', 'c', 'l', 'm', 'n'], ['1', '2', '3', '12', '13', '14']\n empty_cells_tuples = list(itertools.product(empty_letters, empty_numbers))\n empty_cells = []\n for tupl in empty_cells_tuples:\n empty_cells.append(tupl[0] + tupl[1])\n # print board\n for number in range(0, 14):\n print(\"-\" * 76)\n print(str(number + 1).rjust(2, ' '), end=\"|\")\n for letter in letters:\n position = letter + str(number + 1)\n piece = board_dict[position]\n if position not in empty_cells:\n print(str(piece).ljust(2, ' ') + ' |', end=\" \")\n\n else:\n if position.startswith('c'):\n print(' ', end='| ')\n\n else:\n print(' ', end=' ')\n\n print()\n print(\"-\" * 76)\n print(\"\\n\")\n print(\"END OF FOUR_PLAYERS BOARD\")\n\n elif self.name == \"three_players_board\":\n # fill all cells of board_dict with \" \" for empty cells\n letters = [chr(i + 97) for i in range(0, 22)]\n for number in range(0, 22):\n for letter in letters:\n position = letter + str(number + 1)\n board_dict[position] = ' '\n\n # first line of board\n board_str = \" |\"\n for i in range(0, 22):\n board_str += chr(i + 97).ljust(2, ' ') + \" | \"\n\n print(board_str)\n\n empty_cells = []\n for i in range(7):\n for j in range(1, 7 - i + 1):\n position = letters[i] + str(j)\n empty_cells.append(position)\n\n for j in range(1, i + 2):\n position = letters[i + 15] + str(j)\n empty_cells.append(position)\n\n for j in range(10 + i, 17):\n position = letters[i] + str(j)\n empty_cells.append(position)\n\n for j in range(16 - i, 17):\n position = letters[i + 15] + str(j)\n empty_cells.append(position)\n\n board_dict = self.set_piece_in_cell(board_dict)\n\n # print board\n for number in range(0, 16):\n print(\"-\" * 106)\n print(str(number + 1).rjust(2, ' '), end=\"|\")\n for letter in letters:\n position = letter + str(number + 1)\n piece = board_dict[position]\n if position not in empty_cells:\n print(str(piece).ljust(2, ' ') + ' |', end=\" \")\n\n\n else:\n if position == 'g1' or position == 'g16':\n print(' ', end='| ')\n elif position.startswith('c'):\n print(' ', end=' ')\n\n else:\n print(' ', end=' ')\n\n print()\n print(\"-\" * 106)\n print(\"\\n\")\n print(\"END OF THREE_PLAYERS BOARD\")","def show_board(self):\n\n for s in self.board[1:-1]:\n print(''.join(x.symbol for x in s[1:-1]))","def show_board(self, game):\n\n self.screen.clear()\n self.show_banner()\n self.show_score(game)\n self.show_towers(game.board)\n self.screen.refresh()","def show_boards(self):\n\n self.board.render_board()\n print(\"--------------------------------\")\n self.board_to_shots.render_board()","def display_board(self):\n print(f\"{BREAK_STRING}\\n\")\n print(f\"STOCK \\t WASTE \\t\\t\\t\\tFOUNDATION\\n\")\n print(f\"{self.stock}\\t{self.waste}\\t\\t\\t\\t{self.foundations['clubs']}\\t{self.foundations['diamonds']}\"\n f\"\\t{self.foundations['hearts']}\\t{self.foundations['spades']}\\n\")\n print(f\"\\nTABLEAU\\n\")\n for num in range(1, 8):\n print(f\"{num} {self.tableaus[num-1]}\")\n print(f\"{BREAK_STRING}\\n\")","def display_board(self):\n print('*' + '*'.join(['**']*len(self.board[0])) + '*')\n for row in self.board:\n print('|' + ' '.join([('%s' % square) for square in row]) + '|')\n print('*' + '*'.join(['**']*len(self.board[0])) + '*')","def show_board(board) -> None:\n for line in board:\n print('|'.join(line))","def display_board(board):\n print()\n print(\" {} | {} | {} \".format(*board[0:3]))\n print(\"---|---|---\")\n print(\" {} | {} | {} \".format(*board[3:6]))\n print(\"---|---|---\")\n print(\" {} | {} | {} \".format(*board[6:9]))\n print()","def printBoard(self):","def display_board():\n print(board[0], '|', board[1], '|', board[2])\n print(board[3], '|', board[4], '|', board[5])\n print(board[6], '|', board[7], '|', board[8])","def display_board(self):\n print(\"-\" * 9)\n for i in range(0, len(self.game_board), 3):\n row = self.game_board[i:i + 3]\n print('|', *row, '|', sep=' ')\n print('-' * 9)","def show_board(self): \n for row in range(self.n):\n for col in range(self.n):\n if [row, col] in self.queens:\n print (' Q ', end = '')\n else:\n print (' - ', end = '')\n print()\n print()","def display_board(self, board):\n\n print(\"\\n\\t - A - B - C - D - E - F - G - H - \\n\")\n print(\"\\t8 \", board[56], \"|\", board[57], \"|\", board[58], \"|\", board[59], \"|\", board[60], \"|\", board[61], \"|\", board[62], \"|\", board[63])\n print(\"\\t \", \"---------------------------------------\")\n print(\"\\t7 \", board[48], \"|\", board[49], \"|\", board[50], \"|\", board[51], \"|\", board[52], \"|\", board[53], \"|\", board[54], \"|\", board[55])\n print(\"\\t \", \"---------------------------------------\")\n print(\"\\t6 \", board[40], \"|\", board[41], \"|\", board[42], \"|\", board[43], \"|\", board[44], \"|\", board[45], \"|\", board[46], \"|\", board[47])\n print(\"\\t \", \"---------------------------------------\")\n print(\"\\t5 \", board[32], \"|\", board[33], \"|\", board[34], \"|\", board[35], \"|\", board[36], \"|\", board[37], \"|\", board[38], \"|\", board[39])\n print(\"\\t \", \"---------------------------------------\")\n print(\"\\t4 \", board[24], \"|\", board[25], \"|\", board[26], \"|\", board[27], \"|\", board[28], \"|\", board[29], \"|\", board[30], \"|\", board[31])\n print(\"\\t \", \"---------------------------------------\")\n print(\"\\t3 \", board[16], \"|\", board[17], \"|\", board[18], \"|\", board[19], \"|\", board[20], \"|\", board[21], \"|\", board[22], \"|\", board[23])\n print(\"\\t \", \"---------------------------------------\")\n print(\"\\t2 \", board[8], \"|\", board[9], \"|\", board[10], \"|\", board[11], \"|\", board[12], \"|\", board[13], \"|\", board[14], \"|\", board[15])\n print(\"\\t \", \"---------------------------------------\")\n print(\"\\t1 \", board[0], \"|\", board[1], \"|\", board[2], \"|\", board[3], \"|\", board[4], \"|\", board[5], \"|\", board[6], \"|\", board[7])\n print(\"\\n\\t - A - B - C - D - E - F - G - H - \\n\")","def display_board(self):\n\n for i in range(len(self._board[0])):\n row = ''\n for j in range(len(self._board)):\n if self._board[j][i] == '':\n row += ' - '\n else:\n row += ' '+str(self._board[j][i])+' '\n print(row)\n print('............................................')","def showBoard(self):\n \n brd = \"\\n | | \\n\" + \\\n \" \" + self.squares[0] + \" | \" + self.squares[1] + \" | \" + self.squares[2] + \" \\n\" + \\\n \"___|___|___\\n\" + \\\n \" | | \\n\" + \\\n \" \" + self.squares[3] + \" | \" + self.squares[4] + \" | \" + self.squares[5] + \" \\n\" + \\\n \"___|___|___\\n\" + \\\n \" | | \\n\" + \\\n \" \" + self.squares[6] + \" | \" + self.squares[7] + \" | \" + self.squares[8] + \" \\n\" + \\\n \" | | \\n\"\n\n return brd","def printBoard(self):\n if self.side == self.WHITE or self.side == None:\n for r in [8,7,6,5,4,3,2,1]:\n for c in 'abcdefgh':\n p = self.getPiece(c,r) # print a8 first\n if p == None:\n print \" \",\n else:\n print self.getPieceName(p.type),\n print \"\"\n else:\n for r in [1,2,3,4,5,6,7,8]:\n for c in 'hgfedcba':\n p = self.getPiece(c,r) # print h1 first\n if p == None:\n print \" \",\n else:\n print self.getPieceName(p.type),\n print \"\"\n\n for r in [8,7,6,5,4,3,2,1]:\n for c in 'abcdefgh':\n p = self.getPiece(c,r)\n #if p != None and p.header.frame_id == \"chess_board\":\n # print \"Warning, frame is chess_board:\", c+str(r)","def display_board():\n print(\"\\n\")\n print(\"-------------------------------------\")\n print(\"| \" + board[0] + \" | \" + board[1] +\n \" | \" + board[2] + \" 1 | 2 | 3 |\")\n print(\"| \" + board[3] + \" | \" + board[4] +\n \" | \" + board[5] + \" TicTacToe 4 | 5 | 6 |\")\n print(\"| \" + board[6] + \" | \" + board[7] +\n \" | \" + board[8] + \" 7 | 8 | 9 |\")\n print(\"-------------------------------------\")\n print(\"\\n\")","def show(self) -> None:","def show(self):","def show_board(self, boardID):\n return self.app.get('/board/' + boardID, follow_redirects = True)","def show_clubs(self):\n self.client.get(f\"{host}/board\")","def display_board(board):\n print(board[7] + '|' + board[8] + '|' + board[9])\n print(board[4] + '|' + board[5] + '|' + board[6])\n print(board[1] + '|' + board[2] + '|' + board[3])\n pass","def show_board(board):\n for line in board:\n print(line)","def display(self, players, currentPlayerIndex):\n print(\"Hints : \", self.hints)\n print(\"Strikes : \", self.strikes)\n print(\"Cards left in deck : \", hanabi.Hanabi.deck.cardsLeft())\n self.displayField()\n self.displayDiscarded()\n print(\"Hands:\")\n for player in players:\n if player == players[currentPlayerIndex]:\n player.displayKnownHand()\n else:\n player.displayHand()","def show(self):\n\n pass","def _print_board(board):\r\n pass","def display_board(self, board):\r\n print(\" 0 1 2 3 4 5 6 7\")\r\n for x, row in enumerate(board):\r\n sys.stdout.write(str(x))\r\n for val in row:\r\n if val == 1:\r\n sys.stdout.write(\"|b\")\r\n elif val == -1:\r\n sys.stdout.write(\"|w\")\r\n elif val == 2:\r\n sys.stdout.write(\"|B\")\r\n elif val == -2:\r\n sys.stdout.write(\"|W\")\r\n else:\r\n sys.stdout.write(\"| \")\r\n print(\"|\")","def show(self):\n pass","def show_board(self):\n print(self.capacity_list)","def show_board(player_name='player',win=False):\r\n print('\\n'*10)\r\n triple_hash();\r\n print(f' {loc[0]} # {loc[1]} # {loc[2]}')\r\n triple_hash()\r\n print(' #################################################################')\r\n triple_hash()\r\n print(f' {loc[3]} # {loc[4]} # {loc[5]}')\r\n triple_hash()\r\n print(' #################################################################')\r\n triple_hash()\r\n print(f' {loc[6]} # {loc[7]} # {loc[8]}')\r\n triple_hash()\r\n\r\n if win:\r\n print(f'\\n\\ncongratulations, {player_name}, you have won!')","def show(self):\r\n display(self.grid_part)","def show(self,canvas): \n for piece in self.bluh:\n piece.render(canvas)\n\n #create vertical and horizontal bold outline\n for i in range(len(self.board)+1):\n x0=300+self.piecesize*i\n y0=100\n x1=300+self.piecesize*i\n y1=900\n canvas.create_line(x0,y0,x1,y1,width=5,fill=self.mode.color1)\n for a in range(len(self.board)+1):\n for i in range(len(self.board)+1):\n x2=300\n y2=100+self.piecesize*i\n x3=1100\n y3=100+self.piecesize*i\n canvas.create_line(x2,y2,x3,y3,width=5,fill=self.mode.color1)\n for piece in self.bluh:\n if piece.isselected==True:\n piece.dropShadow(canvas)\n piece.render(canvas)\n #print(piece.__repr__())","def display(self): \n print ' ' \n print 'Connect ', NWIN, ' Board '\n print ' ' \n for r in reversed(range(self.getHeight())):\n for c in range(self.getWidth()):\n if self.cell[c][r] == BLACK:\n print '+',\n elif self.cell[c][r] == WHITE:\n print '-',\n else:\n print '.',\n print ' '\n for c in range(self.getWidth()):\n print c,\n print ' '\n print ' '","def display(self):\n for row in self._board_area:\n print(row, end=\"\\n\")","def printBoard(self):\n\t\tkey = [' ', 'X', 'O']\n\t\tprint(' | |')\n\t\tprint(' ' + key[self.state[0][0]] + ' | ' + key[self.state[0][1]] + ' | ' + key[self.state[0][2]])\n\t\tprint(' | |')\n\t\tprint('-----------')\n\t\tprint(' | |')\n\t\tprint(' ' + key[self.state[1][0]] + ' | ' + key[self.state[1][1]] + ' | ' + key[self.state[1][2]])\n\t\tprint(' | |')\n\t\tprint('-----------')\n\t\tprint(' | |')\n\t\tprint(' ' + key[self.state[2][0]] + ' | ' + key[self.state[2][1]] + ' | ' + key[self.state[2][2]])\n\t\tprint(' | |')","def display_board(self):\n os.system('clear')\n print 'Press \"j\" to move Left, \"l\" to move Right, and \"k\" to Invert the piece.'\n for row in self.board:\n print row\n print 'Points: {}'.format(self.points)\n print 'Level: {}'.format(self.level)","def render_board(self):\n print \"\"\n for row in self._board:\n print row","def print_board(self):\n print('Board:')\n print('\\n'.join([''.join(['{:4}'.format(item) for item in row]) for row in self.board]))","def printBoard(self):\n if self.side == self.WHITE or self.side == None:\n for r in [8,7,6,5,4,3,2,1]:\n for c in 'abcdefgh':\n p = self.getPiece(c,r) # print a8 first\n if p == None:\n print \" \",\n else:\n print self.getPieceName(p.type),\n print \"\"\n else:\n for r in [1,2,3,4,5,6,7,8]:\n for c in 'hgfedcba':\n p = self.getPiece(c,r) # print h1 first\n if p == None:\n print \" \",\n else:\n print self.getPieceName(p.type),\n print \"\"","def show_board(self, points=None):\n points = points or self.game.get_points()\n self.clear_board()\n for point in points:\n x_koord = (point[0] * 10) + 1\n y_koord = (point[1] * 10) + 1\n if y_koord < self.window_y:\n pygame.draw.rect(self.display, self.black, pygame.Rect(y_koord, x_koord, 9, 9)) # noqa: E501\n null = [(self.verschiebung_ges[1] * 10) + 1, (self.verschiebung_ges[0] * 10) + 1]\n mid_x = (((self.window_x // 20) + -1) * 10) + 1\n mid_y = (((self.window_x // 20) + -1) * 10) + 1\n pygame.draw.rect(self.display, self.red, pygame.Rect(null[0], null[1], 4.5, 5))\n pygame.draw.rect(self.display, self.green, pygame.Rect((mid_x, mid_y + 4.5, 4.5, 5)))\n Display.update_board()","def print_board(self):\n print_sp = functools.partial(print, end=' ')\n print_sp(' ')\n for i in range(BOARD_SIZE):\n print_sp(i)\n print()\n for i in range(BOARD_SIZE):\n print_sp(i)\n for j in range(BOARD_SIZE):\n e = self.board[j][i]\n print_sp('●') if e == BLACK else print_sp('○') if e == WHITE else print_sp('·')\n print()","def _render_board(self):\n for index, row in enumerate(self._board):\n print(index, end=' ') if index < 10 else print(index, end=' ')\n list(map(lambda x: print(x, end=' '), row))\n print()\n print(' ', end='')\n for i in range(len(self._board)):\n print(i, end=' ') if i < 10 else print(i, end=' ')\n print()","def show(self):\n print('\\n'+'\\n'.join([' '.join([['.', 'O', 'X'][self.board[3*j + i]]\n for i in range(3)]) for j in range(3)]))","def print_board(self):\n print(f'{self.name} BOARD:\\n')\n print(' A B C D E F G H I J ')\n print(' -------------------')\n row_number = 0\n for row in self.board:\n print('%d|%s ' % (row_number, ' '.join(row)))\n row_number += 1\n print(f'\\nLIVES REMAINING: {self.lives}\\n')","def print_board(self):\n print(\" 1 2 3 4 5 6 7\")\n for row in range(self.playable_row_range[0], self.playable_row_range[1]):\n for col in range(self.playable_column_range[0], self.playable_column_range[1]):\n print(\"[{piece}]\".format(piece=self.board[row][col]), end=\" \")\n print('\\n', end=\"\")\n print(\"\\n\")","def api_print_board(self):\n print(self.board)","def display(self):\n\n #player UI\n s = \" \"\n for p in range(WIDTH):\n s += str(p)\n s += \" \"\n\n print(s)\n\n for row in range(HEIGHT):\n\n # player UI\n print(row, end=' ')\n\n for col in range(WIDTH):\n\n if self.board[row][col] == 1:\n print(\"X\", end=' ')\n elif self.board[row][col] == 2:\n print(\"O\", end=' ')\n else:\n print(\"-\", end=' ')\n print()","def print_board(self):\n for i in range(self.size):\n print(\" \".join(self.board[i]))\n print(\"\\n\")","def print_board(self):\n print(self.board)","def print_board(self, board):\n\n for i in range(0, len(self.row_map.keys())):\n for j in range(0, len(self.row_map.keys())):\n print(\" | {:>2}\".format(board[self.row_map[i + 1] + str(j + 1)]), end='')\n print(\"\\n\")\n print(\" --------------------- \")","def show_possibles(self):\n for row in range(self.board_size):\n for col in range(self.board_size):\n poss = list(self.possibles[row][col])\n if poss:\n teil = qbwrdd.Tile(poss, self.board.scene)\n teil.cell = \"poss\"\n cell = row * self.board_size + col\n pos_x, pos_y = self.board.cells[cell].x(), self.board.cells[cell].y()\n if col % 3 > 0:\n pos_x += 2\n self.poss_tiles[row][col] = teil\n teil.draw_tile_at(pos_x, pos_y)","def __show_scoreboard(self):\n self.clear_screen()\n\n print('\\n' * 2, end=\"\")\n for line in self.__fame:\n print((\" \" * 5) + line, end=\"\")\n print('\\n' * 2, end=\"\")\n\n with open(\"mastermind/assets/scores.json\", \"r\") as data:\n board = list(load(data).items())\n\n space = \" \" * 11\n print(f\"{space}RANK {'PLAYER':<30}\" +\n f\"{'TIME':>7} (seconds){'POINTS':>29}\\n\")\n\n lines_printed = 0\n for idx, entry in enumerate(board[:10]):\n lines_printed += 1\n space = \" \" * 10\n n = idx + 1\n year, month, day, time = entry[0].split(\" \")\n points = entry[1][\"points\"]\n playtime = entry[1][\"playtime\"]\n player = entry[1][\"player\"]\n\n print(f\"{space}{n:>4}. {player:<30}\" +\n f\"{playtime:>7,.2f}{points:>36}/15\")\n\n lines = \"\\n\" * (12 - lines_printed)\n print(f\"{lines}{space}\", end=\"\")\n sleep(.25)\n self.cool_print(\"Press ENTER to return to player menu.\",\n newline=False, margin=0)\n input()","def _display_board(state: game.GameState) -> None:\n for row in range(state.get_rows()):\n rowString = \"|\"\n for col in range(state.get_columns()):\n cellValue = state.get_cell_contents(row, col)\n cellState = state.get_cell_state(row, col)\n if cellState == game.EMPTY_CELL:\n rowString += ' '\n elif cellState == game.OCCUPIED_CELL:\n rowString += (' ' + cellValue + ' ')\n elif cellState == game.FALLER_MOVING_CELL:\n rowString += ('[' + cellValue + ']')\n elif cellState == game.FALLER_STOPPED_CELL:\n rowString += ('|' + cellValue + '|')\n elif cellState == game.MATCHED_CELL:\n rowString += ('*' + cellValue + '*')\n rowString += '|'\n print(rowString)\n finalLine = ' '\n for col in range(state.get_columns()):\n finalLine += '---'\n finalLine += ' '\n print(finalLine)","def displayBoard(self):\n res = ''\n for i in range(0, self.size):\n res += '|'\n for j in range(0, self.size):\n res += ' ' + str(self.board[i][j])\n res += '\\n'\n res += '+'\n for i in range(0, self.size * 2):\n res += '-'\n res += '\\n '\n for i in range(1, (self.size + 1)):\n res += (' ' + str(i))\n return res","def print_board(self):\n div = int(math.sqrt(self.BoardSize))\n dash = \"\"\n space = \"\"\n line = \"+\"\n sep = \"|\"\n for i in range(div):\n dash += \"----\"\n space += \" \"\n for i in range(div):\n line += dash + \"+\"\n sep += space + \"|\"\n for i in range(-1, self.BoardSize):\n if i != -1:\n print \"|\",\n for j in range(self.BoardSize):\n if self.CurrentGameBoard[i][j] > 9:\n print self.CurrentGameBoard[i][j],\n elif self.CurrentGameBoard[i][j] > 0:\n print \"\", self.CurrentGameBoard[i][j],\n else:\n print \" \",\n if (j+1 != self.BoardSize):\n if ((j+1)//div != j/div):\n print \"|\",\n else:\n print \"\",\n else:\n print \"|\"\n if ((i+1)//div != i/div):\n print line\n else:\n print sep","def display_board(board):\n spaces = \" \" * 3\n vertical_line = \"|\"\n horizontal_line = \"–\"\n border = [3, 6, 9]\n for i in range(1, len(board)):\n marker = str(board[i])\n if i in border:\n print(spaces + marker, end=spaces + \"\\n\")\n else:\n print(spaces + marker, end=spaces + vertical_line)\n if i in border and i != 9:\n print(horizontal_line * 24)\n print()","def print_board(self):\n self.board.print()","def print_board(self, board):\n print(\"Sudoku Board:\")\n count = 0\n for row in board:\n string = \"\"\n for num in range(len(row)):\n if row[num] != 0:\n string += str(row[num])\n else:\n string += \"_\"\n if num != len(row) - 1:\n string += \" \"\n if (num+1) % 3 == 0 and num != len(row) - 1:\n string += \"| \"\n print(string)\n count += 1\n if count % 3 == 0 and count < 9:\n print(\"_______________________________\")","def display(self, update_board=1):\n\tM,N = self.X, self.Y \n\tif not update_board:\n\t for i in range(0, M):\n\t\tfor j in range(0, N):\n\t\t\tif self.state.has_key( (i,j) ): \n\t\t\t self.scr.addch(j+1, i+1, self.char)\n\t\t\telse:\n\t\t\t self.scr.addch(j+1, i+1, ' ')\n\t self.scr.refresh()\n\t return\n\n\td={} ; self.boring=1\n\tfor i in range(0, M):\n\t L=range( max(0, i-1), min(M, i+2) )\n\t for j in range(0, N):\n\t\ts=0\n\t\tlive=self.state.has_key( (i,j) )\n\t\tfor k in range( max(0, j-1), min(N, j+2) ):\n\t\t for l in L:\n\t\t\tif self.state.has_key( (l,k) ):\n\t\t\t s=s+1\n\t\ts=s-live\n\t\tif s==3: \n\t\t # Birth\n\t\t d[i,j]=1 \n\t\t self.scr.addch(j+1, i+1, self.char)\n\t\t if not live: self.boring=0 \n\t\telif s==2 and live: d[i,j]=1 # Survival\n\t\telif live: \n\t\t # Death\n\t\t self.scr.addch(j+1, i+1, ' ')\n\t\t self.boring=0\n\tself.state=d\n\tself.scr.refresh()","def print_board(self):\n to_join = [\"-\" * self.DIMENSIONS[0]]\n for row in self.grid:\n to_join.append(\"\".join([ch.letter if ch is not None else \" \" for ch in row]))\n\n print(\"\\n\".join(to_join))","def print_hint_board(self):\n board = \"\"\n count = 1 \n for i in range(3):#need to change this in the future\n for j in range(3):#need to change this in the future\n board += str(count) \n count += 1\n if j != 2:#need to change this in the future\n board += \" | \"\n board += \"\\n\"\n return board","def print_board(self):\n board = \"\"\n for i in range(3):#need to change this in the future\n for j in range(3):#need to change this in the future\n board += self.board[i][j]\n if j != 2:#need to change this in the future\n board += \" | \"\n board += \"\\n\"\n return board","def display_board(self, board):\r\n width = self.board_size[1]\r\n top_row_index = \" \".join([str(i) for i in range(width)])\r\n print(\" {}\".format(top_row_index))\r\n for x, row in enumerate(board):\r\n sys.stdout.write(str(x))\r\n for val in row:\r\n if val == 1:\r\n sys.stdout.write(\"|X\")\r\n elif val == -1:\r\n sys.stdout.write(\"|O\")\r\n else:\r\n sys.stdout.write(\"| \")\r\n print(\"|\")","def print_board(self):\n print(\n self.BOARD_TEMPLATE.format(\n *[self.COUNTER_REPRESENTATION[counter] for counter in self.board])\n )","def print_board(self):\n\n print(\"=\" * 10)\n for row in self._board_matrix:\n for entry in row:\n if entry is None:\n print(\"_\", end=\"\")\n else:\n print(entry.length, end=\"\")\n print(\"\")\n print(\"=\" * 10)","def draw_board(self):\n print(\"\\n\" * 10)\n print(\"-PRINTING BOARD-\")\n for row in self.grid:\n for column in row:\n print(column.character(), end=\"\")\n print() # to create a new line","def print_boards(self, board):\n \n attacks_matrix, ships_matrix = board.get_matrixes(self.name)\n print(\"Attacks:\")\n self._print_board(attacks_matrix)\n\n print(\"Ocean:\")\n self._print_board(ships_matrix)","def display(self, grid):\n for i in range(grid.height):\n print(\"-\" + \"-------\"*grid.width)\n for j in range(grid.width):\n if not j:\n print(\"|\", end=\"\") # begin row with vertical line\n a = self.actions.get((i, j), ' ')\n print(\" %s |\" % a, end=\"\")\n print(\"\") # new line\n print(\"-\" + \"-------\"*grid.width, end='\\n\\n')","def display(self) -> None:\n # ask info to display to controller\n self.list_matches = ct.Controls.get_current_matches()\n for elem in self.list_matches: # setup the data to be displayed\n p1 = str(elem.player1)\n p2 = str(elem.player2)\n match = p1 + ' vs ' + p2\n self.lignes.append({'match_instance': elem, 'label': match, 'choice': ['match nul', p1, p2],\n 'result': None})\n for index, elem in enumerate(self.lignes): # display the data\n self.widgets.append(self.my_simple_line(self.master, elem['label'], index + 1, 0, 1, 1, 10, 10))\n elem['result'], menu_option = self.my_option_menu(self.master, elem['choice'], index + 1, 1, 1, 1, 10, 10)\n self.widgets.append(menu_option)\n # display the button to go through next step of tournament\n self.widgets.append(self.my_button(self.master, 'Clôturer ce tour', 0, len(self.lignes) + 1, self.save_scores))","def display_board(self, board):\r\n print(\" 0 1 2\")\r\n for x, row in enumerate(board):\r\n sys.stdout.write(str(x))\r\n for val in row:\r\n if val == 1:\r\n sys.stdout.write(\"|X\")\r\n elif val == -1:\r\n sys.stdout.write(\"|O\")\r\n else:\r\n sys.stdout.write(\"| \")\r\n print(\"|\")","def displayBoard(self, ui):\n #self.ui.mainArea.fill(self.ui.color_background)\n self.board = Board(ui, self.player1, self.player2)","def printBoard(self):\n for i in range(0,6):\n for j in range(0,6):\n print(self.board[i][j], end=\" \")\n print(\"\")\n print(\"\")","def print_board(self):\n \n # How to show empty/p1/p2\n VALS = \".XO\"\n\n print(\"\\n a b c d e f g\")\n print(\" /--+-+-+-+-+-+--\\\\\")\n for r in range(_HEIGHT - 1, -1, -1):\n s = \"%s |\" % r\n for c in range(_WIDTH):\n # Print mark next to most recent move\n mark = \">\" if self.last_play_rc == (r, c) else \" \"\n s += mark + VALS[self.board[r * 7 + c]]\n print(s + \" |\")\n print(\" \\\\--+-+-+-+-+-+--/\")\n print(\" a b c d e f g\\n\")","def print_board(self):\n\n board = self.get_board()\n row = 9\n while row > -1:\n print(row, board[row])\n row -= 1\n print(\" 0 1 2 3 4 5 6 7 8 9\")","def display_board(board):\n print(\" | |\")\n print(\" \" + board[7] + \" | \" + board[8] + \" | \" + board[9])\n print(\" | |\")\n display_hline()\n print(\" | |\")\n print(\" \" + board[4] + \" | \" + board[5] + \" | \" + board[6])\n print(\" | |\")\n display_hline()\n print(\" | |\")\n print(\" \" + board[1] + \" | \" + board[2] + \" | \" + board[3])\n print(\" | |\")","def displayGame(self):\n # row1 & row2 longer, row3 & row4 shorter, proper indented below\n print 'current table:'\n for key in ['row1','row2']:\n rowLs = self.table[key]\n string = ''\n for ele in rowLs:\n tmpStr = str(ele) + '\\t'\n string += tmpStr\n print string\n for key in ['row3','row4']:\n string = '\\t'\n rowLs = self.table[key]\n for ele in rowLs:\n tmpStr = str(ele) + '\\t'\n string += tmpStr\n print string \n print 'discardList:'\n print self.discardLs[0],'\\t',self.discardLs[1],'\\n',self.discardLs[2],'\\t',self.discardLs[3]","def print_board(self):\n print(*self._board, sep=\"\\n\")","def print_board(self):\n num_rows = len(self.board)\n num_cols = len(self.board[0])\n \n for i in range(num_rows):\n if i % 3 == 0 and i != 0:\n print(\"- - - - - - - - - - - -\")\n \n for j in range(num_cols):\n if j % 3 == 0 and j != 0:\n print(\" | \", end=\"\")\n \n if j == 8:\n print(self.board[i][j])\n else:\n number = str(self.board[i][j])\n print(\"{} \".format(number), end='')","def display_board(board_state):\n\n if type(board_state) != str:\n raise TypeError('Given board input must be String')\n\n if len(board_state) != 9:\n raise Exception(\"Input board string length is not 9\")\n\n counter = 0\n # print()\n for position in board_state:\n counter += 1\n if counter % 3 == 0:\n \n if counter != 9:\n paddingString = \"\\n---------\\n\"\n else:\n paddingString = ''\n else:\n paddingString = \" | \"\n\n if position.isnumeric():\n print(\" \", end=paddingString)\n\n else:\n print(position, end=paddingString)\n\n print(\"\\n\\n\")","def show( self):\n def symbol( i):\n return i<0 and (i==-2 and ' ' or '0') or chr(ord('a') + i)\n \n X, Y = np.max( self.board.positions, 0)\n # -2 to indicate outside board.\n display = np.zeros( (X+1,Y+1), dtype=int) - 2 \n for x, y in self.board.positions:\n display[x, y] = -1 # -1 to indicate unoccupied\n for p, i in self.occupation.items():\n x, y = self.board.positions[p]\n display[x, y] = i\n for x in xrange(X+1):\n s = ''.join( [ symbol( display[x, y]) for y in xrange(Y+1) ])\n print s","def show(self):\n raise NotImplementedError","def show(self):\n raise NotImplementedError","def display_menu(self):\n # Gets the number of habits that exist in the habits table\n number_of_habits = len(self.analytics.habits_table())\n # Gets the number of trackings that exist in the trackings table\n number_of_trackings = len(self.analytics.trackings_table())\n\n print(\n \"\"\"\n ________________________________________\n\n WELCOME TO YOUR HABITSBOX\n ________________________________________\n\n Everything can be achieved\n with perseverance and commitment\n\n ---------- Let's get started -----------\n\n Choose a number\n\n 0. Exit\n ----------------------------------------\n 1. Add a new habit\n ----------------------------------------\n \"\"\")\n\n if number_of_habits >= 1:\n print(\n \"\"\"\n 2. Check a habit off\n 3. Delete a habit\n ----------------------------------------\n \"\"\")\n if (number_of_habits == 1):\n print(\"\"\"\n Analysis -------------------------------\n\n 4. See my habit\n ----------------------------------------\n \"\"\")\n elif (number_of_habits > 1): #or (number_of_trackings >= 0):\n print(\n \"\"\"\n Analysis -------------------------------\n\n 4. See a habit\n 5. See all habits registered\n 6. See habits with same periodicity\n \"\"\")\n if (number_of_trackings > 0):\n print(\"\"\" \n 7. See my longest streak of all habits\n ----------------------------------------\n \"\"\")","def display(self):\n for row in self.tile_rows:\n print(row)","def displayBoard(board):\n\n # Background color.\n WINDOW.fill(BORDER)\n\n # Draws out each row.\n for i in range(0, 4):\n displayRow(board[i], 8, i)\n\n pygame.display.update()","def print_board(board):\n win = GraphWin('N-Rainhas', 850, 650)\n win.setBackground(color_rgb(188, 237, 145))\n title = Text(Point(400, 30), \"N-Rainhas\")\n title.setSize(20)\n title.draw(win)\n\n # Desenha tabuleiro principal\n rect = Rectangle(\n Point(150 - 5, 100 - 5),\n Point(650 + 5, 600 + 5)\n )\n rect.setFill('brown')\n rect.draw(win)\n\n # Desenha as casas no tabuleiro\n square = 500 / N\n for i in range(N):\n for j in range(N):\n if (i + j) % 2 == 0:\n x = 150 + i * square\n y = 100 + j * square\n rect = Rectangle(\n Point(x, y),\n Point(x + square, y + square)\n )\n rect.setFill('gray')\n rect.draw(win)\n\n # Desenha as peças no tabuleiro\n x = 150 + i * square\n y = 100 + board[i] * square\n cir = Circle(\n Point(x + 0.5 * square, y + 0.5 * square), 160 / N\n )\n cir.setFill('blue')\n cir.draw(win)\n\n win.getMouse()\n win.close()","def print_board(self):\n\n print(\"Board update:\")\n\n count = 0\n\n for c in self.__game_board:\n i = int(c)\n\n char_to_print = chr(i)\n\n if char_to_print in ('X', 'O'):\n print(chr(i), end='')\n else:\n print(\" \", end='')\n\n printSeparator(count)\n\n count += 1","def display(values):\n \n print(\"Displaying board\")\n for idx, unit_row in enumerate(unit_rows):\n row_string = \"\"\n for idx2, box in enumerate(unit_row):\n row_string += values[box]\n row_string += (9 - len(values[box])) * ' ' + ' '\n if ((idx2 + 1) % 3 == 0 and idx2 + 1 != 9):\n row_string += ' | '\n print('\\n' + row_string)\n if ((idx + 1) % 3 == 0 and idx + 1 != 9):\n print('\\n' + '-' * len(row_string))","async def blacklist_show(self, ctx):\n content = discord.Embed(\n title=f\":scroll: {ctx.guild.name} Blacklist\", color=int(\"ffd983\", 16)\n )\n\n blacklisted_channels = await self.bot.db.execute(\n \"\"\"\n SELECT channel_id FROM blacklisted_channel WHERE guild_id = %s\n \"\"\",\n ctx.guild.id,\n as_list=True,\n )\n blacklisted_members = await self.bot.db.execute(\n \"\"\"\n SELECT user_id FROM blacklisted_member WHERE guild_id = %s\n \"\"\",\n ctx.guild.id,\n as_list=True,\n )\n blacklisted_commands = await self.bot.db.execute(\n \"\"\"\n SELECT command_name FROM blacklisted_command WHERE guild_id = %s\n \"\"\",\n ctx.guild.id,\n as_list=True,\n )\n\n def length_limited_value(rows):\n value = \"\"\n for row in rows:\n if len(value + \"\\n\" + row) > 1019:\n value += \"\\n...\"\n break\n\n value += (\"\\n\" if value != \"\" else \"\") + row\n\n return value\n\n if blacklisted_channels:\n rows = [f\"<#{channel_id}>\" for channel_id in blacklisted_channels]\n content.add_field(\n name=\"Channels\",\n value=length_limited_value(rows),\n )\n if blacklisted_members:\n rows = [f\"<@{user_id}>\" for user_id in blacklisted_members]\n content.add_field(\n name=\"Users\",\n value=length_limited_value(rows),\n )\n if blacklisted_commands:\n rows = [f\"`{ctx.prefix}{command}`\" for command in blacklisted_commands]\n content.add_field(\n name=\"Commands\",\n value=length_limited_value(rows),\n )\n\n if not content.fields:\n content.description = \"Nothing is blacklisted yet!\"\n\n await ctx.send(embed=content)","def show(self):\r\n for card in self.cards_list:\r\n print(card)","def print_board(self):\r\n for row in range(len(self.board)):\r\n line = str(row)+\": \"\r\n for cell in self.board[row]:\r\n line += cell + \" \"\r\n print(line)\r\n print(\" A B C D E\")","def __str__(self) -> str:\n return self.board","def print_board(self):\n line = \"---------------------\"\n for i, row in enumerate(self.board):\n row_string = \"\"\n for j, col in enumerate(row):\n if j == 3 or j == 6:\n row_string += \"| \"\n row_string += str(row[j]) + \" \"\n print(row_string)\n if i == 2 or i == 5:\n print(line)","def PrintBoard():\n\tfor x in xrange(16):\n\t\tif x!=0:\n\t\t\tprint '\\n'\n\t\tfor y in xrange(36):\n\t\t\tprint board[x][y],"],"string":"[\n \"def show_board(self):\\n self._game_board.display()\",\n \"def show_board(self):\\n print(self.game_board)\",\n \"def show_board(self):\\n board_vis = f\\\"\\\\n{'*' * 22}Board state{'*' * 23}\\\\n\\\"\\n str_p2_store=\\\" \\\"+str(self.p2_store()) if self.p2_store()<10 else str(self.p2_store())\\n board_vis += (f\\\" {str_p2_store} - | \\\" +\\n \\\" || \\\".join(\\n [i if len(i) == 2 else ' ' + i for i in list(map(str, self.p2_pits()[::-1]))]) + \\\" | \\\\n\\\")\\n board_vis += f\\\"{'-------' * (self.M + 2)}\\\\n\\\"\\n board_vis += (\\\" | \\\" + \\\" || \\\".join(\\n [i if len(i) == 2 else ' ' + i for i in list(map(str, self.p1_pits()))]) +\\n f\\\" | - {self.p1_store()}\\\\n\\\")\\n board_vis += f\\\"{'*' * 56}\\\\n\\\"\\n print(board_vis)\",\n \"def display_board(self):\\r\\n board = self._board\\r\\n col = \\\"A\\\"\\r\\n row = 1\\r\\n\\r\\n print(end=\\\"|\\\")\\r\\n for i in range(9): # Prints column labels of board\\r\\n print(\\\" \\\" + col + \\\" |\\\", end=\\\"\\\")\\r\\n col = chr(ord(col) + 1)\\r\\n print()\\r\\n\\r\\n for i in range(10): # Prints the board\\r\\n print(end=\\\"|\\\")\\r\\n for j in range(9):\\r\\n if board[i][j].get_piece() is not None:\\r\\n print(board[i][j].get_piece().get_id(), end =\\\"|\\\")\\r\\n else:\\r\\n print(\\\" \\\", end = \\\"|\\\")\\r\\n print(\\\" \\\" + str(row))\\r\\n if i != 4 and i != 9:\\r\\n print(\\\"____\\\" * 9)\\r\\n if i == 4:\\r\\n print(\\\"~~~~~~~~~~~~~~~~RIVER~~~~~~~~~~~~~~~\\\")\\r\\n row += 1\",\n \"def display_board(self):\\n print(self.game_board)\",\n \"def show_board(self):\\n for i in range(self.num_rows):\\n print(' ----'*8)\\n s = \\\"\\\"\\n for j in range(self.num_cols):\\n s += '| {} '.format(self._show_piece(i, j))\\n print(\\\"{}|\\\".format(s))\\n print(' ----'*8)\",\n \"def display(self):\\n board_dict = dict()\\n if self.name == 'two_players_double_board':\\n # fill all cells of board_dict with \\\" \\\" for empty cells\\n letters = [chr(i+97) for i in range(0, 16)]\\n for number in range(0, 12):\\n for letter in letters:\\n position = letter + str(number + 1)\\n board_dict[position] = ' '\\n\\n board_dict = self.set_piece_in_cell(board_dict)\\n # first line of board\\n board_str = \\\" |\\\"\\n for i in range(0, 16):\\n board_str += chr(i + 97).ljust(2, ' ') + \\\" | \\\"\\n\\n print(board_str)\\n\\n # print board\\n for number in range(0, 12):\\n print(\\\"-\\\" * 82)\\n print(str(number + 1).rjust(2, ' '), end=\\\"|\\\")\\n for letter in letters:\\n position = letter + str(number + 1)\\n piece = board_dict[position]\\n print(str(piece).ljust(2, ' ') + ' |', end=\\\" \\\")\\n print()\\n print(\\\"-\\\" * 82)\\n print(\\\"\\\\n\\\")\\n print(\\\"END OF TWO_PLAYERS BOARD\\\")\\n\\n elif self.name == \\\"four_players_board\\\":\\n # fill all cells of board_dict with \\\" \\\" for empty cells\\n letters = [chr(i+97) for i in range(0, 14)]\\n for number in range(0, 14):\\n for letter in letters:\\n position = letter + str(number + 1)\\n board_dict[position] = ' '\\n\\n board_dict = self.set_piece_in_cell(board_dict)\\n # first line of board\\n board_str = \\\" |\\\"\\n for i in range(0, 14):\\n board_str += chr(i + 97).ljust(2, ' ') + \\\" | \\\"\\n\\n print(board_str)\\n\\n empty_letters, empty_numbers = ['a', 'b', 'c', 'l', 'm', 'n'], ['1', '2', '3', '12', '13', '14']\\n empty_cells_tuples = list(itertools.product(empty_letters, empty_numbers))\\n empty_cells = []\\n for tupl in empty_cells_tuples:\\n empty_cells.append(tupl[0] + tupl[1])\\n # print board\\n for number in range(0, 14):\\n print(\\\"-\\\" * 76)\\n print(str(number + 1).rjust(2, ' '), end=\\\"|\\\")\\n for letter in letters:\\n position = letter + str(number + 1)\\n piece = board_dict[position]\\n if position not in empty_cells:\\n print(str(piece).ljust(2, ' ') + ' |', end=\\\" \\\")\\n\\n else:\\n if position.startswith('c'):\\n print(' ', end='| ')\\n\\n else:\\n print(' ', end=' ')\\n\\n print()\\n print(\\\"-\\\" * 76)\\n print(\\\"\\\\n\\\")\\n print(\\\"END OF FOUR_PLAYERS BOARD\\\")\\n\\n elif self.name == \\\"three_players_board\\\":\\n # fill all cells of board_dict with \\\" \\\" for empty cells\\n letters = [chr(i + 97) for i in range(0, 22)]\\n for number in range(0, 22):\\n for letter in letters:\\n position = letter + str(number + 1)\\n board_dict[position] = ' '\\n\\n # first line of board\\n board_str = \\\" |\\\"\\n for i in range(0, 22):\\n board_str += chr(i + 97).ljust(2, ' ') + \\\" | \\\"\\n\\n print(board_str)\\n\\n empty_cells = []\\n for i in range(7):\\n for j in range(1, 7 - i + 1):\\n position = letters[i] + str(j)\\n empty_cells.append(position)\\n\\n for j in range(1, i + 2):\\n position = letters[i + 15] + str(j)\\n empty_cells.append(position)\\n\\n for j in range(10 + i, 17):\\n position = letters[i] + str(j)\\n empty_cells.append(position)\\n\\n for j in range(16 - i, 17):\\n position = letters[i + 15] + str(j)\\n empty_cells.append(position)\\n\\n board_dict = self.set_piece_in_cell(board_dict)\\n\\n # print board\\n for number in range(0, 16):\\n print(\\\"-\\\" * 106)\\n print(str(number + 1).rjust(2, ' '), end=\\\"|\\\")\\n for letter in letters:\\n position = letter + str(number + 1)\\n piece = board_dict[position]\\n if position not in empty_cells:\\n print(str(piece).ljust(2, ' ') + ' |', end=\\\" \\\")\\n\\n\\n else:\\n if position == 'g1' or position == 'g16':\\n print(' ', end='| ')\\n elif position.startswith('c'):\\n print(' ', end=' ')\\n\\n else:\\n print(' ', end=' ')\\n\\n print()\\n print(\\\"-\\\" * 106)\\n print(\\\"\\\\n\\\")\\n print(\\\"END OF THREE_PLAYERS BOARD\\\")\",\n \"def show_board(self):\\n\\n for s in self.board[1:-1]:\\n print(''.join(x.symbol for x in s[1:-1]))\",\n \"def show_board(self, game):\\n\\n self.screen.clear()\\n self.show_banner()\\n self.show_score(game)\\n self.show_towers(game.board)\\n self.screen.refresh()\",\n \"def show_boards(self):\\n\\n self.board.render_board()\\n print(\\\"--------------------------------\\\")\\n self.board_to_shots.render_board()\",\n \"def display_board(self):\\n print(f\\\"{BREAK_STRING}\\\\n\\\")\\n print(f\\\"STOCK \\\\t WASTE \\\\t\\\\t\\\\t\\\\tFOUNDATION\\\\n\\\")\\n print(f\\\"{self.stock}\\\\t{self.waste}\\\\t\\\\t\\\\t\\\\t{self.foundations['clubs']}\\\\t{self.foundations['diamonds']}\\\"\\n f\\\"\\\\t{self.foundations['hearts']}\\\\t{self.foundations['spades']}\\\\n\\\")\\n print(f\\\"\\\\nTABLEAU\\\\n\\\")\\n for num in range(1, 8):\\n print(f\\\"{num} {self.tableaus[num-1]}\\\")\\n print(f\\\"{BREAK_STRING}\\\\n\\\")\",\n \"def display_board(self):\\n print('*' + '*'.join(['**']*len(self.board[0])) + '*')\\n for row in self.board:\\n print('|' + ' '.join([('%s' % square) for square in row]) + '|')\\n print('*' + '*'.join(['**']*len(self.board[0])) + '*')\",\n \"def show_board(board) -> None:\\n for line in board:\\n print('|'.join(line))\",\n \"def display_board(board):\\n print()\\n print(\\\" {} | {} | {} \\\".format(*board[0:3]))\\n print(\\\"---|---|---\\\")\\n print(\\\" {} | {} | {} \\\".format(*board[3:6]))\\n print(\\\"---|---|---\\\")\\n print(\\\" {} | {} | {} \\\".format(*board[6:9]))\\n print()\",\n \"def printBoard(self):\",\n \"def display_board():\\n print(board[0], '|', board[1], '|', board[2])\\n print(board[3], '|', board[4], '|', board[5])\\n print(board[6], '|', board[7], '|', board[8])\",\n \"def display_board(self):\\n print(\\\"-\\\" * 9)\\n for i in range(0, len(self.game_board), 3):\\n row = self.game_board[i:i + 3]\\n print('|', *row, '|', sep=' ')\\n print('-' * 9)\",\n \"def show_board(self): \\n for row in range(self.n):\\n for col in range(self.n):\\n if [row, col] in self.queens:\\n print (' Q ', end = '')\\n else:\\n print (' - ', end = '')\\n print()\\n print()\",\n \"def display_board(self, board):\\n\\n print(\\\"\\\\n\\\\t - A - B - C - D - E - F - G - H - \\\\n\\\")\\n print(\\\"\\\\t8 \\\", board[56], \\\"|\\\", board[57], \\\"|\\\", board[58], \\\"|\\\", board[59], \\\"|\\\", board[60], \\\"|\\\", board[61], \\\"|\\\", board[62], \\\"|\\\", board[63])\\n print(\\\"\\\\t \\\", \\\"---------------------------------------\\\")\\n print(\\\"\\\\t7 \\\", board[48], \\\"|\\\", board[49], \\\"|\\\", board[50], \\\"|\\\", board[51], \\\"|\\\", board[52], \\\"|\\\", board[53], \\\"|\\\", board[54], \\\"|\\\", board[55])\\n print(\\\"\\\\t \\\", \\\"---------------------------------------\\\")\\n print(\\\"\\\\t6 \\\", board[40], \\\"|\\\", board[41], \\\"|\\\", board[42], \\\"|\\\", board[43], \\\"|\\\", board[44], \\\"|\\\", board[45], \\\"|\\\", board[46], \\\"|\\\", board[47])\\n print(\\\"\\\\t \\\", \\\"---------------------------------------\\\")\\n print(\\\"\\\\t5 \\\", board[32], \\\"|\\\", board[33], \\\"|\\\", board[34], \\\"|\\\", board[35], \\\"|\\\", board[36], \\\"|\\\", board[37], \\\"|\\\", board[38], \\\"|\\\", board[39])\\n print(\\\"\\\\t \\\", \\\"---------------------------------------\\\")\\n print(\\\"\\\\t4 \\\", board[24], \\\"|\\\", board[25], \\\"|\\\", board[26], \\\"|\\\", board[27], \\\"|\\\", board[28], \\\"|\\\", board[29], \\\"|\\\", board[30], \\\"|\\\", board[31])\\n print(\\\"\\\\t \\\", \\\"---------------------------------------\\\")\\n print(\\\"\\\\t3 \\\", board[16], \\\"|\\\", board[17], \\\"|\\\", board[18], \\\"|\\\", board[19], \\\"|\\\", board[20], \\\"|\\\", board[21], \\\"|\\\", board[22], \\\"|\\\", board[23])\\n print(\\\"\\\\t \\\", \\\"---------------------------------------\\\")\\n print(\\\"\\\\t2 \\\", board[8], \\\"|\\\", board[9], \\\"|\\\", board[10], \\\"|\\\", board[11], \\\"|\\\", board[12], \\\"|\\\", board[13], \\\"|\\\", board[14], \\\"|\\\", board[15])\\n print(\\\"\\\\t \\\", \\\"---------------------------------------\\\")\\n print(\\\"\\\\t1 \\\", board[0], \\\"|\\\", board[1], \\\"|\\\", board[2], \\\"|\\\", board[3], \\\"|\\\", board[4], \\\"|\\\", board[5], \\\"|\\\", board[6], \\\"|\\\", board[7])\\n print(\\\"\\\\n\\\\t - A - B - C - D - E - F - G - H - \\\\n\\\")\",\n \"def display_board(self):\\n\\n for i in range(len(self._board[0])):\\n row = ''\\n for j in range(len(self._board)):\\n if self._board[j][i] == '':\\n row += ' - '\\n else:\\n row += ' '+str(self._board[j][i])+' '\\n print(row)\\n print('............................................')\",\n \"def showBoard(self):\\n \\n brd = \\\"\\\\n | | \\\\n\\\" + \\\\\\n \\\" \\\" + self.squares[0] + \\\" | \\\" + self.squares[1] + \\\" | \\\" + self.squares[2] + \\\" \\\\n\\\" + \\\\\\n \\\"___|___|___\\\\n\\\" + \\\\\\n \\\" | | \\\\n\\\" + \\\\\\n \\\" \\\" + self.squares[3] + \\\" | \\\" + self.squares[4] + \\\" | \\\" + self.squares[5] + \\\" \\\\n\\\" + \\\\\\n \\\"___|___|___\\\\n\\\" + \\\\\\n \\\" | | \\\\n\\\" + \\\\\\n \\\" \\\" + self.squares[6] + \\\" | \\\" + self.squares[7] + \\\" | \\\" + self.squares[8] + \\\" \\\\n\\\" + \\\\\\n \\\" | | \\\\n\\\"\\n\\n return brd\",\n \"def printBoard(self):\\n if self.side == self.WHITE or self.side == None:\\n for r in [8,7,6,5,4,3,2,1]:\\n for c in 'abcdefgh':\\n p = self.getPiece(c,r) # print a8 first\\n if p == None:\\n print \\\" \\\",\\n else:\\n print self.getPieceName(p.type),\\n print \\\"\\\"\\n else:\\n for r in [1,2,3,4,5,6,7,8]:\\n for c in 'hgfedcba':\\n p = self.getPiece(c,r) # print h1 first\\n if p == None:\\n print \\\" \\\",\\n else:\\n print self.getPieceName(p.type),\\n print \\\"\\\"\\n\\n for r in [8,7,6,5,4,3,2,1]:\\n for c in 'abcdefgh':\\n p = self.getPiece(c,r)\\n #if p != None and p.header.frame_id == \\\"chess_board\\\":\\n # print \\\"Warning, frame is chess_board:\\\", c+str(r)\",\n \"def display_board():\\n print(\\\"\\\\n\\\")\\n print(\\\"-------------------------------------\\\")\\n print(\\\"| \\\" + board[0] + \\\" | \\\" + board[1] +\\n \\\" | \\\" + board[2] + \\\" 1 | 2 | 3 |\\\")\\n print(\\\"| \\\" + board[3] + \\\" | \\\" + board[4] +\\n \\\" | \\\" + board[5] + \\\" TicTacToe 4 | 5 | 6 |\\\")\\n print(\\\"| \\\" + board[6] + \\\" | \\\" + board[7] +\\n \\\" | \\\" + board[8] + \\\" 7 | 8 | 9 |\\\")\\n print(\\\"-------------------------------------\\\")\\n print(\\\"\\\\n\\\")\",\n \"def show(self) -> None:\",\n \"def show(self):\",\n \"def show_board(self, boardID):\\n return self.app.get('/board/' + boardID, follow_redirects = True)\",\n \"def show_clubs(self):\\n self.client.get(f\\\"{host}/board\\\")\",\n \"def display_board(board):\\n print(board[7] + '|' + board[8] + '|' + board[9])\\n print(board[4] + '|' + board[5] + '|' + board[6])\\n print(board[1] + '|' + board[2] + '|' + board[3])\\n pass\",\n \"def show_board(board):\\n for line in board:\\n print(line)\",\n \"def display(self, players, currentPlayerIndex):\\n print(\\\"Hints : \\\", self.hints)\\n print(\\\"Strikes : \\\", self.strikes)\\n print(\\\"Cards left in deck : \\\", hanabi.Hanabi.deck.cardsLeft())\\n self.displayField()\\n self.displayDiscarded()\\n print(\\\"Hands:\\\")\\n for player in players:\\n if player == players[currentPlayerIndex]:\\n player.displayKnownHand()\\n else:\\n player.displayHand()\",\n \"def show(self):\\n\\n pass\",\n \"def _print_board(board):\\r\\n pass\",\n \"def display_board(self, board):\\r\\n print(\\\" 0 1 2 3 4 5 6 7\\\")\\r\\n for x, row in enumerate(board):\\r\\n sys.stdout.write(str(x))\\r\\n for val in row:\\r\\n if val == 1:\\r\\n sys.stdout.write(\\\"|b\\\")\\r\\n elif val == -1:\\r\\n sys.stdout.write(\\\"|w\\\")\\r\\n elif val == 2:\\r\\n sys.stdout.write(\\\"|B\\\")\\r\\n elif val == -2:\\r\\n sys.stdout.write(\\\"|W\\\")\\r\\n else:\\r\\n sys.stdout.write(\\\"| \\\")\\r\\n print(\\\"|\\\")\",\n \"def show(self):\\n pass\",\n \"def show_board(self):\\n print(self.capacity_list)\",\n \"def show_board(player_name='player',win=False):\\r\\n print('\\\\n'*10)\\r\\n triple_hash();\\r\\n print(f' {loc[0]} # {loc[1]} # {loc[2]}')\\r\\n triple_hash()\\r\\n print(' #################################################################')\\r\\n triple_hash()\\r\\n print(f' {loc[3]} # {loc[4]} # {loc[5]}')\\r\\n triple_hash()\\r\\n print(' #################################################################')\\r\\n triple_hash()\\r\\n print(f' {loc[6]} # {loc[7]} # {loc[8]}')\\r\\n triple_hash()\\r\\n\\r\\n if win:\\r\\n print(f'\\\\n\\\\ncongratulations, {player_name}, you have won!')\",\n \"def show(self):\\r\\n display(self.grid_part)\",\n \"def show(self,canvas): \\n for piece in self.bluh:\\n piece.render(canvas)\\n\\n #create vertical and horizontal bold outline\\n for i in range(len(self.board)+1):\\n x0=300+self.piecesize*i\\n y0=100\\n x1=300+self.piecesize*i\\n y1=900\\n canvas.create_line(x0,y0,x1,y1,width=5,fill=self.mode.color1)\\n for a in range(len(self.board)+1):\\n for i in range(len(self.board)+1):\\n x2=300\\n y2=100+self.piecesize*i\\n x3=1100\\n y3=100+self.piecesize*i\\n canvas.create_line(x2,y2,x3,y3,width=5,fill=self.mode.color1)\\n for piece in self.bluh:\\n if piece.isselected==True:\\n piece.dropShadow(canvas)\\n piece.render(canvas)\\n #print(piece.__repr__())\",\n \"def display(self): \\n print ' ' \\n print 'Connect ', NWIN, ' Board '\\n print ' ' \\n for r in reversed(range(self.getHeight())):\\n for c in range(self.getWidth()):\\n if self.cell[c][r] == BLACK:\\n print '+',\\n elif self.cell[c][r] == WHITE:\\n print '-',\\n else:\\n print '.',\\n print ' '\\n for c in range(self.getWidth()):\\n print c,\\n print ' '\\n print ' '\",\n \"def display(self):\\n for row in self._board_area:\\n print(row, end=\\\"\\\\n\\\")\",\n \"def printBoard(self):\\n\\t\\tkey = [' ', 'X', 'O']\\n\\t\\tprint(' | |')\\n\\t\\tprint(' ' + key[self.state[0][0]] + ' | ' + key[self.state[0][1]] + ' | ' + key[self.state[0][2]])\\n\\t\\tprint(' | |')\\n\\t\\tprint('-----------')\\n\\t\\tprint(' | |')\\n\\t\\tprint(' ' + key[self.state[1][0]] + ' | ' + key[self.state[1][1]] + ' | ' + key[self.state[1][2]])\\n\\t\\tprint(' | |')\\n\\t\\tprint('-----------')\\n\\t\\tprint(' | |')\\n\\t\\tprint(' ' + key[self.state[2][0]] + ' | ' + key[self.state[2][1]] + ' | ' + key[self.state[2][2]])\\n\\t\\tprint(' | |')\",\n \"def display_board(self):\\n os.system('clear')\\n print 'Press \\\"j\\\" to move Left, \\\"l\\\" to move Right, and \\\"k\\\" to Invert the piece.'\\n for row in self.board:\\n print row\\n print 'Points: {}'.format(self.points)\\n print 'Level: {}'.format(self.level)\",\n \"def render_board(self):\\n print \\\"\\\"\\n for row in self._board:\\n print row\",\n \"def print_board(self):\\n print('Board:')\\n print('\\\\n'.join([''.join(['{:4}'.format(item) for item in row]) for row in self.board]))\",\n \"def printBoard(self):\\n if self.side == self.WHITE or self.side == None:\\n for r in [8,7,6,5,4,3,2,1]:\\n for c in 'abcdefgh':\\n p = self.getPiece(c,r) # print a8 first\\n if p == None:\\n print \\\" \\\",\\n else:\\n print self.getPieceName(p.type),\\n print \\\"\\\"\\n else:\\n for r in [1,2,3,4,5,6,7,8]:\\n for c in 'hgfedcba':\\n p = self.getPiece(c,r) # print h1 first\\n if p == None:\\n print \\\" \\\",\\n else:\\n print self.getPieceName(p.type),\\n print \\\"\\\"\",\n \"def show_board(self, points=None):\\n points = points or self.game.get_points()\\n self.clear_board()\\n for point in points:\\n x_koord = (point[0] * 10) + 1\\n y_koord = (point[1] * 10) + 1\\n if y_koord < self.window_y:\\n pygame.draw.rect(self.display, self.black, pygame.Rect(y_koord, x_koord, 9, 9)) # noqa: E501\\n null = [(self.verschiebung_ges[1] * 10) + 1, (self.verschiebung_ges[0] * 10) + 1]\\n mid_x = (((self.window_x // 20) + -1) * 10) + 1\\n mid_y = (((self.window_x // 20) + -1) * 10) + 1\\n pygame.draw.rect(self.display, self.red, pygame.Rect(null[0], null[1], 4.5, 5))\\n pygame.draw.rect(self.display, self.green, pygame.Rect((mid_x, mid_y + 4.5, 4.5, 5)))\\n Display.update_board()\",\n \"def print_board(self):\\n print_sp = functools.partial(print, end=' ')\\n print_sp(' ')\\n for i in range(BOARD_SIZE):\\n print_sp(i)\\n print()\\n for i in range(BOARD_SIZE):\\n print_sp(i)\\n for j in range(BOARD_SIZE):\\n e = self.board[j][i]\\n print_sp('●') if e == BLACK else print_sp('○') if e == WHITE else print_sp('·')\\n print()\",\n \"def _render_board(self):\\n for index, row in enumerate(self._board):\\n print(index, end=' ') if index < 10 else print(index, end=' ')\\n list(map(lambda x: print(x, end=' '), row))\\n print()\\n print(' ', end='')\\n for i in range(len(self._board)):\\n print(i, end=' ') if i < 10 else print(i, end=' ')\\n print()\",\n \"def show(self):\\n print('\\\\n'+'\\\\n'.join([' '.join([['.', 'O', 'X'][self.board[3*j + i]]\\n for i in range(3)]) for j in range(3)]))\",\n \"def print_board(self):\\n print(f'{self.name} BOARD:\\\\n')\\n print(' A B C D E F G H I J ')\\n print(' -------------------')\\n row_number = 0\\n for row in self.board:\\n print('%d|%s ' % (row_number, ' '.join(row)))\\n row_number += 1\\n print(f'\\\\nLIVES REMAINING: {self.lives}\\\\n')\",\n \"def print_board(self):\\n print(\\\" 1 2 3 4 5 6 7\\\")\\n for row in range(self.playable_row_range[0], self.playable_row_range[1]):\\n for col in range(self.playable_column_range[0], self.playable_column_range[1]):\\n print(\\\"[{piece}]\\\".format(piece=self.board[row][col]), end=\\\" \\\")\\n print('\\\\n', end=\\\"\\\")\\n print(\\\"\\\\n\\\")\",\n \"def api_print_board(self):\\n print(self.board)\",\n \"def display(self):\\n\\n #player UI\\n s = \\\" \\\"\\n for p in range(WIDTH):\\n s += str(p)\\n s += \\\" \\\"\\n\\n print(s)\\n\\n for row in range(HEIGHT):\\n\\n # player UI\\n print(row, end=' ')\\n\\n for col in range(WIDTH):\\n\\n if self.board[row][col] == 1:\\n print(\\\"X\\\", end=' ')\\n elif self.board[row][col] == 2:\\n print(\\\"O\\\", end=' ')\\n else:\\n print(\\\"-\\\", end=' ')\\n print()\",\n \"def print_board(self):\\n for i in range(self.size):\\n print(\\\" \\\".join(self.board[i]))\\n print(\\\"\\\\n\\\")\",\n \"def print_board(self):\\n print(self.board)\",\n \"def print_board(self, board):\\n\\n for i in range(0, len(self.row_map.keys())):\\n for j in range(0, len(self.row_map.keys())):\\n print(\\\" | {:>2}\\\".format(board[self.row_map[i + 1] + str(j + 1)]), end='')\\n print(\\\"\\\\n\\\")\\n print(\\\" --------------------- \\\")\",\n \"def show_possibles(self):\\n for row in range(self.board_size):\\n for col in range(self.board_size):\\n poss = list(self.possibles[row][col])\\n if poss:\\n teil = qbwrdd.Tile(poss, self.board.scene)\\n teil.cell = \\\"poss\\\"\\n cell = row * self.board_size + col\\n pos_x, pos_y = self.board.cells[cell].x(), self.board.cells[cell].y()\\n if col % 3 > 0:\\n pos_x += 2\\n self.poss_tiles[row][col] = teil\\n teil.draw_tile_at(pos_x, pos_y)\",\n \"def __show_scoreboard(self):\\n self.clear_screen()\\n\\n print('\\\\n' * 2, end=\\\"\\\")\\n for line in self.__fame:\\n print((\\\" \\\" * 5) + line, end=\\\"\\\")\\n print('\\\\n' * 2, end=\\\"\\\")\\n\\n with open(\\\"mastermind/assets/scores.json\\\", \\\"r\\\") as data:\\n board = list(load(data).items())\\n\\n space = \\\" \\\" * 11\\n print(f\\\"{space}RANK {'PLAYER':<30}\\\" +\\n f\\\"{'TIME':>7} (seconds){'POINTS':>29}\\\\n\\\")\\n\\n lines_printed = 0\\n for idx, entry in enumerate(board[:10]):\\n lines_printed += 1\\n space = \\\" \\\" * 10\\n n = idx + 1\\n year, month, day, time = entry[0].split(\\\" \\\")\\n points = entry[1][\\\"points\\\"]\\n playtime = entry[1][\\\"playtime\\\"]\\n player = entry[1][\\\"player\\\"]\\n\\n print(f\\\"{space}{n:>4}. {player:<30}\\\" +\\n f\\\"{playtime:>7,.2f}{points:>36}/15\\\")\\n\\n lines = \\\"\\\\n\\\" * (12 - lines_printed)\\n print(f\\\"{lines}{space}\\\", end=\\\"\\\")\\n sleep(.25)\\n self.cool_print(\\\"Press ENTER to return to player menu.\\\",\\n newline=False, margin=0)\\n input()\",\n \"def _display_board(state: game.GameState) -> None:\\n for row in range(state.get_rows()):\\n rowString = \\\"|\\\"\\n for col in range(state.get_columns()):\\n cellValue = state.get_cell_contents(row, col)\\n cellState = state.get_cell_state(row, col)\\n if cellState == game.EMPTY_CELL:\\n rowString += ' '\\n elif cellState == game.OCCUPIED_CELL:\\n rowString += (' ' + cellValue + ' ')\\n elif cellState == game.FALLER_MOVING_CELL:\\n rowString += ('[' + cellValue + ']')\\n elif cellState == game.FALLER_STOPPED_CELL:\\n rowString += ('|' + cellValue + '|')\\n elif cellState == game.MATCHED_CELL:\\n rowString += ('*' + cellValue + '*')\\n rowString += '|'\\n print(rowString)\\n finalLine = ' '\\n for col in range(state.get_columns()):\\n finalLine += '---'\\n finalLine += ' '\\n print(finalLine)\",\n \"def displayBoard(self):\\n res = ''\\n for i in range(0, self.size):\\n res += '|'\\n for j in range(0, self.size):\\n res += ' ' + str(self.board[i][j])\\n res += '\\\\n'\\n res += '+'\\n for i in range(0, self.size * 2):\\n res += '-'\\n res += '\\\\n '\\n for i in range(1, (self.size + 1)):\\n res += (' ' + str(i))\\n return res\",\n \"def print_board(self):\\n div = int(math.sqrt(self.BoardSize))\\n dash = \\\"\\\"\\n space = \\\"\\\"\\n line = \\\"+\\\"\\n sep = \\\"|\\\"\\n for i in range(div):\\n dash += \\\"----\\\"\\n space += \\\" \\\"\\n for i in range(div):\\n line += dash + \\\"+\\\"\\n sep += space + \\\"|\\\"\\n for i in range(-1, self.BoardSize):\\n if i != -1:\\n print \\\"|\\\",\\n for j in range(self.BoardSize):\\n if self.CurrentGameBoard[i][j] > 9:\\n print self.CurrentGameBoard[i][j],\\n elif self.CurrentGameBoard[i][j] > 0:\\n print \\\"\\\", self.CurrentGameBoard[i][j],\\n else:\\n print \\\" \\\",\\n if (j+1 != self.BoardSize):\\n if ((j+1)//div != j/div):\\n print \\\"|\\\",\\n else:\\n print \\\"\\\",\\n else:\\n print \\\"|\\\"\\n if ((i+1)//div != i/div):\\n print line\\n else:\\n print sep\",\n \"def display_board(board):\\n spaces = \\\" \\\" * 3\\n vertical_line = \\\"|\\\"\\n horizontal_line = \\\"–\\\"\\n border = [3, 6, 9]\\n for i in range(1, len(board)):\\n marker = str(board[i])\\n if i in border:\\n print(spaces + marker, end=spaces + \\\"\\\\n\\\")\\n else:\\n print(spaces + marker, end=spaces + vertical_line)\\n if i in border and i != 9:\\n print(horizontal_line * 24)\\n print()\",\n \"def print_board(self):\\n self.board.print()\",\n \"def print_board(self, board):\\n print(\\\"Sudoku Board:\\\")\\n count = 0\\n for row in board:\\n string = \\\"\\\"\\n for num in range(len(row)):\\n if row[num] != 0:\\n string += str(row[num])\\n else:\\n string += \\\"_\\\"\\n if num != len(row) - 1:\\n string += \\\" \\\"\\n if (num+1) % 3 == 0 and num != len(row) - 1:\\n string += \\\"| \\\"\\n print(string)\\n count += 1\\n if count % 3 == 0 and count < 9:\\n print(\\\"_______________________________\\\")\",\n \"def display(self, update_board=1):\\n\\tM,N = self.X, self.Y \\n\\tif not update_board:\\n\\t for i in range(0, M):\\n\\t\\tfor j in range(0, N):\\n\\t\\t\\tif self.state.has_key( (i,j) ): \\n\\t\\t\\t self.scr.addch(j+1, i+1, self.char)\\n\\t\\t\\telse:\\n\\t\\t\\t self.scr.addch(j+1, i+1, ' ')\\n\\t self.scr.refresh()\\n\\t return\\n\\n\\td={} ; self.boring=1\\n\\tfor i in range(0, M):\\n\\t L=range( max(0, i-1), min(M, i+2) )\\n\\t for j in range(0, N):\\n\\t\\ts=0\\n\\t\\tlive=self.state.has_key( (i,j) )\\n\\t\\tfor k in range( max(0, j-1), min(N, j+2) ):\\n\\t\\t for l in L:\\n\\t\\t\\tif self.state.has_key( (l,k) ):\\n\\t\\t\\t s=s+1\\n\\t\\ts=s-live\\n\\t\\tif s==3: \\n\\t\\t # Birth\\n\\t\\t d[i,j]=1 \\n\\t\\t self.scr.addch(j+1, i+1, self.char)\\n\\t\\t if not live: self.boring=0 \\n\\t\\telif s==2 and live: d[i,j]=1 # Survival\\n\\t\\telif live: \\n\\t\\t # Death\\n\\t\\t self.scr.addch(j+1, i+1, ' ')\\n\\t\\t self.boring=0\\n\\tself.state=d\\n\\tself.scr.refresh()\",\n \"def print_board(self):\\n to_join = [\\\"-\\\" * self.DIMENSIONS[0]]\\n for row in self.grid:\\n to_join.append(\\\"\\\".join([ch.letter if ch is not None else \\\" \\\" for ch in row]))\\n\\n print(\\\"\\\\n\\\".join(to_join))\",\n \"def print_hint_board(self):\\n board = \\\"\\\"\\n count = 1 \\n for i in range(3):#need to change this in the future\\n for j in range(3):#need to change this in the future\\n board += str(count) \\n count += 1\\n if j != 2:#need to change this in the future\\n board += \\\" | \\\"\\n board += \\\"\\\\n\\\"\\n return board\",\n \"def print_board(self):\\n board = \\\"\\\"\\n for i in range(3):#need to change this in the future\\n for j in range(3):#need to change this in the future\\n board += self.board[i][j]\\n if j != 2:#need to change this in the future\\n board += \\\" | \\\"\\n board += \\\"\\\\n\\\"\\n return board\",\n \"def display_board(self, board):\\r\\n width = self.board_size[1]\\r\\n top_row_index = \\\" \\\".join([str(i) for i in range(width)])\\r\\n print(\\\" {}\\\".format(top_row_index))\\r\\n for x, row in enumerate(board):\\r\\n sys.stdout.write(str(x))\\r\\n for val in row:\\r\\n if val == 1:\\r\\n sys.stdout.write(\\\"|X\\\")\\r\\n elif val == -1:\\r\\n sys.stdout.write(\\\"|O\\\")\\r\\n else:\\r\\n sys.stdout.write(\\\"| \\\")\\r\\n print(\\\"|\\\")\",\n \"def print_board(self):\\n print(\\n self.BOARD_TEMPLATE.format(\\n *[self.COUNTER_REPRESENTATION[counter] for counter in self.board])\\n )\",\n \"def print_board(self):\\n\\n print(\\\"=\\\" * 10)\\n for row in self._board_matrix:\\n for entry in row:\\n if entry is None:\\n print(\\\"_\\\", end=\\\"\\\")\\n else:\\n print(entry.length, end=\\\"\\\")\\n print(\\\"\\\")\\n print(\\\"=\\\" * 10)\",\n \"def draw_board(self):\\n print(\\\"\\\\n\\\" * 10)\\n print(\\\"-PRINTING BOARD-\\\")\\n for row in self.grid:\\n for column in row:\\n print(column.character(), end=\\\"\\\")\\n print() # to create a new line\",\n \"def print_boards(self, board):\\n \\n attacks_matrix, ships_matrix = board.get_matrixes(self.name)\\n print(\\\"Attacks:\\\")\\n self._print_board(attacks_matrix)\\n\\n print(\\\"Ocean:\\\")\\n self._print_board(ships_matrix)\",\n \"def display(self, grid):\\n for i in range(grid.height):\\n print(\\\"-\\\" + \\\"-------\\\"*grid.width)\\n for j in range(grid.width):\\n if not j:\\n print(\\\"|\\\", end=\\\"\\\") # begin row with vertical line\\n a = self.actions.get((i, j), ' ')\\n print(\\\" %s |\\\" % a, end=\\\"\\\")\\n print(\\\"\\\") # new line\\n print(\\\"-\\\" + \\\"-------\\\"*grid.width, end='\\\\n\\\\n')\",\n \"def display(self) -> None:\\n # ask info to display to controller\\n self.list_matches = ct.Controls.get_current_matches()\\n for elem in self.list_matches: # setup the data to be displayed\\n p1 = str(elem.player1)\\n p2 = str(elem.player2)\\n match = p1 + ' vs ' + p2\\n self.lignes.append({'match_instance': elem, 'label': match, 'choice': ['match nul', p1, p2],\\n 'result': None})\\n for index, elem in enumerate(self.lignes): # display the data\\n self.widgets.append(self.my_simple_line(self.master, elem['label'], index + 1, 0, 1, 1, 10, 10))\\n elem['result'], menu_option = self.my_option_menu(self.master, elem['choice'], index + 1, 1, 1, 1, 10, 10)\\n self.widgets.append(menu_option)\\n # display the button to go through next step of tournament\\n self.widgets.append(self.my_button(self.master, 'Clôturer ce tour', 0, len(self.lignes) + 1, self.save_scores))\",\n \"def display_board(self, board):\\r\\n print(\\\" 0 1 2\\\")\\r\\n for x, row in enumerate(board):\\r\\n sys.stdout.write(str(x))\\r\\n for val in row:\\r\\n if val == 1:\\r\\n sys.stdout.write(\\\"|X\\\")\\r\\n elif val == -1:\\r\\n sys.stdout.write(\\\"|O\\\")\\r\\n else:\\r\\n sys.stdout.write(\\\"| \\\")\\r\\n print(\\\"|\\\")\",\n \"def displayBoard(self, ui):\\n #self.ui.mainArea.fill(self.ui.color_background)\\n self.board = Board(ui, self.player1, self.player2)\",\n \"def printBoard(self):\\n for i in range(0,6):\\n for j in range(0,6):\\n print(self.board[i][j], end=\\\" \\\")\\n print(\\\"\\\")\\n print(\\\"\\\")\",\n \"def print_board(self):\\n \\n # How to show empty/p1/p2\\n VALS = \\\".XO\\\"\\n\\n print(\\\"\\\\n a b c d e f g\\\")\\n print(\\\" /--+-+-+-+-+-+--\\\\\\\\\\\")\\n for r in range(_HEIGHT - 1, -1, -1):\\n s = \\\"%s |\\\" % r\\n for c in range(_WIDTH):\\n # Print mark next to most recent move\\n mark = \\\">\\\" if self.last_play_rc == (r, c) else \\\" \\\"\\n s += mark + VALS[self.board[r * 7 + c]]\\n print(s + \\\" |\\\")\\n print(\\\" \\\\\\\\--+-+-+-+-+-+--/\\\")\\n print(\\\" a b c d e f g\\\\n\\\")\",\n \"def print_board(self):\\n\\n board = self.get_board()\\n row = 9\\n while row > -1:\\n print(row, board[row])\\n row -= 1\\n print(\\\" 0 1 2 3 4 5 6 7 8 9\\\")\",\n \"def display_board(board):\\n print(\\\" | |\\\")\\n print(\\\" \\\" + board[7] + \\\" | \\\" + board[8] + \\\" | \\\" + board[9])\\n print(\\\" | |\\\")\\n display_hline()\\n print(\\\" | |\\\")\\n print(\\\" \\\" + board[4] + \\\" | \\\" + board[5] + \\\" | \\\" + board[6])\\n print(\\\" | |\\\")\\n display_hline()\\n print(\\\" | |\\\")\\n print(\\\" \\\" + board[1] + \\\" | \\\" + board[2] + \\\" | \\\" + board[3])\\n print(\\\" | |\\\")\",\n \"def displayGame(self):\\n # row1 & row2 longer, row3 & row4 shorter, proper indented below\\n print 'current table:'\\n for key in ['row1','row2']:\\n rowLs = self.table[key]\\n string = ''\\n for ele in rowLs:\\n tmpStr = str(ele) + '\\\\t'\\n string += tmpStr\\n print string\\n for key in ['row3','row4']:\\n string = '\\\\t'\\n rowLs = self.table[key]\\n for ele in rowLs:\\n tmpStr = str(ele) + '\\\\t'\\n string += tmpStr\\n print string \\n print 'discardList:'\\n print self.discardLs[0],'\\\\t',self.discardLs[1],'\\\\n',self.discardLs[2],'\\\\t',self.discardLs[3]\",\n \"def print_board(self):\\n print(*self._board, sep=\\\"\\\\n\\\")\",\n \"def print_board(self):\\n num_rows = len(self.board)\\n num_cols = len(self.board[0])\\n \\n for i in range(num_rows):\\n if i % 3 == 0 and i != 0:\\n print(\\\"- - - - - - - - - - - -\\\")\\n \\n for j in range(num_cols):\\n if j % 3 == 0 and j != 0:\\n print(\\\" | \\\", end=\\\"\\\")\\n \\n if j == 8:\\n print(self.board[i][j])\\n else:\\n number = str(self.board[i][j])\\n print(\\\"{} \\\".format(number), end='')\",\n \"def display_board(board_state):\\n\\n if type(board_state) != str:\\n raise TypeError('Given board input must be String')\\n\\n if len(board_state) != 9:\\n raise Exception(\\\"Input board string length is not 9\\\")\\n\\n counter = 0\\n # print()\\n for position in board_state:\\n counter += 1\\n if counter % 3 == 0:\\n \\n if counter != 9:\\n paddingString = \\\"\\\\n---------\\\\n\\\"\\n else:\\n paddingString = ''\\n else:\\n paddingString = \\\" | \\\"\\n\\n if position.isnumeric():\\n print(\\\" \\\", end=paddingString)\\n\\n else:\\n print(position, end=paddingString)\\n\\n print(\\\"\\\\n\\\\n\\\")\",\n \"def show( self):\\n def symbol( i):\\n return i<0 and (i==-2 and ' ' or '0') or chr(ord('a') + i)\\n \\n X, Y = np.max( self.board.positions, 0)\\n # -2 to indicate outside board.\\n display = np.zeros( (X+1,Y+1), dtype=int) - 2 \\n for x, y in self.board.positions:\\n display[x, y] = -1 # -1 to indicate unoccupied\\n for p, i in self.occupation.items():\\n x, y = self.board.positions[p]\\n display[x, y] = i\\n for x in xrange(X+1):\\n s = ''.join( [ symbol( display[x, y]) for y in xrange(Y+1) ])\\n print s\",\n \"def show(self):\\n raise NotImplementedError\",\n \"def show(self):\\n raise NotImplementedError\",\n \"def display_menu(self):\\n # Gets the number of habits that exist in the habits table\\n number_of_habits = len(self.analytics.habits_table())\\n # Gets the number of trackings that exist in the trackings table\\n number_of_trackings = len(self.analytics.trackings_table())\\n\\n print(\\n \\\"\\\"\\\"\\n ________________________________________\\n\\n WELCOME TO YOUR HABITSBOX\\n ________________________________________\\n\\n Everything can be achieved\\n with perseverance and commitment\\n\\n ---------- Let's get started -----------\\n\\n Choose a number\\n\\n 0. Exit\\n ----------------------------------------\\n 1. Add a new habit\\n ----------------------------------------\\n \\\"\\\"\\\")\\n\\n if number_of_habits >= 1:\\n print(\\n \\\"\\\"\\\"\\n 2. Check a habit off\\n 3. Delete a habit\\n ----------------------------------------\\n \\\"\\\"\\\")\\n if (number_of_habits == 1):\\n print(\\\"\\\"\\\"\\n Analysis -------------------------------\\n\\n 4. See my habit\\n ----------------------------------------\\n \\\"\\\"\\\")\\n elif (number_of_habits > 1): #or (number_of_trackings >= 0):\\n print(\\n \\\"\\\"\\\"\\n Analysis -------------------------------\\n\\n 4. See a habit\\n 5. See all habits registered\\n 6. See habits with same periodicity\\n \\\"\\\"\\\")\\n if (number_of_trackings > 0):\\n print(\\\"\\\"\\\" \\n 7. See my longest streak of all habits\\n ----------------------------------------\\n \\\"\\\"\\\")\",\n \"def display(self):\\n for row in self.tile_rows:\\n print(row)\",\n \"def displayBoard(board):\\n\\n # Background color.\\n WINDOW.fill(BORDER)\\n\\n # Draws out each row.\\n for i in range(0, 4):\\n displayRow(board[i], 8, i)\\n\\n pygame.display.update()\",\n \"def print_board(board):\\n win = GraphWin('N-Rainhas', 850, 650)\\n win.setBackground(color_rgb(188, 237, 145))\\n title = Text(Point(400, 30), \\\"N-Rainhas\\\")\\n title.setSize(20)\\n title.draw(win)\\n\\n # Desenha tabuleiro principal\\n rect = Rectangle(\\n Point(150 - 5, 100 - 5),\\n Point(650 + 5, 600 + 5)\\n )\\n rect.setFill('brown')\\n rect.draw(win)\\n\\n # Desenha as casas no tabuleiro\\n square = 500 / N\\n for i in range(N):\\n for j in range(N):\\n if (i + j) % 2 == 0:\\n x = 150 + i * square\\n y = 100 + j * square\\n rect = Rectangle(\\n Point(x, y),\\n Point(x + square, y + square)\\n )\\n rect.setFill('gray')\\n rect.draw(win)\\n\\n # Desenha as peças no tabuleiro\\n x = 150 + i * square\\n y = 100 + board[i] * square\\n cir = Circle(\\n Point(x + 0.5 * square, y + 0.5 * square), 160 / N\\n )\\n cir.setFill('blue')\\n cir.draw(win)\\n\\n win.getMouse()\\n win.close()\",\n \"def print_board(self):\\n\\n print(\\\"Board update:\\\")\\n\\n count = 0\\n\\n for c in self.__game_board:\\n i = int(c)\\n\\n char_to_print = chr(i)\\n\\n if char_to_print in ('X', 'O'):\\n print(chr(i), end='')\\n else:\\n print(\\\" \\\", end='')\\n\\n printSeparator(count)\\n\\n count += 1\",\n \"def display(values):\\n \\n print(\\\"Displaying board\\\")\\n for idx, unit_row in enumerate(unit_rows):\\n row_string = \\\"\\\"\\n for idx2, box in enumerate(unit_row):\\n row_string += values[box]\\n row_string += (9 - len(values[box])) * ' ' + ' '\\n if ((idx2 + 1) % 3 == 0 and idx2 + 1 != 9):\\n row_string += ' | '\\n print('\\\\n' + row_string)\\n if ((idx + 1) % 3 == 0 and idx + 1 != 9):\\n print('\\\\n' + '-' * len(row_string))\",\n \"async def blacklist_show(self, ctx):\\n content = discord.Embed(\\n title=f\\\":scroll: {ctx.guild.name} Blacklist\\\", color=int(\\\"ffd983\\\", 16)\\n )\\n\\n blacklisted_channels = await self.bot.db.execute(\\n \\\"\\\"\\\"\\n SELECT channel_id FROM blacklisted_channel WHERE guild_id = %s\\n \\\"\\\"\\\",\\n ctx.guild.id,\\n as_list=True,\\n )\\n blacklisted_members = await self.bot.db.execute(\\n \\\"\\\"\\\"\\n SELECT user_id FROM blacklisted_member WHERE guild_id = %s\\n \\\"\\\"\\\",\\n ctx.guild.id,\\n as_list=True,\\n )\\n blacklisted_commands = await self.bot.db.execute(\\n \\\"\\\"\\\"\\n SELECT command_name FROM blacklisted_command WHERE guild_id = %s\\n \\\"\\\"\\\",\\n ctx.guild.id,\\n as_list=True,\\n )\\n\\n def length_limited_value(rows):\\n value = \\\"\\\"\\n for row in rows:\\n if len(value + \\\"\\\\n\\\" + row) > 1019:\\n value += \\\"\\\\n...\\\"\\n break\\n\\n value += (\\\"\\\\n\\\" if value != \\\"\\\" else \\\"\\\") + row\\n\\n return value\\n\\n if blacklisted_channels:\\n rows = [f\\\"<#{channel_id}>\\\" for channel_id in blacklisted_channels]\\n content.add_field(\\n name=\\\"Channels\\\",\\n value=length_limited_value(rows),\\n )\\n if blacklisted_members:\\n rows = [f\\\"<@{user_id}>\\\" for user_id in blacklisted_members]\\n content.add_field(\\n name=\\\"Users\\\",\\n value=length_limited_value(rows),\\n )\\n if blacklisted_commands:\\n rows = [f\\\"`{ctx.prefix}{command}`\\\" for command in blacklisted_commands]\\n content.add_field(\\n name=\\\"Commands\\\",\\n value=length_limited_value(rows),\\n )\\n\\n if not content.fields:\\n content.description = \\\"Nothing is blacklisted yet!\\\"\\n\\n await ctx.send(embed=content)\",\n \"def show(self):\\r\\n for card in self.cards_list:\\r\\n print(card)\",\n \"def print_board(self):\\r\\n for row in range(len(self.board)):\\r\\n line = str(row)+\\\": \\\"\\r\\n for cell in self.board[row]:\\r\\n line += cell + \\\" \\\"\\r\\n print(line)\\r\\n print(\\\" A B C D E\\\")\",\n \"def __str__(self) -> str:\\n return self.board\",\n \"def print_board(self):\\n line = \\\"---------------------\\\"\\n for i, row in enumerate(self.board):\\n row_string = \\\"\\\"\\n for j, col in enumerate(row):\\n if j == 3 or j == 6:\\n row_string += \\\"| \\\"\\n row_string += str(row[j]) + \\\" \\\"\\n print(row_string)\\n if i == 2 or i == 5:\\n print(line)\",\n \"def PrintBoard():\\n\\tfor x in xrange(16):\\n\\t\\tif x!=0:\\n\\t\\t\\tprint '\\\\n'\\n\\t\\tfor y in xrange(36):\\n\\t\\t\\tprint board[x][y],\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72244227","0.6975949","0.68790525","0.67226493","0.6711845","0.66894794","0.66305685","0.6616085","0.6584913","0.6556513","0.652724","0.6510904","0.64619637","0.6442191","0.6401692","0.6401686","0.6382453","0.6379178","0.63448644","0.63213986","0.6290037","0.6289441","0.62813264","0.6276477","0.6261412","0.62589276","0.62588197","0.6256942","0.6250518","0.62374127","0.62109745","0.62085474","0.62078947","0.61987954","0.6184558","0.61685145","0.6160706","0.61359686","0.6125178","0.61163","0.6112031","0.61005646","0.609844","0.6095345","0.60839224","0.6082781","0.6062422","0.60446453","0.6040736","0.603796","0.6035612","0.6021623","0.60128456","0.59923637","0.5981741","0.5965682","0.596182","0.5960153","0.5959415","0.59500796","0.59333074","0.5930781","0.5929777","0.59262687","0.5925619","0.5925464","0.5917196","0.5916626","0.5904814","0.5896919","0.58765554","0.5870741","0.58635753","0.58513165","0.5845969","0.5836312","0.58351934","0.583413","0.5827901","0.58250415","0.582023","0.5805715","0.58038235","0.5782418","0.57758397","0.5775428","0.5773871","0.5773871","0.5749723","0.5749501","0.57488495","0.57347786","0.5730774","0.57294965","0.57294416","0.57177395","0.5715904","0.57121867","0.57105756","0.57007253"],"string":"[\n \"0.72244227\",\n \"0.6975949\",\n \"0.68790525\",\n \"0.67226493\",\n \"0.6711845\",\n \"0.66894794\",\n \"0.66305685\",\n \"0.6616085\",\n \"0.6584913\",\n \"0.6556513\",\n \"0.652724\",\n \"0.6510904\",\n \"0.64619637\",\n \"0.6442191\",\n \"0.6401692\",\n \"0.6401686\",\n \"0.6382453\",\n \"0.6379178\",\n \"0.63448644\",\n \"0.63213986\",\n \"0.6290037\",\n \"0.6289441\",\n \"0.62813264\",\n \"0.6276477\",\n \"0.6261412\",\n \"0.62589276\",\n \"0.62588197\",\n \"0.6256942\",\n \"0.6250518\",\n \"0.62374127\",\n \"0.62109745\",\n \"0.62085474\",\n \"0.62078947\",\n \"0.61987954\",\n \"0.6184558\",\n \"0.61685145\",\n \"0.6160706\",\n \"0.61359686\",\n \"0.6125178\",\n \"0.61163\",\n \"0.6112031\",\n \"0.61005646\",\n \"0.609844\",\n \"0.6095345\",\n \"0.60839224\",\n \"0.6082781\",\n \"0.6062422\",\n \"0.60446453\",\n \"0.6040736\",\n \"0.603796\",\n \"0.6035612\",\n \"0.6021623\",\n \"0.60128456\",\n \"0.59923637\",\n \"0.5981741\",\n \"0.5965682\",\n \"0.596182\",\n \"0.5960153\",\n \"0.5959415\",\n \"0.59500796\",\n \"0.59333074\",\n \"0.5930781\",\n \"0.5929777\",\n \"0.59262687\",\n \"0.5925619\",\n \"0.5925464\",\n \"0.5917196\",\n \"0.5916626\",\n \"0.5904814\",\n \"0.5896919\",\n \"0.58765554\",\n \"0.5870741\",\n \"0.58635753\",\n \"0.58513165\",\n \"0.5845969\",\n \"0.5836312\",\n \"0.58351934\",\n \"0.583413\",\n \"0.5827901\",\n \"0.58250415\",\n \"0.582023\",\n \"0.5805715\",\n \"0.58038235\",\n \"0.5782418\",\n \"0.57758397\",\n \"0.5775428\",\n \"0.5773871\",\n \"0.5773871\",\n \"0.5749723\",\n \"0.5749501\",\n \"0.57488495\",\n \"0.57347786\",\n \"0.5730774\",\n \"0.57294965\",\n \"0.57294416\",\n \"0.57177395\",\n \"0.5715904\",\n \"0.57121867\",\n \"0.57105756\",\n \"0.57007253\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6246042"},"document_rank":{"kind":"string","value":"29"}}},{"rowIdx":17767,"cells":{"query":{"kind":"string","value":"Remove ``>`` from beginning of a line."},"document":{"kind":"string","value":"def clean(self, line):\n m = self.RE.match(line)\n if line.strip() == \">\":\n return \"\"\n elif m:\n return m.group(2)\n else:\n return line"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def clean(self, line):\r\n m = self.RE.match(line)\r\n if line.strip() == \">\":\r\n return \"\"\r\n elif m:\r\n return m.group(2)\r\n else:\r\n return line","def dealFirstLine(line):\n\n print \"%s\" % (line.strip('\\n'))","def _remove_beginning_newlines(lines):\n first_non_blank_line = 0\n\n for line in lines:\n if line.strip():\n break\n\n first_non_blank_line += 1\n\n return lines[first_non_blank_line:]","def strip_start(h, s):\n if h.startswith(s):\n h = h[len(s):]\n return h","def remove_starting_carriage_return_in_output(self, text):\n\n # Display info message\n log.info(\"remove_starting_carriage_return_in_output\")\n\n # Remove the carriage return at the beginning of the string\n output = text.lstrip(\"\\r\\n\\r\")\n\n # Display info message\n log.info(f\"remove_starting_carriage_return_in_output: output = '{output}'\")\n\n # Return the string without the starting carriage return\n return output","def delete_first_line(string):\n lines = string.split('\\n')\n return '\\n'.join(lines[1:])","def rstrip_line(line):\n return line.rstrip()","def strip_warnings(self, line):\n if line[0] == \"|\":\n return \"\"\n else:\n return line","def clean_chunk(chunk):\n return '\\n'.join([x[1:] for x in chunk.split('\\n')\n if x and x[0] not in ('-', '@')])","def _strip_position(line: str) -> str:\n line = \".py\".join(line.split(\".py:\")[1:])\n line = \" \".join(line.split(\" \")[1:])\n return line","def strip_line(line):\n line = line.strip()\n line = line.rstrip('\\n')\n line = line.rstrip('\\t')\n line = (line.split(\"//\"))[0]\n return line","def clean(line):\n line = line.lower().replace(\"\\n\",\" \").replace(\"\\r\",\"\").replace(',',\"\").replace(\">\",\"> \").replace(\"<\", \" <\").replace(\"|\",\" \")\n return line","def trim(line):\n index = 0\n for i in range(len(line)):\n if line[i].isalpha():\n break\n index = index + 1\n return line[index:]","def scratch(line):\n if line.count('~~') >= 2:\n for i in range(0, line.count('~~') - line.count('~~') % 2):\n if i % 2 == 0:\n line = line.replace('~~', '<del>', 1)\n else:\n line = line.replace('~~', '</del>', 1)\n return line","def _PreParse(line: str) -> str:\n line = line.rstrip(\"\\n\")\n\n commentIndex = line.find(\"/\")\n\n # no comment found\n if commentIndex == - 1:\n return line\n\n # truncate\n return line[0:commentIndex]","def strip_tags(line):\n return re.sub(r'<sup[^>]*>.*?</sup>|<a[^>]*>.*?</a>|<[^>]+>', '', unescape(line), flags=re.S)","def _trunc_lines_prepend(self):\n\t\tp = self._edit.get_buffer()\n\t\tnLines = p.get_line_count()\n\t\twhile nLines > 0:\n\t\t\tif nLines <= self._maxLines +1:\n\t\t\t\tbreak\n\t\t\tend = p.get_end_iter()\n\t\t\tstart = p.get_end_iter()\n\t\t\tstart.backward_line()\n\t\t\tp.delete(start, end)\n\t\t\tnLines = p.get_line_count()","def oneline(value):\r\n try:\r\n return mark_safe(newlines.sub('', inbetween.sub('><', value)))\r\n except:\r\n return value","def precmd(self, line):\n if line == \"EOF\":\n return line\n return line.lower()","def filter_line(self, line):\n if line.startswith(\"<\"):\n # Simply filter out all lines beginning with '<', which are metadata\n return None\n\n # Some metadata-like text is also included at the start of lines, followed by \". - \"\n if u\". - \" in line:\n __, __, line = line.partition(u\". - \")\n\n # Remove -s and spaces from the start of lines\n # Not sure why they're often there, but it's just how the transcripts were formatted\n line = line.lstrip(u\"- \")\n\n # Skip lines that are fully surrounded by brackets: they're typically descriptions of what happened\n # E.g. (Applause)\n if line.startswith(u\"(\") and line.endswith(u\")\"):\n return None\n\n # It's common for a speaker's first utterance to start with a marker indicating the original language\n line = language_indicator_re.sub(u\"\", line)\n return line","def precmd(self, line):\n return line.strip()","def redirect_leading(self):\n while len(self.cmd) >= 3 and self.cmd[0] == \"<\":\n self.stdin = safe_open(self.cmd[1], \"r\")\n self.cmd = self.cmd[2:]","def remove_first_line(fname):\n with codecs.open(fname, 'r', 'utf-8') as fin:\n data = fin.read().splitlines(True)\n with codecs.open('temp_file.tsv', 'w','utf-8') as fout:\n fout.writelines(data[1:])\n\n fin.close()\n fout.close()\n # Delete original file and rename temp file to original name\n os.remove(fname)\n os.rename('temp_file.tsv',fname)","def ltrim(self, name, start, end):\r\n return self.format_inline('LTRIM', name, start, end)","def clean_hanging_newline(t):\n if t and t[-1] == \"\\n\":\n return t[:-1]\n return t","def remove_leading_blanks(self, sentence):\n pass","def clean_header(klass, s):\n return re.sub(r\"[\\n\\r\\t]+\", \" \", s).strip()","def _chop_end_misc(line):\n return re.sub(r\"\\s+\\d\\d-\\w\\w\\w-\\d\\d\\s+[1-9][0-9A-Z]{3}\\s*\\Z\", \"\", line)","def rstrip(self) -> String:\n pass","def _remove_new_line(self, message):\n if message.endswith('\\n'):\n return message[:-1]\n return message","def strip_line_ending( line ):\n\n # surely there's a better way?\n while len(line) and line[-1] in '\\n\\r':\n line = line[:-1]\n\n while len(line) and line[0] in '\\n\\r':\n line = line[1:]\n \n return line","def removeQuotes(text):\n lines = []\n for l in io.StringIO(text):\n l = l.strip()\n if l and l[0] != '>':\n lines.append(l)\n return ' '.join(lines)","def readline_strip(stream):\n assert hasattr(stream,\"read\")\n line = stream.readline()\n line = line.rstrip(\"\\n\")\n return line","def lstrip(self) -> String:\n pass","def _prune_ansi(line):\n\n pattern = re.compile('\\x1b[^m]+m')\n match = pattern.search(line)\n while match:\n index = line.find(match.group(0))\n line = line[:index] + line[index+len(match.group(0)):]\n match = pattern.search(line)\n return line","def clean_line(self, line):\n\n if \"#\" in line:\n temp = line.split(\"#\")\n if len(temp) < 2:\n return \"\"\n else:\n temp = temp[0] + \"\\n\"\n\n # make sure the \"#\" isn't in quotes\n if temp.count(\"\\\"\") % 2 == 0:\n line = temp\n\n line = line.replace(\"}\", \" } \").replace(\"{\", \" { \")\n while \"=\" in line:\n line = self.replace_equals(line)\n line = line.lstrip()\n return line","def remove_header_subject(text):\n _before, _blankline, after = text.partition('\\n\\n')\n sub = [l for l in _before.split(\"\\n\") if \"Subject:\" in l]\n final = sub[0] + \"\\n\" + after\n return final","def fix_headers(filename):\n\n counter = 1\n\n for line in fileinput.input(filename, inplace = True):\n if '>' in line:\n line = line.replace(line, '>'+str(counter)+'\\n')\n counter += 1\n sys.stdout.write(line)","def remove_comments_from_line(self, line):\n comment_start = line.find('$')\n if (comment_start >= 0):\n line = line[:comment_start]\n return line.lower()","def remove_leading_whitespace_and_empty_lines(text: str) -> str:\n # We call lstrip() twice on the same line. This is inefficient but ok for small unit tests.\n # Please change it if you want to.\n return '\\n'.join([line.lstrip() for line in text.split('\\n') if line.lstrip() != ''])","def strip_gutenberg_header(input_book_lines):\n\tlines = input_book_lines\n\tcurr_line = 0\n\twhile lines[curr_line].find('START OF THIS PROJECT GUTENBERG EBOOK') == -1:\n\t\tcurr_line += 1\n\treturn lines[curr_line+1:]","def stripUnderline(self, s):\n return s.replace('\\x1f', '').replace('\\x1F', '')","def remove_space(line):\n split_line = line.split()\n return \"\".join(split_line)","def __stripEol(self, txt):\n return txt.replace(\"\\r\", \"\").replace(\"\\n\", \"\")","def fix_horizontal(line):\n\tline = line.rstrip()\n\tline = untabify(line, tab_width)\n\treturn line + '\\n'","def strip_rule(line):\n\n return \" \".join(line.split())","def remove_head_line(self, gtfs_file, path):\n out_list = []\n header = GtfsHeader.return_header(self, gtfs_file).strip()\n in_file = os.path.join(os.path.expanduser(path), '{}.tmp'.format(gtfs_file))\n\n lines = open(in_file).readlines()\n cnt = 0\n for line in lines:\n if header in line:\n cnt += 1\n print('>>> Found header {} in {}.'.format(cnt, gtfs_file))\n lines.remove(line)\n # out_list.append(header.strip())\n\n for line in lines:\n out_list.append(line.strip())\n out_file = in_file\n\n f = open(out_file, 'w')\n for line in out_list:\n f.write('{}\\n'.format(line.strip()))\n f.close()","def unlines(line):\n\n return line.translate(str.maketrans('\\n', ' '))","def clean_fasta(fasta_in=FASTA_OLD, fasta_out=FASTA):\n with open(fasta_in, 'r') as file_in, open(fasta_out, 'a') as file_out:\n file_out.write(file_in.readline())\n for line in file_in:\n if line.startswith(\">\"):\n file_out.write(\"\\n\" + line)\n else:\n file_out.write(line.strip())","def strip_anything_before_h1_from_html(html):\n\n stripped_html = html.strip()\n if '</h1>' in stripped_html:\n return stripped_html.split('</h1>', 1)[1]\n\n return html","def stripline(line, stripboth=False):\n if stripboth:\n return line.lstrip().rstrip()\n return line.rstrip()","def removeSingleChars(self) -> None:\n self.text = re.sub('\\s[^\\n\\s]\\s', ' ', self.text)","def ltrim(self, name, start, end):\n self.connect()\n self._write('LTRIM %s %s %s\\r\\n' % (name, start, end))\n return self._get_simple_response()","def precmd(self, line):\n cmd, args, line = self.parseline(line)\n if '>' in line:\n self.set_outFile(args)\n elif self.outFile:\n self.outFile = None\n return line","def first_line(line):\r\n res = \"\"\r\n # Custom rules for printing first line in tabular format\r\n for i, j in enumerate(line):\r\n if i % 2 == 0:\r\n res += j + \" \"\r\n elif i % 2 != 0 and i != len(line) - 1:\r\n res += j + \"|\"\r\n else:\r\n res += j\r\n print(res)","def _output_skip_line(self):\n self.buf += '...'\n self._pad_horizontally(3)\n\n if self.num_parents >= 3 and self.commit_index < self.num_columns - 1:\n self._update_state(GraphState.PRE_COMMIT)\n else:\n self._update_state(GraphState.COMMIT)","def _remove_path_head(path, head):\n # Bugfix 13 Oct 2017: path.replace(head,'') will remove head from everywhere in the path. This\n # is especially problematic if the user gives the local dir as \".\" (i.e. the current directory)\n # because it will remove periods from filenames\n\n # Find the head at the beginning of the path only. Escape any characters in head that have special\n # meaning in a regular expression (e.g. \".\" means \"any character\")\n head_regex = '^{}'.format(re.escape(head))\n path = re.sub(head_regex, '', path)\n if path.startswith('/'):\n path = path[1:]\n\n return path","def remove_comments(line):\n hashPos = line.find('#')\n return line[:hashPos] if hashPos >= 0 else line","def _get_whitespace(line):\n return line[:-len(line.lstrip())]","def _truncateLine(self, line):\n return line[: irc.MAX_COMMAND_LENGTH - len(self.delimiter)]","def stripBrackets(b):\n\n while b.startswith(b\"> \"):\n b = b[2:]\n return b","def blank_line_before_underline(): # noqa: D416","def _clean(raw_command: str) -> str:\n return raw_command.split('//')[0].strip('\\n ')","def strip_comments(line):\n if \"#\" in line:\n return line[:line.find(\"#\")]\n else:\n return line","def render_first_line(self, comment=False):\n if comment:\n line = '#' + str(self.created) + ' ' + ' '.join(self.tags) + \"\\n\"\n else:\n line = '*' + str(self.created) + ' ' + ' '.join(self.tags) + \"\\n\"\n return unicode(line)","def trim_fasta(fasta_lines, output_length):\r\n for seq_id, seq in parse_fasta(fasta_lines):\r\n yield '>%s\\n%s\\n' % (seq_id, seq[:output_length])","def emptyline(self):\n self.do_ls(\"\")","def strip_begin_end_public_key(key):\n return key.replace(\"\\n\", \"\")\\\n .replace(\"-----BEGIN PUBLIC KEY-----\", \"\").replace(\n \"-----END PUBLIC KEY-----\", \"\")","def __remove_break_line__(self, string):\n return string.rstrip()","def stripNewLinesFromStartAndEnd(string):\n return string.lstrip().rstrip()","def fix_ending(x):\n x = strip_stoich_wrapper(x)\n x = re.sub(r'(?<=[a-zA-Z])\\-(?=[a-zA-Z]$)', ' ', x)\n return x","def strip_other_charcter():\n pass","def _preprocess(self, source):\n source = source.replace(u'\\n', u'').strip()\n source = re.sub(r'<br\\s*\\/?\\s*>', u' ', source, re.I)\n source = re.sub(r'\\s\\s+', u' ', source)\n return source","def _chop_end_codes(line):\n return re.sub(r\"\\s\\s\\s\\s+[\\w]{4}.\\s+\\d*\\Z\", \"\", line)","def str_first_at_front(self):\n return str(self)","def beginning_of_line():\n app = get_app()\n before_cursor = app.current_buffer.document.current_line_before_cursor\n\n return bool(\n len(before_cursor) == 0 and not app.current_buffer.document.on_first_line\n )","def move_to_first_line(self) -> None:\n try:\n first_line_length: int = self.buffer.index('\\n')\n except ValueError:\n return\n\n current_column: int = self.buffer.get_column(self.index)\n new_column: int = min(current_column, first_line_length)\n\n self.index = new_column","def remove_tags(raw):\n cleanr = re.compile('<.*?>')\n cleantext = re.sub(cleanr, ' ', raw)\n return cleantext","def _reset_leading_whitespace(self):\n self._leading_whitespace = ''","def remove_html(x: str) -> str:\n regex = r\"<.+?>\"\n return re.sub(regex, \"\", x)","def _start_magic(line):\n return start(line)","def remove_starting_comments(sql: str) -> str:\n commentless_sql = sql\n while True:\n start_comment = COMMENT_START_SQL_RE.match(commentless_sql)\n if start_comment is None:\n break\n commentless_sql = commentless_sql[start_comment.end() :]\n return commentless_sql","def backspace(self):\n # If the position is at the beggining of a line that is not the first\n # line then join the line to the end of the line above it.\n if self.pos == 0 and self.line > 0:\n self.pos = self.buffer.line_length(self.line - 1)\n self.buffer.join_lines(self.line - 1, self.line)\n self.line -= 1\n elif not (self.pos == 0 and self.line == 0):\n # Delete the character before the cursor and move the position back 1\n self.buffer.delete_char(self.line, self.pos - 1)\n self.pos -= 1\n \n self.has_changes = True","def stripBold(self, s):\n return s.replace('\\x02', '')","def precmd(self, line):\n return line","def no_blank_line_before_section(): # noqa: D416","def __trim(self, text):\n\n if isinstance(text, str) and \\\n text[0] == \"(\" and \\\n text[-1] == \")\":\n text = text[1:-1]\n return text","def _rstrip(line, JUNK='\\n \\t'):\r\n\r\n i = len(line)\r\n while i > 0 and line[i-1] in JUNK:\r\n i -= 1\r\n return line[:i]","def emptyline(self):","def cut_line(self):\r\n self.parachute.pop(0)","def remove_field_name(str):\r\n str = re.sub('<[^>]*>', ' ', str)\r\n return str","def strip_header(book):\n\theader_regex = header_string_regex()\n\theader_match = re.search(header_regex, book)\n\n\theader_end = 0\n\tif header_match:\n\t\theader_end = header_match.end()\n\n\treturn book[header_end:]","def strip_leading_ch_symbol(text: str) -> str:\n ch_symbols = \"！？｡＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰–—‘’‛“”„‟…‧﹏.\"\n text = re.sub(r'^[%s]+' % ch_symbols, \"\", text)\n return text","def _strip_excerpt(self, raw_html):\n clean_regex = re.compile(\"<.*?>\")\n clean_text = re.sub(clean_regex, \"\", raw_html)\n return html.unescape(clean_text).replace(\"\\n\", \"\")","def LTrim(text):\n return str(text).lstrip()","def strip_leading_chars(val):\n for i, c in enumerate(val):\n if c in \"0123456789.\":\n return val[i:]\n return \"\"","def strip_tags(initial_string):\n result = re.sub('<[^<]+?>', '', initial_string)\n return result","def bytes_strip(buf: bytes) -> bytes:\n if b'\\x00' in buf:\n return buf[:buf.find(b'\\x00')]\n else:\n return buf","def strip(self, value):\n raise NotImplementedError","def trim(self, start, end):"],"string":"[\n \"def clean(self, line):\\r\\n m = self.RE.match(line)\\r\\n if line.strip() == \\\">\\\":\\r\\n return \\\"\\\"\\r\\n elif m:\\r\\n return m.group(2)\\r\\n else:\\r\\n return line\",\n \"def dealFirstLine(line):\\n\\n print \\\"%s\\\" % (line.strip('\\\\n'))\",\n \"def _remove_beginning_newlines(lines):\\n first_non_blank_line = 0\\n\\n for line in lines:\\n if line.strip():\\n break\\n\\n first_non_blank_line += 1\\n\\n return lines[first_non_blank_line:]\",\n \"def strip_start(h, s):\\n if h.startswith(s):\\n h = h[len(s):]\\n return h\",\n \"def remove_starting_carriage_return_in_output(self, text):\\n\\n # Display info message\\n log.info(\\\"remove_starting_carriage_return_in_output\\\")\\n\\n # Remove the carriage return at the beginning of the string\\n output = text.lstrip(\\\"\\\\r\\\\n\\\\r\\\")\\n\\n # Display info message\\n log.info(f\\\"remove_starting_carriage_return_in_output: output = '{output}'\\\")\\n\\n # Return the string without the starting carriage return\\n return output\",\n \"def delete_first_line(string):\\n lines = string.split('\\\\n')\\n return '\\\\n'.join(lines[1:])\",\n \"def rstrip_line(line):\\n return line.rstrip()\",\n \"def strip_warnings(self, line):\\n if line[0] == \\\"|\\\":\\n return \\\"\\\"\\n else:\\n return line\",\n \"def clean_chunk(chunk):\\n return '\\\\n'.join([x[1:] for x in chunk.split('\\\\n')\\n if x and x[0] not in ('-', '@')])\",\n \"def _strip_position(line: str) -> str:\\n line = \\\".py\\\".join(line.split(\\\".py:\\\")[1:])\\n line = \\\" \\\".join(line.split(\\\" \\\")[1:])\\n return line\",\n \"def strip_line(line):\\n line = line.strip()\\n line = line.rstrip('\\\\n')\\n line = line.rstrip('\\\\t')\\n line = (line.split(\\\"//\\\"))[0]\\n return line\",\n \"def clean(line):\\n line = line.lower().replace(\\\"\\\\n\\\",\\\" \\\").replace(\\\"\\\\r\\\",\\\"\\\").replace(',',\\\"\\\").replace(\\\">\\\",\\\"> \\\").replace(\\\"<\\\", \\\" <\\\").replace(\\\"|\\\",\\\" \\\")\\n return line\",\n \"def trim(line):\\n index = 0\\n for i in range(len(line)):\\n if line[i].isalpha():\\n break\\n index = index + 1\\n return line[index:]\",\n \"def scratch(line):\\n if line.count('~~') >= 2:\\n for i in range(0, line.count('~~') - line.count('~~') % 2):\\n if i % 2 == 0:\\n line = line.replace('~~', '<del>', 1)\\n else:\\n line = line.replace('~~', '</del>', 1)\\n return line\",\n \"def _PreParse(line: str) -> str:\\n line = line.rstrip(\\\"\\\\n\\\")\\n\\n commentIndex = line.find(\\\"/\\\")\\n\\n # no comment found\\n if commentIndex == - 1:\\n return line\\n\\n # truncate\\n return line[0:commentIndex]\",\n \"def strip_tags(line):\\n return re.sub(r'<sup[^>]*>.*?</sup>|<a[^>]*>.*?</a>|<[^>]+>', '', unescape(line), flags=re.S)\",\n \"def _trunc_lines_prepend(self):\\n\\t\\tp = self._edit.get_buffer()\\n\\t\\tnLines = p.get_line_count()\\n\\t\\twhile nLines > 0:\\n\\t\\t\\tif nLines <= self._maxLines +1:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\tend = p.get_end_iter()\\n\\t\\t\\tstart = p.get_end_iter()\\n\\t\\t\\tstart.backward_line()\\n\\t\\t\\tp.delete(start, end)\\n\\t\\t\\tnLines = p.get_line_count()\",\n \"def oneline(value):\\r\\n try:\\r\\n return mark_safe(newlines.sub('', inbetween.sub('><', value)))\\r\\n except:\\r\\n return value\",\n \"def precmd(self, line):\\n if line == \\\"EOF\\\":\\n return line\\n return line.lower()\",\n \"def filter_line(self, line):\\n if line.startswith(\\\"<\\\"):\\n # Simply filter out all lines beginning with '<', which are metadata\\n return None\\n\\n # Some metadata-like text is also included at the start of lines, followed by \\\". - \\\"\\n if u\\\". - \\\" in line:\\n __, __, line = line.partition(u\\\". - \\\")\\n\\n # Remove -s and spaces from the start of lines\\n # Not sure why they're often there, but it's just how the transcripts were formatted\\n line = line.lstrip(u\\\"- \\\")\\n\\n # Skip lines that are fully surrounded by brackets: they're typically descriptions of what happened\\n # E.g. (Applause)\\n if line.startswith(u\\\"(\\\") and line.endswith(u\\\")\\\"):\\n return None\\n\\n # It's common for a speaker's first utterance to start with a marker indicating the original language\\n line = language_indicator_re.sub(u\\\"\\\", line)\\n return line\",\n \"def precmd(self, line):\\n return line.strip()\",\n \"def redirect_leading(self):\\n while len(self.cmd) >= 3 and self.cmd[0] == \\\"<\\\":\\n self.stdin = safe_open(self.cmd[1], \\\"r\\\")\\n self.cmd = self.cmd[2:]\",\n \"def remove_first_line(fname):\\n with codecs.open(fname, 'r', 'utf-8') as fin:\\n data = fin.read().splitlines(True)\\n with codecs.open('temp_file.tsv', 'w','utf-8') as fout:\\n fout.writelines(data[1:])\\n\\n fin.close()\\n fout.close()\\n # Delete original file and rename temp file to original name\\n os.remove(fname)\\n os.rename('temp_file.tsv',fname)\",\n \"def ltrim(self, name, start, end):\\r\\n return self.format_inline('LTRIM', name, start, end)\",\n \"def clean_hanging_newline(t):\\n if t and t[-1] == \\\"\\\\n\\\":\\n return t[:-1]\\n return t\",\n \"def remove_leading_blanks(self, sentence):\\n pass\",\n \"def clean_header(klass, s):\\n return re.sub(r\\\"[\\\\n\\\\r\\\\t]+\\\", \\\" \\\", s).strip()\",\n \"def _chop_end_misc(line):\\n return re.sub(r\\\"\\\\s+\\\\d\\\\d-\\\\w\\\\w\\\\w-\\\\d\\\\d\\\\s+[1-9][0-9A-Z]{3}\\\\s*\\\\Z\\\", \\\"\\\", line)\",\n \"def rstrip(self) -> String:\\n pass\",\n \"def _remove_new_line(self, message):\\n if message.endswith('\\\\n'):\\n return message[:-1]\\n return message\",\n \"def strip_line_ending( line ):\\n\\n # surely there's a better way?\\n while len(line) and line[-1] in '\\\\n\\\\r':\\n line = line[:-1]\\n\\n while len(line) and line[0] in '\\\\n\\\\r':\\n line = line[1:]\\n \\n return line\",\n \"def removeQuotes(text):\\n lines = []\\n for l in io.StringIO(text):\\n l = l.strip()\\n if l and l[0] != '>':\\n lines.append(l)\\n return ' '.join(lines)\",\n \"def readline_strip(stream):\\n assert hasattr(stream,\\\"read\\\")\\n line = stream.readline()\\n line = line.rstrip(\\\"\\\\n\\\")\\n return line\",\n \"def lstrip(self) -> String:\\n pass\",\n \"def _prune_ansi(line):\\n\\n pattern = re.compile('\\\\x1b[^m]+m')\\n match = pattern.search(line)\\n while match:\\n index = line.find(match.group(0))\\n line = line[:index] + line[index+len(match.group(0)):]\\n match = pattern.search(line)\\n return line\",\n \"def clean_line(self, line):\\n\\n if \\\"#\\\" in line:\\n temp = line.split(\\\"#\\\")\\n if len(temp) < 2:\\n return \\\"\\\"\\n else:\\n temp = temp[0] + \\\"\\\\n\\\"\\n\\n # make sure the \\\"#\\\" isn't in quotes\\n if temp.count(\\\"\\\\\\\"\\\") % 2 == 0:\\n line = temp\\n\\n line = line.replace(\\\"}\\\", \\\" } \\\").replace(\\\"{\\\", \\\" { \\\")\\n while \\\"=\\\" in line:\\n line = self.replace_equals(line)\\n line = line.lstrip()\\n return line\",\n \"def remove_header_subject(text):\\n _before, _blankline, after = text.partition('\\\\n\\\\n')\\n sub = [l for l in _before.split(\\\"\\\\n\\\") if \\\"Subject:\\\" in l]\\n final = sub[0] + \\\"\\\\n\\\" + after\\n return final\",\n \"def fix_headers(filename):\\n\\n counter = 1\\n\\n for line in fileinput.input(filename, inplace = True):\\n if '>' in line:\\n line = line.replace(line, '>'+str(counter)+'\\\\n')\\n counter += 1\\n sys.stdout.write(line)\",\n \"def remove_comments_from_line(self, line):\\n comment_start = line.find('$')\\n if (comment_start >= 0):\\n line = line[:comment_start]\\n return line.lower()\",\n \"def remove_leading_whitespace_and_empty_lines(text: str) -> str:\\n # We call lstrip() twice on the same line. This is inefficient but ok for small unit tests.\\n # Please change it if you want to.\\n return '\\\\n'.join([line.lstrip() for line in text.split('\\\\n') if line.lstrip() != ''])\",\n \"def strip_gutenberg_header(input_book_lines):\\n\\tlines = input_book_lines\\n\\tcurr_line = 0\\n\\twhile lines[curr_line].find('START OF THIS PROJECT GUTENBERG EBOOK') == -1:\\n\\t\\tcurr_line += 1\\n\\treturn lines[curr_line+1:]\",\n \"def stripUnderline(self, s):\\n return s.replace('\\\\x1f', '').replace('\\\\x1F', '')\",\n \"def remove_space(line):\\n split_line = line.split()\\n return \\\"\\\".join(split_line)\",\n \"def __stripEol(self, txt):\\n return txt.replace(\\\"\\\\r\\\", \\\"\\\").replace(\\\"\\\\n\\\", \\\"\\\")\",\n \"def fix_horizontal(line):\\n\\tline = line.rstrip()\\n\\tline = untabify(line, tab_width)\\n\\treturn line + '\\\\n'\",\n \"def strip_rule(line):\\n\\n return \\\" \\\".join(line.split())\",\n \"def remove_head_line(self, gtfs_file, path):\\n out_list = []\\n header = GtfsHeader.return_header(self, gtfs_file).strip()\\n in_file = os.path.join(os.path.expanduser(path), '{}.tmp'.format(gtfs_file))\\n\\n lines = open(in_file).readlines()\\n cnt = 0\\n for line in lines:\\n if header in line:\\n cnt += 1\\n print('>>> Found header {} in {}.'.format(cnt, gtfs_file))\\n lines.remove(line)\\n # out_list.append(header.strip())\\n\\n for line in lines:\\n out_list.append(line.strip())\\n out_file = in_file\\n\\n f = open(out_file, 'w')\\n for line in out_list:\\n f.write('{}\\\\n'.format(line.strip()))\\n f.close()\",\n \"def unlines(line):\\n\\n return line.translate(str.maketrans('\\\\n', ' '))\",\n \"def clean_fasta(fasta_in=FASTA_OLD, fasta_out=FASTA):\\n with open(fasta_in, 'r') as file_in, open(fasta_out, 'a') as file_out:\\n file_out.write(file_in.readline())\\n for line in file_in:\\n if line.startswith(\\\">\\\"):\\n file_out.write(\\\"\\\\n\\\" + line)\\n else:\\n file_out.write(line.strip())\",\n \"def strip_anything_before_h1_from_html(html):\\n\\n stripped_html = html.strip()\\n if '</h1>' in stripped_html:\\n return stripped_html.split('</h1>', 1)[1]\\n\\n return html\",\n \"def stripline(line, stripboth=False):\\n if stripboth:\\n return line.lstrip().rstrip()\\n return line.rstrip()\",\n \"def removeSingleChars(self) -> None:\\n self.text = re.sub('\\\\s[^\\\\n\\\\s]\\\\s', ' ', self.text)\",\n \"def ltrim(self, name, start, end):\\n self.connect()\\n self._write('LTRIM %s %s %s\\\\r\\\\n' % (name, start, end))\\n return self._get_simple_response()\",\n \"def precmd(self, line):\\n cmd, args, line = self.parseline(line)\\n if '>' in line:\\n self.set_outFile(args)\\n elif self.outFile:\\n self.outFile = None\\n return line\",\n \"def first_line(line):\\r\\n res = \\\"\\\"\\r\\n # Custom rules for printing first line in tabular format\\r\\n for i, j in enumerate(line):\\r\\n if i % 2 == 0:\\r\\n res += j + \\\" \\\"\\r\\n elif i % 2 != 0 and i != len(line) - 1:\\r\\n res += j + \\\"|\\\"\\r\\n else:\\r\\n res += j\\r\\n print(res)\",\n \"def _output_skip_line(self):\\n self.buf += '...'\\n self._pad_horizontally(3)\\n\\n if self.num_parents >= 3 and self.commit_index < self.num_columns - 1:\\n self._update_state(GraphState.PRE_COMMIT)\\n else:\\n self._update_state(GraphState.COMMIT)\",\n \"def _remove_path_head(path, head):\\n # Bugfix 13 Oct 2017: path.replace(head,'') will remove head from everywhere in the path. This\\n # is especially problematic if the user gives the local dir as \\\".\\\" (i.e. the current directory)\\n # because it will remove periods from filenames\\n\\n # Find the head at the beginning of the path only. Escape any characters in head that have special\\n # meaning in a regular expression (e.g. \\\".\\\" means \\\"any character\\\")\\n head_regex = '^{}'.format(re.escape(head))\\n path = re.sub(head_regex, '', path)\\n if path.startswith('/'):\\n path = path[1:]\\n\\n return path\",\n \"def remove_comments(line):\\n hashPos = line.find('#')\\n return line[:hashPos] if hashPos >= 0 else line\",\n \"def _get_whitespace(line):\\n return line[:-len(line.lstrip())]\",\n \"def _truncateLine(self, line):\\n return line[: irc.MAX_COMMAND_LENGTH - len(self.delimiter)]\",\n \"def stripBrackets(b):\\n\\n while b.startswith(b\\\"> \\\"):\\n b = b[2:]\\n return b\",\n \"def blank_line_before_underline(): # noqa: D416\",\n \"def _clean(raw_command: str) -> str:\\n return raw_command.split('//')[0].strip('\\\\n ')\",\n \"def strip_comments(line):\\n if \\\"#\\\" in line:\\n return line[:line.find(\\\"#\\\")]\\n else:\\n return line\",\n \"def render_first_line(self, comment=False):\\n if comment:\\n line = '#' + str(self.created) + ' ' + ' '.join(self.tags) + \\\"\\\\n\\\"\\n else:\\n line = '*' + str(self.created) + ' ' + ' '.join(self.tags) + \\\"\\\\n\\\"\\n return unicode(line)\",\n \"def trim_fasta(fasta_lines, output_length):\\r\\n for seq_id, seq in parse_fasta(fasta_lines):\\r\\n yield '>%s\\\\n%s\\\\n' % (seq_id, seq[:output_length])\",\n \"def emptyline(self):\\n self.do_ls(\\\"\\\")\",\n \"def strip_begin_end_public_key(key):\\n return key.replace(\\\"\\\\n\\\", \\\"\\\")\\\\\\n .replace(\\\"-----BEGIN PUBLIC KEY-----\\\", \\\"\\\").replace(\\n \\\"-----END PUBLIC KEY-----\\\", \\\"\\\")\",\n \"def __remove_break_line__(self, string):\\n return string.rstrip()\",\n \"def stripNewLinesFromStartAndEnd(string):\\n return string.lstrip().rstrip()\",\n \"def fix_ending(x):\\n x = strip_stoich_wrapper(x)\\n x = re.sub(r'(?<=[a-zA-Z])\\\\-(?=[a-zA-Z]$)', ' ', x)\\n return x\",\n \"def strip_other_charcter():\\n pass\",\n \"def _preprocess(self, source):\\n source = source.replace(u'\\\\n', u'').strip()\\n source = re.sub(r'<br\\\\s*\\\\/?\\\\s*>', u' ', source, re.I)\\n source = re.sub(r'\\\\s\\\\s+', u' ', source)\\n return source\",\n \"def _chop_end_codes(line):\\n return re.sub(r\\\"\\\\s\\\\s\\\\s\\\\s+[\\\\w]{4}.\\\\s+\\\\d*\\\\Z\\\", \\\"\\\", line)\",\n \"def str_first_at_front(self):\\n return str(self)\",\n \"def beginning_of_line():\\n app = get_app()\\n before_cursor = app.current_buffer.document.current_line_before_cursor\\n\\n return bool(\\n len(before_cursor) == 0 and not app.current_buffer.document.on_first_line\\n )\",\n \"def move_to_first_line(self) -> None:\\n try:\\n first_line_length: int = self.buffer.index('\\\\n')\\n except ValueError:\\n return\\n\\n current_column: int = self.buffer.get_column(self.index)\\n new_column: int = min(current_column, first_line_length)\\n\\n self.index = new_column\",\n \"def remove_tags(raw):\\n cleanr = re.compile('<.*?>')\\n cleantext = re.sub(cleanr, ' ', raw)\\n return cleantext\",\n \"def _reset_leading_whitespace(self):\\n self._leading_whitespace = ''\",\n \"def remove_html(x: str) -> str:\\n regex = r\\\"<.+?>\\\"\\n return re.sub(regex, \\\"\\\", x)\",\n \"def _start_magic(line):\\n return start(line)\",\n \"def remove_starting_comments(sql: str) -> str:\\n commentless_sql = sql\\n while True:\\n start_comment = COMMENT_START_SQL_RE.match(commentless_sql)\\n if start_comment is None:\\n break\\n commentless_sql = commentless_sql[start_comment.end() :]\\n return commentless_sql\",\n \"def backspace(self):\\n # If the position is at the beggining of a line that is not the first\\n # line then join the line to the end of the line above it.\\n if self.pos == 0 and self.line > 0:\\n self.pos = self.buffer.line_length(self.line - 1)\\n self.buffer.join_lines(self.line - 1, self.line)\\n self.line -= 1\\n elif not (self.pos == 0 and self.line == 0):\\n # Delete the character before the cursor and move the position back 1\\n self.buffer.delete_char(self.line, self.pos - 1)\\n self.pos -= 1\\n \\n self.has_changes = True\",\n \"def stripBold(self, s):\\n return s.replace('\\\\x02', '')\",\n \"def precmd(self, line):\\n return line\",\n \"def no_blank_line_before_section(): # noqa: D416\",\n \"def __trim(self, text):\\n\\n if isinstance(text, str) and \\\\\\n text[0] == \\\"(\\\" and \\\\\\n text[-1] == \\\")\\\":\\n text = text[1:-1]\\n return text\",\n \"def _rstrip(line, JUNK='\\\\n \\\\t'):\\r\\n\\r\\n i = len(line)\\r\\n while i > 0 and line[i-1] in JUNK:\\r\\n i -= 1\\r\\n return line[:i]\",\n \"def emptyline(self):\",\n \"def cut_line(self):\\r\\n self.parachute.pop(0)\",\n \"def remove_field_name(str):\\r\\n str = re.sub('<[^>]*>', ' ', str)\\r\\n return str\",\n \"def strip_header(book):\\n\\theader_regex = header_string_regex()\\n\\theader_match = re.search(header_regex, book)\\n\\n\\theader_end = 0\\n\\tif header_match:\\n\\t\\theader_end = header_match.end()\\n\\n\\treturn book[header_end:]\",\n \"def strip_leading_ch_symbol(text: str) -> str:\\n ch_symbols = \\\"！？｡＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰–—‘’‛“”„‟…‧﹏.\\\"\\n text = re.sub(r'^[%s]+' % ch_symbols, \\\"\\\", text)\\n return text\",\n \"def _strip_excerpt(self, raw_html):\\n clean_regex = re.compile(\\\"<.*?>\\\")\\n clean_text = re.sub(clean_regex, \\\"\\\", raw_html)\\n return html.unescape(clean_text).replace(\\\"\\\\n\\\", \\\"\\\")\",\n \"def LTrim(text):\\n return str(text).lstrip()\",\n \"def strip_leading_chars(val):\\n for i, c in enumerate(val):\\n if c in \\\"0123456789.\\\":\\n return val[i:]\\n return \\\"\\\"\",\n \"def strip_tags(initial_string):\\n result = re.sub('<[^<]+?>', '', initial_string)\\n return result\",\n \"def bytes_strip(buf: bytes) -> bytes:\\n if b'\\\\x00' in buf:\\n return buf[:buf.find(b'\\\\x00')]\\n else:\\n return buf\",\n \"def strip(self, value):\\n raise NotImplementedError\",\n \"def trim(self, start, end):\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7322892","0.65217817","0.6428228","0.64100033","0.6187956","0.61572856","0.6113667","0.6068053","0.6057279","0.6052678","0.5984401","0.59601843","0.5782217","0.5741116","0.5701244","0.5687602","0.56819564","0.5668284","0.5666465","0.5642651","0.5623701","0.5614793","0.5612808","0.5611076","0.5607446","0.55495036","0.55462813","0.5538137","0.5527812","0.54940754","0.54808414","0.5478213","0.5457218","0.5451707","0.5450555","0.544898","0.54488057","0.54453284","0.5438384","0.5437094","0.5418871","0.54167134","0.54019743","0.54005337","0.53816557","0.5380091","0.5372608","0.5372077","0.53706443","0.53587884","0.53493124","0.5346437","0.5344656","0.5342691","0.53228825","0.53195363","0.53136086","0.53084093","0.529721","0.52891845","0.5284412","0.52765226","0.5251249","0.5244012","0.52403456","0.52380043","0.5229766","0.5220766","0.52205706","0.52199817","0.52183884","0.52130234","0.5205097","0.52017343","0.5191343","0.5190015","0.5188229","0.5179044","0.51742077","0.51712596","0.5169283","0.5156123","0.51558256","0.5152595","0.5151997","0.51506907","0.51471686","0.51437545","0.51343685","0.51341206","0.512706","0.51258105","0.512495","0.5119043","0.51147276","0.5111111","0.51099306","0.5098977","0.5093208","0.50913566"],"string":"[\n \"0.7322892\",\n \"0.65217817\",\n \"0.6428228\",\n \"0.64100033\",\n \"0.6187956\",\n \"0.61572856\",\n \"0.6113667\",\n \"0.6068053\",\n \"0.6057279\",\n \"0.6052678\",\n \"0.5984401\",\n \"0.59601843\",\n \"0.5782217\",\n \"0.5741116\",\n \"0.5701244\",\n \"0.5687602\",\n \"0.56819564\",\n \"0.5668284\",\n \"0.5666465\",\n \"0.5642651\",\n \"0.5623701\",\n \"0.5614793\",\n \"0.5612808\",\n \"0.5611076\",\n \"0.5607446\",\n \"0.55495036\",\n \"0.55462813\",\n \"0.5538137\",\n \"0.5527812\",\n \"0.54940754\",\n \"0.54808414\",\n \"0.5478213\",\n \"0.5457218\",\n \"0.5451707\",\n \"0.5450555\",\n \"0.544898\",\n \"0.54488057\",\n \"0.54453284\",\n \"0.5438384\",\n \"0.5437094\",\n \"0.5418871\",\n \"0.54167134\",\n \"0.54019743\",\n \"0.54005337\",\n \"0.53816557\",\n \"0.5380091\",\n \"0.5372608\",\n \"0.5372077\",\n \"0.53706443\",\n \"0.53587884\",\n \"0.53493124\",\n \"0.5346437\",\n \"0.5344656\",\n \"0.5342691\",\n \"0.53228825\",\n \"0.53195363\",\n \"0.53136086\",\n \"0.53084093\",\n \"0.529721\",\n \"0.52891845\",\n \"0.5284412\",\n \"0.52765226\",\n \"0.5251249\",\n \"0.5244012\",\n \"0.52403456\",\n \"0.52380043\",\n \"0.5229766\",\n \"0.5220766\",\n \"0.52205706\",\n \"0.52199817\",\n \"0.52183884\",\n \"0.52130234\",\n \"0.5205097\",\n \"0.52017343\",\n \"0.5191343\",\n \"0.5190015\",\n \"0.5188229\",\n \"0.5179044\",\n \"0.51742077\",\n \"0.51712596\",\n \"0.5169283\",\n \"0.5156123\",\n \"0.51558256\",\n \"0.5152595\",\n \"0.5151997\",\n \"0.51506907\",\n \"0.51471686\",\n \"0.51437545\",\n \"0.51343685\",\n \"0.51341206\",\n \"0.512706\",\n \"0.51258105\",\n \"0.512495\",\n \"0.5119043\",\n \"0.51147276\",\n \"0.5111111\",\n \"0.51099306\",\n \"0.5098977\",\n \"0.5093208\",\n \"0.50913566\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7390938"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17768,"cells":{"query":{"kind":"string","value":"Convert ttyrec files to videos"},"document":{"kind":"string","value":"def main(ctx, ttyrec, encoding, ibm, outfile, size, fps, font_size, font_file,\n bold_font_file, info, info_all):\n if ibm:\n encoding = 'cp437'\n fp, def_outfile = open_or_get(ttyrec)\n try:\n with fp:\n updates = list(read_ttyrec(fp, encoding=encoding, errors='replace'))\n except ShortTTYRecError as e:\n ctx.fail(str(e))\n if info or info_all:\n about = ttyrec_info(updates, show_all=info_all)\n click.echo(json.dumps(about, sort_keys=True, indent=4))\n return\n if len(updates) < 2:\n ctx.fail(\n 'ttyrec only has {} update{}; need at least two to make a video'\n .format(len(updates), 's' if len(updates) != 1 else '')\n )\n duration = updates[-1].timestamp - updates[0].timestamp\n click.echo(\n f'ttyrec length: {duration} ({len(updates)} distinct frames)',\n err=True,\n )\n imgr = ScreenRenderer(\n font = ImageFont.truetype(font_file, size=font_size),\n bold_font = ImageFont.truetype(bold_font_file, size=font_size),\n font_size = font_size,\n columns = size[0],\n lines = size[1],\n )\n imageio.plugins.ffmpeg.download()\n if outfile is None:\n outfile = def_outfile\n click.echo(f'Writing {outfile} ...', err=True)\n with click.progressbar(\n imgr.render_updates(updates, fps, block_size=MACRO_BLOCK_SIZE),\n length=ceil(duration.total_seconds() * fps),\n ) as mov_frames:\n imageio.mimwrite(outfile, map(np.asarray, mov_frames), fps=fps)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def seqIo_toVid(fName, ext='avi'):\n\n assert fName[-3:]=='seq', 'Not a seq file'\n sr = seqIo_reader(fName)\n N = sr.header['numFrames']\n h = sr.header['height']\n w = sr.header['width']\n fps = sr.header['fps']\n\n out = fName[:-3]+ext\n sw = skvideo.io.FFmpegWriter(out)\n # sw = cv2.VideoWriter(out, -1, fps, (w, h))\n timer = pb.ProgressBar(widgets=['Converting ', pb.Percentage(), ' -- ',\n pb.FormatLabel('Frame %(value)d'), '/',\n pb.FormatLabel('%(max)d'), ' [', pb.Timer(), '] ',\n pb.Bar(), ' (', pb.ETA(), ') '], maxval=N)\n\n for f in range(N):\n I, ts = sr.getFrame(f)\n sw.writeFrame(Image.fromarray(I))\n # sw.write(I)\n timer.update(f)\n timer.finish()\n # cv2.destroyAllWindows()\n # sw.release()\n sw.close()\n sr.close()\n print(out + ' converted')","def video_files():\n p = parse_cmdline(get_parser=get_parser_files)\n log.setup_main_handler(\n mods=(\"fogtools\", \"typhon\", \"fogpy\", \"sattools\", \"fcitools\", \"satpy\",\n \"pyresample\"),\n level=logging.INFO)\n vis.show_video_abi_glm(\n files=p.files,\n img_out=p.filename_pattern_image,\n vid_out=p.filename_pattern_video,\n out_dir=p.outdir)\n print(\"Files written to:\", p.outdir)","def check_video_timestamps(movie_file, desired_format='.mp4', desired_framerate=30):\n\n check_video_format(movie_file, desired_format='.mp4', original_format='.avi')\n\n new_movie_file = movie_file+'_tt'+desired_format\n if not os.path.isfile(new_movie_file):\n #Convert file to 30 fps\n cmd = ['ffmpeg', '-i', movie_file+desired_format]\n cmd += ['-r', str(desired_framerate)]\n cmd += ['-y', movie_file+'_t'+desired_format]\n cmd_string = ''.join([\"%s \" % el for el in cmd]) \n #print '-->Running: ', cmd_string\n p = subprocess.Popen(cmd, shell=False)\n p.wait()\n\n #Add timecode text to video\n cmd = 'ffmpeg -i '+movie_file+'_t'+desired_format+' -vf drawtext=\\\"fontfile=/opt/X11/share/fonts/TTF/VeraMoBd.ttf: timecode=\\'00\\:00\\:00\\:00\\':rate=30: fontcolor=white@0.8: x=7: y=460\\\" -an -y '+movie_file+'_tt'+desired_format\n args = shlex.split(cmd)\n #print args\n p = subprocess.Popen(args, shell=False)\n p.wait()\n\n os.remove(movie_file+'_t'+desired_format)\n\n return new_movie_file","def make_video(pattern, plotdir, moviedir, movienametag):\n images_list = glob('%s/%s'%(plotdir, pattern))\n images_list.sort()\n # save all required files into tmp_moviedir, with simple filenames: %.4d.png\n tmp_moviedir = '%s/tmp_movie_%s'%(plotdir, movienametag)\n os.system('mkdir -p %s'%tmp_moviedir)\n for i in range(len(images_list)):\n fname = images_list[i].split('%s/'%plotdir)[-1].split('.png')[0]\n os.system('cp %s/%s.png %s/%.4d.png'%(plotdir, fname, tmp_moviedir, i))\n\n os.system('avconv -i %s'%tmp_moviedir +'/%04d.png ' \\\n +' -y -c:v libx264 -pix_fmt yuv420p %s/%s.mp4'%(moviedir, movienametag))","def make_video(input_files, width=0, height=0, frame_rate=24, crf=20, output_path=\"video.mp4\"):\n if isinstance(input_files, list):\n from PIL import Image # pylint: disable=C0415\n\n with Image.open(input_files[0]) as img:\n width, height = img.size\n tmp_dir = \"tmp_ffmpeg_dir\"\n os.mkdir(tmp_dir)\n if width % 2 != 0:\n print(f\"Width ({width}) not divisible by 2\")\n width -= 1\n if height % 2 != 0:\n print(f\"Height ({width}) not divisible by 2\")\n height -= 1\n for i, inp in enumerate(input_files):\n shutil.copy(inp, os.path.join(tmp_dir, f\"{i:06d}.png\"))\n inputs = f\"{tmp_dir}/%06d.png\"\n command = ffmpeg_common_args(frame_rate, inputs, width, height, crf, output_path)\n ret = os.system(command)\n assert ret == 0, \"ffmpeg failed to generate video file.\"\n for i in range(len(input_files)):\n os.remove(os.path.join(tmp_dir, f\"{i:06d}.png\"))\n os.rmdir(tmp_dir)\n elif isinstance(input_files, str):\n assert width != 0 and height != 0\n command = ffmpeg_common_args(frame_rate, input_files, width, height, crf, output_path)\n ret = os.system(command)\n assert ret == 0, \"ffmpeg failed to generate video file.\"\n else:\n assert (\n False\n ), f'input_files should be list (of files) or str (of file template, e.g., \"%04d.png\") instead of {type(input_files)}'","def convert(processed_dir: str, video_file: str):\n\n video_name = osp.splitext(osp.basename(video_file))[0]\n out_dir = processed_dir + video_name\n\n # create img dir\n if not osp.exists(processed_dir):\n os.mkdir(processed_dir)\n\n # Create dir for video file if not existent\n # this is where we save our images\n if not osp.exists(out_dir):\n os.mkdir(out_dir)\n\n if osp.exists(out_dir):\n os.mkdir(out_dir + \"/kermit/\")\n os.mkdir(out_dir + \"/not_kermit/\")\n\n # open video file for processing\n cap = cv.VideoCapture(video_file)\n frame_rate = cap.get(5) # frame rate\n\n sec = 0\n total_count = (60*25)+50 # just an approximation\n pbar = tqdm.tqdm(total=total_count, leave=False)\n\n count = 0\n while (cap.isOpened()):\n frame_id = cap.get(1) # current frame number\n frame_exists, curr_frame = cap.read()\n\n if not frame_exists:\n break\n else:\n if (frame_id % math.floor(frame_rate) == 0):\n # output is : video_file/<video_file>_frameNr.jpg\n cv.imwrite(osp.join(out_dir, '{}_{}.jpg'.format(video_name,count)), curr_frame)\n count = count + 1\n pbar.update(1)\n\n pbar.close()\n # release resources\n cap.release()","def make_video(data,\n xdim, ydim, sample_read_rows, sample_read_cols, image_write_rows, image_write_cols,\n directory, filename, fps = 24.0, start_frame = 1, end_frame = None, timestamp = False, fontsize = 30, ts_pos = (0,0), save_raw = False):\n\n #Command to send via the command prompt which specifies the pipe parameters\n # command = ['ffmpeg',\n # '-y', # (optional) overwrite output file if it exists\n # '-f', 'image2pipe',\n # '-vcodec', 'mjpeg', #'mjpeg',\n # '-r', '1',\n # '-r', str(fps), # frames per second\n # '-i', '-', # The input comes from a pipe\n # '-an', # Tells FFMPEG not to expect any audio\n # '-vcodec', 'mpeg4',\n # '-b:v', '5000k',\n # directory + filename + \"/\"+filename+\".mp4\",\n # '-hide_banner',\n # '-loglevel', 'panic']\n\n # Create directories if they don't exist\n if not os.path.exists(os.path.join(directory, filename, 'frames/')):\n os.makedirs(os.path.join(directory, filename, 'frames/'))\n if save_raw and not os.path.exists(os.path.join(directory, filename, 'frames-raw/')):\n os.makedirs(os.path.join(directory, filename, 'frames-raw/'))\n\n if end_frame == None:\n end_frame = data.FrameCount\n\n cm = colormap.get_cmap('viridis')\n\n for i, frame_offset in enumerate(tqdm.tqdm(range(start_frame, end_frame))):\n frame = FrameRead(data, frame_offset)\n frame_image = np.zeros([ydim, xdim], dtype=np.uint8)\n frame_image[image_write_rows, image_write_cols] = frame.frame_data[sample_read_rows, sample_read_cols]\n\n rgb_im = Image.fromarray(cm(frame_image, bytes=True)).convert('RGB')\n rgb_im.save(os.path.join(directory, filename, 'frames/', f'{i}.jpg'), 'JPEG')\n\n if save_raw:\n Image.fromarray(np.uint8(frame.frame_data), mode='L').save(os.path.join(directory, filename, 'frames-raw/', f'{i}.jpg'), 'JPEG')","def __convert_video(self, v_dir):\r\n self.video_dir = v_dir\r\n vid_capt = cv2.VideoCapture(v_dir)\r\n curr_frame = 0\r\n # clear directory if it already exists, else create it\r\n if os.path.exists(self.FRAME_FOLDER):\r\n for file in os.listdir(self.FRAME_FOLDER):\r\n file_path = os.path.join(self.FRAME_FOLDER, file)\r\n try:\r\n if os.path.isfile(file_path):\r\n os.unlink(file_path)\r\n except Exception as e:\r\n print(e)\r\n else:\r\n os.makedirs(self.FRAME_FOLDER)\r\n\r\n while True:\r\n # ret is return value, once it turns False, video is over\r\n ret, frame = vid_capt.read()\r\n if not ret:\r\n break\r\n f_name = self.FRAME_FOLDER + '/' + self.vid_name + 'frame' + str(curr_frame) + '.jpg'\r\n cv2.imwrite(f_name, frame)\r\n curr_frame += 1\r\n\r\n vid_capt.release()\r\n cv2.destroyAllWindows()","def check_video_format(movie_file, desired_format='.mp4', original_format='.avi'):\n\n if not os.path.isfile(movie_file+original_format):\n print 'Error. avi file does not exist:'+movie_file+'.avi'\n if not os.path.isfile(movie_file+desired_format):\n cmd = ['ffmpeg']\n cmd += ['-i', movie_file+original_format]\n cmd += [movie_file+desired_format]\n cmd_string = ''.join([\"%s \" % el for el in cmd])\n #print '-->Running: ', cmd_string\n p = subprocess.Popen(cmd, shell=False)\n p.wait()","def start_recording(codec, filename=time.strftime(\"%Y-%m-%d_%H-%M-%S\")):\n global video_writer\n folder = 'video_out/' # eventually replace this with the SD card folder\n # TODO: also include branch name and/or commit ID\n path = folder + filename + '.' + filetype\n print \"Saving video to: %s\" % path\n\n height = videoinput.frame_height\n if settings.sidebyside:\n width = 2*videoinput.frame_width\n else:\n width = videoinput.frame_width\n\n try:\n video_writer = cv2.VideoWriter(path, codec, 30, (width, height))\n except:\n print \"Failed to open video file for writing!\"","def write_video(frames, filename, fps=20):\n \n # On Mac systems, copy ffmeg binaries to your PATH (http://ffmpegmac.net/)\n \n if platform.system() == 'Windows':\n err_str = 'Don\\'t know how to write a movie for %s platform' % platform.system()\n raise NotImplementedError(err_str)\n\n \n if len(frames.shape) == 4:\n pix_fmt = 'rgb24'\n else:\n pix_fmt = 'gray'\n \n # normalize\n max_pix_val = np.percentile(frames, 99.9)\n if frames.dtype in (np.bool, bool):\n frames = frames.astype(np.uint8)\n frames -= frames.min()\n frames[frames>max_pix_val] = max_pix_val\n if max_pix_val > 0:\n frames *= 255. / max_pix_val\n frames = frames.astype(np.uint8)\n \n # figure out which av program is installed\n program_name = ''\n try:\n subprocess.check_call(['avconv', '-h'], stdout=DEVNULL, stderr=DEVNULL)\n program_name = 'avconv'\n except OSError:\n try:\n subprocess.check_call(['ffmpeg', '-h'], stdout=DEVNULL, stderr=DEVNULL)\n program_name = 'ffmpeg'\n except OSError:\n pass\n if not program_name:\n raise OSError('Can\\'t find avconv or ffmpeg')\n \n # prepare pipe to av converter program\n size_str = '%ix%i' % (frames.shape[1], frames.shape[2])\n cmd = [program_name,\n '-y', # (optional) overwrite output file if it exists\n '-f', 'rawvideo',\n '-vcodec','rawvideo',\n '-s', size_str, # size of one frame\n '-pix_fmt', pix_fmt,\n '-r', str(fps), # frames per second\n '-i', '-', # input comes from a pipe\n '-an', # no audio\n '-qscale', '1',\n '-vcodec','mjpeg',\n filename]\n \n pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=DEVNULL, stderr=subprocess.STDOUT)\n \n # write frames \n for frame in frames:\n frame = np.fliplr(frame)\n pipe.stdin.write(frame.tostring())\n pipe.stdin.close()\n pipe.wait()","def reencode(filepath, loglevel='panic'):\n try:\n import ffmpeg\n except ImportError:\n logger.error(\n 'Import Error! Cant import ffmpeg. '\n 'Annotations operations will be limited. import manually and fix errors')\n raise\n if not os.path.isfile(filepath):\n raise IOError('File doesnt exists: {}'.format(filepath))\n # re encode video without b frame and as mp4\n basename, ext = os.path.splitext(filepath)\n output_filepath = os.path.join(basename, os.path.basename(filepath).replace(ext, '.mp4'))\n if not os.path.isdir(os.path.dirname(output_filepath)):\n os.makedirs(os.path.dirname(output_filepath))\n try:\n stream = ffmpeg.input(filepath, **{'loglevel': loglevel}).output(output_filepath,\n **{'x264opts': 'bframes=0',\n 'f': 'mp4'})\n ffmpeg.overwrite_output(stream).run()\n except Exception as e:\n logger.exception('ffmpeg error in disassemble:')\n raise\n\n output_probe = Videos.get_info(output_filepath)\n start_time = eval(output_probe['streams'][0]['start_time'])\n fps = eval(output_probe['streams'][0]['avg_frame_rate'])\n has_b_frames = output_probe['streams'][0]['has_b_frames']\n start_frame = fps * start_time\n if start_time != 0:\n logger.warning('Video start_time is not 0!')\n if has_b_frames != 0:\n logger.warning('Video still has b frames!')\n return output_filepath","def stream_frames(video_capture):","def play_video_file(fname : str):\n cap = cv2.VideoCapture(fname)\n fps = cap.get(5)\n font = cv2.FONT_HERSHEY_SIMPLEX\n fontScale = 1\n fontColor = (0, 0, 0)\n lineType = 2\n\n myvideo = []\n while cap.isOpened():\n ret, frame = cap.read()\n\n if ret is True:\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n cv2.putText(gray, 'Time: ' + str(round(cap.get(0) / 1000, 2)),\n (10, 30),\n font,\n fontScale,\n fontColor,\n lineType)\n cv2.putText(gray, 'Frame: ' + str(int(cap.get(1))),\n (10, 70),\n font,\n fontScale,\n fontColor,\n lineType)\n myvideo.append(gray)\n #cv2.imshow('frame', gray)\n #cv2.waitKey(10)\n #if cv2.waitKey(delay=2) & 0xFF == ord('q'):\n # break\n else:\n break\n\n cap.release()\n\n if fps < 60:\n for frame in myvideo:\n cv2.imshow('frame', frame)\n cv2.waitKey(10)\n else:\n for ind, frame in enumerate(myvideo):\n if ind % 3 == 0:\n cv2.imshow('frame', frame)\n cv2.waitKey(10)\n else:\n continue\n cv2.destroyAllWindows()","def convert_files(enumerated_src_file):\n i, src_file = enumerated_src_file\n src_file = src_file.strip()\n file_extension, acodec, quality = audio_codec()\n\n dst_file = '.'.join(src_file.split('.')[:-1]) + file_extension\n sys.stdout.write(str(i + 1) + ': ' + src_file + ' -> ' + dst_file + '\\n')\n subprocess.call(['ffmpeg', '-i', src_file, '-vn', '-acodec',\n acodec, '-aq', quality, dst_file, '-loglevel', 'quiet'])\n return src_file","def process_video(input_file, output_file):\n # video = VideoFileClip(input_file).subclip(40,44) # from 38s to 46s\n video = VideoFileClip(input_file)\n annotated_video = video.fl_image(process_pipeline)\n annotated_video.write_videofile(output_file, audio=False)","def process_video(input_file, output_file):\n # video = VideoFileClip(input_file).subclip(40,44) # from 38s to 46s\n video = VideoFileClip(input_file)\n annotated_video = video.fl_image(process_pipeline)\n annotated_video.write_videofile(output_file, audio=False)","def create_video(input_file, output_file):\n input_video = VideoFileClip(input_file)\n output_video = input_video.fl_image(detect_lane.fit_and_plot)\n output_video.write_videofile(output_file, audio=False)","def makeVideo():\n os.system(\"cd video && ffmpeg -r 10 -i img%05d.jpg -vcodec mpeg4 -y caronthehill_clip.mp4\")","def convert_video(video_file, output_file_name):\n video_stream = cv2.VideoCapture(video_file)\n total_frames = video_stream.get(cv2.CAP_PROP_FRAME_COUNT)\n background = get_median_frame(video_stream)\n video_stream.release()\n #reopen for processing:\n video_stream = cv2.VideoCapture(video_file)\n #ready an output writer\n writer = cv2.VideoWriter(output_file_name, \n cv2.VideoWriter_fourcc(*\"MP4V\"), fps,(1080,1920)) #(1920,1080))\n frameCnt=0\n pos = [] #Array for the coordinates\n while(frameCnt < total_frames-1):\n frameCnt+=1\n ret, frame = video_stream.read()\n dframe = background_subtraction(frame,background)\n cnts = find_contours(dframe)\n x,y = find_lowest_contour(cnts)\n pos.append([x,y])\n if len(pos): \n cv2.polylines(frame,np.int32([pos]),False,(0, 255, 0),2)\n writer.write(cv2.resize(frame, (1080,1920))) ## size probably shoudn't be fixed.\n writer.release()\n video_stream.release()\n return pos","def process_video(lane, fname, output):\n\tclip = VideoFileClip(fname)\n\toutput_name = output\n\toutput_clip = clip.fl_image(lane.pipeline)\n\toutput_clip.write_videofile(output_name, audio=False)\n\tprint ('Video processed successfully')","def make_seret(processed_files_directory='files/',fps=5):\r\n # Sort files in processed images directory\r\n files = sort_files(processed_files_directory)\r\n # Create list as container for the movie.\r\n img_array = []\r\n # For each file\r\n for file in files:\r\n file_format = file.split(\".\")\r\n if file_format[-1] == 'jpg': # verify that we will include jpg files only in the movie\r\n # Read the file\r\n img = cv2.imread(file)\r\n # Extract height, width, channels from image\r\n height, width, layers = img.shape\r\n # size = (width, height)\r\n size = (width, height)\r\n # Append image to movie container\r\n img_array.append(img)\r\n # Create a video writer for the movie\r\n out = cv2.VideoWriter(processed_files_directory+'initial.avi', cv2.VideoWriter_fourcc(*'DIVX'), fps, size)\r\n # For each image in container\r\n for image in img_array:\r\n # Write image by video writer\r\n out.write(image)\r\n # Release video writer.\r\n out.release()","def __init__(self,vid_path:str,num_frames:int=None,vid_flow_direction:str='left'):\n \n self.num_frames=num_frames\n if vid_path.split('.')[-1]=='cine' or vid_flow_direction!='left':\n #This is a cine file or needs to be rotated, convert to mp4\n print('Converting .cine file to mp4 (lossless)')\n #detect platform so we can correct file paths for ffmpeg\n is_win=re.compile('.*[Ww]in.*')\n if is_win.match(sys.platform):\n corrected_vid_path='\"'+vid_path+'\"'\n else:\n #Put escape characters in front of spaces in file name\n corrected_vid_path=[]\n for c in vid_path:\n if c==' ':\n corrected_vid_path.append('\\\\')\n corrected_vid_path.append(c)\n corrected_vid_path=''.join(corrected_vid_path)\n if vid_flow_direction=='up':\n rotate='-vf \"transpose=2\" '\n elif vid_flow_direction=='left':\n rotate=''\n elif vid_flow_direction=='right':\n rotate='-vf \"transpose=2,transpose=2\" '\n else:\n raise Exception(\"vid_flow_direction must be 'up', 'left' or 'right'\")\n if num_frames!=None:\n frames='-frames:v {0} '.format(num_frames)\n else:\n frames=''\n os_handle,new_file_path=tempfile.mkstemp(suffix='.mp4')\n #close file, we don't work with it directly\n os.close(os_handle)\n ffmpeg_command='ffmpeg -y -i {orig_file} {frames}{rotate}-f mp4 -crf 0 {new_file}'.format(orig_file=corrected_vid_path,rotate=rotate,new_file=new_file_path,frames=frames)\n print(ffmpeg_command)\n list(os.popen(ffmpeg_command))\n self.vid_path=new_file_path\n self.delete_file=True\n stats=os.stat(new_file_path)\n if stats.st_size==0:\n raise Exception('File conversion failed, check that ffmpeg is on PATH')\n else:\n #Not a cine\n self.vid_path=vid_path\n self.delete_file=False","def show_video(path: str): \n video_path = sorted(glob(path + \"/*.mp4\"))[-1]\n video = io.open(video_path, 'r+b').read()\n encoded = base64.b64encode(video)\n\n return HTML(data='''<video alt=\"test\" controls>\n <source src=\"data:video/mp4;base64,{0}\" type=\"video/mp4\" /> </video>'''\n .format(encoded.decode('ascii')))","def create_video():\n print(\"Generating output video\")\n frame_array = []\n files = [f for f in os.listdir(MODIFIED_FRAMES_DIR) if isfile(join(MODIFIED_FRAMES_DIR, f))]\n #for sorting the file names properly\n # files.sort(key = lambda x: x[3:-4])\n files = sorted(files,key=lambda x: int(os.path.splitext(x)[0]))\n for i in range(len(files)):\n filename= MODIFIED_FRAMES_DIR + files[i]\n # print(filename)\n #reading each files\n img = cv2.imread(filename)\n height, width, layers = img.shape\n size = (width,height)\n \n #inserting the frames into an image array\n frame_array.append(img)\n \n out = cv2.VideoWriter(OUTPUT_FILE,cv2.VideoWriter_fourcc(*'DIVX'), FRAME_RATE, size)\n for i in range(len(frame_array)):\n # writing to a image array\n out.write(frame_array[i])\n out.release()\n print(\"Output video generated successfully...\")\n\n # img_array = []\n # for filename in glob.glob(MODIFIED_FRAMES_DIR+'/*.jpg'):\n # img = cv2.imread(filename)\n # height, width, layers = img.shape\n # size = (width,height)\n # img_array.append(img)\n\n # height, width, layers = img_array[0].shape\n # size = (width,height)\n # out = cv2.VideoWriter('output.mov',cv2.VideoWriter_fourcc(*'DIVX'), 15, size) \n # for i in range(len(img_array)):\n # out.write(img_array[i])\n # out.release()","def make_movie(processed_files_directory='files/', WITH_SUBTITLES=False, WITH_AUDIO=False):\r\n # Declare the text for sub-titles\r\n\r\n if WITH_SUBTITLES: # if the user is willing to have subtitles in the movie\r\n with open(processed_files_directory+'subtitles.txt', 'r', encoding='utf8') as f:\r\n txt = f.read() # read the subtitles file\r\n # Split text to lines.\r\n subtitles = txt.split('\\n')\r\n # Declare VideoFileClip from the movie that I already have.\r\n clip = VideoFileClip(processed_files_directory + \"initial.avi\")\r\n # Declare duration of one sub-title as total duration of the video divided by number of lines.\r\n duration = clip.duration/len(subtitles)\r\n # Set start to zero.\r\n start=0\r\n # Set container for the clips.\r\n videos=[]\r\n # Loop all sub-titles\r\n for line in subtitles:\r\n # Make text clip from the reversed Hebrew text\r\n txt_clip = TextClip(line[::-1], fontsize=30, color='yellow', font='Calibri')\r\n # Set position to the bottom of screen.\r\n txt_clip = txt_clip.set_position('bottom').set_duration(duration)\r\n # Make sub clip of the movie with same duration as text clip.\r\n sub_clip = clip.subclip(start,start+duration)\r\n # Set CompositeVideoClip from the text clip and sub clip.\r\n video = CompositeVideoClip([sub_clip, txt_clip])\r\n # Insert the video to the clips container\r\n videos.append(video)\r\n # Set start time for next sub-title.\r\n start+=duration\r\n # Concatenate all clips of the container.\r\n res = concatenate_videoclips(videos)\r\n clip = res # now the clip is res\r\n else:\r\n clip = VideoFileClip(processed_files_directory+ \"initial.avi\") # the clip won't have subtitles\r\n\r\n\r\n # Set audio clip from mp3 file.\r\n if WITH_AUDIO: # if the user has chosen to include soundtrack in the movie\r\n f = 'audio.mp3' # change to mp3 soundtrack file of the movie\r\n # set the duration of the audioclip to max(duration of clip), even if the audioclip is longer\r\n audioclip = AudioFileClip(processed_files_directory+f)\r\n\r\n # check if the clip length is bigger than the\r\n if clip.duration > audioclip.duration:\r\n number_of_duplicated = int(np.ceil(clip.duration/audioclip.duration))\r\n # duplicate the audioclip in order to later fit the movie's duration\r\n audioclip = concatenate_audioclips([AudioFileClip(processed_files_directory+f) for i in range(number_of_duplicated)])\r\n\r\n # Now fit the audioclip duration to the movie's\r\n audioclip = audioclip.set_duration(clip.duration)\r\n\r\n # Set audio for the container.\r\n if not WITH_SUBTITLES: # if the user wanted to have audio included without subtitles\r\n videoclip = clip.set_audio(audioclip)\r\n else: # if the user wanted to have both audio and subtitles\r\n videoclip = res.set_audio(audioclip)\r\n else:\r\n videoclip = clip # if the user didn't want audio in the movie\r\n\r\n # Write the video file.\r\n f = 'final_movie.mp4' # change to the desired movie filename\r\n videoclip.write_videofile(processed_files_directory+f)","def readVideo(self):\n vid = cv2.VideoCapture(self.fname)\n imgstack = []\n # grab = True\n grab, img = vid.read()\n while grab:\n imgstack.append(\n Frame(\n cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),\n self.starttime\n + datetime.timedelta(seconds=self.frame_dt * self.length),\n )\n )\n self.length += 1\n grab, img = vid.read()\n self.frames = imgstack","def write_video_ffmpeg(\n itr: Iterator[np.ndarray],\n out_file: str | Path,\n fps: int = 30,\n out_fps: int = 30,\n vcodec: str = \"libx264\",\n input_fmt: str = \"rgb24\",\n output_fmt: str = \"yuv420p\",\n quite=False\n) -> None:\n\n first_img = next(itr)\n height, width, _ = first_img.shape\n\n stream = ffmpeg.input(\"pipe:\", format=\"rawvideo\", pix_fmt=input_fmt, s=f\"{width}x{height}\", r=fps)\n stream = ffmpeg.output(stream, str(out_file), pix_fmt=output_fmt, vcodec=vcodec, r=out_fps)\n if quite:\n stream = stream.global_args('-loglevel', 'quiet')\n stream = ffmpeg.overwrite_output(stream)\n stream = ffmpeg.run_async(stream, pipe_stdin=True)\n\n def write_frame(img: np.ndarray) -> None:\n stream.stdin.write(as_uint8(img).tobytes())\n\n # Writes all the video frames to the file.\n write_frame(first_img)\n for img in itr:\n write_frame(img)\n\n stream.stdin.close()\n stream.wait()\n print('Done.')","def split_video_random(file_path, start_pos, split_length, out_path):\n s_cmd = \" -i '%s'\"%(file_path) #use default CODEC\n try:\n\tfileext = file_path.split(\".\")[-1]\n except IndexError as e:\n\traise IndexError(\"No ext. in filename. Error: \" + str(e))\n\n split_start = start_pos\n split_length = split_length\n head, tail = os.path.split(file_path)\n name, ext = tail.split('.')\n filebase=name+'_'+str(start_pos)+'-'+str(split_length)\n\n dstfilebase = out_path + '/' + filebase # create output file base\n\n #split_str = \"\"\n #split_str += \" -ss \" + str(split_start) + \" -t \" + str(split_length) + \" '\"+ dstfilebase + \".\" + fileext + \"'\"\n\n s_str = \"\"\t\n #s_str += \"ffmpeg\"+\" -ss \"+str(split_start)+\" -t \"+str(split_length) + s_cmd + \" '\"+dstfilebase + \".\" + fileext + \"'\"\n s_str += \"ffmpeg\" + \" -ss \" + str(split_start) + s_cmd + \" -t \" + str(split_length) + \" '\"+ dstfilebase + \".\" + fileext + \"'\"\n print(\"########################################################\")\n #print \"About to run: \"+split_cmd+split_str\n print(\"About to run: \"+s_str)\n print(\"########################################################\")\n #output = subprocess.Popen(split_cmd+split_str, shell = True, stdout = subprocess.PIPE).stdout.read()\n output = subprocess.Popen(s_str, shell=True, stdout=subprocess.PIPE).stdout.read()","def video_files_to_tfrecords(output_file, filepaths, label_dict, downsample_graph):\n\n if type(filepaths) != list:\n filepaths = [filepaths] # catch single inputs (not a normal case)\n\n tqkws = {\n 'total': len(filepaths),\n 'unit': ' videos',\n 'desc': 'Serializing video frames'\n }\n\n video_placeholder, downsampled = downsample_graph\n\n with tf.python_io.TFRecordWriter(output_file) as writer:\n for path in tqdm.tqdm(filepaths, **tqkws):\n video_array = video_to_array(path)\n label = label_dict[os.path.split(os.path.abspath(os.path.join(path, os.pardir)))[-1]]\n\n l = video_array.shape[0]\n w = video_array.shape[2]\n h = video_array.shape[1]\n\n if h != 240 or w != 320:\n continue\n\n downsampled_video_array = downsampled.eval({video_placeholder: video_array})\n feature_dict = {\n 'height': _int_feature(h),\n 'width': _int_feature(w),\n 'length': _int_feature(l),\n 'video': _bytes_feature(downsampled_video_array.astype(np.uint8).tostring()),\n 'label': _int_feature(label)\n }\n\n observation = tf.train.Example(features=tf.train.Features(feature=feature_dict))\n\n writer.write(observation.SerializeToString())","def transcodetomp4(file_in, logger):\n\n import subprocess\n\n file_out = file_in.replace('.mkv', '.mp4')\n\n if os.path.isfile('/usr/bin/avconv'):\n\n convert_command = 'su securityspy -c \\\"/usr/bin/avconv -i \"{}\" -f mp4 -vcodec copy -acodec '.format(file_in) + \\\n 'libfaac -b:a 112k -ac 2 -y \"{}\"'.format(file_out) + \"\\\"\"\n\n try:\n subprocess.check_call(convert_command, shell=True)\n except subprocess.CalledProcessError:\n logger.error(\"The command to transcode: {} --- failed...\".format(convert_command))\n return file_in\n\n return file_out\n else:\n return file_in\n # fin","def main():\n convert(\"env_100000.mp4\", TargetFormat.GIF)","def convert_video_path_and_save(video_path, output_path=\"output.mp4\", temp_folder = \"./temp\",\n frame_frequency=24, image_reducer=100, fontSize=10, spacing=1.1, maxsize=None, chars=\" .*:+%S0#@\",\n logs=False, processes=4, progress_tracker=None):\n\n if logs:\n start_time = time.time()\n print (\"Converting video...\")\n \n # set up a capture temporarily so we can grab some basic info about it\n capture = cv2.VideoCapture(video_path)\n if not capture.isOpened():\n print (\"Could not read video. Please enter a valid video file!\")\n exit(0)\n\n fps = capture.get(cv2.CAP_PROP_FPS)\n bitrate = int(capture.get(cv2.CAP_PROP_BITRATE))\n total_frames = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))\n frames_included = int(total_frames / frame_frequency)\n # total_frames / fps gives us our video duration.\n video_duration = total_frames / fps\n # frames included / video duration gives new fps\n new_fps = (total_frames / frame_frequency) / video_duration\n\n capture.release()\n\n # First, we grab all the frames we need and store them in a temp folder\n # After that, we convert all the image frames in the temp folder, and save them back in the temp folder\n # Then, we write them to video and save to disk\n # To utilize mutli processing, we separate grabbing frames and converting the frames into batches\n\n while os.path.isdir(temp_folder):\n temp_folder += \"_\"\n temp_folder += \"/\"\n os.mkdir(temp_folder)\n\n # initial setup\n # we divide our work into batches\n batches = processes\n frames_per_batch = int(total_frames / batches / frame_frequency)\n if progress_tracker is None:\n progress_tracker = Value(\"f\", 0, lock=True)\n # progress: saved frames + converted frames + written frames\n progress_step = 100 / (frames_included * 3)\n\n # grab the frames, and write to separate batch folders\n save_frames_processes = []\n for batch in range(batches):\n starting_frame = batch * frames_per_batch * frame_frequency\n batch_folder = temp_folder + str(batch) + \"/\"\n os.mkdir(batch_folder)\n args = (\n starting_frame,\n starting_frame + frames_per_batch * frame_frequency,\n video_path,\n batch_folder,\n frame_frequency,\n logs,\n progress_tracker,\n progress_step\n )\n p = Process(target=_save_frames, args=args)\n p.daemon = True\n p.start()\n save_frames_processes.append(p)\n for p in save_frames_processes:\n p.join()\n\n # convert all the frames in each batch folder\n convert_processes = []\n for batch in range(batches):\n batch_folder = temp_folder + str(batch) + \"/\"\n args = (\n batch_folder,\n frames_per_batch,\n image_reducer,\n fontSize, spacing, maxsize, chars,\n logs, progress_tracker, progress_step\n )\n p = Process(target=_convert_batch, args=args)\n p.daemon = True\n p.start()\n convert_processes.append(p)\n for p in convert_processes:\n p.join()\n\n # if no extension was assigned, automatically assign .mp4\n output_name, output_ext = os.path.splitext(output_path)\n if output_ext == \"\":\n output_ext = \".mp4\"\n # if final output path was specified, then modify it (append _Copy to it)\n final_output_path = output_name + output_ext\n while os.path.isfile(final_output_path):\n if logs : print (final_output_path, \"already exists!\")\n final_output_path = os.path.splitext(final_output_path)[0] + \"_Copy\" + output_ext\n\n # video settings\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n video_out = imageio.get_writer(final_output_path, fps=new_fps, quality=None, bitrate=(bitrate * 1024 * 2.5))\n size = None\n\n # write images to new video\n for batch in range(1, batches + 1):\n batch_folder = temp_folder + str(batch - 1) + \"/\"\n for i in range(1, frames_per_batch + 1):\n img = cv2.imread(batch_folder + str(i) + \".jpg\", 2)\n if size is None:\n height, width = img.shape\n size = (width, height)\n video_out.append_data(img)\n with progress_tracker.get_lock():\n progress_tracker.value += progress_step\n if logs : print (\"Progress: %.4f%%\" % progress_tracker.value, end=\"\\r\")\n video_out.close()\n shutil.rmtree(temp_folder)\n\n # when we are done, there might be some rounding errors when converting some stuff to integers, thus it doesn't appear to be done\n # So we just simply set it to 100\n with progress_tracker.get_lock():\n progress_tracker.value = 100\n\n if logs:\n print (\"=\" * 30)\n print (\"SUMMARY:\")\n print (\"-\" * 20)\n print (\"Progress: %.4f%%\" % progress_tracker.value)\n print (\"Total frames found:\", str(total_frames))\n print (\"Frames included and converted:\", str(frames_per_batch * batches))\n print (\"Original FPS:\", str(fps))\n print(\"New FPS:\", str(new_fps))\n print (\"Resolution:\", str(size))\n print (\"Saved to\", final_output_path)\n print (\"Time took: %.4f secs\" % (time.time() - start_time))","def tiff2mp4(path):\n video = tifffile.imread(path)\n nFrames, h,w = video.shape\n fps = int(input('Input desired output fps:'))\n # dur=1/fps \n pathout =path[:-4]+'_'+str(fps)+'.mp4' \n # pathout2 =path[:-4]+'_St.tif'\n codec = cv2.VideoWriter_fourcc(*'H264')\n out = cv2.VideoWriter(pathout, codec , fps, (w, h))\n print(\"---------------------------------------------\")\n print('Converting Tiff stack to the movie') \n for i in tqdm.tqdm(range(nFrames)): \n img=video[i] \n out.write(img)\n out.release()\n cv2.destroyAllWindows()\n print(\"==============================================\")\n print(\"MP4 convertion Done!\")","def list_files_to_convert():\n for root, dirs, files in os.walk(video_dir):\n file_list = [name for name in files if not name.endswith('.mp3')]\n for name in file_list:\n filepath = os.path.join(root, name)\n media_info = MediaInfo.parse(filepath, library_file=dll_path)\n for track in media_info.tracks:\n if 'Audio' in track.track_type:\n # print(track.track_type, track.bit_rate)\n # print(filepath, \"Is an Audio/Video file, and should be converted because a sound track is found\")\n yield dict(path=filepath, info=media_info)","def videoFrames(filename, framerate=1):\n vid_file = os.path.join(os.path.dirname(os.getcwd()), \"Database\", \"Video\", filename)\n print(vid_file)\n assert os.path.isfile(vid_file), \"Given path is not a valid file\"\n tmpdir = os.path.join(os.getcwd(), \"tmp\")\n subprocess.run(\n [\n \"ffmpeg\",\n \"-i\",\n vid_file,\n \"-r\",\n f\"{framerate}\",\n os.path.join(tmpdir, \"img_%04d.jpg\"),\n ]\n )\n return [os.path.join(tmpdir, i) for i in os.listdir(tmpdir) if not i.endswith(\".wav\")]","def main():\n print(\"This is a library for reading video sequences into python via ffmpeg. \")\n print(\"Provides the 'Video_Reader' iterator class. \")\n print(\"Requires ffmpeg be installed. \")","def loadVideo( iFileName, iFrameSize = (576, 720) ):\n import sys\n import subprocess as sp\n # ustvari klic ffmpeg in preusmeri izhod v cevovod\n command = [ 'ffmpeg',\n '-i', iFileName,\n '-f', 'image2pipe',\n '-pix_fmt', 'rgb24',\n '-vcodec', 'rawvideo', '-']\n pipe = sp.Popen(command, stdout = sp.PIPE, bufsize=10**8)\n # definiraj novo spremeljivko\n oVideo = np.array([])\n iFrameSize = np.asarray( iFrameSize )\n frameCount = 0\n # zacni neskoncno zanko\n while True:\n frameCount += 1\n# print( 'Berem okvir %d ...' % frameCount )\n print(\"\\rBerem okvir %d ...\" % frameCount, end=\"\")\n # preberi Y*X*3 bajtov (= 1 okvir)\n raw_frame = pipe.stdout.read(np.prod(iFrameSize)*3)\n # pretvori prebrane podatke v numpy polje\n frame = np.fromstring(raw_frame, dtype='uint8') \n # preveri ce je velikost ustrezna, sicer prekini zanko\n if frame.size != (np.prod(iFrameSize)*3):\n print(\" koncano!\\n\")\n break;\n # preoblikuj dimenzije in pretvori v sivinsko sliko\n frame = colorToGray( frame.reshape((iFrameSize[0],iFrameSize[1],3)) )\n # sprazni medpomnilnik \n pipe.stdout.flush() \n # vnesi okvir v izhodno sprememnljivko\n if oVideo.size == 0:\n oVideo = frame\n oVideo = oVideo[...,None]\n else:\n oVideo = np.concatenate((oVideo,frame[...,None]), axis=2)\n # zapri cevovod\n pipe.terminate()\n # vrni izhodno spremenljivko\n return oVideo","def prepare_video(path_to_video: str, number_of_images=87) -> None:\n\n temp_video = path.join(path_to_video, 'temp_outpy.mp4')\n video = path.join(path_to_video, 'outpy.h264')\n\n # create mp4 video for metadata and compute video duration\n subprocess.run(['ffmpeg', '-i', video, '-c', 'copy', temp_video])\n result = subprocess.run([\"ffprobe\", \"-v\", \"error\", \"-show_entries\",\n \"format=duration\", \"-of\",\n \"default=noprint_wrappers=1:nokey=1\", temp_video],\n stdout=subprocess.PIPE,\n stderr=subprocess.STDOUT)\n video_duration = float(result.stdout)\n\n # create images folder\n path_to_images = path.join(path_to_video, 'images')\n if path.exists(path_to_images) and path.isdir(path_to_images):\n shutil.rmtree(path_to_images)\n makedirs(path_to_images)\n\n # split the given video into images\n subprocess.run(['ffmpeg', '-i', temp_video, '-r', str(number_of_images / video_duration), '-f', 'image2',\n path.join(path_to_images, 'image%d.jpg')])\n\n # remove extra files\n remove_extra_images(path_to_images, number_of_images)\n remove(temp_video)","def convert(fname_src, verbose=False):\n if not os.path.isfile(fname_src):\n raise IOError('File not found: %s' % fname_src)\n\n # File names.\n b, e = os.path.splitext(fname_src)\n fname_dst = b + '.m4a'\n\n # Build command.\n cmd = 'ffmpeg -y -i \"%s\" \"%s\"' % (fname_src, fname_dst)\n\n t0 = time.time()\n std_out, std_err = run_cmd(cmd)\n dt = time.time() - t0\n\n if dt < 0.01:\n raise Exception('Problem processing file: %s %s %s %s' % (fname_src, std_out, std_err, cmd))\n\n if std_out.lower().find('error') >= 0:\n raise Exception('Problem processing file: %s %s %s %s' % (fname_src, std_out, std_err, cmd))\n\n # Done.\n return fname_dst","def generate_video(sign, issue, output):\n\n videos = {\n \"Climate Change\": \"ClimateChange.mp4\",\n \"Green Jobs\": \"GreenJobs.mp4\",\n \"Tourism\": \"Tourism.mp4\",\n \"Small Business\": \"SmallBusiness.mp4\",\n \"Public health\": \"PublicHealth.mp4\",\n \"Education Funding\": \"EducationFunding.mp4\"\n }\n\n video_path = CWD(f\"Assets/{videos[issue]}\")\n\n frame = cv2.imread(sign)\n frame = cv2.resize(frame, (1920, 1080))\n height, width, layers = frame.shape\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n video = cv2.VideoWriter(CWD(\"temp.mp4\"), fourcc, 1, (width, height))\n for i in range(5):\n video.write(frame)\n video.release()\n\n image_clip = VideoFileClip(CWD(\"temp.mp4\"))\n original_video = VideoFileClip(video_path)\n final_video = concatenate_videoclips([original_video, image_clip], method=\"compose\")\n\n final_video.write_videofile(output)\n os.remove(CWD(\"temp.mp4\"))","def video2():\n return Response(gen_frames(2),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def obtenerVideo(camara):\n val, frame = camara.read()\n return val, frame","def seqIo_frImgs(fName, header=[], aviName=[], Is=[], sDir=[], name='I', ndig=5, f0=0, f1=1e6):\n \n if aviName!=[]: #avi movie exists\n vc = cv2.VideoCapture(aviName)\n if vc.isOpened(): rval = True\n else:\n rval = False\n print('video not readable')\n return\n fps = vc.get(cv2.cv.CV_CAP_PROP_FPS)\n NUM_FRAMES = int(vc.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))\n print(NUM_FRAMES)\n IM_TOP_H = vc.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)\n IM_TOP_W = vc.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)\n header['width']=IM_TOP_W\n header['height']=IM_TOP_H\n header['fps']=fps\n\n sw = seqIo_writer(fName,header)\n print('creating seq from AVI')\n # initialize timer\n timer = pb.ProgressBar(widgets=['Converting ', pb.Percentage(), ' -- ',\n pb.FormatLabel('Frame %(value)d'), '/',\n pb.FormatLabel('%(max)d'), ' [', pb.Timer(), '] ',\n pb.Bar(), ' (', pb.ETA(), ') '], maxval=NUM_FRAMES)\n for f in range(NUM_FRAMES):\n rval, im = vc.read()\n if rval:\n im= im.astype(np.uint8)\n sw.addFrame(im)\n timer.update(f)\n sw.close()\n timer.finish()\n elif Is==[]:\n assert(os.path.isdir(sDir))\n sw = seqIo_writer(fName,header)\n frmstr = '%s/%s%%0%ii.%s' % (sDir,name,ndig,header.ext)\n for frame in range(f0,f1):\n f = frmstr % frame\n if not os.path.isfile(f):break\n fid = open(f, 'r')\n if fid<0: sw.close(); assert(False)\n I = fid.read()\n fid.close()\n b = bytearray(I)\n assert (b[0] == 255 and b[1] == 216 and b[-2] == 255 and b[-1] == 217); # JPG\n I = np.array(list(b)).astype(np.uint8)\n sw.addFrame(I,0,0)\n sw.close()\n if frame==f0: print('No images found')\n else:\n nd = len(Is.shape)\n if nd==2: nd=3\n assert(nd<=4)\n nFrm = Is.shape[nd-1]\n header['height']=Is.shape[0]\n header['width']=Is.shape[1]\n sw =seqIo_writer(fName,header)\n if nd==3:\n for f in range(nFrm): sw.addFrame(Is[:,:,f])\n if nd==4:\n for f in range(nFrm): sw.addFrame(Is[:,:,:,f])\n sw.close()","def convert_multiple(self, video_files, out, brate, _format):\n\n for video in video_files:\n self.to_audio(os.path.abspath(video),\n out, brate, _format)","def make_video(self, mp4=True, gif=True):\n fn = self.get_output_filename(\".mp4\")\n command = (\n (get_ffmpeg_path() + f\" -loglevel panic -framerate {self.framerate} -i \")\n + os.path.join(self.frame_directory, FRAME_FN_TEMPLATE)\n + \" -s:v \"\n + str(self.width)\n + \"x\"\n + str(self.height)\n + \" -c:v libx264 -profile:v high -crf 1 -pix_fmt yuv420p -y \"\n + fn\n )\n\n os.system(command)\n\n if gif:\n mp4_to_gif(\n self.get_output_filename(\".mp4\"),\n self.get_output_filename(\".gif\"),\n self.framerate,\n )\n\n if not mp4:\n os.remove(fn)","def processVideo(inputRootFolder='/home/pi/Logging/UnprocessedVideo',\n outputFolder='/home/pi/Logging/Unsent',\n cam_framerate=10,\n delay=15):\n logging.info('Processing videos')\n sleep(delay) # hardcoded sleep function to ensure that the video has finished saving\n # Create directories if necessary\n try:\n os.makedirs(inputRootFolder)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise # This was not a \"directory exists\" error\n try:\n os.makedirs(outputFolder)\n except OSError as e:\n if e.errno != errno.EEXIST:\n raise # This was not a \"directory exists\" error\n # Get the list of subdirectories\n f = []\n for (dirpath, dirnames, filenames) in os.walk(inputRootFolder):\n f.extend(dirnames)\n # Go through each subdirectory\n for folder in f:\n folderName = os.path.join(inputRootFolder,folder)\n videoListName = '%s/videoList.txt' % folderName #file that will contain list of videos\n videoList = io.open(videoListName, 'w')\n for fileName in sorted(os.listdir(folderName)): #add each video in the folder to the file\n if (fileName.startswith('Video')):\n videoString = (\"file '%s/%s'\\n\" % (folderName, fileName))\n videoList.write(videoString)\n videoList.close()\n outputFile = '%s/%s.mp4' % (outputFolder, folder)\n #concatenate the videos\n subprocess.call(['ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i',\n videoListName, '-c', 'copy', outputFile], shell=False)\n shutil.rmtree(folderName, ignore_errors=True) #delete the folder\n logging.info('Processed videos')","def process_videos(chapter_info):\n\n print(\"Processing chapter_info:\", chapter_info)\n\n # getting creation time of the first chapter\n # TODO update when adding multiple directory proccessing\n os.chdir(DIR_VIDEO_FILES)\n print(\"1st chapter\", chapter_info[1][0])\n chap1_time = time.strftime(\n r\"%Y-%m-%d_%H-%M\", time.localtime(os.path.getctime(chapter_info[1][0])))\n print(\"1st chapter creation\", chap1_time)\n\n # output_file = f\"M_GH00{chapter_info[0]}_{chap1_time}.MP4\"\n output_file = f\"{chap1_time}_GH00{chapter_info[0]}_MRG.MP4\"\n if os.path.isfile(output_file):\n print(f\"Chapter already processed, found file: {output_file}\")\n return\n\n # preparing text file containing file list for merging (for ffmpeg)\n video_list_file = chapter_info[0] + \"_merge.txt\"\n with open(video_list_file, \"w\") as f:\n for video_chapter in chapter_info[1]:\n f.write(f\"file {video_chapter}\\n\")\n\n command = f\"{FFMPEG_EXE} -f concat -i {video_list_file} -c copy {DIR_OUTPUT}{output_file}\"\n print(\"command =\", command)\n # p = subprocess.run(\"dir\", shell=True, capture_output=True)\n # p = subprocess.run(\"dir\", shell=True, stdout=subprocess.PIPE, text=True)\n p = subprocess.run(command, stdout=subprocess.PIPE, text=True)\n print(\"returncode =\", p.returncode)\n # print(\"stdout =\", p.stdout)\n os.remove(video_list_file) # remove file list after merging\n # rename original chapters after processing\n for video_chapter in chapter_info[1]:\n os.rename(video_chapter, f\"OK_{video_chapter}\")","def generate_video_metadata(absolute_paths):\n\n vids = []\n\n bad_fn = \"/share/pi/cleemess/file-conversion-pipeline/bad_mp4s.txt\"\n good_fn = \"/share/pi/cleemess/file-conversion-pipeline/good_mp4s.txt\"\n # if os.path.exists(bad_fn):\n # os.remove(bad_fn)\n\n if os.path.exists(bad_fn):\n with open(bad_fn) as f:\n bad_paths = set([line.strip() for line in f.readlines()])\n else:\n bad_paths = set()\n\n if os.path.exists(good_fn):\n with open(good_fn) as f:\n good_paths = set([line.strip() for line in f.readlines()])\n else:\n good_paths = set()\n \n with tqdm(list(absolute_paths)) as pbar:\n for absolute_path in pbar:\n if absolute_path in bad_paths or absolute_path in good_paths:\n continue\n\n cmd = \"ffprobe -v quiet -print_format json -show_streams %s\" % absolute_path\n try:\n subprocess.check_output(shlex.split(cmd)).decode(\"utf-8\")\n with open(good_fn, \"a\") as f:\n f.write(absolute_path + \"\\n\")\n good_paths.add(absolute_path)\n except KeyboardInterrupt:\n raise\n except Exception as e:\n with open(bad_fn, \"a\") as f:\n f.write(absolute_path + \"\\n\")\n bad_paths.add(absolute_path)\n # print(e)\n # print(cmd)\n # raise\n\n pbar.set_description(f\"{len(good_paths)}, {len(bad_paths)}\")\n return vids","def _generate_video(\n out_file,\n n_frames=5,\n width=100,\n height=50,\n seed=0,\n fps=24,\n broken=False,\n):\n is_mpeg = out_file.endswith(\".mpeg\")\n video_format = \"libx264rgb\"\n pixel_format = \"rgb24\"\n\n if is_mpeg:\n video_format = \"mpeg1video\"\n pixel_format = \"yuv420p\"\n\n if broken:\n n_frames = 0\n\n np.random.seed(seed)\n container = av.open(out_file, mode=\"w\")\n stream = container.add_stream(video_format, rate=fps)\n stream.width = width\n stream.height = height\n stream.pix_fmt = pixel_format\n\n if is_mpeg:\n frames = [av.VideoFrame(width, height, pixel_format) for i in range(n_frames)]\n else:\n # save lossless video\n stream.options[\"crf\"] = \"0\"\n images = (np.random.randn(n_frames, height, width, 3) * 255).astype(np.uint8)\n frames = [\n av.VideoFrame.from_ndarray(image, format=pixel_format) for image in images\n ]\n\n for frame in frames:\n for packet in stream.encode(frame):\n container.mux(packet)\n\n if not broken:\n # flush the stream\n # video cannot be loaded if this is omitted\n packet = stream.encode(None)\n container.mux(packet)\n\n container.close()\n\n pil_images = [frame.to_image() for frame in frames]\n return pil_images","def frames_to_video(frames, path, scale=2.0):\n\n width, height = frames.shape[-2:]\n width = int(width * scale)\n height = int(height * scale)\n fourcc = cv2.VideoWriter_fourcc(*'MP4V')\n fps = 30\n out = cv2.VideoWriter(path, fourcc, fps, (width, height))\n for frame in frames:\n frame = cv2.merge([frame, frame, frame])\n if scale != 1:\n frame = cv2.resize(frame, (width, height), interpolation=cv2.INTER_LINEAR)\n out.write(frame.astype(np.uint8))\n\n out.release()","def video_audio_files(video_name, path_curr_vid, path_current_frames):\n video_file = f\"{path_curr_vid}{video_name}\"\n audio_file = f\"{path_current_frames}{video_name.replace(VIDEO_EXT,AUDIO_EXT)}\"\n return video_file, audio_file","def generateVideo(textFilePath,extractedPath,createdVideoPath):\r\n\t\t#Check if the save directory exists, If not create directory\r\n\t\tif not os.path.exists(createdVideoPath):\r\n\t\t\tos.mkdir(createdVideoPath)\r\n\t\t#Open the text file\r\n\t\tfile = open(textFilePath)\r\n\t\tvideoPath = createdVideoPath\r\n\r\n\t\tfor hashedData in file:\r\n\t\t\thashedData = hashedData.split(\"\\n\")[0]\r\n\t\t\timage_folder = extractedPath + \"/\" + \"data\" + \"/\" + hashedData\r\n\t\t\tvideo_name = hashedData + \".avi\"\r\n\t\t\timages = os.listdir(image_folder)\r\n\t\t\tframe = cv2.imread(os.path.join(image_folder, images[0]))\r\n\t\t\theight, width, layers = frame.shape\r\n\t\t\t#declare the video writter\r\n\t\t\tvideo = cv2.VideoWriter(videoPath + \"/\" +video_name, 0, 1, (width,height))\r\n\t\t\t#Write all images to a single video\r\n\t\t\tfor image in images:\r\n\t\t\t\tvideo.write(cv2.imread(os.path.join(image_folder, image)))\r\n\r\n\t\t\tcv2.destroyAllWindows()\r\n\t\t\tvideo.release()","def main(_):\n print('argument to expand', ARGS.video_in)\n print('argument expanded', glob.glob(ARGS.video_in))\n video_count = 0\n for video_filename in glob.glob(ARGS.video_in):\n print('start parsing', video_filename)\n data = skvideo.io.ffprobe(video_filename)['video']\n rate_str = six.ensure_str(data['@r_frame_rate']).split('/')\n rate = float(rate_str[0]) / float(rate_str[1])\n print('detected frame rate:', rate)\n\n print('load frames:')\n video = skvideo.io.vreader(video_filename)\n frame_count = 0\n file_count = 0\n for frame in video:\n if (frame_count > ARGS.offset) and \\\n ((frame_count-ARGS.offset)%ARGS.skip == 0) and \\\n (frame_count/rate >= ARGS.from_s) and \\\n (frame_count/rate <= ARGS.to_s or ARGS.to_s == -1):\n print(frame_count,)\n img = Image.fromarray(frame)\n if ARGS.crop:\n img = crop(img, ARGS.size)\n # save file\n file_number = file_count + video_count * ARGS.multiple + ARGS.start\n if ARGS.format_ext.lower() == 'jpg':\n file_out = os.path.join(ARGS.path_out,\n 'f{:07d}.jpg'.format(file_number))\n img.save(file_out, 'JPEG')\n elif ARGS.format_ext.lower() == 'png':\n file_out = os.path.join(ARGS.path_out,\n 'f{:07d}.png'.format(file_number))\n img.save(file_out, 'PNG')\n else:\n print('unrecognize format', ARGS.format_ext)\n sys.exit()\n file_count += 1\n frame_count += 1\n video_count += 1","def adorn_video(self, iterator):\n return iterator \\\n | select(lambda f: f + (\n video_to_npy(f[1],\n # note weird thing here, width doesn't work they appear to be inverted\n height=self.video_size,\n squarecrop=self.squarecrop,\n fps=self.framerate,\n maxlength=self.max_length,\n # save a npy replacement\n outfile=self.get_numpy_filename(f[1]),\n use_cache=self.use_cache\n ),))","def __init__(self, input_file_path, convert_to_bgr=False):\n self.__yuv_video = YuvDecoder(input_file_path, convert_to_bgr=True)\n print('After INSTANTIATION')\n self.__yuv_video.start()","def transcode(path, outpath):\n\n needs_transcode = determine_transcode(path)\n logger.info(f\"Transcoding {path} to {outpath}...\")\n\n cmd = [\n \"ffmpeg\", \"-y\",\n \"-i\", path,\n \"-an\",\n \"-metadata:s\", \"handler_name=tator\",\n \"-vcodec\", \"libx264\",\n \"-g\", \"25\",\n \"-preset\", \"fast\",\n \"-pix_fmt\", \"yuv420p\",\n \"-vf\", \"pad=ceil(iw/2)*2:ceil(ih/2)*2\",\n \"-movflags\",\n \"faststart+frag_keyframe+empty_moov+default_base_moof\",\n \"-tune\", \"fastdecode\",\n ]\n\n if needs_transcode[1]:\n #Resize to 720p\n cmd.extend([\"-vf\", \"scale=-2:720\"])\n\n cmd.append(outpath)\n logger.info('ffmpeg cmd = {}'.format(cmd))\n subprocess.run(cmd, check=True)\n logger.info(\"Transcoding finished!\")","def mpg2avi(filename):\n assert filename.endswith('.mpg')\n ofile = '%s.avi' % os.path.splitext(filename)[0]\n run_shell_cmd('ffmpeg -y -i %s -qscale:v 2 %s' % (filename, ofile), ignore=True)\n return ofile","def start_ffmpeg_record(stream, stream_url, formatted_date):\n filename = stream + '_' + formatted_date\n save_video_dir = 'rover_stream/' + stream\n subprocess.Popen(['mkdir rover_stream'], shell=True)\n subprocess.Popen(['mkdir ' + save_video_dir], shell=True)\n proc_video[stream] = subprocess.Popen(['ffmpeg -i ' + stream_url + ' -acodec copy -vcodec copy ' + save_video_dir + '/' + filename + '.mp4'], stdin=PIPE, shell=True)","def video():\n return Response(gen(Camera()),\n mimetype='multipart/x-mixed-replace; boundary=frame')","def save_video(foldername, songname, songlen, num_steps, output):\n num_steps_by_len = num_steps / songlen\n p = subprocess.Popen(['ffmpeg', '-f', 'image2', '-r', str(num_steps_by_len), '-i', '%d.png', '-c:v', 'libx264', '-pix_fmt', 'yuv420p', '-vf', 'pad=ceil(iw/2)*2:ceil(ih/2)*2', 'movie.mp4'], cwd=foldername)\n p.wait()\n\n p = subprocess.Popen(['ffmpeg', '-i', 'movie.mp4', '-i', '../audio_files/' + songname + '.mp3', '-map', '0:v', '-map', '1:a', '-c', 'copy', output], cwd=foldername)\n p.wait()","def recordVideo(args, env, model, filename):\n # env = model.get_env()\n images = []\n images = images + runAGame(model, env, args.method == 'centralized')\n images = images + runAGame(model, env, args.method == 'centralized')\n images = images + runAGame(model, env, args.method == 'centralized')\n images[0].save(filename + '.gif',\n format='GIF',\n append_images=images[1:],\n save_all=True,\n duration=500,\n loop=0)\n print('Video saved:', filename)","def test_plenty_of_video_files():\n # make sure that there is one sequence per video file\n pipe = VideoPipe(\n batch_size=BATCH_SIZE, data=PLENTY_VIDEO_FILES, step=1000000, sequence_length=1)\n pipe.build()\n iters = math.ceil(len(os.listdir(PLENTY_VIDEO_DIRECTORY)) / BATCH_SIZE)\n for i in range(iters):\n print(\"Iter \" + str(i))\n pipe.run()","def main(path):\n logger.info(f'Processing video file {path}')\n # Extract audio\n audio_file = extract_audio(path, pipeline_config.audio_target_dir)\n\n # Generate sound classification results and speech recogniser results\n sound_results = SoundRecogniser().process_file(audio_file)\n sound_results = process_overlap(sound_results)\n speech_results = SpeechRecogniser().process_file(audio_file)\n\n # NLP\n wrds = get_words(speech_results)\n nlp = SpaCyNaturalLanguageProcessor(pipeline_config.spacy_model)\n custom_nlp = SpaCyNaturalLanguageProcessor(pipeline_config.custom_spacy_model)\n processor = nlp.get_spacy_results_processor(wrds, speech_results)\n custom_processor = custom_nlp.get_spacy_results_processor(wrds, speech_results)\n chunk_results = processor.process_speech_results_chunk()\n ner_results = processor.process_speech_results_ner()\n ner_results.extend(custom_processor.process_speech_results_ner())\n match_results = processor.process_speech_results_match()\n speech_results = nlp.process_spurious_words(speech_results, chunk_results)\n\n # Add Speech recogniser results, sound classification results and NLP results to a subtitle file\n subs_1 = save_to_subtitles(speech_results,\n lambda speech_result: speech_result['word'])\n subs_1 = compress_subs(subs_1)\n subs_2 = save_to_subtitles(sound_results,\n lambda sound_result: sound_result['class'])\n subs_2 = flatten_subs(subs_2)\n subs_3 = save_to_subtitles(chunk_results,\n lambda chunk_result: f'{chunk_result[\"word\"]} ({chunk_result[\"head\"]})')\n subs_4 = save_to_subtitles(ner_results,\n lambda ner_result: f'{ner_result[\"type\"]} {ner_result[\"word\"]}')\n subs_5 = save_to_subtitles(match_results,\n lambda match_result: match_result[\"word\"])\n\n combined_subs = append_subs(None, subs_1, style='bottom')\n combined_subs = append_subs(combined_subs, subs_2, exclude=['bottom'], style='top', formatter=lambda x: f'({x})')\n combined_subs = append_subs(combined_subs, subs_3, style='left')\n combined_subs = append_subs(combined_subs, subs_4, style='right')\n combined_subs = append_subs(combined_subs, subs_5, style='bottom_left_pred')\n combined_subs = remove_tiny_subs(combined_subs, duration_millis=1000, left_millis=None,\n right_millis=None, style='top')\n subtitle_file_name = os.path.splitext(path)[0] + '.ass'\n create_styles(combined_subs)\n combined_subs.save(subtitle_file_name)\n\n # Burn to a video\n burn_subtitles_into_video(path, subtitle_file_name, pipeline_config.audio_target_dir)\n logger.info(f'Done processing {audio_file}')","def avi2mpg(filename):\n assert filename.endswith('.avi')\n ofile = '%s.mpg' % os.path.splitext(filename)[0]\n run_shell_cmd('ffmpeg -y -i %s -qscale:v 1 %s' % (filename, ofile), ignore=True)\n return ofile","def filter_videos(\n files: list\n):\n#cSpell:words webm vchd rmvb gifv xvid vidx\n video_extensions = [\n \"WEBM\",\n \"MPG\",\"MP2\", \"MPEG\", \"MPE\", \"MPV\",\n \"OGV\",\"OGG\",\n \"MP4\", \"M4P\", \"M4V\",\n \"AVI\",\n \"WMV\",\n \"MOV\",\"QT\",\n \"FLV\",\"SWF\",\n \"F4V\",\"F4P\",\"F4A\",\"F4B\",\n \"VCHD\",\n \"RMVB\",\"RM\",\n \"VOB\",\n \"MKV\",\n \"MTS\", \"M2TS\", \"TS\",\n \"MNG\",\n \"GIFV\",\n \"GIF\",\n \"DRC\",\n \"XVID\",\n \"VIDX\",\n \"ASF\",\n \"AMV\",\n \"M2V\",\n \"SVI\",\n \"3GP\",\n \"MXF\",\n \"ROQ\",\n \"NSV\",\n \"3G2\",\n ]\n return filter_files_by_extension(files, video_extensions)\n ...","def video(self, file, draw_frames):\n # Just loop through the generator as we're only interested\n # in the output at the end.\n for _ in self.video_generator(file, draw_frames):\n continue\n\n self._save_tracks(file)","def convert_to_mp3(filename: str, title: str, start: int=None, end: int=None) -> list:\n\t# setup args for ffmpeg\n\tfile_a = f\"{path_to_wrk_dir}{filename}.mp4\" # input file\n\tfile_b = f\"{path_to_wrk_dir}{title}.mp3\" # output file\n\tfiles_b = [] # this list need if file more than 30 mb\n\targs = [\n\t\t\"/usr/bin/ffmpeg\", # path to ffmpeg\n\t\t\"-i\", # flag for input file\n\t\tfile_a, # input file\n\t\t\"-acodec\", # setup codec\n\t\t\"libmp3lame\", # codec name\n\t\t]\n\n\t# now need setup timings for target encode\n\tif start is not None and start != 0:\n\t\targs = args + [\"-ss\", str(start)]\n\tif end is not None and end != 0:\n\t\targs = args + [\"-t\", str(end - start)]\n\n\t# and last part for args to ffmpeg\n\targs = args + [\n\t\t\"-metadata\", # setup metadata for file\n\t\tf\"title={title}\", # title\n\t\t\"-metadata\",\n\t\tf\"artist={title}\", # and artist\n\t\t\"-b:a\", # setup bitrate\n\t\t\"320k\", # setup max bitrate\n\t\tfile_b,\n\t\t]\n\tprint(f\"{args}\")\n\t# start subprocess for encoding\n\tpopen = subprocess.Popen(args)\n\tpopen.wait()\n\n\t# check size file. if he more than 30 mb, bot need split him to chunks.\n\tsize = getsize(file_b) / 1024 / 1024\n\tif size > 30 and ( start or end is None ):\n\t\t# setup args for split to chunks\n\t\targs = [\n\t\t\t\"ffprobe\",\n\t\t\t\"-show_entries\",\n\t\t\t\"format=duration\",\n\t\t\t\"-i\",\n\t\t\tfile_b,\n\t\t\t]\n\n\t\t# get duration video.\n\t\tpopen = subprocess.Popen(args, stdout=subprocess.PIPE)\n\t\tpopen.wait()\n\t\toutput = popen.stdout.read()\n\t\t# now we know how long this audio file\n\t\t# split to 10 min chunks\n\t\tdur = re.findall(r\"\\d{1,10}\", str(output))\n\t\t# get chunks count for loop\n\t\tcount_chunks = (int(dur[0]) // 600) + 1\n\t\tfor chunk_start_time in range(0, count_chunks):\n\t\t\t# setup args for split\n\t\t\t# big parts of args the same for encode\n\t\t\targs = [\n\t\t\t\t\"/usr/bin/ffmpeg\",\n\t\t\t\t\"-i\",\n\t\t\t\tfile_b,\n\t\t\t\t\"-ss\",\n\t\t\t\tf\"{chunk_start_time * 600}\", # when start chunk\n\t\t\t\t\"-t\",\n\t\t\t\t\"600\", # 10 mints duration\n\t\t\t\t\"-acodec\",\n\t\t\t\t\"copy\", # copy\n\t\t\t\t\"-b:a\",\n\t\t\t\t\"320k\",\n\t\t\t\tf\"{path_to_wrk_dir}{title}_{chunk_start_time}.mp3\", # now we have path to video with chunk number.\n\t\t\t]\n\t\t\ttry:\n\t\t\t\t# start process for cut chunk\n\t\t\t\tpopen = subprocess.Popen(args, stdout=subprocess.PIPE)\n\t\t\t\tpopen.wait()\n\t\t\t# handle except.\n\t\t\texcept Exception as e:\n\t\t\t\tprint(f\"Exception - {e}\")\n\t\t\tfiles_b.append(f\"{path_to_wrk_dir}{title}_{chunk_start_time}.mp3\") # append name of file in list\n\t\tremove(file_b)\n\ttry:\n\t\t# remove tmp file\n\t\tremove(file_a)\n\t# handle except\n\texcept FileNotFoundError:\n\t\tfiles = get_file_list(path_to_wrk_dir)\n\t\tfor i in files:\n\t\t\tif -1 != f\"{path_to_wrk_dir}{i}\".find(f\"{filename}\") and f\"{i}\".find(f\".mp3\") == -1:\n\t\t\t\ttry:\n\t\t\t\t\tremove(f\"{path_to_wrk_dir}{i}\")\n\t\t\t\texcept FileNotFoundError:\n\t\t\t\t\tprint(f\"can't remove file {path_to_wrk_dir}{i}\")\n\tif len(files_b) == 0:\n\t\treturn [file_b]\n\telse:\n\t\treturn files_b","def ffmpeg_subclip_video_file(filename, t1, t2):\n subprocess.call(['ffmpeg', '-i', filename, '-ss', str(t1), '-to', str(t2), '-c', 'copy', '-y', filename.split('.')[0] + '_subclip.mp4'])\n return","def _read_file(self):\n extension = self.path.split('.')[-1]\n if extension!='avi':\n raise Exception(\"Invalid Format\")\n\n return cv2.VideoCapture(self.path)","def convert_to_mp4(filepath=''):\n print \"Converting \" + filepath + \" to mp4...\"\n base = os.path.splitext(filepath)\n basename = base[0]\n subprocess.call([\n 'convert',\n '-coalesce',\n '-background',\n 'white',\n filepath,\n basename + '%05d.png'\n ])\n\n frame_rate = get_frame_rate(filepath)\n print \"Using frame rate of \" + frame_rate\n\n # avconv -r 8 -i frame%02d.png -qscale 4 test.mp4\n # convert frames to avi\n subprocess.call([\n 'avconv',\n '-r',\n frame_rate,\n '-i',\n basename + '%05d.png',\n '-qscale',\n '4',\n '-b:a',\n '192k',\n '-y',\n '-loglevel',\n 'quiet',\n '-vf',\n 'scale=trunc(iw/2)*2:trunc(ih/2)*2',\n basename + '.mp4'\n ])\n\n # clean up\n for fl in glob.glob(basename + '*png'):\n os.remove(fl)\n return basename + '.mp4'","def _decode_frames_by_av_module(\n full_path,\n video_start_pts=0,\n video_end_pts=None,\n audio_start_pts=0,\n audio_end_pts=None,\n):\n if video_end_pts is None:\n video_end_pts = float(\"inf\")\n if audio_end_pts is None:\n audio_end_pts = float(\"inf\")\n container = av.open(full_path)\n\n video_frames = []\n vtimebase = torch.zeros([0], dtype=torch.int32)\n if container.streams.video:\n video_frames = _read_from_stream(\n container,\n video_start_pts,\n video_end_pts,\n container.streams.video[0],\n {\"video\": 0},\n )\n # container.streams.video[0].average_rate is not a reliable estimator of\n # frame rate. It can be wrong for certain codec, such as VP80\n # So we do not return video fps here\n vtimebase = _fraction_to_tensor(container.streams.video[0].time_base)\n\n audio_frames = []\n atimebase = torch.zeros([0], dtype=torch.int32)\n if container.streams.audio:\n audio_frames = _read_from_stream(\n container,\n audio_start_pts,\n audio_end_pts,\n container.streams.audio[0],\n {\"audio\": 0},\n )\n atimebase = _fraction_to_tensor(container.streams.audio[0].time_base)\n\n container.close()\n vframes = [frame.to_rgb().to_ndarray() for frame in video_frames]\n vframes = torch.as_tensor(np.stack(vframes))\n\n vframe_pts = torch.tensor([frame.pts for frame in video_frames], dtype=torch.int64)\n\n aframes = [frame.to_ndarray() for frame in audio_frames]\n if aframes:\n aframes = np.transpose(np.concatenate(aframes, axis=1))\n aframes = torch.as_tensor(aframes)\n else:\n aframes = torch.empty((1, 0), dtype=torch.float32)\n\n aframe_pts = torch.tensor([audio_frame.pts for audio_frame in audio_frames], dtype=torch.int64)\n\n return DecoderResult(\n vframes=vframes,\n vframe_pts=vframe_pts,\n vtimebase=vtimebase,\n aframes=aframes,\n aframe_pts=aframe_pts,\n atimebase=atimebase,\n )","def process_video(video_dir, save_dir):\n for sig_vid in tqdm(find_files(video_dir, '*.{}'.format(VID_FORMAT))):\n \n vc = cv2.VideoCapture(sig_vid) \n width = int(vc.get(cv2.CAP_PROP_FRAME_WIDTH))\n height = int(vc.get(cv2.CAP_PROP_FRAME_HEIGHT))\n rig_bot_height, rig_bot_width = height // 2, width // 2\n\n if rig_bot_height == 540 and rig_bot_width == 960:\n # right bottom, r_h, l_w, r_w\n iou = [390, 90, 890]\n\n elif rig_bot_height == 720 and rig_bot_width == 1280:\n log.info('high resolution video, please confirm iou param')\n\n else:\n assert 'please confirm video resolution'\n\n count = 0\n cout_save = 0\n\n while vc: \n rval, frame = vc.read() \n\n if rval == True:\n count += 1\n # fisheye extract front preview\n ext_region = frame[rig_bot_height:, rig_bot_width:]\n cv2.imshow('ori frame', ext_region)\n\n key = cv2.waitKey(0) & 0xFF\n if key == ord('q'):\n break\n\n elif key == ord('s'): \n # Interval 20 frame save \n if cout_save % 20 == 0 or cout_save > 20: \n file_name = create_files(save_dir, sig_vid)\n img_res = process_frame(ext_region, iou)\n cv2.imwrite(os.path.join(save_dir, file_name)+\"/\"+ file_name+\"_{}.jpg\".format(count),img_res)\n cout_save = 0\n log.info('successful save current frame {}'.format(count))\n\n else:\n cout_save += 1\n continue\n cout_save += 1\n\n else:\n # skip current frame and cout pre save frame interval\n if cout_save > 0:\n cout_save += 1\n continue\n\n else:\n break\n \n vc.release()\n cv2.destroyAllWindows()","def merge(vidlist,weekNumber):\n #generate day for file name\n today = date.today()\n d = today.strftime(\"%Y_%m_%d\")\n #resizing video\n clips = []\n for vid in vidlist:\n if vid.endswith(\".mp4\"):\n video = VideoFileClip(vid)\n ratio = video.h / video.w\n if ratio < (16/9 - 0.01):\n video = video.resize(width=1080)\n else:\n video = video.resize(height=1920)\n clips.append(video)\n finalrender = concatenate_videoclips(clips,method='compose')\n finalrender.write_videofile(str(weekNumber)+'.mp4',codec='libx264')","def process_video(self):\n if os.path.isfile(self.source):\n self.cap = cv2.VideoCapture(self.source)\n else:\n try:\n file_name = \"input.mp4\"\n self.source = self.source.replace('open', 'uc')\n print( \"\\nDownloading video file from drive link to %s\\n\"%file_name)\n gdown.download(self.source, file_name, quiet=False)\n print( \"%s downloaded!\\n\"%file_name )\n self.cap = cv2.VideoCapture(file_name)\n except Exception:\n raise RuntimeError(\"Invalid source input, please specify a Google drive link or a downloaded local file as input \\n\")\n\n\n assert self.cap.isOpened(), \"Failed to open %s\" % self.source\n\n self.w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n self.h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n self.fps = self.cap.get(cv2.CAP_PROP_FPS) \n self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\n return","def _decode(item):\n tivo_filename = item.filename()\n logger.info(\"Decoding %s\" % tivo_filename)\n\n mpeg_filename = item.filename(ext=\"mpg\")\n videos_dir = item.vdir()\n\n p = subprocess.Popen([\"/usr/local/bin/tivodecode\", \"--mak\", os.environ[\"MAK\"], \n \"--out\", mpeg_filename, tivo_filename], cwd=videos_dir,\n stderr=subprocess.STDOUT, stdout=subprocess.PIPE)\n rc = p.wait()\n\n logger.info(\"tivodecode returned %d\" % rc)\n logger.info(\"tivodecode output: '%s'\" % p.stdout.read())\n if rc == 0:\n # success!\n item.decoded = True\n item.save()\n else:\n raise Exception(\"Tivodecode failed on file '%s' with rc %d\" %\n (tivo_filename, rc))","def play(self, path=None):\n if path is None:\n path = self.download()\n\n # Clear the users out buffer before playing the video\n os.system('cls' if os.name == 'nt' else 'clear')\n\n # Better colors for display\n utils.term.bgcolor = 'white'\n count = 0\n\n vid = cv2.VideoCapture(path)\n\n while(vid.isOpened()):\n # Read the frame\n ret, frame = vid.read()\n count += 1\n\n # Convert to work with PIL and fabulous\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n image = Image.fromarray(frame)\n\n display = fi.Image(\"demo.png\")\n display.img = image.convert(\"RGBA\")\n display.resize()\n print str(display)[:-1]\n\n # Clear the stdout buffer after MAX_FRAME number frames\n if count % self.MAX_BUF_SIZE == 0:\n os.system('cls' if os.name == 'nt' else 'clear')","def main():\n input_video = sys.argv[1]\n input_audio = sys.argv[2]\n output_video = sys.argv[3]\n set_audio(input_video, input_audio, output_video)","def generate_still_from_video(self,\n in_path: str\n ) -> Tuple[bytes, float, str]:\n out_filepath = f\"/tmp/{uuid4()}.jpg\"\n command = [\n \"ffmpeg\",\n \"-i\", in_path,\n \"-vframes\", \"1\",\n out_filepath\n ]\n\n process = subprocess.Popen(command,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n process.wait()\n stderr = process.stderr.read().decode(\"utf-8\")\n\n # Parse start timecode\n timecode = self.parse_start_timecode_from_stderr(stderr)\n\n # Read new file back in and delete\n try:\n with open(out_filepath, \"rb\") as f:\n file_out_bytes = f.read()\n os.remove(out_filepath)\n except FileNotFoundError:\n raise TranscodeError(\"FFmpeg returned a non-zero code.\\n\" + stderr)\n\n return file_out_bytes, timecode, stderr","def extract_vob(in_vob, guid):\n\t#Detect interlacing.\n\tmediainfo_command = \"mediainfo --Inform='Video;%ScanType%,%ScanOrder%' \" + in_vob\n\tprint(mediainfo_command)\n\tprocess = subprocess.Popen(mediainfo_command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\t(cout, cerr) = process.communicate()\n\texit_code = process.wait()\n\tif exit_code != 0:\n\t\traise Exception(\"Calling Mediainfo on {in_vob} failed with exit code {exit_code}.\".format(in_vob=in_vob, exit_code=exit_code))\n\tmediainfo_parts = cout.decode(\"utf-8\").split(\",\")\n\tis_interlaced = mediainfo_parts[0] == \"Interlaced\"\n\tfield_order = mediainfo_parts[1].lower().strip()\n\tprint(\"Interlace detection:\", is_interlaced, field_order, \"(\", mediainfo_parts, \")\")\n\n\tffmpeg_command = [\"ffmpeg\", \"-i\", in_vob]\n\tprint(ffmpeg_command)\n\tprocess = subprocess.Popen(ffmpeg_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\t(cout, cerr) = process.communicate()\n\tprocess.wait() #Ignore the exit code. It always fails.\n\tvobinfo = cerr.decode(\"utf-8\")\n\ttracks = []\n\tfor match in re.finditer(r\" Stream #0:(\\d+)\\[0x[0-9a-f]+\\]: (\\w+): ([^\\n]+)\", vobinfo):\n\t\ttrack_nr = match.group(1)\n\t\ttrack_type = match.group(2)\n\t\ttrack_codec = match.group(3)\n\t\tnew_track = track.Track()\n\t\tnew_track.from_vob(track_nr, track_type, track_codec, is_interlaced, field_order)\n\t\tnew_track.file_name = guid + \"-T\" + str(new_track.track_nr) + \".\" + new_track.codec\n\t\tif new_track.type != \"unknown\":\n\t\t\ttracks.append(new_track)\n\n\t#Generate the parameters to pass to ffmpeg.\n\ttrack_params = [\"-i\", in_vob]\n\tfor track_metadata in tracks:\n\t\ttrack_params.append(\"-map\")\n\t\ttrack_params.append(\"0:\" + str(track_metadata.track_nr))\n\t\ttrack_params.append(\"-c\")\n\t\ttrack_params.append(\"copy\")\n\t\ttrack_params.append(track_metadata.file_name)\n\n\t#Extract all tracks.\n\tprint(\"---- Extracting tracks...\")\n\tffmpeg(*track_params)\n\n\treturn tracks","def convert_data(data_dir, dname):\n # Get videos from the original dataset\n seq_generator = get_seq(data_dir, dname)\n # Process videos\n for n, (f, k, seq) in enumerate(seq_generator):\n # Create a directory for the video\n f = os.path.splitext(os.path.basename(f))[0]\n dirname = os.path.join(data_dir, 'processed_data', dname, f, f'{k:03d}')\n os.makedirs(dirname)\n # Save all frames in .png files\n for i, img in enumerate(seq):\n img.save(os.path.join(dirname, f'{i:03d}.png'), 'PNG')\n print(f'{dirname} ({n + 1})')","def mp4_to_webm(srcfile, destfile, overwrite=False):\n syspkgs.check_installs([\"ffmpeg\"])\n cmd = [\n \"ffmpeg\",\n \"-i\",\n srcfile,\n \"-c:v\",\n \"libvpx\",\n \"-crf\",\n \"10\",\n \"-b:v\",\n \"1M\",\n \"-c:a\",\n \"libvorbis\",\n destfile,\n ]\n if overwrite:\n cmd.insert(1, \"-y\")\n print(\" \".join(cmd))\n return subprocess.check_output(cmd, encoding=\"utf-8\")","def encode_videos(self):\n\t\t\n\t\tself.face_detector = MTCNN()\n\t\tencoder = MyVGGFace(self.vgg_l, self.vgg_v)\n\t\t\n\t\tfolder = self.folders['raw_video_folder']\n\t\t\n\t\tfor (dirpath, _, filenames) in os.walk(folder):\n\t\t\tif platform == 'linux' or platform == 'linux2' or platform == 'darwin':\n\t\t\t\t# linux and OSX\n\t\t\t\tsplit_path = dirpath.split('/')\n\t\t\telse:\n\t\t\t\t# windows\n\t\t\t\tsplit_path = dirpath.split('\\\\')\n\t\t\tif filenames:\n\t\t\t\tif self.options.verbose:\n\t\t\t\t\tprint(f'Extracting features from {dirpath}')\n\t\t\t\tfor file in progressbar.progressbar(filenames):\n\t\t\t\t\tencode_path = (f'{self.feature_folder}/{split_path[-2]}', f'{file[:14]}.pic')\n\t\t\t\t\tcoord_path = (f'{self.folders[\"facial_data\"]}', f'{file[:14]}.pic')\n\t\t\t\t\tif file.endswith('.mp4') and not os.path.exists(f'{encode_path[0]}/{encode_path[1]}'):\n\t\t\t\t\t\tfaces, coords = self.video_faces(f'{dirpath}/{file}', f'{coord_path[0]}/{coord_path[1]}')\n\t\t\t\t\t\tencoding = encoder.vggface_encoding(faces)\n\t\t\t\t\t\tsave_to_file(coord_path[0], coord_path[1], coords)\n\t\t\t\t\t\tsave_to_file(encode_path[0], encode_path[1], encoding.reshape(encoding.shape[0], -1))\n\t\t\t\t\t\tdel faces, encoding\n\t\t\t\t\t\tgc.collect()","def video_to_frames(video_filename,output_dir):\n cap = cv2.VideoCapture(video_filename)\n video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1\n vid_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n vid_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n vid_fps = int(cap.get(cv2.CAP_PROP_FPS))\n print(\"vid_res=%d x %d, fps=%d\\n\" % (vid_width, vid_height,vid_fps))\n crop_width=int(vid_width/128)*128\n crop_height=int(vid_height/128)*128\n grab_step=int(vid_fps/2)\n if cap.isOpened() and video_length > 0:\n count = 0\n frame_id=0\n success, image = cap.read()\n while success and frame_id <= 9999:\n if count%grab_step==0:\n crop_img = image[0:crop_width, 0:crop_height]\n resized_img = cv2.resize(crop_img, (128, 128)) \n cv2.imwrite(output_dir+\"/frame%05d.jpg\" % frame_id, resized_img)\n frame_id+=1\n success, image = cap.read()\n count += 1\n return 0","def video_times():\n p = parse_cmdline(get_parser=get_parser_times)\n log.setup_main_handler(\n mods=(\"fogtools\", \"typhon\", \"fogpy\", \"sattools\", \"fcitools\", \"satpy\",\n \"pyresample\"),\n level=logging.DEBUG)\n vis.show_video_abi_glm_times(\n start_date=p.start_time,\n end_date=p.end_time,\n img_out=p.filename_pattern_image,\n vid_out=p.filename_pattern_video,\n out_dir=p.outdir,\n sector=p.sector,\n area=p.area)\n print(\"Files written to:\", p.outdir)","def get_video_as_images():\n experiments = ['me1.mp4']\n try:\n if (os.path.isdir(\"dump\")):\n shutil.rmtree('dump')\n except OSError:\n print (\"Deletion of the directory failed\")\n exit()\n os.system('mkdir dump')\n for experiment in experiments:\n exp_no_ext = experiment.split('.')[0]\n subdir_cmd = \"dump/{0}\".format(exp_no_ext)\n os.mkdir(subdir_cmd)\n os.system('ffmpeg -i videos/%s dump/%s/%s%%03d.jpg' % (experiment, exp_no_ext, exp_no_ext))\n run_all(exp_no_ext)","def video2rosbag(ifile, ofile, topic='/camera/image_raw'):\n\n from cv_bridge import CvBridge\n cap = cv2.VideoCapture(ifile)\n fps = 20\n wait = 1.0/fps\n bridge = CvBridge()\n bag = rosbag.Bag(ofile, 'w')\n while(cap.isOpened()):\n ret, frame = cap.read()\n if ret:\n msg = bridge.cv2_to_imgmsg(frame, encoding='bgr8')\n bag.write(topic, msg)\n time.sleep(wait)\n else:\n break\n cap.release()\n bag.close()","def video_to_frames(input_loc, output_loc, frame_skip):\n try:\n os.mkdir(output_loc)\n except OSError as e:\n print(e)\n pass\n # Log the time\n time_start = time.time()\n # Start capturing the feed\n cap = cv2.VideoCapture(input_loc)\n # Find the number of frames\n video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1\n print (\"Number of frames: \", video_length)\n count = 0\n actual_frames = 0\n print (\"Converting video..\\n\")\n # Start converting the video\n while cap.isOpened():\n # Extract the frame\n ret, frame = (None, None)\n for i in range(frame_skip):\n ret, frame = cap.read()\n actual_frames += 1\n cv2.imwrite(output_loc + \"/%#05d.jpg\" % (actual_frames), frame)\n count += 1\n # Write the results back to output location.\n # If there are no more frames left\n if (actual_frames > (video_length) - frame_skip):\n # Log the time again\n time_end = time.time()\n # Release the feed\n cap.release()\n # Print stats\n print (\"Done extracting frames.\\n%d frames extracted\" % count)\n print (\"It took %d seconds forconversion.\" % (time_end-time_start))\n break","def convert_to_img(vid_file, output_folder):\n cam = cv2.VideoCapture(vid_file)\n counter = 0\n ret = True\n while(ret):\n ret, frame = cam.read()\n if not ret:\n break\n\n cv2.imshow('frame', frame)\n cv2.imwrite(os.path.join(output_folder, str(counter) + \".png\"), frame)\n counter += 1\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n cam.release()\n cv2.destroyAllWindows()","def process_group(pattern, params):\n # check subdirectory according to filter options\n subdir = params['label']\n # and the parameters label\n print 'Processing:', subdir\n\n # search for videos matching the pattern\n search = os.path.join(ROOT_RAWDATA_DIR, pattern)\n print 'Search pattern:', search\n flist = sorted(glob.glob(search))\n\n # for each matching video\n for f in flist:\n # video structures (copied from LEGOS FTP) is yyyymmdd/HH/MM.mp4\n # and we want to store frames as yyyymmdd/yyyymmdd_HH/yyyymmdd_HHMM/yyyymmdd_HHMM_<index>.<format>\n # so: recursively split to extract basename, hour and date\n p, fname = os.path.split(f)\n p, hour = os.path.split(p)\n p, date = os.path.split(p)\n minute, _ = os.path.splitext(fname)\n # compute output dir, and prefix for frames\n outdir = os.path.join(ROOT_PREPROC_DIR,\n subdir, # according to parameters\n date,\n '{}_{}'.format(date, hour),\n '{}_{}{}'.format(date, hour, minute),\n )\n prefix = '{}_{}{}_'.format(date, hour, minute)\n # create output directory if neeeded\n if not os.path.exists(outdir):\n print 'Creating output directory', outdir\n os.makedirs(outdir, 0755)\n # call decoder\n command = ['python', '-u', 'decoder.py',\n f,\n '-o', outdir,\n '-p', prefix,\n '-l', params['label'],\n '-f', str(params['image_format']),\n '-m', str(params['median_length']),\n '-r', str(params['resolution']),\n '-O', str(params['origin'][0]), str(params['origin'][1]),\n '-d', str(params['dimensions'][0]), str(params['dimensions'][1]),\n '-a', str(params['rotation']),\n ]\n subprocess.call(command)","def get_video_from_camera(cls):\r\n cap = cv2.VideoCapture(0)\r\n width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\r\n heigth = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\r\n fps = 30\r\n save_file = None\r\n \r\n Tk().withdraw()\r\n save_file = asksaveasfilename(defaultextension=\".mp4\")\r\n \r\n if save_file!=None:\r\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\r\n out = cv2.VideoWriter(filename=save_file, fourcc=fourcc, fps=fps, \r\n frameSize=(width, heigth),isColor=True)\r\n \r\n while cap.isOpened():\r\n ret, frame = cap.read()\r\n cv2.imshow('Play video', frame)\r\n out.write(frame)\r\n if cv2.waitKey(30)==27:\r\n break\r\n cv2.destroyAllWindows()\r\n out.release()\r\n cap.release()\r\n return save_file","def create_video_task_structure(obj):\n parent_path = resource_path(obj.__parent__)\n obj_path = resource_path(obj)\n types = ['.mkv', '.avi'] #FIXME: Settings, formats etc...\n results = []\n for mtype in types:\n task_c = chain(blob_to_tmp_file.s(obj_path), convert_file.s(mtype), tmp_to_blob.s(parent_path, obj_path))\n results.append(task_c())\n return results","def startVideo(self,fname):\n\n\n try:\n fourcc = cv2.cv.CV_FOURCC(*'DIVX')\n\n except Exception as e:\n #print \"Exception \",e.args\n fourcc = cv2.VideoWriter_fourcc(*'DIVX')\n\n self.video = cv2.VideoWriter(fname, fourcc, 10, (self.screenWidth, self.screenHeight))\n if self.video is None:\n print \"VideoWriter failed to start.\"\n else:\n print \"VideoWriter started ok\"","def transcode(filePath: str) -> str:\n asset_uuid = uuid.uuid4()\n outPath = os.path.join(\"/tmp\", str(asset_uuid)+'.mp4')\n ffmpeg.input(filePath).output(outPath).run()\n return outPath","def load_video(self, filename):\n \n reader = imageio.get_reader(filename, 'ffmpeg')\n \n return np.array(list(reader), dtype=np.float32)","def generate_frame(video_path, video_name, second, label, dest_path):\n print \"video_path\", video_path\n print 'video_name',video_name\n print 'second',second\n print 'label',label\n print 'dest_path',dest_path\n\n vidcap = cv2.VideoCapture(os.path.join(video_path, video_name))\n vidcap.set(0, int(second*1000))\n success, image = vidcap.read()\n if success:\n cv2.imwrite(os.path.join(dest_path, video_name+\"_\"+str(second)+\"_\"+str(label)+\".jpg\"), image)","def watch_movie():\r\n if os.path.isfile('files/final_movie.mp4'): # if the file exists\r\n with open('files/final_movie.mp4', 'rb') as f:\r\n video_data = f.read()\r\n st.video(video_data)\r\n else: # if the file doesn't exist, let the user know\r\n st.header(\"You haven't created a movie yet!\")","def convert(self, infile, outfile, opts, timeout=10, preopts=None, postopts=None):\n if os.name == 'nt':\n timeout = 0\n\n if not os.path.exists(infile):\n raise FFMpegError(\"Input file doesn't exist: \" + infile)\n\n cmds = [self.ffmpeg_path]\n if preopts:\n cmds.extend(preopts)\n cmds.extend(['-i', infile])\n\n # Move additional inputs to the front of the line\n for ind, command in enumerate(opts):\n if command == '-i':\n cmds.extend(['-i', opts[ind + 1]])\n del opts[ind]\n del opts[ind]\n\n cmds.extend(opts)\n if postopts:\n cmds.extend(postopts)\n cmds.extend(['-y', outfile])\n\n if timeout:\n def on_sigalrm(*_):\n signal.signal(signal.SIGALRM, signal.SIG_DFL)\n raise Exception('timed out while waiting for ffmpeg')\n\n signal.signal(signal.SIGALRM, on_sigalrm)\n\n try:\n p = self._spawn(cmds)\n except OSError:\n raise FFMpegError('Error while calling ffmpeg binary')\n\n yielded = False\n buf = ''\n total_output = ''\n pat = re.compile(r'time=([0-9.:]+) ')\n\n while True:\n if timeout:\n signal.alarm(timeout)\n\n ret = p.stderr.read(10)\n\n if timeout:\n signal.alarm(0)\n\n if not ret:\n # For small or very fast jobs, ffmpeg may never output a '\\r'. When EOF is reached, yield if we haven't yet.\n if not yielded:\n yielded = True\n yield 10\n break\n\n try:\n ret = ret.decode(console_encoding)\n except UnicodeDecodeError:\n try:\n ret = ret.decode(console_encoding, errors=\"ignore\")\n except:\n pass\n\n total_output += ret\n buf += ret\n if '\\r' in buf:\n line, buf = buf.split('\\r', 1)\n\n tmp = pat.findall(line)\n if len(tmp) == 1:\n timespec = tmp[0]\n if ':' in timespec:\n timecode = 0\n for part in timespec.split(':'):\n timecode = 60 * timecode + float(part)\n else:\n timecode = float(tmp[0])\n yielded = True\n yield timecode\n\n if timeout:\n signal.signal(signal.SIGALRM, signal.SIG_DFL)\n\n p.communicate() # wait for process to exit\n\n if total_output == '':\n raise FFMpegError('Error while calling ffmpeg binary')\n\n cmd = ' '.join(cmds)\n if '\\n' in total_output:\n line = total_output.split('\\n')[-2]\n\n if line.startswith('Received signal'):\n # Received signal 15: terminating.\n raise FFMpegConvertError(line.split(':')[0], cmd, total_output, pid=p.pid)\n if line.startswith(infile + ': '):\n err = line[len(infile) + 2:]\n raise FFMpegConvertError('Encoding error', cmd, total_output,\n err, pid=p.pid)\n if line.startswith('Error while '):\n raise FFMpegConvertError('Encoding error', cmd, total_output,\n line, pid=p.pid)\n if not yielded:\n raise FFMpegConvertError('Unknown ffmpeg error', cmd,\n total_output, line, pid=p.pid)\n if p.returncode != 0:\n raise FFMpegConvertError('Exited with code %d' % p.returncode, cmd,\n total_output, pid=p.pid)\n\n return outfile","def f2gif(path,fps):\n print(\"==============================================\")\n print(\"Convert file to GIF!\")\n pathout = path[:-4]+'_'+str(fps)+'.gif'\n if path.endswith('.tif'): \n# import tifffile\n im = tifffile.imread(path)\n nFrames, h,w = im.shape\n dur=1/fps\n clip = []\n for i in range(nFrames):\n fr = cv2.cvtColor(im[i],cv2.COLOR_GRAY2RGB)\n clip.append(mp.ImageClip(fr).set_duration(dur))\n video = mp.concatenate_videoclips(clip, method=\"compose\",ismask=False)#ismask=True to make grayscale\n\n else:\n video = mp.VideoFileClip(path)\n fpsIn = int(video.fps)\n if fps != fpsIn:\n print(\"Conflict in fps! \\n\", \"[0] Use fps of input file;\\n\", \"[1] Use desired fps w/o speedup;\\n\",\n \"[2] Use desired fps w/ speedup:\")\n k = input('Input your selection: ')\n if k == 2:\n sf = fps/fpsIn\n video =video.fx(mp.vfx.speedx, sf)# Not working when sf<1\n elif k == 0:\n fps = fpsIn\n\n video.write_gif(pathout,fps=fps)\n video.reader.close()# To fix handel error problem\n# if path.endswith('.gif'):\n# clip.write_videofile(pathout,fps=fps,codec='libx264', bitrate='32 M',preset='ultrafast')\n print(\"==============================================\")\n print(\"MP4 convertion Done!\")","def __call__(self, device, timeout, bitrate, compress):\n if device is None:\n devices = android.list_devices()\n device = console.prompt_for_options(\"Choose device: \", devices)\n current_dir = os.getcwd()\n file_path_on_device = android.record_video(device, timeout, bitrate)\n file_name = os.path.basename(file_path_on_device)\n android.download_file(device, file_path_on_device, current_dir)\n android.remove_file(device, file_path_on_device)\n result_file_path = os.path.join(current_dir, file_name)\n if compress:\n console.compress_video(result_file_path)\n log.info(\"Find result at \" + result_file_path)"],"string":"[\n \"def seqIo_toVid(fName, ext='avi'):\\n\\n assert fName[-3:]=='seq', 'Not a seq file'\\n sr = seqIo_reader(fName)\\n N = sr.header['numFrames']\\n h = sr.header['height']\\n w = sr.header['width']\\n fps = sr.header['fps']\\n\\n out = fName[:-3]+ext\\n sw = skvideo.io.FFmpegWriter(out)\\n # sw = cv2.VideoWriter(out, -1, fps, (w, h))\\n timer = pb.ProgressBar(widgets=['Converting ', pb.Percentage(), ' -- ',\\n pb.FormatLabel('Frame %(value)d'), '/',\\n pb.FormatLabel('%(max)d'), ' [', pb.Timer(), '] ',\\n pb.Bar(), ' (', pb.ETA(), ') '], maxval=N)\\n\\n for f in range(N):\\n I, ts = sr.getFrame(f)\\n sw.writeFrame(Image.fromarray(I))\\n # sw.write(I)\\n timer.update(f)\\n timer.finish()\\n # cv2.destroyAllWindows()\\n # sw.release()\\n sw.close()\\n sr.close()\\n print(out + ' converted')\",\n \"def video_files():\\n p = parse_cmdline(get_parser=get_parser_files)\\n log.setup_main_handler(\\n mods=(\\\"fogtools\\\", \\\"typhon\\\", \\\"fogpy\\\", \\\"sattools\\\", \\\"fcitools\\\", \\\"satpy\\\",\\n \\\"pyresample\\\"),\\n level=logging.INFO)\\n vis.show_video_abi_glm(\\n files=p.files,\\n img_out=p.filename_pattern_image,\\n vid_out=p.filename_pattern_video,\\n out_dir=p.outdir)\\n print(\\\"Files written to:\\\", p.outdir)\",\n \"def check_video_timestamps(movie_file, desired_format='.mp4', desired_framerate=30):\\n\\n check_video_format(movie_file, desired_format='.mp4', original_format='.avi')\\n\\n new_movie_file = movie_file+'_tt'+desired_format\\n if not os.path.isfile(new_movie_file):\\n #Convert file to 30 fps\\n cmd = ['ffmpeg', '-i', movie_file+desired_format]\\n cmd += ['-r', str(desired_framerate)]\\n cmd += ['-y', movie_file+'_t'+desired_format]\\n cmd_string = ''.join([\\\"%s \\\" % el for el in cmd]) \\n #print '-->Running: ', cmd_string\\n p = subprocess.Popen(cmd, shell=False)\\n p.wait()\\n\\n #Add timecode text to video\\n cmd = 'ffmpeg -i '+movie_file+'_t'+desired_format+' -vf drawtext=\\\\\\\"fontfile=/opt/X11/share/fonts/TTF/VeraMoBd.ttf: timecode=\\\\'00\\\\:00\\\\:00\\\\:00\\\\':rate=30: fontcolor=white@0.8: x=7: y=460\\\\\\\" -an -y '+movie_file+'_tt'+desired_format\\n args = shlex.split(cmd)\\n #print args\\n p = subprocess.Popen(args, shell=False)\\n p.wait()\\n\\n os.remove(movie_file+'_t'+desired_format)\\n\\n return new_movie_file\",\n \"def make_video(pattern, plotdir, moviedir, movienametag):\\n images_list = glob('%s/%s'%(plotdir, pattern))\\n images_list.sort()\\n # save all required files into tmp_moviedir, with simple filenames: %.4d.png\\n tmp_moviedir = '%s/tmp_movie_%s'%(plotdir, movienametag)\\n os.system('mkdir -p %s'%tmp_moviedir)\\n for i in range(len(images_list)):\\n fname = images_list[i].split('%s/'%plotdir)[-1].split('.png')[0]\\n os.system('cp %s/%s.png %s/%.4d.png'%(plotdir, fname, tmp_moviedir, i))\\n\\n os.system('avconv -i %s'%tmp_moviedir +'/%04d.png ' \\\\\\n +' -y -c:v libx264 -pix_fmt yuv420p %s/%s.mp4'%(moviedir, movienametag))\",\n \"def make_video(input_files, width=0, height=0, frame_rate=24, crf=20, output_path=\\\"video.mp4\\\"):\\n if isinstance(input_files, list):\\n from PIL import Image # pylint: disable=C0415\\n\\n with Image.open(input_files[0]) as img:\\n width, height = img.size\\n tmp_dir = \\\"tmp_ffmpeg_dir\\\"\\n os.mkdir(tmp_dir)\\n if width % 2 != 0:\\n print(f\\\"Width ({width}) not divisible by 2\\\")\\n width -= 1\\n if height % 2 != 0:\\n print(f\\\"Height ({width}) not divisible by 2\\\")\\n height -= 1\\n for i, inp in enumerate(input_files):\\n shutil.copy(inp, os.path.join(tmp_dir, f\\\"{i:06d}.png\\\"))\\n inputs = f\\\"{tmp_dir}/%06d.png\\\"\\n command = ffmpeg_common_args(frame_rate, inputs, width, height, crf, output_path)\\n ret = os.system(command)\\n assert ret == 0, \\\"ffmpeg failed to generate video file.\\\"\\n for i in range(len(input_files)):\\n os.remove(os.path.join(tmp_dir, f\\\"{i:06d}.png\\\"))\\n os.rmdir(tmp_dir)\\n elif isinstance(input_files, str):\\n assert width != 0 and height != 0\\n command = ffmpeg_common_args(frame_rate, input_files, width, height, crf, output_path)\\n ret = os.system(command)\\n assert ret == 0, \\\"ffmpeg failed to generate video file.\\\"\\n else:\\n assert (\\n False\\n ), f'input_files should be list (of files) or str (of file template, e.g., \\\"%04d.png\\\") instead of {type(input_files)}'\",\n \"def convert(processed_dir: str, video_file: str):\\n\\n video_name = osp.splitext(osp.basename(video_file))[0]\\n out_dir = processed_dir + video_name\\n\\n # create img dir\\n if not osp.exists(processed_dir):\\n os.mkdir(processed_dir)\\n\\n # Create dir for video file if not existent\\n # this is where we save our images\\n if not osp.exists(out_dir):\\n os.mkdir(out_dir)\\n\\n if osp.exists(out_dir):\\n os.mkdir(out_dir + \\\"/kermit/\\\")\\n os.mkdir(out_dir + \\\"/not_kermit/\\\")\\n\\n # open video file for processing\\n cap = cv.VideoCapture(video_file)\\n frame_rate = cap.get(5) # frame rate\\n\\n sec = 0\\n total_count = (60*25)+50 # just an approximation\\n pbar = tqdm.tqdm(total=total_count, leave=False)\\n\\n count = 0\\n while (cap.isOpened()):\\n frame_id = cap.get(1) # current frame number\\n frame_exists, curr_frame = cap.read()\\n\\n if not frame_exists:\\n break\\n else:\\n if (frame_id % math.floor(frame_rate) == 0):\\n # output is : video_file/<video_file>_frameNr.jpg\\n cv.imwrite(osp.join(out_dir, '{}_{}.jpg'.format(video_name,count)), curr_frame)\\n count = count + 1\\n pbar.update(1)\\n\\n pbar.close()\\n # release resources\\n cap.release()\",\n \"def make_video(data,\\n xdim, ydim, sample_read_rows, sample_read_cols, image_write_rows, image_write_cols,\\n directory, filename, fps = 24.0, start_frame = 1, end_frame = None, timestamp = False, fontsize = 30, ts_pos = (0,0), save_raw = False):\\n\\n #Command to send via the command prompt which specifies the pipe parameters\\n # command = ['ffmpeg',\\n # '-y', # (optional) overwrite output file if it exists\\n # '-f', 'image2pipe',\\n # '-vcodec', 'mjpeg', #'mjpeg',\\n # '-r', '1',\\n # '-r', str(fps), # frames per second\\n # '-i', '-', # The input comes from a pipe\\n # '-an', # Tells FFMPEG not to expect any audio\\n # '-vcodec', 'mpeg4',\\n # '-b:v', '5000k',\\n # directory + filename + \\\"/\\\"+filename+\\\".mp4\\\",\\n # '-hide_banner',\\n # '-loglevel', 'panic']\\n\\n # Create directories if they don't exist\\n if not os.path.exists(os.path.join(directory, filename, 'frames/')):\\n os.makedirs(os.path.join(directory, filename, 'frames/'))\\n if save_raw and not os.path.exists(os.path.join(directory, filename, 'frames-raw/')):\\n os.makedirs(os.path.join(directory, filename, 'frames-raw/'))\\n\\n if end_frame == None:\\n end_frame = data.FrameCount\\n\\n cm = colormap.get_cmap('viridis')\\n\\n for i, frame_offset in enumerate(tqdm.tqdm(range(start_frame, end_frame))):\\n frame = FrameRead(data, frame_offset)\\n frame_image = np.zeros([ydim, xdim], dtype=np.uint8)\\n frame_image[image_write_rows, image_write_cols] = frame.frame_data[sample_read_rows, sample_read_cols]\\n\\n rgb_im = Image.fromarray(cm(frame_image, bytes=True)).convert('RGB')\\n rgb_im.save(os.path.join(directory, filename, 'frames/', f'{i}.jpg'), 'JPEG')\\n\\n if save_raw:\\n Image.fromarray(np.uint8(frame.frame_data), mode='L').save(os.path.join(directory, filename, 'frames-raw/', f'{i}.jpg'), 'JPEG')\",\n \"def __convert_video(self, v_dir):\\r\\n self.video_dir = v_dir\\r\\n vid_capt = cv2.VideoCapture(v_dir)\\r\\n curr_frame = 0\\r\\n # clear directory if it already exists, else create it\\r\\n if os.path.exists(self.FRAME_FOLDER):\\r\\n for file in os.listdir(self.FRAME_FOLDER):\\r\\n file_path = os.path.join(self.FRAME_FOLDER, file)\\r\\n try:\\r\\n if os.path.isfile(file_path):\\r\\n os.unlink(file_path)\\r\\n except Exception as e:\\r\\n print(e)\\r\\n else:\\r\\n os.makedirs(self.FRAME_FOLDER)\\r\\n\\r\\n while True:\\r\\n # ret is return value, once it turns False, video is over\\r\\n ret, frame = vid_capt.read()\\r\\n if not ret:\\r\\n break\\r\\n f_name = self.FRAME_FOLDER + '/' + self.vid_name + 'frame' + str(curr_frame) + '.jpg'\\r\\n cv2.imwrite(f_name, frame)\\r\\n curr_frame += 1\\r\\n\\r\\n vid_capt.release()\\r\\n cv2.destroyAllWindows()\",\n \"def check_video_format(movie_file, desired_format='.mp4', original_format='.avi'):\\n\\n if not os.path.isfile(movie_file+original_format):\\n print 'Error. avi file does not exist:'+movie_file+'.avi'\\n if not os.path.isfile(movie_file+desired_format):\\n cmd = ['ffmpeg']\\n cmd += ['-i', movie_file+original_format]\\n cmd += [movie_file+desired_format]\\n cmd_string = ''.join([\\\"%s \\\" % el for el in cmd])\\n #print '-->Running: ', cmd_string\\n p = subprocess.Popen(cmd, shell=False)\\n p.wait()\",\n \"def start_recording(codec, filename=time.strftime(\\\"%Y-%m-%d_%H-%M-%S\\\")):\\n global video_writer\\n folder = 'video_out/' # eventually replace this with the SD card folder\\n # TODO: also include branch name and/or commit ID\\n path = folder + filename + '.' + filetype\\n print \\\"Saving video to: %s\\\" % path\\n\\n height = videoinput.frame_height\\n if settings.sidebyside:\\n width = 2*videoinput.frame_width\\n else:\\n width = videoinput.frame_width\\n\\n try:\\n video_writer = cv2.VideoWriter(path, codec, 30, (width, height))\\n except:\\n print \\\"Failed to open video file for writing!\\\"\",\n \"def write_video(frames, filename, fps=20):\\n \\n # On Mac systems, copy ffmeg binaries to your PATH (http://ffmpegmac.net/)\\n \\n if platform.system() == 'Windows':\\n err_str = 'Don\\\\'t know how to write a movie for %s platform' % platform.system()\\n raise NotImplementedError(err_str)\\n\\n \\n if len(frames.shape) == 4:\\n pix_fmt = 'rgb24'\\n else:\\n pix_fmt = 'gray'\\n \\n # normalize\\n max_pix_val = np.percentile(frames, 99.9)\\n if frames.dtype in (np.bool, bool):\\n frames = frames.astype(np.uint8)\\n frames -= frames.min()\\n frames[frames>max_pix_val] = max_pix_val\\n if max_pix_val > 0:\\n frames *= 255. / max_pix_val\\n frames = frames.astype(np.uint8)\\n \\n # figure out which av program is installed\\n program_name = ''\\n try:\\n subprocess.check_call(['avconv', '-h'], stdout=DEVNULL, stderr=DEVNULL)\\n program_name = 'avconv'\\n except OSError:\\n try:\\n subprocess.check_call(['ffmpeg', '-h'], stdout=DEVNULL, stderr=DEVNULL)\\n program_name = 'ffmpeg'\\n except OSError:\\n pass\\n if not program_name:\\n raise OSError('Can\\\\'t find avconv or ffmpeg')\\n \\n # prepare pipe to av converter program\\n size_str = '%ix%i' % (frames.shape[1], frames.shape[2])\\n cmd = [program_name,\\n '-y', # (optional) overwrite output file if it exists\\n '-f', 'rawvideo',\\n '-vcodec','rawvideo',\\n '-s', size_str, # size of one frame\\n '-pix_fmt', pix_fmt,\\n '-r', str(fps), # frames per second\\n '-i', '-', # input comes from a pipe\\n '-an', # no audio\\n '-qscale', '1',\\n '-vcodec','mjpeg',\\n filename]\\n \\n pipe = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=DEVNULL, stderr=subprocess.STDOUT)\\n \\n # write frames \\n for frame in frames:\\n frame = np.fliplr(frame)\\n pipe.stdin.write(frame.tostring())\\n pipe.stdin.close()\\n pipe.wait()\",\n \"def reencode(filepath, loglevel='panic'):\\n try:\\n import ffmpeg\\n except ImportError:\\n logger.error(\\n 'Import Error! Cant import ffmpeg. '\\n 'Annotations operations will be limited. import manually and fix errors')\\n raise\\n if not os.path.isfile(filepath):\\n raise IOError('File doesnt exists: {}'.format(filepath))\\n # re encode video without b frame and as mp4\\n basename, ext = os.path.splitext(filepath)\\n output_filepath = os.path.join(basename, os.path.basename(filepath).replace(ext, '.mp4'))\\n if not os.path.isdir(os.path.dirname(output_filepath)):\\n os.makedirs(os.path.dirname(output_filepath))\\n try:\\n stream = ffmpeg.input(filepath, **{'loglevel': loglevel}).output(output_filepath,\\n **{'x264opts': 'bframes=0',\\n 'f': 'mp4'})\\n ffmpeg.overwrite_output(stream).run()\\n except Exception as e:\\n logger.exception('ffmpeg error in disassemble:')\\n raise\\n\\n output_probe = Videos.get_info(output_filepath)\\n start_time = eval(output_probe['streams'][0]['start_time'])\\n fps = eval(output_probe['streams'][0]['avg_frame_rate'])\\n has_b_frames = output_probe['streams'][0]['has_b_frames']\\n start_frame = fps * start_time\\n if start_time != 0:\\n logger.warning('Video start_time is not 0!')\\n if has_b_frames != 0:\\n logger.warning('Video still has b frames!')\\n return output_filepath\",\n \"def stream_frames(video_capture):\",\n \"def play_video_file(fname : str):\\n cap = cv2.VideoCapture(fname)\\n fps = cap.get(5)\\n font = cv2.FONT_HERSHEY_SIMPLEX\\n fontScale = 1\\n fontColor = (0, 0, 0)\\n lineType = 2\\n\\n myvideo = []\\n while cap.isOpened():\\n ret, frame = cap.read()\\n\\n if ret is True:\\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\\n cv2.putText(gray, 'Time: ' + str(round(cap.get(0) / 1000, 2)),\\n (10, 30),\\n font,\\n fontScale,\\n fontColor,\\n lineType)\\n cv2.putText(gray, 'Frame: ' + str(int(cap.get(1))),\\n (10, 70),\\n font,\\n fontScale,\\n fontColor,\\n lineType)\\n myvideo.append(gray)\\n #cv2.imshow('frame', gray)\\n #cv2.waitKey(10)\\n #if cv2.waitKey(delay=2) & 0xFF == ord('q'):\\n # break\\n else:\\n break\\n\\n cap.release()\\n\\n if fps < 60:\\n for frame in myvideo:\\n cv2.imshow('frame', frame)\\n cv2.waitKey(10)\\n else:\\n for ind, frame in enumerate(myvideo):\\n if ind % 3 == 0:\\n cv2.imshow('frame', frame)\\n cv2.waitKey(10)\\n else:\\n continue\\n cv2.destroyAllWindows()\",\n \"def convert_files(enumerated_src_file):\\n i, src_file = enumerated_src_file\\n src_file = src_file.strip()\\n file_extension, acodec, quality = audio_codec()\\n\\n dst_file = '.'.join(src_file.split('.')[:-1]) + file_extension\\n sys.stdout.write(str(i + 1) + ': ' + src_file + ' -> ' + dst_file + '\\\\n')\\n subprocess.call(['ffmpeg', '-i', src_file, '-vn', '-acodec',\\n acodec, '-aq', quality, dst_file, '-loglevel', 'quiet'])\\n return src_file\",\n \"def process_video(input_file, output_file):\\n # video = VideoFileClip(input_file).subclip(40,44) # from 38s to 46s\\n video = VideoFileClip(input_file)\\n annotated_video = video.fl_image(process_pipeline)\\n annotated_video.write_videofile(output_file, audio=False)\",\n \"def process_video(input_file, output_file):\\n # video = VideoFileClip(input_file).subclip(40,44) # from 38s to 46s\\n video = VideoFileClip(input_file)\\n annotated_video = video.fl_image(process_pipeline)\\n annotated_video.write_videofile(output_file, audio=False)\",\n \"def create_video(input_file, output_file):\\n input_video = VideoFileClip(input_file)\\n output_video = input_video.fl_image(detect_lane.fit_and_plot)\\n output_video.write_videofile(output_file, audio=False)\",\n \"def makeVideo():\\n os.system(\\\"cd video && ffmpeg -r 10 -i img%05d.jpg -vcodec mpeg4 -y caronthehill_clip.mp4\\\")\",\n \"def convert_video(video_file, output_file_name):\\n video_stream = cv2.VideoCapture(video_file)\\n total_frames = video_stream.get(cv2.CAP_PROP_FRAME_COUNT)\\n background = get_median_frame(video_stream)\\n video_stream.release()\\n #reopen for processing:\\n video_stream = cv2.VideoCapture(video_file)\\n #ready an output writer\\n writer = cv2.VideoWriter(output_file_name, \\n cv2.VideoWriter_fourcc(*\\\"MP4V\\\"), fps,(1080,1920)) #(1920,1080))\\n frameCnt=0\\n pos = [] #Array for the coordinates\\n while(frameCnt < total_frames-1):\\n frameCnt+=1\\n ret, frame = video_stream.read()\\n dframe = background_subtraction(frame,background)\\n cnts = find_contours(dframe)\\n x,y = find_lowest_contour(cnts)\\n pos.append([x,y])\\n if len(pos): \\n cv2.polylines(frame,np.int32([pos]),False,(0, 255, 0),2)\\n writer.write(cv2.resize(frame, (1080,1920))) ## size probably shoudn't be fixed.\\n writer.release()\\n video_stream.release()\\n return pos\",\n \"def process_video(lane, fname, output):\\n\\tclip = VideoFileClip(fname)\\n\\toutput_name = output\\n\\toutput_clip = clip.fl_image(lane.pipeline)\\n\\toutput_clip.write_videofile(output_name, audio=False)\\n\\tprint ('Video processed successfully')\",\n \"def make_seret(processed_files_directory='files/',fps=5):\\r\\n # Sort files in processed images directory\\r\\n files = sort_files(processed_files_directory)\\r\\n # Create list as container for the movie.\\r\\n img_array = []\\r\\n # For each file\\r\\n for file in files:\\r\\n file_format = file.split(\\\".\\\")\\r\\n if file_format[-1] == 'jpg': # verify that we will include jpg files only in the movie\\r\\n # Read the file\\r\\n img = cv2.imread(file)\\r\\n # Extract height, width, channels from image\\r\\n height, width, layers = img.shape\\r\\n # size = (width, height)\\r\\n size = (width, height)\\r\\n # Append image to movie container\\r\\n img_array.append(img)\\r\\n # Create a video writer for the movie\\r\\n out = cv2.VideoWriter(processed_files_directory+'initial.avi', cv2.VideoWriter_fourcc(*'DIVX'), fps, size)\\r\\n # For each image in container\\r\\n for image in img_array:\\r\\n # Write image by video writer\\r\\n out.write(image)\\r\\n # Release video writer.\\r\\n out.release()\",\n \"def __init__(self,vid_path:str,num_frames:int=None,vid_flow_direction:str='left'):\\n \\n self.num_frames=num_frames\\n if vid_path.split('.')[-1]=='cine' or vid_flow_direction!='left':\\n #This is a cine file or needs to be rotated, convert to mp4\\n print('Converting .cine file to mp4 (lossless)')\\n #detect platform so we can correct file paths for ffmpeg\\n is_win=re.compile('.*[Ww]in.*')\\n if is_win.match(sys.platform):\\n corrected_vid_path='\\\"'+vid_path+'\\\"'\\n else:\\n #Put escape characters in front of spaces in file name\\n corrected_vid_path=[]\\n for c in vid_path:\\n if c==' ':\\n corrected_vid_path.append('\\\\\\\\')\\n corrected_vid_path.append(c)\\n corrected_vid_path=''.join(corrected_vid_path)\\n if vid_flow_direction=='up':\\n rotate='-vf \\\"transpose=2\\\" '\\n elif vid_flow_direction=='left':\\n rotate=''\\n elif vid_flow_direction=='right':\\n rotate='-vf \\\"transpose=2,transpose=2\\\" '\\n else:\\n raise Exception(\\\"vid_flow_direction must be 'up', 'left' or 'right'\\\")\\n if num_frames!=None:\\n frames='-frames:v {0} '.format(num_frames)\\n else:\\n frames=''\\n os_handle,new_file_path=tempfile.mkstemp(suffix='.mp4')\\n #close file, we don't work with it directly\\n os.close(os_handle)\\n ffmpeg_command='ffmpeg -y -i {orig_file} {frames}{rotate}-f mp4 -crf 0 {new_file}'.format(orig_file=corrected_vid_path,rotate=rotate,new_file=new_file_path,frames=frames)\\n print(ffmpeg_command)\\n list(os.popen(ffmpeg_command))\\n self.vid_path=new_file_path\\n self.delete_file=True\\n stats=os.stat(new_file_path)\\n if stats.st_size==0:\\n raise Exception('File conversion failed, check that ffmpeg is on PATH')\\n else:\\n #Not a cine\\n self.vid_path=vid_path\\n self.delete_file=False\",\n \"def show_video(path: str): \\n video_path = sorted(glob(path + \\\"/*.mp4\\\"))[-1]\\n video = io.open(video_path, 'r+b').read()\\n encoded = base64.b64encode(video)\\n\\n return HTML(data='''<video alt=\\\"test\\\" controls>\\n <source src=\\\"data:video/mp4;base64,{0}\\\" type=\\\"video/mp4\\\" /> </video>'''\\n .format(encoded.decode('ascii')))\",\n \"def create_video():\\n print(\\\"Generating output video\\\")\\n frame_array = []\\n files = [f for f in os.listdir(MODIFIED_FRAMES_DIR) if isfile(join(MODIFIED_FRAMES_DIR, f))]\\n #for sorting the file names properly\\n # files.sort(key = lambda x: x[3:-4])\\n files = sorted(files,key=lambda x: int(os.path.splitext(x)[0]))\\n for i in range(len(files)):\\n filename= MODIFIED_FRAMES_DIR + files[i]\\n # print(filename)\\n #reading each files\\n img = cv2.imread(filename)\\n height, width, layers = img.shape\\n size = (width,height)\\n \\n #inserting the frames into an image array\\n frame_array.append(img)\\n \\n out = cv2.VideoWriter(OUTPUT_FILE,cv2.VideoWriter_fourcc(*'DIVX'), FRAME_RATE, size)\\n for i in range(len(frame_array)):\\n # writing to a image array\\n out.write(frame_array[i])\\n out.release()\\n print(\\\"Output video generated successfully...\\\")\\n\\n # img_array = []\\n # for filename in glob.glob(MODIFIED_FRAMES_DIR+'/*.jpg'):\\n # img = cv2.imread(filename)\\n # height, width, layers = img.shape\\n # size = (width,height)\\n # img_array.append(img)\\n\\n # height, width, layers = img_array[0].shape\\n # size = (width,height)\\n # out = cv2.VideoWriter('output.mov',cv2.VideoWriter_fourcc(*'DIVX'), 15, size) \\n # for i in range(len(img_array)):\\n # out.write(img_array[i])\\n # out.release()\",\n \"def make_movie(processed_files_directory='files/', WITH_SUBTITLES=False, WITH_AUDIO=False):\\r\\n # Declare the text for sub-titles\\r\\n\\r\\n if WITH_SUBTITLES: # if the user is willing to have subtitles in the movie\\r\\n with open(processed_files_directory+'subtitles.txt', 'r', encoding='utf8') as f:\\r\\n txt = f.read() # read the subtitles file\\r\\n # Split text to lines.\\r\\n subtitles = txt.split('\\\\n')\\r\\n # Declare VideoFileClip from the movie that I already have.\\r\\n clip = VideoFileClip(processed_files_directory + \\\"initial.avi\\\")\\r\\n # Declare duration of one sub-title as total duration of the video divided by number of lines.\\r\\n duration = clip.duration/len(subtitles)\\r\\n # Set start to zero.\\r\\n start=0\\r\\n # Set container for the clips.\\r\\n videos=[]\\r\\n # Loop all sub-titles\\r\\n for line in subtitles:\\r\\n # Make text clip from the reversed Hebrew text\\r\\n txt_clip = TextClip(line[::-1], fontsize=30, color='yellow', font='Calibri')\\r\\n # Set position to the bottom of screen.\\r\\n txt_clip = txt_clip.set_position('bottom').set_duration(duration)\\r\\n # Make sub clip of the movie with same duration as text clip.\\r\\n sub_clip = clip.subclip(start,start+duration)\\r\\n # Set CompositeVideoClip from the text clip and sub clip.\\r\\n video = CompositeVideoClip([sub_clip, txt_clip])\\r\\n # Insert the video to the clips container\\r\\n videos.append(video)\\r\\n # Set start time for next sub-title.\\r\\n start+=duration\\r\\n # Concatenate all clips of the container.\\r\\n res = concatenate_videoclips(videos)\\r\\n clip = res # now the clip is res\\r\\n else:\\r\\n clip = VideoFileClip(processed_files_directory+ \\\"initial.avi\\\") # the clip won't have subtitles\\r\\n\\r\\n\\r\\n # Set audio clip from mp3 file.\\r\\n if WITH_AUDIO: # if the user has chosen to include soundtrack in the movie\\r\\n f = 'audio.mp3' # change to mp3 soundtrack file of the movie\\r\\n # set the duration of the audioclip to max(duration of clip), even if the audioclip is longer\\r\\n audioclip = AudioFileClip(processed_files_directory+f)\\r\\n\\r\\n # check if the clip length is bigger than the\\r\\n if clip.duration > audioclip.duration:\\r\\n number_of_duplicated = int(np.ceil(clip.duration/audioclip.duration))\\r\\n # duplicate the audioclip in order to later fit the movie's duration\\r\\n audioclip = concatenate_audioclips([AudioFileClip(processed_files_directory+f) for i in range(number_of_duplicated)])\\r\\n\\r\\n # Now fit the audioclip duration to the movie's\\r\\n audioclip = audioclip.set_duration(clip.duration)\\r\\n\\r\\n # Set audio for the container.\\r\\n if not WITH_SUBTITLES: # if the user wanted to have audio included without subtitles\\r\\n videoclip = clip.set_audio(audioclip)\\r\\n else: # if the user wanted to have both audio and subtitles\\r\\n videoclip = res.set_audio(audioclip)\\r\\n else:\\r\\n videoclip = clip # if the user didn't want audio in the movie\\r\\n\\r\\n # Write the video file.\\r\\n f = 'final_movie.mp4' # change to the desired movie filename\\r\\n videoclip.write_videofile(processed_files_directory+f)\",\n \"def readVideo(self):\\n vid = cv2.VideoCapture(self.fname)\\n imgstack = []\\n # grab = True\\n grab, img = vid.read()\\n while grab:\\n imgstack.append(\\n Frame(\\n cv2.cvtColor(img, cv2.COLOR_BGR2GRAY),\\n self.starttime\\n + datetime.timedelta(seconds=self.frame_dt * self.length),\\n )\\n )\\n self.length += 1\\n grab, img = vid.read()\\n self.frames = imgstack\",\n \"def write_video_ffmpeg(\\n itr: Iterator[np.ndarray],\\n out_file: str | Path,\\n fps: int = 30,\\n out_fps: int = 30,\\n vcodec: str = \\\"libx264\\\",\\n input_fmt: str = \\\"rgb24\\\",\\n output_fmt: str = \\\"yuv420p\\\",\\n quite=False\\n) -> None:\\n\\n first_img = next(itr)\\n height, width, _ = first_img.shape\\n\\n stream = ffmpeg.input(\\\"pipe:\\\", format=\\\"rawvideo\\\", pix_fmt=input_fmt, s=f\\\"{width}x{height}\\\", r=fps)\\n stream = ffmpeg.output(stream, str(out_file), pix_fmt=output_fmt, vcodec=vcodec, r=out_fps)\\n if quite:\\n stream = stream.global_args('-loglevel', 'quiet')\\n stream = ffmpeg.overwrite_output(stream)\\n stream = ffmpeg.run_async(stream, pipe_stdin=True)\\n\\n def write_frame(img: np.ndarray) -> None:\\n stream.stdin.write(as_uint8(img).tobytes())\\n\\n # Writes all the video frames to the file.\\n write_frame(first_img)\\n for img in itr:\\n write_frame(img)\\n\\n stream.stdin.close()\\n stream.wait()\\n print('Done.')\",\n \"def split_video_random(file_path, start_pos, split_length, out_path):\\n s_cmd = \\\" -i '%s'\\\"%(file_path) #use default CODEC\\n try:\\n\\tfileext = file_path.split(\\\".\\\")[-1]\\n except IndexError as e:\\n\\traise IndexError(\\\"No ext. in filename. Error: \\\" + str(e))\\n\\n split_start = start_pos\\n split_length = split_length\\n head, tail = os.path.split(file_path)\\n name, ext = tail.split('.')\\n filebase=name+'_'+str(start_pos)+'-'+str(split_length)\\n\\n dstfilebase = out_path + '/' + filebase # create output file base\\n\\n #split_str = \\\"\\\"\\n #split_str += \\\" -ss \\\" + str(split_start) + \\\" -t \\\" + str(split_length) + \\\" '\\\"+ dstfilebase + \\\".\\\" + fileext + \\\"'\\\"\\n\\n s_str = \\\"\\\"\\t\\n #s_str += \\\"ffmpeg\\\"+\\\" -ss \\\"+str(split_start)+\\\" -t \\\"+str(split_length) + s_cmd + \\\" '\\\"+dstfilebase + \\\".\\\" + fileext + \\\"'\\\"\\n s_str += \\\"ffmpeg\\\" + \\\" -ss \\\" + str(split_start) + s_cmd + \\\" -t \\\" + str(split_length) + \\\" '\\\"+ dstfilebase + \\\".\\\" + fileext + \\\"'\\\"\\n print(\\\"########################################################\\\")\\n #print \\\"About to run: \\\"+split_cmd+split_str\\n print(\\\"About to run: \\\"+s_str)\\n print(\\\"########################################################\\\")\\n #output = subprocess.Popen(split_cmd+split_str, shell = True, stdout = subprocess.PIPE).stdout.read()\\n output = subprocess.Popen(s_str, shell=True, stdout=subprocess.PIPE).stdout.read()\",\n \"def video_files_to_tfrecords(output_file, filepaths, label_dict, downsample_graph):\\n\\n if type(filepaths) != list:\\n filepaths = [filepaths] # catch single inputs (not a normal case)\\n\\n tqkws = {\\n 'total': len(filepaths),\\n 'unit': ' videos',\\n 'desc': 'Serializing video frames'\\n }\\n\\n video_placeholder, downsampled = downsample_graph\\n\\n with tf.python_io.TFRecordWriter(output_file) as writer:\\n for path in tqdm.tqdm(filepaths, **tqkws):\\n video_array = video_to_array(path)\\n label = label_dict[os.path.split(os.path.abspath(os.path.join(path, os.pardir)))[-1]]\\n\\n l = video_array.shape[0]\\n w = video_array.shape[2]\\n h = video_array.shape[1]\\n\\n if h != 240 or w != 320:\\n continue\\n\\n downsampled_video_array = downsampled.eval({video_placeholder: video_array})\\n feature_dict = {\\n 'height': _int_feature(h),\\n 'width': _int_feature(w),\\n 'length': _int_feature(l),\\n 'video': _bytes_feature(downsampled_video_array.astype(np.uint8).tostring()),\\n 'label': _int_feature(label)\\n }\\n\\n observation = tf.train.Example(features=tf.train.Features(feature=feature_dict))\\n\\n writer.write(observation.SerializeToString())\",\n \"def transcodetomp4(file_in, logger):\\n\\n import subprocess\\n\\n file_out = file_in.replace('.mkv', '.mp4')\\n\\n if os.path.isfile('/usr/bin/avconv'):\\n\\n convert_command = 'su securityspy -c \\\\\\\"/usr/bin/avconv -i \\\"{}\\\" -f mp4 -vcodec copy -acodec '.format(file_in) + \\\\\\n 'libfaac -b:a 112k -ac 2 -y \\\"{}\\\"'.format(file_out) + \\\"\\\\\\\"\\\"\\n\\n try:\\n subprocess.check_call(convert_command, shell=True)\\n except subprocess.CalledProcessError:\\n logger.error(\\\"The command to transcode: {} --- failed...\\\".format(convert_command))\\n return file_in\\n\\n return file_out\\n else:\\n return file_in\\n # fin\",\n \"def main():\\n convert(\\\"env_100000.mp4\\\", TargetFormat.GIF)\",\n \"def convert_video_path_and_save(video_path, output_path=\\\"output.mp4\\\", temp_folder = \\\"./temp\\\",\\n frame_frequency=24, image_reducer=100, fontSize=10, spacing=1.1, maxsize=None, chars=\\\" .*:+%S0#@\\\",\\n logs=False, processes=4, progress_tracker=None):\\n\\n if logs:\\n start_time = time.time()\\n print (\\\"Converting video...\\\")\\n \\n # set up a capture temporarily so we can grab some basic info about it\\n capture = cv2.VideoCapture(video_path)\\n if not capture.isOpened():\\n print (\\\"Could not read video. Please enter a valid video file!\\\")\\n exit(0)\\n\\n fps = capture.get(cv2.CAP_PROP_FPS)\\n bitrate = int(capture.get(cv2.CAP_PROP_BITRATE))\\n total_frames = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))\\n frames_included = int(total_frames / frame_frequency)\\n # total_frames / fps gives us our video duration.\\n video_duration = total_frames / fps\\n # frames included / video duration gives new fps\\n new_fps = (total_frames / frame_frequency) / video_duration\\n\\n capture.release()\\n\\n # First, we grab all the frames we need and store them in a temp folder\\n # After that, we convert all the image frames in the temp folder, and save them back in the temp folder\\n # Then, we write them to video and save to disk\\n # To utilize mutli processing, we separate grabbing frames and converting the frames into batches\\n\\n while os.path.isdir(temp_folder):\\n temp_folder += \\\"_\\\"\\n temp_folder += \\\"/\\\"\\n os.mkdir(temp_folder)\\n\\n # initial setup\\n # we divide our work into batches\\n batches = processes\\n frames_per_batch = int(total_frames / batches / frame_frequency)\\n if progress_tracker is None:\\n progress_tracker = Value(\\\"f\\\", 0, lock=True)\\n # progress: saved frames + converted frames + written frames\\n progress_step = 100 / (frames_included * 3)\\n\\n # grab the frames, and write to separate batch folders\\n save_frames_processes = []\\n for batch in range(batches):\\n starting_frame = batch * frames_per_batch * frame_frequency\\n batch_folder = temp_folder + str(batch) + \\\"/\\\"\\n os.mkdir(batch_folder)\\n args = (\\n starting_frame,\\n starting_frame + frames_per_batch * frame_frequency,\\n video_path,\\n batch_folder,\\n frame_frequency,\\n logs,\\n progress_tracker,\\n progress_step\\n )\\n p = Process(target=_save_frames, args=args)\\n p.daemon = True\\n p.start()\\n save_frames_processes.append(p)\\n for p in save_frames_processes:\\n p.join()\\n\\n # convert all the frames in each batch folder\\n convert_processes = []\\n for batch in range(batches):\\n batch_folder = temp_folder + str(batch) + \\\"/\\\"\\n args = (\\n batch_folder,\\n frames_per_batch,\\n image_reducer,\\n fontSize, spacing, maxsize, chars,\\n logs, progress_tracker, progress_step\\n )\\n p = Process(target=_convert_batch, args=args)\\n p.daemon = True\\n p.start()\\n convert_processes.append(p)\\n for p in convert_processes:\\n p.join()\\n\\n # if no extension was assigned, automatically assign .mp4\\n output_name, output_ext = os.path.splitext(output_path)\\n if output_ext == \\\"\\\":\\n output_ext = \\\".mp4\\\"\\n # if final output path was specified, then modify it (append _Copy to it)\\n final_output_path = output_name + output_ext\\n while os.path.isfile(final_output_path):\\n if logs : print (final_output_path, \\\"already exists!\\\")\\n final_output_path = os.path.splitext(final_output_path)[0] + \\\"_Copy\\\" + output_ext\\n\\n # video settings\\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\\n video_out = imageio.get_writer(final_output_path, fps=new_fps, quality=None, bitrate=(bitrate * 1024 * 2.5))\\n size = None\\n\\n # write images to new video\\n for batch in range(1, batches + 1):\\n batch_folder = temp_folder + str(batch - 1) + \\\"/\\\"\\n for i in range(1, frames_per_batch + 1):\\n img = cv2.imread(batch_folder + str(i) + \\\".jpg\\\", 2)\\n if size is None:\\n height, width = img.shape\\n size = (width, height)\\n video_out.append_data(img)\\n with progress_tracker.get_lock():\\n progress_tracker.value += progress_step\\n if logs : print (\\\"Progress: %.4f%%\\\" % progress_tracker.value, end=\\\"\\\\r\\\")\\n video_out.close()\\n shutil.rmtree(temp_folder)\\n\\n # when we are done, there might be some rounding errors when converting some stuff to integers, thus it doesn't appear to be done\\n # So we just simply set it to 100\\n with progress_tracker.get_lock():\\n progress_tracker.value = 100\\n\\n if logs:\\n print (\\\"=\\\" * 30)\\n print (\\\"SUMMARY:\\\")\\n print (\\\"-\\\" * 20)\\n print (\\\"Progress: %.4f%%\\\" % progress_tracker.value)\\n print (\\\"Total frames found:\\\", str(total_frames))\\n print (\\\"Frames included and converted:\\\", str(frames_per_batch * batches))\\n print (\\\"Original FPS:\\\", str(fps))\\n print(\\\"New FPS:\\\", str(new_fps))\\n print (\\\"Resolution:\\\", str(size))\\n print (\\\"Saved to\\\", final_output_path)\\n print (\\\"Time took: %.4f secs\\\" % (time.time() - start_time))\",\n \"def tiff2mp4(path):\\n video = tifffile.imread(path)\\n nFrames, h,w = video.shape\\n fps = int(input('Input desired output fps:'))\\n # dur=1/fps \\n pathout =path[:-4]+'_'+str(fps)+'.mp4' \\n # pathout2 =path[:-4]+'_St.tif'\\n codec = cv2.VideoWriter_fourcc(*'H264')\\n out = cv2.VideoWriter(pathout, codec , fps, (w, h))\\n print(\\\"---------------------------------------------\\\")\\n print('Converting Tiff stack to the movie') \\n for i in tqdm.tqdm(range(nFrames)): \\n img=video[i] \\n out.write(img)\\n out.release()\\n cv2.destroyAllWindows()\\n print(\\\"==============================================\\\")\\n print(\\\"MP4 convertion Done!\\\")\",\n \"def list_files_to_convert():\\n for root, dirs, files in os.walk(video_dir):\\n file_list = [name for name in files if not name.endswith('.mp3')]\\n for name in file_list:\\n filepath = os.path.join(root, name)\\n media_info = MediaInfo.parse(filepath, library_file=dll_path)\\n for track in media_info.tracks:\\n if 'Audio' in track.track_type:\\n # print(track.track_type, track.bit_rate)\\n # print(filepath, \\\"Is an Audio/Video file, and should be converted because a sound track is found\\\")\\n yield dict(path=filepath, info=media_info)\",\n \"def videoFrames(filename, framerate=1):\\n vid_file = os.path.join(os.path.dirname(os.getcwd()), \\\"Database\\\", \\\"Video\\\", filename)\\n print(vid_file)\\n assert os.path.isfile(vid_file), \\\"Given path is not a valid file\\\"\\n tmpdir = os.path.join(os.getcwd(), \\\"tmp\\\")\\n subprocess.run(\\n [\\n \\\"ffmpeg\\\",\\n \\\"-i\\\",\\n vid_file,\\n \\\"-r\\\",\\n f\\\"{framerate}\\\",\\n os.path.join(tmpdir, \\\"img_%04d.jpg\\\"),\\n ]\\n )\\n return [os.path.join(tmpdir, i) for i in os.listdir(tmpdir) if not i.endswith(\\\".wav\\\")]\",\n \"def main():\\n print(\\\"This is a library for reading video sequences into python via ffmpeg. \\\")\\n print(\\\"Provides the 'Video_Reader' iterator class. \\\")\\n print(\\\"Requires ffmpeg be installed. \\\")\",\n \"def loadVideo( iFileName, iFrameSize = (576, 720) ):\\n import sys\\n import subprocess as sp\\n # ustvari klic ffmpeg in preusmeri izhod v cevovod\\n command = [ 'ffmpeg',\\n '-i', iFileName,\\n '-f', 'image2pipe',\\n '-pix_fmt', 'rgb24',\\n '-vcodec', 'rawvideo', '-']\\n pipe = sp.Popen(command, stdout = sp.PIPE, bufsize=10**8)\\n # definiraj novo spremeljivko\\n oVideo = np.array([])\\n iFrameSize = np.asarray( iFrameSize )\\n frameCount = 0\\n # zacni neskoncno zanko\\n while True:\\n frameCount += 1\\n# print( 'Berem okvir %d ...' % frameCount )\\n print(\\\"\\\\rBerem okvir %d ...\\\" % frameCount, end=\\\"\\\")\\n # preberi Y*X*3 bajtov (= 1 okvir)\\n raw_frame = pipe.stdout.read(np.prod(iFrameSize)*3)\\n # pretvori prebrane podatke v numpy polje\\n frame = np.fromstring(raw_frame, dtype='uint8') \\n # preveri ce je velikost ustrezna, sicer prekini zanko\\n if frame.size != (np.prod(iFrameSize)*3):\\n print(\\\" koncano!\\\\n\\\")\\n break;\\n # preoblikuj dimenzije in pretvori v sivinsko sliko\\n frame = colorToGray( frame.reshape((iFrameSize[0],iFrameSize[1],3)) )\\n # sprazni medpomnilnik \\n pipe.stdout.flush() \\n # vnesi okvir v izhodno sprememnljivko\\n if oVideo.size == 0:\\n oVideo = frame\\n oVideo = oVideo[...,None]\\n else:\\n oVideo = np.concatenate((oVideo,frame[...,None]), axis=2)\\n # zapri cevovod\\n pipe.terminate()\\n # vrni izhodno spremenljivko\\n return oVideo\",\n \"def prepare_video(path_to_video: str, number_of_images=87) -> None:\\n\\n temp_video = path.join(path_to_video, 'temp_outpy.mp4')\\n video = path.join(path_to_video, 'outpy.h264')\\n\\n # create mp4 video for metadata and compute video duration\\n subprocess.run(['ffmpeg', '-i', video, '-c', 'copy', temp_video])\\n result = subprocess.run([\\\"ffprobe\\\", \\\"-v\\\", \\\"error\\\", \\\"-show_entries\\\",\\n \\\"format=duration\\\", \\\"-of\\\",\\n \\\"default=noprint_wrappers=1:nokey=1\\\", temp_video],\\n stdout=subprocess.PIPE,\\n stderr=subprocess.STDOUT)\\n video_duration = float(result.stdout)\\n\\n # create images folder\\n path_to_images = path.join(path_to_video, 'images')\\n if path.exists(path_to_images) and path.isdir(path_to_images):\\n shutil.rmtree(path_to_images)\\n makedirs(path_to_images)\\n\\n # split the given video into images\\n subprocess.run(['ffmpeg', '-i', temp_video, '-r', str(number_of_images / video_duration), '-f', 'image2',\\n path.join(path_to_images, 'image%d.jpg')])\\n\\n # remove extra files\\n remove_extra_images(path_to_images, number_of_images)\\n remove(temp_video)\",\n \"def convert(fname_src, verbose=False):\\n if not os.path.isfile(fname_src):\\n raise IOError('File not found: %s' % fname_src)\\n\\n # File names.\\n b, e = os.path.splitext(fname_src)\\n fname_dst = b + '.m4a'\\n\\n # Build command.\\n cmd = 'ffmpeg -y -i \\\"%s\\\" \\\"%s\\\"' % (fname_src, fname_dst)\\n\\n t0 = time.time()\\n std_out, std_err = run_cmd(cmd)\\n dt = time.time() - t0\\n\\n if dt < 0.01:\\n raise Exception('Problem processing file: %s %s %s %s' % (fname_src, std_out, std_err, cmd))\\n\\n if std_out.lower().find('error') >= 0:\\n raise Exception('Problem processing file: %s %s %s %s' % (fname_src, std_out, std_err, cmd))\\n\\n # Done.\\n return fname_dst\",\n \"def generate_video(sign, issue, output):\\n\\n videos = {\\n \\\"Climate Change\\\": \\\"ClimateChange.mp4\\\",\\n \\\"Green Jobs\\\": \\\"GreenJobs.mp4\\\",\\n \\\"Tourism\\\": \\\"Tourism.mp4\\\",\\n \\\"Small Business\\\": \\\"SmallBusiness.mp4\\\",\\n \\\"Public health\\\": \\\"PublicHealth.mp4\\\",\\n \\\"Education Funding\\\": \\\"EducationFunding.mp4\\\"\\n }\\n\\n video_path = CWD(f\\\"Assets/{videos[issue]}\\\")\\n\\n frame = cv2.imread(sign)\\n frame = cv2.resize(frame, (1920, 1080))\\n height, width, layers = frame.shape\\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\\n video = cv2.VideoWriter(CWD(\\\"temp.mp4\\\"), fourcc, 1, (width, height))\\n for i in range(5):\\n video.write(frame)\\n video.release()\\n\\n image_clip = VideoFileClip(CWD(\\\"temp.mp4\\\"))\\n original_video = VideoFileClip(video_path)\\n final_video = concatenate_videoclips([original_video, image_clip], method=\\\"compose\\\")\\n\\n final_video.write_videofile(output)\\n os.remove(CWD(\\\"temp.mp4\\\"))\",\n \"def video2():\\n return Response(gen_frames(2),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def obtenerVideo(camara):\\n val, frame = camara.read()\\n return val, frame\",\n \"def seqIo_frImgs(fName, header=[], aviName=[], Is=[], sDir=[], name='I', ndig=5, f0=0, f1=1e6):\\n \\n if aviName!=[]: #avi movie exists\\n vc = cv2.VideoCapture(aviName)\\n if vc.isOpened(): rval = True\\n else:\\n rval = False\\n print('video not readable')\\n return\\n fps = vc.get(cv2.cv.CV_CAP_PROP_FPS)\\n NUM_FRAMES = int(vc.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))\\n print(NUM_FRAMES)\\n IM_TOP_H = vc.get(cv2.cv.CV_CAP_PROP_FRAME_HEIGHT)\\n IM_TOP_W = vc.get(cv2.cv.CV_CAP_PROP_FRAME_WIDTH)\\n header['width']=IM_TOP_W\\n header['height']=IM_TOP_H\\n header['fps']=fps\\n\\n sw = seqIo_writer(fName,header)\\n print('creating seq from AVI')\\n # initialize timer\\n timer = pb.ProgressBar(widgets=['Converting ', pb.Percentage(), ' -- ',\\n pb.FormatLabel('Frame %(value)d'), '/',\\n pb.FormatLabel('%(max)d'), ' [', pb.Timer(), '] ',\\n pb.Bar(), ' (', pb.ETA(), ') '], maxval=NUM_FRAMES)\\n for f in range(NUM_FRAMES):\\n rval, im = vc.read()\\n if rval:\\n im= im.astype(np.uint8)\\n sw.addFrame(im)\\n timer.update(f)\\n sw.close()\\n timer.finish()\\n elif Is==[]:\\n assert(os.path.isdir(sDir))\\n sw = seqIo_writer(fName,header)\\n frmstr = '%s/%s%%0%ii.%s' % (sDir,name,ndig,header.ext)\\n for frame in range(f0,f1):\\n f = frmstr % frame\\n if not os.path.isfile(f):break\\n fid = open(f, 'r')\\n if fid<0: sw.close(); assert(False)\\n I = fid.read()\\n fid.close()\\n b = bytearray(I)\\n assert (b[0] == 255 and b[1] == 216 and b[-2] == 255 and b[-1] == 217); # JPG\\n I = np.array(list(b)).astype(np.uint8)\\n sw.addFrame(I,0,0)\\n sw.close()\\n if frame==f0: print('No images found')\\n else:\\n nd = len(Is.shape)\\n if nd==2: nd=3\\n assert(nd<=4)\\n nFrm = Is.shape[nd-1]\\n header['height']=Is.shape[0]\\n header['width']=Is.shape[1]\\n sw =seqIo_writer(fName,header)\\n if nd==3:\\n for f in range(nFrm): sw.addFrame(Is[:,:,f])\\n if nd==4:\\n for f in range(nFrm): sw.addFrame(Is[:,:,:,f])\\n sw.close()\",\n \"def convert_multiple(self, video_files, out, brate, _format):\\n\\n for video in video_files:\\n self.to_audio(os.path.abspath(video),\\n out, brate, _format)\",\n \"def make_video(self, mp4=True, gif=True):\\n fn = self.get_output_filename(\\\".mp4\\\")\\n command = (\\n (get_ffmpeg_path() + f\\\" -loglevel panic -framerate {self.framerate} -i \\\")\\n + os.path.join(self.frame_directory, FRAME_FN_TEMPLATE)\\n + \\\" -s:v \\\"\\n + str(self.width)\\n + \\\"x\\\"\\n + str(self.height)\\n + \\\" -c:v libx264 -profile:v high -crf 1 -pix_fmt yuv420p -y \\\"\\n + fn\\n )\\n\\n os.system(command)\\n\\n if gif:\\n mp4_to_gif(\\n self.get_output_filename(\\\".mp4\\\"),\\n self.get_output_filename(\\\".gif\\\"),\\n self.framerate,\\n )\\n\\n if not mp4:\\n os.remove(fn)\",\n \"def processVideo(inputRootFolder='/home/pi/Logging/UnprocessedVideo',\\n outputFolder='/home/pi/Logging/Unsent',\\n cam_framerate=10,\\n delay=15):\\n logging.info('Processing videos')\\n sleep(delay) # hardcoded sleep function to ensure that the video has finished saving\\n # Create directories if necessary\\n try:\\n os.makedirs(inputRootFolder)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise # This was not a \\\"directory exists\\\" error\\n try:\\n os.makedirs(outputFolder)\\n except OSError as e:\\n if e.errno != errno.EEXIST:\\n raise # This was not a \\\"directory exists\\\" error\\n # Get the list of subdirectories\\n f = []\\n for (dirpath, dirnames, filenames) in os.walk(inputRootFolder):\\n f.extend(dirnames)\\n # Go through each subdirectory\\n for folder in f:\\n folderName = os.path.join(inputRootFolder,folder)\\n videoListName = '%s/videoList.txt' % folderName #file that will contain list of videos\\n videoList = io.open(videoListName, 'w')\\n for fileName in sorted(os.listdir(folderName)): #add each video in the folder to the file\\n if (fileName.startswith('Video')):\\n videoString = (\\\"file '%s/%s'\\\\n\\\" % (folderName, fileName))\\n videoList.write(videoString)\\n videoList.close()\\n outputFile = '%s/%s.mp4' % (outputFolder, folder)\\n #concatenate the videos\\n subprocess.call(['ffmpeg', '-y', '-f', 'concat', '-safe', '0', '-i',\\n videoListName, '-c', 'copy', outputFile], shell=False)\\n shutil.rmtree(folderName, ignore_errors=True) #delete the folder\\n logging.info('Processed videos')\",\n \"def process_videos(chapter_info):\\n\\n print(\\\"Processing chapter_info:\\\", chapter_info)\\n\\n # getting creation time of the first chapter\\n # TODO update when adding multiple directory proccessing\\n os.chdir(DIR_VIDEO_FILES)\\n print(\\\"1st chapter\\\", chapter_info[1][0])\\n chap1_time = time.strftime(\\n r\\\"%Y-%m-%d_%H-%M\\\", time.localtime(os.path.getctime(chapter_info[1][0])))\\n print(\\\"1st chapter creation\\\", chap1_time)\\n\\n # output_file = f\\\"M_GH00{chapter_info[0]}_{chap1_time}.MP4\\\"\\n output_file = f\\\"{chap1_time}_GH00{chapter_info[0]}_MRG.MP4\\\"\\n if os.path.isfile(output_file):\\n print(f\\\"Chapter already processed, found file: {output_file}\\\")\\n return\\n\\n # preparing text file containing file list for merging (for ffmpeg)\\n video_list_file = chapter_info[0] + \\\"_merge.txt\\\"\\n with open(video_list_file, \\\"w\\\") as f:\\n for video_chapter in chapter_info[1]:\\n f.write(f\\\"file {video_chapter}\\\\n\\\")\\n\\n command = f\\\"{FFMPEG_EXE} -f concat -i {video_list_file} -c copy {DIR_OUTPUT}{output_file}\\\"\\n print(\\\"command =\\\", command)\\n # p = subprocess.run(\\\"dir\\\", shell=True, capture_output=True)\\n # p = subprocess.run(\\\"dir\\\", shell=True, stdout=subprocess.PIPE, text=True)\\n p = subprocess.run(command, stdout=subprocess.PIPE, text=True)\\n print(\\\"returncode =\\\", p.returncode)\\n # print(\\\"stdout =\\\", p.stdout)\\n os.remove(video_list_file) # remove file list after merging\\n # rename original chapters after processing\\n for video_chapter in chapter_info[1]:\\n os.rename(video_chapter, f\\\"OK_{video_chapter}\\\")\",\n \"def generate_video_metadata(absolute_paths):\\n\\n vids = []\\n\\n bad_fn = \\\"/share/pi/cleemess/file-conversion-pipeline/bad_mp4s.txt\\\"\\n good_fn = \\\"/share/pi/cleemess/file-conversion-pipeline/good_mp4s.txt\\\"\\n # if os.path.exists(bad_fn):\\n # os.remove(bad_fn)\\n\\n if os.path.exists(bad_fn):\\n with open(bad_fn) as f:\\n bad_paths = set([line.strip() for line in f.readlines()])\\n else:\\n bad_paths = set()\\n\\n if os.path.exists(good_fn):\\n with open(good_fn) as f:\\n good_paths = set([line.strip() for line in f.readlines()])\\n else:\\n good_paths = set()\\n \\n with tqdm(list(absolute_paths)) as pbar:\\n for absolute_path in pbar:\\n if absolute_path in bad_paths or absolute_path in good_paths:\\n continue\\n\\n cmd = \\\"ffprobe -v quiet -print_format json -show_streams %s\\\" % absolute_path\\n try:\\n subprocess.check_output(shlex.split(cmd)).decode(\\\"utf-8\\\")\\n with open(good_fn, \\\"a\\\") as f:\\n f.write(absolute_path + \\\"\\\\n\\\")\\n good_paths.add(absolute_path)\\n except KeyboardInterrupt:\\n raise\\n except Exception as e:\\n with open(bad_fn, \\\"a\\\") as f:\\n f.write(absolute_path + \\\"\\\\n\\\")\\n bad_paths.add(absolute_path)\\n # print(e)\\n # print(cmd)\\n # raise\\n\\n pbar.set_description(f\\\"{len(good_paths)}, {len(bad_paths)}\\\")\\n return vids\",\n \"def _generate_video(\\n out_file,\\n n_frames=5,\\n width=100,\\n height=50,\\n seed=0,\\n fps=24,\\n broken=False,\\n):\\n is_mpeg = out_file.endswith(\\\".mpeg\\\")\\n video_format = \\\"libx264rgb\\\"\\n pixel_format = \\\"rgb24\\\"\\n\\n if is_mpeg:\\n video_format = \\\"mpeg1video\\\"\\n pixel_format = \\\"yuv420p\\\"\\n\\n if broken:\\n n_frames = 0\\n\\n np.random.seed(seed)\\n container = av.open(out_file, mode=\\\"w\\\")\\n stream = container.add_stream(video_format, rate=fps)\\n stream.width = width\\n stream.height = height\\n stream.pix_fmt = pixel_format\\n\\n if is_mpeg:\\n frames = [av.VideoFrame(width, height, pixel_format) for i in range(n_frames)]\\n else:\\n # save lossless video\\n stream.options[\\\"crf\\\"] = \\\"0\\\"\\n images = (np.random.randn(n_frames, height, width, 3) * 255).astype(np.uint8)\\n frames = [\\n av.VideoFrame.from_ndarray(image, format=pixel_format) for image in images\\n ]\\n\\n for frame in frames:\\n for packet in stream.encode(frame):\\n container.mux(packet)\\n\\n if not broken:\\n # flush the stream\\n # video cannot be loaded if this is omitted\\n packet = stream.encode(None)\\n container.mux(packet)\\n\\n container.close()\\n\\n pil_images = [frame.to_image() for frame in frames]\\n return pil_images\",\n \"def frames_to_video(frames, path, scale=2.0):\\n\\n width, height = frames.shape[-2:]\\n width = int(width * scale)\\n height = int(height * scale)\\n fourcc = cv2.VideoWriter_fourcc(*'MP4V')\\n fps = 30\\n out = cv2.VideoWriter(path, fourcc, fps, (width, height))\\n for frame in frames:\\n frame = cv2.merge([frame, frame, frame])\\n if scale != 1:\\n frame = cv2.resize(frame, (width, height), interpolation=cv2.INTER_LINEAR)\\n out.write(frame.astype(np.uint8))\\n\\n out.release()\",\n \"def video_audio_files(video_name, path_curr_vid, path_current_frames):\\n video_file = f\\\"{path_curr_vid}{video_name}\\\"\\n audio_file = f\\\"{path_current_frames}{video_name.replace(VIDEO_EXT,AUDIO_EXT)}\\\"\\n return video_file, audio_file\",\n \"def generateVideo(textFilePath,extractedPath,createdVideoPath):\\r\\n\\t\\t#Check if the save directory exists, If not create directory\\r\\n\\t\\tif not os.path.exists(createdVideoPath):\\r\\n\\t\\t\\tos.mkdir(createdVideoPath)\\r\\n\\t\\t#Open the text file\\r\\n\\t\\tfile = open(textFilePath)\\r\\n\\t\\tvideoPath = createdVideoPath\\r\\n\\r\\n\\t\\tfor hashedData in file:\\r\\n\\t\\t\\thashedData = hashedData.split(\\\"\\\\n\\\")[0]\\r\\n\\t\\t\\timage_folder = extractedPath + \\\"/\\\" + \\\"data\\\" + \\\"/\\\" + hashedData\\r\\n\\t\\t\\tvideo_name = hashedData + \\\".avi\\\"\\r\\n\\t\\t\\timages = os.listdir(image_folder)\\r\\n\\t\\t\\tframe = cv2.imread(os.path.join(image_folder, images[0]))\\r\\n\\t\\t\\theight, width, layers = frame.shape\\r\\n\\t\\t\\t#declare the video writter\\r\\n\\t\\t\\tvideo = cv2.VideoWriter(videoPath + \\\"/\\\" +video_name, 0, 1, (width,height))\\r\\n\\t\\t\\t#Write all images to a single video\\r\\n\\t\\t\\tfor image in images:\\r\\n\\t\\t\\t\\tvideo.write(cv2.imread(os.path.join(image_folder, image)))\\r\\n\\r\\n\\t\\t\\tcv2.destroyAllWindows()\\r\\n\\t\\t\\tvideo.release()\",\n \"def main(_):\\n print('argument to expand', ARGS.video_in)\\n print('argument expanded', glob.glob(ARGS.video_in))\\n video_count = 0\\n for video_filename in glob.glob(ARGS.video_in):\\n print('start parsing', video_filename)\\n data = skvideo.io.ffprobe(video_filename)['video']\\n rate_str = six.ensure_str(data['@r_frame_rate']).split('/')\\n rate = float(rate_str[0]) / float(rate_str[1])\\n print('detected frame rate:', rate)\\n\\n print('load frames:')\\n video = skvideo.io.vreader(video_filename)\\n frame_count = 0\\n file_count = 0\\n for frame in video:\\n if (frame_count > ARGS.offset) and \\\\\\n ((frame_count-ARGS.offset)%ARGS.skip == 0) and \\\\\\n (frame_count/rate >= ARGS.from_s) and \\\\\\n (frame_count/rate <= ARGS.to_s or ARGS.to_s == -1):\\n print(frame_count,)\\n img = Image.fromarray(frame)\\n if ARGS.crop:\\n img = crop(img, ARGS.size)\\n # save file\\n file_number = file_count + video_count * ARGS.multiple + ARGS.start\\n if ARGS.format_ext.lower() == 'jpg':\\n file_out = os.path.join(ARGS.path_out,\\n 'f{:07d}.jpg'.format(file_number))\\n img.save(file_out, 'JPEG')\\n elif ARGS.format_ext.lower() == 'png':\\n file_out = os.path.join(ARGS.path_out,\\n 'f{:07d}.png'.format(file_number))\\n img.save(file_out, 'PNG')\\n else:\\n print('unrecognize format', ARGS.format_ext)\\n sys.exit()\\n file_count += 1\\n frame_count += 1\\n video_count += 1\",\n \"def adorn_video(self, iterator):\\n return iterator \\\\\\n | select(lambda f: f + (\\n video_to_npy(f[1],\\n # note weird thing here, width doesn't work they appear to be inverted\\n height=self.video_size,\\n squarecrop=self.squarecrop,\\n fps=self.framerate,\\n maxlength=self.max_length,\\n # save a npy replacement\\n outfile=self.get_numpy_filename(f[1]),\\n use_cache=self.use_cache\\n ),))\",\n \"def __init__(self, input_file_path, convert_to_bgr=False):\\n self.__yuv_video = YuvDecoder(input_file_path, convert_to_bgr=True)\\n print('After INSTANTIATION')\\n self.__yuv_video.start()\",\n \"def transcode(path, outpath):\\n\\n needs_transcode = determine_transcode(path)\\n logger.info(f\\\"Transcoding {path} to {outpath}...\\\")\\n\\n cmd = [\\n \\\"ffmpeg\\\", \\\"-y\\\",\\n \\\"-i\\\", path,\\n \\\"-an\\\",\\n \\\"-metadata:s\\\", \\\"handler_name=tator\\\",\\n \\\"-vcodec\\\", \\\"libx264\\\",\\n \\\"-g\\\", \\\"25\\\",\\n \\\"-preset\\\", \\\"fast\\\",\\n \\\"-pix_fmt\\\", \\\"yuv420p\\\",\\n \\\"-vf\\\", \\\"pad=ceil(iw/2)*2:ceil(ih/2)*2\\\",\\n \\\"-movflags\\\",\\n \\\"faststart+frag_keyframe+empty_moov+default_base_moof\\\",\\n \\\"-tune\\\", \\\"fastdecode\\\",\\n ]\\n\\n if needs_transcode[1]:\\n #Resize to 720p\\n cmd.extend([\\\"-vf\\\", \\\"scale=-2:720\\\"])\\n\\n cmd.append(outpath)\\n logger.info('ffmpeg cmd = {}'.format(cmd))\\n subprocess.run(cmd, check=True)\\n logger.info(\\\"Transcoding finished!\\\")\",\n \"def mpg2avi(filename):\\n assert filename.endswith('.mpg')\\n ofile = '%s.avi' % os.path.splitext(filename)[0]\\n run_shell_cmd('ffmpeg -y -i %s -qscale:v 2 %s' % (filename, ofile), ignore=True)\\n return ofile\",\n \"def start_ffmpeg_record(stream, stream_url, formatted_date):\\n filename = stream + '_' + formatted_date\\n save_video_dir = 'rover_stream/' + stream\\n subprocess.Popen(['mkdir rover_stream'], shell=True)\\n subprocess.Popen(['mkdir ' + save_video_dir], shell=True)\\n proc_video[stream] = subprocess.Popen(['ffmpeg -i ' + stream_url + ' -acodec copy -vcodec copy ' + save_video_dir + '/' + filename + '.mp4'], stdin=PIPE, shell=True)\",\n \"def video():\\n return Response(gen(Camera()),\\n mimetype='multipart/x-mixed-replace; boundary=frame')\",\n \"def save_video(foldername, songname, songlen, num_steps, output):\\n num_steps_by_len = num_steps / songlen\\n p = subprocess.Popen(['ffmpeg', '-f', 'image2', '-r', str(num_steps_by_len), '-i', '%d.png', '-c:v', 'libx264', '-pix_fmt', 'yuv420p', '-vf', 'pad=ceil(iw/2)*2:ceil(ih/2)*2', 'movie.mp4'], cwd=foldername)\\n p.wait()\\n\\n p = subprocess.Popen(['ffmpeg', '-i', 'movie.mp4', '-i', '../audio_files/' + songname + '.mp3', '-map', '0:v', '-map', '1:a', '-c', 'copy', output], cwd=foldername)\\n p.wait()\",\n \"def recordVideo(args, env, model, filename):\\n # env = model.get_env()\\n images = []\\n images = images + runAGame(model, env, args.method == 'centralized')\\n images = images + runAGame(model, env, args.method == 'centralized')\\n images = images + runAGame(model, env, args.method == 'centralized')\\n images[0].save(filename + '.gif',\\n format='GIF',\\n append_images=images[1:],\\n save_all=True,\\n duration=500,\\n loop=0)\\n print('Video saved:', filename)\",\n \"def test_plenty_of_video_files():\\n # make sure that there is one sequence per video file\\n pipe = VideoPipe(\\n batch_size=BATCH_SIZE, data=PLENTY_VIDEO_FILES, step=1000000, sequence_length=1)\\n pipe.build()\\n iters = math.ceil(len(os.listdir(PLENTY_VIDEO_DIRECTORY)) / BATCH_SIZE)\\n for i in range(iters):\\n print(\\\"Iter \\\" + str(i))\\n pipe.run()\",\n \"def main(path):\\n logger.info(f'Processing video file {path}')\\n # Extract audio\\n audio_file = extract_audio(path, pipeline_config.audio_target_dir)\\n\\n # Generate sound classification results and speech recogniser results\\n sound_results = SoundRecogniser().process_file(audio_file)\\n sound_results = process_overlap(sound_results)\\n speech_results = SpeechRecogniser().process_file(audio_file)\\n\\n # NLP\\n wrds = get_words(speech_results)\\n nlp = SpaCyNaturalLanguageProcessor(pipeline_config.spacy_model)\\n custom_nlp = SpaCyNaturalLanguageProcessor(pipeline_config.custom_spacy_model)\\n processor = nlp.get_spacy_results_processor(wrds, speech_results)\\n custom_processor = custom_nlp.get_spacy_results_processor(wrds, speech_results)\\n chunk_results = processor.process_speech_results_chunk()\\n ner_results = processor.process_speech_results_ner()\\n ner_results.extend(custom_processor.process_speech_results_ner())\\n match_results = processor.process_speech_results_match()\\n speech_results = nlp.process_spurious_words(speech_results, chunk_results)\\n\\n # Add Speech recogniser results, sound classification results and NLP results to a subtitle file\\n subs_1 = save_to_subtitles(speech_results,\\n lambda speech_result: speech_result['word'])\\n subs_1 = compress_subs(subs_1)\\n subs_2 = save_to_subtitles(sound_results,\\n lambda sound_result: sound_result['class'])\\n subs_2 = flatten_subs(subs_2)\\n subs_3 = save_to_subtitles(chunk_results,\\n lambda chunk_result: f'{chunk_result[\\\"word\\\"]} ({chunk_result[\\\"head\\\"]})')\\n subs_4 = save_to_subtitles(ner_results,\\n lambda ner_result: f'{ner_result[\\\"type\\\"]} {ner_result[\\\"word\\\"]}')\\n subs_5 = save_to_subtitles(match_results,\\n lambda match_result: match_result[\\\"word\\\"])\\n\\n combined_subs = append_subs(None, subs_1, style='bottom')\\n combined_subs = append_subs(combined_subs, subs_2, exclude=['bottom'], style='top', formatter=lambda x: f'({x})')\\n combined_subs = append_subs(combined_subs, subs_3, style='left')\\n combined_subs = append_subs(combined_subs, subs_4, style='right')\\n combined_subs = append_subs(combined_subs, subs_5, style='bottom_left_pred')\\n combined_subs = remove_tiny_subs(combined_subs, duration_millis=1000, left_millis=None,\\n right_millis=None, style='top')\\n subtitle_file_name = os.path.splitext(path)[0] + '.ass'\\n create_styles(combined_subs)\\n combined_subs.save(subtitle_file_name)\\n\\n # Burn to a video\\n burn_subtitles_into_video(path, subtitle_file_name, pipeline_config.audio_target_dir)\\n logger.info(f'Done processing {audio_file}')\",\n \"def avi2mpg(filename):\\n assert filename.endswith('.avi')\\n ofile = '%s.mpg' % os.path.splitext(filename)[0]\\n run_shell_cmd('ffmpeg -y -i %s -qscale:v 1 %s' % (filename, ofile), ignore=True)\\n return ofile\",\n \"def filter_videos(\\n files: list\\n):\\n#cSpell:words webm vchd rmvb gifv xvid vidx\\n video_extensions = [\\n \\\"WEBM\\\",\\n \\\"MPG\\\",\\\"MP2\\\", \\\"MPEG\\\", \\\"MPE\\\", \\\"MPV\\\",\\n \\\"OGV\\\",\\\"OGG\\\",\\n \\\"MP4\\\", \\\"M4P\\\", \\\"M4V\\\",\\n \\\"AVI\\\",\\n \\\"WMV\\\",\\n \\\"MOV\\\",\\\"QT\\\",\\n \\\"FLV\\\",\\\"SWF\\\",\\n \\\"F4V\\\",\\\"F4P\\\",\\\"F4A\\\",\\\"F4B\\\",\\n \\\"VCHD\\\",\\n \\\"RMVB\\\",\\\"RM\\\",\\n \\\"VOB\\\",\\n \\\"MKV\\\",\\n \\\"MTS\\\", \\\"M2TS\\\", \\\"TS\\\",\\n \\\"MNG\\\",\\n \\\"GIFV\\\",\\n \\\"GIF\\\",\\n \\\"DRC\\\",\\n \\\"XVID\\\",\\n \\\"VIDX\\\",\\n \\\"ASF\\\",\\n \\\"AMV\\\",\\n \\\"M2V\\\",\\n \\\"SVI\\\",\\n \\\"3GP\\\",\\n \\\"MXF\\\",\\n \\\"ROQ\\\",\\n \\\"NSV\\\",\\n \\\"3G2\\\",\\n ]\\n return filter_files_by_extension(files, video_extensions)\\n ...\",\n \"def video(self, file, draw_frames):\\n # Just loop through the generator as we're only interested\\n # in the output at the end.\\n for _ in self.video_generator(file, draw_frames):\\n continue\\n\\n self._save_tracks(file)\",\n \"def convert_to_mp3(filename: str, title: str, start: int=None, end: int=None) -> list:\\n\\t# setup args for ffmpeg\\n\\tfile_a = f\\\"{path_to_wrk_dir}{filename}.mp4\\\" # input file\\n\\tfile_b = f\\\"{path_to_wrk_dir}{title}.mp3\\\" # output file\\n\\tfiles_b = [] # this list need if file more than 30 mb\\n\\targs = [\\n\\t\\t\\\"/usr/bin/ffmpeg\\\", # path to ffmpeg\\n\\t\\t\\\"-i\\\", # flag for input file\\n\\t\\tfile_a, # input file\\n\\t\\t\\\"-acodec\\\", # setup codec\\n\\t\\t\\\"libmp3lame\\\", # codec name\\n\\t\\t]\\n\\n\\t# now need setup timings for target encode\\n\\tif start is not None and start != 0:\\n\\t\\targs = args + [\\\"-ss\\\", str(start)]\\n\\tif end is not None and end != 0:\\n\\t\\targs = args + [\\\"-t\\\", str(end - start)]\\n\\n\\t# and last part for args to ffmpeg\\n\\targs = args + [\\n\\t\\t\\\"-metadata\\\", # setup metadata for file\\n\\t\\tf\\\"title={title}\\\", # title\\n\\t\\t\\\"-metadata\\\",\\n\\t\\tf\\\"artist={title}\\\", # and artist\\n\\t\\t\\\"-b:a\\\", # setup bitrate\\n\\t\\t\\\"320k\\\", # setup max bitrate\\n\\t\\tfile_b,\\n\\t\\t]\\n\\tprint(f\\\"{args}\\\")\\n\\t# start subprocess for encoding\\n\\tpopen = subprocess.Popen(args)\\n\\tpopen.wait()\\n\\n\\t# check size file. if he more than 30 mb, bot need split him to chunks.\\n\\tsize = getsize(file_b) / 1024 / 1024\\n\\tif size > 30 and ( start or end is None ):\\n\\t\\t# setup args for split to chunks\\n\\t\\targs = [\\n\\t\\t\\t\\\"ffprobe\\\",\\n\\t\\t\\t\\\"-show_entries\\\",\\n\\t\\t\\t\\\"format=duration\\\",\\n\\t\\t\\t\\\"-i\\\",\\n\\t\\t\\tfile_b,\\n\\t\\t\\t]\\n\\n\\t\\t# get duration video.\\n\\t\\tpopen = subprocess.Popen(args, stdout=subprocess.PIPE)\\n\\t\\tpopen.wait()\\n\\t\\toutput = popen.stdout.read()\\n\\t\\t# now we know how long this audio file\\n\\t\\t# split to 10 min chunks\\n\\t\\tdur = re.findall(r\\\"\\\\d{1,10}\\\", str(output))\\n\\t\\t# get chunks count for loop\\n\\t\\tcount_chunks = (int(dur[0]) // 600) + 1\\n\\t\\tfor chunk_start_time in range(0, count_chunks):\\n\\t\\t\\t# setup args for split\\n\\t\\t\\t# big parts of args the same for encode\\n\\t\\t\\targs = [\\n\\t\\t\\t\\t\\\"/usr/bin/ffmpeg\\\",\\n\\t\\t\\t\\t\\\"-i\\\",\\n\\t\\t\\t\\tfile_b,\\n\\t\\t\\t\\t\\\"-ss\\\",\\n\\t\\t\\t\\tf\\\"{chunk_start_time * 600}\\\", # when start chunk\\n\\t\\t\\t\\t\\\"-t\\\",\\n\\t\\t\\t\\t\\\"600\\\", # 10 mints duration\\n\\t\\t\\t\\t\\\"-acodec\\\",\\n\\t\\t\\t\\t\\\"copy\\\", # copy\\n\\t\\t\\t\\t\\\"-b:a\\\",\\n\\t\\t\\t\\t\\\"320k\\\",\\n\\t\\t\\t\\tf\\\"{path_to_wrk_dir}{title}_{chunk_start_time}.mp3\\\", # now we have path to video with chunk number.\\n\\t\\t\\t]\\n\\t\\t\\ttry:\\n\\t\\t\\t\\t# start process for cut chunk\\n\\t\\t\\t\\tpopen = subprocess.Popen(args, stdout=subprocess.PIPE)\\n\\t\\t\\t\\tpopen.wait()\\n\\t\\t\\t# handle except.\\n\\t\\t\\texcept Exception as e:\\n\\t\\t\\t\\tprint(f\\\"Exception - {e}\\\")\\n\\t\\t\\tfiles_b.append(f\\\"{path_to_wrk_dir}{title}_{chunk_start_time}.mp3\\\") # append name of file in list\\n\\t\\tremove(file_b)\\n\\ttry:\\n\\t\\t# remove tmp file\\n\\t\\tremove(file_a)\\n\\t# handle except\\n\\texcept FileNotFoundError:\\n\\t\\tfiles = get_file_list(path_to_wrk_dir)\\n\\t\\tfor i in files:\\n\\t\\t\\tif -1 != f\\\"{path_to_wrk_dir}{i}\\\".find(f\\\"{filename}\\\") and f\\\"{i}\\\".find(f\\\".mp3\\\") == -1:\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tremove(f\\\"{path_to_wrk_dir}{i}\\\")\\n\\t\\t\\t\\texcept FileNotFoundError:\\n\\t\\t\\t\\t\\tprint(f\\\"can't remove file {path_to_wrk_dir}{i}\\\")\\n\\tif len(files_b) == 0:\\n\\t\\treturn [file_b]\\n\\telse:\\n\\t\\treturn files_b\",\n \"def ffmpeg_subclip_video_file(filename, t1, t2):\\n subprocess.call(['ffmpeg', '-i', filename, '-ss', str(t1), '-to', str(t2), '-c', 'copy', '-y', filename.split('.')[0] + '_subclip.mp4'])\\n return\",\n \"def _read_file(self):\\n extension = self.path.split('.')[-1]\\n if extension!='avi':\\n raise Exception(\\\"Invalid Format\\\")\\n\\n return cv2.VideoCapture(self.path)\",\n \"def convert_to_mp4(filepath=''):\\n print \\\"Converting \\\" + filepath + \\\" to mp4...\\\"\\n base = os.path.splitext(filepath)\\n basename = base[0]\\n subprocess.call([\\n 'convert',\\n '-coalesce',\\n '-background',\\n 'white',\\n filepath,\\n basename + '%05d.png'\\n ])\\n\\n frame_rate = get_frame_rate(filepath)\\n print \\\"Using frame rate of \\\" + frame_rate\\n\\n # avconv -r 8 -i frame%02d.png -qscale 4 test.mp4\\n # convert frames to avi\\n subprocess.call([\\n 'avconv',\\n '-r',\\n frame_rate,\\n '-i',\\n basename + '%05d.png',\\n '-qscale',\\n '4',\\n '-b:a',\\n '192k',\\n '-y',\\n '-loglevel',\\n 'quiet',\\n '-vf',\\n 'scale=trunc(iw/2)*2:trunc(ih/2)*2',\\n basename + '.mp4'\\n ])\\n\\n # clean up\\n for fl in glob.glob(basename + '*png'):\\n os.remove(fl)\\n return basename + '.mp4'\",\n \"def _decode_frames_by_av_module(\\n full_path,\\n video_start_pts=0,\\n video_end_pts=None,\\n audio_start_pts=0,\\n audio_end_pts=None,\\n):\\n if video_end_pts is None:\\n video_end_pts = float(\\\"inf\\\")\\n if audio_end_pts is None:\\n audio_end_pts = float(\\\"inf\\\")\\n container = av.open(full_path)\\n\\n video_frames = []\\n vtimebase = torch.zeros([0], dtype=torch.int32)\\n if container.streams.video:\\n video_frames = _read_from_stream(\\n container,\\n video_start_pts,\\n video_end_pts,\\n container.streams.video[0],\\n {\\\"video\\\": 0},\\n )\\n # container.streams.video[0].average_rate is not a reliable estimator of\\n # frame rate. It can be wrong for certain codec, such as VP80\\n # So we do not return video fps here\\n vtimebase = _fraction_to_tensor(container.streams.video[0].time_base)\\n\\n audio_frames = []\\n atimebase = torch.zeros([0], dtype=torch.int32)\\n if container.streams.audio:\\n audio_frames = _read_from_stream(\\n container,\\n audio_start_pts,\\n audio_end_pts,\\n container.streams.audio[0],\\n {\\\"audio\\\": 0},\\n )\\n atimebase = _fraction_to_tensor(container.streams.audio[0].time_base)\\n\\n container.close()\\n vframes = [frame.to_rgb().to_ndarray() for frame in video_frames]\\n vframes = torch.as_tensor(np.stack(vframes))\\n\\n vframe_pts = torch.tensor([frame.pts for frame in video_frames], dtype=torch.int64)\\n\\n aframes = [frame.to_ndarray() for frame in audio_frames]\\n if aframes:\\n aframes = np.transpose(np.concatenate(aframes, axis=1))\\n aframes = torch.as_tensor(aframes)\\n else:\\n aframes = torch.empty((1, 0), dtype=torch.float32)\\n\\n aframe_pts = torch.tensor([audio_frame.pts for audio_frame in audio_frames], dtype=torch.int64)\\n\\n return DecoderResult(\\n vframes=vframes,\\n vframe_pts=vframe_pts,\\n vtimebase=vtimebase,\\n aframes=aframes,\\n aframe_pts=aframe_pts,\\n atimebase=atimebase,\\n )\",\n \"def process_video(video_dir, save_dir):\\n for sig_vid in tqdm(find_files(video_dir, '*.{}'.format(VID_FORMAT))):\\n \\n vc = cv2.VideoCapture(sig_vid) \\n width = int(vc.get(cv2.CAP_PROP_FRAME_WIDTH))\\n height = int(vc.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n rig_bot_height, rig_bot_width = height // 2, width // 2\\n\\n if rig_bot_height == 540 and rig_bot_width == 960:\\n # right bottom, r_h, l_w, r_w\\n iou = [390, 90, 890]\\n\\n elif rig_bot_height == 720 and rig_bot_width == 1280:\\n log.info('high resolution video, please confirm iou param')\\n\\n else:\\n assert 'please confirm video resolution'\\n\\n count = 0\\n cout_save = 0\\n\\n while vc: \\n rval, frame = vc.read() \\n\\n if rval == True:\\n count += 1\\n # fisheye extract front preview\\n ext_region = frame[rig_bot_height:, rig_bot_width:]\\n cv2.imshow('ori frame', ext_region)\\n\\n key = cv2.waitKey(0) & 0xFF\\n if key == ord('q'):\\n break\\n\\n elif key == ord('s'): \\n # Interval 20 frame save \\n if cout_save % 20 == 0 or cout_save > 20: \\n file_name = create_files(save_dir, sig_vid)\\n img_res = process_frame(ext_region, iou)\\n cv2.imwrite(os.path.join(save_dir, file_name)+\\\"/\\\"+ file_name+\\\"_{}.jpg\\\".format(count),img_res)\\n cout_save = 0\\n log.info('successful save current frame {}'.format(count))\\n\\n else:\\n cout_save += 1\\n continue\\n cout_save += 1\\n\\n else:\\n # skip current frame and cout pre save frame interval\\n if cout_save > 0:\\n cout_save += 1\\n continue\\n\\n else:\\n break\\n \\n vc.release()\\n cv2.destroyAllWindows()\",\n \"def merge(vidlist,weekNumber):\\n #generate day for file name\\n today = date.today()\\n d = today.strftime(\\\"%Y_%m_%d\\\")\\n #resizing video\\n clips = []\\n for vid in vidlist:\\n if vid.endswith(\\\".mp4\\\"):\\n video = VideoFileClip(vid)\\n ratio = video.h / video.w\\n if ratio < (16/9 - 0.01):\\n video = video.resize(width=1080)\\n else:\\n video = video.resize(height=1920)\\n clips.append(video)\\n finalrender = concatenate_videoclips(clips,method='compose')\\n finalrender.write_videofile(str(weekNumber)+'.mp4',codec='libx264')\",\n \"def process_video(self):\\n if os.path.isfile(self.source):\\n self.cap = cv2.VideoCapture(self.source)\\n else:\\n try:\\n file_name = \\\"input.mp4\\\"\\n self.source = self.source.replace('open', 'uc')\\n print( \\\"\\\\nDownloading video file from drive link to %s\\\\n\\\"%file_name)\\n gdown.download(self.source, file_name, quiet=False)\\n print( \\\"%s downloaded!\\\\n\\\"%file_name )\\n self.cap = cv2.VideoCapture(file_name)\\n except Exception:\\n raise RuntimeError(\\\"Invalid source input, please specify a Google drive link or a downloaded local file as input \\\\n\\\")\\n\\n\\n assert self.cap.isOpened(), \\\"Failed to open %s\\\" % self.source\\n\\n self.w = int(self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))\\n self.h = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n self.fps = self.cap.get(cv2.CAP_PROP_FPS) \\n self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))\\n return\",\n \"def _decode(item):\\n tivo_filename = item.filename()\\n logger.info(\\\"Decoding %s\\\" % tivo_filename)\\n\\n mpeg_filename = item.filename(ext=\\\"mpg\\\")\\n videos_dir = item.vdir()\\n\\n p = subprocess.Popen([\\\"/usr/local/bin/tivodecode\\\", \\\"--mak\\\", os.environ[\\\"MAK\\\"], \\n \\\"--out\\\", mpeg_filename, tivo_filename], cwd=videos_dir,\\n stderr=subprocess.STDOUT, stdout=subprocess.PIPE)\\n rc = p.wait()\\n\\n logger.info(\\\"tivodecode returned %d\\\" % rc)\\n logger.info(\\\"tivodecode output: '%s'\\\" % p.stdout.read())\\n if rc == 0:\\n # success!\\n item.decoded = True\\n item.save()\\n else:\\n raise Exception(\\\"Tivodecode failed on file '%s' with rc %d\\\" %\\n (tivo_filename, rc))\",\n \"def play(self, path=None):\\n if path is None:\\n path = self.download()\\n\\n # Clear the users out buffer before playing the video\\n os.system('cls' if os.name == 'nt' else 'clear')\\n\\n # Better colors for display\\n utils.term.bgcolor = 'white'\\n count = 0\\n\\n vid = cv2.VideoCapture(path)\\n\\n while(vid.isOpened()):\\n # Read the frame\\n ret, frame = vid.read()\\n count += 1\\n\\n # Convert to work with PIL and fabulous\\n frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\\n image = Image.fromarray(frame)\\n\\n display = fi.Image(\\\"demo.png\\\")\\n display.img = image.convert(\\\"RGBA\\\")\\n display.resize()\\n print str(display)[:-1]\\n\\n # Clear the stdout buffer after MAX_FRAME number frames\\n if count % self.MAX_BUF_SIZE == 0:\\n os.system('cls' if os.name == 'nt' else 'clear')\",\n \"def main():\\n input_video = sys.argv[1]\\n input_audio = sys.argv[2]\\n output_video = sys.argv[3]\\n set_audio(input_video, input_audio, output_video)\",\n \"def generate_still_from_video(self,\\n in_path: str\\n ) -> Tuple[bytes, float, str]:\\n out_filepath = f\\\"/tmp/{uuid4()}.jpg\\\"\\n command = [\\n \\\"ffmpeg\\\",\\n \\\"-i\\\", in_path,\\n \\\"-vframes\\\", \\\"1\\\",\\n out_filepath\\n ]\\n\\n process = subprocess.Popen(command,\\n stdout=subprocess.PIPE,\\n stderr=subprocess.PIPE)\\n process.wait()\\n stderr = process.stderr.read().decode(\\\"utf-8\\\")\\n\\n # Parse start timecode\\n timecode = self.parse_start_timecode_from_stderr(stderr)\\n\\n # Read new file back in and delete\\n try:\\n with open(out_filepath, \\\"rb\\\") as f:\\n file_out_bytes = f.read()\\n os.remove(out_filepath)\\n except FileNotFoundError:\\n raise TranscodeError(\\\"FFmpeg returned a non-zero code.\\\\n\\\" + stderr)\\n\\n return file_out_bytes, timecode, stderr\",\n \"def extract_vob(in_vob, guid):\\n\\t#Detect interlacing.\\n\\tmediainfo_command = \\\"mediainfo --Inform='Video;%ScanType%,%ScanOrder%' \\\" + in_vob\\n\\tprint(mediainfo_command)\\n\\tprocess = subprocess.Popen(mediainfo_command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n\\t(cout, cerr) = process.communicate()\\n\\texit_code = process.wait()\\n\\tif exit_code != 0:\\n\\t\\traise Exception(\\\"Calling Mediainfo on {in_vob} failed with exit code {exit_code}.\\\".format(in_vob=in_vob, exit_code=exit_code))\\n\\tmediainfo_parts = cout.decode(\\\"utf-8\\\").split(\\\",\\\")\\n\\tis_interlaced = mediainfo_parts[0] == \\\"Interlaced\\\"\\n\\tfield_order = mediainfo_parts[1].lower().strip()\\n\\tprint(\\\"Interlace detection:\\\", is_interlaced, field_order, \\\"(\\\", mediainfo_parts, \\\")\\\")\\n\\n\\tffmpeg_command = [\\\"ffmpeg\\\", \\\"-i\\\", in_vob]\\n\\tprint(ffmpeg_command)\\n\\tprocess = subprocess.Popen(ffmpeg_command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\\n\\t(cout, cerr) = process.communicate()\\n\\tprocess.wait() #Ignore the exit code. It always fails.\\n\\tvobinfo = cerr.decode(\\\"utf-8\\\")\\n\\ttracks = []\\n\\tfor match in re.finditer(r\\\" Stream #0:(\\\\d+)\\\\[0x[0-9a-f]+\\\\]: (\\\\w+): ([^\\\\n]+)\\\", vobinfo):\\n\\t\\ttrack_nr = match.group(1)\\n\\t\\ttrack_type = match.group(2)\\n\\t\\ttrack_codec = match.group(3)\\n\\t\\tnew_track = track.Track()\\n\\t\\tnew_track.from_vob(track_nr, track_type, track_codec, is_interlaced, field_order)\\n\\t\\tnew_track.file_name = guid + \\\"-T\\\" + str(new_track.track_nr) + \\\".\\\" + new_track.codec\\n\\t\\tif new_track.type != \\\"unknown\\\":\\n\\t\\t\\ttracks.append(new_track)\\n\\n\\t#Generate the parameters to pass to ffmpeg.\\n\\ttrack_params = [\\\"-i\\\", in_vob]\\n\\tfor track_metadata in tracks:\\n\\t\\ttrack_params.append(\\\"-map\\\")\\n\\t\\ttrack_params.append(\\\"0:\\\" + str(track_metadata.track_nr))\\n\\t\\ttrack_params.append(\\\"-c\\\")\\n\\t\\ttrack_params.append(\\\"copy\\\")\\n\\t\\ttrack_params.append(track_metadata.file_name)\\n\\n\\t#Extract all tracks.\\n\\tprint(\\\"---- Extracting tracks...\\\")\\n\\tffmpeg(*track_params)\\n\\n\\treturn tracks\",\n \"def convert_data(data_dir, dname):\\n # Get videos from the original dataset\\n seq_generator = get_seq(data_dir, dname)\\n # Process videos\\n for n, (f, k, seq) in enumerate(seq_generator):\\n # Create a directory for the video\\n f = os.path.splitext(os.path.basename(f))[0]\\n dirname = os.path.join(data_dir, 'processed_data', dname, f, f'{k:03d}')\\n os.makedirs(dirname)\\n # Save all frames in .png files\\n for i, img in enumerate(seq):\\n img.save(os.path.join(dirname, f'{i:03d}.png'), 'PNG')\\n print(f'{dirname} ({n + 1})')\",\n \"def mp4_to_webm(srcfile, destfile, overwrite=False):\\n syspkgs.check_installs([\\\"ffmpeg\\\"])\\n cmd = [\\n \\\"ffmpeg\\\",\\n \\\"-i\\\",\\n srcfile,\\n \\\"-c:v\\\",\\n \\\"libvpx\\\",\\n \\\"-crf\\\",\\n \\\"10\\\",\\n \\\"-b:v\\\",\\n \\\"1M\\\",\\n \\\"-c:a\\\",\\n \\\"libvorbis\\\",\\n destfile,\\n ]\\n if overwrite:\\n cmd.insert(1, \\\"-y\\\")\\n print(\\\" \\\".join(cmd))\\n return subprocess.check_output(cmd, encoding=\\\"utf-8\\\")\",\n \"def encode_videos(self):\\n\\t\\t\\n\\t\\tself.face_detector = MTCNN()\\n\\t\\tencoder = MyVGGFace(self.vgg_l, self.vgg_v)\\n\\t\\t\\n\\t\\tfolder = self.folders['raw_video_folder']\\n\\t\\t\\n\\t\\tfor (dirpath, _, filenames) in os.walk(folder):\\n\\t\\t\\tif platform == 'linux' or platform == 'linux2' or platform == 'darwin':\\n\\t\\t\\t\\t# linux and OSX\\n\\t\\t\\t\\tsplit_path = dirpath.split('/')\\n\\t\\t\\telse:\\n\\t\\t\\t\\t# windows\\n\\t\\t\\t\\tsplit_path = dirpath.split('\\\\\\\\')\\n\\t\\t\\tif filenames:\\n\\t\\t\\t\\tif self.options.verbose:\\n\\t\\t\\t\\t\\tprint(f'Extracting features from {dirpath}')\\n\\t\\t\\t\\tfor file in progressbar.progressbar(filenames):\\n\\t\\t\\t\\t\\tencode_path = (f'{self.feature_folder}/{split_path[-2]}', f'{file[:14]}.pic')\\n\\t\\t\\t\\t\\tcoord_path = (f'{self.folders[\\\"facial_data\\\"]}', f'{file[:14]}.pic')\\n\\t\\t\\t\\t\\tif file.endswith('.mp4') and not os.path.exists(f'{encode_path[0]}/{encode_path[1]}'):\\n\\t\\t\\t\\t\\t\\tfaces, coords = self.video_faces(f'{dirpath}/{file}', f'{coord_path[0]}/{coord_path[1]}')\\n\\t\\t\\t\\t\\t\\tencoding = encoder.vggface_encoding(faces)\\n\\t\\t\\t\\t\\t\\tsave_to_file(coord_path[0], coord_path[1], coords)\\n\\t\\t\\t\\t\\t\\tsave_to_file(encode_path[0], encode_path[1], encoding.reshape(encoding.shape[0], -1))\\n\\t\\t\\t\\t\\t\\tdel faces, encoding\\n\\t\\t\\t\\t\\t\\tgc.collect()\",\n \"def video_to_frames(video_filename,output_dir):\\n cap = cv2.VideoCapture(video_filename)\\n video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1\\n vid_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\\n vid_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\n vid_fps = int(cap.get(cv2.CAP_PROP_FPS))\\n print(\\\"vid_res=%d x %d, fps=%d\\\\n\\\" % (vid_width, vid_height,vid_fps))\\n crop_width=int(vid_width/128)*128\\n crop_height=int(vid_height/128)*128\\n grab_step=int(vid_fps/2)\\n if cap.isOpened() and video_length > 0:\\n count = 0\\n frame_id=0\\n success, image = cap.read()\\n while success and frame_id <= 9999:\\n if count%grab_step==0:\\n crop_img = image[0:crop_width, 0:crop_height]\\n resized_img = cv2.resize(crop_img, (128, 128)) \\n cv2.imwrite(output_dir+\\\"/frame%05d.jpg\\\" % frame_id, resized_img)\\n frame_id+=1\\n success, image = cap.read()\\n count += 1\\n return 0\",\n \"def video_times():\\n p = parse_cmdline(get_parser=get_parser_times)\\n log.setup_main_handler(\\n mods=(\\\"fogtools\\\", \\\"typhon\\\", \\\"fogpy\\\", \\\"sattools\\\", \\\"fcitools\\\", \\\"satpy\\\",\\n \\\"pyresample\\\"),\\n level=logging.DEBUG)\\n vis.show_video_abi_glm_times(\\n start_date=p.start_time,\\n end_date=p.end_time,\\n img_out=p.filename_pattern_image,\\n vid_out=p.filename_pattern_video,\\n out_dir=p.outdir,\\n sector=p.sector,\\n area=p.area)\\n print(\\\"Files written to:\\\", p.outdir)\",\n \"def get_video_as_images():\\n experiments = ['me1.mp4']\\n try:\\n if (os.path.isdir(\\\"dump\\\")):\\n shutil.rmtree('dump')\\n except OSError:\\n print (\\\"Deletion of the directory failed\\\")\\n exit()\\n os.system('mkdir dump')\\n for experiment in experiments:\\n exp_no_ext = experiment.split('.')[0]\\n subdir_cmd = \\\"dump/{0}\\\".format(exp_no_ext)\\n os.mkdir(subdir_cmd)\\n os.system('ffmpeg -i videos/%s dump/%s/%s%%03d.jpg' % (experiment, exp_no_ext, exp_no_ext))\\n run_all(exp_no_ext)\",\n \"def video2rosbag(ifile, ofile, topic='/camera/image_raw'):\\n\\n from cv_bridge import CvBridge\\n cap = cv2.VideoCapture(ifile)\\n fps = 20\\n wait = 1.0/fps\\n bridge = CvBridge()\\n bag = rosbag.Bag(ofile, 'w')\\n while(cap.isOpened()):\\n ret, frame = cap.read()\\n if ret:\\n msg = bridge.cv2_to_imgmsg(frame, encoding='bgr8')\\n bag.write(topic, msg)\\n time.sleep(wait)\\n else:\\n break\\n cap.release()\\n bag.close()\",\n \"def video_to_frames(input_loc, output_loc, frame_skip):\\n try:\\n os.mkdir(output_loc)\\n except OSError as e:\\n print(e)\\n pass\\n # Log the time\\n time_start = time.time()\\n # Start capturing the feed\\n cap = cv2.VideoCapture(input_loc)\\n # Find the number of frames\\n video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1\\n print (\\\"Number of frames: \\\", video_length)\\n count = 0\\n actual_frames = 0\\n print (\\\"Converting video..\\\\n\\\")\\n # Start converting the video\\n while cap.isOpened():\\n # Extract the frame\\n ret, frame = (None, None)\\n for i in range(frame_skip):\\n ret, frame = cap.read()\\n actual_frames += 1\\n cv2.imwrite(output_loc + \\\"/%#05d.jpg\\\" % (actual_frames), frame)\\n count += 1\\n # Write the results back to output location.\\n # If there are no more frames left\\n if (actual_frames > (video_length) - frame_skip):\\n # Log the time again\\n time_end = time.time()\\n # Release the feed\\n cap.release()\\n # Print stats\\n print (\\\"Done extracting frames.\\\\n%d frames extracted\\\" % count)\\n print (\\\"It took %d seconds forconversion.\\\" % (time_end-time_start))\\n break\",\n \"def convert_to_img(vid_file, output_folder):\\n cam = cv2.VideoCapture(vid_file)\\n counter = 0\\n ret = True\\n while(ret):\\n ret, frame = cam.read()\\n if not ret:\\n break\\n\\n cv2.imshow('frame', frame)\\n cv2.imwrite(os.path.join(output_folder, str(counter) + \\\".png\\\"), frame)\\n counter += 1\\n if cv2.waitKey(1) & 0xFF == ord('q'):\\n break\\n\\n cam.release()\\n cv2.destroyAllWindows()\",\n \"def process_group(pattern, params):\\n # check subdirectory according to filter options\\n subdir = params['label']\\n # and the parameters label\\n print 'Processing:', subdir\\n\\n # search for videos matching the pattern\\n search = os.path.join(ROOT_RAWDATA_DIR, pattern)\\n print 'Search pattern:', search\\n flist = sorted(glob.glob(search))\\n\\n # for each matching video\\n for f in flist:\\n # video structures (copied from LEGOS FTP) is yyyymmdd/HH/MM.mp4\\n # and we want to store frames as yyyymmdd/yyyymmdd_HH/yyyymmdd_HHMM/yyyymmdd_HHMM_<index>.<format>\\n # so: recursively split to extract basename, hour and date\\n p, fname = os.path.split(f)\\n p, hour = os.path.split(p)\\n p, date = os.path.split(p)\\n minute, _ = os.path.splitext(fname)\\n # compute output dir, and prefix for frames\\n outdir = os.path.join(ROOT_PREPROC_DIR,\\n subdir, # according to parameters\\n date,\\n '{}_{}'.format(date, hour),\\n '{}_{}{}'.format(date, hour, minute),\\n )\\n prefix = '{}_{}{}_'.format(date, hour, minute)\\n # create output directory if neeeded\\n if not os.path.exists(outdir):\\n print 'Creating output directory', outdir\\n os.makedirs(outdir, 0755)\\n # call decoder\\n command = ['python', '-u', 'decoder.py',\\n f,\\n '-o', outdir,\\n '-p', prefix,\\n '-l', params['label'],\\n '-f', str(params['image_format']),\\n '-m', str(params['median_length']),\\n '-r', str(params['resolution']),\\n '-O', str(params['origin'][0]), str(params['origin'][1]),\\n '-d', str(params['dimensions'][0]), str(params['dimensions'][1]),\\n '-a', str(params['rotation']),\\n ]\\n subprocess.call(command)\",\n \"def get_video_from_camera(cls):\\r\\n cap = cv2.VideoCapture(0)\\r\\n width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\\r\\n heigth = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\\r\\n fps = 30\\r\\n save_file = None\\r\\n \\r\\n Tk().withdraw()\\r\\n save_file = asksaveasfilename(defaultextension=\\\".mp4\\\")\\r\\n \\r\\n if save_file!=None:\\r\\n fourcc = cv2.VideoWriter_fourcc(*'mp4v')\\r\\n out = cv2.VideoWriter(filename=save_file, fourcc=fourcc, fps=fps, \\r\\n frameSize=(width, heigth),isColor=True)\\r\\n \\r\\n while cap.isOpened():\\r\\n ret, frame = cap.read()\\r\\n cv2.imshow('Play video', frame)\\r\\n out.write(frame)\\r\\n if cv2.waitKey(30)==27:\\r\\n break\\r\\n cv2.destroyAllWindows()\\r\\n out.release()\\r\\n cap.release()\\r\\n return save_file\",\n \"def create_video_task_structure(obj):\\n parent_path = resource_path(obj.__parent__)\\n obj_path = resource_path(obj)\\n types = ['.mkv', '.avi'] #FIXME: Settings, formats etc...\\n results = []\\n for mtype in types:\\n task_c = chain(blob_to_tmp_file.s(obj_path), convert_file.s(mtype), tmp_to_blob.s(parent_path, obj_path))\\n results.append(task_c())\\n return results\",\n \"def startVideo(self,fname):\\n\\n\\n try:\\n fourcc = cv2.cv.CV_FOURCC(*'DIVX')\\n\\n except Exception as e:\\n #print \\\"Exception \\\",e.args\\n fourcc = cv2.VideoWriter_fourcc(*'DIVX')\\n\\n self.video = cv2.VideoWriter(fname, fourcc, 10, (self.screenWidth, self.screenHeight))\\n if self.video is None:\\n print \\\"VideoWriter failed to start.\\\"\\n else:\\n print \\\"VideoWriter started ok\\\"\",\n \"def transcode(filePath: str) -> str:\\n asset_uuid = uuid.uuid4()\\n outPath = os.path.join(\\\"/tmp\\\", str(asset_uuid)+'.mp4')\\n ffmpeg.input(filePath).output(outPath).run()\\n return outPath\",\n \"def load_video(self, filename):\\n \\n reader = imageio.get_reader(filename, 'ffmpeg')\\n \\n return np.array(list(reader), dtype=np.float32)\",\n \"def generate_frame(video_path, video_name, second, label, dest_path):\\n print \\\"video_path\\\", video_path\\n print 'video_name',video_name\\n print 'second',second\\n print 'label',label\\n print 'dest_path',dest_path\\n\\n vidcap = cv2.VideoCapture(os.path.join(video_path, video_name))\\n vidcap.set(0, int(second*1000))\\n success, image = vidcap.read()\\n if success:\\n cv2.imwrite(os.path.join(dest_path, video_name+\\\"_\\\"+str(second)+\\\"_\\\"+str(label)+\\\".jpg\\\"), image)\",\n \"def watch_movie():\\r\\n if os.path.isfile('files/final_movie.mp4'): # if the file exists\\r\\n with open('files/final_movie.mp4', 'rb') as f:\\r\\n video_data = f.read()\\r\\n st.video(video_data)\\r\\n else: # if the file doesn't exist, let the user know\\r\\n st.header(\\\"You haven't created a movie yet!\\\")\",\n \"def convert(self, infile, outfile, opts, timeout=10, preopts=None, postopts=None):\\n if os.name == 'nt':\\n timeout = 0\\n\\n if not os.path.exists(infile):\\n raise FFMpegError(\\\"Input file doesn't exist: \\\" + infile)\\n\\n cmds = [self.ffmpeg_path]\\n if preopts:\\n cmds.extend(preopts)\\n cmds.extend(['-i', infile])\\n\\n # Move additional inputs to the front of the line\\n for ind, command in enumerate(opts):\\n if command == '-i':\\n cmds.extend(['-i', opts[ind + 1]])\\n del opts[ind]\\n del opts[ind]\\n\\n cmds.extend(opts)\\n if postopts:\\n cmds.extend(postopts)\\n cmds.extend(['-y', outfile])\\n\\n if timeout:\\n def on_sigalrm(*_):\\n signal.signal(signal.SIGALRM, signal.SIG_DFL)\\n raise Exception('timed out while waiting for ffmpeg')\\n\\n signal.signal(signal.SIGALRM, on_sigalrm)\\n\\n try:\\n p = self._spawn(cmds)\\n except OSError:\\n raise FFMpegError('Error while calling ffmpeg binary')\\n\\n yielded = False\\n buf = ''\\n total_output = ''\\n pat = re.compile(r'time=([0-9.:]+) ')\\n\\n while True:\\n if timeout:\\n signal.alarm(timeout)\\n\\n ret = p.stderr.read(10)\\n\\n if timeout:\\n signal.alarm(0)\\n\\n if not ret:\\n # For small or very fast jobs, ffmpeg may never output a '\\\\r'. When EOF is reached, yield if we haven't yet.\\n if not yielded:\\n yielded = True\\n yield 10\\n break\\n\\n try:\\n ret = ret.decode(console_encoding)\\n except UnicodeDecodeError:\\n try:\\n ret = ret.decode(console_encoding, errors=\\\"ignore\\\")\\n except:\\n pass\\n\\n total_output += ret\\n buf += ret\\n if '\\\\r' in buf:\\n line, buf = buf.split('\\\\r', 1)\\n\\n tmp = pat.findall(line)\\n if len(tmp) == 1:\\n timespec = tmp[0]\\n if ':' in timespec:\\n timecode = 0\\n for part in timespec.split(':'):\\n timecode = 60 * timecode + float(part)\\n else:\\n timecode = float(tmp[0])\\n yielded = True\\n yield timecode\\n\\n if timeout:\\n signal.signal(signal.SIGALRM, signal.SIG_DFL)\\n\\n p.communicate() # wait for process to exit\\n\\n if total_output == '':\\n raise FFMpegError('Error while calling ffmpeg binary')\\n\\n cmd = ' '.join(cmds)\\n if '\\\\n' in total_output:\\n line = total_output.split('\\\\n')[-2]\\n\\n if line.startswith('Received signal'):\\n # Received signal 15: terminating.\\n raise FFMpegConvertError(line.split(':')[0], cmd, total_output, pid=p.pid)\\n if line.startswith(infile + ': '):\\n err = line[len(infile) + 2:]\\n raise FFMpegConvertError('Encoding error', cmd, total_output,\\n err, pid=p.pid)\\n if line.startswith('Error while '):\\n raise FFMpegConvertError('Encoding error', cmd, total_output,\\n line, pid=p.pid)\\n if not yielded:\\n raise FFMpegConvertError('Unknown ffmpeg error', cmd,\\n total_output, line, pid=p.pid)\\n if p.returncode != 0:\\n raise FFMpegConvertError('Exited with code %d' % p.returncode, cmd,\\n total_output, pid=p.pid)\\n\\n return outfile\",\n \"def f2gif(path,fps):\\n print(\\\"==============================================\\\")\\n print(\\\"Convert file to GIF!\\\")\\n pathout = path[:-4]+'_'+str(fps)+'.gif'\\n if path.endswith('.tif'): \\n# import tifffile\\n im = tifffile.imread(path)\\n nFrames, h,w = im.shape\\n dur=1/fps\\n clip = []\\n for i in range(nFrames):\\n fr = cv2.cvtColor(im[i],cv2.COLOR_GRAY2RGB)\\n clip.append(mp.ImageClip(fr).set_duration(dur))\\n video = mp.concatenate_videoclips(clip, method=\\\"compose\\\",ismask=False)#ismask=True to make grayscale\\n\\n else:\\n video = mp.VideoFileClip(path)\\n fpsIn = int(video.fps)\\n if fps != fpsIn:\\n print(\\\"Conflict in fps! \\\\n\\\", \\\"[0] Use fps of input file;\\\\n\\\", \\\"[1] Use desired fps w/o speedup;\\\\n\\\",\\n \\\"[2] Use desired fps w/ speedup:\\\")\\n k = input('Input your selection: ')\\n if k == 2:\\n sf = fps/fpsIn\\n video =video.fx(mp.vfx.speedx, sf)# Not working when sf<1\\n elif k == 0:\\n fps = fpsIn\\n\\n video.write_gif(pathout,fps=fps)\\n video.reader.close()# To fix handel error problem\\n# if path.endswith('.gif'):\\n# clip.write_videofile(pathout,fps=fps,codec='libx264', bitrate='32 M',preset='ultrafast')\\n print(\\\"==============================================\\\")\\n print(\\\"MP4 convertion Done!\\\")\",\n \"def __call__(self, device, timeout, bitrate, compress):\\n if device is None:\\n devices = android.list_devices()\\n device = console.prompt_for_options(\\\"Choose device: \\\", devices)\\n current_dir = os.getcwd()\\n file_path_on_device = android.record_video(device, timeout, bitrate)\\n file_name = os.path.basename(file_path_on_device)\\n android.download_file(device, file_path_on_device, current_dir)\\n android.remove_file(device, file_path_on_device)\\n result_file_path = os.path.join(current_dir, file_name)\\n if compress:\\n console.compress_video(result_file_path)\\n log.info(\\\"Find result at \\\" + result_file_path)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.67228967","0.6123915","0.6026224","0.6026062","0.6018919","0.60161096","0.5921599","0.58913285","0.58782333","0.58761436","0.5870621","0.5852439","0.58420694","0.5806366","0.5802016","0.57724094","0.57724094","0.574259","0.5720971","0.5720353","0.57061726","0.57024807","0.56999785","0.56236637","0.5607386","0.5605964","0.55996925","0.55873966","0.5584279","0.55832636","0.5581374","0.5541743","0.55340767","0.55245894","0.5522144","0.5486773","0.54668874","0.54582506","0.5448679","0.54467225","0.54465836","0.5445959","0.5441157","0.5435368","0.54239225","0.5405827","0.5405566","0.54039377","0.54000694","0.5381512","0.5380654","0.53799665","0.53631353","0.536065","0.53597987","0.53551173","0.5349687","0.5335461","0.53241944","0.5298811","0.52939063","0.52701116","0.526651","0.5266368","0.5261351","0.525034","0.5248964","0.52484673","0.5246383","0.5236251","0.52226335","0.521553","0.521476","0.5194506","0.5193272","0.51879704","0.51876587","0.5187234","0.51823246","0.51817757","0.51783353","0.51690847","0.5167684","0.516631","0.5159132","0.51309955","0.5124228","0.5124009","0.5121145","0.5120048","0.51189786","0.510917","0.51057386","0.5104716","0.5101034","0.50990087","0.5097411","0.50973403","0.5093291","0.5088719"],"string":"[\n \"0.67228967\",\n \"0.6123915\",\n \"0.6026224\",\n \"0.6026062\",\n \"0.6018919\",\n \"0.60161096\",\n \"0.5921599\",\n \"0.58913285\",\n \"0.58782333\",\n \"0.58761436\",\n \"0.5870621\",\n \"0.5852439\",\n \"0.58420694\",\n \"0.5806366\",\n \"0.5802016\",\n \"0.57724094\",\n \"0.57724094\",\n \"0.574259\",\n \"0.5720971\",\n \"0.5720353\",\n \"0.57061726\",\n \"0.57024807\",\n \"0.56999785\",\n \"0.56236637\",\n \"0.5607386\",\n \"0.5605964\",\n \"0.55996925\",\n \"0.55873966\",\n \"0.5584279\",\n \"0.55832636\",\n \"0.5581374\",\n \"0.5541743\",\n \"0.55340767\",\n \"0.55245894\",\n \"0.5522144\",\n \"0.5486773\",\n \"0.54668874\",\n \"0.54582506\",\n \"0.5448679\",\n \"0.54467225\",\n \"0.54465836\",\n \"0.5445959\",\n \"0.5441157\",\n \"0.5435368\",\n \"0.54239225\",\n \"0.5405827\",\n \"0.5405566\",\n \"0.54039377\",\n \"0.54000694\",\n \"0.5381512\",\n \"0.5380654\",\n \"0.53799665\",\n \"0.53631353\",\n \"0.536065\",\n \"0.53597987\",\n \"0.53551173\",\n \"0.5349687\",\n \"0.5335461\",\n \"0.53241944\",\n \"0.5298811\",\n \"0.52939063\",\n \"0.52701116\",\n \"0.526651\",\n \"0.5266368\",\n \"0.5261351\",\n \"0.525034\",\n \"0.5248964\",\n \"0.52484673\",\n \"0.5246383\",\n \"0.5236251\",\n \"0.52226335\",\n \"0.521553\",\n \"0.521476\",\n \"0.5194506\",\n \"0.5193272\",\n \"0.51879704\",\n \"0.51876587\",\n \"0.5187234\",\n \"0.51823246\",\n \"0.51817757\",\n \"0.51783353\",\n \"0.51690847\",\n \"0.5167684\",\n \"0.516631\",\n \"0.5159132\",\n \"0.51309955\",\n \"0.5124228\",\n \"0.5124009\",\n \"0.5121145\",\n \"0.5120048\",\n \"0.51189786\",\n \"0.510917\",\n \"0.51057386\",\n \"0.5104716\",\n \"0.5101034\",\n \"0.50990087\",\n \"0.5097411\",\n \"0.50973403\",\n \"0.5093291\",\n \"0.5088719\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.70133644"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17769,"cells":{"query":{"kind":"string","value":"Set the SQLAlchemy query"},"document":{"kind":"string","value":"def set_sa_query(self, query):\n self.sa_query = query"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def set_query(self, query):\n return self.set_param(\"query\", query)","def query(self, query):\n self._query = query","def set_query(self, query):\n query = pylastica.query.Query.create(query)\n data = query.to_dict()\n return self.set_param('query', data['query'])","def make_query(self):","def query(self, query):\n\n self._query = query","def query(self, query):\n\n self._query = query","def query(self, query):\n\n self._query = query","def set_session_query_tag(self) -> None:\n if self.service_connection.queryTag:\n self.engine.execute(\n SNOWFLAKE_SESSION_TAG_QUERY.format(\n query_tag=self.service_connection.queryTag\n )\n )","def query(self, query):","def query(cls):\n query_class = cls.query_class\n return query_class(orm_class=cls)","def sql_query(self, new_query):\n self._project.sql_query = new_query","def set_queries(self, **kwargs):\n for k, v in kwargs.items():\n self._query_dict[k] = v","def _make_query(self):\r\n raise NotImplementedError()","def execute_query(self, query):\n self.recordset = self.con.execute(query)\n return self","def setQuery(self, tester=None):\n if self.testing:\n self.user = tester\n self.r3.user = self.user\n self.query, self.artist = self.user.search()\n self.case = tester.case\n else:\n self.user = User()\n self.r3.user = self.user\n self.query, self.artist = self.user.search()\n self.case = case(caseFromQuery(self))\n self.DB.open_connection()","def generate_query(self):\n return","def query(self, **kwargs):","def setup(self, query: str, docache=False, recache=False) -> None:\n self.q = Query(query, docache, recache)","def query(self):\n pass","def test_override_query_class(self):\n db = Alchy(self.app, session_options={'query_cls': BaseQuery})\n\n self.assertEquals(db.Query, BaseQuery)\n self.assertIsInstance(db.session.query(), BaseQuery)","def _run_query(self):","def q(cls) -> Query:\n if not cls.s:\n raise M2Error('No DB session defined')\n return cls.s.query(cls)","def set_query(self, query, site_id=None, unique=False):\n return self.get_queryset().set_query(query, site_id, unique)","def determine_query():\n return query if query is not None \\\n else f\"SELECT * FROM '{table}';\"","def query(self):\n return Query(self)","def query(self, **kwargs):\n\n return query.query(self._host, self._session, **kwargs)","def query(self):\r\n raise NotImplementedError","def query(self):\n return self.__query","def __init__(__self__, *,\n query: Optional[str] = None):\n if query is not None:\n pulumi.set(__self__, \"query\", query)","def __init__(self, query_return_values=None, dbsession=None, **kwargs):\n assert query_return_values, 'query_return_values truthiness test failed'\n assert dbsession, 'dbsession truthiness test failed'\n super().__init__(query_return_values=query_return_values, **kwargs)\n self.dbsession = dbsession","def query(self, value: str):\n self._query = value","def get_query(self):\n return self.query_class(self)","def query(self):\n return Query(from_=self)","def query(self) -> None:\n raise NotImplementedError()","def _query(self, model_class, **kwargs):\r\n query = model_class.objects\r\n if self.select_for_update:\r\n query = query.select_for_update()\r\n query = query.filter(**kwargs)\r\n return query","def sql_query(self):\n return self._project.sql_query","def _reset_query(self):\n self.query = pysnow.QueryBuilder()\n self.desired_response_fields = list()","def query(self):","def test_default_query_class(self):\n db = Alchy(self.app)\n\n self.assertEquals(db.Query, alchy.QueryModel)\n self.assertIsInstance(db.session.query(), alchy.QueryModel)","def execute_query(self, *args, **kwargs):","def query(self, *sql):\n self.cursor.execute(*sql)\n self.conn.commit()","def run_query(self):\n return _run_query(self.query)","def test_entity_query_initialization(self, test_domain):\n dao = test_domain.repository_for(Person)._dao\n query = dao.query\n\n assert query is not None\n assert isinstance(query, QuerySet)\n assert query._criteria == QuerySet(dao, test_domain, Person)._criteria","def visit_query(self, query):\n return query","def query(self, value):\n \n self._query = str(value) if value else None","def query(self):\n return self.session.query","def apply_model_to_query(self, query):\n pass","def query(self, query, view=None):\n if isinstance(query, six.string_types):\n query = Q.parse(query)\n if view is None:\n l = lambda: super(DiscoDB, self).query(query)\n else:\n if not isinstance(view, DiscoDBView):\n view = self.make_view(view)\n l = lambda: super(DiscoDB, self).query(query, view=view)\n return DiscoDBLazyInquiry(l)","def queries(self, queries):\n\n self._queries = queries","def __init__(self, sql_engine: sqlalchemy.engine.Engine) -> None:\n self._sql_engine = sql_engine","def _db_query(self, cls, id):\r\n query = db.session.query(self.__class__)\r\n if not id:\r\n limit, offset = self._set_limit_and_offset()\r\n query = self._filter_query(query, limit, offset)\r\n else:\r\n query = [query.get(id)]\r\n return query","def query(engine: sa.engine, query_params: Dict[str, Any]) -> sa.engine.ResultProxy:\r\n with engine.connect() as con:\r\n attr_col = [query_params[Toml.ATTR]]\r\n cols = [sa.Column(x) for x in attr_col + query_params[Toml.COLUMNS]]\r\n table = query_params[Toml.TABLE]\r\n s = sa.select(cols)\r\n s.append_from(sa.text(table))\r\n s.append_whereclause(sa.text(query_params[Toml.EXCLUDE_BY] + ' not in :exclude')\r\n .bindparams(sa.bindparam('exclude', expanding=True)))\r\n return con.execute(s, {\r\n 'exclude': query_params[Toml.EXCLUDE]\r\n })","def query_runtime(self, query_runtime):\n\n self._query_runtime = query_runtime","def filter(self, **kwargs):\n kwargs['query'] += ' FROM {0}'\n return kwargs","def query_table_is(self, query_table):\n self.query_table = query_table\n logger.info(\"Query table is: {query_table}\".format(query_table=query_table))","def query(self, search, query):\n if query:\n return search.query(\n \"simple_query_string\",\n query=query,\n fields=self.fields,\n default_operator='and',\n lenient=True\n )\n return search","def query(self):\n return self._query","def query(self):\n return self._query","def query(self):\n return self._query","def _sql_where(self, cursor, table, prefix=None, aggregate=False):\n assert False, \"subclass responsibility\"","def query(cls):\r\n return cls._session.query(cls)","def __search(self):\n query = self.__query.query()\n self.__engine.search(query)","def sql_query(self, query_text, edges = False):\n q = QueryWrapper(self.graph, QueryString(query_text, 'sql'),\n edges = edges, debug = self._debug)\n return q","def make_query(self, query):\n return Transaction(self, query)","def query(self):\n return self.snowflake_options.query","def query_db(cls):\n return g.db.query(cls)","def do_add_query_field(self):\n raise NotImplementedError","def query_id(self, query_id):\n\n self._query_id = query_id","def query(self):\n \n return self._query","def run(self):\n query = self.query\n\n # count before filtering\n # self.cardinality = query.add_columns(self.columns[0].sqla_expr).count()\n\n self._set_column_filter_expressions()\n self._set_global_filter_expression()\n self._set_sort_expressions()\n self._set_yadcf_data(query)\n\n # apply filters\n query = query.filter(\n *[e for e in self.filter_expressions if e is not None])\n self.filtered_query = deepcopy(query)\n\n # self.cardinality_filtered = query.add_columns(\n # self.columns[0].sqla_expr).count()\n\n # apply sorts\n query = query.order_by(\n *[e for e in self.sort_expressions if e is not None])\n\n # add paging options\n length = int(self.params.get('length'))\n if length >= 0:\n query = query.limit(length)\n elif length == -1:\n pass\n else:\n raise(ValueError(\n 'Length should be a positive integer or -1 to disable'))\n query = query.offset(int(self.params.get('start')))\n\n # add columns to query\n query = query.add_columns(\n *[c.sqla_expr for c in self.columns])\n\n self.filtered_query = self.filtered_query.add_columns(\n *[c.sqla_expr for c in self.columns])\n\n self.query = query\n # fetch the result of the queries\n column_names = [col.mData if col.mData else str(i)\n for i, col in enumerate(self.columns)]\n # self.results = [{k: v for k, v in zip(\n # column_names, row)} for row in query.all()]","def _send_query(self, query) -> None:\n Cli3App.instance().session.send_query(query)","def commitQuery(self):\r\n\t\tself.session.commit()","def query(mdx_stmt):","def construct_query(self):\n reader = QueryReader(filepath=self.filepath, filename=self.filename, raw_sql=self.raw_sql, params=self.params)\n return reader.sql","def query(cls, **kwds):\n #NOTE: Only static properties can be indexed by homer, \n # so we don't worry about querying for dynamic properties\n query = \"\"\n started = False\n for name in kwds:\n if not started:\n pattern = \"%s=:%s\" % (name, name)\n query += pattern\n started = True\n else:\n pattern = \" AND %s=:%s\" % (name, name)\n query += pattern\n\n q = 'SELECT * FROM %s WHERE %s' % (cls.kind(), query)\n query = CqlQuery(cls, q, **kwds)\n query.convert = True\n return query","def soql_query(self, query):\n self.builtin.log(\"Running SOQL Query: {}\".format(query))\n return self.cumulusci.sf.query_all(query)","def filter_sqlalchemy_query(self, query, request):\n query = query.filter(self.sqla_column == request.matchdict[self.key])\n\n if self.parent_router:\n query = query.join(self.sqla_rel)\n query = self.parent_router.filter_sqlalchemy_query(\n query, request)\n\n return query","def query(self):\n self.session_status()\n try:\n if self.keyword['limit'] == None:\n query = self.session.query(self.keyword['tablename']).filter(\n self.keyword['filter']).all()\n else:\n query = self.session.query(self.keyword['tablename']).filter(\n self.keyword['filter']).limit(self.keyword['limit']).all()\n except OperationalError:\n self.retry += 1\n if self.retry > self.max_retry_time:\n raise OperationalError, 'RETRY OUT'\n time.sleep(3)\n self.session.close()\n self.query()\n\n self.session.close()\n\n if not query:\n return []\n self.retry = 0\n return query","def conform_query(cls, query):\n return query","def query(self, sql, rowfactory=None, *args, **kwgs):\n return query(self.conn, sql, rowfactory, *args, **kwgs)","def send_sql_query(self, query):\n # TODO: MAKE SQL SELECT and SQL INSERT modules\n self.cursor.execute(query)\n self.cnx.commit()","def __getattr__(self, name):\n return getattr(SQLQuerySet(proxy=self), name)","def _query_frame_set(self, value):\n self._query_frame = self._prep_frame(value)","def build_query(self):\r\n query = super().build_query()\r\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\r\n algorithm = request.args.get('algorithm')\r\n if algorithm is not None:\r\n query = query.filter(MlModelMetric.algorithm == algorithm)\r\n return query","def select(self, query):\n return Transaction(self, query)","def query(self, db, term):\n raise NotImplementedError('GenericEngine.query is an abstract method.')","def execute_query(self, query):\n with self.db_engine.connect() as conn:\n try:\n result = conn.execute(query)\n\n except Exception as e:\n logger.error(e)\n\n result = None\n \n return result","def __prepare_query(self, query, stopwords=[], stemming_func=None):\n pass","def _query(self, with_clause='', fields={}, groupby='', from_clause=''):\n fields['woo_instance_id'] = \", s.woo_instance_id as woo_instance_id\"\n groupby += ', s.woo_instance_id'\n return super(SaleReport, self)._query(with_clause, fields, groupby, from_clause)","def __init__(self, query, page=1, limit=20):\n self.query = query\n self.page = page\n self.limit = limit\n self.__prepare()","def query(function):\n @functools.wraps(function)\n def marked(self, *args, **kwargs):\n self.session.mark_query([self.key] if hasattr(self, 'key') else [])\n return function(self, *args, **kwargs)\n return marked","def get_query(self):\n q = db.Query(self.KIND,keys_only=self.KEYS_ONLY)\n for prop, value in self.FILTERS:\n q.filter(\"%s =\" % prop, value)\n if self.ancestor:\n q.ancestor(self.ancestor)\n q.order(self.ORDER_BY)\n return q","def sql(self, q):\r\n params = base.get_params(None, locals())\r\n url = '{0}/{1}'.format(self.get_url(), 'sql')\r\n\r\n return http.Request('POST', url, params), parsers.parse_json","def query(self, query):\n\n self.query_pass(query, node=self)\n\n self.clean(node=self)","def _exec (self, sql, **kwargs):\n\n kwargs['id'] = self.id\n cursor = self.connection.cursor ()\n cursor.execute (sql.replace ('table_name', self.table_name), kwargs)\n return cursor","def orwhere(self, query):\n if self.query is None:\n self.query = query\n\n else:\n self.query |= query\n\n return self","def __init__(self, db):\n self.table_name = \"query_latent_space\"\n self.db = db","def gws_q(self, query, attribute, data):\n \n gws_q = query.format(attribute, data) #attribute = SQL table\n \n query_df = db.query(gws_q) \n return query_df","def query(self, q, **kwargs):\n return self._client.query(self._db_name, q, **kwargs)","def build(cls):\n return condor_db.session()"],"string":"[\n \"def set_query(self, query):\\n return self.set_param(\\\"query\\\", query)\",\n \"def query(self, query):\\n self._query = query\",\n \"def set_query(self, query):\\n query = pylastica.query.Query.create(query)\\n data = query.to_dict()\\n return self.set_param('query', data['query'])\",\n \"def make_query(self):\",\n \"def query(self, query):\\n\\n self._query = query\",\n \"def query(self, query):\\n\\n self._query = query\",\n \"def query(self, query):\\n\\n self._query = query\",\n \"def set_session_query_tag(self) -> None:\\n if self.service_connection.queryTag:\\n self.engine.execute(\\n SNOWFLAKE_SESSION_TAG_QUERY.format(\\n query_tag=self.service_connection.queryTag\\n )\\n )\",\n \"def query(self, query):\",\n \"def query(cls):\\n query_class = cls.query_class\\n return query_class(orm_class=cls)\",\n \"def sql_query(self, new_query):\\n self._project.sql_query = new_query\",\n \"def set_queries(self, **kwargs):\\n for k, v in kwargs.items():\\n self._query_dict[k] = v\",\n \"def _make_query(self):\\r\\n raise NotImplementedError()\",\n \"def execute_query(self, query):\\n self.recordset = self.con.execute(query)\\n return self\",\n \"def setQuery(self, tester=None):\\n if self.testing:\\n self.user = tester\\n self.r3.user = self.user\\n self.query, self.artist = self.user.search()\\n self.case = tester.case\\n else:\\n self.user = User()\\n self.r3.user = self.user\\n self.query, self.artist = self.user.search()\\n self.case = case(caseFromQuery(self))\\n self.DB.open_connection()\",\n \"def generate_query(self):\\n return\",\n \"def query(self, **kwargs):\",\n \"def setup(self, query: str, docache=False, recache=False) -> None:\\n self.q = Query(query, docache, recache)\",\n \"def query(self):\\n pass\",\n \"def test_override_query_class(self):\\n db = Alchy(self.app, session_options={'query_cls': BaseQuery})\\n\\n self.assertEquals(db.Query, BaseQuery)\\n self.assertIsInstance(db.session.query(), BaseQuery)\",\n \"def _run_query(self):\",\n \"def q(cls) -> Query:\\n if not cls.s:\\n raise M2Error('No DB session defined')\\n return cls.s.query(cls)\",\n \"def set_query(self, query, site_id=None, unique=False):\\n return self.get_queryset().set_query(query, site_id, unique)\",\n \"def determine_query():\\n return query if query is not None \\\\\\n else f\\\"SELECT * FROM '{table}';\\\"\",\n \"def query(self):\\n return Query(self)\",\n \"def query(self, **kwargs):\\n\\n return query.query(self._host, self._session, **kwargs)\",\n \"def query(self):\\r\\n raise NotImplementedError\",\n \"def query(self):\\n return self.__query\",\n \"def __init__(__self__, *,\\n query: Optional[str] = None):\\n if query is not None:\\n pulumi.set(__self__, \\\"query\\\", query)\",\n \"def __init__(self, query_return_values=None, dbsession=None, **kwargs):\\n assert query_return_values, 'query_return_values truthiness test failed'\\n assert dbsession, 'dbsession truthiness test failed'\\n super().__init__(query_return_values=query_return_values, **kwargs)\\n self.dbsession = dbsession\",\n \"def query(self, value: str):\\n self._query = value\",\n \"def get_query(self):\\n return self.query_class(self)\",\n \"def query(self):\\n return Query(from_=self)\",\n \"def query(self) -> None:\\n raise NotImplementedError()\",\n \"def _query(self, model_class, **kwargs):\\r\\n query = model_class.objects\\r\\n if self.select_for_update:\\r\\n query = query.select_for_update()\\r\\n query = query.filter(**kwargs)\\r\\n return query\",\n \"def sql_query(self):\\n return self._project.sql_query\",\n \"def _reset_query(self):\\n self.query = pysnow.QueryBuilder()\\n self.desired_response_fields = list()\",\n \"def query(self):\",\n \"def test_default_query_class(self):\\n db = Alchy(self.app)\\n\\n self.assertEquals(db.Query, alchy.QueryModel)\\n self.assertIsInstance(db.session.query(), alchy.QueryModel)\",\n \"def execute_query(self, *args, **kwargs):\",\n \"def query(self, *sql):\\n self.cursor.execute(*sql)\\n self.conn.commit()\",\n \"def run_query(self):\\n return _run_query(self.query)\",\n \"def test_entity_query_initialization(self, test_domain):\\n dao = test_domain.repository_for(Person)._dao\\n query = dao.query\\n\\n assert query is not None\\n assert isinstance(query, QuerySet)\\n assert query._criteria == QuerySet(dao, test_domain, Person)._criteria\",\n \"def visit_query(self, query):\\n return query\",\n \"def query(self, value):\\n \\n self._query = str(value) if value else None\",\n \"def query(self):\\n return self.session.query\",\n \"def apply_model_to_query(self, query):\\n pass\",\n \"def query(self, query, view=None):\\n if isinstance(query, six.string_types):\\n query = Q.parse(query)\\n if view is None:\\n l = lambda: super(DiscoDB, self).query(query)\\n else:\\n if not isinstance(view, DiscoDBView):\\n view = self.make_view(view)\\n l = lambda: super(DiscoDB, self).query(query, view=view)\\n return DiscoDBLazyInquiry(l)\",\n \"def queries(self, queries):\\n\\n self._queries = queries\",\n \"def __init__(self, sql_engine: sqlalchemy.engine.Engine) -> None:\\n self._sql_engine = sql_engine\",\n \"def _db_query(self, cls, id):\\r\\n query = db.session.query(self.__class__)\\r\\n if not id:\\r\\n limit, offset = self._set_limit_and_offset()\\r\\n query = self._filter_query(query, limit, offset)\\r\\n else:\\r\\n query = [query.get(id)]\\r\\n return query\",\n \"def query(engine: sa.engine, query_params: Dict[str, Any]) -> sa.engine.ResultProxy:\\r\\n with engine.connect() as con:\\r\\n attr_col = [query_params[Toml.ATTR]]\\r\\n cols = [sa.Column(x) for x in attr_col + query_params[Toml.COLUMNS]]\\r\\n table = query_params[Toml.TABLE]\\r\\n s = sa.select(cols)\\r\\n s.append_from(sa.text(table))\\r\\n s.append_whereclause(sa.text(query_params[Toml.EXCLUDE_BY] + ' not in :exclude')\\r\\n .bindparams(sa.bindparam('exclude', expanding=True)))\\r\\n return con.execute(s, {\\r\\n 'exclude': query_params[Toml.EXCLUDE]\\r\\n })\",\n \"def query_runtime(self, query_runtime):\\n\\n self._query_runtime = query_runtime\",\n \"def filter(self, **kwargs):\\n kwargs['query'] += ' FROM {0}'\\n return kwargs\",\n \"def query_table_is(self, query_table):\\n self.query_table = query_table\\n logger.info(\\\"Query table is: {query_table}\\\".format(query_table=query_table))\",\n \"def query(self, search, query):\\n if query:\\n return search.query(\\n \\\"simple_query_string\\\",\\n query=query,\\n fields=self.fields,\\n default_operator='and',\\n lenient=True\\n )\\n return search\",\n \"def query(self):\\n return self._query\",\n \"def query(self):\\n return self._query\",\n \"def query(self):\\n return self._query\",\n \"def _sql_where(self, cursor, table, prefix=None, aggregate=False):\\n assert False, \\\"subclass responsibility\\\"\",\n \"def query(cls):\\r\\n return cls._session.query(cls)\",\n \"def __search(self):\\n query = self.__query.query()\\n self.__engine.search(query)\",\n \"def sql_query(self, query_text, edges = False):\\n q = QueryWrapper(self.graph, QueryString(query_text, 'sql'),\\n edges = edges, debug = self._debug)\\n return q\",\n \"def make_query(self, query):\\n return Transaction(self, query)\",\n \"def query(self):\\n return self.snowflake_options.query\",\n \"def query_db(cls):\\n return g.db.query(cls)\",\n \"def do_add_query_field(self):\\n raise NotImplementedError\",\n \"def query_id(self, query_id):\\n\\n self._query_id = query_id\",\n \"def query(self):\\n \\n return self._query\",\n \"def run(self):\\n query = self.query\\n\\n # count before filtering\\n # self.cardinality = query.add_columns(self.columns[0].sqla_expr).count()\\n\\n self._set_column_filter_expressions()\\n self._set_global_filter_expression()\\n self._set_sort_expressions()\\n self._set_yadcf_data(query)\\n\\n # apply filters\\n query = query.filter(\\n *[e for e in self.filter_expressions if e is not None])\\n self.filtered_query = deepcopy(query)\\n\\n # self.cardinality_filtered = query.add_columns(\\n # self.columns[0].sqla_expr).count()\\n\\n # apply sorts\\n query = query.order_by(\\n *[e for e in self.sort_expressions if e is not None])\\n\\n # add paging options\\n length = int(self.params.get('length'))\\n if length >= 0:\\n query = query.limit(length)\\n elif length == -1:\\n pass\\n else:\\n raise(ValueError(\\n 'Length should be a positive integer or -1 to disable'))\\n query = query.offset(int(self.params.get('start')))\\n\\n # add columns to query\\n query = query.add_columns(\\n *[c.sqla_expr for c in self.columns])\\n\\n self.filtered_query = self.filtered_query.add_columns(\\n *[c.sqla_expr for c in self.columns])\\n\\n self.query = query\\n # fetch the result of the queries\\n column_names = [col.mData if col.mData else str(i)\\n for i, col in enumerate(self.columns)]\\n # self.results = [{k: v for k, v in zip(\\n # column_names, row)} for row in query.all()]\",\n \"def _send_query(self, query) -> None:\\n Cli3App.instance().session.send_query(query)\",\n \"def commitQuery(self):\\r\\n\\t\\tself.session.commit()\",\n \"def query(mdx_stmt):\",\n \"def construct_query(self):\\n reader = QueryReader(filepath=self.filepath, filename=self.filename, raw_sql=self.raw_sql, params=self.params)\\n return reader.sql\",\n \"def query(cls, **kwds):\\n #NOTE: Only static properties can be indexed by homer, \\n # so we don't worry about querying for dynamic properties\\n query = \\\"\\\"\\n started = False\\n for name in kwds:\\n if not started:\\n pattern = \\\"%s=:%s\\\" % (name, name)\\n query += pattern\\n started = True\\n else:\\n pattern = \\\" AND %s=:%s\\\" % (name, name)\\n query += pattern\\n\\n q = 'SELECT * FROM %s WHERE %s' % (cls.kind(), query)\\n query = CqlQuery(cls, q, **kwds)\\n query.convert = True\\n return query\",\n \"def soql_query(self, query):\\n self.builtin.log(\\\"Running SOQL Query: {}\\\".format(query))\\n return self.cumulusci.sf.query_all(query)\",\n \"def filter_sqlalchemy_query(self, query, request):\\n query = query.filter(self.sqla_column == request.matchdict[self.key])\\n\\n if self.parent_router:\\n query = query.join(self.sqla_rel)\\n query = self.parent_router.filter_sqlalchemy_query(\\n query, request)\\n\\n return query\",\n \"def query(self):\\n self.session_status()\\n try:\\n if self.keyword['limit'] == None:\\n query = self.session.query(self.keyword['tablename']).filter(\\n self.keyword['filter']).all()\\n else:\\n query = self.session.query(self.keyword['tablename']).filter(\\n self.keyword['filter']).limit(self.keyword['limit']).all()\\n except OperationalError:\\n self.retry += 1\\n if self.retry > self.max_retry_time:\\n raise OperationalError, 'RETRY OUT'\\n time.sleep(3)\\n self.session.close()\\n self.query()\\n\\n self.session.close()\\n\\n if not query:\\n return []\\n self.retry = 0\\n return query\",\n \"def conform_query(cls, query):\\n return query\",\n \"def query(self, sql, rowfactory=None, *args, **kwgs):\\n return query(self.conn, sql, rowfactory, *args, **kwgs)\",\n \"def send_sql_query(self, query):\\n # TODO: MAKE SQL SELECT and SQL INSERT modules\\n self.cursor.execute(query)\\n self.cnx.commit()\",\n \"def __getattr__(self, name):\\n return getattr(SQLQuerySet(proxy=self), name)\",\n \"def _query_frame_set(self, value):\\n self._query_frame = self._prep_frame(value)\",\n \"def build_query(self):\\r\\n query = super().build_query()\\r\\n query = query.filter(Metric.metric_id == MlModelMetric.metric_id)\\r\\n algorithm = request.args.get('algorithm')\\r\\n if algorithm is not None:\\r\\n query = query.filter(MlModelMetric.algorithm == algorithm)\\r\\n return query\",\n \"def select(self, query):\\n return Transaction(self, query)\",\n \"def query(self, db, term):\\n raise NotImplementedError('GenericEngine.query is an abstract method.')\",\n \"def execute_query(self, query):\\n with self.db_engine.connect() as conn:\\n try:\\n result = conn.execute(query)\\n\\n except Exception as e:\\n logger.error(e)\\n\\n result = None\\n \\n return result\",\n \"def __prepare_query(self, query, stopwords=[], stemming_func=None):\\n pass\",\n \"def _query(self, with_clause='', fields={}, groupby='', from_clause=''):\\n fields['woo_instance_id'] = \\\", s.woo_instance_id as woo_instance_id\\\"\\n groupby += ', s.woo_instance_id'\\n return super(SaleReport, self)._query(with_clause, fields, groupby, from_clause)\",\n \"def __init__(self, query, page=1, limit=20):\\n self.query = query\\n self.page = page\\n self.limit = limit\\n self.__prepare()\",\n \"def query(function):\\n @functools.wraps(function)\\n def marked(self, *args, **kwargs):\\n self.session.mark_query([self.key] if hasattr(self, 'key') else [])\\n return function(self, *args, **kwargs)\\n return marked\",\n \"def get_query(self):\\n q = db.Query(self.KIND,keys_only=self.KEYS_ONLY)\\n for prop, value in self.FILTERS:\\n q.filter(\\\"%s =\\\" % prop, value)\\n if self.ancestor:\\n q.ancestor(self.ancestor)\\n q.order(self.ORDER_BY)\\n return q\",\n \"def sql(self, q):\\r\\n params = base.get_params(None, locals())\\r\\n url = '{0}/{1}'.format(self.get_url(), 'sql')\\r\\n\\r\\n return http.Request('POST', url, params), parsers.parse_json\",\n \"def query(self, query):\\n\\n self.query_pass(query, node=self)\\n\\n self.clean(node=self)\",\n \"def _exec (self, sql, **kwargs):\\n\\n kwargs['id'] = self.id\\n cursor = self.connection.cursor ()\\n cursor.execute (sql.replace ('table_name', self.table_name), kwargs)\\n return cursor\",\n \"def orwhere(self, query):\\n if self.query is None:\\n self.query = query\\n\\n else:\\n self.query |= query\\n\\n return self\",\n \"def __init__(self, db):\\n self.table_name = \\\"query_latent_space\\\"\\n self.db = db\",\n \"def gws_q(self, query, attribute, data):\\n \\n gws_q = query.format(attribute, data) #attribute = SQL table\\n \\n query_df = db.query(gws_q) \\n return query_df\",\n \"def query(self, q, **kwargs):\\n return self._client.query(self._db_name, q, **kwargs)\",\n \"def build(cls):\\n return condor_db.session()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7269278","0.71905774","0.70059764","0.6978373","0.69707596","0.69707596","0.69707596","0.67561483","0.668698","0.6570881","0.64990544","0.6484255","0.64412546","0.6415398","0.63802713","0.6367533","0.62840843","0.6221531","0.6157206","0.6138676","0.61083823","0.60484827","0.60378987","0.60301864","0.60043126","0.5987234","0.59648293","0.5953579","0.5945616","0.59350985","0.59278244","0.5898927","0.5898345","0.58822715","0.58740455","0.5872985","0.58720934","0.5862571","0.5841268","0.5815269","0.5809925","0.5809875","0.5797098","0.5771835","0.5771696","0.57640535","0.5751599","0.5723165","0.57145756","0.5714562","0.5701191","0.56683767","0.56619436","0.5659664","0.56460494","0.56436473","0.563712","0.563712","0.563712","0.5631614","0.5630297","0.56213576","0.5621304","0.56074446","0.56064886","0.5589577","0.5587386","0.558033","0.55759203","0.55664736","0.5520791","0.5518041","0.55126524","0.55122524","0.55114174","0.5474799","0.5467649","0.54671973","0.5464284","0.5459774","0.5445643","0.54388726","0.5429844","0.5423559","0.5419432","0.5371121","0.5368037","0.536632","0.53522974","0.53504825","0.53489405","0.53457505","0.5345371","0.53426266","0.5340846","0.5340239","0.53299457","0.53199375","0.53194183","0.53179485"],"string":"[\n \"0.7269278\",\n \"0.71905774\",\n \"0.70059764\",\n \"0.6978373\",\n \"0.69707596\",\n \"0.69707596\",\n \"0.69707596\",\n \"0.67561483\",\n \"0.668698\",\n \"0.6570881\",\n \"0.64990544\",\n \"0.6484255\",\n \"0.64412546\",\n \"0.6415398\",\n \"0.63802713\",\n \"0.6367533\",\n \"0.62840843\",\n \"0.6221531\",\n \"0.6157206\",\n \"0.6138676\",\n \"0.61083823\",\n \"0.60484827\",\n \"0.60378987\",\n \"0.60301864\",\n \"0.60043126\",\n \"0.5987234\",\n \"0.59648293\",\n \"0.5953579\",\n \"0.5945616\",\n \"0.59350985\",\n \"0.59278244\",\n \"0.5898927\",\n \"0.5898345\",\n \"0.58822715\",\n \"0.58740455\",\n \"0.5872985\",\n \"0.58720934\",\n \"0.5862571\",\n \"0.5841268\",\n \"0.5815269\",\n \"0.5809925\",\n \"0.5809875\",\n \"0.5797098\",\n \"0.5771835\",\n \"0.5771696\",\n \"0.57640535\",\n \"0.5751599\",\n \"0.5723165\",\n \"0.57145756\",\n \"0.5714562\",\n \"0.5701191\",\n \"0.56683767\",\n \"0.56619436\",\n \"0.5659664\",\n \"0.56460494\",\n \"0.56436473\",\n \"0.563712\",\n \"0.563712\",\n \"0.563712\",\n \"0.5631614\",\n \"0.5630297\",\n \"0.56213576\",\n \"0.5621304\",\n \"0.56074446\",\n \"0.56064886\",\n \"0.5589577\",\n \"0.5587386\",\n \"0.558033\",\n \"0.55759203\",\n \"0.55664736\",\n \"0.5520791\",\n \"0.5518041\",\n \"0.55126524\",\n \"0.55122524\",\n \"0.55114174\",\n \"0.5474799\",\n \"0.5467649\",\n \"0.54671973\",\n \"0.5464284\",\n \"0.5459774\",\n \"0.5445643\",\n \"0.54388726\",\n \"0.5429844\",\n \"0.5423559\",\n \"0.5419432\",\n \"0.5371121\",\n \"0.5368037\",\n \"0.536632\",\n \"0.53522974\",\n \"0.53504825\",\n \"0.53489405\",\n \"0.53457505\",\n \"0.5345371\",\n \"0.53426266\",\n \"0.5340846\",\n \"0.5340239\",\n \"0.53299457\",\n \"0.53199375\",\n \"0.53194183\",\n \"0.53179485\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7026373"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":17770,"cells":{"query":{"kind":"string","value":"Set the \"entity_class_registry\" field"},"document":{"kind":"string","value":"def set_entity_class_registry(self, entity_class_registry):\n self.entity_class_registry = entity_class_registry"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def register_class(self, entity_class):\n key = entity_class.__collection_name__\n\n if key not in self._registered_types:\n self._registered_types[key] = entity_class","def _extract_entity_class_registry(self):\n for description in self.sa_query.column_descriptions:\n if \"entity\" in description:\n declarative_meta = description[\"entity\"]\n _class_registry = getattr(\n declarative_meta, \"_decl_class_registry\", None)\n if _class_registry is not None:\n entity_class_registry = {}\n for elmnt in _class_registry.values():\n if type(elmnt) is DeclarativeMeta:\n description = elmnt.__table__.description\n entity_class_registry[description] = elmnt\n return entity_class_registry\n return None","def set_target_registry(args):\n if 'target_registry' not in args:\n return\n\n if args['target_registry'] == '':\n args['target_registry'] = None\n return\n\n args['target_registry'] = (\n AuthenticatedRegistry.query.filter_by(\n base_name=args['target_registry'])).first()\n\n if args['target_registry'] is None:\n raise NoModelError('Registry')","def entity_reg(hass):\n return mock_registry(hass)","def setEntityLoader(resolver):\n ret = libxml2mod.xmlSetEntityLoader(resolver)\n return ret","def register_bundle(self, cls):\n return self.register_entity('bundle', cls)","def _register(registry, cls):\n assert issubclass(cls, Registrable)\n\n reg_attr = f\"_{cls.__name__}_registered\"\n if getattr(cls, reg_attr, False):\n return cls\n\n name = cls.__fieldtype__()\n assert (\n name not in registry\n ), f\"{cls!r} cannot be registered as {name!r}: already used by {registry[name]!r}\"\n\n registry[name] = cls\n setattr(cls, reg_attr, True)\n return cls","def register_driver(self, key, cls):\n self.drivers.update({key: cls})","def register(cls, class_):\n cls._registered[class_.tag()] = class_","def register_group(self, cls):\n return self.register_entity('group', cls)","def __init__(self):\n self.registry = {}","def _register(cls):\n clsid_path = \"Software\\\\Classes\\\\CLSID\\\\\" + cls._reg_clsid_\n progid_path = \"Software\\\\Classes\\\\\" + cls._reg_progid_\n spec = cls.__module__ + \".\" + cls.__name__\n\n # register the class information\n win32api.RegSetValue(win32con.HKEY_CURRENT_USER, clsid_path, win32con.REG_SZ, cls._reg_desc_)\n win32api.RegSetValue(win32con.HKEY_CURRENT_USER, clsid_path + \"\\\\ProgID\", win32con.REG_SZ, cls._reg_progid_)\n win32api.RegSetValue(win32con.HKEY_CURRENT_USER, clsid_path + \"\\\\PythonCOM\", win32con.REG_SZ, spec)\n hkey = win32api.RegCreateKey(win32con.HKEY_CURRENT_USER, clsid_path + \"\\\\InprocServer32\")\n win32api.RegSetValueEx(hkey, None, None, win32con.REG_SZ, pythoncom.__file__)\n win32api.RegSetValueEx(hkey, \"ThreadingModel\", None, win32con.REG_SZ, \"Both\")\n\n # and add the progid\n win32api.RegSetValue(win32con.HKEY_CURRENT_USER, progid_path, win32con.REG_SZ, cls._reg_desc_)\n win32api.RegSetValue(win32con.HKEY_CURRENT_USER, progid_path + \"\\\\CLSID\", win32con.REG_SZ, cls._reg_clsid_)","def entity_type(self, entity_type):\n self._entity_type = entity_type","def __init__(self):\n self._registry = {}","def register(cls):\n register(cls, cls.provided_class)","def _class(self, _class):\n\n self.__class = _class","def _class(self, _class):\n\n self.__class = _class","def entity_registry_enabled_default(self):\n return False","def entity_type(self, entity_type):\n\n self._entity_type = entity_type","def register_class(cls):\n if cls is RegisteredType:\n raise \"Please do _not_ register RegisteredType!\"\n \n cid = RegisteredType._reg[autoid]\n RegisteredType._reg['classes'][cls] = cid\n RegisteredType._reg['classids'][cid] = cls\n RegisteredType._reg['autoid'] += 1","def set_entity(cls, entity):\n # Preparing auto increment\n entity_count = cls.get_entity_count()\n new_key = \"entity:\" + str(entity_count + 1)\n\n # Set key to Entity\n entity.entity_key = new_key\n\n # Execute HMSET for assigning hash structure\n result = cls.db.hmset(new_key, entity.extract())\n\n # If success, increase key\n if result:\n cls.set_entity_count(entity_count + 1)\n return result","def entity_registry_enabled_default(self) -> bool:\n return False","def setEntityTypes(self, value):\n return self._set(entityTypes=value)","def entity_type(self, entity_type: str):\n\n self._entity_type = entity_type","def entity(self, entity):\n\n self._entity = entity","def include(self, registry):\n for cls in registry.values():\n db_to_element = {}\n\n props = sorted([(k,v) for k,v in cls.__dict__.items()\n if isinstance(v, Property)]\n , key=lambda p:p[1].instance_idx)\n for prop_name, prop_value in props:\n value_name = prop_value.name\n if value_name:\n db_to_element[value_name] = prop_name\n prop_name = value_name\n else:\n db_to_element[prop_name] = prop_name\n\n self.guard_reserved_words(prop_name, cls)\n\n self.props_from_db[cls] = self.create_props_mapping(db_to_element)\n self.init_broker_for_class(cls)\n self.registry[cls.registry_name] = cls","def write_registry(self) -> None:\n self.manager.write_registry()","def set(self, obj: _T) -> None:\n\n self.registry[self.scopefunc()] = obj","def save(self, *args, **kwargs):\n self.entity_type = \"Person\"\n super().save(*args, **kwargs)","def _register(self, aggregate_root_entity):\n if not isinstance(aggregate_root_entity, self._aggregate_root_entity_class()):\n raise TypeError(\"{!r} is not of type {} therefore cannot be store in a {}\"\n .format(aggregate_root_entity, self._aggregate_root_entity_class().__name__, self.__class__.__name__))\n self._track(aggregate_root_entity)\n self._intern(aggregate_root_entity)","def register(dbengine, backendclass):\n backendregistry.register(dbengine, backendclass)","def __init__(cls, name, bases, dct):\n #Create a registry for *this* class\n cls._Registry = {}\n #Insert a reference to this class in it's *base class'* registry\n cls._super(bases)._Registry[name] = cls","def decorator(zha_ent: _ZhaGroupEntityT) -> _ZhaGroupEntityT:\n self._group_registry[component] = zha_ent\n return zha_ent","def registry(self):\n return self.__registry","def setProductComponent(self, *args):\n return _libsbml.SpeciesTypeComponentMapInProduct_setProductComponent(self, *args)","def register(self, cls):\n if not issubclass(cls, FormCallback):\n raise InvalidRegistryItemType(\n \"Invalid item type `{0}` for registry \"\n \"`{1}`\".format(cls, self.__class__)\n )\n\n # uid = self.uidfy(cls)\n # If item has not been forced yet, add/replace its' value in the\n # registry.\n\n if cls in self._registry[cls.stage]:\n return False\n else:\n self._registry[cls.stage].append(cls)\n return True","def extend_entity(cls):\n # Generate the corresponding tag entity\n tag_entity = f\"{cls.__name__}Tag\"\n plural = f\"{cls.__name__.lower()}s\"\n fields = {\n \"_table\": tag_entity,\n plural: Set(cls.__name__),\n \"objects\": property(lambda t: getattr(t, plural, [])),\n }\n #fields['_indexes_'] = [Index(fields['first_name'],fields['last_name'],is_pk=False,is_unique=False)]\n entity = type(tag_entity, (Tag, ), fields)\n cls.db_tags = Set(tag_entity)\n cls.tags = lazy_property(_get_tag_handler)","def register_orm_base(self, base):\n for model in utils.searchable_sqlalchemy_models(base):\n self.register_type(model.es_type_name, model.es_properties, model)","def register(self, klass):\n if klass not in self.extensions:\n self.extensions.append(klass)","def save(self, *args, **kwargs):\n self.entity_type = \"Charity\"\n super().save(*args, **kwargs)","def register(cls, class_to_register):\n cls.registered_loaders.append(class_to_register)\n return class_to_register","def autodiscover():\n \n _autodiscover(registry)","def register_node_type_from_module(self, modname, classname):\n nodelist.register_node_type_from_module(modname, classname)","def set_key_class(self, key_class):\r\n self.key_class = key_class","def set_state_with_entry(\n hass: HomeAssistant,\n entry: er.RegistryEntry,\n state,\n additional_attributes=None,\n new_entity_id=None,\n):\n attributes = {}\n\n if entry.original_name:\n attributes[ATTR_FRIENDLY_NAME] = entry.original_name\n if entry.unit_of_measurement:\n attributes[ATTR_UNIT_OF_MEASUREMENT] = entry.unit_of_measurement\n if entry.original_device_class:\n attributes[ATTR_DEVICE_CLASS] = entry.original_device_class\n\n if additional_attributes:\n attributes = {**attributes, **additional_attributes}\n\n hass.states.async_set(\n entity_id=new_entity_id if new_entity_id else entry.entity_id,\n new_state=state,\n attributes=attributes,\n )","def register(self, regclass: Any, reg_path: str, reg_id: str) -> None:\n reg_id = self.get_reg_name(reg_path, reg_id)\n if reg_id in self._reg_class:\n logger.warning('re-register id: {}'.format(reg_id))\n if not self.allow_replace:\n raise ValueError('Cannot re-register id: {}'.format(reg_id))\n self._reg_class[reg_id] = regclass\n logger.debug('registered: {}'.format(reg_id))","def register(widget):\n w = widget.class_traits()\n _registry.register(w['_model_module'].default_value,\n w['_model_module_version'].default_value,\n w['_model_name'].default_value,\n w['_view_module'].default_value,\n w['_view_module_version'].default_value,\n w['_view_name'].default_value,\n widget)\n return widget","def register_config(cls):\n _configs[cls.__name__] = cls","def create_all(self, registry):\n for cls in registry.values():\n self.create_class(cls)","def register_class(obj):\r\n try:\r\n KnownClass.objects.get(module_name=obj.__module__, class_name=obj.__class__.__name__)\r\n except DoesNotExist:\r\n # Create it\r\n KnownClass(module_name = obj.__module__, class_name = obj.__class__.__name__).save()","def register(self, new_entry: BaseRegistryEntry) -> None:\n self._REGISTERED_ENVS[new_entry.identifier] = new_entry","def _register(self, form_class, check_middleware=True):\n if not issubclass(form_class, _ConfigFormBase):\n raise ValueError(\n \"The form does not inherit from `forms.ConfigForm`\")\n\n self._registry.add(form_class)\n\n if check_middleware:\n _check_backend()","def set_hypercubes_classes(self):\n print(\"Setting the BaseGrid hypercubes' classes.\")\n list_of_all_hc = list(self.hypercubes.flatten())\n print(\"Number of hypercubes: \" + str(len(list_of_all_hc)))\n for hypercube in list_of_all_hc:\n hypercube.set_hypercube_class()\n if self.child_grid:\n self.child_grid.set_hypercubes_classes()","def registry(self):\n return self._registry","def _default_registry(self):\n registry = JSONSerializer.instance().registry.copy()\n registry.update({m.__model__: m for m in find_sql_models()})\n return registry","def extension(klass):\n registry.register(klass)\n return klass","def test_register_manual_keys(self):\n registry = ClassRegistry()\n\n @registry.register('fire')\n class Charizard(Pokemon):\n pass\n\n @registry.register('water')\n class Blastoise(Pokemon):\n pass\n\n # By default, you have to specify a registry key when\n # registering new classes. We'll see how to assign\n # registry keys automatically in the next test.\n with self.assertRaises(ValueError):\n # noinspection PyUnusedLocal\n @registry.register\n class Venusaur(Pokemon):\n pass\n\n self.assertIsInstance(registry['fire'], Charizard)\n self.assertIsInstance(registry['water'], Blastoise)","def register(self, cls):\r\n\r\n # Do all checks and complain before changing any state.\r\n if len(cls.tags) == 0:\r\n raise ValueError(\"No tags specified for class {0}\".format(cls.__name__))\r\n\r\n for t in cls.tags:\r\n if t in self._mapping:\r\n other_cls = self._mapping[t]\r\n if cls == other_cls:\r\n # registering the same class multiple times seems silly, but ok\r\n continue\r\n raise ValueError(\"Tag {0} already registered by class {1}.\"\r\n \" Can't register for class {2}\"\r\n .format(t, other_cls.__name__, cls.__name__))\r\n\r\n # Ok, should be good to change state now.\r\n for t in cls.tags:\r\n self._mapping[t] = cls\r\n\r\n # Returning the cls means we can use this as a decorator.\r\n return cls","def setup_platform(hass, config, add_entities, discovery_info=None):\n \n entity = LiberationRecap()\n add_entities([entity], True)\n \n return True","def register_root(cls):\n if RegisteredType._reg['root_class'] is None:\n\n del RegisteredType._reg\n RegisteredType._reg = {\n 'classes' : { 'classid_key' : 'type'},\n 'autoid' : 0,\n 'classids' : { 'type' : 'classid_key' },\n }\n RegisteredType._reg['root_class'] = cls \n cls.register_class()","def register(self):\n\n RPCObjectsRegistry.add(self)","def set_element_class_lookup(self, lookup=None): # real signature unknown; restored from __doc__\n pass","def registry_host(self, registry_host: str):\n\n self._registry_host = registry_host","def set_searchable_content_types(value):\n record = dict(interface=ICoverSettings, name='searchable_content_types')\n api.portal.set_registry_record(value=value, **record)","def register_user(self, cls):\n return self.register_entity('user', cls)","def update_entities(self):\n raise NotImplementedError()","def _initiate_registry_from_torchlib(\n self, torchlib_registry: registration.Registry\n ):\n for aten_name, aten_overloads_func in torchlib_registry.items():\n for func in aten_overloads_func.overloads:\n self.register(\n aten_name,\n self._opset_version,\n func,\n custom=False,\n )","def setup_class(klass):","def setup_class(klass):","def register(self, cls, force=False):\n if not issubclass(cls, self.type):\n raise InvalidRegistryItemType(\n \"Invalid item type `{0}` for registry \"\n \"`{1}`\".format(cls, self.__class__)\n )\n\n # If item has not been forced yet, add/replace its' value in the\n # registry.\n if force:\n\n if cls.uid not in self._forced:\n self._registry[cls.uid] = cls\n self._forced.append(cls.uid)\n return True\n else:\n return False\n\n else:\n\n if cls.uid in self._registry:\n return False\n else:\n self._registry[cls.uid] = cls\n return True","def values_for_registry(cls, registry):\n cls.validate(registry)\n return {\n 'type': cls.attr_type,\n 'verbose_name': cls.attr_verbose_name,\n 'key': cls.attr_key,\n 'class': cls.get_class(),\n '_class': cls\n }","def register(self):\n raise NotImplementedError","def register(self):\n raise NotImplementedError","def update_registry_for_model(self, config):\n for builder in self._builders:\n builder.update_registry_for_model(config)","def set_EntityID(self, value):\n super(EntityOverviewInputSet, self)._set_input('EntityID', value)","def entity_registry_enabled_default(self) -> bool:\n # these sensors are only here for backwards compatibility, disable them by default\n return False","def _set_registry_client(self, registry_name: str) -> None:\n rg_ = self._operation_scope._resource_group_name\n sub_ = self._operation_scope._subscription_id\n registry_ = self._operation_scope.registry_name\n client_ = self._service_client\n environment_versions_operation_ = self._version_operations\n\n try:\n _client, _rg, _sub = get_registry_client(self._service_client._config.credential, registry_name)\n self._operation_scope.registry_name = registry_name\n self._operation_scope._resource_group_name = _rg\n self._operation_scope._subscription_id = _sub\n self._service_client = _client\n self._version_operations = _client.environment_versions\n yield\n finally:\n self._operation_scope.registry_name = registry_\n self._operation_scope._resource_group_name = rg_\n self._operation_scope._subscription_id = sub_\n self._service_client = client_\n self._version_operations = environment_versions_operation_","def _refresh_registry(cls) -> None:\n cls.objects_dict.clear()\n\n # Add new object instances to the registry.\n for name, clazz in inspect.getmembers(\n objects, predicate=inspect.isclass):\n if name == 'BaseObject':\n continue\n\n ancestor_names = [\n base_class.__name__ for base_class in inspect.getmro(clazz)]\n\n assert 'BaseObject' in ancestor_names\n cls.objects_dict[clazz.__name__] = clazz","def entity_id(self, entity_id):\n\n self._entity_id = entity_id","def __set_registry(values):\n try:\n _write_key = winreg.OpenKey(_registry, \"SYSTEM\\\\CurrentControlSet\\\\Control\\\\Session Manager\", 0, winreg.KEY_WRITE)\n except PermissionError:\n raise PermissionError(\"Permission Denied to write registry key.\")\n winreg.SetValueEx(_write_key, \"PendingFileRenameOperations\", 0, winreg.REG_MULTI_SZ, values)","def add_etl_class(self, etl_class):\n\n if etl_class.identifier in self.etl_classes:\n raise DuplicateConfigException(\n \"Builder already has etl_class with identifier : {}\".format(\n etl_class.identifier\n )\n )\n\n self.etl_classes[etl_class.identifier] = etl_class","def register(cls, package_type):\r\n if not issubclass(package_type, cls):\r\n raise TypeError('package_type must be a subclass of Package.')\r\n cls._REGISTRY.add(package_type)","def set_class_list(self, L):\n\t\tself.class_list = L","def register(self):\n raise NotImplementedError()","def new_entity(self, new_entity):\n\n self._new_entity = new_entity","def setReactantComponent(self, *args):\n return _libsbml.SpeciesTypeComponentMapInProduct_setReactantComponent(self, *args)","def remote_registerEngine(self, engineReference):","def register(self, cls, force=False):\n if not issubclass(cls, self.type):\n raise InvalidRegistryItemType(\n \"Invalid item type `{0}` for registry \"\n \"`{1}`\".format(cls, self.__class__)\n )\n\n uid = BasePluginWidgetRegistry.namify(cls.theme_uid, cls.plugin_uid)\n\n # If item has not been forced yet, add/replace its' value in the\n # registry.\n if force:\n\n if uid not in self._forced:\n self._registry[uid] = cls\n self._forced.append(uid)\n return True\n else:\n return False\n\n else:\n\n if uid in self._registry:\n return False\n else:\n self._registry[uid] = cls\n return True","def reidentify_entity(self, qid, new_qid):\n # We assume this is a new entity\n if self._entity_symbols.qid_exists(new_qid):\n raise ValueError(\n f\"The entity {new_qid} already exists. Please call update_entity instead.\"\n )\n self._entity_symbols.reidentify_entity(qid, new_qid)\n for type_sys in self._type_systems:\n self._type_systems[type_sys].reidentify_entity(qid, new_qid)\n if self._kg_symbols is not None:\n self._kg_symbols.reidentify_entity(qid, new_qid)","def register(self):\n # Set the evaluation state of this instance to REGISTER, as it has been\n # recognized by the root object.\n self.root.registry.append(self)\n self.eval_state = COMMAND_EVAL_REGISTER\n\n # Loop through children and register them too, recursively.\n for ch in self.children:\n # Only register the child if it has not been registered yet;\n # therefore its evaluation state has been set to NONE.\n if ch.eval_state == COMMAND_EVAL_NONE:\n ch.register()","def _fill_class_dicts():\n global _taxonomy_classes\n global _data_classes\n if not _taxonomy_classes:\n _taxonomy_classes = get_taxonomies()\n if not _data_classes:\n stack = []\n next_module = data\n while next_module is not None:\n stack += _inspect_module(next_module)\n if stack:\n next_module = stack.pop()\n else:\n next_module = None","def add_ents(self, ents: Iterable['Entity']) -> None:\n ents = list(ents)\n self.entities.extend(ents)\n for item in ents:\n self.by_class[item['classname'].casefold()].add(item)\n self.by_target[item['targetname', ''].casefold() or None].add(item)\n if 'nodeid' in item:\n try:\n node_id = int(item['nodeid'])\n except (TypeError, ValueError):\n pass\n else:\n item['nodeid'] = str(self.node_id.get_id(node_id))","def _register(cls):\r\n command_name = cls.__dict__.get('__command__', None)\r\n if command_name:\r\n Command._commands[command_name] = cls","def register(cls, basedir, *allowed_target_types):\r\n cls._register(basedir, *allowed_target_types)","def on_registered(self):\r\n super().on_registered()\r\n\r\n # Register type information\r\n cls = self.__class__\r\n\r\n subclass_cache = cls._of_subclass_cache\r\n type_cache = cls._of_type_cache\r\n\r\n # Cache subtypes\r\n for base_cls in cls.__mro__:\r\n try:\r\n instances = subclass_cache[base_cls]\r\n\r\n except KeyError:\r\n instances = subclass_cache[base_cls] = set()\r\n\r\n instances.add(self)\r\n\r\n # Cache the type\r\n try:\r\n instances = type_cache[cls]\r\n\r\n except KeyError:\r\n instances = type_cache[cls] = set()\r\n\r\n instances.add(self)\r\n\r\n ReplicableRegisteredSignal.invoke(target=self)","def register_classes():\n CoaddSplit.register_class()\n CoaddSplit_SG.register_class()","def register_class(self, cls, *, name=None):\n cls_name = self.host.cache_class(cls, name)\n self.register_constant(cls, cls_name)","def _register_resource(self, name):\n GlobalClass.register(self)\n tag = self.module.name.lower()\n group = self.config.xpath(self.xpath + \"/\" + tag)[0]\n group_mysql4 = group.tag + \"_mysql4\"\n resource_model = find_or_create(group, \"resourceModel\")\n resource_model.text = group_mysql4\n group_mysql4 = find_or_create(self.type_elem, group_mysql4)\n class_ = find_or_create(group_mysql4, \"class\")\n class_.text = \"%s_%s_%s_Mysql4\" % (self.module.namespace,\n self.module.name,\n self.type.capitalize())\n entities = find_or_create(group_mysql4, \"entities\")\n name_lower = find_or_create(entities, name.lower())\n table = find_or_create(name_lower, \"table\")\n table.text = self.table or group.tag + \"_\" + name_lower.tag\n self.put_config(self.config)","def initializePredefinedEntities():\n libxml2mod.xmlInitializePredefinedEntities()","def create_class(self, cls):\n\n cls_name = cls.registry_name\n\n bases = [base for base in cls.__bases__ if self.valid_element_base(base)]\n if not bases:\n raise TypeError(\n 'Unexpected base class(es) in Graph.create_class'\n ' - try the declarative bases')\n\n extends = None\n if bases[0] is bases[0].decl_root:\n extends = ['V', 'E'][bases[0].decl_type]\n else:\n extends = ','.join([base.registry_name for base in bases])\n\n #if not self.client.command(\n # 'SELECT FROM ( SELECT expand( classes ) FROM metadata:schema ) WHERE name = \"{}\"'\n # .format(cls_name)):\n try:\n self.client.command(\n 'CREATE CLASS {0} EXTENDS {1}'.format(cls_name, extends))\n except pyorient.PyOrientSchemaException:\n # Class already exists\n pass\n\n db_to_element = {}\n\n props = sorted([(k,v) for k,v in cls.__dict__.items()\n if isinstance(v, Property)]\n , key=lambda p:p[1].instance_idx)\n for prop_name, prop_value in props:\n value_name = prop_value.name\n if value_name:\n db_to_element[value_name] = prop_name\n prop_name = value_name\n else:\n db_to_element[prop_name] = prop_name\n\n self.guard_reserved_words(prop_name, cls)\n\n class_prop = '{0}.{1}'.format(cls_name, prop_name)\n\n linked_to = None\n if isinstance(prop_value, LinkedClassProperty):\n type_linked_to = prop_value.linked_to\n\n linked_to = getattr(type_linked_to, 'registry_name', None)\n if not linked_to:\n link_bases = getattr(type_linked_to, '__bases__', None)\n if link_bases and \\\n isinstance(prop_value, LinkedProperty) and \\\n link_bases[0] is Property:\n linked_to = type_linked_to.__name__\n\n try:\n self.client.command(\n 'CREATE PROPERTY {0} {1} {2}'\n .format(class_prop\n , type(prop_value).__name__\n , linked_to or ''))\n except pyorient.PyOrientCommandException:\n # Property already exists\n pass\n\n if prop_value.default is not None:\n if self.server_version >= (2,1,0):\n self.client.command(\n 'ALTER PROPERTY {0} DEFAULT {1}'\n .format(class_prop,\n PropertyEncoder.encode(prop_value.default)))\n\n self.client.command(\n 'ALTER PROPERTY {0} NOTNULL {1}'\n .format(class_prop\n , str(not prop_value.nullable).lower()))\n\n self.client.command(\n 'ALTER PROPERTY {} MANDATORY {}'\n .format(class_prop\n , str(prop_value.mandatory).lower()))\n\n self.client.command(\n 'ALTER PROPERTY {} READONLY {}'\n .format(class_prop\n , str(prop_value.readonly).lower()))\n\n # TODO Add support for composite indexes\n if prop_value.indexed:\n try:\n self.client.command(\n 'CREATE INDEX {0} {1}'\n .format(class_prop\n , 'UNIQUE' if prop_value.unique\n else 'NOTUNIQUE'))\n except pyorient.PyOrientIndexException:\n # Index already exists\n pass\n\n self.props_from_db[cls] = self.create_props_mapping(db_to_element)\n self.init_broker_for_class(cls)\n self.registry[cls_name] = cls"],"string":"[\n \"def register_class(self, entity_class):\\n key = entity_class.__collection_name__\\n\\n if key not in self._registered_types:\\n self._registered_types[key] = entity_class\",\n \"def _extract_entity_class_registry(self):\\n for description in self.sa_query.column_descriptions:\\n if \\\"entity\\\" in description:\\n declarative_meta = description[\\\"entity\\\"]\\n _class_registry = getattr(\\n declarative_meta, \\\"_decl_class_registry\\\", None)\\n if _class_registry is not None:\\n entity_class_registry = {}\\n for elmnt in _class_registry.values():\\n if type(elmnt) is DeclarativeMeta:\\n description = elmnt.__table__.description\\n entity_class_registry[description] = elmnt\\n return entity_class_registry\\n return None\",\n \"def set_target_registry(args):\\n if 'target_registry' not in args:\\n return\\n\\n if args['target_registry'] == '':\\n args['target_registry'] = None\\n return\\n\\n args['target_registry'] = (\\n AuthenticatedRegistry.query.filter_by(\\n base_name=args['target_registry'])).first()\\n\\n if args['target_registry'] is None:\\n raise NoModelError('Registry')\",\n \"def entity_reg(hass):\\n return mock_registry(hass)\",\n \"def setEntityLoader(resolver):\\n ret = libxml2mod.xmlSetEntityLoader(resolver)\\n return ret\",\n \"def register_bundle(self, cls):\\n return self.register_entity('bundle', cls)\",\n \"def _register(registry, cls):\\n assert issubclass(cls, Registrable)\\n\\n reg_attr = f\\\"_{cls.__name__}_registered\\\"\\n if getattr(cls, reg_attr, False):\\n return cls\\n\\n name = cls.__fieldtype__()\\n assert (\\n name not in registry\\n ), f\\\"{cls!r} cannot be registered as {name!r}: already used by {registry[name]!r}\\\"\\n\\n registry[name] = cls\\n setattr(cls, reg_attr, True)\\n return cls\",\n \"def register_driver(self, key, cls):\\n self.drivers.update({key: cls})\",\n \"def register(cls, class_):\\n cls._registered[class_.tag()] = class_\",\n \"def register_group(self, cls):\\n return self.register_entity('group', cls)\",\n \"def __init__(self):\\n self.registry = {}\",\n \"def _register(cls):\\n clsid_path = \\\"Software\\\\\\\\Classes\\\\\\\\CLSID\\\\\\\\\\\" + cls._reg_clsid_\\n progid_path = \\\"Software\\\\\\\\Classes\\\\\\\\\\\" + cls._reg_progid_\\n spec = cls.__module__ + \\\".\\\" + cls.__name__\\n\\n # register the class information\\n win32api.RegSetValue(win32con.HKEY_CURRENT_USER, clsid_path, win32con.REG_SZ, cls._reg_desc_)\\n win32api.RegSetValue(win32con.HKEY_CURRENT_USER, clsid_path + \\\"\\\\\\\\ProgID\\\", win32con.REG_SZ, cls._reg_progid_)\\n win32api.RegSetValue(win32con.HKEY_CURRENT_USER, clsid_path + \\\"\\\\\\\\PythonCOM\\\", win32con.REG_SZ, spec)\\n hkey = win32api.RegCreateKey(win32con.HKEY_CURRENT_USER, clsid_path + \\\"\\\\\\\\InprocServer32\\\")\\n win32api.RegSetValueEx(hkey, None, None, win32con.REG_SZ, pythoncom.__file__)\\n win32api.RegSetValueEx(hkey, \\\"ThreadingModel\\\", None, win32con.REG_SZ, \\\"Both\\\")\\n\\n # and add the progid\\n win32api.RegSetValue(win32con.HKEY_CURRENT_USER, progid_path, win32con.REG_SZ, cls._reg_desc_)\\n win32api.RegSetValue(win32con.HKEY_CURRENT_USER, progid_path + \\\"\\\\\\\\CLSID\\\", win32con.REG_SZ, cls._reg_clsid_)\",\n \"def entity_type(self, entity_type):\\n self._entity_type = entity_type\",\n \"def __init__(self):\\n self._registry = {}\",\n \"def register(cls):\\n register(cls, cls.provided_class)\",\n \"def _class(self, _class):\\n\\n self.__class = _class\",\n \"def _class(self, _class):\\n\\n self.__class = _class\",\n \"def entity_registry_enabled_default(self):\\n return False\",\n \"def entity_type(self, entity_type):\\n\\n self._entity_type = entity_type\",\n \"def register_class(cls):\\n if cls is RegisteredType:\\n raise \\\"Please do _not_ register RegisteredType!\\\"\\n \\n cid = RegisteredType._reg[autoid]\\n RegisteredType._reg['classes'][cls] = cid\\n RegisteredType._reg['classids'][cid] = cls\\n RegisteredType._reg['autoid'] += 1\",\n \"def set_entity(cls, entity):\\n # Preparing auto increment\\n entity_count = cls.get_entity_count()\\n new_key = \\\"entity:\\\" + str(entity_count + 1)\\n\\n # Set key to Entity\\n entity.entity_key = new_key\\n\\n # Execute HMSET for assigning hash structure\\n result = cls.db.hmset(new_key, entity.extract())\\n\\n # If success, increase key\\n if result:\\n cls.set_entity_count(entity_count + 1)\\n return result\",\n \"def entity_registry_enabled_default(self) -> bool:\\n return False\",\n \"def setEntityTypes(self, value):\\n return self._set(entityTypes=value)\",\n \"def entity_type(self, entity_type: str):\\n\\n self._entity_type = entity_type\",\n \"def entity(self, entity):\\n\\n self._entity = entity\",\n \"def include(self, registry):\\n for cls in registry.values():\\n db_to_element = {}\\n\\n props = sorted([(k,v) for k,v in cls.__dict__.items()\\n if isinstance(v, Property)]\\n , key=lambda p:p[1].instance_idx)\\n for prop_name, prop_value in props:\\n value_name = prop_value.name\\n if value_name:\\n db_to_element[value_name] = prop_name\\n prop_name = value_name\\n else:\\n db_to_element[prop_name] = prop_name\\n\\n self.guard_reserved_words(prop_name, cls)\\n\\n self.props_from_db[cls] = self.create_props_mapping(db_to_element)\\n self.init_broker_for_class(cls)\\n self.registry[cls.registry_name] = cls\",\n \"def write_registry(self) -> None:\\n self.manager.write_registry()\",\n \"def set(self, obj: _T) -> None:\\n\\n self.registry[self.scopefunc()] = obj\",\n \"def save(self, *args, **kwargs):\\n self.entity_type = \\\"Person\\\"\\n super().save(*args, **kwargs)\",\n \"def _register(self, aggregate_root_entity):\\n if not isinstance(aggregate_root_entity, self._aggregate_root_entity_class()):\\n raise TypeError(\\\"{!r} is not of type {} therefore cannot be store in a {}\\\"\\n .format(aggregate_root_entity, self._aggregate_root_entity_class().__name__, self.__class__.__name__))\\n self._track(aggregate_root_entity)\\n self._intern(aggregate_root_entity)\",\n \"def register(dbengine, backendclass):\\n backendregistry.register(dbengine, backendclass)\",\n \"def __init__(cls, name, bases, dct):\\n #Create a registry for *this* class\\n cls._Registry = {}\\n #Insert a reference to this class in it's *base class'* registry\\n cls._super(bases)._Registry[name] = cls\",\n \"def decorator(zha_ent: _ZhaGroupEntityT) -> _ZhaGroupEntityT:\\n self._group_registry[component] = zha_ent\\n return zha_ent\",\n \"def registry(self):\\n return self.__registry\",\n \"def setProductComponent(self, *args):\\n return _libsbml.SpeciesTypeComponentMapInProduct_setProductComponent(self, *args)\",\n \"def register(self, cls):\\n if not issubclass(cls, FormCallback):\\n raise InvalidRegistryItemType(\\n \\\"Invalid item type `{0}` for registry \\\"\\n \\\"`{1}`\\\".format(cls, self.__class__)\\n )\\n\\n # uid = self.uidfy(cls)\\n # If item has not been forced yet, add/replace its' value in the\\n # registry.\\n\\n if cls in self._registry[cls.stage]:\\n return False\\n else:\\n self._registry[cls.stage].append(cls)\\n return True\",\n \"def extend_entity(cls):\\n # Generate the corresponding tag entity\\n tag_entity = f\\\"{cls.__name__}Tag\\\"\\n plural = f\\\"{cls.__name__.lower()}s\\\"\\n fields = {\\n \\\"_table\\\": tag_entity,\\n plural: Set(cls.__name__),\\n \\\"objects\\\": property(lambda t: getattr(t, plural, [])),\\n }\\n #fields['_indexes_'] = [Index(fields['first_name'],fields['last_name'],is_pk=False,is_unique=False)]\\n entity = type(tag_entity, (Tag, ), fields)\\n cls.db_tags = Set(tag_entity)\\n cls.tags = lazy_property(_get_tag_handler)\",\n \"def register_orm_base(self, base):\\n for model in utils.searchable_sqlalchemy_models(base):\\n self.register_type(model.es_type_name, model.es_properties, model)\",\n \"def register(self, klass):\\n if klass not in self.extensions:\\n self.extensions.append(klass)\",\n \"def save(self, *args, **kwargs):\\n self.entity_type = \\\"Charity\\\"\\n super().save(*args, **kwargs)\",\n \"def register(cls, class_to_register):\\n cls.registered_loaders.append(class_to_register)\\n return class_to_register\",\n \"def autodiscover():\\n \\n _autodiscover(registry)\",\n \"def register_node_type_from_module(self, modname, classname):\\n nodelist.register_node_type_from_module(modname, classname)\",\n \"def set_key_class(self, key_class):\\r\\n self.key_class = key_class\",\n \"def set_state_with_entry(\\n hass: HomeAssistant,\\n entry: er.RegistryEntry,\\n state,\\n additional_attributes=None,\\n new_entity_id=None,\\n):\\n attributes = {}\\n\\n if entry.original_name:\\n attributes[ATTR_FRIENDLY_NAME] = entry.original_name\\n if entry.unit_of_measurement:\\n attributes[ATTR_UNIT_OF_MEASUREMENT] = entry.unit_of_measurement\\n if entry.original_device_class:\\n attributes[ATTR_DEVICE_CLASS] = entry.original_device_class\\n\\n if additional_attributes:\\n attributes = {**attributes, **additional_attributes}\\n\\n hass.states.async_set(\\n entity_id=new_entity_id if new_entity_id else entry.entity_id,\\n new_state=state,\\n attributes=attributes,\\n )\",\n \"def register(self, regclass: Any, reg_path: str, reg_id: str) -> None:\\n reg_id = self.get_reg_name(reg_path, reg_id)\\n if reg_id in self._reg_class:\\n logger.warning('re-register id: {}'.format(reg_id))\\n if not self.allow_replace:\\n raise ValueError('Cannot re-register id: {}'.format(reg_id))\\n self._reg_class[reg_id] = regclass\\n logger.debug('registered: {}'.format(reg_id))\",\n \"def register(widget):\\n w = widget.class_traits()\\n _registry.register(w['_model_module'].default_value,\\n w['_model_module_version'].default_value,\\n w['_model_name'].default_value,\\n w['_view_module'].default_value,\\n w['_view_module_version'].default_value,\\n w['_view_name'].default_value,\\n widget)\\n return widget\",\n \"def register_config(cls):\\n _configs[cls.__name__] = cls\",\n \"def create_all(self, registry):\\n for cls in registry.values():\\n self.create_class(cls)\",\n \"def register_class(obj):\\r\\n try:\\r\\n KnownClass.objects.get(module_name=obj.__module__, class_name=obj.__class__.__name__)\\r\\n except DoesNotExist:\\r\\n # Create it\\r\\n KnownClass(module_name = obj.__module__, class_name = obj.__class__.__name__).save()\",\n \"def register(self, new_entry: BaseRegistryEntry) -> None:\\n self._REGISTERED_ENVS[new_entry.identifier] = new_entry\",\n \"def _register(self, form_class, check_middleware=True):\\n if not issubclass(form_class, _ConfigFormBase):\\n raise ValueError(\\n \\\"The form does not inherit from `forms.ConfigForm`\\\")\\n\\n self._registry.add(form_class)\\n\\n if check_middleware:\\n _check_backend()\",\n \"def set_hypercubes_classes(self):\\n print(\\\"Setting the BaseGrid hypercubes' classes.\\\")\\n list_of_all_hc = list(self.hypercubes.flatten())\\n print(\\\"Number of hypercubes: \\\" + str(len(list_of_all_hc)))\\n for hypercube in list_of_all_hc:\\n hypercube.set_hypercube_class()\\n if self.child_grid:\\n self.child_grid.set_hypercubes_classes()\",\n \"def registry(self):\\n return self._registry\",\n \"def _default_registry(self):\\n registry = JSONSerializer.instance().registry.copy()\\n registry.update({m.__model__: m for m in find_sql_models()})\\n return registry\",\n \"def extension(klass):\\n registry.register(klass)\\n return klass\",\n \"def test_register_manual_keys(self):\\n registry = ClassRegistry()\\n\\n @registry.register('fire')\\n class Charizard(Pokemon):\\n pass\\n\\n @registry.register('water')\\n class Blastoise(Pokemon):\\n pass\\n\\n # By default, you have to specify a registry key when\\n # registering new classes. We'll see how to assign\\n # registry keys automatically in the next test.\\n with self.assertRaises(ValueError):\\n # noinspection PyUnusedLocal\\n @registry.register\\n class Venusaur(Pokemon):\\n pass\\n\\n self.assertIsInstance(registry['fire'], Charizard)\\n self.assertIsInstance(registry['water'], Blastoise)\",\n \"def register(self, cls):\\r\\n\\r\\n # Do all checks and complain before changing any state.\\r\\n if len(cls.tags) == 0:\\r\\n raise ValueError(\\\"No tags specified for class {0}\\\".format(cls.__name__))\\r\\n\\r\\n for t in cls.tags:\\r\\n if t in self._mapping:\\r\\n other_cls = self._mapping[t]\\r\\n if cls == other_cls:\\r\\n # registering the same class multiple times seems silly, but ok\\r\\n continue\\r\\n raise ValueError(\\\"Tag {0} already registered by class {1}.\\\"\\r\\n \\\" Can't register for class {2}\\\"\\r\\n .format(t, other_cls.__name__, cls.__name__))\\r\\n\\r\\n # Ok, should be good to change state now.\\r\\n for t in cls.tags:\\r\\n self._mapping[t] = cls\\r\\n\\r\\n # Returning the cls means we can use this as a decorator.\\r\\n return cls\",\n \"def setup_platform(hass, config, add_entities, discovery_info=None):\\n \\n entity = LiberationRecap()\\n add_entities([entity], True)\\n \\n return True\",\n \"def register_root(cls):\\n if RegisteredType._reg['root_class'] is None:\\n\\n del RegisteredType._reg\\n RegisteredType._reg = {\\n 'classes' : { 'classid_key' : 'type'},\\n 'autoid' : 0,\\n 'classids' : { 'type' : 'classid_key' },\\n }\\n RegisteredType._reg['root_class'] = cls \\n cls.register_class()\",\n \"def register(self):\\n\\n RPCObjectsRegistry.add(self)\",\n \"def set_element_class_lookup(self, lookup=None): # real signature unknown; restored from __doc__\\n pass\",\n \"def registry_host(self, registry_host: str):\\n\\n self._registry_host = registry_host\",\n \"def set_searchable_content_types(value):\\n record = dict(interface=ICoverSettings, name='searchable_content_types')\\n api.portal.set_registry_record(value=value, **record)\",\n \"def register_user(self, cls):\\n return self.register_entity('user', cls)\",\n \"def update_entities(self):\\n raise NotImplementedError()\",\n \"def _initiate_registry_from_torchlib(\\n self, torchlib_registry: registration.Registry\\n ):\\n for aten_name, aten_overloads_func in torchlib_registry.items():\\n for func in aten_overloads_func.overloads:\\n self.register(\\n aten_name,\\n self._opset_version,\\n func,\\n custom=False,\\n )\",\n \"def setup_class(klass):\",\n \"def setup_class(klass):\",\n \"def register(self, cls, force=False):\\n if not issubclass(cls, self.type):\\n raise InvalidRegistryItemType(\\n \\\"Invalid item type `{0}` for registry \\\"\\n \\\"`{1}`\\\".format(cls, self.__class__)\\n )\\n\\n # If item has not been forced yet, add/replace its' value in the\\n # registry.\\n if force:\\n\\n if cls.uid not in self._forced:\\n self._registry[cls.uid] = cls\\n self._forced.append(cls.uid)\\n return True\\n else:\\n return False\\n\\n else:\\n\\n if cls.uid in self._registry:\\n return False\\n else:\\n self._registry[cls.uid] = cls\\n return True\",\n \"def values_for_registry(cls, registry):\\n cls.validate(registry)\\n return {\\n 'type': cls.attr_type,\\n 'verbose_name': cls.attr_verbose_name,\\n 'key': cls.attr_key,\\n 'class': cls.get_class(),\\n '_class': cls\\n }\",\n \"def register(self):\\n raise NotImplementedError\",\n \"def register(self):\\n raise NotImplementedError\",\n \"def update_registry_for_model(self, config):\\n for builder in self._builders:\\n builder.update_registry_for_model(config)\",\n \"def set_EntityID(self, value):\\n super(EntityOverviewInputSet, self)._set_input('EntityID', value)\",\n \"def entity_registry_enabled_default(self) -> bool:\\n # these sensors are only here for backwards compatibility, disable them by default\\n return False\",\n \"def _set_registry_client(self, registry_name: str) -> None:\\n rg_ = self._operation_scope._resource_group_name\\n sub_ = self._operation_scope._subscription_id\\n registry_ = self._operation_scope.registry_name\\n client_ = self._service_client\\n environment_versions_operation_ = self._version_operations\\n\\n try:\\n _client, _rg, _sub = get_registry_client(self._service_client._config.credential, registry_name)\\n self._operation_scope.registry_name = registry_name\\n self._operation_scope._resource_group_name = _rg\\n self._operation_scope._subscription_id = _sub\\n self._service_client = _client\\n self._version_operations = _client.environment_versions\\n yield\\n finally:\\n self._operation_scope.registry_name = registry_\\n self._operation_scope._resource_group_name = rg_\\n self._operation_scope._subscription_id = sub_\\n self._service_client = client_\\n self._version_operations = environment_versions_operation_\",\n \"def _refresh_registry(cls) -> None:\\n cls.objects_dict.clear()\\n\\n # Add new object instances to the registry.\\n for name, clazz in inspect.getmembers(\\n objects, predicate=inspect.isclass):\\n if name == 'BaseObject':\\n continue\\n\\n ancestor_names = [\\n base_class.__name__ for base_class in inspect.getmro(clazz)]\\n\\n assert 'BaseObject' in ancestor_names\\n cls.objects_dict[clazz.__name__] = clazz\",\n \"def entity_id(self, entity_id):\\n\\n self._entity_id = entity_id\",\n \"def __set_registry(values):\\n try:\\n _write_key = winreg.OpenKey(_registry, \\\"SYSTEM\\\\\\\\CurrentControlSet\\\\\\\\Control\\\\\\\\Session Manager\\\", 0, winreg.KEY_WRITE)\\n except PermissionError:\\n raise PermissionError(\\\"Permission Denied to write registry key.\\\")\\n winreg.SetValueEx(_write_key, \\\"PendingFileRenameOperations\\\", 0, winreg.REG_MULTI_SZ, values)\",\n \"def add_etl_class(self, etl_class):\\n\\n if etl_class.identifier in self.etl_classes:\\n raise DuplicateConfigException(\\n \\\"Builder already has etl_class with identifier : {}\\\".format(\\n etl_class.identifier\\n )\\n )\\n\\n self.etl_classes[etl_class.identifier] = etl_class\",\n \"def register(cls, package_type):\\r\\n if not issubclass(package_type, cls):\\r\\n raise TypeError('package_type must be a subclass of Package.')\\r\\n cls._REGISTRY.add(package_type)\",\n \"def set_class_list(self, L):\\n\\t\\tself.class_list = L\",\n \"def register(self):\\n raise NotImplementedError()\",\n \"def new_entity(self, new_entity):\\n\\n self._new_entity = new_entity\",\n \"def setReactantComponent(self, *args):\\n return _libsbml.SpeciesTypeComponentMapInProduct_setReactantComponent(self, *args)\",\n \"def remote_registerEngine(self, engineReference):\",\n \"def register(self, cls, force=False):\\n if not issubclass(cls, self.type):\\n raise InvalidRegistryItemType(\\n \\\"Invalid item type `{0}` for registry \\\"\\n \\\"`{1}`\\\".format(cls, self.__class__)\\n )\\n\\n uid = BasePluginWidgetRegistry.namify(cls.theme_uid, cls.plugin_uid)\\n\\n # If item has not been forced yet, add/replace its' value in the\\n # registry.\\n if force:\\n\\n if uid not in self._forced:\\n self._registry[uid] = cls\\n self._forced.append(uid)\\n return True\\n else:\\n return False\\n\\n else:\\n\\n if uid in self._registry:\\n return False\\n else:\\n self._registry[uid] = cls\\n return True\",\n \"def reidentify_entity(self, qid, new_qid):\\n # We assume this is a new entity\\n if self._entity_symbols.qid_exists(new_qid):\\n raise ValueError(\\n f\\\"The entity {new_qid} already exists. Please call update_entity instead.\\\"\\n )\\n self._entity_symbols.reidentify_entity(qid, new_qid)\\n for type_sys in self._type_systems:\\n self._type_systems[type_sys].reidentify_entity(qid, new_qid)\\n if self._kg_symbols is not None:\\n self._kg_symbols.reidentify_entity(qid, new_qid)\",\n \"def register(self):\\n # Set the evaluation state of this instance to REGISTER, as it has been\\n # recognized by the root object.\\n self.root.registry.append(self)\\n self.eval_state = COMMAND_EVAL_REGISTER\\n\\n # Loop through children and register them too, recursively.\\n for ch in self.children:\\n # Only register the child if it has not been registered yet;\\n # therefore its evaluation state has been set to NONE.\\n if ch.eval_state == COMMAND_EVAL_NONE:\\n ch.register()\",\n \"def _fill_class_dicts():\\n global _taxonomy_classes\\n global _data_classes\\n if not _taxonomy_classes:\\n _taxonomy_classes = get_taxonomies()\\n if not _data_classes:\\n stack = []\\n next_module = data\\n while next_module is not None:\\n stack += _inspect_module(next_module)\\n if stack:\\n next_module = stack.pop()\\n else:\\n next_module = None\",\n \"def add_ents(self, ents: Iterable['Entity']) -> None:\\n ents = list(ents)\\n self.entities.extend(ents)\\n for item in ents:\\n self.by_class[item['classname'].casefold()].add(item)\\n self.by_target[item['targetname', ''].casefold() or None].add(item)\\n if 'nodeid' in item:\\n try:\\n node_id = int(item['nodeid'])\\n except (TypeError, ValueError):\\n pass\\n else:\\n item['nodeid'] = str(self.node_id.get_id(node_id))\",\n \"def _register(cls):\\r\\n command_name = cls.__dict__.get('__command__', None)\\r\\n if command_name:\\r\\n Command._commands[command_name] = cls\",\n \"def register(cls, basedir, *allowed_target_types):\\r\\n cls._register(basedir, *allowed_target_types)\",\n \"def on_registered(self):\\r\\n super().on_registered()\\r\\n\\r\\n # Register type information\\r\\n cls = self.__class__\\r\\n\\r\\n subclass_cache = cls._of_subclass_cache\\r\\n type_cache = cls._of_type_cache\\r\\n\\r\\n # Cache subtypes\\r\\n for base_cls in cls.__mro__:\\r\\n try:\\r\\n instances = subclass_cache[base_cls]\\r\\n\\r\\n except KeyError:\\r\\n instances = subclass_cache[base_cls] = set()\\r\\n\\r\\n instances.add(self)\\r\\n\\r\\n # Cache the type\\r\\n try:\\r\\n instances = type_cache[cls]\\r\\n\\r\\n except KeyError:\\r\\n instances = type_cache[cls] = set()\\r\\n\\r\\n instances.add(self)\\r\\n\\r\\n ReplicableRegisteredSignal.invoke(target=self)\",\n \"def register_classes():\\n CoaddSplit.register_class()\\n CoaddSplit_SG.register_class()\",\n \"def register_class(self, cls, *, name=None):\\n cls_name = self.host.cache_class(cls, name)\\n self.register_constant(cls, cls_name)\",\n \"def _register_resource(self, name):\\n GlobalClass.register(self)\\n tag = self.module.name.lower()\\n group = self.config.xpath(self.xpath + \\\"/\\\" + tag)[0]\\n group_mysql4 = group.tag + \\\"_mysql4\\\"\\n resource_model = find_or_create(group, \\\"resourceModel\\\")\\n resource_model.text = group_mysql4\\n group_mysql4 = find_or_create(self.type_elem, group_mysql4)\\n class_ = find_or_create(group_mysql4, \\\"class\\\")\\n class_.text = \\\"%s_%s_%s_Mysql4\\\" % (self.module.namespace,\\n self.module.name,\\n self.type.capitalize())\\n entities = find_or_create(group_mysql4, \\\"entities\\\")\\n name_lower = find_or_create(entities, name.lower())\\n table = find_or_create(name_lower, \\\"table\\\")\\n table.text = self.table or group.tag + \\\"_\\\" + name_lower.tag\\n self.put_config(self.config)\",\n \"def initializePredefinedEntities():\\n libxml2mod.xmlInitializePredefinedEntities()\",\n \"def create_class(self, cls):\\n\\n cls_name = cls.registry_name\\n\\n bases = [base for base in cls.__bases__ if self.valid_element_base(base)]\\n if not bases:\\n raise TypeError(\\n 'Unexpected base class(es) in Graph.create_class'\\n ' - try the declarative bases')\\n\\n extends = None\\n if bases[0] is bases[0].decl_root:\\n extends = ['V', 'E'][bases[0].decl_type]\\n else:\\n extends = ','.join([base.registry_name for base in bases])\\n\\n #if not self.client.command(\\n # 'SELECT FROM ( SELECT expand( classes ) FROM metadata:schema ) WHERE name = \\\"{}\\\"'\\n # .format(cls_name)):\\n try:\\n self.client.command(\\n 'CREATE CLASS {0} EXTENDS {1}'.format(cls_name, extends))\\n except pyorient.PyOrientSchemaException:\\n # Class already exists\\n pass\\n\\n db_to_element = {}\\n\\n props = sorted([(k,v) for k,v in cls.__dict__.items()\\n if isinstance(v, Property)]\\n , key=lambda p:p[1].instance_idx)\\n for prop_name, prop_value in props:\\n value_name = prop_value.name\\n if value_name:\\n db_to_element[value_name] = prop_name\\n prop_name = value_name\\n else:\\n db_to_element[prop_name] = prop_name\\n\\n self.guard_reserved_words(prop_name, cls)\\n\\n class_prop = '{0}.{1}'.format(cls_name, prop_name)\\n\\n linked_to = None\\n if isinstance(prop_value, LinkedClassProperty):\\n type_linked_to = prop_value.linked_to\\n\\n linked_to = getattr(type_linked_to, 'registry_name', None)\\n if not linked_to:\\n link_bases = getattr(type_linked_to, '__bases__', None)\\n if link_bases and \\\\\\n isinstance(prop_value, LinkedProperty) and \\\\\\n link_bases[0] is Property:\\n linked_to = type_linked_to.__name__\\n\\n try:\\n self.client.command(\\n 'CREATE PROPERTY {0} {1} {2}'\\n .format(class_prop\\n , type(prop_value).__name__\\n , linked_to or ''))\\n except pyorient.PyOrientCommandException:\\n # Property already exists\\n pass\\n\\n if prop_value.default is not None:\\n if self.server_version >= (2,1,0):\\n self.client.command(\\n 'ALTER PROPERTY {0} DEFAULT {1}'\\n .format(class_prop,\\n PropertyEncoder.encode(prop_value.default)))\\n\\n self.client.command(\\n 'ALTER PROPERTY {0} NOTNULL {1}'\\n .format(class_prop\\n , str(not prop_value.nullable).lower()))\\n\\n self.client.command(\\n 'ALTER PROPERTY {} MANDATORY {}'\\n .format(class_prop\\n , str(prop_value.mandatory).lower()))\\n\\n self.client.command(\\n 'ALTER PROPERTY {} READONLY {}'\\n .format(class_prop\\n , str(prop_value.readonly).lower()))\\n\\n # TODO Add support for composite indexes\\n if prop_value.indexed:\\n try:\\n self.client.command(\\n 'CREATE INDEX {0} {1}'\\n .format(class_prop\\n , 'UNIQUE' if prop_value.unique\\n else 'NOTUNIQUE'))\\n except pyorient.PyOrientIndexException:\\n # Index already exists\\n pass\\n\\n self.props_from_db[cls] = self.create_props_mapping(db_to_element)\\n self.init_broker_for_class(cls)\\n self.registry[cls_name] = cls\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6296474","0.6145517","0.5784777","0.57402545","0.5640025","0.5552346","0.54653853","0.52816427","0.5269132","0.5262372","0.5256132","0.52219176","0.5213515","0.5207139","0.5203314","0.52020997","0.52020997","0.51808226","0.5144154","0.51290417","0.51211494","0.50926924","0.50775343","0.50489837","0.5045451","0.5003249","0.49861822","0.49723077","0.49671492","0.49576122","0.49472478","0.4939789","0.49301082","0.49087113","0.48693752","0.48602334","0.48591053","0.4845741","0.483096","0.48206922","0.48171642","0.4797765","0.47959676","0.47913992","0.4783749","0.47822425","0.4767188","0.47652432","0.47573176","0.4745003","0.47319165","0.47297823","0.4719262","0.4705221","0.46970627","0.46937492","0.46839488","0.4668164","0.46669587","0.4664681","0.46630418","0.46546596","0.4652964","0.46505758","0.4644093","0.4640944","0.4618305","0.46132442","0.46132442","0.46088806","0.46068776","0.4600091","0.4600091","0.45972466","0.4597083","0.45905563","0.45887843","0.45856425","0.4584535","0.45839432","0.4583261","0.4578922","0.45757562","0.45660606","0.45599517","0.45545846","0.45532772","0.45522645","0.45354712","0.45326573","0.45056802","0.45056528","0.45047426","0.44944748","0.44923788","0.44878638","0.4474737","0.44716415","0.44664082","0.44661292"],"string":"[\n \"0.6296474\",\n \"0.6145517\",\n \"0.5784777\",\n \"0.57402545\",\n \"0.5640025\",\n \"0.5552346\",\n \"0.54653853\",\n \"0.52816427\",\n \"0.5269132\",\n \"0.5262372\",\n \"0.5256132\",\n \"0.52219176\",\n \"0.5213515\",\n \"0.5207139\",\n \"0.5203314\",\n \"0.52020997\",\n \"0.52020997\",\n \"0.51808226\",\n \"0.5144154\",\n \"0.51290417\",\n \"0.51211494\",\n \"0.50926924\",\n \"0.50775343\",\n \"0.50489837\",\n \"0.5045451\",\n \"0.5003249\",\n \"0.49861822\",\n \"0.49723077\",\n \"0.49671492\",\n \"0.49576122\",\n \"0.49472478\",\n \"0.4939789\",\n \"0.49301082\",\n \"0.49087113\",\n \"0.48693752\",\n \"0.48602334\",\n \"0.48591053\",\n \"0.4845741\",\n \"0.483096\",\n \"0.48206922\",\n \"0.48171642\",\n \"0.4797765\",\n \"0.47959676\",\n \"0.47913992\",\n \"0.4783749\",\n \"0.47822425\",\n \"0.4767188\",\n \"0.47652432\",\n \"0.47573176\",\n \"0.4745003\",\n \"0.47319165\",\n \"0.47297823\",\n \"0.4719262\",\n \"0.4705221\",\n \"0.46970627\",\n \"0.46937492\",\n \"0.46839488\",\n \"0.4668164\",\n \"0.46669587\",\n \"0.4664681\",\n \"0.46630418\",\n \"0.46546596\",\n \"0.4652964\",\n \"0.46505758\",\n \"0.4644093\",\n \"0.4640944\",\n \"0.4618305\",\n \"0.46132442\",\n \"0.46132442\",\n \"0.46088806\",\n \"0.46068776\",\n \"0.4600091\",\n \"0.4600091\",\n \"0.45972466\",\n \"0.4597083\",\n \"0.45905563\",\n \"0.45887843\",\n \"0.45856425\",\n \"0.4584535\",\n \"0.45839432\",\n \"0.4583261\",\n \"0.4578922\",\n \"0.45757562\",\n \"0.45660606\",\n \"0.45599517\",\n \"0.45545846\",\n \"0.45532772\",\n \"0.45522645\",\n \"0.45354712\",\n \"0.45326573\",\n \"0.45056802\",\n \"0.45056528\",\n \"0.45047426\",\n \"0.44944748\",\n \"0.44923788\",\n \"0.44878638\",\n \"0.4474737\",\n \"0.44716415\",\n \"0.44664082\",\n \"0.44661292\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8463285"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17771,"cells":{"query":{"kind":"string","value":"Extract an entity class registry from one of the models of the inner SQLAlchemy query. This result of this function is used by several SQLAlchemy components during the extraction of the SQL query from a SQLAlchemy query."},"document":{"kind":"string","value":"def _extract_entity_class_registry(self):\n for description in self.sa_query.column_descriptions:\n if \"entity\" in description:\n declarative_meta = description[\"entity\"]\n _class_registry = getattr(\n declarative_meta, \"_decl_class_registry\", None)\n if _class_registry is not None:\n entity_class_registry = {}\n for elmnt in _class_registry.values():\n if type(elmnt) is DeclarativeMeta:\n description = elmnt.__table__.description\n entity_class_registry[description] = elmnt\n return entity_class_registry\n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_real_object(self):\n query_string = dedent(f\"\"\"\\\n import app.config.models_importer as models_importer\n\n class_ = models_importer.all_models['{self.ref_class}']\n \n class_.query.get({self.ref_id})\"\"\")\n\n return exec(query_string)","def _namespaced_query(session, obj_outer, cls_inner, name_inner):\n return session.query(cls_inner) \\\n .filter_by(owner = obj_outer) \\\n .filter_by(label = name_inner).first()","def get_models_query():\n query = db.session.query(Products.model).distinct()\n return query","def resolve_model(root: Entity, *args) -> str:\n return 'entities.entity'","def models(self, model=None):\n for query in self.__queries:\n if isinstance(query, orb.Query):\n yield query.model(model)\n else:\n for model in query.models(model):\n yield model","def expand(self, graph):\n for triple in self.g:\n graph.add(triple)\n entity_tags = defaultdict(set)\n res = graph.query(\n \"\"\"SELECT ?ent ?tag WHERE {\n ?ent brick:hasTag ?tag\n }\"\"\"\n )\n for ent, tag in res:\n entity_tags[ent].add(tag)\n for entity, tagset in entity_tags.items():\n tagset = list(map(lambda x: x.split(\"#\")[-1], tagset))\n lookup = self.lookup_tagset(tagset)\n if len(lookup) == 0:\n continue\n klasses = list(lookup[0][0])\n graph.add((entity, A, BRICK[klasses[0]]))","def register_orm_base(self, base):\n for model in utils.searchable_sqlalchemy_models(base):\n self.register_type(model.es_type_name, model.es_properties, model)","def entity_classes_to_map_over(cls):\n return [exp_models.ExplorationSnapshotMetadataModel]","def infer_model(self, model):\n\n from .graph import Graph\n\n entities = model[\"rows\"]\n # index the entities by their ID field\n entities = {e[\"id\"].replace('\"', \"\"): {\"tags\": e} for e in entities}\n # TODO: add e['dis'] for a descriptive label?\n brickgraph = Graph(load_brick=False)\n\n # marker tag pass\n for entity_id, entity in entities.items():\n marker_tags = {\n k for k, v in entity[\"tags\"].items() if v == \"m:\" or v == \"M\"\n }\n for f in self._filters:\n marker_tags = list(filter(f, marker_tags))\n # translate tags\n entity_tagset = list(self._translate_tags(marker_tags))\n\n equip_ref = entity[\"tags\"].get(\"equipRef\")\n # infer tags for single entity\n triples, _ = self.infer_entity(\n entity_tagset, identifier=entity_id, equip_ref=equip_ref\n )\n brickgraph.add(*triples)\n self._generated_triples.extend(triples)\n\n # take a pass through for relationships\n for entity_id, entity in entities.items():\n relships = {k: v for k, v in entity[\"tags\"].items() if k.endswith(\"Ref\")}\n # equip_entity_id = entity_id.replace(' ', '_') + '_equip'\n point_entity_id = entity_id.replace(\" \", \"_\") + \"_point\"\n if \"equipRef\" not in relships:\n continue\n reffed_equip = (\n relships[\"equipRef\"].replace(\" \", \"_\").replace('\"', \"\") + \"_equip\"\n )\n if self._BLDG[point_entity_id] in brickgraph.nodes:\n triple = (\n self._BLDG[reffed_equip],\n BRICK.hasPoint,\n self._BLDG[point_entity_id],\n )\n brickgraph.add(triple)\n self._generated_triples.append(triple)\n return brickgraph","def reverse_entity_type_subclass_map(entity_type_subclass_map):\n entity_type_superclass_map = {}\n\n for superclass, subclasses in tqdm(entity_type_subclass_map.items()):\n for subclass in subclasses:\n if subclass['id'] in entity_type_superclass_map:\n entity_type_superclass_map[subclass['id']].append(superclass)\n else:\n entity_type_superclass_map[subclass['id']] = [superclass]\n\n return entity_type_superclass_map\n\n\n#def get_type_index():\n \"\"\"\n Create an index s.t. each type gets an ID (e.g. person -> 1,\n organization -> 2, ...\n \"\"\"\n #return dict(enumerate(type_list))\n\n\n#def get_index_of_type(entity_type):\n \"\"\"\n Return the index of a given entity type.\n \"\"\"\n #return type_list.index(entity_type)\n\n\n#def get_type_by_index(index):\n \"\"\"\n Return the type of a given index.\n \"\"\"\n #return type_list[index - 1] # TODO re-train model with new indices","def to_entity(cls, model_obj: \"SqlalchemyModel\"):\n item_dict = {}\n for field_name in attributes(cls.meta_.entity_cls):\n item_dict[field_name] = getattr(model_obj, field_name, None)\n return cls.meta_.entity_cls(item_dict)","def _get_model_class_from_table(self, table):\r\n try:\r\n model_class = [m for m in get_models() if connection.introspection.table_name_converter(m._meta.db_table) in map(connection.introspection.table_name_converter,[table])][0] \r\n m2m = False \r\n except IndexError:\r\n try: \r\n # this is a many to many field \r\n model_class = [f.rel.to for m in get_models() for f in m._meta.local_many_to_many if f.m2m_db_table() == table][0] \r\n m2m = True \r\n except IndexError: \r\n # this is an inner join \r\n table = self.query.alias_map[table][0]\r\n return self._get_model_class_from_table(table)\r\n return model_class, m2m","def fetchall(self):\n rows = self.cursor.fetchall()\n\n if self.model.single:\n for row in rows:\n yield self.__instance_from_db(self.model, row)\n else:\n for row in rows:\n yield tuple(self.__instance_from_db(m, row) for m in self.model.models)","def all(self, cls=None):\n results = {}\n if cls is not None:\n # TODO cls always is a class\n if type(cls) == str:\n cls = eval(cls)\n for instance in self.__session.query(cls):\n key = \"{}.{}\".format(cls.__name__, instance.id)\n results[key] = instance\n return results\n else:\n for table in self.__tables:\n for instance in self.__session.query(eval(table)):\n key = \"{}.{}\".format(table, instance.id)\n results[key] = instance\n return results","def _default_registry(self):\n registry = JSONSerializer.instance().registry.copy()\n registry.update({m.__model__: m for m in find_sql_models()})\n return registry","def _get_related_models(self, parent_model):\r\n related_models = set()\r\n rev_reversemapping = dict([(v,k) for k,v in self._reversemapping.iteritems()])\r\n if rev_reversemapping:\r\n for attname, related in self._get_reverse_relations(parent_model):\r\n related_models.add((rev_reversemapping[attname], related.model))\r\n\r\n for field in parent_model._meta.fields:\r\n if field.rel and field.rel.to._meta.db_table in self.query.tables and field.rel.to != parent_model:\r\n related_models.add((field.attname, field.rel.to))\r\n \r\n for attname, model_class in related_models:\r\n yield attname, model_class\r\n if attname.endswith(\"_id\"):\r\n attname = attname[:-3]\r\n for join_attname, model_klass in self._get_related_models(model_class):\r\n yield LOOKUP_SEP.join((attname,join_attname)), model_klass","def query(cls):\n query_class = cls.query_class\n return query_class(orm_class=cls)","def all(self, cls=None):\n obj_dict = {}\n all_objs = []\n if cls:\n all_objs = self.__session.query(eval(cls)).all()\n else:\n for table in self.all_classes:\n all_objs += self.__session.query(eval(table)).all()\n obj_dict = {obj.__class__.__name__ + '.' + obj.id: obj\n for obj in all_objs}\n # TODO BUG: includes <sqlalchemy> object in dict\n return obj_dict","def expand(self, model=None, ignoreFilter=False):\n model = self.__model or model\n if not model:\n raise orb.errors.QueryInvalid('Could not traverse: {0}'.format(self.__column))\n\n schema = model.schema()\n parts = self.__column.split('.')\n\n # expand the current column\n lookup = schema.column(parts[0], raise_=False) or schema.collector(parts[0])\n\n if lookup:\n # utilize query filters to generate\n # a new filter based on this object\n query_filter = lookup.queryFilterMethod()\n if callable(query_filter) and not ignoreFilter:\n new_q = query_filter(model, self)\n if new_q:\n return new_q.expand(model, ignoreFilter=True)\n else:\n return None\n\n # otherwise, check to see if the lookup\n # has a shortcut to look through\n elif isinstance(lookup, orb.Column) and lookup.shortcut():\n parts = lookup.shortcut().split('.')\n lookup = schema.column(parts[0], raise_=False)\n\n if len(parts) == 1:\n return self\n else:\n if isinstance(lookup, orb.Collector):\n return orb.Query(model).in_(lookup.collectExpand(self, parts))\n\n elif isinstance(lookup, orb.ReferenceColumn):\n rmodel = lookup.referenceModel()\n sub_q = self.copy()\n sub_q._Query__column = '.'.join(parts[1:])\n sub_q._Query__model = rmodel\n records = rmodel.select(columns=[rmodel.schema().idColumn()], where=sub_q)\n return orb.Query(model, parts[0]).in_(records)\n\n else:\n raise orb.errors.QueryInvalid('Could not traverse: {0}'.format(self.__column))","def resolve_entities(root, info, ids: list[int], **kwargs):\n return Entity.objects.filter(id__in=ids)","def matching_objects(self, filter_deleted):\n from rome.core.orm.utils import get_literal_query\n from rome.lang.sql_parser import QueryParser\n from rome.core.rows.rows import construct_rows\n\n read_deleted = self.read_deleted\n if filter_deleted:\n read_deleted = \"no\"\n\n if self._autoflush:\n if self.session is not None:\n self.session.commit()\n\n if not self.query_tree:\n sql_query = get_literal_query(self.sa_query)\n parser = QueryParser()\n query_tree = parser.parse(sql_query)\n else:\n query_tree = self.query_tree\n\n if not self.entity_class_registry:\n self.entity_class_registry = self._extract_entity_class_registry()\n entity_class_registry = self.entity_class_registry\n\n # Collecting variables of sub queries\n subqueries_variables = {}\n for (variable_name, sub_query_tree) in query_tree.variables.iteritems():\n sub_query = Query()\n sub_query.set_query_tree(sub_query_tree)\n sub_query.set_entity_class_registry(entity_class_registry)\n result = sub_query.all()\n subqueries_variables[variable_name] = result\n\n rows = construct_rows(query_tree,\n entity_class_registry,\n read_deleted=read_deleted,\n subqueries_variables= subqueries_variables)\n\n def row_function(row, column_descriptions, decoder):\n from rome.core.session.utils import ObjectAttributeRefresher\n final_row = []\n one_is_an_object = False\n object_attribute_refresher = ObjectAttributeRefresher()\n for column_description in column_descriptions:\n if type(column_description[\"type\"]) in [Integer, String]:\n row_key = column_description[\"entity\"].__table__.name.capitalize(\n )\n property_name = column_description[\"name\"]\n value = None\n if row_key in row and property_name in row[row_key]:\n value = row[row_key].get(property_name, None)\n else:\n # It seems that we are parsing the result of a function call\n column_description_expr = column_description.get(\"expr\",\n None)\n if column_description_expr is not None:\n property_name = str(column_description_expr)\n value = row.get(property_name, None)\n if value is not None:\n final_row += [value]\n else:\n logging.error(\n \"Could not understand how to get the value of '%s' with this: '%s'\"\n % (column_description.get(\"expr\", \"??\"), row))\n elif type(column_description[\"type\"]) == DeclarativeMeta:\n one_is_an_object = True\n row_key = column_description[\"entity\"].__table__.name\n new_object = column_description[\"entity\"]()\n attribute_names = map(lambda x: x.key, list(\n column_description[\"entity\"].__table__.columns))\n for attribute_name in attribute_names:\n value = decoder.decode(row[row_key].get(attribute_name,\n None))\n setattr(new_object, attribute_name, value)\n\n if \"___version_number\" in row[row_key]:\n setattr(new_object, \"___version_number\", row[row_key][\"___version_number\"])\n\n load_options = None\n if hasattr(self.sa_query, \"_with_options\"):\n load_options = self.sa_query._with_options\n object_attribute_refresher.refresh(new_object, load_options=load_options)\n final_row += [new_object]\n else:\n logging.error(\"Unsupported type: '%s'\" %\n (column_description[\"type\"]))\n if not one_is_an_object:\n return [final_row]\n else:\n return final_row\n\n def row_function_subquery(row, attributes, decoder):\n result = []\n for attribute in attributes:\n tablename = attribute.split(\".\")[0]\n attribute_name = attribute.split(\".\")[1]\n result += [row[tablename][attribute_name]]\n return result\n\n decoder = Decoder()\n\n if len(self.sa_query.column_descriptions) > 0:\n final_rows = map(lambda r: row_function(\n r, self.sa_query.column_descriptions, decoder), rows)\n else:\n final_rows = map(lambda r: row_function_subquery(\n r, self.query_tree.attributes, decoder), rows)\n\n if len(self.sa_query.column_descriptions) <= 1:\n # Flatten the list\n final_rows = [item for sublist in final_rows for item in sublist]\n\n # Add watcher on objects\n if self.session is not None:\n for obj in final_rows:\n if hasattr(obj, \"id\"):\n self.session.watch(obj)\n\n return final_rows","def init_model(connection):\n db = connection\n\n for obj in common.__dict__.itervalues():\n if type(obj) == type and issubclass(obj, common.Model) and hasattr(obj, '__tablename__'):\n tablename = getattr(obj, '__tablename__')\n obj._object_store = Domain(db, tablename)\n collection_to_class[obj._object_store] = obj","def entity_classes_to_map_over(cls):\n\n return [user_models.UserSubscriptionsModel]","def apply_model_to_query(self, query):\n pass","def entity_classes_to_map_over(cls):\n return [exp_models.ExplorationRightsSnapshotMetadataModel]","def get_model(self):\n return QueryS","def _egg_module(self, query):\n return process.extractOne(query, self.egg_names,\n scorer=fuzz.ratio,\n score_cutoff=self.egg_threshold)","def get_query(self):\n return self.query_class(self)","def _get_model_from_table_name(table_name: str) -> Optional[Type[RDSModel]]:\n table_model = None\n try:\n if hasattr(Base, '_decl_class_registry'):\n models = Base._decl_class_registry.values() # sqlalchemy < 1.4\n else:\n models = Base.registry._class_registry.values()\n\n for model in models:\n if hasattr(model, '__tablename__') and model.__tablename__ == table_name:\n table_model = model\n except Exception as e:\n LOGGER.exception(f'Failed to get model for the table: {table_name} from rds model base')\n raise e\n\n return table_model","def to_orm(self):\n data = {}\n for key, value in self:\n # If field is Type[ORMModel],\n # recursively convert to an ORM object.\n if hasattr(value, \"__orm__\"):\n data[key] = value.to_orm()\n # If the field is a dictionary, iterate over\n # values and convert any ORM models to ORM objects\n # else leave them alone.\n elif isinstance(value, dict):\n nested_data = {}\n for nested_key, nested_value in value:\n if hasattr(nested_value, \"__orm__\"):\n nested_data[key] = nested_value.to_orm()\n else:\n nested_data[key] = value\n data[key] = nested_data\n # If the field is an iterable, iterate through list\n # and convert ORM Models to ORM objects.\n #\n # There has to be a better way to write this conditional...\n elif (\n isinstance(value, collections.Iterable) and\n type(value) not in (str, bytearray, bytes)\n ):\n nested_data = []\n for nested_value in value:\n if hasattr(nested_value, \"__orm__\"):\n nested_data.append(nested_value.to_orm())\n else:\n nested_data.append(nested_value)\n # Convert iterable to the appropriate type at the\n # end.\n data[key] = type(value)(nested_data)\n # Leave the value alone if its not an ORMModel\n else:\n data[key] = value\n return self.__orm__(**data)","def models(self):\n return self._base.classes","def get_mapper(mixed):\n if isinstance(mixed, orm._MapperEntity):\n mixed = mixed.expr\n elif isinstance(mixed, orm.sa.Column):\n mixed = mixed.table\n elif isinstance(mixed, orm._ColumnEntity):\n mixed = mixed.expr\n\n if isinstance(mixed, orm.sa.orm.Mapper):\n return mixed\n if isinstance(mixed, orm.sa.orm.util.AliasedClass):\n return orm.sa.inspect(mixed).mapper\n if isinstance(mixed, orm.sa.sql.selectable.Alias):\n mixed = mixed.element\n if isinstance(mixed, orm.AliasedInsp):\n return mixed.mapper\n if isinstance(mixed, orm.sa.orm.attributes.InstrumentedAttribute):\n mixed = mixed.class_\n if isinstance(mixed, orm.sa.Table):\n if hasattr(orm.mapperlib, '_all_registries'):\n all_mappers = set()\n for mapper_registry in orm.mapperlib._all_registries():\n all_mappers.update(mapper_registry.mappers)\n else: # SQLAlchemy <1.4\n all_mappers = orm.mapperlib._mapper_registry\n mappers = [\n mapper for mapper in all_mappers\n if mixed in {mapper.local_table}\n ]\n if len(mappers) > 1:\n raise Exception('Still to many mappers %s' % str(mappers))\n if not mappers:\n raise ValueError(\n \"Could not get mapper for table '%s'.\" % mixed.name\n )\n else:\n return mappers[0]\n if not orm.isclass(mixed):\n mixed = type(mixed)\n return orm.sa.inspect(mixed)","def get_entity(expression):\n if expression:\n namespace = sys.modules.copy()\n namespace.update(__main__.__dict__)\n try:\n return eval(expression, namespace)\n except BaseException:\n # An uncaught exception closes idle, and eval can raise any\n # exception, especially if user classes are involved.\n return None","def _merge_by_query(self, obj_dict):\n _res = self.__session.query(obj_dict[\"class\"]).filter_by(**obj_dict[\"query_dict\"]).first()\n\n if _res is None:\n self._add(obj_dict[\"instance\"])\n else:\n if hasattr(obj_dict[\"instance\"], 'attributes') and \\\n hasattr(obj_dict[\"instance\"], 'p_key'):\n for attr in obj_dict[\"instance\"].attributes:\n if attr not in obj_dict[\"instance\"].p_key:\n setattr(_res, attr, getattr(obj_dict[\"instance\"], attr))\n # updating the instance\n obj_dict[\"instance\"] = _res\n else:\n raise AttributeError(\"Class variable (attributes / p_key) not set for %s\" %\n (obj_dict[\"instance\"],))","def _related_fields(self):\r\n model_class, m2m = self._get_model_class_from_table(self.model._meta.db_table) \r\n related_fields = {\r\n self.model._meta.pk.attname: model_class\r\n }\r\n for attname, model_class in self._get_related_models(self.model):\r\n related_fields[attname] = model_class\r\n return related_fields","def build_mapping(self, vertex, edge, auto_plural=False):\n\n mc = type(vertex)\n registry = {}\n\n schema = self.client.command(\n 'SELECT FROM (SELECT expand(classes) FROM metadata:schema)'\n ' WHERE name NOT IN [\\'ORole\\', \\'ORestricted\\', \\'OTriggered\\','\n ' \\'ORIDs\\', \\'OUser\\', \\'OIdentity\\', \\'OSchedule\\','\n ' \\'OFunction\\'] ORDER BY defaultClusterId')\n\n for c in schema:\n class_def = c.oRecordData\n\n sup = class_def['superClass']\n if sup is not None:\n base_names = class_def.get('superClasses', [sup])\n bases = tuple(\n vertex if b == 'V' else edge if b == 'E'\n else registry[b] if b in registry\n else self.registry[b]\n for b in base_names\n if b == 'V' or b == 'E'\n or b in registry or b in self.registry)\n class_name = class_def['name']\n\n if bases:\n props = { p['name']:self.property_from_schema(p)\n for p in class_def['properties'] }\n props['decl_type'] = bases[0].decl_type\n\n if auto_plural:\n props['registry_plural'] = class_name\n registry[class_name] = mc(class_name, bases, props)\n\n return registry","def _to_entity(self):\n obj_dict = copy.deepcopy(vars(self))\n exclude_from_indexes = ()\n try:\n exclude_from_indexes = self._exclude_from_indexes_\n except AttributeError:\n pass\n\n try:\n key = self.key\n except AttributeError:\n key = CustomKey(self.__class__.__name__)\n\n entity = datastore.Entity(key=key, exclude_from_indexes=exclude_from_indexes)\n for dict_key, dict_val in obj_dict.copy().items():\n if dict_val is not None:\n if isinstance(dict_val, BaseModel):\n # If the value is an instance of BaseModel, convert the instance\n # into a \"dotted\" dictionary compatible with NDB entities.\n del obj_dict[dict_key]\n obj_dict.update(dict_val.dottify(dict_key))\n if isinstance(dict_val, list) and len(dict_val) > 0 and isinstance(dict_val[0], BaseModel):\n # if the value is a list of BaseModel objects\n dotted_dict_list = []\n dotted_dict = dict()\n for i, val in enumerate(dict_val):\n dotted_dict_list.append(val.dottify(dict_key))\n for dict_ in dotted_dict_list:\n for k, v in dict_.items():\n temp_val = dotted_dict.get(k) or []\n temp_val.append(v)\n dotted_dict[k] = temp_val\n del obj_dict[dict_key]\n obj_dict.update(dotted_dict)\n else:\n # if the value is False-y i.e. the key has not been set in the object,\n # delete the key from the object\n del obj_dict[dict_key]\n entity.update(obj_dict)\n return entity","def query_orm(orm, strip_fields=[\"__version__\"]):\n columns = filter(lambda col: col.name not in strip_fields, orm.__table__.columns)\n with db_session(\"production\") as session:\n query = session.query(*columns)\n return list(map(object_as_dict, query.all()))","def entity_classes_to_map_over(cls):\n return [user_models.UserSettingsModel]","def entity_classes_to_map_over(cls):\n return [user_models.UserSettingsModel]","def entity_classes_to_map_over(cls):\n return [user_models.UserSettingsModel]","def entity_classes_to_map_over(cls):\n return [user_models.UserSettingsModel]","def entity_classes_to_map_over(cls):\n return [user_models.UserSettingsModel]","def get_entities(self, row_id):\n return self.get(row_id).entities","def _build(cls, model_class, *args, **kwargs):\n for k in kwargs.keys():\n if k in model_class.relationships():\n rel_key = '{}_id'.format(k)\n try:\n kwargs[rel_key] = str(kwargs[k].id)\n except AttributeError:\n pass\n obj = super(BaseFactory, cls)._build(model_class, *args, **kwargs)\n obj_dict = obj.to_dict()\n try:\n db.session.expunge(obj)\n except InvalidRequestError:\n pass\n return obj_dict","def entity_classes_to_map_over(cls):\n return [feedback_models.GeneralFeedbackThreadModel,\n feedback_models.GeneralFeedbackMessageModel,\n feedback_models.GeneralFeedbackThreadUserModel,\n email_models.GeneralFeedbackEmailReplyToIdModel,\n user_models.UserSubscriptionsModel]","def entity_classes_to_map_over(cls):\n return [feedback_models.GeneralFeedbackThreadModel,\n feedback_models.GeneralFeedbackMessageModel,\n feedback_models.GeneralFeedbackThreadUserModel,\n email_models.GeneralFeedbackEmailReplyToIdModel,\n user_models.UserSubscriptionsModel]","def as_real_class(self):\n model = self.content_type.model_class()\n if model == Defense:\n return self\n return model.objects.get(id=self.id)","def __iter__(self):\n return self._database.select(self.as_sql(), self._model_cls)","def me_class(self):\n return self._entity_class","def all(self, *args, **kwargs):\n list_to_return = []\n if not self.object_type:\n return list_to_return\n class_name = eval(self.object_type)\n if self.objects_id:\n for id in self.objects_id.split(';'):\n if id:\n list_to_return.append(class_name.objects.get(id=id))\n return list_to_return","def gather_entities(self):\n entitylist = set()\n for entity in self.entities.all():\n entitylist.add(entity)\n entitylist.update(entity.get_ancestors())\n return entitylist #set(entity for entity in entitylist if not entity.abstract_entity)","def serialize_model(result):\n relation_prefix = '/' # this prefix is added to fields that are a relation\n\n # Note: unloaded property is used to discard fields that are not loaded, ie. lazily loaded,\n # such as relationships (by default), and fields not specified in query select clause.\n selected = (lambda field: not field in sqlalchemy.orm.attributes.instance_state(result).unloaded)\n fields = list(filter(selected, result._sa_instance_state.attrs.keys()))\n\n object = {}\n for field in fields:\n\n try:\n value = getattr(result, field)\n except AttributeError:\n continue # we are permissive\n\n if not is_relationship(getattr(result.__class__, field)):\n object[field] = value\n\n else:\n if isinstance(value, sqlalchemy.orm.collections.InstrumentedList):\n # ..n relationship: value is a list of sqla models\n object[relation_prefix + field] = list(map(lambda result: result.id, value))\n\n elif isinstance(type(value), sqlalchemy.ext.declarative.api.DeclarativeMeta):\n # ..1 relationship: value is a sqla model\n object[relation_prefix + field] = value.id\n\n else:\n # ..1 relationship: value shall be empty\n object[relation_prefix + field] = value\n\n return object","def get_available_entities_models():\n return ['concat', 'bahdanau', 'luong']","def all(self, cls=None):\n if not self.__session:\n self.reload()\n objects = {}\n if type(cls) == str:\n cls = classes.get(cls, None)\n if cls:\n for obj in self.__session.query(cls):\n objects[obj.__class__.__name__ + '.' + obj.id] = obj\n else:\n for cls in classes.values():\n for obj in self.__session.query(cls):\n objects[obj.__class__.__name__ + '.' + obj.id] = obj\n return objects","def test_joined(engine, sessionmaker):\n # Defining specification\n spec = {\n \"components\": {\n \"schemas\": {\n \"Employee\": {\n \"properties\": {\n \"id\": {\"type\": \"integer\", \"x-primary-key\": True},\n \"name\": {\"type\": \"string\"},\n \"type\": {\"type\": \"string\"},\n },\n \"x-tablename\": \"employee\",\n \"type\": \"object\",\n \"x-kwargs\": {\n \"__mapper_args__\": {\n \"polymorphic_on\": \"type\",\n \"polymorphic_identity\": \"employee\",\n }\n },\n },\n \"Manager\": {\n \"allOf\": [\n {\"$ref\": \"#/components/schemas/Employee\"},\n {\n \"x-tablename\": \"manager\",\n \"x-inherits\": \"Employee\",\n \"type\": \"object\",\n \"properties\": {\n \"id\": {\n \"type\": \"integer\",\n \"x-primary-key\": True,\n \"x-foreign-key\": \"employee.id\",\n },\n \"manager_data\": {\"type\": \"string\"},\n },\n \"x-kwargs\": {\n \"__mapper_args__\": {\"polymorphic_identity\": \"manager\"}\n },\n },\n ]\n },\n \"Engineer\": {\n \"allOf\": [\n {\"$ref\": \"#/components/schemas/Employee\"},\n {\n \"x-tablename\": \"engineer\",\n \"x-inherits\": \"Employee\",\n \"type\": \"object\",\n \"properties\": {\n \"id\": {\n \"type\": \"integer\",\n \"x-primary-key\": True,\n \"x-foreign-key\": \"employee.id\",\n },\n \"engineer_info\": {\"type\": \"string\"},\n },\n \"x-kwargs\": {\n \"__mapper_args__\": {\"polymorphic_identity\": \"engineer\"}\n },\n },\n ]\n },\n }\n }\n }\n # Creating model factory\n base = declarative.declarative_base()\n model_factory = open_alchemy.init_model_factory(spec=spec, base=base)\n employee = model_factory(name=\"Employee\")\n manager = model_factory(name=\"Manager\")\n engineer = model_factory(name=\"Engineer\")\n\n # Creating models\n base.metadata.create_all(engine)\n # Creating instance of models\n employee_instance = employee(id=1, name=\"employee 1\")\n manager_instance = manager(id=2, name=\"employee 2\", manager_data=\"manager data 2\")\n engineer_instance = engineer(\n id=3, name=\"employee 3\", engineer_info=\"engineer info 3\"\n )\n session = sessionmaker()\n session.add(employee_instance)\n session.add(manager_instance)\n session.add(engineer_instance)\n session.flush()\n\n # Querying session for employee\n queried_employee = session.query(employee).first()\n assert queried_employee.id == 1\n assert queried_employee.name == \"employee 1\"\n assert queried_employee.type == \"employee\"\n # Querying session for manager\n queried_manager = session.query(manager).first()\n assert queried_manager.id == 2\n assert queried_manager.name == \"employee 2\"\n assert queried_manager.type == \"manager\"\n assert queried_manager.manager_data == \"manager data 2\"\n # Querying session for engineer\n queried_engineer = session.query(engineer).first()\n assert queried_engineer.id == 3\n assert queried_engineer.name == \"employee 3\"\n assert queried_engineer.type == \"engineer\"\n assert queried_engineer.engineer_info == \"engineer info 3\"","def get_entities_of_type(self, typename, type_system):\n if type_system not in self._type_systems:\n raise ValueError(\n f\"The type system {type_system} is not one of {self._type_systems.keys()}\"\n )\n return self._type_systems[type_system].get_entities_of_type(typename)","def include(self, registry):\n for cls in registry.values():\n db_to_element = {}\n\n props = sorted([(k,v) for k,v in cls.__dict__.items()\n if isinstance(v, Property)]\n , key=lambda p:p[1].instance_idx)\n for prop_name, prop_value in props:\n value_name = prop_value.name\n if value_name:\n db_to_element[value_name] = prop_name\n prop_name = value_name\n else:\n db_to_element[prop_name] = prop_name\n\n self.guard_reserved_words(prop_name, cls)\n\n self.props_from_db[cls] = self.create_props_mapping(db_to_element)\n self.init_broker_for_class(cls)\n self.registry[cls.registry_name] = cls","def readEntities(self):\n entities = {}\n \n # Regexes must be greedy to prevent matching outer entity and end_entity strings\n # Regexes have re.DOTALL to match newlines\n for m in re.finditer(\"ENTITY (.*?)END_ENTITY;\", self.data, re.DOTALL):\n entity = {}\n raw_entity_str = m.groups()[0]\n\n entity[\"name\"] = re.search(\"(.*?)[;|\\s]\", raw_entity_str).groups()[0].upper()\n \n is_supertype = re.search(\"SUPERTYPE\", raw_entity_str) != None\n if is_supertype:\n supertypeofmatch = re.search(\".*SUPERTYPE OF \\((.*?)\\)\", raw_entity_str)\n is_abstract_supertype = re.search(\"ABSTRACT SUPERTYPE\", raw_entity_str) != None\n \n is_subtype = re.search(\"SUBTYPE\", raw_entity_str) != None\n\n if is_supertype and is_subtype:\n if is_abstract_supertype:\n # abstract supertype of ... subtype of ... ;\n match = re.search(\".*ABSTRACT SUPERTYPE OF\\s+\\((.*?)\\)\\s+SUBTYPE OF\\s+\\((.*?)\\);\", raw_entity_str, re.DOTALL)\n entity[\"isabstract\"] = True\n supertypeof,subtypeof = match.groups()\n entity[\"supertype\"] = subtypeof.upper()\n supertypeof = re.sub('\\s', '', supertypeof)\n supertypeofmatch = re.search(\".*\\((.*?)\\)\", supertypeof, re.DOTALL)\n subtypes = supertypeofmatch.groups()[0].upper().split(',')\n entity[\"subtypes\"] = subtypes\n else:\n # supertype of ... subtype of ... ;\n match = re.search(\".*SUPERTYPE OF\\s+\\((.*?)\\)\\s+SUBTYPE OF\\s+\\((.*?)\\);\", raw_entity_str, re.DOTALL)\n entity[\"isabstract\"] = False\n supertypeof,subtypeof = match.groups()\n entity[\"supertype\"] = subtypeof.upper()\n supertypeof = re.sub('\\s', '', supertypeof)\n supertypeofmatch = re.search(\".*\\((.*?)\\)\", supertypeof, re.DOTALL)\n subtypes = supertypeofmatch.groups()[0].upper().split(',')\n entity[\"subtypes\"] = subtypes\n elif is_subtype:\n # subtype of ... ;\n subtypeofmatch = re.search(\".*SUBTYPE OF \\((.*?)\\);\", raw_entity_str)\n entity[\"supertype\"] = subtypeofmatch.groups()[0].upper() if subtypeofmatch else None\n\n # find the shortest string matched from the end of the entity type header to the\n # first occurence of a NO_ATTR string (when it occurs on a new line)\n inner_str = re.search(\";(.*?)$\", raw_entity_str, re.DOTALL).groups()[0]\n\n attrs_str = min([inner_str.partition(\"\\r\\n \"+a)[0] for a in self.NO_ATTR])\n attrs = []\n for am in re.finditer(\"(\\S*?) : (.*?);\", attrs_str, re.DOTALL):\n name, attr_type = [s.replace(\"\\r\\n\\t\",\"\") for s in am.groups()]\n attrs.append((name, attr_type))\n \n entity[\"attributes\"] = attrs\n entities[entity[\"name\"]] = entity\n \n\n return entities","def query(self, klass):\n return self.getSession().query(klass)","def objects(self, cls):\n for name, info in direct_fields(self.__class__).items():\n if issubclass(cls, info.sub_fields[0].type_):\n return getattr(self, name)\n raise TypeError(cls)","def _get_related_objects(obj, parent_class=False):\n foreign_managers = _get_related_managers(obj, parent_class)\n\n related_objects = []\n for manager in foreign_managers:\n related_objects += manager.all()\n\n return related_objects","def all(self, cls=None):\n all_dicts = {}\n if cls is not None:\n objects = self.__session.query(models.classes[cls]).all()\n for obj in objects:\n key = obj.__class__.__name__ + \".\" + obj.id\n all_dicts[key] = obj\n return all_dicts\n else:\n # Checks class for BaseModel as an excemption otherwise\n # Checks all classes\n for key, value in models.classes.items():\n if key != \"BaseModel\":\n objects = self.__session.query(value).all()\n for obj in objects:\n table = obj.__class__.__name__ + \".\" + obj.id\n all_dicts[table] = obj\n return all_dicts","def find_sql_models():\n for model in find_subclasses(SQLModel):\n # Get model Meta class\n meta = getattr(model, 'Meta', None)\n if meta:\n # If this is marked as abstract ignore it\n if getattr(meta, 'abstract', False):\n continue\n yield model","def _load_related_objects(context, cls, db_data):\n obj_data = db_data.copy()\n for name, (obj_cls, _) in cls.object_fields.items():\n if obj_data.get(name):\n obj_data[name] = obj_cls(context, **obj_data.get(name).as_dict())\n else:\n del obj_data[name]\n\n return obj_data","def as_leaf_class(self):\n\n # get the leaf class\n content_type = self.content_type\n Model = content_type.model_class()\n \n # get the appropriate object\n if (Model == self.__class__):\n return self\n return Model.objects.get(pk=self.pk)","def map_database(connection):\n eng = create_engine(connection)\n metadata = MetaData()\n metadata.reflect(eng)\n base = automap_base(metadata=metadata)\n base.prepare()\n return base.classes, eng","def expand_type_query(owner_name: str, type_name: []) -> 'session query':\n critical_level = get_critical_level(owner_name, type_name)\n\n ordered = db.session.query(\n func.sum(SpecificOrders.quantity).label('number')\n )\\\n .join(Orders, Orders.order_id == SpecificOrders.order_id)\\\n .filter(\n and_(\n Orders.supplier_name == owner_name,\n SpecificOrders.type_name.in_(type_name)\n )\n\n )\n\n query = select([Products.type_name.label('Type'),\n Products.serial_number.label('Serial number'),\n Products.producent.label('Producent'),\n Products.model.label('Model'),\n Products.product_condition.label('Condition'),\n Products.additonal_info.label('Additional info'),\n Products.appear_in_order.label('Bind to order')])\\\n .where(\n and_(\n Products.owner_name == owner_name,\n Products.type_name.in_(type_name)\n )\n ).order_by(Products.appear_in_order.asc(),Products.serial_number.asc())\n return db.session.query(aliased(query)), ordered, critical_level","def get_entity_type_superclass_map(entity_types):\n _logger.info(f'Creating entity type superclass map...')\n entity_type_subclass_map = get_entity_type_subclass_map(entity_types)\n return reverse_entity_type_subclass_map(entity_type_subclass_map)","def get_model(params):\r\n module_name, class_name = params.model.name.rsplit('.', 1)\r\n i = importlib.import_module(module_name)\r\n return getattr(i, class_name)","def extract_product(product):\r\n product = \"%\" + product + \"%\"\r\n cursor.execute(\"USE openfoodfacts;\")\r\n cursor.execute(\"\"\"SELECT Food.id, Food.name, categories_id, nutri_score, url, stores \\\r\n FROM Food \\\r\n INNER JOIN Categories ON Food.categories_id LIKE Categories.name\\\r\n WHERE Food.name LIKE %s;\"\"\", (product))\r\n product = cursor.fetchone()\r\n product_class = cl.Food(product)\r\n return product_class","def all_entity_classes():\n persistent_classes = Entity._decl_class_registry.values()\n # with sqlalchemy 0.8 _decl_class_registry holds object that are not classes\n return [ cls for cls in persistent_classes\n if isclass(cls) and issubclass(cls, Entity) ]","def query(cls):\r\n return cls._session.query(cls)","def _inspect_module(module):\n module_list = getmembers(module, predicate=ismodule)\n classes = getmembers(module, predicate=isclass)\n for (name, cls) in classes:\n if issubclass(cls, db.Model) and not issubclass(cls, Taxonomy):\n if cls is not db.Model:\n _data_classes[name] = cls\n return [mod[1] for mod in module_list]","def _raw(self, query: Any, data: Any = None):\n assert isinstance(query, str)\n\n conn = self._get_session()\n try:\n results = conn.execute(query)\n\n entity_items = []\n for item in results:\n entity = self.model_cls.to_entity(item)\n entity.state_.mark_retrieved()\n entity_items.append(entity)\n\n result = ResultSet(\n offset=0,\n limit=len(entity_items),\n total=len(entity_items),\n items=entity_items,\n )\n except DatabaseError as exc:\n logger.error(f\"Error while running raw query: {exc}\")\n raise\n finally:\n if not current_uow:\n conn.commit()\n conn.close()\n\n return result","def _get_entities(self, entity_name):\n return get_entities(self.attributes.workspace.namespace, self.attributes.workspace.name, entity_name)","def get_query(params, filters):\n engine, session = connect_to_db(DB_NAME, CORE_DIR)\n\n q = session.query(*params) # kick off the query\n\n classes = []\n for p in params:\n parent_class = p.parent.class_\n classes.append(parent_class) if parent_class not in classes else None # need order, so hack around a set...\n\n\n base = classes.pop(0) # grab first class from list\n linked = [base] # the first class is inherently already in the join-chain\n\n if classes: # any more classes?\n for c in classes:\n relations_for_c = relations.get(c.__name__)\n\n if not relations_for_c:\n msg = f'{c.__name__} does not have any defined relationships.'\n raise NotImplementedError(msg)\n\n relation = relations_for_c.get(base.__name__)\n\n if relation:\n q = q.join(c, relation.key == relation.fkey)\n else:\n msg = f'{c.__name__} is not directly related to {base} in the schema.'\n raise NotImplementedError(msg)\n\n for f in filters:\n q = q.filter(f)\n\n return q.all()[:10]","def _get_global_entities(nest):\n entities = nest.get('global_entities', None)\n if not entities:\n return []\n return list(entities.keys())","def of(cls, type_):\n\n for subcls in Mapping.__subclasses__():\n if subcls.type() == type_:\n return subcls()\n\n return None","async def _parse_entities(self, responses: SourceResponses) -> Entities:\n installed_dependencies: dict[str, dict[str, str]] = {}\n for response in responses:\n installed_dependencies.update(await response.json(content_type=None))\n return Entities(\n Entity(\n key=f'{dependency}@{versions.get(\"current\", \"?\")}',\n name=dependency,\n current=versions.get(\"current\", \"unknown\"),\n wanted=versions.get(\"wanted\", \"unknown\"),\n latest=versions.get(\"latest\", \"unknown\"),\n )\n for dependency, versions in installed_dependencies.items()\n )","def entity_classes_to_map_over(cls):\n return [\n exp_models.ExplorationRightsModel,\n collection_models.CollectionRightsModel,\n feedback_models.GeneralFeedbackMessageModel]","def select_or_run(self, *args, **kwargs):\n query_rows = self.bind_signature(args, kwargs)\n\n model_instances = []\n for query_row in query_rows:\n model_instances.append(self.get_or_run(**query_row))\n\n if len(model_instances) == 1: # TODO: Is this actually a good API?\n return model_instances[0]\n return model_instances","def relate(self, qs):\n model_map = {}\n item_map = {}\n for item in qs:\n object_id = getattr(item, self._object_id_field)\n content_type = getattr(item, self._content_type_field)\n model_map.setdefault(content_type, {}) \\\n [object_id] = item.id\n item_map[item.id] = item\n for ct, items_ in model_map.items():\n for o in ct.model_class().objects.select_related() \\\n .filter(id__in=items_.keys()).all():\n setattr(item_map[items_[o.id]],self._content_object_field, o)\n return qs","def all(self, cls=None):\n # create a dictionary\n obj_dict = {}\n if cls is None:\n classes = [State, City, User, Place, Review, Amenity]\n for class_name in classes:\n for obj in self.__session.query(class_name):\n key = obj.__class__.__name__ + '.' + obj.id\n obj_dict[key] = obj\n else:\n for obj in self.__session.query(cls):\n key = obj.__class__.__name__ + '.' + obj.id\n obj_dict[key] = obj\n return obj_dict","def test_extraction_with_model_class(self: Any, mock_method: Any) -> None:\n config_dict = {\n 'extractor.sqlalchemy.conn_string': 'TEST_CONNECTION',\n 'extractor.sqlalchemy.extract_sql': 'SELECT 1 FROM TEST_TABLE;',\n 'extractor.sqlalchemy.model_class':\n 'tests.unit.extractor.test_sql_alchemy_extractor.TableMetadataResult'\n }\n self.conf = ConfigFactory.from_dict(config_dict)\n\n extractor = SQLAlchemyExtractor()\n extractor.results = [dict(database='test_database',\n schema='test_schema',\n name='test_table',\n description='test_description',\n column_name='test_column_name',\n column_type='test_column_type',\n column_comment='test_column_comment',\n owner='test_owner')]\n\n extractor.init(Scoped.get_scoped_conf(conf=self.conf,\n scope=extractor.get_scope()))\n\n result = extractor.extract()\n\n self.assertIsInstance(result, TableMetadataResult)\n self.assertEqual(result.name, 'test_table')","def resolve(self, term: str, labels: istr = None) -> List[Entity]:","def deep_entities(model = 'bahdanau'):\n assert isinstance(model, str), 'model must be a string'\n model = model.lower()\n if model in ['concat', 'bahdanau', 'luong']:\n if not os.path.isfile(PATH_ENTITIES[model]['model']):\n print('downloading ENTITIES frozen %s model' % (model))\n download_file(\n S3_PATH_ENTITIES[model]['model'], PATH_ENTITIES[model]['model']\n )\n if not os.path.isfile(PATH_ENTITIES[model]['setting']):\n print('downloading ENTITIES %s dictionary' % (model))\n download_file(\n S3_PATH_ENTITIES[model]['setting'],\n PATH_ENTITIES[model]['setting'],\n )\n with open(PATH_ENTITIES[model]['setting'], 'r') as fopen:\n nodes = json.loads(fopen.read())\n g = load_graph(PATH_ENTITIES[model]['model'])\n return TAGGING(\n g.get_tensor_by_name('import/Placeholder:0'),\n g.get_tensor_by_name('import/Placeholder_1:0'),\n g.get_tensor_by_name('import/logits:0'),\n nodes,\n tf.InteractiveSession(graph = g),\n )\n\n else:\n raise Exception(\n 'model not supported, please check supported models from malaya.get_available_entities_models()'\n )","def find_objs(self, cls, **attr):\n nodes = getattr(self.graph, getattr(models, cls).element_plural).query(**attr).all()\n return nodes","def create_class(self, cls):\n\n cls_name = cls.registry_name\n\n bases = [base for base in cls.__bases__ if self.valid_element_base(base)]\n if not bases:\n raise TypeError(\n 'Unexpected base class(es) in Graph.create_class'\n ' - try the declarative bases')\n\n extends = None\n if bases[0] is bases[0].decl_root:\n extends = ['V', 'E'][bases[0].decl_type]\n else:\n extends = ','.join([base.registry_name for base in bases])\n\n #if not self.client.command(\n # 'SELECT FROM ( SELECT expand( classes ) FROM metadata:schema ) WHERE name = \"{}\"'\n # .format(cls_name)):\n try:\n self.client.command(\n 'CREATE CLASS {0} EXTENDS {1}'.format(cls_name, extends))\n except pyorient.PyOrientSchemaException:\n # Class already exists\n pass\n\n db_to_element = {}\n\n props = sorted([(k,v) for k,v in cls.__dict__.items()\n if isinstance(v, Property)]\n , key=lambda p:p[1].instance_idx)\n for prop_name, prop_value in props:\n value_name = prop_value.name\n if value_name:\n db_to_element[value_name] = prop_name\n prop_name = value_name\n else:\n db_to_element[prop_name] = prop_name\n\n self.guard_reserved_words(prop_name, cls)\n\n class_prop = '{0}.{1}'.format(cls_name, prop_name)\n\n linked_to = None\n if isinstance(prop_value, LinkedClassProperty):\n type_linked_to = prop_value.linked_to\n\n linked_to = getattr(type_linked_to, 'registry_name', None)\n if not linked_to:\n link_bases = getattr(type_linked_to, '__bases__', None)\n if link_bases and \\\n isinstance(prop_value, LinkedProperty) and \\\n link_bases[0] is Property:\n linked_to = type_linked_to.__name__\n\n try:\n self.client.command(\n 'CREATE PROPERTY {0} {1} {2}'\n .format(class_prop\n , type(prop_value).__name__\n , linked_to or ''))\n except pyorient.PyOrientCommandException:\n # Property already exists\n pass\n\n if prop_value.default is not None:\n if self.server_version >= (2,1,0):\n self.client.command(\n 'ALTER PROPERTY {0} DEFAULT {1}'\n .format(class_prop,\n PropertyEncoder.encode(prop_value.default)))\n\n self.client.command(\n 'ALTER PROPERTY {0} NOTNULL {1}'\n .format(class_prop\n , str(not prop_value.nullable).lower()))\n\n self.client.command(\n 'ALTER PROPERTY {} MANDATORY {}'\n .format(class_prop\n , str(prop_value.mandatory).lower()))\n\n self.client.command(\n 'ALTER PROPERTY {} READONLY {}'\n .format(class_prop\n , str(prop_value.readonly).lower()))\n\n # TODO Add support for composite indexes\n if prop_value.indexed:\n try:\n self.client.command(\n 'CREATE INDEX {0} {1}'\n .format(class_prop\n , 'UNIQUE' if prop_value.unique\n else 'NOTUNIQUE'))\n except pyorient.PyOrientIndexException:\n # Index already exists\n pass\n\n self.props_from_db[cls] = self.create_props_mapping(db_to_element)\n self.init_broker_for_class(cls)\n self.registry[cls_name] = cls","def python_type(self) -> type:\n return SearchMetaEntity","def _process_related_model_searches(query: dict) -> dict:\n multi_terms = {\n \"collections\": \"collection\",\n \"publishers\": \"publisher\",\n \"repositories\": \"repository\",\n \"original_coverages\": \"coverage\",\n \"subjects\": \"subject\",\n \"contributors\": \"contributor\",\n \"donors\": \"donor\",\n \"languages\": \"language\",\n \"translations\": \"translation\",\n \"themes\": \"theme\",\n }\n\n # Rename each term to singular\n for key, value in multi_terms.items():\n if key in query.keys():\n query[value] = query.pop(key)\n\n # Build list of terms we need to parse\n terms_to_parse = []\n for term in multi_terms.values():\n if term in query.keys():\n terms_to_parse.append(term)\n\n # transform each term list into a list of IDs\n for term in terms_to_parse:\n query[term] = [str(item.id) for item in query[term]]\n\n # Special handling for langauges, translations, themes.\n # Unlike they above, they only accept singular values\n for term in [\"language\", \"translation\", \"theme\"]:\n if term in query.keys():\n if len(query[term]) > 1:\n logging.error(\"[!] Cannot filter for more than one %s\", term)\n raise exceptions.InvalidSearchFieldError\n # Pull out the singleton.\n query[term] = query[term][0]\n\n # Return the reformatted query.\n return query","def all(self, cls=None):\n if cls:\n objects = self.__session.query(cls).all()\n else:\n classes = [State, City] # , User, Place, Review, Amenity]\n objects = []\n for c in classes:\n objects += self.__session.query(c)\n return {\"{}.{}\".format(type(obj).__name__, obj.id): obj for obj in\n objects}","def expand_entities(self, entityIterable):\n for e in entityIterable:\n if self.expand or self.select:\n e.Expand(self.expand, self.select)\n yield e","def set_entity_class_registry(self, entity_class_registry):\n self.entity_class_registry = entity_class_registry","def get_entity(obj):\n return obj.or_expression.and_expression.cmp_expression.arith_expression. \\\n mul_expression.unary_expression.pow_expression.primary_expression. \\\n entity","def declarative_base(model):\n for parent in model.__bases__:\n try:\n parent.metadata\n return declarative_base(parent)\n except AttributeError:\n pass\n return model","def resolve_model(root: Quote, *args) -> str:\n return 'quotes.quote'","def __call__(self, context=None):\n if context is None:\n context = self.context\n return self.entity.resolve(context)","def all(self, cls=None):\n query_data = {}\n\n if cls is None:\n for valid_key, valid_class in DBStorage.CNC.items():\n for instance in self.__session.query(valid_class):\n key = type(instance).__name__ + \".\" + instance.id\n query_data.update({key: instance})\n return query_data\n else:\n for instance in self.__session.query(DBStorage.CNC[cls]):\n key = type(instance).__name__ + \".\" + instance.id\n query_data.update({key: instance})\n return query_data","def getentities(self):\n entities = {}\n\n # The following will create lots of errors in suds.client, one\n # for every type that is not an entity. Disable their logger\n # temporarily to avoid cluttering the log.\n sudslog = logging.getLogger('suds.client')\n sudssav = sudslog.disabled\n sudslog.disabled = True\n for t in self.gettypes():\n try:\n info = EntityInfo(t, self.client)\n except ICATError:\n continue\n entities[t] = info\n sudslog.disabled = sudssav\n\n return entities"],"string":"[\n \"def get_real_object(self):\\n query_string = dedent(f\\\"\\\"\\\"\\\\\\n import app.config.models_importer as models_importer\\n\\n class_ = models_importer.all_models['{self.ref_class}']\\n \\n class_.query.get({self.ref_id})\\\"\\\"\\\")\\n\\n return exec(query_string)\",\n \"def _namespaced_query(session, obj_outer, cls_inner, name_inner):\\n return session.query(cls_inner) \\\\\\n .filter_by(owner = obj_outer) \\\\\\n .filter_by(label = name_inner).first()\",\n \"def get_models_query():\\n query = db.session.query(Products.model).distinct()\\n return query\",\n \"def resolve_model(root: Entity, *args) -> str:\\n return 'entities.entity'\",\n \"def models(self, model=None):\\n for query in self.__queries:\\n if isinstance(query, orb.Query):\\n yield query.model(model)\\n else:\\n for model in query.models(model):\\n yield model\",\n \"def expand(self, graph):\\n for triple in self.g:\\n graph.add(triple)\\n entity_tags = defaultdict(set)\\n res = graph.query(\\n \\\"\\\"\\\"SELECT ?ent ?tag WHERE {\\n ?ent brick:hasTag ?tag\\n }\\\"\\\"\\\"\\n )\\n for ent, tag in res:\\n entity_tags[ent].add(tag)\\n for entity, tagset in entity_tags.items():\\n tagset = list(map(lambda x: x.split(\\\"#\\\")[-1], tagset))\\n lookup = self.lookup_tagset(tagset)\\n if len(lookup) == 0:\\n continue\\n klasses = list(lookup[0][0])\\n graph.add((entity, A, BRICK[klasses[0]]))\",\n \"def register_orm_base(self, base):\\n for model in utils.searchable_sqlalchemy_models(base):\\n self.register_type(model.es_type_name, model.es_properties, model)\",\n \"def entity_classes_to_map_over(cls):\\n return [exp_models.ExplorationSnapshotMetadataModel]\",\n \"def infer_model(self, model):\\n\\n from .graph import Graph\\n\\n entities = model[\\\"rows\\\"]\\n # index the entities by their ID field\\n entities = {e[\\\"id\\\"].replace('\\\"', \\\"\\\"): {\\\"tags\\\": e} for e in entities}\\n # TODO: add e['dis'] for a descriptive label?\\n brickgraph = Graph(load_brick=False)\\n\\n # marker tag pass\\n for entity_id, entity in entities.items():\\n marker_tags = {\\n k for k, v in entity[\\\"tags\\\"].items() if v == \\\"m:\\\" or v == \\\"M\\\"\\n }\\n for f in self._filters:\\n marker_tags = list(filter(f, marker_tags))\\n # translate tags\\n entity_tagset = list(self._translate_tags(marker_tags))\\n\\n equip_ref = entity[\\\"tags\\\"].get(\\\"equipRef\\\")\\n # infer tags for single entity\\n triples, _ = self.infer_entity(\\n entity_tagset, identifier=entity_id, equip_ref=equip_ref\\n )\\n brickgraph.add(*triples)\\n self._generated_triples.extend(triples)\\n\\n # take a pass through for relationships\\n for entity_id, entity in entities.items():\\n relships = {k: v for k, v in entity[\\\"tags\\\"].items() if k.endswith(\\\"Ref\\\")}\\n # equip_entity_id = entity_id.replace(' ', '_') + '_equip'\\n point_entity_id = entity_id.replace(\\\" \\\", \\\"_\\\") + \\\"_point\\\"\\n if \\\"equipRef\\\" not in relships:\\n continue\\n reffed_equip = (\\n relships[\\\"equipRef\\\"].replace(\\\" \\\", \\\"_\\\").replace('\\\"', \\\"\\\") + \\\"_equip\\\"\\n )\\n if self._BLDG[point_entity_id] in brickgraph.nodes:\\n triple = (\\n self._BLDG[reffed_equip],\\n BRICK.hasPoint,\\n self._BLDG[point_entity_id],\\n )\\n brickgraph.add(triple)\\n self._generated_triples.append(triple)\\n return brickgraph\",\n \"def reverse_entity_type_subclass_map(entity_type_subclass_map):\\n entity_type_superclass_map = {}\\n\\n for superclass, subclasses in tqdm(entity_type_subclass_map.items()):\\n for subclass in subclasses:\\n if subclass['id'] in entity_type_superclass_map:\\n entity_type_superclass_map[subclass['id']].append(superclass)\\n else:\\n entity_type_superclass_map[subclass['id']] = [superclass]\\n\\n return entity_type_superclass_map\\n\\n\\n#def get_type_index():\\n \\\"\\\"\\\"\\n Create an index s.t. each type gets an ID (e.g. person -> 1,\\n organization -> 2, ...\\n \\\"\\\"\\\"\\n #return dict(enumerate(type_list))\\n\\n\\n#def get_index_of_type(entity_type):\\n \\\"\\\"\\\"\\n Return the index of a given entity type.\\n \\\"\\\"\\\"\\n #return type_list.index(entity_type)\\n\\n\\n#def get_type_by_index(index):\\n \\\"\\\"\\\"\\n Return the type of a given index.\\n \\\"\\\"\\\"\\n #return type_list[index - 1] # TODO re-train model with new indices\",\n \"def to_entity(cls, model_obj: \\\"SqlalchemyModel\\\"):\\n item_dict = {}\\n for field_name in attributes(cls.meta_.entity_cls):\\n item_dict[field_name] = getattr(model_obj, field_name, None)\\n return cls.meta_.entity_cls(item_dict)\",\n \"def _get_model_class_from_table(self, table):\\r\\n try:\\r\\n model_class = [m for m in get_models() if connection.introspection.table_name_converter(m._meta.db_table) in map(connection.introspection.table_name_converter,[table])][0] \\r\\n m2m = False \\r\\n except IndexError:\\r\\n try: \\r\\n # this is a many to many field \\r\\n model_class = [f.rel.to for m in get_models() for f in m._meta.local_many_to_many if f.m2m_db_table() == table][0] \\r\\n m2m = True \\r\\n except IndexError: \\r\\n # this is an inner join \\r\\n table = self.query.alias_map[table][0]\\r\\n return self._get_model_class_from_table(table)\\r\\n return model_class, m2m\",\n \"def fetchall(self):\\n rows = self.cursor.fetchall()\\n\\n if self.model.single:\\n for row in rows:\\n yield self.__instance_from_db(self.model, row)\\n else:\\n for row in rows:\\n yield tuple(self.__instance_from_db(m, row) for m in self.model.models)\",\n \"def all(self, cls=None):\\n results = {}\\n if cls is not None:\\n # TODO cls always is a class\\n if type(cls) == str:\\n cls = eval(cls)\\n for instance in self.__session.query(cls):\\n key = \\\"{}.{}\\\".format(cls.__name__, instance.id)\\n results[key] = instance\\n return results\\n else:\\n for table in self.__tables:\\n for instance in self.__session.query(eval(table)):\\n key = \\\"{}.{}\\\".format(table, instance.id)\\n results[key] = instance\\n return results\",\n \"def _default_registry(self):\\n registry = JSONSerializer.instance().registry.copy()\\n registry.update({m.__model__: m for m in find_sql_models()})\\n return registry\",\n \"def _get_related_models(self, parent_model):\\r\\n related_models = set()\\r\\n rev_reversemapping = dict([(v,k) for k,v in self._reversemapping.iteritems()])\\r\\n if rev_reversemapping:\\r\\n for attname, related in self._get_reverse_relations(parent_model):\\r\\n related_models.add((rev_reversemapping[attname], related.model))\\r\\n\\r\\n for field in parent_model._meta.fields:\\r\\n if field.rel and field.rel.to._meta.db_table in self.query.tables and field.rel.to != parent_model:\\r\\n related_models.add((field.attname, field.rel.to))\\r\\n \\r\\n for attname, model_class in related_models:\\r\\n yield attname, model_class\\r\\n if attname.endswith(\\\"_id\\\"):\\r\\n attname = attname[:-3]\\r\\n for join_attname, model_klass in self._get_related_models(model_class):\\r\\n yield LOOKUP_SEP.join((attname,join_attname)), model_klass\",\n \"def query(cls):\\n query_class = cls.query_class\\n return query_class(orm_class=cls)\",\n \"def all(self, cls=None):\\n obj_dict = {}\\n all_objs = []\\n if cls:\\n all_objs = self.__session.query(eval(cls)).all()\\n else:\\n for table in self.all_classes:\\n all_objs += self.__session.query(eval(table)).all()\\n obj_dict = {obj.__class__.__name__ + '.' + obj.id: obj\\n for obj in all_objs}\\n # TODO BUG: includes <sqlalchemy> object in dict\\n return obj_dict\",\n \"def expand(self, model=None, ignoreFilter=False):\\n model = self.__model or model\\n if not model:\\n raise orb.errors.QueryInvalid('Could not traverse: {0}'.format(self.__column))\\n\\n schema = model.schema()\\n parts = self.__column.split('.')\\n\\n # expand the current column\\n lookup = schema.column(parts[0], raise_=False) or schema.collector(parts[0])\\n\\n if lookup:\\n # utilize query filters to generate\\n # a new filter based on this object\\n query_filter = lookup.queryFilterMethod()\\n if callable(query_filter) and not ignoreFilter:\\n new_q = query_filter(model, self)\\n if new_q:\\n return new_q.expand(model, ignoreFilter=True)\\n else:\\n return None\\n\\n # otherwise, check to see if the lookup\\n # has a shortcut to look through\\n elif isinstance(lookup, orb.Column) and lookup.shortcut():\\n parts = lookup.shortcut().split('.')\\n lookup = schema.column(parts[0], raise_=False)\\n\\n if len(parts) == 1:\\n return self\\n else:\\n if isinstance(lookup, orb.Collector):\\n return orb.Query(model).in_(lookup.collectExpand(self, parts))\\n\\n elif isinstance(lookup, orb.ReferenceColumn):\\n rmodel = lookup.referenceModel()\\n sub_q = self.copy()\\n sub_q._Query__column = '.'.join(parts[1:])\\n sub_q._Query__model = rmodel\\n records = rmodel.select(columns=[rmodel.schema().idColumn()], where=sub_q)\\n return orb.Query(model, parts[0]).in_(records)\\n\\n else:\\n raise orb.errors.QueryInvalid('Could not traverse: {0}'.format(self.__column))\",\n \"def resolve_entities(root, info, ids: list[int], **kwargs):\\n return Entity.objects.filter(id__in=ids)\",\n \"def matching_objects(self, filter_deleted):\\n from rome.core.orm.utils import get_literal_query\\n from rome.lang.sql_parser import QueryParser\\n from rome.core.rows.rows import construct_rows\\n\\n read_deleted = self.read_deleted\\n if filter_deleted:\\n read_deleted = \\\"no\\\"\\n\\n if self._autoflush:\\n if self.session is not None:\\n self.session.commit()\\n\\n if not self.query_tree:\\n sql_query = get_literal_query(self.sa_query)\\n parser = QueryParser()\\n query_tree = parser.parse(sql_query)\\n else:\\n query_tree = self.query_tree\\n\\n if not self.entity_class_registry:\\n self.entity_class_registry = self._extract_entity_class_registry()\\n entity_class_registry = self.entity_class_registry\\n\\n # Collecting variables of sub queries\\n subqueries_variables = {}\\n for (variable_name, sub_query_tree) in query_tree.variables.iteritems():\\n sub_query = Query()\\n sub_query.set_query_tree(sub_query_tree)\\n sub_query.set_entity_class_registry(entity_class_registry)\\n result = sub_query.all()\\n subqueries_variables[variable_name] = result\\n\\n rows = construct_rows(query_tree,\\n entity_class_registry,\\n read_deleted=read_deleted,\\n subqueries_variables= subqueries_variables)\\n\\n def row_function(row, column_descriptions, decoder):\\n from rome.core.session.utils import ObjectAttributeRefresher\\n final_row = []\\n one_is_an_object = False\\n object_attribute_refresher = ObjectAttributeRefresher()\\n for column_description in column_descriptions:\\n if type(column_description[\\\"type\\\"]) in [Integer, String]:\\n row_key = column_description[\\\"entity\\\"].__table__.name.capitalize(\\n )\\n property_name = column_description[\\\"name\\\"]\\n value = None\\n if row_key in row and property_name in row[row_key]:\\n value = row[row_key].get(property_name, None)\\n else:\\n # It seems that we are parsing the result of a function call\\n column_description_expr = column_description.get(\\\"expr\\\",\\n None)\\n if column_description_expr is not None:\\n property_name = str(column_description_expr)\\n value = row.get(property_name, None)\\n if value is not None:\\n final_row += [value]\\n else:\\n logging.error(\\n \\\"Could not understand how to get the value of '%s' with this: '%s'\\\"\\n % (column_description.get(\\\"expr\\\", \\\"??\\\"), row))\\n elif type(column_description[\\\"type\\\"]) == DeclarativeMeta:\\n one_is_an_object = True\\n row_key = column_description[\\\"entity\\\"].__table__.name\\n new_object = column_description[\\\"entity\\\"]()\\n attribute_names = map(lambda x: x.key, list(\\n column_description[\\\"entity\\\"].__table__.columns))\\n for attribute_name in attribute_names:\\n value = decoder.decode(row[row_key].get(attribute_name,\\n None))\\n setattr(new_object, attribute_name, value)\\n\\n if \\\"___version_number\\\" in row[row_key]:\\n setattr(new_object, \\\"___version_number\\\", row[row_key][\\\"___version_number\\\"])\\n\\n load_options = None\\n if hasattr(self.sa_query, \\\"_with_options\\\"):\\n load_options = self.sa_query._with_options\\n object_attribute_refresher.refresh(new_object, load_options=load_options)\\n final_row += [new_object]\\n else:\\n logging.error(\\\"Unsupported type: '%s'\\\" %\\n (column_description[\\\"type\\\"]))\\n if not one_is_an_object:\\n return [final_row]\\n else:\\n return final_row\\n\\n def row_function_subquery(row, attributes, decoder):\\n result = []\\n for attribute in attributes:\\n tablename = attribute.split(\\\".\\\")[0]\\n attribute_name = attribute.split(\\\".\\\")[1]\\n result += [row[tablename][attribute_name]]\\n return result\\n\\n decoder = Decoder()\\n\\n if len(self.sa_query.column_descriptions) > 0:\\n final_rows = map(lambda r: row_function(\\n r, self.sa_query.column_descriptions, decoder), rows)\\n else:\\n final_rows = map(lambda r: row_function_subquery(\\n r, self.query_tree.attributes, decoder), rows)\\n\\n if len(self.sa_query.column_descriptions) <= 1:\\n # Flatten the list\\n final_rows = [item for sublist in final_rows for item in sublist]\\n\\n # Add watcher on objects\\n if self.session is not None:\\n for obj in final_rows:\\n if hasattr(obj, \\\"id\\\"):\\n self.session.watch(obj)\\n\\n return final_rows\",\n \"def init_model(connection):\\n db = connection\\n\\n for obj in common.__dict__.itervalues():\\n if type(obj) == type and issubclass(obj, common.Model) and hasattr(obj, '__tablename__'):\\n tablename = getattr(obj, '__tablename__')\\n obj._object_store = Domain(db, tablename)\\n collection_to_class[obj._object_store] = obj\",\n \"def entity_classes_to_map_over(cls):\\n\\n return [user_models.UserSubscriptionsModel]\",\n \"def apply_model_to_query(self, query):\\n pass\",\n \"def entity_classes_to_map_over(cls):\\n return [exp_models.ExplorationRightsSnapshotMetadataModel]\",\n \"def get_model(self):\\n return QueryS\",\n \"def _egg_module(self, query):\\n return process.extractOne(query, self.egg_names,\\n scorer=fuzz.ratio,\\n score_cutoff=self.egg_threshold)\",\n \"def get_query(self):\\n return self.query_class(self)\",\n \"def _get_model_from_table_name(table_name: str) -> Optional[Type[RDSModel]]:\\n table_model = None\\n try:\\n if hasattr(Base, '_decl_class_registry'):\\n models = Base._decl_class_registry.values() # sqlalchemy < 1.4\\n else:\\n models = Base.registry._class_registry.values()\\n\\n for model in models:\\n if hasattr(model, '__tablename__') and model.__tablename__ == table_name:\\n table_model = model\\n except Exception as e:\\n LOGGER.exception(f'Failed to get model for the table: {table_name} from rds model base')\\n raise e\\n\\n return table_model\",\n \"def to_orm(self):\\n data = {}\\n for key, value in self:\\n # If field is Type[ORMModel],\\n # recursively convert to an ORM object.\\n if hasattr(value, \\\"__orm__\\\"):\\n data[key] = value.to_orm()\\n # If the field is a dictionary, iterate over\\n # values and convert any ORM models to ORM objects\\n # else leave them alone.\\n elif isinstance(value, dict):\\n nested_data = {}\\n for nested_key, nested_value in value:\\n if hasattr(nested_value, \\\"__orm__\\\"):\\n nested_data[key] = nested_value.to_orm()\\n else:\\n nested_data[key] = value\\n data[key] = nested_data\\n # If the field is an iterable, iterate through list\\n # and convert ORM Models to ORM objects.\\n #\\n # There has to be a better way to write this conditional...\\n elif (\\n isinstance(value, collections.Iterable) and\\n type(value) not in (str, bytearray, bytes)\\n ):\\n nested_data = []\\n for nested_value in value:\\n if hasattr(nested_value, \\\"__orm__\\\"):\\n nested_data.append(nested_value.to_orm())\\n else:\\n nested_data.append(nested_value)\\n # Convert iterable to the appropriate type at the\\n # end.\\n data[key] = type(value)(nested_data)\\n # Leave the value alone if its not an ORMModel\\n else:\\n data[key] = value\\n return self.__orm__(**data)\",\n \"def models(self):\\n return self._base.classes\",\n \"def get_mapper(mixed):\\n if isinstance(mixed, orm._MapperEntity):\\n mixed = mixed.expr\\n elif isinstance(mixed, orm.sa.Column):\\n mixed = mixed.table\\n elif isinstance(mixed, orm._ColumnEntity):\\n mixed = mixed.expr\\n\\n if isinstance(mixed, orm.sa.orm.Mapper):\\n return mixed\\n if isinstance(mixed, orm.sa.orm.util.AliasedClass):\\n return orm.sa.inspect(mixed).mapper\\n if isinstance(mixed, orm.sa.sql.selectable.Alias):\\n mixed = mixed.element\\n if isinstance(mixed, orm.AliasedInsp):\\n return mixed.mapper\\n if isinstance(mixed, orm.sa.orm.attributes.InstrumentedAttribute):\\n mixed = mixed.class_\\n if isinstance(mixed, orm.sa.Table):\\n if hasattr(orm.mapperlib, '_all_registries'):\\n all_mappers = set()\\n for mapper_registry in orm.mapperlib._all_registries():\\n all_mappers.update(mapper_registry.mappers)\\n else: # SQLAlchemy <1.4\\n all_mappers = orm.mapperlib._mapper_registry\\n mappers = [\\n mapper for mapper in all_mappers\\n if mixed in {mapper.local_table}\\n ]\\n if len(mappers) > 1:\\n raise Exception('Still to many mappers %s' % str(mappers))\\n if not mappers:\\n raise ValueError(\\n \\\"Could not get mapper for table '%s'.\\\" % mixed.name\\n )\\n else:\\n return mappers[0]\\n if not orm.isclass(mixed):\\n mixed = type(mixed)\\n return orm.sa.inspect(mixed)\",\n \"def get_entity(expression):\\n if expression:\\n namespace = sys.modules.copy()\\n namespace.update(__main__.__dict__)\\n try:\\n return eval(expression, namespace)\\n except BaseException:\\n # An uncaught exception closes idle, and eval can raise any\\n # exception, especially if user classes are involved.\\n return None\",\n \"def _merge_by_query(self, obj_dict):\\n _res = self.__session.query(obj_dict[\\\"class\\\"]).filter_by(**obj_dict[\\\"query_dict\\\"]).first()\\n\\n if _res is None:\\n self._add(obj_dict[\\\"instance\\\"])\\n else:\\n if hasattr(obj_dict[\\\"instance\\\"], 'attributes') and \\\\\\n hasattr(obj_dict[\\\"instance\\\"], 'p_key'):\\n for attr in obj_dict[\\\"instance\\\"].attributes:\\n if attr not in obj_dict[\\\"instance\\\"].p_key:\\n setattr(_res, attr, getattr(obj_dict[\\\"instance\\\"], attr))\\n # updating the instance\\n obj_dict[\\\"instance\\\"] = _res\\n else:\\n raise AttributeError(\\\"Class variable (attributes / p_key) not set for %s\\\" %\\n (obj_dict[\\\"instance\\\"],))\",\n \"def _related_fields(self):\\r\\n model_class, m2m = self._get_model_class_from_table(self.model._meta.db_table) \\r\\n related_fields = {\\r\\n self.model._meta.pk.attname: model_class\\r\\n }\\r\\n for attname, model_class in self._get_related_models(self.model):\\r\\n related_fields[attname] = model_class\\r\\n return related_fields\",\n \"def build_mapping(self, vertex, edge, auto_plural=False):\\n\\n mc = type(vertex)\\n registry = {}\\n\\n schema = self.client.command(\\n 'SELECT FROM (SELECT expand(classes) FROM metadata:schema)'\\n ' WHERE name NOT IN [\\\\'ORole\\\\', \\\\'ORestricted\\\\', \\\\'OTriggered\\\\','\\n ' \\\\'ORIDs\\\\', \\\\'OUser\\\\', \\\\'OIdentity\\\\', \\\\'OSchedule\\\\','\\n ' \\\\'OFunction\\\\'] ORDER BY defaultClusterId')\\n\\n for c in schema:\\n class_def = c.oRecordData\\n\\n sup = class_def['superClass']\\n if sup is not None:\\n base_names = class_def.get('superClasses', [sup])\\n bases = tuple(\\n vertex if b == 'V' else edge if b == 'E'\\n else registry[b] if b in registry\\n else self.registry[b]\\n for b in base_names\\n if b == 'V' or b == 'E'\\n or b in registry or b in self.registry)\\n class_name = class_def['name']\\n\\n if bases:\\n props = { p['name']:self.property_from_schema(p)\\n for p in class_def['properties'] }\\n props['decl_type'] = bases[0].decl_type\\n\\n if auto_plural:\\n props['registry_plural'] = class_name\\n registry[class_name] = mc(class_name, bases, props)\\n\\n return registry\",\n \"def _to_entity(self):\\n obj_dict = copy.deepcopy(vars(self))\\n exclude_from_indexes = ()\\n try:\\n exclude_from_indexes = self._exclude_from_indexes_\\n except AttributeError:\\n pass\\n\\n try:\\n key = self.key\\n except AttributeError:\\n key = CustomKey(self.__class__.__name__)\\n\\n entity = datastore.Entity(key=key, exclude_from_indexes=exclude_from_indexes)\\n for dict_key, dict_val in obj_dict.copy().items():\\n if dict_val is not None:\\n if isinstance(dict_val, BaseModel):\\n # If the value is an instance of BaseModel, convert the instance\\n # into a \\\"dotted\\\" dictionary compatible with NDB entities.\\n del obj_dict[dict_key]\\n obj_dict.update(dict_val.dottify(dict_key))\\n if isinstance(dict_val, list) and len(dict_val) > 0 and isinstance(dict_val[0], BaseModel):\\n # if the value is a list of BaseModel objects\\n dotted_dict_list = []\\n dotted_dict = dict()\\n for i, val in enumerate(dict_val):\\n dotted_dict_list.append(val.dottify(dict_key))\\n for dict_ in dotted_dict_list:\\n for k, v in dict_.items():\\n temp_val = dotted_dict.get(k) or []\\n temp_val.append(v)\\n dotted_dict[k] = temp_val\\n del obj_dict[dict_key]\\n obj_dict.update(dotted_dict)\\n else:\\n # if the value is False-y i.e. the key has not been set in the object,\\n # delete the key from the object\\n del obj_dict[dict_key]\\n entity.update(obj_dict)\\n return entity\",\n \"def query_orm(orm, strip_fields=[\\\"__version__\\\"]):\\n columns = filter(lambda col: col.name not in strip_fields, orm.__table__.columns)\\n with db_session(\\\"production\\\") as session:\\n query = session.query(*columns)\\n return list(map(object_as_dict, query.all()))\",\n \"def entity_classes_to_map_over(cls):\\n return [user_models.UserSettingsModel]\",\n \"def entity_classes_to_map_over(cls):\\n return [user_models.UserSettingsModel]\",\n \"def entity_classes_to_map_over(cls):\\n return [user_models.UserSettingsModel]\",\n \"def entity_classes_to_map_over(cls):\\n return [user_models.UserSettingsModel]\",\n \"def entity_classes_to_map_over(cls):\\n return [user_models.UserSettingsModel]\",\n \"def get_entities(self, row_id):\\n return self.get(row_id).entities\",\n \"def _build(cls, model_class, *args, **kwargs):\\n for k in kwargs.keys():\\n if k in model_class.relationships():\\n rel_key = '{}_id'.format(k)\\n try:\\n kwargs[rel_key] = str(kwargs[k].id)\\n except AttributeError:\\n pass\\n obj = super(BaseFactory, cls)._build(model_class, *args, **kwargs)\\n obj_dict = obj.to_dict()\\n try:\\n db.session.expunge(obj)\\n except InvalidRequestError:\\n pass\\n return obj_dict\",\n \"def entity_classes_to_map_over(cls):\\n return [feedback_models.GeneralFeedbackThreadModel,\\n feedback_models.GeneralFeedbackMessageModel,\\n feedback_models.GeneralFeedbackThreadUserModel,\\n email_models.GeneralFeedbackEmailReplyToIdModel,\\n user_models.UserSubscriptionsModel]\",\n \"def entity_classes_to_map_over(cls):\\n return [feedback_models.GeneralFeedbackThreadModel,\\n feedback_models.GeneralFeedbackMessageModel,\\n feedback_models.GeneralFeedbackThreadUserModel,\\n email_models.GeneralFeedbackEmailReplyToIdModel,\\n user_models.UserSubscriptionsModel]\",\n \"def as_real_class(self):\\n model = self.content_type.model_class()\\n if model == Defense:\\n return self\\n return model.objects.get(id=self.id)\",\n \"def __iter__(self):\\n return self._database.select(self.as_sql(), self._model_cls)\",\n \"def me_class(self):\\n return self._entity_class\",\n \"def all(self, *args, **kwargs):\\n list_to_return = []\\n if not self.object_type:\\n return list_to_return\\n class_name = eval(self.object_type)\\n if self.objects_id:\\n for id in self.objects_id.split(';'):\\n if id:\\n list_to_return.append(class_name.objects.get(id=id))\\n return list_to_return\",\n \"def gather_entities(self):\\n entitylist = set()\\n for entity in self.entities.all():\\n entitylist.add(entity)\\n entitylist.update(entity.get_ancestors())\\n return entitylist #set(entity for entity in entitylist if not entity.abstract_entity)\",\n \"def serialize_model(result):\\n relation_prefix = '/' # this prefix is added to fields that are a relation\\n\\n # Note: unloaded property is used to discard fields that are not loaded, ie. lazily loaded,\\n # such as relationships (by default), and fields not specified in query select clause.\\n selected = (lambda field: not field in sqlalchemy.orm.attributes.instance_state(result).unloaded)\\n fields = list(filter(selected, result._sa_instance_state.attrs.keys()))\\n\\n object = {}\\n for field in fields:\\n\\n try:\\n value = getattr(result, field)\\n except AttributeError:\\n continue # we are permissive\\n\\n if not is_relationship(getattr(result.__class__, field)):\\n object[field] = value\\n\\n else:\\n if isinstance(value, sqlalchemy.orm.collections.InstrumentedList):\\n # ..n relationship: value is a list of sqla models\\n object[relation_prefix + field] = list(map(lambda result: result.id, value))\\n\\n elif isinstance(type(value), sqlalchemy.ext.declarative.api.DeclarativeMeta):\\n # ..1 relationship: value is a sqla model\\n object[relation_prefix + field] = value.id\\n\\n else:\\n # ..1 relationship: value shall be empty\\n object[relation_prefix + field] = value\\n\\n return object\",\n \"def get_available_entities_models():\\n return ['concat', 'bahdanau', 'luong']\",\n \"def all(self, cls=None):\\n if not self.__session:\\n self.reload()\\n objects = {}\\n if type(cls) == str:\\n cls = classes.get(cls, None)\\n if cls:\\n for obj in self.__session.query(cls):\\n objects[obj.__class__.__name__ + '.' + obj.id] = obj\\n else:\\n for cls in classes.values():\\n for obj in self.__session.query(cls):\\n objects[obj.__class__.__name__ + '.' + obj.id] = obj\\n return objects\",\n \"def test_joined(engine, sessionmaker):\\n # Defining specification\\n spec = {\\n \\\"components\\\": {\\n \\\"schemas\\\": {\\n \\\"Employee\\\": {\\n \\\"properties\\\": {\\n \\\"id\\\": {\\\"type\\\": \\\"integer\\\", \\\"x-primary-key\\\": True},\\n \\\"name\\\": {\\\"type\\\": \\\"string\\\"},\\n \\\"type\\\": {\\\"type\\\": \\\"string\\\"},\\n },\\n \\\"x-tablename\\\": \\\"employee\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"x-kwargs\\\": {\\n \\\"__mapper_args__\\\": {\\n \\\"polymorphic_on\\\": \\\"type\\\",\\n \\\"polymorphic_identity\\\": \\\"employee\\\",\\n }\\n },\\n },\\n \\\"Manager\\\": {\\n \\\"allOf\\\": [\\n {\\\"$ref\\\": \\\"#/components/schemas/Employee\\\"},\\n {\\n \\\"x-tablename\\\": \\\"manager\\\",\\n \\\"x-inherits\\\": \\\"Employee\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"id\\\": {\\n \\\"type\\\": \\\"integer\\\",\\n \\\"x-primary-key\\\": True,\\n \\\"x-foreign-key\\\": \\\"employee.id\\\",\\n },\\n \\\"manager_data\\\": {\\\"type\\\": \\\"string\\\"},\\n },\\n \\\"x-kwargs\\\": {\\n \\\"__mapper_args__\\\": {\\\"polymorphic_identity\\\": \\\"manager\\\"}\\n },\\n },\\n ]\\n },\\n \\\"Engineer\\\": {\\n \\\"allOf\\\": [\\n {\\\"$ref\\\": \\\"#/components/schemas/Employee\\\"},\\n {\\n \\\"x-tablename\\\": \\\"engineer\\\",\\n \\\"x-inherits\\\": \\\"Employee\\\",\\n \\\"type\\\": \\\"object\\\",\\n \\\"properties\\\": {\\n \\\"id\\\": {\\n \\\"type\\\": \\\"integer\\\",\\n \\\"x-primary-key\\\": True,\\n \\\"x-foreign-key\\\": \\\"employee.id\\\",\\n },\\n \\\"engineer_info\\\": {\\\"type\\\": \\\"string\\\"},\\n },\\n \\\"x-kwargs\\\": {\\n \\\"__mapper_args__\\\": {\\\"polymorphic_identity\\\": \\\"engineer\\\"}\\n },\\n },\\n ]\\n },\\n }\\n }\\n }\\n # Creating model factory\\n base = declarative.declarative_base()\\n model_factory = open_alchemy.init_model_factory(spec=spec, base=base)\\n employee = model_factory(name=\\\"Employee\\\")\\n manager = model_factory(name=\\\"Manager\\\")\\n engineer = model_factory(name=\\\"Engineer\\\")\\n\\n # Creating models\\n base.metadata.create_all(engine)\\n # Creating instance of models\\n employee_instance = employee(id=1, name=\\\"employee 1\\\")\\n manager_instance = manager(id=2, name=\\\"employee 2\\\", manager_data=\\\"manager data 2\\\")\\n engineer_instance = engineer(\\n id=3, name=\\\"employee 3\\\", engineer_info=\\\"engineer info 3\\\"\\n )\\n session = sessionmaker()\\n session.add(employee_instance)\\n session.add(manager_instance)\\n session.add(engineer_instance)\\n session.flush()\\n\\n # Querying session for employee\\n queried_employee = session.query(employee).first()\\n assert queried_employee.id == 1\\n assert queried_employee.name == \\\"employee 1\\\"\\n assert queried_employee.type == \\\"employee\\\"\\n # Querying session for manager\\n queried_manager = session.query(manager).first()\\n assert queried_manager.id == 2\\n assert queried_manager.name == \\\"employee 2\\\"\\n assert queried_manager.type == \\\"manager\\\"\\n assert queried_manager.manager_data == \\\"manager data 2\\\"\\n # Querying session for engineer\\n queried_engineer = session.query(engineer).first()\\n assert queried_engineer.id == 3\\n assert queried_engineer.name == \\\"employee 3\\\"\\n assert queried_engineer.type == \\\"engineer\\\"\\n assert queried_engineer.engineer_info == \\\"engineer info 3\\\"\",\n \"def get_entities_of_type(self, typename, type_system):\\n if type_system not in self._type_systems:\\n raise ValueError(\\n f\\\"The type system {type_system} is not one of {self._type_systems.keys()}\\\"\\n )\\n return self._type_systems[type_system].get_entities_of_type(typename)\",\n \"def include(self, registry):\\n for cls in registry.values():\\n db_to_element = {}\\n\\n props = sorted([(k,v) for k,v in cls.__dict__.items()\\n if isinstance(v, Property)]\\n , key=lambda p:p[1].instance_idx)\\n for prop_name, prop_value in props:\\n value_name = prop_value.name\\n if value_name:\\n db_to_element[value_name] = prop_name\\n prop_name = value_name\\n else:\\n db_to_element[prop_name] = prop_name\\n\\n self.guard_reserved_words(prop_name, cls)\\n\\n self.props_from_db[cls] = self.create_props_mapping(db_to_element)\\n self.init_broker_for_class(cls)\\n self.registry[cls.registry_name] = cls\",\n \"def readEntities(self):\\n entities = {}\\n \\n # Regexes must be greedy to prevent matching outer entity and end_entity strings\\n # Regexes have re.DOTALL to match newlines\\n for m in re.finditer(\\\"ENTITY (.*?)END_ENTITY;\\\", self.data, re.DOTALL):\\n entity = {}\\n raw_entity_str = m.groups()[0]\\n\\n entity[\\\"name\\\"] = re.search(\\\"(.*?)[;|\\\\s]\\\", raw_entity_str).groups()[0].upper()\\n \\n is_supertype = re.search(\\\"SUPERTYPE\\\", raw_entity_str) != None\\n if is_supertype:\\n supertypeofmatch = re.search(\\\".*SUPERTYPE OF \\\\((.*?)\\\\)\\\", raw_entity_str)\\n is_abstract_supertype = re.search(\\\"ABSTRACT SUPERTYPE\\\", raw_entity_str) != None\\n \\n is_subtype = re.search(\\\"SUBTYPE\\\", raw_entity_str) != None\\n\\n if is_supertype and is_subtype:\\n if is_abstract_supertype:\\n # abstract supertype of ... subtype of ... ;\\n match = re.search(\\\".*ABSTRACT SUPERTYPE OF\\\\s+\\\\((.*?)\\\\)\\\\s+SUBTYPE OF\\\\s+\\\\((.*?)\\\\);\\\", raw_entity_str, re.DOTALL)\\n entity[\\\"isabstract\\\"] = True\\n supertypeof,subtypeof = match.groups()\\n entity[\\\"supertype\\\"] = subtypeof.upper()\\n supertypeof = re.sub('\\\\s', '', supertypeof)\\n supertypeofmatch = re.search(\\\".*\\\\((.*?)\\\\)\\\", supertypeof, re.DOTALL)\\n subtypes = supertypeofmatch.groups()[0].upper().split(',')\\n entity[\\\"subtypes\\\"] = subtypes\\n else:\\n # supertype of ... subtype of ... ;\\n match = re.search(\\\".*SUPERTYPE OF\\\\s+\\\\((.*?)\\\\)\\\\s+SUBTYPE OF\\\\s+\\\\((.*?)\\\\);\\\", raw_entity_str, re.DOTALL)\\n entity[\\\"isabstract\\\"] = False\\n supertypeof,subtypeof = match.groups()\\n entity[\\\"supertype\\\"] = subtypeof.upper()\\n supertypeof = re.sub('\\\\s', '', supertypeof)\\n supertypeofmatch = re.search(\\\".*\\\\((.*?)\\\\)\\\", supertypeof, re.DOTALL)\\n subtypes = supertypeofmatch.groups()[0].upper().split(',')\\n entity[\\\"subtypes\\\"] = subtypes\\n elif is_subtype:\\n # subtype of ... ;\\n subtypeofmatch = re.search(\\\".*SUBTYPE OF \\\\((.*?)\\\\);\\\", raw_entity_str)\\n entity[\\\"supertype\\\"] = subtypeofmatch.groups()[0].upper() if subtypeofmatch else None\\n\\n # find the shortest string matched from the end of the entity type header to the\\n # first occurence of a NO_ATTR string (when it occurs on a new line)\\n inner_str = re.search(\\\";(.*?)$\\\", raw_entity_str, re.DOTALL).groups()[0]\\n\\n attrs_str = min([inner_str.partition(\\\"\\\\r\\\\n \\\"+a)[0] for a in self.NO_ATTR])\\n attrs = []\\n for am in re.finditer(\\\"(\\\\S*?) : (.*?);\\\", attrs_str, re.DOTALL):\\n name, attr_type = [s.replace(\\\"\\\\r\\\\n\\\\t\\\",\\\"\\\") for s in am.groups()]\\n attrs.append((name, attr_type))\\n \\n entity[\\\"attributes\\\"] = attrs\\n entities[entity[\\\"name\\\"]] = entity\\n \\n\\n return entities\",\n \"def query(self, klass):\\n return self.getSession().query(klass)\",\n \"def objects(self, cls):\\n for name, info in direct_fields(self.__class__).items():\\n if issubclass(cls, info.sub_fields[0].type_):\\n return getattr(self, name)\\n raise TypeError(cls)\",\n \"def _get_related_objects(obj, parent_class=False):\\n foreign_managers = _get_related_managers(obj, parent_class)\\n\\n related_objects = []\\n for manager in foreign_managers:\\n related_objects += manager.all()\\n\\n return related_objects\",\n \"def all(self, cls=None):\\n all_dicts = {}\\n if cls is not None:\\n objects = self.__session.query(models.classes[cls]).all()\\n for obj in objects:\\n key = obj.__class__.__name__ + \\\".\\\" + obj.id\\n all_dicts[key] = obj\\n return all_dicts\\n else:\\n # Checks class for BaseModel as an excemption otherwise\\n # Checks all classes\\n for key, value in models.classes.items():\\n if key != \\\"BaseModel\\\":\\n objects = self.__session.query(value).all()\\n for obj in objects:\\n table = obj.__class__.__name__ + \\\".\\\" + obj.id\\n all_dicts[table] = obj\\n return all_dicts\",\n \"def find_sql_models():\\n for model in find_subclasses(SQLModel):\\n # Get model Meta class\\n meta = getattr(model, 'Meta', None)\\n if meta:\\n # If this is marked as abstract ignore it\\n if getattr(meta, 'abstract', False):\\n continue\\n yield model\",\n \"def _load_related_objects(context, cls, db_data):\\n obj_data = db_data.copy()\\n for name, (obj_cls, _) in cls.object_fields.items():\\n if obj_data.get(name):\\n obj_data[name] = obj_cls(context, **obj_data.get(name).as_dict())\\n else:\\n del obj_data[name]\\n\\n return obj_data\",\n \"def as_leaf_class(self):\\n\\n # get the leaf class\\n content_type = self.content_type\\n Model = content_type.model_class()\\n \\n # get the appropriate object\\n if (Model == self.__class__):\\n return self\\n return Model.objects.get(pk=self.pk)\",\n \"def map_database(connection):\\n eng = create_engine(connection)\\n metadata = MetaData()\\n metadata.reflect(eng)\\n base = automap_base(metadata=metadata)\\n base.prepare()\\n return base.classes, eng\",\n \"def expand_type_query(owner_name: str, type_name: []) -> 'session query':\\n critical_level = get_critical_level(owner_name, type_name)\\n\\n ordered = db.session.query(\\n func.sum(SpecificOrders.quantity).label('number')\\n )\\\\\\n .join(Orders, Orders.order_id == SpecificOrders.order_id)\\\\\\n .filter(\\n and_(\\n Orders.supplier_name == owner_name,\\n SpecificOrders.type_name.in_(type_name)\\n )\\n\\n )\\n\\n query = select([Products.type_name.label('Type'),\\n Products.serial_number.label('Serial number'),\\n Products.producent.label('Producent'),\\n Products.model.label('Model'),\\n Products.product_condition.label('Condition'),\\n Products.additonal_info.label('Additional info'),\\n Products.appear_in_order.label('Bind to order')])\\\\\\n .where(\\n and_(\\n Products.owner_name == owner_name,\\n Products.type_name.in_(type_name)\\n )\\n ).order_by(Products.appear_in_order.asc(),Products.serial_number.asc())\\n return db.session.query(aliased(query)), ordered, critical_level\",\n \"def get_entity_type_superclass_map(entity_types):\\n _logger.info(f'Creating entity type superclass map...')\\n entity_type_subclass_map = get_entity_type_subclass_map(entity_types)\\n return reverse_entity_type_subclass_map(entity_type_subclass_map)\",\n \"def get_model(params):\\r\\n module_name, class_name = params.model.name.rsplit('.', 1)\\r\\n i = importlib.import_module(module_name)\\r\\n return getattr(i, class_name)\",\n \"def extract_product(product):\\r\\n product = \\\"%\\\" + product + \\\"%\\\"\\r\\n cursor.execute(\\\"USE openfoodfacts;\\\")\\r\\n cursor.execute(\\\"\\\"\\\"SELECT Food.id, Food.name, categories_id, nutri_score, url, stores \\\\\\r\\n FROM Food \\\\\\r\\n INNER JOIN Categories ON Food.categories_id LIKE Categories.name\\\\\\r\\n WHERE Food.name LIKE %s;\\\"\\\"\\\", (product))\\r\\n product = cursor.fetchone()\\r\\n product_class = cl.Food(product)\\r\\n return product_class\",\n \"def all_entity_classes():\\n persistent_classes = Entity._decl_class_registry.values()\\n # with sqlalchemy 0.8 _decl_class_registry holds object that are not classes\\n return [ cls for cls in persistent_classes\\n if isclass(cls) and issubclass(cls, Entity) ]\",\n \"def query(cls):\\r\\n return cls._session.query(cls)\",\n \"def _inspect_module(module):\\n module_list = getmembers(module, predicate=ismodule)\\n classes = getmembers(module, predicate=isclass)\\n for (name, cls) in classes:\\n if issubclass(cls, db.Model) and not issubclass(cls, Taxonomy):\\n if cls is not db.Model:\\n _data_classes[name] = cls\\n return [mod[1] for mod in module_list]\",\n \"def _raw(self, query: Any, data: Any = None):\\n assert isinstance(query, str)\\n\\n conn = self._get_session()\\n try:\\n results = conn.execute(query)\\n\\n entity_items = []\\n for item in results:\\n entity = self.model_cls.to_entity(item)\\n entity.state_.mark_retrieved()\\n entity_items.append(entity)\\n\\n result = ResultSet(\\n offset=0,\\n limit=len(entity_items),\\n total=len(entity_items),\\n items=entity_items,\\n )\\n except DatabaseError as exc:\\n logger.error(f\\\"Error while running raw query: {exc}\\\")\\n raise\\n finally:\\n if not current_uow:\\n conn.commit()\\n conn.close()\\n\\n return result\",\n \"def _get_entities(self, entity_name):\\n return get_entities(self.attributes.workspace.namespace, self.attributes.workspace.name, entity_name)\",\n \"def get_query(params, filters):\\n engine, session = connect_to_db(DB_NAME, CORE_DIR)\\n\\n q = session.query(*params) # kick off the query\\n\\n classes = []\\n for p in params:\\n parent_class = p.parent.class_\\n classes.append(parent_class) if parent_class not in classes else None # need order, so hack around a set...\\n\\n\\n base = classes.pop(0) # grab first class from list\\n linked = [base] # the first class is inherently already in the join-chain\\n\\n if classes: # any more classes?\\n for c in classes:\\n relations_for_c = relations.get(c.__name__)\\n\\n if not relations_for_c:\\n msg = f'{c.__name__} does not have any defined relationships.'\\n raise NotImplementedError(msg)\\n\\n relation = relations_for_c.get(base.__name__)\\n\\n if relation:\\n q = q.join(c, relation.key == relation.fkey)\\n else:\\n msg = f'{c.__name__} is not directly related to {base} in the schema.'\\n raise NotImplementedError(msg)\\n\\n for f in filters:\\n q = q.filter(f)\\n\\n return q.all()[:10]\",\n \"def _get_global_entities(nest):\\n entities = nest.get('global_entities', None)\\n if not entities:\\n return []\\n return list(entities.keys())\",\n \"def of(cls, type_):\\n\\n for subcls in Mapping.__subclasses__():\\n if subcls.type() == type_:\\n return subcls()\\n\\n return None\",\n \"async def _parse_entities(self, responses: SourceResponses) -> Entities:\\n installed_dependencies: dict[str, dict[str, str]] = {}\\n for response in responses:\\n installed_dependencies.update(await response.json(content_type=None))\\n return Entities(\\n Entity(\\n key=f'{dependency}@{versions.get(\\\"current\\\", \\\"?\\\")}',\\n name=dependency,\\n current=versions.get(\\\"current\\\", \\\"unknown\\\"),\\n wanted=versions.get(\\\"wanted\\\", \\\"unknown\\\"),\\n latest=versions.get(\\\"latest\\\", \\\"unknown\\\"),\\n )\\n for dependency, versions in installed_dependencies.items()\\n )\",\n \"def entity_classes_to_map_over(cls):\\n return [\\n exp_models.ExplorationRightsModel,\\n collection_models.CollectionRightsModel,\\n feedback_models.GeneralFeedbackMessageModel]\",\n \"def select_or_run(self, *args, **kwargs):\\n query_rows = self.bind_signature(args, kwargs)\\n\\n model_instances = []\\n for query_row in query_rows:\\n model_instances.append(self.get_or_run(**query_row))\\n\\n if len(model_instances) == 1: # TODO: Is this actually a good API?\\n return model_instances[0]\\n return model_instances\",\n \"def relate(self, qs):\\n model_map = {}\\n item_map = {}\\n for item in qs:\\n object_id = getattr(item, self._object_id_field)\\n content_type = getattr(item, self._content_type_field)\\n model_map.setdefault(content_type, {}) \\\\\\n [object_id] = item.id\\n item_map[item.id] = item\\n for ct, items_ in model_map.items():\\n for o in ct.model_class().objects.select_related() \\\\\\n .filter(id__in=items_.keys()).all():\\n setattr(item_map[items_[o.id]],self._content_object_field, o)\\n return qs\",\n \"def all(self, cls=None):\\n # create a dictionary\\n obj_dict = {}\\n if cls is None:\\n classes = [State, City, User, Place, Review, Amenity]\\n for class_name in classes:\\n for obj in self.__session.query(class_name):\\n key = obj.__class__.__name__ + '.' + obj.id\\n obj_dict[key] = obj\\n else:\\n for obj in self.__session.query(cls):\\n key = obj.__class__.__name__ + '.' + obj.id\\n obj_dict[key] = obj\\n return obj_dict\",\n \"def test_extraction_with_model_class(self: Any, mock_method: Any) -> None:\\n config_dict = {\\n 'extractor.sqlalchemy.conn_string': 'TEST_CONNECTION',\\n 'extractor.sqlalchemy.extract_sql': 'SELECT 1 FROM TEST_TABLE;',\\n 'extractor.sqlalchemy.model_class':\\n 'tests.unit.extractor.test_sql_alchemy_extractor.TableMetadataResult'\\n }\\n self.conf = ConfigFactory.from_dict(config_dict)\\n\\n extractor = SQLAlchemyExtractor()\\n extractor.results = [dict(database='test_database',\\n schema='test_schema',\\n name='test_table',\\n description='test_description',\\n column_name='test_column_name',\\n column_type='test_column_type',\\n column_comment='test_column_comment',\\n owner='test_owner')]\\n\\n extractor.init(Scoped.get_scoped_conf(conf=self.conf,\\n scope=extractor.get_scope()))\\n\\n result = extractor.extract()\\n\\n self.assertIsInstance(result, TableMetadataResult)\\n self.assertEqual(result.name, 'test_table')\",\n \"def resolve(self, term: str, labels: istr = None) -> List[Entity]:\",\n \"def deep_entities(model = 'bahdanau'):\\n assert isinstance(model, str), 'model must be a string'\\n model = model.lower()\\n if model in ['concat', 'bahdanau', 'luong']:\\n if not os.path.isfile(PATH_ENTITIES[model]['model']):\\n print('downloading ENTITIES frozen %s model' % (model))\\n download_file(\\n S3_PATH_ENTITIES[model]['model'], PATH_ENTITIES[model]['model']\\n )\\n if not os.path.isfile(PATH_ENTITIES[model]['setting']):\\n print('downloading ENTITIES %s dictionary' % (model))\\n download_file(\\n S3_PATH_ENTITIES[model]['setting'],\\n PATH_ENTITIES[model]['setting'],\\n )\\n with open(PATH_ENTITIES[model]['setting'], 'r') as fopen:\\n nodes = json.loads(fopen.read())\\n g = load_graph(PATH_ENTITIES[model]['model'])\\n return TAGGING(\\n g.get_tensor_by_name('import/Placeholder:0'),\\n g.get_tensor_by_name('import/Placeholder_1:0'),\\n g.get_tensor_by_name('import/logits:0'),\\n nodes,\\n tf.InteractiveSession(graph = g),\\n )\\n\\n else:\\n raise Exception(\\n 'model not supported, please check supported models from malaya.get_available_entities_models()'\\n )\",\n \"def find_objs(self, cls, **attr):\\n nodes = getattr(self.graph, getattr(models, cls).element_plural).query(**attr).all()\\n return nodes\",\n \"def create_class(self, cls):\\n\\n cls_name = cls.registry_name\\n\\n bases = [base for base in cls.__bases__ if self.valid_element_base(base)]\\n if not bases:\\n raise TypeError(\\n 'Unexpected base class(es) in Graph.create_class'\\n ' - try the declarative bases')\\n\\n extends = None\\n if bases[0] is bases[0].decl_root:\\n extends = ['V', 'E'][bases[0].decl_type]\\n else:\\n extends = ','.join([base.registry_name for base in bases])\\n\\n #if not self.client.command(\\n # 'SELECT FROM ( SELECT expand( classes ) FROM metadata:schema ) WHERE name = \\\"{}\\\"'\\n # .format(cls_name)):\\n try:\\n self.client.command(\\n 'CREATE CLASS {0} EXTENDS {1}'.format(cls_name, extends))\\n except pyorient.PyOrientSchemaException:\\n # Class already exists\\n pass\\n\\n db_to_element = {}\\n\\n props = sorted([(k,v) for k,v in cls.__dict__.items()\\n if isinstance(v, Property)]\\n , key=lambda p:p[1].instance_idx)\\n for prop_name, prop_value in props:\\n value_name = prop_value.name\\n if value_name:\\n db_to_element[value_name] = prop_name\\n prop_name = value_name\\n else:\\n db_to_element[prop_name] = prop_name\\n\\n self.guard_reserved_words(prop_name, cls)\\n\\n class_prop = '{0}.{1}'.format(cls_name, prop_name)\\n\\n linked_to = None\\n if isinstance(prop_value, LinkedClassProperty):\\n type_linked_to = prop_value.linked_to\\n\\n linked_to = getattr(type_linked_to, 'registry_name', None)\\n if not linked_to:\\n link_bases = getattr(type_linked_to, '__bases__', None)\\n if link_bases and \\\\\\n isinstance(prop_value, LinkedProperty) and \\\\\\n link_bases[0] is Property:\\n linked_to = type_linked_to.__name__\\n\\n try:\\n self.client.command(\\n 'CREATE PROPERTY {0} {1} {2}'\\n .format(class_prop\\n , type(prop_value).__name__\\n , linked_to or ''))\\n except pyorient.PyOrientCommandException:\\n # Property already exists\\n pass\\n\\n if prop_value.default is not None:\\n if self.server_version >= (2,1,0):\\n self.client.command(\\n 'ALTER PROPERTY {0} DEFAULT {1}'\\n .format(class_prop,\\n PropertyEncoder.encode(prop_value.default)))\\n\\n self.client.command(\\n 'ALTER PROPERTY {0} NOTNULL {1}'\\n .format(class_prop\\n , str(not prop_value.nullable).lower()))\\n\\n self.client.command(\\n 'ALTER PROPERTY {} MANDATORY {}'\\n .format(class_prop\\n , str(prop_value.mandatory).lower()))\\n\\n self.client.command(\\n 'ALTER PROPERTY {} READONLY {}'\\n .format(class_prop\\n , str(prop_value.readonly).lower()))\\n\\n # TODO Add support for composite indexes\\n if prop_value.indexed:\\n try:\\n self.client.command(\\n 'CREATE INDEX {0} {1}'\\n .format(class_prop\\n , 'UNIQUE' if prop_value.unique\\n else 'NOTUNIQUE'))\\n except pyorient.PyOrientIndexException:\\n # Index already exists\\n pass\\n\\n self.props_from_db[cls] = self.create_props_mapping(db_to_element)\\n self.init_broker_for_class(cls)\\n self.registry[cls_name] = cls\",\n \"def python_type(self) -> type:\\n return SearchMetaEntity\",\n \"def _process_related_model_searches(query: dict) -> dict:\\n multi_terms = {\\n \\\"collections\\\": \\\"collection\\\",\\n \\\"publishers\\\": \\\"publisher\\\",\\n \\\"repositories\\\": \\\"repository\\\",\\n \\\"original_coverages\\\": \\\"coverage\\\",\\n \\\"subjects\\\": \\\"subject\\\",\\n \\\"contributors\\\": \\\"contributor\\\",\\n \\\"donors\\\": \\\"donor\\\",\\n \\\"languages\\\": \\\"language\\\",\\n \\\"translations\\\": \\\"translation\\\",\\n \\\"themes\\\": \\\"theme\\\",\\n }\\n\\n # Rename each term to singular\\n for key, value in multi_terms.items():\\n if key in query.keys():\\n query[value] = query.pop(key)\\n\\n # Build list of terms we need to parse\\n terms_to_parse = []\\n for term in multi_terms.values():\\n if term in query.keys():\\n terms_to_parse.append(term)\\n\\n # transform each term list into a list of IDs\\n for term in terms_to_parse:\\n query[term] = [str(item.id) for item in query[term]]\\n\\n # Special handling for langauges, translations, themes.\\n # Unlike they above, they only accept singular values\\n for term in [\\\"language\\\", \\\"translation\\\", \\\"theme\\\"]:\\n if term in query.keys():\\n if len(query[term]) > 1:\\n logging.error(\\\"[!] Cannot filter for more than one %s\\\", term)\\n raise exceptions.InvalidSearchFieldError\\n # Pull out the singleton.\\n query[term] = query[term][0]\\n\\n # Return the reformatted query.\\n return query\",\n \"def all(self, cls=None):\\n if cls:\\n objects = self.__session.query(cls).all()\\n else:\\n classes = [State, City] # , User, Place, Review, Amenity]\\n objects = []\\n for c in classes:\\n objects += self.__session.query(c)\\n return {\\\"{}.{}\\\".format(type(obj).__name__, obj.id): obj for obj in\\n objects}\",\n \"def expand_entities(self, entityIterable):\\n for e in entityIterable:\\n if self.expand or self.select:\\n e.Expand(self.expand, self.select)\\n yield e\",\n \"def set_entity_class_registry(self, entity_class_registry):\\n self.entity_class_registry = entity_class_registry\",\n \"def get_entity(obj):\\n return obj.or_expression.and_expression.cmp_expression.arith_expression. \\\\\\n mul_expression.unary_expression.pow_expression.primary_expression. \\\\\\n entity\",\n \"def declarative_base(model):\\n for parent in model.__bases__:\\n try:\\n parent.metadata\\n return declarative_base(parent)\\n except AttributeError:\\n pass\\n return model\",\n \"def resolve_model(root: Quote, *args) -> str:\\n return 'quotes.quote'\",\n \"def __call__(self, context=None):\\n if context is None:\\n context = self.context\\n return self.entity.resolve(context)\",\n \"def all(self, cls=None):\\n query_data = {}\\n\\n if cls is None:\\n for valid_key, valid_class in DBStorage.CNC.items():\\n for instance in self.__session.query(valid_class):\\n key = type(instance).__name__ + \\\".\\\" + instance.id\\n query_data.update({key: instance})\\n return query_data\\n else:\\n for instance in self.__session.query(DBStorage.CNC[cls]):\\n key = type(instance).__name__ + \\\".\\\" + instance.id\\n query_data.update({key: instance})\\n return query_data\",\n \"def getentities(self):\\n entities = {}\\n\\n # The following will create lots of errors in suds.client, one\\n # for every type that is not an entity. Disable their logger\\n # temporarily to avoid cluttering the log.\\n sudslog = logging.getLogger('suds.client')\\n sudssav = sudslog.disabled\\n sudslog.disabled = True\\n for t in self.gettypes():\\n try:\\n info = EntityInfo(t, self.client)\\n except ICATError:\\n continue\\n entities[t] = info\\n sudslog.disabled = sudssav\\n\\n return entities\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5555714","0.5259711","0.5109347","0.50943285","0.50772786","0.49984002","0.4953237","0.49206263","0.47721955","0.47709483","0.47572222","0.47535753","0.47392863","0.47341767","0.47276932","0.46964145","0.46887028","0.46723166","0.4664276","0.4661921","0.4645294","0.4644923","0.4642833","0.4636559","0.4629262","0.4627161","0.46072057","0.4599009","0.45899713","0.45689103","0.4538446","0.4535646","0.4522507","0.45184544","0.45078188","0.45016804","0.449783","0.4479749","0.4468496","0.4468496","0.4468496","0.4468496","0.4468496","0.44523048","0.4441625","0.4437932","0.4437932","0.44308606","0.4421625","0.44193035","0.44174305","0.44142163","0.44124135","0.44106796","0.43993783","0.43934947","0.43823528","0.43800318","0.43771487","0.43771163","0.43654346","0.43600038","0.43583548","0.43499583","0.43494","0.43379486","0.43259764","0.43233147","0.43168494","0.43089738","0.43066752","0.43062165","0.43042263","0.42978424","0.42967612","0.42939612","0.42904374","0.42893055","0.4281305","0.4276777","0.4275497","0.42709514","0.42703378","0.42635366","0.426206","0.42566386","0.42527494","0.42309365","0.423071","0.42287916","0.4228048","0.4225867","0.42238292","0.4221839","0.42217404","0.4201312","0.41970813","0.4193007","0.41867837","0.41853398"],"string":"[\n \"0.5555714\",\n \"0.5259711\",\n \"0.5109347\",\n \"0.50943285\",\n \"0.50772786\",\n \"0.49984002\",\n \"0.4953237\",\n \"0.49206263\",\n \"0.47721955\",\n \"0.47709483\",\n \"0.47572222\",\n \"0.47535753\",\n \"0.47392863\",\n \"0.47341767\",\n \"0.47276932\",\n \"0.46964145\",\n \"0.46887028\",\n \"0.46723166\",\n \"0.4664276\",\n \"0.4661921\",\n \"0.4645294\",\n \"0.4644923\",\n \"0.4642833\",\n \"0.4636559\",\n \"0.4629262\",\n \"0.4627161\",\n \"0.46072057\",\n \"0.4599009\",\n \"0.45899713\",\n \"0.45689103\",\n \"0.4538446\",\n \"0.4535646\",\n \"0.4522507\",\n \"0.45184544\",\n \"0.45078188\",\n \"0.45016804\",\n \"0.449783\",\n \"0.4479749\",\n \"0.4468496\",\n \"0.4468496\",\n \"0.4468496\",\n \"0.4468496\",\n \"0.4468496\",\n \"0.44523048\",\n \"0.4441625\",\n \"0.4437932\",\n \"0.4437932\",\n \"0.44308606\",\n \"0.4421625\",\n \"0.44193035\",\n \"0.44174305\",\n \"0.44142163\",\n \"0.44124135\",\n \"0.44106796\",\n \"0.43993783\",\n \"0.43934947\",\n \"0.43823528\",\n \"0.43800318\",\n \"0.43771487\",\n \"0.43771163\",\n \"0.43654346\",\n \"0.43600038\",\n \"0.43583548\",\n \"0.43499583\",\n \"0.43494\",\n \"0.43379486\",\n \"0.43259764\",\n \"0.43233147\",\n \"0.43168494\",\n \"0.43089738\",\n \"0.43066752\",\n \"0.43062165\",\n \"0.43042263\",\n \"0.42978424\",\n \"0.42967612\",\n \"0.42939612\",\n \"0.42904374\",\n \"0.42893055\",\n \"0.4281305\",\n \"0.4276777\",\n \"0.4275497\",\n \"0.42709514\",\n \"0.42703378\",\n \"0.42635366\",\n \"0.426206\",\n \"0.42566386\",\n \"0.42527494\",\n \"0.42309365\",\n \"0.423071\",\n \"0.42287916\",\n \"0.4228048\",\n \"0.4225867\",\n \"0.42238292\",\n \"0.4221839\",\n \"0.42217404\",\n \"0.4201312\",\n \"0.41970813\",\n \"0.4193007\",\n \"0.41867837\",\n \"0.41853398\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.75623906"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17772,"cells":{"query":{"kind":"string","value":"Execute the query, and return its result as rows"},"document":{"kind":"string","value":"def matching_objects(self, filter_deleted):\n from rome.core.orm.utils import get_literal_query\n from rome.lang.sql_parser import QueryParser\n from rome.core.rows.rows import construct_rows\n\n read_deleted = self.read_deleted\n if filter_deleted:\n read_deleted = \"no\"\n\n if self._autoflush:\n if self.session is not None:\n self.session.commit()\n\n if not self.query_tree:\n sql_query = get_literal_query(self.sa_query)\n parser = QueryParser()\n query_tree = parser.parse(sql_query)\n else:\n query_tree = self.query_tree\n\n if not self.entity_class_registry:\n self.entity_class_registry = self._extract_entity_class_registry()\n entity_class_registry = self.entity_class_registry\n\n # Collecting variables of sub queries\n subqueries_variables = {}\n for (variable_name, sub_query_tree) in query_tree.variables.iteritems():\n sub_query = Query()\n sub_query.set_query_tree(sub_query_tree)\n sub_query.set_entity_class_registry(entity_class_registry)\n result = sub_query.all()\n subqueries_variables[variable_name] = result\n\n rows = construct_rows(query_tree,\n entity_class_registry,\n read_deleted=read_deleted,\n subqueries_variables= subqueries_variables)\n\n def row_function(row, column_descriptions, decoder):\n from rome.core.session.utils import ObjectAttributeRefresher\n final_row = []\n one_is_an_object = False\n object_attribute_refresher = ObjectAttributeRefresher()\n for column_description in column_descriptions:\n if type(column_description[\"type\"]) in [Integer, String]:\n row_key = column_description[\"entity\"].__table__.name.capitalize(\n )\n property_name = column_description[\"name\"]\n value = None\n if row_key in row and property_name in row[row_key]:\n value = row[row_key].get(property_name, None)\n else:\n # It seems that we are parsing the result of a function call\n column_description_expr = column_description.get(\"expr\",\n None)\n if column_description_expr is not None:\n property_name = str(column_description_expr)\n value = row.get(property_name, None)\n if value is not None:\n final_row += [value]\n else:\n logging.error(\n \"Could not understand how to get the value of '%s' with this: '%s'\"\n % (column_description.get(\"expr\", \"??\"), row))\n elif type(column_description[\"type\"]) == DeclarativeMeta:\n one_is_an_object = True\n row_key = column_description[\"entity\"].__table__.name\n new_object = column_description[\"entity\"]()\n attribute_names = map(lambda x: x.key, list(\n column_description[\"entity\"].__table__.columns))\n for attribute_name in attribute_names:\n value = decoder.decode(row[row_key].get(attribute_name,\n None))\n setattr(new_object, attribute_name, value)\n\n if \"___version_number\" in row[row_key]:\n setattr(new_object, \"___version_number\", row[row_key][\"___version_number\"])\n\n load_options = None\n if hasattr(self.sa_query, \"_with_options\"):\n load_options = self.sa_query._with_options\n object_attribute_refresher.refresh(new_object, load_options=load_options)\n final_row += [new_object]\n else:\n logging.error(\"Unsupported type: '%s'\" %\n (column_description[\"type\"]))\n if not one_is_an_object:\n return [final_row]\n else:\n return final_row\n\n def row_function_subquery(row, attributes, decoder):\n result = []\n for attribute in attributes:\n tablename = attribute.split(\".\")[0]\n attribute_name = attribute.split(\".\")[1]\n result += [row[tablename][attribute_name]]\n return result\n\n decoder = Decoder()\n\n if len(self.sa_query.column_descriptions) > 0:\n final_rows = map(lambda r: row_function(\n r, self.sa_query.column_descriptions, decoder), rows)\n else:\n final_rows = map(lambda r: row_function_subquery(\n r, self.query_tree.attributes, decoder), rows)\n\n if len(self.sa_query.column_descriptions) <= 1:\n # Flatten the list\n final_rows = [item for sublist in final_rows for item in sublist]\n\n # Add watcher on objects\n if self.session is not None:\n for obj in final_rows:\n if hasattr(obj, \"id\"):\n self.session.watch(obj)\n\n return final_rows"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def execute(self, query):\n with self.conn.cursor() as cur:\n # Execute the query\n try:\n cur.execute(query)\n except Exception as exc:\n print(\"Unable to execute query. Error was {0}\".format(str(exc)))\n exit()\n rows = cur.fetchall()\n return rows","def executeQuery(query):\n c = db.cursor()\n c.execute(query)\n rows = c.fetchall()\n db.close()\n return rows","def _run_query(self, query):\n cursor = self.conn.cursor()\n cursor.execute(query)\n return cursor.fetchall()","def _query_and_fetchall(self, query):\n with self._connect() as conn:\n cur = conn.cursor()\n cur.execute(query)\n results = cur.fetchall()\n\n return results","def query(self, sql):\n try:\n res_cursor = self.connection.execute(text(sql))\n except Exception as e: \n raise e(\"SQL execution error!\")\n \n rows = (Row(res_cursor.keys(), record) for record in res_cursor)\n results = RowsCollection(rows)\n return results","def query(self, sql):\n self.cursor.execute(sql)\n result = self.cursor.fetchall()\n return result","def get_rows(query, params=None):\n cur = get_cursor()\n cur.execute(query, params)\n rows = cur.fetchall()\n return rows","def execute_and_fetch(db, query):\n rows = []\n\n db.query(query)\n res = db.use_result()\n row = res.fetch_row()\n while row != ():\n rows.append(row[0])\n row = res.fetch_row()\n return rows","async def query(self, stmt, *args):\n\n with (await self.application.db.cursor()) as cur:\n await cur.execute(stmt, args)\n return [self.row_to_obj(row, cur)\n for row in await cur.fetchall()]","def exec_results(query):\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n c.execute(query)\n result = c.fetchall()\n db.close()\n return result","def run_query(query):\r\n db = psycopg2.connect('dbname=' + database)\r\n connect = db.cursor()\r\n connect.execute(query)\r\n rows = connect.fetchall()\r\n db.close()\r\n return rows","def execute_query(query):\n c.execute(query)\n return c.fetchall()","async def db_query(self, *args, **kwargs):\n rows = []\n async with self.db_pool.acquire() as conn:\n async with conn.cursor(cursor_factory=DictCursor) as cur:\n await cur.execute(*args, **kwargs)\n try:\n async for row in cur:\n rows.append(row)\n except psycopg2.ProgrammingError:\n # No results\n pass\n return rows","def run_query(conn, query):\n\tcur = conn.cursor()\n\tcur.execute(query)\n\trows = cur.fetchall()\n\treturn rows","async def db_execute(self, *args, **kwargs):\n rows = []\n async with self.db_pool.acquire() as conn:\n async with conn.cursor() as cur:\n await cur.execute(*args, **kwargs)\n try:\n async for row in cur:\n rows.append(row)\n except psycopg2.ProgrammingError:\n # No results\n pass\n return rows","async def fetchall(self):\n\n self._check_executed()\n\n if self._query_id == -1:\n msg = \"query didn't result in a resultset\"\n self._exception_handler(ProgrammingError, msg)\n\n result = self._rows[self.rownumber - self._offset :]\n self.rownumber = len(self._rows) + self._offset\n\n # slide the window over the resultset\n while await self.nextset():\n result += self._rows\n self.rownumber = len(self._rows) + self._offset\n print(\n \"\\nresult: \",\n self._rows,\n \"\\n\\n\",\n )\n return result","def _get_rows_from_query(self, query, data):\n to_return = []\n results = self.db_conn.cursor().execute(query, data)\n for result in results:\n to_return.append(result)\n return to_return","def query(self, query: str, *args, **kwargs):\n cursor = self._cursor()\n try:\n self._execute(cursor, query, args, kwargs)\n column_names = [d[0] for d in cursor.description]\n return [Row(zip(column_names, row)) for row in cursor]\n finally:\n cursor.close()","def execute(self, qry):\n def internal():\n print 'qry = ', qry\n self._cur = self.get_cursor()\n print 'self._cur = ', self._cur\n self._cur.execute(qry)\n # self.conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor).execute(qry)\n rows = self._cur.fetchall()\n return rows\n\n return self._retry(internal)","def get_query_results(self, conn, sql):\n\n cur = conn.cursor()\n cur.execute(sql)\n yield from cur.fetchall()","def _execute_query(self, query, values):\n with self as plasticDB:\n cursor = plasticDB.connection.cursor()\n cursor.execute(query,values)\n if not cursor.description:\n return []\n rs = RecordSet(initialData=cursor.fetchall(), recordType=next(zip(*cursor.description)))\n return rs","def run(self):\n rows = None\n if self.sql.startswith('select'):\n conn = self.table.connect()\n with conn.cursor() as curs:\n try:\n curs.execute(self.sql)\n except conn.DatabaseError as exc:\n error, = exc.args\n logging.error(f\"\"\"error executing {self.sql}:\n {error.code}\"\"\")\n self.excep = exc\n raise exc\n else:\n rows = curs.fetchall()\n # logging.critical(f\"\"\"executed {self.sql}\"\"\")\n self.result_exec = rows","def get_query_results(query):\n\n db, c = connect()\n c.execute(query)\n results = c.fetchall()\n db.close()\n return results","def _run_hive_query(cursor, query, has_results=False):\n _print_debug('Executing query: {}'.format(query))\n cursor.execute(query)\n\n if has_results:\n rows = cursor.fetchall()\n _print_debug('Query returned {} rows'.format(len(rows)))\n return rows","def execute_query(self) -> dict or list:\n pass","def _query_all(self, sql, data=None):\n\n conn = psycopg2.connect(self.connect_args)\n cur = conn.cursor()\n cur.execute(sql, data)\n result = cur.fetchall()\n cur.close()\n conn.close()\n return result","def get_results(self, table=None, query=None):\n def determine_query():\n \"\"\"\n Determines the query by checking if a query arg was passed.\n \"\"\"\n return query if query is not None \\\n else f\"SELECT * FROM '{table}';\"\n\n def __to_list(__results):\n \"\"\"\n Converts DB results to a (persistent) list.\n \"\"\"\n rows = []\n for row in __results:\n rows.append(row)\n \n __results.close()\n\n return rows\n\n\n # Determine query and execute\n results = __to_list(self.execute_query(determine_query()))\n\n return results","def query(self, query):\n\n config = {\n 'user' : self.db_user,\n 'password' : self.db_pwd,\n 'database' : self.db_name,\n 'host' : self.db_host,\n 'unix_socket' : self.db_socket,\n 'port' : self.db_port,\n 'charset' : 'utf8'\n }\n\n try: \n cnx = mysql.connector.connect(**config)\n cursor = cnx.cursor(dictionary=True) # To return rows as dictionaries.\n\n cursor.execute(query[0], query[1])\n \n # Putting the result rows in an array.\n result = []\n for row in cursor:\n result.append(row)\n\n except mysql.connector.Error as err:\n return None\n else:\n cursor.close()\n cnx.close()\n return result","def _exec1(self, sql):\n result = self._exec(sql)\n return [row[0] for row in result]","def execute(self, sql):\n with self.connection.cursor() as dbc:\n if sql[-1] != ';':\n sql += ';'\n dbc.execute(sql)\n self.last_row = dbc.lastrowid\n try:\n return dbc.fetchall()\n except:\n return","def _runQuery(self, query, connection):\n with connection as c:\n cursor = c.cursor()\n cursor.execute(query)\n results = []\n result = cursor.fetchone()\n while result is not None:\n numCols = len(result)\n item = {}\n for col in range(0, numCols):\n key = cursor.description[col][0]\n value = result[col]\n try:\n #handle oracle clob datatypes\n value = str(result[col].read())\n except AttributeError:\n pass\n item[key] = value\n #item = {cursor.description[col][0]: result[col] for col in range(0, numCols)}\n results.append(item)\n result = cursor.fetchone()\n return results","def get_result(query):\r\n db = psycopg2.connect(database=DBNAME)\r\n c = db.cursor()\r\n c.execute(query)\r\n results = c.fetchall()\r\n db.close()\r\n return results","def getResults(query):\n # Tells psycopg2 which database we are talking about\n db = psycopg2.connect(database=\"news\")\n # Curser is a local variable commonly use in sql/python connections\n curser = db.cursor()\n # Please refer to https://dev.mysql.com/doc/connector-python/en/\n # for more information about how this block of code works\n curser.execute(query)\n i = curser.fetchall()\n db.close()\n return i","def execute_query(conn, query):\r\n cur = conn.cursor()\r\n cur.execute(query)\r\n conn.commit()\r\n return cur.fetchall()","def _execute_query(self,\n sql_connection: Connection,\n sql_query: Union[str, Query]) -> List[Dict[str, Any]]:\n return_result: List[Dict[str, Any]] = []\n result: ResultProxy = sql_connection.execute(sql_query)\n if result and result.returns_rows:\n return_result: List[Dict[str, Any]] = [dict(row) for row in result]\n return return_result","def run_query(cur, query, show_results=False):\n num_rows = cur.execute(query)\n print('the query returned {} rows'.format(num_rows))\n if show_results:\n for row in cur.fetchall():\n print(row)","def execute_sql(self, result_type=MULTI):\r\n try:\r\n sql, params = self.as_sql()\r\n #import pdb; pdb.set_trace()\r\n if not sql:\r\n raise EmptyResultSet\r\n except EmptyResultSet:\r\n if result_type == MULTI:\r\n return iter([])\r\n else:\r\n return\r\n\r\n cursor = self.connection.cursor() \r\n cursor.execute(sql, params)\r\n\r\n if not result_type:\r\n return cursor\r\n if result_type == SINGLE:\r\n if self.query.ordering_aliases:\r\n return cursor.fetchone()[:-len(self.query.ordering_aliases)]\r\n return cursor.fetchone()\r\n\r\n # The MULTI case.\r\n if self.query.ordering_aliases:\r\n result = order_modified_iter(cursor, len(self.query.ordering_aliases),\r\n self.connection.features.empty_fetchmany_value)\r\n else:\r\n result = iter((lambda: cursor.fetchmany(GET_ITERATOR_CHUNK_SIZE)),\r\n self.connection.features.empty_fetchmany_value)\r\n if not self.connection.features.can_use_chunked_reads:\r\n # If we are using non-chunked reads, we return the same data\r\n # structure as normally, but ensure it is all read into memory\r\n # before going any further.\r\n return list(result)\r\n return result","def select_query(self):\n query = db.select([self.tables])\n print(query)\n ResultProxy = self.connection.execute(query)\n ResultSet = ResultProxy.fetchall()\n return ResultSet","async def fetch_row(self, query: str, args: Optional[Iterable]=None) -> List:\n stats.inc('queries', 'SQL')\n async with self.pool.acquire() as conn:\n async with conn.cursor() as cur:\n await cur.execute(query, args)\n ret = await cur.fetchone()\n return ret","def query(self, query, *args, **kwargs):\n # TODO: do not return an sqlite3 Errors\n\n if not self.begin():\n return None\n\n self.execute(query, *args, **kwargs)\n\n # create clever result\n ret = list()\n for row in self.cursor:\n if len(row) == 1: # (2)\n ret.append(row[0])\n else: # (3)\n ret.append(row)\n if len(ret) == 1: # (1)\n ret = ret[0]\n\n self.end()\n\n return ret","def query_fetchall(self, q, param=None):\r\n try:\r\n c = self.connection.cursor()\r\n if param is None:\r\n c.execute(q)\r\n else:\r\n c.execute(q, param)\r\n self.logger.log(logger.LogLevel.DEBUG, 'database.fetchall: %s | %s' % (q, param)) \r\n return c.fetchall()\r\n except Exception as e:\r\n self.logger.log(logger.LogLevel.ERROR, 'database.fetchall: %s. %s | %s' % (e, q, param))\r\n return False","def execute(self, sql, values=None):\n c = self.conn.cursor()\n self.lock.acquire()\n hasReturn = sql.lstrip().upper().startswith(\"SELECT\")\n\n result = []\n try:\n if values:\n c.executemany(sql, values)\n else:\n c.execute(sql)\n\n if hasReturn:\n result = c.fetchall()\n\n except Exception, e:\n Log.error(traceback.format_exc())\n self.conn.rollback()\n finally:\n self.lock.release()\n\n if hasReturn:\n return result","def runningwithqueries(query):\n print(\"\\nRunning Query: \" + str(query) + \"\\nResult :\\n\")\n crsr = cnxn.execute(query)\n columns = [column[0] for column in crsr.description]\n print(columns)\n for row in crsr.fetchall():\n print(row)\n crsr.close()","def query(self, sql, rowfactory=None, *args, **kwgs):\n return query(self.conn, sql, rowfactory, *args, **kwgs)","def execute_query(query):\n db = psycopg2.connect(\"dbname=news\")\n c = db.cursor()\n c.execute(query)\n results = c.fetchall()\n db.close()\n return results","def query(self, query, *args, **kwargs):\n c = self._conn.cursor()\n easy_query(c.execute, query, *args, **kwargs)\n if c.description is None:\n return c.rowcount\n return Results(c.description, c.fetchall())","def db_results(self, query):\n try:\n # Connect to database\n db = psycopg2.connect(\"dbname=news\")\n c = db.cursor()\n # executing query and getting all results\n c.execute(query)\n db_res = c.fetchall()\n # closing connection\n db.close()\n return db_res\n except Error:\n print (\"Error when attempting to connect to db\")","def sql_query_fetch_list(self,sql):\n\t\tif self.connected:\n#\t\t\t self.__cursor.execute(sql)\n#\t\t\t results = np.asarray(self.__cursor.fetchall())\n\t\t\tcur = self.__engine.execute(sql)\n\t\t\tresults = np.asarray(cur.fetchall())\n\t\t\tcur.close()\n\t\t\treturn results\n\t\telse:\n\t\t\tprint ('db not connected yet. Do connect first')","def perform_query(query):\r\n\r\n # setup and perform query\r\n client = boto3.client('athena')\r\n DATABASE = 'default'\r\n output='s3://ruesdefrance-query-results/'\r\n \r\n queryStart = client.start_query_execution(\r\n QueryString=query,\r\n QueryExecutionContext={\r\n 'Database': DATABASE\r\n },\r\n ResultConfiguration={\r\n 'OutputLocation': output,\r\n }\r\n )\r\n\r\n # get query execution ID\r\n queryId = queryStart['QueryExecutionId']\r\n \r\n # prepare results if request succeeds\r\n status = ''\r\n while status not in ['SUCCEEDED','FAILED']:\r\n test_exec = client.get_query_execution(QueryExecutionId = queryId)\r\n status = test_exec['QueryExecution']['Status']['State']\r\n if status == 'SUCCEEDED':\r\n results = client.get_query_results(QueryExecutionId = queryId)\r\n rows = []\r\n for row in results['ResultSet']['Rows']:\r\n #print(row)\r\n rows.append(row['Data'])\r\n elif status == 'FAILED':\r\n print('FAILED')\r\n time.sleep(1)\r\n\r\n # extract column names and content of rows \r\n columns = rows[0]\r\n rows = rows[1:]\r\n print(\"rows length {}\".format(len(rows)))\r\n print(rows)\r\n \r\n return rows","def _execute_query(self, sql):\n url = self.db_url + \"?\" + urlencode({'action': 'doQuery', 'SQL': sql})\n install_opener(build_opener(self.auth_handler, self.cookie_handler))\n response = urlopen(url)\n cookie_jar.save(ignore_discard=True)\n\n # Check for OK response\n line = response.readline()\n if bytes(line) != b\"#OK\\n\":\n raise Exception(response.readlines())\n\n # Skip rows until we reach QUERYTIMEOUT\n while True:\n line = bytes(response.readline())\n if line == b\"\":\n raise Exception(\"Unexpected end of file while reading result\"\n \"header\")\n elif line.startswith(b\"#QUERYTIMEOUT\"):\n break\n\n # Skip QUERYTIME\n if not(bytes(response.readline()).startswith(b\"#QUERYTIME\")):\n raise Exception(\"Don't understand result header!\")\n\n # Read column info\n # (also discards line with full list of column names)\n columns = []\n while True:\n line = bytes(response.readline())\n if not line.startswith(b\"#\"):\n break\n else:\n m = re.match(b\"^#COLUMN ([0-9]+) name=([\\w]+) \"\n b\"JDBC_TYPE=(-?[0-9]+) JDBC_TYPENAME=([\\w]+)\\n$\",\n line)\n if m is not None:\n columns.append(m.groups())\n else:\n raise Exception(\"Don't understand column info: \"+line)\n\n # Construct record type for the output\n types = [numpy_dtype[col[3]] for col in columns]\n try:\n # Python 2 compatible\n names = [col[1] for col in columns]\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\n except TypeError:\n # Python 3 compatible\n names = [col[1].decode() for col in columns]\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\n\n # Return the data as a record array\n return np.genfromtxt(response, dtype=dtype, delimiter=\",\")","def __get_results(self, query):\n return self.mysql.query_multi_with_fetchall_as_dict(query)","def returnAll(self):\n try:\n # self.checkValName()\n self.dbc.execute(self.query, self.val)\n self.results = self.dbc.fetchall()\n except MySQLdb.Error as e:\n print \"Query failed: %s \" % e\n raise","def query(self, query, *parameters, **kwparameters):\n cursor = self._cursor()\n try:\n self._execute(cursor, query, parameters, kwparameters)\n column_names = [d[0] for d in cursor.description]\n\n if self.grace_result:\n return [GraceDict(zip(column_names, row)) for row in cursor]\n else:\n return [zip(column_names, row) for row in cursor]\n finally:\n cursor.close()","def returnAll(self):\n try:\n # self.checkValName()\n self.conn.execute(self.query, self.val)\n self.results = self.cursor.fetchall()\n except Exception as e:\n print \"Query failed: %s \" % e\n raise","def run_query(query):\n db.query(query)\n dbResult = db.store_result()\n dbFetched = dbResult.fetch_row(maxrows = 0, how = 2)\n df = pd.DataFrame.from_records(dbFetched)\n return df","def get_rows(self, tbl):\n statmt = \"select * from %s\" % tbl\n self.cur.execute(statmt)\n rows = list(self.cur.fetchall())\n return rows","async def fetchall(self, query, connection=None):\n async with self.connection(connection) as conn:\n r = await conn.execute(query)\n return await r.fetchall()","def query(self):\n self.session_status()\n try:\n if self.keyword['limit'] == None:\n query = self.session.query(self.keyword['tablename']).filter(\n self.keyword['filter']).all()\n else:\n query = self.session.query(self.keyword['tablename']).filter(\n self.keyword['filter']).limit(self.keyword['limit']).all()\n except OperationalError:\n self.retry += 1\n if self.retry > self.max_retry_time:\n raise OperationalError, 'RETRY OUT'\n time.sleep(3)\n self.session.close()\n self.query()\n\n self.session.close()\n\n if not query:\n return []\n self.retry = 0\n return query","def do_query(self, query):\n query = query.replace('\\n', ' ')\n self.cursor.execute(query)\n return self.cursor.fetchall()","def _perform_query(self, from_row=0, max_rows=-1):\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\n results = result.get(\"results\", [])\n\n self._total_results = len(results)\n self._count_valid = True\n\n for item in results:\n yield self._doc_class(self._cb, item[\"id\"], item)","def execute_many(self, sql, args=None):\r\n args = args or None\r\n with Executer(self) as cursor:\r\n rows = cursor.executemany(sql, args)\r\n return rows","def execute(self, sql):\r\n try:\r\n with self.connection.cursor() as cursor:\r\n cursor.execute(sql)\r\n self.connection.commit()\r\n try:\r\n count = cursor.rowcount\r\n res = cursor.fetchall()\r\n except psycopg2.ProgrammingError:\r\n res = count\r\n return res\r\n except psycopg2.Error as e:\r\n # print(sql)\r\n raise e","def rpc_database_get_rows(self, *args):\n\t\targs = list(args)\n\t\toffset = 0\n\t\tfields = self.path.split('/')[1:-2]\n\t\tif len(args) == (len(fields) + 1):\n\t\t\toffset = (args.pop() * VIEW_ROW_COUNT)\n\t\tassert len(fields) == len(args)\n\t\ttable_name = self.path.split('/')[-2]\n\t\ttable = DATABASE_TABLE_OBJECTS.get(table_name)\n\t\tassert table\n\n\t\t# it's critical that the columns are in the order that the client is expecting\n\t\tcolumns = DATABASE_TABLES[table_name]\n\t\trows = []\n\t\tsession = db_manager.Session()\n\t\tquery = session.query(table)\n\t\tquery = query.filter_by(**dict(zip((f + '_id' for f in fields), args)))\n\t\tfor row in query[offset:offset + VIEW_ROW_COUNT]:\n\t\t\trows.append([getattr(row, c) for c in columns])\n\t\tsession.close()\n\t\tif not len(rows):\n\t\t\treturn None\n\t\treturn {'columns': columns, 'rows': rows}","def query(self, query, params=()):\n with self.get_pool().connection() as conn:\n c = conn.cursor()\n try:\n c.execute(query, params)\n conn.commit()\n except IntegrityError:\n conn.rollback()\n raise\n if c.description is None:\n return c.rowcount\n return Results(c.description, c.fetchall())","def execute(self,sql):\n # self.results = self.execute_silent(sql)\n # return self.results\n # sql = self.format_sql(sql, **kwargs)\n sql_list = sql.split(';')\n for stmt in sql_list:\n if stmt:\n stmt = stmt.strip()\n if len(stmt) < 10:\n break\n result = self.execute_silent(stmt)\n #if result is not None,It's select stmt.\n if result:\n return result","def get_query_results(query):\n try:\n dbconn = psycopg2.connect(database=\"news\")\n cursor = dbconn.cursor()\n cursor.execute(query)\n result = cursor.fetchall()\n dbconn.close()\n return result\n except Exception as e:\n print(e)\n exit(1)","async def all(\n self,\n statement: Executable,\n params: t.Optional[\n t.Union[t.Mapping[str, t.Any], t.Sequence[t.Mapping[str, t.Any]]]\n ] = None,\n execution_options: t.Optional[t.Mapping[str, t.Any]] = None,\n bind_arguments: t.Optional[t.Mapping[str, t.Any]] = None,\n ) -> t.List[t.Any]:\n result = await self.execute(\n statement, params, execution_options=execution_options, bind_arguments=bind_arguments\n )\n if isinstance(result, sa.engine.CursorResult):\n items = result.all()\n else:\n if (\n result.raw.context.compiled.compile_state\n and result.raw.context.compiled.compile_state.multi_row_eager_loaders\n ):\n result = result.unique()\n items = result.scalars().all()\n return items","def query(self, query, *variables, limit=None):\n self.cursor.execute(query, variables)\n try:\n if limit:\n return self.cursor.fetchmany(limit)\n else:\n return self.cursor.fetchall()\n except psycopg2.ProgrammingError:\n return None","def fetch_all(self, sql):\n result = []\n\n curs = self.q(sql, True)\n cols = curs.column_names\n for row in curs:\n row_result = {}\n for field in cols:\n k = cols.index(field)\n row_result[cols[k]] = row[k]\n #print cols[k], row[k]\n result.append(row_result)\n curs.close()\n return result","def query(cursor, query):\n out = []\n c = cursor.execute(query)\n out.append(c.fetchall())\n # will return list of tuples for each query\n return out","def get_execute_table(self, limit=None):\n query = self.select_all()\n self.cur.execute(query)\n if limit is None:\n result = self.cur.fetchall()\n else:\n result = self.cur.fetchmany(limit)\n return to_data_frame(result)","def get_execute_table(self, limit=None):\n query = self.select_all()\n self.cur.execute(query)\n if limit is None:\n result = self.cur.fetchall()\n else:\n result = self.cur.fetchmany(limit)\n return to_data_frame(result)","def fetch_all(cls, sql, **kwargs):\n conn = kwargs['conn']\n\n cursor = conn.cursor(dictionary=True, buffered=False)\n for _ in cursor.execute(sql, kwargs.get('args'), multi=True):\n pass\n\n result = cursor.fetchall()\n cls.close(conn, cursor)\n\n return result","def execute(self):\n if self.sql is None:\n self.sql = self.construct_query()\n # Only SQL strings can be split, not (e.g.) SQLAlchemy statements.\n if self.multiple_statements and isinstance(self.sql, str):\n statements = self._split_sql()\n else:\n statements = [self.sql]\n single_statement = True if len(statements) == 1 and self.filename else False\n try:\n for statement in statements:\n result_proxy = self.cm.conn.execute(statement)\n log_string = self.filename if single_statement else str(statement)[:25]\n self.logger.info(\"Executed {} against {}\".format(log_string, self.cm.db))\n if result_proxy.cursor:\n return self.fetch_results(result_proxy)\n except Exception as e:\n self.logger.exception(e)\n raise","def query(self, query, params=None):\n cur = self.connection.cursor()\n cur.execute(query, params)\n result = [list(row) for row in cur.fetchall()]\n colnames = [desc[0] for desc in cur.description]\n cur.close()\n return colnames, result","def iter(self, query: str, *args, **kwargs):\n self._ensure_connected()\n cursor = SSCursor(self._db)\n try:\n self._execute(cursor, query, args, kwargs)\n column_names = [d[0] for d in cursor.description]\n for row in cursor:\n yield Row(zip(column_names, row))\n finally:\n cursor.close()","def read(self, query):\r\n t1 = time.time()\r\n if self.database in ['redshift', 'postgres']:\r\n ret = postgres_helper.fetchall(config=self.conf, sql=query)\r\n else:\r\n raise Exception(\"database not supported yet: '{}'\"\r\n .format(self.database))\r\n t2 = time.time()\r\n t = t2 - t1\r\n print('Finished in {:.2f} seconds.'.format(t))\r\n return ret","def execute_query(self, *args, **kwargs):","def query(self, q, *args):\n\n if self.dbtype == 'pg':\n print q\n return self.db.execute(q, *args).fetchall()\n else:\n cur = self.db.cursor()\n try:\n print q\n print args\n if args:\n cur.execute(q, args)\n else:\n cur.execute(q)\n ret = cur.fetchall()\n return ret\n except:\n self.db.rollback()\n raise\n finally:\n cur.close()","def execute_fetchall(self, sql, sql_args=None, dictcursor=False):\n # Check that sql arguments have the correct type\n self._check_sql_args(sql_args)\n # Execute the query\n try:\n pgcursor = self.get_postgres_cursor(dictcursor)\n pgcursor.execute(sql, sql_args)\n result = pgcursor.fetchall()\n self._connection.commit()\n except PostgresError, e:\n self._connection.rollback()\n raise RuntimeError(\"Error running SQL query: %s\", str(e))\n finally:\n pgcursor.close()\n return result","def read_items(self, query=None, val=None):\n with self.conn:\n with self.conn.cursor() as curr:\n curr.execute(query, val)\n rows = curr.fetchall()\n self.conn.close()\n return rows","def execute(sql, args=()):\n res = con.execute(sql, args)\n con.commit()\n return res.fetchall()","def _runsql(self):\n self.logger.info(\"Running SQL where sequence > %s\" % self.seq)\n try:\n results = self.engine.execute(self.sql,\n (self.max_rows, self.seq)).fetchall()\n except sqlalchemy.exc.ProgrammingError, err:\n self.logger.critical(\"Error connecting to DB : %s\" % err)\n return None\n self.logger.info('Fetched %d rows from DB' % len(results))\n if not len(results):\n self.logger.info(\"No rows returned from DB. Finished loading\")\n return False\n return results","def do(self, executor):\n sql, kw = self._assemble()\n return executor.execute(\n sql, kw\n )","def get(self, question, rows):\n con.query(question)\n result = con.use_result()\n return result.fetch_row(rows)","def cursorExecute(self, query):\n return self.cursor.execute(query)","def execute_query(query):\n\n db = psycopg2.connect(database=\"news\")\n cursor = db.cursor()\n cursor.execute(query)\n query_result = cursor.fetchall()\n db.close()\n return query_result","def fetch_all(self, sql: str, values: Union[Tuple, Mapping] = None) -> Any:\n with self.cursor() as cursor:\n cursor.execute(sql, values)\n return cursor.fetchall()","def fetchSqlRecords(self, sql):\r\n cursor = self.c.cursor()\r\n cursor.execute(sql)\r\n r = cursor.fetchall()\r\n cursor.close()\r\n return r","def execute(self, sql):\n return self.db.execute(sql)","def quarry(conn, query, params=None, returns=True):\n # Create cursor object\n cur = conn.cursor()\n\n # Execute the query\n if params is not None:\n cur.execute(query, params)\n else:\n cur.execute(query)\n\n # If returns is True, assign the query to variable\n if returns:\n results = cur.fetchall()\n pprint(results)\n\n # If not, do not create variable to return\n # Close the cursor and the connection\n cur.close()\n conn.commit()\n\n # If returns is True, return the return value\n if returns:\n return results","def select_records(conn, qry, row_cnt=None):\n result = None\n try:\n cur = conn.cursor()\n cur.execute(qry)\n result = PostgresStuff.get_results(cur, row_cnt)\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if cur is not None:\n cur.close()\n return result","def run_select_query(query, args = None):\n cursor = db.get_cursor()\n cursor.execute(query, args)\n return cursor.fetchall()","def execute(self) -> TableResult:\n self._t_env._before_execute()\n return TableResult(self._j_statement_set.execute())","def fetch_data_from_db(query):\n cursor.execute(query)\n result = cursor.fetchall()\n return result","def get_rows(self, logger):\n engine = self.connecting_database(logger)\n\n # Returning all rows from the table using Pandas\n logger.info(\"Getting rows . . .\")\n df = pd.read_sql('bt_challenge_boa', con=engine)\n\n logger.info(\"Total Rows: {}\".format(str(len(df))))\n logger.info(\"Saving the CSV file . . .\")\n df.to_csv(self.path + self.outpufile, index=False, doublequote=True)","def execute_read_query(connection, query):\n cursor = connection.cursor()\n result = None\n try:\n cursor.execute(query)\n result = cursor.fetchall()\n return result\n except psycopg2.OperationalError as e:\n print(f\"The error '{e}' occurred\")","def get_row_result(self, cmd, fields):\n msg_type, msg = self.protocol.build_execute_statement(\"mysqlx\", cmd, fields)\n self.protocol.send_msg(msg_type, msg)\n return RowResult(self)","def query(self, query: str, values: Tuple = None) -> None:\n cursor = self.connection.cursor()\n cursor.execute(query, values)\n result = cursor.fetchall()\n cursor.close()\n return result","def _raw(self, query: Any, data: Any = None):\n assert isinstance(query, str)\n\n conn = self._get_session()\n try:\n results = conn.execute(query)\n\n entity_items = []\n for item in results:\n entity = self.model_cls.to_entity(item)\n entity.state_.mark_retrieved()\n entity_items.append(entity)\n\n result = ResultSet(\n offset=0,\n limit=len(entity_items),\n total=len(entity_items),\n items=entity_items,\n )\n except DatabaseError as exc:\n logger.error(f\"Error while running raw query: {exc}\")\n raise\n finally:\n if not current_uow:\n conn.commit()\n conn.close()\n\n return result","def query(self, query, cs_type=None, *parameters, **kwparameters):\n cursor = self._cursor(cs_type)\n try:\n self._execute(cursor, query, parameters, kwparameters)\n if cs_type in [\"SSCursor\", \"SSDictCursor\"]:\n while 1:\n try:\n row = cursor.fetchone()\n except Exception, e:\n cursor.close()\n raise e\n if row:\n yield row\n else:\n break\n else:\n yield [Row(row) if isinstance(row, dict) else row for row in cursor]\n except Exception, e:\n cursor.close()"],"string":"[\n \"def execute(self, query):\\n with self.conn.cursor() as cur:\\n # Execute the query\\n try:\\n cur.execute(query)\\n except Exception as exc:\\n print(\\\"Unable to execute query. Error was {0}\\\".format(str(exc)))\\n exit()\\n rows = cur.fetchall()\\n return rows\",\n \"def executeQuery(query):\\n c = db.cursor()\\n c.execute(query)\\n rows = c.fetchall()\\n db.close()\\n return rows\",\n \"def _run_query(self, query):\\n cursor = self.conn.cursor()\\n cursor.execute(query)\\n return cursor.fetchall()\",\n \"def _query_and_fetchall(self, query):\\n with self._connect() as conn:\\n cur = conn.cursor()\\n cur.execute(query)\\n results = cur.fetchall()\\n\\n return results\",\n \"def query(self, sql):\\n try:\\n res_cursor = self.connection.execute(text(sql))\\n except Exception as e: \\n raise e(\\\"SQL execution error!\\\")\\n \\n rows = (Row(res_cursor.keys(), record) for record in res_cursor)\\n results = RowsCollection(rows)\\n return results\",\n \"def query(self, sql):\\n self.cursor.execute(sql)\\n result = self.cursor.fetchall()\\n return result\",\n \"def get_rows(query, params=None):\\n cur = get_cursor()\\n cur.execute(query, params)\\n rows = cur.fetchall()\\n return rows\",\n \"def execute_and_fetch(db, query):\\n rows = []\\n\\n db.query(query)\\n res = db.use_result()\\n row = res.fetch_row()\\n while row != ():\\n rows.append(row[0])\\n row = res.fetch_row()\\n return rows\",\n \"async def query(self, stmt, *args):\\n\\n with (await self.application.db.cursor()) as cur:\\n await cur.execute(stmt, args)\\n return [self.row_to_obj(row, cur)\\n for row in await cur.fetchall()]\",\n \"def exec_results(query):\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n c.execute(query)\\n result = c.fetchall()\\n db.close()\\n return result\",\n \"def run_query(query):\\r\\n db = psycopg2.connect('dbname=' + database)\\r\\n connect = db.cursor()\\r\\n connect.execute(query)\\r\\n rows = connect.fetchall()\\r\\n db.close()\\r\\n return rows\",\n \"def execute_query(query):\\n c.execute(query)\\n return c.fetchall()\",\n \"async def db_query(self, *args, **kwargs):\\n rows = []\\n async with self.db_pool.acquire() as conn:\\n async with conn.cursor(cursor_factory=DictCursor) as cur:\\n await cur.execute(*args, **kwargs)\\n try:\\n async for row in cur:\\n rows.append(row)\\n except psycopg2.ProgrammingError:\\n # No results\\n pass\\n return rows\",\n \"def run_query(conn, query):\\n\\tcur = conn.cursor()\\n\\tcur.execute(query)\\n\\trows = cur.fetchall()\\n\\treturn rows\",\n \"async def db_execute(self, *args, **kwargs):\\n rows = []\\n async with self.db_pool.acquire() as conn:\\n async with conn.cursor() as cur:\\n await cur.execute(*args, **kwargs)\\n try:\\n async for row in cur:\\n rows.append(row)\\n except psycopg2.ProgrammingError:\\n # No results\\n pass\\n return rows\",\n \"async def fetchall(self):\\n\\n self._check_executed()\\n\\n if self._query_id == -1:\\n msg = \\\"query didn't result in a resultset\\\"\\n self._exception_handler(ProgrammingError, msg)\\n\\n result = self._rows[self.rownumber - self._offset :]\\n self.rownumber = len(self._rows) + self._offset\\n\\n # slide the window over the resultset\\n while await self.nextset():\\n result += self._rows\\n self.rownumber = len(self._rows) + self._offset\\n print(\\n \\\"\\\\nresult: \\\",\\n self._rows,\\n \\\"\\\\n\\\\n\\\",\\n )\\n return result\",\n \"def _get_rows_from_query(self, query, data):\\n to_return = []\\n results = self.db_conn.cursor().execute(query, data)\\n for result in results:\\n to_return.append(result)\\n return to_return\",\n \"def query(self, query: str, *args, **kwargs):\\n cursor = self._cursor()\\n try:\\n self._execute(cursor, query, args, kwargs)\\n column_names = [d[0] for d in cursor.description]\\n return [Row(zip(column_names, row)) for row in cursor]\\n finally:\\n cursor.close()\",\n \"def execute(self, qry):\\n def internal():\\n print 'qry = ', qry\\n self._cur = self.get_cursor()\\n print 'self._cur = ', self._cur\\n self._cur.execute(qry)\\n # self.conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor).execute(qry)\\n rows = self._cur.fetchall()\\n return rows\\n\\n return self._retry(internal)\",\n \"def get_query_results(self, conn, sql):\\n\\n cur = conn.cursor()\\n cur.execute(sql)\\n yield from cur.fetchall()\",\n \"def _execute_query(self, query, values):\\n with self as plasticDB:\\n cursor = plasticDB.connection.cursor()\\n cursor.execute(query,values)\\n if not cursor.description:\\n return []\\n rs = RecordSet(initialData=cursor.fetchall(), recordType=next(zip(*cursor.description)))\\n return rs\",\n \"def run(self):\\n rows = None\\n if self.sql.startswith('select'):\\n conn = self.table.connect()\\n with conn.cursor() as curs:\\n try:\\n curs.execute(self.sql)\\n except conn.DatabaseError as exc:\\n error, = exc.args\\n logging.error(f\\\"\\\"\\\"error executing {self.sql}:\\n {error.code}\\\"\\\"\\\")\\n self.excep = exc\\n raise exc\\n else:\\n rows = curs.fetchall()\\n # logging.critical(f\\\"\\\"\\\"executed {self.sql}\\\"\\\"\\\")\\n self.result_exec = rows\",\n \"def get_query_results(query):\\n\\n db, c = connect()\\n c.execute(query)\\n results = c.fetchall()\\n db.close()\\n return results\",\n \"def _run_hive_query(cursor, query, has_results=False):\\n _print_debug('Executing query: {}'.format(query))\\n cursor.execute(query)\\n\\n if has_results:\\n rows = cursor.fetchall()\\n _print_debug('Query returned {} rows'.format(len(rows)))\\n return rows\",\n \"def execute_query(self) -> dict or list:\\n pass\",\n \"def _query_all(self, sql, data=None):\\n\\n conn = psycopg2.connect(self.connect_args)\\n cur = conn.cursor()\\n cur.execute(sql, data)\\n result = cur.fetchall()\\n cur.close()\\n conn.close()\\n return result\",\n \"def get_results(self, table=None, query=None):\\n def determine_query():\\n \\\"\\\"\\\"\\n Determines the query by checking if a query arg was passed.\\n \\\"\\\"\\\"\\n return query if query is not None \\\\\\n else f\\\"SELECT * FROM '{table}';\\\"\\n\\n def __to_list(__results):\\n \\\"\\\"\\\"\\n Converts DB results to a (persistent) list.\\n \\\"\\\"\\\"\\n rows = []\\n for row in __results:\\n rows.append(row)\\n \\n __results.close()\\n\\n return rows\\n\\n\\n # Determine query and execute\\n results = __to_list(self.execute_query(determine_query()))\\n\\n return results\",\n \"def query(self, query):\\n\\n config = {\\n 'user' : self.db_user,\\n 'password' : self.db_pwd,\\n 'database' : self.db_name,\\n 'host' : self.db_host,\\n 'unix_socket' : self.db_socket,\\n 'port' : self.db_port,\\n 'charset' : 'utf8'\\n }\\n\\n try: \\n cnx = mysql.connector.connect(**config)\\n cursor = cnx.cursor(dictionary=True) # To return rows as dictionaries.\\n\\n cursor.execute(query[0], query[1])\\n \\n # Putting the result rows in an array.\\n result = []\\n for row in cursor:\\n result.append(row)\\n\\n except mysql.connector.Error as err:\\n return None\\n else:\\n cursor.close()\\n cnx.close()\\n return result\",\n \"def _exec1(self, sql):\\n result = self._exec(sql)\\n return [row[0] for row in result]\",\n \"def execute(self, sql):\\n with self.connection.cursor() as dbc:\\n if sql[-1] != ';':\\n sql += ';'\\n dbc.execute(sql)\\n self.last_row = dbc.lastrowid\\n try:\\n return dbc.fetchall()\\n except:\\n return\",\n \"def _runQuery(self, query, connection):\\n with connection as c:\\n cursor = c.cursor()\\n cursor.execute(query)\\n results = []\\n result = cursor.fetchone()\\n while result is not None:\\n numCols = len(result)\\n item = {}\\n for col in range(0, numCols):\\n key = cursor.description[col][0]\\n value = result[col]\\n try:\\n #handle oracle clob datatypes\\n value = str(result[col].read())\\n except AttributeError:\\n pass\\n item[key] = value\\n #item = {cursor.description[col][0]: result[col] for col in range(0, numCols)}\\n results.append(item)\\n result = cursor.fetchone()\\n return results\",\n \"def get_result(query):\\r\\n db = psycopg2.connect(database=DBNAME)\\r\\n c = db.cursor()\\r\\n c.execute(query)\\r\\n results = c.fetchall()\\r\\n db.close()\\r\\n return results\",\n \"def getResults(query):\\n # Tells psycopg2 which database we are talking about\\n db = psycopg2.connect(database=\\\"news\\\")\\n # Curser is a local variable commonly use in sql/python connections\\n curser = db.cursor()\\n # Please refer to https://dev.mysql.com/doc/connector-python/en/\\n # for more information about how this block of code works\\n curser.execute(query)\\n i = curser.fetchall()\\n db.close()\\n return i\",\n \"def execute_query(conn, query):\\r\\n cur = conn.cursor()\\r\\n cur.execute(query)\\r\\n conn.commit()\\r\\n return cur.fetchall()\",\n \"def _execute_query(self,\\n sql_connection: Connection,\\n sql_query: Union[str, Query]) -> List[Dict[str, Any]]:\\n return_result: List[Dict[str, Any]] = []\\n result: ResultProxy = sql_connection.execute(sql_query)\\n if result and result.returns_rows:\\n return_result: List[Dict[str, Any]] = [dict(row) for row in result]\\n return return_result\",\n \"def run_query(cur, query, show_results=False):\\n num_rows = cur.execute(query)\\n print('the query returned {} rows'.format(num_rows))\\n if show_results:\\n for row in cur.fetchall():\\n print(row)\",\n \"def execute_sql(self, result_type=MULTI):\\r\\n try:\\r\\n sql, params = self.as_sql()\\r\\n #import pdb; pdb.set_trace()\\r\\n if not sql:\\r\\n raise EmptyResultSet\\r\\n except EmptyResultSet:\\r\\n if result_type == MULTI:\\r\\n return iter([])\\r\\n else:\\r\\n return\\r\\n\\r\\n cursor = self.connection.cursor() \\r\\n cursor.execute(sql, params)\\r\\n\\r\\n if not result_type:\\r\\n return cursor\\r\\n if result_type == SINGLE:\\r\\n if self.query.ordering_aliases:\\r\\n return cursor.fetchone()[:-len(self.query.ordering_aliases)]\\r\\n return cursor.fetchone()\\r\\n\\r\\n # The MULTI case.\\r\\n if self.query.ordering_aliases:\\r\\n result = order_modified_iter(cursor, len(self.query.ordering_aliases),\\r\\n self.connection.features.empty_fetchmany_value)\\r\\n else:\\r\\n result = iter((lambda: cursor.fetchmany(GET_ITERATOR_CHUNK_SIZE)),\\r\\n self.connection.features.empty_fetchmany_value)\\r\\n if not self.connection.features.can_use_chunked_reads:\\r\\n # If we are using non-chunked reads, we return the same data\\r\\n # structure as normally, but ensure it is all read into memory\\r\\n # before going any further.\\r\\n return list(result)\\r\\n return result\",\n \"def select_query(self):\\n query = db.select([self.tables])\\n print(query)\\n ResultProxy = self.connection.execute(query)\\n ResultSet = ResultProxy.fetchall()\\n return ResultSet\",\n \"async def fetch_row(self, query: str, args: Optional[Iterable]=None) -> List:\\n stats.inc('queries', 'SQL')\\n async with self.pool.acquire() as conn:\\n async with conn.cursor() as cur:\\n await cur.execute(query, args)\\n ret = await cur.fetchone()\\n return ret\",\n \"def query(self, query, *args, **kwargs):\\n # TODO: do not return an sqlite3 Errors\\n\\n if not self.begin():\\n return None\\n\\n self.execute(query, *args, **kwargs)\\n\\n # create clever result\\n ret = list()\\n for row in self.cursor:\\n if len(row) == 1: # (2)\\n ret.append(row[0])\\n else: # (3)\\n ret.append(row)\\n if len(ret) == 1: # (1)\\n ret = ret[0]\\n\\n self.end()\\n\\n return ret\",\n \"def query_fetchall(self, q, param=None):\\r\\n try:\\r\\n c = self.connection.cursor()\\r\\n if param is None:\\r\\n c.execute(q)\\r\\n else:\\r\\n c.execute(q, param)\\r\\n self.logger.log(logger.LogLevel.DEBUG, 'database.fetchall: %s | %s' % (q, param)) \\r\\n return c.fetchall()\\r\\n except Exception as e:\\r\\n self.logger.log(logger.LogLevel.ERROR, 'database.fetchall: %s. %s | %s' % (e, q, param))\\r\\n return False\",\n \"def execute(self, sql, values=None):\\n c = self.conn.cursor()\\n self.lock.acquire()\\n hasReturn = sql.lstrip().upper().startswith(\\\"SELECT\\\")\\n\\n result = []\\n try:\\n if values:\\n c.executemany(sql, values)\\n else:\\n c.execute(sql)\\n\\n if hasReturn:\\n result = c.fetchall()\\n\\n except Exception, e:\\n Log.error(traceback.format_exc())\\n self.conn.rollback()\\n finally:\\n self.lock.release()\\n\\n if hasReturn:\\n return result\",\n \"def runningwithqueries(query):\\n print(\\\"\\\\nRunning Query: \\\" + str(query) + \\\"\\\\nResult :\\\\n\\\")\\n crsr = cnxn.execute(query)\\n columns = [column[0] for column in crsr.description]\\n print(columns)\\n for row in crsr.fetchall():\\n print(row)\\n crsr.close()\",\n \"def query(self, sql, rowfactory=None, *args, **kwgs):\\n return query(self.conn, sql, rowfactory, *args, **kwgs)\",\n \"def execute_query(query):\\n db = psycopg2.connect(\\\"dbname=news\\\")\\n c = db.cursor()\\n c.execute(query)\\n results = c.fetchall()\\n db.close()\\n return results\",\n \"def query(self, query, *args, **kwargs):\\n c = self._conn.cursor()\\n easy_query(c.execute, query, *args, **kwargs)\\n if c.description is None:\\n return c.rowcount\\n return Results(c.description, c.fetchall())\",\n \"def db_results(self, query):\\n try:\\n # Connect to database\\n db = psycopg2.connect(\\\"dbname=news\\\")\\n c = db.cursor()\\n # executing query and getting all results\\n c.execute(query)\\n db_res = c.fetchall()\\n # closing connection\\n db.close()\\n return db_res\\n except Error:\\n print (\\\"Error when attempting to connect to db\\\")\",\n \"def sql_query_fetch_list(self,sql):\\n\\t\\tif self.connected:\\n#\\t\\t\\t self.__cursor.execute(sql)\\n#\\t\\t\\t results = np.asarray(self.__cursor.fetchall())\\n\\t\\t\\tcur = self.__engine.execute(sql)\\n\\t\\t\\tresults = np.asarray(cur.fetchall())\\n\\t\\t\\tcur.close()\\n\\t\\t\\treturn results\\n\\t\\telse:\\n\\t\\t\\tprint ('db not connected yet. Do connect first')\",\n \"def perform_query(query):\\r\\n\\r\\n # setup and perform query\\r\\n client = boto3.client('athena')\\r\\n DATABASE = 'default'\\r\\n output='s3://ruesdefrance-query-results/'\\r\\n \\r\\n queryStart = client.start_query_execution(\\r\\n QueryString=query,\\r\\n QueryExecutionContext={\\r\\n 'Database': DATABASE\\r\\n },\\r\\n ResultConfiguration={\\r\\n 'OutputLocation': output,\\r\\n }\\r\\n )\\r\\n\\r\\n # get query execution ID\\r\\n queryId = queryStart['QueryExecutionId']\\r\\n \\r\\n # prepare results if request succeeds\\r\\n status = ''\\r\\n while status not in ['SUCCEEDED','FAILED']:\\r\\n test_exec = client.get_query_execution(QueryExecutionId = queryId)\\r\\n status = test_exec['QueryExecution']['Status']['State']\\r\\n if status == 'SUCCEEDED':\\r\\n results = client.get_query_results(QueryExecutionId = queryId)\\r\\n rows = []\\r\\n for row in results['ResultSet']['Rows']:\\r\\n #print(row)\\r\\n rows.append(row['Data'])\\r\\n elif status == 'FAILED':\\r\\n print('FAILED')\\r\\n time.sleep(1)\\r\\n\\r\\n # extract column names and content of rows \\r\\n columns = rows[0]\\r\\n rows = rows[1:]\\r\\n print(\\\"rows length {}\\\".format(len(rows)))\\r\\n print(rows)\\r\\n \\r\\n return rows\",\n \"def _execute_query(self, sql):\\n url = self.db_url + \\\"?\\\" + urlencode({'action': 'doQuery', 'SQL': sql})\\n install_opener(build_opener(self.auth_handler, self.cookie_handler))\\n response = urlopen(url)\\n cookie_jar.save(ignore_discard=True)\\n\\n # Check for OK response\\n line = response.readline()\\n if bytes(line) != b\\\"#OK\\\\n\\\":\\n raise Exception(response.readlines())\\n\\n # Skip rows until we reach QUERYTIMEOUT\\n while True:\\n line = bytes(response.readline())\\n if line == b\\\"\\\":\\n raise Exception(\\\"Unexpected end of file while reading result\\\"\\n \\\"header\\\")\\n elif line.startswith(b\\\"#QUERYTIMEOUT\\\"):\\n break\\n\\n # Skip QUERYTIME\\n if not(bytes(response.readline()).startswith(b\\\"#QUERYTIME\\\")):\\n raise Exception(\\\"Don't understand result header!\\\")\\n\\n # Read column info\\n # (also discards line with full list of column names)\\n columns = []\\n while True:\\n line = bytes(response.readline())\\n if not line.startswith(b\\\"#\\\"):\\n break\\n else:\\n m = re.match(b\\\"^#COLUMN ([0-9]+) name=([\\\\w]+) \\\"\\n b\\\"JDBC_TYPE=(-?[0-9]+) JDBC_TYPENAME=([\\\\w]+)\\\\n$\\\",\\n line)\\n if m is not None:\\n columns.append(m.groups())\\n else:\\n raise Exception(\\\"Don't understand column info: \\\"+line)\\n\\n # Construct record type for the output\\n types = [numpy_dtype[col[3]] for col in columns]\\n try:\\n # Python 2 compatible\\n names = [col[1] for col in columns]\\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\\n except TypeError:\\n # Python 3 compatible\\n names = [col[1].decode() for col in columns]\\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\\n\\n # Return the data as a record array\\n return np.genfromtxt(response, dtype=dtype, delimiter=\\\",\\\")\",\n \"def __get_results(self, query):\\n return self.mysql.query_multi_with_fetchall_as_dict(query)\",\n \"def returnAll(self):\\n try:\\n # self.checkValName()\\n self.dbc.execute(self.query, self.val)\\n self.results = self.dbc.fetchall()\\n except MySQLdb.Error as e:\\n print \\\"Query failed: %s \\\" % e\\n raise\",\n \"def query(self, query, *parameters, **kwparameters):\\n cursor = self._cursor()\\n try:\\n self._execute(cursor, query, parameters, kwparameters)\\n column_names = [d[0] for d in cursor.description]\\n\\n if self.grace_result:\\n return [GraceDict(zip(column_names, row)) for row in cursor]\\n else:\\n return [zip(column_names, row) for row in cursor]\\n finally:\\n cursor.close()\",\n \"def returnAll(self):\\n try:\\n # self.checkValName()\\n self.conn.execute(self.query, self.val)\\n self.results = self.cursor.fetchall()\\n except Exception as e:\\n print \\\"Query failed: %s \\\" % e\\n raise\",\n \"def run_query(query):\\n db.query(query)\\n dbResult = db.store_result()\\n dbFetched = dbResult.fetch_row(maxrows = 0, how = 2)\\n df = pd.DataFrame.from_records(dbFetched)\\n return df\",\n \"def get_rows(self, tbl):\\n statmt = \\\"select * from %s\\\" % tbl\\n self.cur.execute(statmt)\\n rows = list(self.cur.fetchall())\\n return rows\",\n \"async def fetchall(self, query, connection=None):\\n async with self.connection(connection) as conn:\\n r = await conn.execute(query)\\n return await r.fetchall()\",\n \"def query(self):\\n self.session_status()\\n try:\\n if self.keyword['limit'] == None:\\n query = self.session.query(self.keyword['tablename']).filter(\\n self.keyword['filter']).all()\\n else:\\n query = self.session.query(self.keyword['tablename']).filter(\\n self.keyword['filter']).limit(self.keyword['limit']).all()\\n except OperationalError:\\n self.retry += 1\\n if self.retry > self.max_retry_time:\\n raise OperationalError, 'RETRY OUT'\\n time.sleep(3)\\n self.session.close()\\n self.query()\\n\\n self.session.close()\\n\\n if not query:\\n return []\\n self.retry = 0\\n return query\",\n \"def do_query(self, query):\\n query = query.replace('\\\\n', ' ')\\n self.cursor.execute(query)\\n return self.cursor.fetchall()\",\n \"def _perform_query(self, from_row=0, max_rows=-1):\\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\\n results = result.get(\\\"results\\\", [])\\n\\n self._total_results = len(results)\\n self._count_valid = True\\n\\n for item in results:\\n yield self._doc_class(self._cb, item[\\\"id\\\"], item)\",\n \"def execute_many(self, sql, args=None):\\r\\n args = args or None\\r\\n with Executer(self) as cursor:\\r\\n rows = cursor.executemany(sql, args)\\r\\n return rows\",\n \"def execute(self, sql):\\r\\n try:\\r\\n with self.connection.cursor() as cursor:\\r\\n cursor.execute(sql)\\r\\n self.connection.commit()\\r\\n try:\\r\\n count = cursor.rowcount\\r\\n res = cursor.fetchall()\\r\\n except psycopg2.ProgrammingError:\\r\\n res = count\\r\\n return res\\r\\n except psycopg2.Error as e:\\r\\n # print(sql)\\r\\n raise e\",\n \"def rpc_database_get_rows(self, *args):\\n\\t\\targs = list(args)\\n\\t\\toffset = 0\\n\\t\\tfields = self.path.split('/')[1:-2]\\n\\t\\tif len(args) == (len(fields) + 1):\\n\\t\\t\\toffset = (args.pop() * VIEW_ROW_COUNT)\\n\\t\\tassert len(fields) == len(args)\\n\\t\\ttable_name = self.path.split('/')[-2]\\n\\t\\ttable = DATABASE_TABLE_OBJECTS.get(table_name)\\n\\t\\tassert table\\n\\n\\t\\t# it's critical that the columns are in the order that the client is expecting\\n\\t\\tcolumns = DATABASE_TABLES[table_name]\\n\\t\\trows = []\\n\\t\\tsession = db_manager.Session()\\n\\t\\tquery = session.query(table)\\n\\t\\tquery = query.filter_by(**dict(zip((f + '_id' for f in fields), args)))\\n\\t\\tfor row in query[offset:offset + VIEW_ROW_COUNT]:\\n\\t\\t\\trows.append([getattr(row, c) for c in columns])\\n\\t\\tsession.close()\\n\\t\\tif not len(rows):\\n\\t\\t\\treturn None\\n\\t\\treturn {'columns': columns, 'rows': rows}\",\n \"def query(self, query, params=()):\\n with self.get_pool().connection() as conn:\\n c = conn.cursor()\\n try:\\n c.execute(query, params)\\n conn.commit()\\n except IntegrityError:\\n conn.rollback()\\n raise\\n if c.description is None:\\n return c.rowcount\\n return Results(c.description, c.fetchall())\",\n \"def execute(self,sql):\\n # self.results = self.execute_silent(sql)\\n # return self.results\\n # sql = self.format_sql(sql, **kwargs)\\n sql_list = sql.split(';')\\n for stmt in sql_list:\\n if stmt:\\n stmt = stmt.strip()\\n if len(stmt) < 10:\\n break\\n result = self.execute_silent(stmt)\\n #if result is not None,It's select stmt.\\n if result:\\n return result\",\n \"def get_query_results(query):\\n try:\\n dbconn = psycopg2.connect(database=\\\"news\\\")\\n cursor = dbconn.cursor()\\n cursor.execute(query)\\n result = cursor.fetchall()\\n dbconn.close()\\n return result\\n except Exception as e:\\n print(e)\\n exit(1)\",\n \"async def all(\\n self,\\n statement: Executable,\\n params: t.Optional[\\n t.Union[t.Mapping[str, t.Any], t.Sequence[t.Mapping[str, t.Any]]]\\n ] = None,\\n execution_options: t.Optional[t.Mapping[str, t.Any]] = None,\\n bind_arguments: t.Optional[t.Mapping[str, t.Any]] = None,\\n ) -> t.List[t.Any]:\\n result = await self.execute(\\n statement, params, execution_options=execution_options, bind_arguments=bind_arguments\\n )\\n if isinstance(result, sa.engine.CursorResult):\\n items = result.all()\\n else:\\n if (\\n result.raw.context.compiled.compile_state\\n and result.raw.context.compiled.compile_state.multi_row_eager_loaders\\n ):\\n result = result.unique()\\n items = result.scalars().all()\\n return items\",\n \"def query(self, query, *variables, limit=None):\\n self.cursor.execute(query, variables)\\n try:\\n if limit:\\n return self.cursor.fetchmany(limit)\\n else:\\n return self.cursor.fetchall()\\n except psycopg2.ProgrammingError:\\n return None\",\n \"def fetch_all(self, sql):\\n result = []\\n\\n curs = self.q(sql, True)\\n cols = curs.column_names\\n for row in curs:\\n row_result = {}\\n for field in cols:\\n k = cols.index(field)\\n row_result[cols[k]] = row[k]\\n #print cols[k], row[k]\\n result.append(row_result)\\n curs.close()\\n return result\",\n \"def query(cursor, query):\\n out = []\\n c = cursor.execute(query)\\n out.append(c.fetchall())\\n # will return list of tuples for each query\\n return out\",\n \"def get_execute_table(self, limit=None):\\n query = self.select_all()\\n self.cur.execute(query)\\n if limit is None:\\n result = self.cur.fetchall()\\n else:\\n result = self.cur.fetchmany(limit)\\n return to_data_frame(result)\",\n \"def get_execute_table(self, limit=None):\\n query = self.select_all()\\n self.cur.execute(query)\\n if limit is None:\\n result = self.cur.fetchall()\\n else:\\n result = self.cur.fetchmany(limit)\\n return to_data_frame(result)\",\n \"def fetch_all(cls, sql, **kwargs):\\n conn = kwargs['conn']\\n\\n cursor = conn.cursor(dictionary=True, buffered=False)\\n for _ in cursor.execute(sql, kwargs.get('args'), multi=True):\\n pass\\n\\n result = cursor.fetchall()\\n cls.close(conn, cursor)\\n\\n return result\",\n \"def execute(self):\\n if self.sql is None:\\n self.sql = self.construct_query()\\n # Only SQL strings can be split, not (e.g.) SQLAlchemy statements.\\n if self.multiple_statements and isinstance(self.sql, str):\\n statements = self._split_sql()\\n else:\\n statements = [self.sql]\\n single_statement = True if len(statements) == 1 and self.filename else False\\n try:\\n for statement in statements:\\n result_proxy = self.cm.conn.execute(statement)\\n log_string = self.filename if single_statement else str(statement)[:25]\\n self.logger.info(\\\"Executed {} against {}\\\".format(log_string, self.cm.db))\\n if result_proxy.cursor:\\n return self.fetch_results(result_proxy)\\n except Exception as e:\\n self.logger.exception(e)\\n raise\",\n \"def query(self, query, params=None):\\n cur = self.connection.cursor()\\n cur.execute(query, params)\\n result = [list(row) for row in cur.fetchall()]\\n colnames = [desc[0] for desc in cur.description]\\n cur.close()\\n return colnames, result\",\n \"def iter(self, query: str, *args, **kwargs):\\n self._ensure_connected()\\n cursor = SSCursor(self._db)\\n try:\\n self._execute(cursor, query, args, kwargs)\\n column_names = [d[0] for d in cursor.description]\\n for row in cursor:\\n yield Row(zip(column_names, row))\\n finally:\\n cursor.close()\",\n \"def read(self, query):\\r\\n t1 = time.time()\\r\\n if self.database in ['redshift', 'postgres']:\\r\\n ret = postgres_helper.fetchall(config=self.conf, sql=query)\\r\\n else:\\r\\n raise Exception(\\\"database not supported yet: '{}'\\\"\\r\\n .format(self.database))\\r\\n t2 = time.time()\\r\\n t = t2 - t1\\r\\n print('Finished in {:.2f} seconds.'.format(t))\\r\\n return ret\",\n \"def execute_query(self, *args, **kwargs):\",\n \"def query(self, q, *args):\\n\\n if self.dbtype == 'pg':\\n print q\\n return self.db.execute(q, *args).fetchall()\\n else:\\n cur = self.db.cursor()\\n try:\\n print q\\n print args\\n if args:\\n cur.execute(q, args)\\n else:\\n cur.execute(q)\\n ret = cur.fetchall()\\n return ret\\n except:\\n self.db.rollback()\\n raise\\n finally:\\n cur.close()\",\n \"def execute_fetchall(self, sql, sql_args=None, dictcursor=False):\\n # Check that sql arguments have the correct type\\n self._check_sql_args(sql_args)\\n # Execute the query\\n try:\\n pgcursor = self.get_postgres_cursor(dictcursor)\\n pgcursor.execute(sql, sql_args)\\n result = pgcursor.fetchall()\\n self._connection.commit()\\n except PostgresError, e:\\n self._connection.rollback()\\n raise RuntimeError(\\\"Error running SQL query: %s\\\", str(e))\\n finally:\\n pgcursor.close()\\n return result\",\n \"def read_items(self, query=None, val=None):\\n with self.conn:\\n with self.conn.cursor() as curr:\\n curr.execute(query, val)\\n rows = curr.fetchall()\\n self.conn.close()\\n return rows\",\n \"def execute(sql, args=()):\\n res = con.execute(sql, args)\\n con.commit()\\n return res.fetchall()\",\n \"def _runsql(self):\\n self.logger.info(\\\"Running SQL where sequence > %s\\\" % self.seq)\\n try:\\n results = self.engine.execute(self.sql,\\n (self.max_rows, self.seq)).fetchall()\\n except sqlalchemy.exc.ProgrammingError, err:\\n self.logger.critical(\\\"Error connecting to DB : %s\\\" % err)\\n return None\\n self.logger.info('Fetched %d rows from DB' % len(results))\\n if not len(results):\\n self.logger.info(\\\"No rows returned from DB. Finished loading\\\")\\n return False\\n return results\",\n \"def do(self, executor):\\n sql, kw = self._assemble()\\n return executor.execute(\\n sql, kw\\n )\",\n \"def get(self, question, rows):\\n con.query(question)\\n result = con.use_result()\\n return result.fetch_row(rows)\",\n \"def cursorExecute(self, query):\\n return self.cursor.execute(query)\",\n \"def execute_query(query):\\n\\n db = psycopg2.connect(database=\\\"news\\\")\\n cursor = db.cursor()\\n cursor.execute(query)\\n query_result = cursor.fetchall()\\n db.close()\\n return query_result\",\n \"def fetch_all(self, sql: str, values: Union[Tuple, Mapping] = None) -> Any:\\n with self.cursor() as cursor:\\n cursor.execute(sql, values)\\n return cursor.fetchall()\",\n \"def fetchSqlRecords(self, sql):\\r\\n cursor = self.c.cursor()\\r\\n cursor.execute(sql)\\r\\n r = cursor.fetchall()\\r\\n cursor.close()\\r\\n return r\",\n \"def execute(self, sql):\\n return self.db.execute(sql)\",\n \"def quarry(conn, query, params=None, returns=True):\\n # Create cursor object\\n cur = conn.cursor()\\n\\n # Execute the query\\n if params is not None:\\n cur.execute(query, params)\\n else:\\n cur.execute(query)\\n\\n # If returns is True, assign the query to variable\\n if returns:\\n results = cur.fetchall()\\n pprint(results)\\n\\n # If not, do not create variable to return\\n # Close the cursor and the connection\\n cur.close()\\n conn.commit()\\n\\n # If returns is True, return the return value\\n if returns:\\n return results\",\n \"def select_records(conn, qry, row_cnt=None):\\n result = None\\n try:\\n cur = conn.cursor()\\n cur.execute(qry)\\n result = PostgresStuff.get_results(cur, row_cnt)\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if cur is not None:\\n cur.close()\\n return result\",\n \"def run_select_query(query, args = None):\\n cursor = db.get_cursor()\\n cursor.execute(query, args)\\n return cursor.fetchall()\",\n \"def execute(self) -> TableResult:\\n self._t_env._before_execute()\\n return TableResult(self._j_statement_set.execute())\",\n \"def fetch_data_from_db(query):\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return result\",\n \"def get_rows(self, logger):\\n engine = self.connecting_database(logger)\\n\\n # Returning all rows from the table using Pandas\\n logger.info(\\\"Getting rows . . .\\\")\\n df = pd.read_sql('bt_challenge_boa', con=engine)\\n\\n logger.info(\\\"Total Rows: {}\\\".format(str(len(df))))\\n logger.info(\\\"Saving the CSV file . . .\\\")\\n df.to_csv(self.path + self.outpufile, index=False, doublequote=True)\",\n \"def execute_read_query(connection, query):\\n cursor = connection.cursor()\\n result = None\\n try:\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return result\\n except psycopg2.OperationalError as e:\\n print(f\\\"The error '{e}' occurred\\\")\",\n \"def get_row_result(self, cmd, fields):\\n msg_type, msg = self.protocol.build_execute_statement(\\\"mysqlx\\\", cmd, fields)\\n self.protocol.send_msg(msg_type, msg)\\n return RowResult(self)\",\n \"def query(self, query: str, values: Tuple = None) -> None:\\n cursor = self.connection.cursor()\\n cursor.execute(query, values)\\n result = cursor.fetchall()\\n cursor.close()\\n return result\",\n \"def _raw(self, query: Any, data: Any = None):\\n assert isinstance(query, str)\\n\\n conn = self._get_session()\\n try:\\n results = conn.execute(query)\\n\\n entity_items = []\\n for item in results:\\n entity = self.model_cls.to_entity(item)\\n entity.state_.mark_retrieved()\\n entity_items.append(entity)\\n\\n result = ResultSet(\\n offset=0,\\n limit=len(entity_items),\\n total=len(entity_items),\\n items=entity_items,\\n )\\n except DatabaseError as exc:\\n logger.error(f\\\"Error while running raw query: {exc}\\\")\\n raise\\n finally:\\n if not current_uow:\\n conn.commit()\\n conn.close()\\n\\n return result\",\n \"def query(self, query, cs_type=None, *parameters, **kwparameters):\\n cursor = self._cursor(cs_type)\\n try:\\n self._execute(cursor, query, parameters, kwparameters)\\n if cs_type in [\\\"SSCursor\\\", \\\"SSDictCursor\\\"]:\\n while 1:\\n try:\\n row = cursor.fetchone()\\n except Exception, e:\\n cursor.close()\\n raise e\\n if row:\\n yield row\\n else:\\n break\\n else:\\n yield [Row(row) if isinstance(row, dict) else row for row in cursor]\\n except Exception, e:\\n cursor.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8222279","0.7665415","0.76420516","0.7597238","0.7581581","0.75336605","0.75020784","0.75014627","0.7499659","0.7481231","0.7469864","0.7459134","0.74526256","0.7436286","0.7409394","0.7389792","0.73760194","0.7298189","0.72854406","0.7282933","0.72528124","0.7228975","0.71666473","0.7154071","0.7142645","0.71133345","0.70816755","0.70469224","0.7007278","0.69506156","0.6935742","0.6925394","0.6911915","0.6906911","0.690582","0.689136","0.68842816","0.68750256","0.68169165","0.6816377","0.679434","0.6778791","0.6759126","0.67482877","0.67481303","0.67374563","0.67306924","0.6729264","0.6729096","0.67128146","0.6706587","0.669726","0.6696921","0.66879344","0.66801685","0.66768974","0.6671884","0.6656017","0.66518945","0.66438746","0.6635801","0.662961","0.66288483","0.6628148","0.662425","0.6618321","0.660416","0.6600763","0.65939105","0.6587136","0.65858454","0.65858454","0.6585757","0.65797436","0.6577498","0.6562563","0.65610266","0.65555376","0.6552248","0.65512323","0.6544374","0.6544091","0.65400875","0.652207","0.651537","0.6500751","0.6499338","0.6498897","0.64954","0.64938784","0.64930445","0.64901775","0.64874625","0.64687353","0.6466278","0.6457103","0.6454987","0.64514977","0.64431643","0.64362997","0.643316"],"string":"[\n \"0.8222279\",\n \"0.7665415\",\n \"0.76420516\",\n \"0.7597238\",\n \"0.7581581\",\n \"0.75336605\",\n \"0.75020784\",\n \"0.75014627\",\n \"0.7499659\",\n \"0.7481231\",\n \"0.7469864\",\n \"0.7459134\",\n \"0.74526256\",\n \"0.7436286\",\n \"0.7409394\",\n \"0.7389792\",\n \"0.73760194\",\n \"0.7298189\",\n \"0.72854406\",\n \"0.7282933\",\n \"0.72528124\",\n \"0.7228975\",\n \"0.71666473\",\n \"0.7154071\",\n \"0.7142645\",\n \"0.71133345\",\n \"0.70816755\",\n \"0.70469224\",\n \"0.7007278\",\n \"0.69506156\",\n \"0.6935742\",\n \"0.6925394\",\n \"0.6911915\",\n \"0.6906911\",\n \"0.690582\",\n \"0.689136\",\n \"0.68842816\",\n \"0.68750256\",\n \"0.68169165\",\n \"0.6816377\",\n \"0.679434\",\n \"0.6778791\",\n \"0.6759126\",\n \"0.67482877\",\n \"0.67481303\",\n \"0.67374563\",\n \"0.67306924\",\n \"0.6729264\",\n \"0.6729096\",\n \"0.67128146\",\n \"0.6706587\",\n \"0.669726\",\n \"0.6696921\",\n \"0.66879344\",\n \"0.66801685\",\n \"0.66768974\",\n \"0.6671884\",\n \"0.6656017\",\n \"0.66518945\",\n \"0.66438746\",\n \"0.6635801\",\n \"0.662961\",\n \"0.66288483\",\n \"0.6628148\",\n \"0.662425\",\n \"0.6618321\",\n \"0.660416\",\n \"0.6600763\",\n \"0.65939105\",\n \"0.6587136\",\n \"0.65858454\",\n \"0.65858454\",\n \"0.6585757\",\n \"0.65797436\",\n \"0.6577498\",\n \"0.6562563\",\n \"0.65610266\",\n \"0.65555376\",\n \"0.6552248\",\n \"0.65512323\",\n \"0.6544374\",\n \"0.6544091\",\n \"0.65400875\",\n \"0.652207\",\n \"0.651537\",\n \"0.6500751\",\n \"0.6499338\",\n \"0.6498897\",\n \"0.64954\",\n \"0.64938784\",\n \"0.64930445\",\n \"0.64901775\",\n \"0.64874625\",\n \"0.64687353\",\n \"0.6466278\",\n \"0.6457103\",\n \"0.6454987\",\n \"0.64514977\",\n \"0.64431643\",\n \"0.64362997\",\n \"0.643316\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17773,"cells":{"query":{"kind":"string","value":"Execute the query, and return its result as rows"},"document":{"kind":"string","value":"def all(self, filter_deleted=False):\n objects = self.matching_objects(filter_deleted=filter_deleted)\n return objects"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def execute(self, query):\n with self.conn.cursor() as cur:\n # Execute the query\n try:\n cur.execute(query)\n except Exception as exc:\n print(\"Unable to execute query. Error was {0}\".format(str(exc)))\n exit()\n rows = cur.fetchall()\n return rows","def executeQuery(query):\n c = db.cursor()\n c.execute(query)\n rows = c.fetchall()\n db.close()\n return rows","def _run_query(self, query):\n cursor = self.conn.cursor()\n cursor.execute(query)\n return cursor.fetchall()","def _query_and_fetchall(self, query):\n with self._connect() as conn:\n cur = conn.cursor()\n cur.execute(query)\n results = cur.fetchall()\n\n return results","def query(self, sql):\n try:\n res_cursor = self.connection.execute(text(sql))\n except Exception as e: \n raise e(\"SQL execution error!\")\n \n rows = (Row(res_cursor.keys(), record) for record in res_cursor)\n results = RowsCollection(rows)\n return results","def query(self, sql):\n self.cursor.execute(sql)\n result = self.cursor.fetchall()\n return result","def get_rows(query, params=None):\n cur = get_cursor()\n cur.execute(query, params)\n rows = cur.fetchall()\n return rows","def execute_and_fetch(db, query):\n rows = []\n\n db.query(query)\n res = db.use_result()\n row = res.fetch_row()\n while row != ():\n rows.append(row[0])\n row = res.fetch_row()\n return rows","async def query(self, stmt, *args):\n\n with (await self.application.db.cursor()) as cur:\n await cur.execute(stmt, args)\n return [self.row_to_obj(row, cur)\n for row in await cur.fetchall()]","def exec_results(query):\n db = psycopg2.connect(database=DBNAME)\n c = db.cursor()\n c.execute(query)\n result = c.fetchall()\n db.close()\n return result","def run_query(query):\r\n db = psycopg2.connect('dbname=' + database)\r\n connect = db.cursor()\r\n connect.execute(query)\r\n rows = connect.fetchall()\r\n db.close()\r\n return rows","def execute_query(query):\n c.execute(query)\n return c.fetchall()","async def db_query(self, *args, **kwargs):\n rows = []\n async with self.db_pool.acquire() as conn:\n async with conn.cursor(cursor_factory=DictCursor) as cur:\n await cur.execute(*args, **kwargs)\n try:\n async for row in cur:\n rows.append(row)\n except psycopg2.ProgrammingError:\n # No results\n pass\n return rows","def run_query(conn, query):\n\tcur = conn.cursor()\n\tcur.execute(query)\n\trows = cur.fetchall()\n\treturn rows","async def db_execute(self, *args, **kwargs):\n rows = []\n async with self.db_pool.acquire() as conn:\n async with conn.cursor() as cur:\n await cur.execute(*args, **kwargs)\n try:\n async for row in cur:\n rows.append(row)\n except psycopg2.ProgrammingError:\n # No results\n pass\n return rows","async def fetchall(self):\n\n self._check_executed()\n\n if self._query_id == -1:\n msg = \"query didn't result in a resultset\"\n self._exception_handler(ProgrammingError, msg)\n\n result = self._rows[self.rownumber - self._offset :]\n self.rownumber = len(self._rows) + self._offset\n\n # slide the window over the resultset\n while await self.nextset():\n result += self._rows\n self.rownumber = len(self._rows) + self._offset\n print(\n \"\\nresult: \",\n self._rows,\n \"\\n\\n\",\n )\n return result","def _get_rows_from_query(self, query, data):\n to_return = []\n results = self.db_conn.cursor().execute(query, data)\n for result in results:\n to_return.append(result)\n return to_return","def query(self, query: str, *args, **kwargs):\n cursor = self._cursor()\n try:\n self._execute(cursor, query, args, kwargs)\n column_names = [d[0] for d in cursor.description]\n return [Row(zip(column_names, row)) for row in cursor]\n finally:\n cursor.close()","def execute(self, qry):\n def internal():\n print 'qry = ', qry\n self._cur = self.get_cursor()\n print 'self._cur = ', self._cur\n self._cur.execute(qry)\n # self.conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor).execute(qry)\n rows = self._cur.fetchall()\n return rows\n\n return self._retry(internal)","def get_query_results(self, conn, sql):\n\n cur = conn.cursor()\n cur.execute(sql)\n yield from cur.fetchall()","def _execute_query(self, query, values):\n with self as plasticDB:\n cursor = plasticDB.connection.cursor()\n cursor.execute(query,values)\n if not cursor.description:\n return []\n rs = RecordSet(initialData=cursor.fetchall(), recordType=next(zip(*cursor.description)))\n return rs","def run(self):\n rows = None\n if self.sql.startswith('select'):\n conn = self.table.connect()\n with conn.cursor() as curs:\n try:\n curs.execute(self.sql)\n except conn.DatabaseError as exc:\n error, = exc.args\n logging.error(f\"\"\"error executing {self.sql}:\n {error.code}\"\"\")\n self.excep = exc\n raise exc\n else:\n rows = curs.fetchall()\n # logging.critical(f\"\"\"executed {self.sql}\"\"\")\n self.result_exec = rows","def get_query_results(query):\n\n db, c = connect()\n c.execute(query)\n results = c.fetchall()\n db.close()\n return results","def _run_hive_query(cursor, query, has_results=False):\n _print_debug('Executing query: {}'.format(query))\n cursor.execute(query)\n\n if has_results:\n rows = cursor.fetchall()\n _print_debug('Query returned {} rows'.format(len(rows)))\n return rows","def execute_query(self) -> dict or list:\n pass","def _query_all(self, sql, data=None):\n\n conn = psycopg2.connect(self.connect_args)\n cur = conn.cursor()\n cur.execute(sql, data)\n result = cur.fetchall()\n cur.close()\n conn.close()\n return result","def get_results(self, table=None, query=None):\n def determine_query():\n \"\"\"\n Determines the query by checking if a query arg was passed.\n \"\"\"\n return query if query is not None \\\n else f\"SELECT * FROM '{table}';\"\n\n def __to_list(__results):\n \"\"\"\n Converts DB results to a (persistent) list.\n \"\"\"\n rows = []\n for row in __results:\n rows.append(row)\n \n __results.close()\n\n return rows\n\n\n # Determine query and execute\n results = __to_list(self.execute_query(determine_query()))\n\n return results","def query(self, query):\n\n config = {\n 'user' : self.db_user,\n 'password' : self.db_pwd,\n 'database' : self.db_name,\n 'host' : self.db_host,\n 'unix_socket' : self.db_socket,\n 'port' : self.db_port,\n 'charset' : 'utf8'\n }\n\n try: \n cnx = mysql.connector.connect(**config)\n cursor = cnx.cursor(dictionary=True) # To return rows as dictionaries.\n\n cursor.execute(query[0], query[1])\n \n # Putting the result rows in an array.\n result = []\n for row in cursor:\n result.append(row)\n\n except mysql.connector.Error as err:\n return None\n else:\n cursor.close()\n cnx.close()\n return result","def _exec1(self, sql):\n result = self._exec(sql)\n return [row[0] for row in result]","def execute(self, sql):\n with self.connection.cursor() as dbc:\n if sql[-1] != ';':\n sql += ';'\n dbc.execute(sql)\n self.last_row = dbc.lastrowid\n try:\n return dbc.fetchall()\n except:\n return","def _runQuery(self, query, connection):\n with connection as c:\n cursor = c.cursor()\n cursor.execute(query)\n results = []\n result = cursor.fetchone()\n while result is not None:\n numCols = len(result)\n item = {}\n for col in range(0, numCols):\n key = cursor.description[col][0]\n value = result[col]\n try:\n #handle oracle clob datatypes\n value = str(result[col].read())\n except AttributeError:\n pass\n item[key] = value\n #item = {cursor.description[col][0]: result[col] for col in range(0, numCols)}\n results.append(item)\n result = cursor.fetchone()\n return results","def get_result(query):\r\n db = psycopg2.connect(database=DBNAME)\r\n c = db.cursor()\r\n c.execute(query)\r\n results = c.fetchall()\r\n db.close()\r\n return results","def getResults(query):\n # Tells psycopg2 which database we are talking about\n db = psycopg2.connect(database=\"news\")\n # Curser is a local variable commonly use in sql/python connections\n curser = db.cursor()\n # Please refer to https://dev.mysql.com/doc/connector-python/en/\n # for more information about how this block of code works\n curser.execute(query)\n i = curser.fetchall()\n db.close()\n return i","def execute_query(conn, query):\r\n cur = conn.cursor()\r\n cur.execute(query)\r\n conn.commit()\r\n return cur.fetchall()","def _execute_query(self,\n sql_connection: Connection,\n sql_query: Union[str, Query]) -> List[Dict[str, Any]]:\n return_result: List[Dict[str, Any]] = []\n result: ResultProxy = sql_connection.execute(sql_query)\n if result and result.returns_rows:\n return_result: List[Dict[str, Any]] = [dict(row) for row in result]\n return return_result","def run_query(cur, query, show_results=False):\n num_rows = cur.execute(query)\n print('the query returned {} rows'.format(num_rows))\n if show_results:\n for row in cur.fetchall():\n print(row)","def execute_sql(self, result_type=MULTI):\r\n try:\r\n sql, params = self.as_sql()\r\n #import pdb; pdb.set_trace()\r\n if not sql:\r\n raise EmptyResultSet\r\n except EmptyResultSet:\r\n if result_type == MULTI:\r\n return iter([])\r\n else:\r\n return\r\n\r\n cursor = self.connection.cursor() \r\n cursor.execute(sql, params)\r\n\r\n if not result_type:\r\n return cursor\r\n if result_type == SINGLE:\r\n if self.query.ordering_aliases:\r\n return cursor.fetchone()[:-len(self.query.ordering_aliases)]\r\n return cursor.fetchone()\r\n\r\n # The MULTI case.\r\n if self.query.ordering_aliases:\r\n result = order_modified_iter(cursor, len(self.query.ordering_aliases),\r\n self.connection.features.empty_fetchmany_value)\r\n else:\r\n result = iter((lambda: cursor.fetchmany(GET_ITERATOR_CHUNK_SIZE)),\r\n self.connection.features.empty_fetchmany_value)\r\n if not self.connection.features.can_use_chunked_reads:\r\n # If we are using non-chunked reads, we return the same data\r\n # structure as normally, but ensure it is all read into memory\r\n # before going any further.\r\n return list(result)\r\n return result","def select_query(self):\n query = db.select([self.tables])\n print(query)\n ResultProxy = self.connection.execute(query)\n ResultSet = ResultProxy.fetchall()\n return ResultSet","async def fetch_row(self, query: str, args: Optional[Iterable]=None) -> List:\n stats.inc('queries', 'SQL')\n async with self.pool.acquire() as conn:\n async with conn.cursor() as cur:\n await cur.execute(query, args)\n ret = await cur.fetchone()\n return ret","def query(self, query, *args, **kwargs):\n # TODO: do not return an sqlite3 Errors\n\n if not self.begin():\n return None\n\n self.execute(query, *args, **kwargs)\n\n # create clever result\n ret = list()\n for row in self.cursor:\n if len(row) == 1: # (2)\n ret.append(row[0])\n else: # (3)\n ret.append(row)\n if len(ret) == 1: # (1)\n ret = ret[0]\n\n self.end()\n\n return ret","def query_fetchall(self, q, param=None):\r\n try:\r\n c = self.connection.cursor()\r\n if param is None:\r\n c.execute(q)\r\n else:\r\n c.execute(q, param)\r\n self.logger.log(logger.LogLevel.DEBUG, 'database.fetchall: %s | %s' % (q, param)) \r\n return c.fetchall()\r\n except Exception as e:\r\n self.logger.log(logger.LogLevel.ERROR, 'database.fetchall: %s. %s | %s' % (e, q, param))\r\n return False","def execute(self, sql, values=None):\n c = self.conn.cursor()\n self.lock.acquire()\n hasReturn = sql.lstrip().upper().startswith(\"SELECT\")\n\n result = []\n try:\n if values:\n c.executemany(sql, values)\n else:\n c.execute(sql)\n\n if hasReturn:\n result = c.fetchall()\n\n except Exception, e:\n Log.error(traceback.format_exc())\n self.conn.rollback()\n finally:\n self.lock.release()\n\n if hasReturn:\n return result","def runningwithqueries(query):\n print(\"\\nRunning Query: \" + str(query) + \"\\nResult :\\n\")\n crsr = cnxn.execute(query)\n columns = [column[0] for column in crsr.description]\n print(columns)\n for row in crsr.fetchall():\n print(row)\n crsr.close()","def query(self, sql, rowfactory=None, *args, **kwgs):\n return query(self.conn, sql, rowfactory, *args, **kwgs)","def execute_query(query):\n db = psycopg2.connect(\"dbname=news\")\n c = db.cursor()\n c.execute(query)\n results = c.fetchall()\n db.close()\n return results","def query(self, query, *args, **kwargs):\n c = self._conn.cursor()\n easy_query(c.execute, query, *args, **kwargs)\n if c.description is None:\n return c.rowcount\n return Results(c.description, c.fetchall())","def db_results(self, query):\n try:\n # Connect to database\n db = psycopg2.connect(\"dbname=news\")\n c = db.cursor()\n # executing query and getting all results\n c.execute(query)\n db_res = c.fetchall()\n # closing connection\n db.close()\n return db_res\n except Error:\n print (\"Error when attempting to connect to db\")","def sql_query_fetch_list(self,sql):\n\t\tif self.connected:\n#\t\t\t self.__cursor.execute(sql)\n#\t\t\t results = np.asarray(self.__cursor.fetchall())\n\t\t\tcur = self.__engine.execute(sql)\n\t\t\tresults = np.asarray(cur.fetchall())\n\t\t\tcur.close()\n\t\t\treturn results\n\t\telse:\n\t\t\tprint ('db not connected yet. Do connect first')","def perform_query(query):\r\n\r\n # setup and perform query\r\n client = boto3.client('athena')\r\n DATABASE = 'default'\r\n output='s3://ruesdefrance-query-results/'\r\n \r\n queryStart = client.start_query_execution(\r\n QueryString=query,\r\n QueryExecutionContext={\r\n 'Database': DATABASE\r\n },\r\n ResultConfiguration={\r\n 'OutputLocation': output,\r\n }\r\n )\r\n\r\n # get query execution ID\r\n queryId = queryStart['QueryExecutionId']\r\n \r\n # prepare results if request succeeds\r\n status = ''\r\n while status not in ['SUCCEEDED','FAILED']:\r\n test_exec = client.get_query_execution(QueryExecutionId = queryId)\r\n status = test_exec['QueryExecution']['Status']['State']\r\n if status == 'SUCCEEDED':\r\n results = client.get_query_results(QueryExecutionId = queryId)\r\n rows = []\r\n for row in results['ResultSet']['Rows']:\r\n #print(row)\r\n rows.append(row['Data'])\r\n elif status == 'FAILED':\r\n print('FAILED')\r\n time.sleep(1)\r\n\r\n # extract column names and content of rows \r\n columns = rows[0]\r\n rows = rows[1:]\r\n print(\"rows length {}\".format(len(rows)))\r\n print(rows)\r\n \r\n return rows","def _execute_query(self, sql):\n url = self.db_url + \"?\" + urlencode({'action': 'doQuery', 'SQL': sql})\n install_opener(build_opener(self.auth_handler, self.cookie_handler))\n response = urlopen(url)\n cookie_jar.save(ignore_discard=True)\n\n # Check for OK response\n line = response.readline()\n if bytes(line) != b\"#OK\\n\":\n raise Exception(response.readlines())\n\n # Skip rows until we reach QUERYTIMEOUT\n while True:\n line = bytes(response.readline())\n if line == b\"\":\n raise Exception(\"Unexpected end of file while reading result\"\n \"header\")\n elif line.startswith(b\"#QUERYTIMEOUT\"):\n break\n\n # Skip QUERYTIME\n if not(bytes(response.readline()).startswith(b\"#QUERYTIME\")):\n raise Exception(\"Don't understand result header!\")\n\n # Read column info\n # (also discards line with full list of column names)\n columns = []\n while True:\n line = bytes(response.readline())\n if not line.startswith(b\"#\"):\n break\n else:\n m = re.match(b\"^#COLUMN ([0-9]+) name=([\\w]+) \"\n b\"JDBC_TYPE=(-?[0-9]+) JDBC_TYPENAME=([\\w]+)\\n$\",\n line)\n if m is not None:\n columns.append(m.groups())\n else:\n raise Exception(\"Don't understand column info: \"+line)\n\n # Construct record type for the output\n types = [numpy_dtype[col[3]] for col in columns]\n try:\n # Python 2 compatible\n names = [col[1] for col in columns]\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\n except TypeError:\n # Python 3 compatible\n names = [col[1].decode() for col in columns]\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\n\n # Return the data as a record array\n return np.genfromtxt(response, dtype=dtype, delimiter=\",\")","def __get_results(self, query):\n return self.mysql.query_multi_with_fetchall_as_dict(query)","def returnAll(self):\n try:\n # self.checkValName()\n self.dbc.execute(self.query, self.val)\n self.results = self.dbc.fetchall()\n except MySQLdb.Error as e:\n print \"Query failed: %s \" % e\n raise","def query(self, query, *parameters, **kwparameters):\n cursor = self._cursor()\n try:\n self._execute(cursor, query, parameters, kwparameters)\n column_names = [d[0] for d in cursor.description]\n\n if self.grace_result:\n return [GraceDict(zip(column_names, row)) for row in cursor]\n else:\n return [zip(column_names, row) for row in cursor]\n finally:\n cursor.close()","def returnAll(self):\n try:\n # self.checkValName()\n self.conn.execute(self.query, self.val)\n self.results = self.cursor.fetchall()\n except Exception as e:\n print \"Query failed: %s \" % e\n raise","def run_query(query):\n db.query(query)\n dbResult = db.store_result()\n dbFetched = dbResult.fetch_row(maxrows = 0, how = 2)\n df = pd.DataFrame.from_records(dbFetched)\n return df","def get_rows(self, tbl):\n statmt = \"select * from %s\" % tbl\n self.cur.execute(statmt)\n rows = list(self.cur.fetchall())\n return rows","async def fetchall(self, query, connection=None):\n async with self.connection(connection) as conn:\n r = await conn.execute(query)\n return await r.fetchall()","def query(self):\n self.session_status()\n try:\n if self.keyword['limit'] == None:\n query = self.session.query(self.keyword['tablename']).filter(\n self.keyword['filter']).all()\n else:\n query = self.session.query(self.keyword['tablename']).filter(\n self.keyword['filter']).limit(self.keyword['limit']).all()\n except OperationalError:\n self.retry += 1\n if self.retry > self.max_retry_time:\n raise OperationalError, 'RETRY OUT'\n time.sleep(3)\n self.session.close()\n self.query()\n\n self.session.close()\n\n if not query:\n return []\n self.retry = 0\n return query","def do_query(self, query):\n query = query.replace('\\n', ' ')\n self.cursor.execute(query)\n return self.cursor.fetchall()","def _perform_query(self, from_row=0, max_rows=-1):\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\n results = result.get(\"results\", [])\n\n self._total_results = len(results)\n self._count_valid = True\n\n for item in results:\n yield self._doc_class(self._cb, item[\"id\"], item)","def execute_many(self, sql, args=None):\r\n args = args or None\r\n with Executer(self) as cursor:\r\n rows = cursor.executemany(sql, args)\r\n return rows","def execute(self, sql):\r\n try:\r\n with self.connection.cursor() as cursor:\r\n cursor.execute(sql)\r\n self.connection.commit()\r\n try:\r\n count = cursor.rowcount\r\n res = cursor.fetchall()\r\n except psycopg2.ProgrammingError:\r\n res = count\r\n return res\r\n except psycopg2.Error as e:\r\n # print(sql)\r\n raise e","def rpc_database_get_rows(self, *args):\n\t\targs = list(args)\n\t\toffset = 0\n\t\tfields = self.path.split('/')[1:-2]\n\t\tif len(args) == (len(fields) + 1):\n\t\t\toffset = (args.pop() * VIEW_ROW_COUNT)\n\t\tassert len(fields) == len(args)\n\t\ttable_name = self.path.split('/')[-2]\n\t\ttable = DATABASE_TABLE_OBJECTS.get(table_name)\n\t\tassert table\n\n\t\t# it's critical that the columns are in the order that the client is expecting\n\t\tcolumns = DATABASE_TABLES[table_name]\n\t\trows = []\n\t\tsession = db_manager.Session()\n\t\tquery = session.query(table)\n\t\tquery = query.filter_by(**dict(zip((f + '_id' for f in fields), args)))\n\t\tfor row in query[offset:offset + VIEW_ROW_COUNT]:\n\t\t\trows.append([getattr(row, c) for c in columns])\n\t\tsession.close()\n\t\tif not len(rows):\n\t\t\treturn None\n\t\treturn {'columns': columns, 'rows': rows}","def query(self, query, params=()):\n with self.get_pool().connection() as conn:\n c = conn.cursor()\n try:\n c.execute(query, params)\n conn.commit()\n except IntegrityError:\n conn.rollback()\n raise\n if c.description is None:\n return c.rowcount\n return Results(c.description, c.fetchall())","def execute(self,sql):\n # self.results = self.execute_silent(sql)\n # return self.results\n # sql = self.format_sql(sql, **kwargs)\n sql_list = sql.split(';')\n for stmt in sql_list:\n if stmt:\n stmt = stmt.strip()\n if len(stmt) < 10:\n break\n result = self.execute_silent(stmt)\n #if result is not None,It's select stmt.\n if result:\n return result","def get_query_results(query):\n try:\n dbconn = psycopg2.connect(database=\"news\")\n cursor = dbconn.cursor()\n cursor.execute(query)\n result = cursor.fetchall()\n dbconn.close()\n return result\n except Exception as e:\n print(e)\n exit(1)","async def all(\n self,\n statement: Executable,\n params: t.Optional[\n t.Union[t.Mapping[str, t.Any], t.Sequence[t.Mapping[str, t.Any]]]\n ] = None,\n execution_options: t.Optional[t.Mapping[str, t.Any]] = None,\n bind_arguments: t.Optional[t.Mapping[str, t.Any]] = None,\n ) -> t.List[t.Any]:\n result = await self.execute(\n statement, params, execution_options=execution_options, bind_arguments=bind_arguments\n )\n if isinstance(result, sa.engine.CursorResult):\n items = result.all()\n else:\n if (\n result.raw.context.compiled.compile_state\n and result.raw.context.compiled.compile_state.multi_row_eager_loaders\n ):\n result = result.unique()\n items = result.scalars().all()\n return items","def query(self, query, *variables, limit=None):\n self.cursor.execute(query, variables)\n try:\n if limit:\n return self.cursor.fetchmany(limit)\n else:\n return self.cursor.fetchall()\n except psycopg2.ProgrammingError:\n return None","def fetch_all(self, sql):\n result = []\n\n curs = self.q(sql, True)\n cols = curs.column_names\n for row in curs:\n row_result = {}\n for field in cols:\n k = cols.index(field)\n row_result[cols[k]] = row[k]\n #print cols[k], row[k]\n result.append(row_result)\n curs.close()\n return result","def query(cursor, query):\n out = []\n c = cursor.execute(query)\n out.append(c.fetchall())\n # will return list of tuples for each query\n return out","def get_execute_table(self, limit=None):\n query = self.select_all()\n self.cur.execute(query)\n if limit is None:\n result = self.cur.fetchall()\n else:\n result = self.cur.fetchmany(limit)\n return to_data_frame(result)","def get_execute_table(self, limit=None):\n query = self.select_all()\n self.cur.execute(query)\n if limit is None:\n result = self.cur.fetchall()\n else:\n result = self.cur.fetchmany(limit)\n return to_data_frame(result)","def fetch_all(cls, sql, **kwargs):\n conn = kwargs['conn']\n\n cursor = conn.cursor(dictionary=True, buffered=False)\n for _ in cursor.execute(sql, kwargs.get('args'), multi=True):\n pass\n\n result = cursor.fetchall()\n cls.close(conn, cursor)\n\n return result","def execute(self):\n if self.sql is None:\n self.sql = self.construct_query()\n # Only SQL strings can be split, not (e.g.) SQLAlchemy statements.\n if self.multiple_statements and isinstance(self.sql, str):\n statements = self._split_sql()\n else:\n statements = [self.sql]\n single_statement = True if len(statements) == 1 and self.filename else False\n try:\n for statement in statements:\n result_proxy = self.cm.conn.execute(statement)\n log_string = self.filename if single_statement else str(statement)[:25]\n self.logger.info(\"Executed {} against {}\".format(log_string, self.cm.db))\n if result_proxy.cursor:\n return self.fetch_results(result_proxy)\n except Exception as e:\n self.logger.exception(e)\n raise","def query(self, query, params=None):\n cur = self.connection.cursor()\n cur.execute(query, params)\n result = [list(row) for row in cur.fetchall()]\n colnames = [desc[0] for desc in cur.description]\n cur.close()\n return colnames, result","def iter(self, query: str, *args, **kwargs):\n self._ensure_connected()\n cursor = SSCursor(self._db)\n try:\n self._execute(cursor, query, args, kwargs)\n column_names = [d[0] for d in cursor.description]\n for row in cursor:\n yield Row(zip(column_names, row))\n finally:\n cursor.close()","def read(self, query):\r\n t1 = time.time()\r\n if self.database in ['redshift', 'postgres']:\r\n ret = postgres_helper.fetchall(config=self.conf, sql=query)\r\n else:\r\n raise Exception(\"database not supported yet: '{}'\"\r\n .format(self.database))\r\n t2 = time.time()\r\n t = t2 - t1\r\n print('Finished in {:.2f} seconds.'.format(t))\r\n return ret","def execute_query(self, *args, **kwargs):","def query(self, q, *args):\n\n if self.dbtype == 'pg':\n print q\n return self.db.execute(q, *args).fetchall()\n else:\n cur = self.db.cursor()\n try:\n print q\n print args\n if args:\n cur.execute(q, args)\n else:\n cur.execute(q)\n ret = cur.fetchall()\n return ret\n except:\n self.db.rollback()\n raise\n finally:\n cur.close()","def execute_fetchall(self, sql, sql_args=None, dictcursor=False):\n # Check that sql arguments have the correct type\n self._check_sql_args(sql_args)\n # Execute the query\n try:\n pgcursor = self.get_postgres_cursor(dictcursor)\n pgcursor.execute(sql, sql_args)\n result = pgcursor.fetchall()\n self._connection.commit()\n except PostgresError, e:\n self._connection.rollback()\n raise RuntimeError(\"Error running SQL query: %s\", str(e))\n finally:\n pgcursor.close()\n return result","def read_items(self, query=None, val=None):\n with self.conn:\n with self.conn.cursor() as curr:\n curr.execute(query, val)\n rows = curr.fetchall()\n self.conn.close()\n return rows","def execute(sql, args=()):\n res = con.execute(sql, args)\n con.commit()\n return res.fetchall()","def _runsql(self):\n self.logger.info(\"Running SQL where sequence > %s\" % self.seq)\n try:\n results = self.engine.execute(self.sql,\n (self.max_rows, self.seq)).fetchall()\n except sqlalchemy.exc.ProgrammingError, err:\n self.logger.critical(\"Error connecting to DB : %s\" % err)\n return None\n self.logger.info('Fetched %d rows from DB' % len(results))\n if not len(results):\n self.logger.info(\"No rows returned from DB. Finished loading\")\n return False\n return results","def do(self, executor):\n sql, kw = self._assemble()\n return executor.execute(\n sql, kw\n )","def get(self, question, rows):\n con.query(question)\n result = con.use_result()\n return result.fetch_row(rows)","def cursorExecute(self, query):\n return self.cursor.execute(query)","def execute_query(query):\n\n db = psycopg2.connect(database=\"news\")\n cursor = db.cursor()\n cursor.execute(query)\n query_result = cursor.fetchall()\n db.close()\n return query_result","def fetch_all(self, sql: str, values: Union[Tuple, Mapping] = None) -> Any:\n with self.cursor() as cursor:\n cursor.execute(sql, values)\n return cursor.fetchall()","def fetchSqlRecords(self, sql):\r\n cursor = self.c.cursor()\r\n cursor.execute(sql)\r\n r = cursor.fetchall()\r\n cursor.close()\r\n return r","def execute(self, sql):\n return self.db.execute(sql)","def quarry(conn, query, params=None, returns=True):\n # Create cursor object\n cur = conn.cursor()\n\n # Execute the query\n if params is not None:\n cur.execute(query, params)\n else:\n cur.execute(query)\n\n # If returns is True, assign the query to variable\n if returns:\n results = cur.fetchall()\n pprint(results)\n\n # If not, do not create variable to return\n # Close the cursor and the connection\n cur.close()\n conn.commit()\n\n # If returns is True, return the return value\n if returns:\n return results","def select_records(conn, qry, row_cnt=None):\n result = None\n try:\n cur = conn.cursor()\n cur.execute(qry)\n result = PostgresStuff.get_results(cur, row_cnt)\n except (Exception, psycopg2.DatabaseError) as error:\n print(error)\n finally:\n if cur is not None:\n cur.close()\n return result","def run_select_query(query, args = None):\n cursor = db.get_cursor()\n cursor.execute(query, args)\n return cursor.fetchall()","def execute(self) -> TableResult:\n self._t_env._before_execute()\n return TableResult(self._j_statement_set.execute())","def fetch_data_from_db(query):\n cursor.execute(query)\n result = cursor.fetchall()\n return result","def get_rows(self, logger):\n engine = self.connecting_database(logger)\n\n # Returning all rows from the table using Pandas\n logger.info(\"Getting rows . . .\")\n df = pd.read_sql('bt_challenge_boa', con=engine)\n\n logger.info(\"Total Rows: {}\".format(str(len(df))))\n logger.info(\"Saving the CSV file . . .\")\n df.to_csv(self.path + self.outpufile, index=False, doublequote=True)","def execute_read_query(connection, query):\n cursor = connection.cursor()\n result = None\n try:\n cursor.execute(query)\n result = cursor.fetchall()\n return result\n except psycopg2.OperationalError as e:\n print(f\"The error '{e}' occurred\")","def get_row_result(self, cmd, fields):\n msg_type, msg = self.protocol.build_execute_statement(\"mysqlx\", cmd, fields)\n self.protocol.send_msg(msg_type, msg)\n return RowResult(self)","def query(self, query: str, values: Tuple = None) -> None:\n cursor = self.connection.cursor()\n cursor.execute(query, values)\n result = cursor.fetchall()\n cursor.close()\n return result","def _raw(self, query: Any, data: Any = None):\n assert isinstance(query, str)\n\n conn = self._get_session()\n try:\n results = conn.execute(query)\n\n entity_items = []\n for item in results:\n entity = self.model_cls.to_entity(item)\n entity.state_.mark_retrieved()\n entity_items.append(entity)\n\n result = ResultSet(\n offset=0,\n limit=len(entity_items),\n total=len(entity_items),\n items=entity_items,\n )\n except DatabaseError as exc:\n logger.error(f\"Error while running raw query: {exc}\")\n raise\n finally:\n if not current_uow:\n conn.commit()\n conn.close()\n\n return result","def query(self, query, cs_type=None, *parameters, **kwparameters):\n cursor = self._cursor(cs_type)\n try:\n self._execute(cursor, query, parameters, kwparameters)\n if cs_type in [\"SSCursor\", \"SSDictCursor\"]:\n while 1:\n try:\n row = cursor.fetchone()\n except Exception, e:\n cursor.close()\n raise e\n if row:\n yield row\n else:\n break\n else:\n yield [Row(row) if isinstance(row, dict) else row for row in cursor]\n except Exception, e:\n cursor.close()"],"string":"[\n \"def execute(self, query):\\n with self.conn.cursor() as cur:\\n # Execute the query\\n try:\\n cur.execute(query)\\n except Exception as exc:\\n print(\\\"Unable to execute query. Error was {0}\\\".format(str(exc)))\\n exit()\\n rows = cur.fetchall()\\n return rows\",\n \"def executeQuery(query):\\n c = db.cursor()\\n c.execute(query)\\n rows = c.fetchall()\\n db.close()\\n return rows\",\n \"def _run_query(self, query):\\n cursor = self.conn.cursor()\\n cursor.execute(query)\\n return cursor.fetchall()\",\n \"def _query_and_fetchall(self, query):\\n with self._connect() as conn:\\n cur = conn.cursor()\\n cur.execute(query)\\n results = cur.fetchall()\\n\\n return results\",\n \"def query(self, sql):\\n try:\\n res_cursor = self.connection.execute(text(sql))\\n except Exception as e: \\n raise e(\\\"SQL execution error!\\\")\\n \\n rows = (Row(res_cursor.keys(), record) for record in res_cursor)\\n results = RowsCollection(rows)\\n return results\",\n \"def query(self, sql):\\n self.cursor.execute(sql)\\n result = self.cursor.fetchall()\\n return result\",\n \"def get_rows(query, params=None):\\n cur = get_cursor()\\n cur.execute(query, params)\\n rows = cur.fetchall()\\n return rows\",\n \"def execute_and_fetch(db, query):\\n rows = []\\n\\n db.query(query)\\n res = db.use_result()\\n row = res.fetch_row()\\n while row != ():\\n rows.append(row[0])\\n row = res.fetch_row()\\n return rows\",\n \"async def query(self, stmt, *args):\\n\\n with (await self.application.db.cursor()) as cur:\\n await cur.execute(stmt, args)\\n return [self.row_to_obj(row, cur)\\n for row in await cur.fetchall()]\",\n \"def exec_results(query):\\n db = psycopg2.connect(database=DBNAME)\\n c = db.cursor()\\n c.execute(query)\\n result = c.fetchall()\\n db.close()\\n return result\",\n \"def run_query(query):\\r\\n db = psycopg2.connect('dbname=' + database)\\r\\n connect = db.cursor()\\r\\n connect.execute(query)\\r\\n rows = connect.fetchall()\\r\\n db.close()\\r\\n return rows\",\n \"def execute_query(query):\\n c.execute(query)\\n return c.fetchall()\",\n \"async def db_query(self, *args, **kwargs):\\n rows = []\\n async with self.db_pool.acquire() as conn:\\n async with conn.cursor(cursor_factory=DictCursor) as cur:\\n await cur.execute(*args, **kwargs)\\n try:\\n async for row in cur:\\n rows.append(row)\\n except psycopg2.ProgrammingError:\\n # No results\\n pass\\n return rows\",\n \"def run_query(conn, query):\\n\\tcur = conn.cursor()\\n\\tcur.execute(query)\\n\\trows = cur.fetchall()\\n\\treturn rows\",\n \"async def db_execute(self, *args, **kwargs):\\n rows = []\\n async with self.db_pool.acquire() as conn:\\n async with conn.cursor() as cur:\\n await cur.execute(*args, **kwargs)\\n try:\\n async for row in cur:\\n rows.append(row)\\n except psycopg2.ProgrammingError:\\n # No results\\n pass\\n return rows\",\n \"async def fetchall(self):\\n\\n self._check_executed()\\n\\n if self._query_id == -1:\\n msg = \\\"query didn't result in a resultset\\\"\\n self._exception_handler(ProgrammingError, msg)\\n\\n result = self._rows[self.rownumber - self._offset :]\\n self.rownumber = len(self._rows) + self._offset\\n\\n # slide the window over the resultset\\n while await self.nextset():\\n result += self._rows\\n self.rownumber = len(self._rows) + self._offset\\n print(\\n \\\"\\\\nresult: \\\",\\n self._rows,\\n \\\"\\\\n\\\\n\\\",\\n )\\n return result\",\n \"def _get_rows_from_query(self, query, data):\\n to_return = []\\n results = self.db_conn.cursor().execute(query, data)\\n for result in results:\\n to_return.append(result)\\n return to_return\",\n \"def query(self, query: str, *args, **kwargs):\\n cursor = self._cursor()\\n try:\\n self._execute(cursor, query, args, kwargs)\\n column_names = [d[0] for d in cursor.description]\\n return [Row(zip(column_names, row)) for row in cursor]\\n finally:\\n cursor.close()\",\n \"def execute(self, qry):\\n def internal():\\n print 'qry = ', qry\\n self._cur = self.get_cursor()\\n print 'self._cur = ', self._cur\\n self._cur.execute(qry)\\n # self.conn.cursor(cursor_factory=psycopg2.extras.RealDictCursor).execute(qry)\\n rows = self._cur.fetchall()\\n return rows\\n\\n return self._retry(internal)\",\n \"def get_query_results(self, conn, sql):\\n\\n cur = conn.cursor()\\n cur.execute(sql)\\n yield from cur.fetchall()\",\n \"def _execute_query(self, query, values):\\n with self as plasticDB:\\n cursor = plasticDB.connection.cursor()\\n cursor.execute(query,values)\\n if not cursor.description:\\n return []\\n rs = RecordSet(initialData=cursor.fetchall(), recordType=next(zip(*cursor.description)))\\n return rs\",\n \"def run(self):\\n rows = None\\n if self.sql.startswith('select'):\\n conn = self.table.connect()\\n with conn.cursor() as curs:\\n try:\\n curs.execute(self.sql)\\n except conn.DatabaseError as exc:\\n error, = exc.args\\n logging.error(f\\\"\\\"\\\"error executing {self.sql}:\\n {error.code}\\\"\\\"\\\")\\n self.excep = exc\\n raise exc\\n else:\\n rows = curs.fetchall()\\n # logging.critical(f\\\"\\\"\\\"executed {self.sql}\\\"\\\"\\\")\\n self.result_exec = rows\",\n \"def get_query_results(query):\\n\\n db, c = connect()\\n c.execute(query)\\n results = c.fetchall()\\n db.close()\\n return results\",\n \"def _run_hive_query(cursor, query, has_results=False):\\n _print_debug('Executing query: {}'.format(query))\\n cursor.execute(query)\\n\\n if has_results:\\n rows = cursor.fetchall()\\n _print_debug('Query returned {} rows'.format(len(rows)))\\n return rows\",\n \"def execute_query(self) -> dict or list:\\n pass\",\n \"def _query_all(self, sql, data=None):\\n\\n conn = psycopg2.connect(self.connect_args)\\n cur = conn.cursor()\\n cur.execute(sql, data)\\n result = cur.fetchall()\\n cur.close()\\n conn.close()\\n return result\",\n \"def get_results(self, table=None, query=None):\\n def determine_query():\\n \\\"\\\"\\\"\\n Determines the query by checking if a query arg was passed.\\n \\\"\\\"\\\"\\n return query if query is not None \\\\\\n else f\\\"SELECT * FROM '{table}';\\\"\\n\\n def __to_list(__results):\\n \\\"\\\"\\\"\\n Converts DB results to a (persistent) list.\\n \\\"\\\"\\\"\\n rows = []\\n for row in __results:\\n rows.append(row)\\n \\n __results.close()\\n\\n return rows\\n\\n\\n # Determine query and execute\\n results = __to_list(self.execute_query(determine_query()))\\n\\n return results\",\n \"def query(self, query):\\n\\n config = {\\n 'user' : self.db_user,\\n 'password' : self.db_pwd,\\n 'database' : self.db_name,\\n 'host' : self.db_host,\\n 'unix_socket' : self.db_socket,\\n 'port' : self.db_port,\\n 'charset' : 'utf8'\\n }\\n\\n try: \\n cnx = mysql.connector.connect(**config)\\n cursor = cnx.cursor(dictionary=True) # To return rows as dictionaries.\\n\\n cursor.execute(query[0], query[1])\\n \\n # Putting the result rows in an array.\\n result = []\\n for row in cursor:\\n result.append(row)\\n\\n except mysql.connector.Error as err:\\n return None\\n else:\\n cursor.close()\\n cnx.close()\\n return result\",\n \"def _exec1(self, sql):\\n result = self._exec(sql)\\n return [row[0] for row in result]\",\n \"def execute(self, sql):\\n with self.connection.cursor() as dbc:\\n if sql[-1] != ';':\\n sql += ';'\\n dbc.execute(sql)\\n self.last_row = dbc.lastrowid\\n try:\\n return dbc.fetchall()\\n except:\\n return\",\n \"def _runQuery(self, query, connection):\\n with connection as c:\\n cursor = c.cursor()\\n cursor.execute(query)\\n results = []\\n result = cursor.fetchone()\\n while result is not None:\\n numCols = len(result)\\n item = {}\\n for col in range(0, numCols):\\n key = cursor.description[col][0]\\n value = result[col]\\n try:\\n #handle oracle clob datatypes\\n value = str(result[col].read())\\n except AttributeError:\\n pass\\n item[key] = value\\n #item = {cursor.description[col][0]: result[col] for col in range(0, numCols)}\\n results.append(item)\\n result = cursor.fetchone()\\n return results\",\n \"def get_result(query):\\r\\n db = psycopg2.connect(database=DBNAME)\\r\\n c = db.cursor()\\r\\n c.execute(query)\\r\\n results = c.fetchall()\\r\\n db.close()\\r\\n return results\",\n \"def getResults(query):\\n # Tells psycopg2 which database we are talking about\\n db = psycopg2.connect(database=\\\"news\\\")\\n # Curser is a local variable commonly use in sql/python connections\\n curser = db.cursor()\\n # Please refer to https://dev.mysql.com/doc/connector-python/en/\\n # for more information about how this block of code works\\n curser.execute(query)\\n i = curser.fetchall()\\n db.close()\\n return i\",\n \"def execute_query(conn, query):\\r\\n cur = conn.cursor()\\r\\n cur.execute(query)\\r\\n conn.commit()\\r\\n return cur.fetchall()\",\n \"def _execute_query(self,\\n sql_connection: Connection,\\n sql_query: Union[str, Query]) -> List[Dict[str, Any]]:\\n return_result: List[Dict[str, Any]] = []\\n result: ResultProxy = sql_connection.execute(sql_query)\\n if result and result.returns_rows:\\n return_result: List[Dict[str, Any]] = [dict(row) for row in result]\\n return return_result\",\n \"def run_query(cur, query, show_results=False):\\n num_rows = cur.execute(query)\\n print('the query returned {} rows'.format(num_rows))\\n if show_results:\\n for row in cur.fetchall():\\n print(row)\",\n \"def execute_sql(self, result_type=MULTI):\\r\\n try:\\r\\n sql, params = self.as_sql()\\r\\n #import pdb; pdb.set_trace()\\r\\n if not sql:\\r\\n raise EmptyResultSet\\r\\n except EmptyResultSet:\\r\\n if result_type == MULTI:\\r\\n return iter([])\\r\\n else:\\r\\n return\\r\\n\\r\\n cursor = self.connection.cursor() \\r\\n cursor.execute(sql, params)\\r\\n\\r\\n if not result_type:\\r\\n return cursor\\r\\n if result_type == SINGLE:\\r\\n if self.query.ordering_aliases:\\r\\n return cursor.fetchone()[:-len(self.query.ordering_aliases)]\\r\\n return cursor.fetchone()\\r\\n\\r\\n # The MULTI case.\\r\\n if self.query.ordering_aliases:\\r\\n result = order_modified_iter(cursor, len(self.query.ordering_aliases),\\r\\n self.connection.features.empty_fetchmany_value)\\r\\n else:\\r\\n result = iter((lambda: cursor.fetchmany(GET_ITERATOR_CHUNK_SIZE)),\\r\\n self.connection.features.empty_fetchmany_value)\\r\\n if not self.connection.features.can_use_chunked_reads:\\r\\n # If we are using non-chunked reads, we return the same data\\r\\n # structure as normally, but ensure it is all read into memory\\r\\n # before going any further.\\r\\n return list(result)\\r\\n return result\",\n \"def select_query(self):\\n query = db.select([self.tables])\\n print(query)\\n ResultProxy = self.connection.execute(query)\\n ResultSet = ResultProxy.fetchall()\\n return ResultSet\",\n \"async def fetch_row(self, query: str, args: Optional[Iterable]=None) -> List:\\n stats.inc('queries', 'SQL')\\n async with self.pool.acquire() as conn:\\n async with conn.cursor() as cur:\\n await cur.execute(query, args)\\n ret = await cur.fetchone()\\n return ret\",\n \"def query(self, query, *args, **kwargs):\\n # TODO: do not return an sqlite3 Errors\\n\\n if not self.begin():\\n return None\\n\\n self.execute(query, *args, **kwargs)\\n\\n # create clever result\\n ret = list()\\n for row in self.cursor:\\n if len(row) == 1: # (2)\\n ret.append(row[0])\\n else: # (3)\\n ret.append(row)\\n if len(ret) == 1: # (1)\\n ret = ret[0]\\n\\n self.end()\\n\\n return ret\",\n \"def query_fetchall(self, q, param=None):\\r\\n try:\\r\\n c = self.connection.cursor()\\r\\n if param is None:\\r\\n c.execute(q)\\r\\n else:\\r\\n c.execute(q, param)\\r\\n self.logger.log(logger.LogLevel.DEBUG, 'database.fetchall: %s | %s' % (q, param)) \\r\\n return c.fetchall()\\r\\n except Exception as e:\\r\\n self.logger.log(logger.LogLevel.ERROR, 'database.fetchall: %s. %s | %s' % (e, q, param))\\r\\n return False\",\n \"def execute(self, sql, values=None):\\n c = self.conn.cursor()\\n self.lock.acquire()\\n hasReturn = sql.lstrip().upper().startswith(\\\"SELECT\\\")\\n\\n result = []\\n try:\\n if values:\\n c.executemany(sql, values)\\n else:\\n c.execute(sql)\\n\\n if hasReturn:\\n result = c.fetchall()\\n\\n except Exception, e:\\n Log.error(traceback.format_exc())\\n self.conn.rollback()\\n finally:\\n self.lock.release()\\n\\n if hasReturn:\\n return result\",\n \"def runningwithqueries(query):\\n print(\\\"\\\\nRunning Query: \\\" + str(query) + \\\"\\\\nResult :\\\\n\\\")\\n crsr = cnxn.execute(query)\\n columns = [column[0] for column in crsr.description]\\n print(columns)\\n for row in crsr.fetchall():\\n print(row)\\n crsr.close()\",\n \"def query(self, sql, rowfactory=None, *args, **kwgs):\\n return query(self.conn, sql, rowfactory, *args, **kwgs)\",\n \"def execute_query(query):\\n db = psycopg2.connect(\\\"dbname=news\\\")\\n c = db.cursor()\\n c.execute(query)\\n results = c.fetchall()\\n db.close()\\n return results\",\n \"def query(self, query, *args, **kwargs):\\n c = self._conn.cursor()\\n easy_query(c.execute, query, *args, **kwargs)\\n if c.description is None:\\n return c.rowcount\\n return Results(c.description, c.fetchall())\",\n \"def db_results(self, query):\\n try:\\n # Connect to database\\n db = psycopg2.connect(\\\"dbname=news\\\")\\n c = db.cursor()\\n # executing query and getting all results\\n c.execute(query)\\n db_res = c.fetchall()\\n # closing connection\\n db.close()\\n return db_res\\n except Error:\\n print (\\\"Error when attempting to connect to db\\\")\",\n \"def sql_query_fetch_list(self,sql):\\n\\t\\tif self.connected:\\n#\\t\\t\\t self.__cursor.execute(sql)\\n#\\t\\t\\t results = np.asarray(self.__cursor.fetchall())\\n\\t\\t\\tcur = self.__engine.execute(sql)\\n\\t\\t\\tresults = np.asarray(cur.fetchall())\\n\\t\\t\\tcur.close()\\n\\t\\t\\treturn results\\n\\t\\telse:\\n\\t\\t\\tprint ('db not connected yet. Do connect first')\",\n \"def perform_query(query):\\r\\n\\r\\n # setup and perform query\\r\\n client = boto3.client('athena')\\r\\n DATABASE = 'default'\\r\\n output='s3://ruesdefrance-query-results/'\\r\\n \\r\\n queryStart = client.start_query_execution(\\r\\n QueryString=query,\\r\\n QueryExecutionContext={\\r\\n 'Database': DATABASE\\r\\n },\\r\\n ResultConfiguration={\\r\\n 'OutputLocation': output,\\r\\n }\\r\\n )\\r\\n\\r\\n # get query execution ID\\r\\n queryId = queryStart['QueryExecutionId']\\r\\n \\r\\n # prepare results if request succeeds\\r\\n status = ''\\r\\n while status not in ['SUCCEEDED','FAILED']:\\r\\n test_exec = client.get_query_execution(QueryExecutionId = queryId)\\r\\n status = test_exec['QueryExecution']['Status']['State']\\r\\n if status == 'SUCCEEDED':\\r\\n results = client.get_query_results(QueryExecutionId = queryId)\\r\\n rows = []\\r\\n for row in results['ResultSet']['Rows']:\\r\\n #print(row)\\r\\n rows.append(row['Data'])\\r\\n elif status == 'FAILED':\\r\\n print('FAILED')\\r\\n time.sleep(1)\\r\\n\\r\\n # extract column names and content of rows \\r\\n columns = rows[0]\\r\\n rows = rows[1:]\\r\\n print(\\\"rows length {}\\\".format(len(rows)))\\r\\n print(rows)\\r\\n \\r\\n return rows\",\n \"def _execute_query(self, sql):\\n url = self.db_url + \\\"?\\\" + urlencode({'action': 'doQuery', 'SQL': sql})\\n install_opener(build_opener(self.auth_handler, self.cookie_handler))\\n response = urlopen(url)\\n cookie_jar.save(ignore_discard=True)\\n\\n # Check for OK response\\n line = response.readline()\\n if bytes(line) != b\\\"#OK\\\\n\\\":\\n raise Exception(response.readlines())\\n\\n # Skip rows until we reach QUERYTIMEOUT\\n while True:\\n line = bytes(response.readline())\\n if line == b\\\"\\\":\\n raise Exception(\\\"Unexpected end of file while reading result\\\"\\n \\\"header\\\")\\n elif line.startswith(b\\\"#QUERYTIMEOUT\\\"):\\n break\\n\\n # Skip QUERYTIME\\n if not(bytes(response.readline()).startswith(b\\\"#QUERYTIME\\\")):\\n raise Exception(\\\"Don't understand result header!\\\")\\n\\n # Read column info\\n # (also discards line with full list of column names)\\n columns = []\\n while True:\\n line = bytes(response.readline())\\n if not line.startswith(b\\\"#\\\"):\\n break\\n else:\\n m = re.match(b\\\"^#COLUMN ([0-9]+) name=([\\\\w]+) \\\"\\n b\\\"JDBC_TYPE=(-?[0-9]+) JDBC_TYPENAME=([\\\\w]+)\\\\n$\\\",\\n line)\\n if m is not None:\\n columns.append(m.groups())\\n else:\\n raise Exception(\\\"Don't understand column info: \\\"+line)\\n\\n # Construct record type for the output\\n types = [numpy_dtype[col[3]] for col in columns]\\n try:\\n # Python 2 compatible\\n names = [col[1] for col in columns]\\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\\n except TypeError:\\n # Python 3 compatible\\n names = [col[1].decode() for col in columns]\\n dtype = np.dtype([(n, t) for n, t in zip(names, types)])\\n\\n # Return the data as a record array\\n return np.genfromtxt(response, dtype=dtype, delimiter=\\\",\\\")\",\n \"def __get_results(self, query):\\n return self.mysql.query_multi_with_fetchall_as_dict(query)\",\n \"def returnAll(self):\\n try:\\n # self.checkValName()\\n self.dbc.execute(self.query, self.val)\\n self.results = self.dbc.fetchall()\\n except MySQLdb.Error as e:\\n print \\\"Query failed: %s \\\" % e\\n raise\",\n \"def query(self, query, *parameters, **kwparameters):\\n cursor = self._cursor()\\n try:\\n self._execute(cursor, query, parameters, kwparameters)\\n column_names = [d[0] for d in cursor.description]\\n\\n if self.grace_result:\\n return [GraceDict(zip(column_names, row)) for row in cursor]\\n else:\\n return [zip(column_names, row) for row in cursor]\\n finally:\\n cursor.close()\",\n \"def returnAll(self):\\n try:\\n # self.checkValName()\\n self.conn.execute(self.query, self.val)\\n self.results = self.cursor.fetchall()\\n except Exception as e:\\n print \\\"Query failed: %s \\\" % e\\n raise\",\n \"def run_query(query):\\n db.query(query)\\n dbResult = db.store_result()\\n dbFetched = dbResult.fetch_row(maxrows = 0, how = 2)\\n df = pd.DataFrame.from_records(dbFetched)\\n return df\",\n \"def get_rows(self, tbl):\\n statmt = \\\"select * from %s\\\" % tbl\\n self.cur.execute(statmt)\\n rows = list(self.cur.fetchall())\\n return rows\",\n \"async def fetchall(self, query, connection=None):\\n async with self.connection(connection) as conn:\\n r = await conn.execute(query)\\n return await r.fetchall()\",\n \"def query(self):\\n self.session_status()\\n try:\\n if self.keyword['limit'] == None:\\n query = self.session.query(self.keyword['tablename']).filter(\\n self.keyword['filter']).all()\\n else:\\n query = self.session.query(self.keyword['tablename']).filter(\\n self.keyword['filter']).limit(self.keyword['limit']).all()\\n except OperationalError:\\n self.retry += 1\\n if self.retry > self.max_retry_time:\\n raise OperationalError, 'RETRY OUT'\\n time.sleep(3)\\n self.session.close()\\n self.query()\\n\\n self.session.close()\\n\\n if not query:\\n return []\\n self.retry = 0\\n return query\",\n \"def do_query(self, query):\\n query = query.replace('\\\\n', ' ')\\n self.cursor.execute(query)\\n return self.cursor.fetchall()\",\n \"def _perform_query(self, from_row=0, max_rows=-1):\\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\\n results = result.get(\\\"results\\\", [])\\n\\n self._total_results = len(results)\\n self._count_valid = True\\n\\n for item in results:\\n yield self._doc_class(self._cb, item[\\\"id\\\"], item)\",\n \"def execute_many(self, sql, args=None):\\r\\n args = args or None\\r\\n with Executer(self) as cursor:\\r\\n rows = cursor.executemany(sql, args)\\r\\n return rows\",\n \"def execute(self, sql):\\r\\n try:\\r\\n with self.connection.cursor() as cursor:\\r\\n cursor.execute(sql)\\r\\n self.connection.commit()\\r\\n try:\\r\\n count = cursor.rowcount\\r\\n res = cursor.fetchall()\\r\\n except psycopg2.ProgrammingError:\\r\\n res = count\\r\\n return res\\r\\n except psycopg2.Error as e:\\r\\n # print(sql)\\r\\n raise e\",\n \"def rpc_database_get_rows(self, *args):\\n\\t\\targs = list(args)\\n\\t\\toffset = 0\\n\\t\\tfields = self.path.split('/')[1:-2]\\n\\t\\tif len(args) == (len(fields) + 1):\\n\\t\\t\\toffset = (args.pop() * VIEW_ROW_COUNT)\\n\\t\\tassert len(fields) == len(args)\\n\\t\\ttable_name = self.path.split('/')[-2]\\n\\t\\ttable = DATABASE_TABLE_OBJECTS.get(table_name)\\n\\t\\tassert table\\n\\n\\t\\t# it's critical that the columns are in the order that the client is expecting\\n\\t\\tcolumns = DATABASE_TABLES[table_name]\\n\\t\\trows = []\\n\\t\\tsession = db_manager.Session()\\n\\t\\tquery = session.query(table)\\n\\t\\tquery = query.filter_by(**dict(zip((f + '_id' for f in fields), args)))\\n\\t\\tfor row in query[offset:offset + VIEW_ROW_COUNT]:\\n\\t\\t\\trows.append([getattr(row, c) for c in columns])\\n\\t\\tsession.close()\\n\\t\\tif not len(rows):\\n\\t\\t\\treturn None\\n\\t\\treturn {'columns': columns, 'rows': rows}\",\n \"def query(self, query, params=()):\\n with self.get_pool().connection() as conn:\\n c = conn.cursor()\\n try:\\n c.execute(query, params)\\n conn.commit()\\n except IntegrityError:\\n conn.rollback()\\n raise\\n if c.description is None:\\n return c.rowcount\\n return Results(c.description, c.fetchall())\",\n \"def execute(self,sql):\\n # self.results = self.execute_silent(sql)\\n # return self.results\\n # sql = self.format_sql(sql, **kwargs)\\n sql_list = sql.split(';')\\n for stmt in sql_list:\\n if stmt:\\n stmt = stmt.strip()\\n if len(stmt) < 10:\\n break\\n result = self.execute_silent(stmt)\\n #if result is not None,It's select stmt.\\n if result:\\n return result\",\n \"def get_query_results(query):\\n try:\\n dbconn = psycopg2.connect(database=\\\"news\\\")\\n cursor = dbconn.cursor()\\n cursor.execute(query)\\n result = cursor.fetchall()\\n dbconn.close()\\n return result\\n except Exception as e:\\n print(e)\\n exit(1)\",\n \"async def all(\\n self,\\n statement: Executable,\\n params: t.Optional[\\n t.Union[t.Mapping[str, t.Any], t.Sequence[t.Mapping[str, t.Any]]]\\n ] = None,\\n execution_options: t.Optional[t.Mapping[str, t.Any]] = None,\\n bind_arguments: t.Optional[t.Mapping[str, t.Any]] = None,\\n ) -> t.List[t.Any]:\\n result = await self.execute(\\n statement, params, execution_options=execution_options, bind_arguments=bind_arguments\\n )\\n if isinstance(result, sa.engine.CursorResult):\\n items = result.all()\\n else:\\n if (\\n result.raw.context.compiled.compile_state\\n and result.raw.context.compiled.compile_state.multi_row_eager_loaders\\n ):\\n result = result.unique()\\n items = result.scalars().all()\\n return items\",\n \"def query(self, query, *variables, limit=None):\\n self.cursor.execute(query, variables)\\n try:\\n if limit:\\n return self.cursor.fetchmany(limit)\\n else:\\n return self.cursor.fetchall()\\n except psycopg2.ProgrammingError:\\n return None\",\n \"def fetch_all(self, sql):\\n result = []\\n\\n curs = self.q(sql, True)\\n cols = curs.column_names\\n for row in curs:\\n row_result = {}\\n for field in cols:\\n k = cols.index(field)\\n row_result[cols[k]] = row[k]\\n #print cols[k], row[k]\\n result.append(row_result)\\n curs.close()\\n return result\",\n \"def query(cursor, query):\\n out = []\\n c = cursor.execute(query)\\n out.append(c.fetchall())\\n # will return list of tuples for each query\\n return out\",\n \"def get_execute_table(self, limit=None):\\n query = self.select_all()\\n self.cur.execute(query)\\n if limit is None:\\n result = self.cur.fetchall()\\n else:\\n result = self.cur.fetchmany(limit)\\n return to_data_frame(result)\",\n \"def get_execute_table(self, limit=None):\\n query = self.select_all()\\n self.cur.execute(query)\\n if limit is None:\\n result = self.cur.fetchall()\\n else:\\n result = self.cur.fetchmany(limit)\\n return to_data_frame(result)\",\n \"def fetch_all(cls, sql, **kwargs):\\n conn = kwargs['conn']\\n\\n cursor = conn.cursor(dictionary=True, buffered=False)\\n for _ in cursor.execute(sql, kwargs.get('args'), multi=True):\\n pass\\n\\n result = cursor.fetchall()\\n cls.close(conn, cursor)\\n\\n return result\",\n \"def execute(self):\\n if self.sql is None:\\n self.sql = self.construct_query()\\n # Only SQL strings can be split, not (e.g.) SQLAlchemy statements.\\n if self.multiple_statements and isinstance(self.sql, str):\\n statements = self._split_sql()\\n else:\\n statements = [self.sql]\\n single_statement = True if len(statements) == 1 and self.filename else False\\n try:\\n for statement in statements:\\n result_proxy = self.cm.conn.execute(statement)\\n log_string = self.filename if single_statement else str(statement)[:25]\\n self.logger.info(\\\"Executed {} against {}\\\".format(log_string, self.cm.db))\\n if result_proxy.cursor:\\n return self.fetch_results(result_proxy)\\n except Exception as e:\\n self.logger.exception(e)\\n raise\",\n \"def query(self, query, params=None):\\n cur = self.connection.cursor()\\n cur.execute(query, params)\\n result = [list(row) for row in cur.fetchall()]\\n colnames = [desc[0] for desc in cur.description]\\n cur.close()\\n return colnames, result\",\n \"def iter(self, query: str, *args, **kwargs):\\n self._ensure_connected()\\n cursor = SSCursor(self._db)\\n try:\\n self._execute(cursor, query, args, kwargs)\\n column_names = [d[0] for d in cursor.description]\\n for row in cursor:\\n yield Row(zip(column_names, row))\\n finally:\\n cursor.close()\",\n \"def read(self, query):\\r\\n t1 = time.time()\\r\\n if self.database in ['redshift', 'postgres']:\\r\\n ret = postgres_helper.fetchall(config=self.conf, sql=query)\\r\\n else:\\r\\n raise Exception(\\\"database not supported yet: '{}'\\\"\\r\\n .format(self.database))\\r\\n t2 = time.time()\\r\\n t = t2 - t1\\r\\n print('Finished in {:.2f} seconds.'.format(t))\\r\\n return ret\",\n \"def execute_query(self, *args, **kwargs):\",\n \"def query(self, q, *args):\\n\\n if self.dbtype == 'pg':\\n print q\\n return self.db.execute(q, *args).fetchall()\\n else:\\n cur = self.db.cursor()\\n try:\\n print q\\n print args\\n if args:\\n cur.execute(q, args)\\n else:\\n cur.execute(q)\\n ret = cur.fetchall()\\n return ret\\n except:\\n self.db.rollback()\\n raise\\n finally:\\n cur.close()\",\n \"def execute_fetchall(self, sql, sql_args=None, dictcursor=False):\\n # Check that sql arguments have the correct type\\n self._check_sql_args(sql_args)\\n # Execute the query\\n try:\\n pgcursor = self.get_postgres_cursor(dictcursor)\\n pgcursor.execute(sql, sql_args)\\n result = pgcursor.fetchall()\\n self._connection.commit()\\n except PostgresError, e:\\n self._connection.rollback()\\n raise RuntimeError(\\\"Error running SQL query: %s\\\", str(e))\\n finally:\\n pgcursor.close()\\n return result\",\n \"def read_items(self, query=None, val=None):\\n with self.conn:\\n with self.conn.cursor() as curr:\\n curr.execute(query, val)\\n rows = curr.fetchall()\\n self.conn.close()\\n return rows\",\n \"def execute(sql, args=()):\\n res = con.execute(sql, args)\\n con.commit()\\n return res.fetchall()\",\n \"def _runsql(self):\\n self.logger.info(\\\"Running SQL where sequence > %s\\\" % self.seq)\\n try:\\n results = self.engine.execute(self.sql,\\n (self.max_rows, self.seq)).fetchall()\\n except sqlalchemy.exc.ProgrammingError, err:\\n self.logger.critical(\\\"Error connecting to DB : %s\\\" % err)\\n return None\\n self.logger.info('Fetched %d rows from DB' % len(results))\\n if not len(results):\\n self.logger.info(\\\"No rows returned from DB. Finished loading\\\")\\n return False\\n return results\",\n \"def do(self, executor):\\n sql, kw = self._assemble()\\n return executor.execute(\\n sql, kw\\n )\",\n \"def get(self, question, rows):\\n con.query(question)\\n result = con.use_result()\\n return result.fetch_row(rows)\",\n \"def cursorExecute(self, query):\\n return self.cursor.execute(query)\",\n \"def execute_query(query):\\n\\n db = psycopg2.connect(database=\\\"news\\\")\\n cursor = db.cursor()\\n cursor.execute(query)\\n query_result = cursor.fetchall()\\n db.close()\\n return query_result\",\n \"def fetch_all(self, sql: str, values: Union[Tuple, Mapping] = None) -> Any:\\n with self.cursor() as cursor:\\n cursor.execute(sql, values)\\n return cursor.fetchall()\",\n \"def fetchSqlRecords(self, sql):\\r\\n cursor = self.c.cursor()\\r\\n cursor.execute(sql)\\r\\n r = cursor.fetchall()\\r\\n cursor.close()\\r\\n return r\",\n \"def execute(self, sql):\\n return self.db.execute(sql)\",\n \"def quarry(conn, query, params=None, returns=True):\\n # Create cursor object\\n cur = conn.cursor()\\n\\n # Execute the query\\n if params is not None:\\n cur.execute(query, params)\\n else:\\n cur.execute(query)\\n\\n # If returns is True, assign the query to variable\\n if returns:\\n results = cur.fetchall()\\n pprint(results)\\n\\n # If not, do not create variable to return\\n # Close the cursor and the connection\\n cur.close()\\n conn.commit()\\n\\n # If returns is True, return the return value\\n if returns:\\n return results\",\n \"def select_records(conn, qry, row_cnt=None):\\n result = None\\n try:\\n cur = conn.cursor()\\n cur.execute(qry)\\n result = PostgresStuff.get_results(cur, row_cnt)\\n except (Exception, psycopg2.DatabaseError) as error:\\n print(error)\\n finally:\\n if cur is not None:\\n cur.close()\\n return result\",\n \"def run_select_query(query, args = None):\\n cursor = db.get_cursor()\\n cursor.execute(query, args)\\n return cursor.fetchall()\",\n \"def execute(self) -> TableResult:\\n self._t_env._before_execute()\\n return TableResult(self._j_statement_set.execute())\",\n \"def fetch_data_from_db(query):\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return result\",\n \"def get_rows(self, logger):\\n engine = self.connecting_database(logger)\\n\\n # Returning all rows from the table using Pandas\\n logger.info(\\\"Getting rows . . .\\\")\\n df = pd.read_sql('bt_challenge_boa', con=engine)\\n\\n logger.info(\\\"Total Rows: {}\\\".format(str(len(df))))\\n logger.info(\\\"Saving the CSV file . . .\\\")\\n df.to_csv(self.path + self.outpufile, index=False, doublequote=True)\",\n \"def execute_read_query(connection, query):\\n cursor = connection.cursor()\\n result = None\\n try:\\n cursor.execute(query)\\n result = cursor.fetchall()\\n return result\\n except psycopg2.OperationalError as e:\\n print(f\\\"The error '{e}' occurred\\\")\",\n \"def get_row_result(self, cmd, fields):\\n msg_type, msg = self.protocol.build_execute_statement(\\\"mysqlx\\\", cmd, fields)\\n self.protocol.send_msg(msg_type, msg)\\n return RowResult(self)\",\n \"def query(self, query: str, values: Tuple = None) -> None:\\n cursor = self.connection.cursor()\\n cursor.execute(query, values)\\n result = cursor.fetchall()\\n cursor.close()\\n return result\",\n \"def _raw(self, query: Any, data: Any = None):\\n assert isinstance(query, str)\\n\\n conn = self._get_session()\\n try:\\n results = conn.execute(query)\\n\\n entity_items = []\\n for item in results:\\n entity = self.model_cls.to_entity(item)\\n entity.state_.mark_retrieved()\\n entity_items.append(entity)\\n\\n result = ResultSet(\\n offset=0,\\n limit=len(entity_items),\\n total=len(entity_items),\\n items=entity_items,\\n )\\n except DatabaseError as exc:\\n logger.error(f\\\"Error while running raw query: {exc}\\\")\\n raise\\n finally:\\n if not current_uow:\\n conn.commit()\\n conn.close()\\n\\n return result\",\n \"def query(self, query, cs_type=None, *parameters, **kwparameters):\\n cursor = self._cursor(cs_type)\\n try:\\n self._execute(cursor, query, parameters, kwparameters)\\n if cs_type in [\\\"SSCursor\\\", \\\"SSDictCursor\\\"]:\\n while 1:\\n try:\\n row = cursor.fetchone()\\n except Exception, e:\\n cursor.close()\\n raise e\\n if row:\\n yield row\\n else:\\n break\\n else:\\n yield [Row(row) if isinstance(row, dict) else row for row in cursor]\\n except Exception, e:\\n cursor.close()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8222279","0.7665415","0.76420516","0.7597238","0.7581581","0.75336605","0.75020784","0.75014627","0.7499659","0.7481231","0.7469864","0.7459134","0.74526256","0.7436286","0.7409394","0.7389792","0.73760194","0.7298189","0.72854406","0.7282933","0.72528124","0.7228975","0.71666473","0.7154071","0.7142645","0.71133345","0.70816755","0.70469224","0.7007278","0.69506156","0.6935742","0.6925394","0.6911915","0.6906911","0.690582","0.689136","0.68842816","0.68750256","0.68169165","0.6816377","0.679434","0.6778791","0.6759126","0.67482877","0.67481303","0.67374563","0.67306924","0.6729264","0.6729096","0.67128146","0.6706587","0.669726","0.6696921","0.66879344","0.66801685","0.66768974","0.6671884","0.6656017","0.66518945","0.66438746","0.6635801","0.662961","0.66288483","0.6628148","0.662425","0.6618321","0.660416","0.6600763","0.65939105","0.6587136","0.65858454","0.65858454","0.6585757","0.65797436","0.6577498","0.6562563","0.65610266","0.65555376","0.6552248","0.65512323","0.6544374","0.6544091","0.65400875","0.652207","0.651537","0.6500751","0.6499338","0.6498897","0.64954","0.64938784","0.64930445","0.64901775","0.64874625","0.64687353","0.6466278","0.6457103","0.6454987","0.64514977","0.64431643","0.64362997","0.643316"],"string":"[\n \"0.8222279\",\n \"0.7665415\",\n \"0.76420516\",\n \"0.7597238\",\n \"0.7581581\",\n \"0.75336605\",\n \"0.75020784\",\n \"0.75014627\",\n \"0.7499659\",\n \"0.7481231\",\n \"0.7469864\",\n \"0.7459134\",\n \"0.74526256\",\n \"0.7436286\",\n \"0.7409394\",\n \"0.7389792\",\n \"0.73760194\",\n \"0.7298189\",\n \"0.72854406\",\n \"0.7282933\",\n \"0.72528124\",\n \"0.7228975\",\n \"0.71666473\",\n \"0.7154071\",\n \"0.7142645\",\n \"0.71133345\",\n \"0.70816755\",\n \"0.70469224\",\n \"0.7007278\",\n \"0.69506156\",\n \"0.6935742\",\n \"0.6925394\",\n \"0.6911915\",\n \"0.6906911\",\n \"0.690582\",\n \"0.689136\",\n \"0.68842816\",\n \"0.68750256\",\n \"0.68169165\",\n \"0.6816377\",\n \"0.679434\",\n \"0.6778791\",\n \"0.6759126\",\n \"0.67482877\",\n \"0.67481303\",\n \"0.67374563\",\n \"0.67306924\",\n \"0.6729264\",\n \"0.6729096\",\n \"0.67128146\",\n \"0.6706587\",\n \"0.669726\",\n \"0.6696921\",\n \"0.66879344\",\n \"0.66801685\",\n \"0.66768974\",\n \"0.6671884\",\n \"0.6656017\",\n \"0.66518945\",\n \"0.66438746\",\n \"0.6635801\",\n \"0.662961\",\n \"0.66288483\",\n \"0.6628148\",\n \"0.662425\",\n \"0.6618321\",\n \"0.660416\",\n \"0.6600763\",\n \"0.65939105\",\n \"0.6587136\",\n \"0.65858454\",\n \"0.65858454\",\n \"0.6585757\",\n \"0.65797436\",\n \"0.6577498\",\n \"0.6562563\",\n \"0.65610266\",\n \"0.65555376\",\n \"0.6552248\",\n \"0.65512323\",\n \"0.6544374\",\n \"0.6544091\",\n \"0.65400875\",\n \"0.652207\",\n \"0.651537\",\n \"0.6500751\",\n \"0.6499338\",\n \"0.6498897\",\n \"0.64954\",\n \"0.64938784\",\n \"0.64930445\",\n \"0.64901775\",\n \"0.64874625\",\n \"0.64687353\",\n \"0.6466278\",\n \"0.6457103\",\n \"0.6454987\",\n \"0.64514977\",\n \"0.64431643\",\n \"0.64362997\",\n \"0.643316\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17774,"cells":{"query":{"kind":"string","value":"Executes the query and returns the first matching row."},"document":{"kind":"string","value":"def first(self, filter_deleted=False):\n objects = self.matching_objects(filter_deleted=filter_deleted)\n\n if len(objects) > 0:\n value = objects[0]\n if self.session is not None:\n if hasattr(value, \"id\"):\n self.session.watch(value)\n return value\n else:\n return None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def fetchone(self):\n\n self._check_executed()\n\n if self._query_id == -1:\n msg = \"query didn't result in a resultset\"\n self._exception_handler(ProgrammingError, msg)\n\n if self.rownumber >= self.rowcount:\n return None\n\n if self.rownumber >= (self._offset + len(self._rows)):\n self.nextset()\n\n result = self._rows[self.rownumber - self._offset]\n self.rownumber += 1\n return result","def query_one(self, sql: str) -> Any:\n with self.connection.cursor() as cursor:\n self.connection.ping(reconnect=True)\n cursor.execute(sql)\n row = cursor.fetchone()\n self.connection.commit()\n return row","def fetch_one(self, sql):\n curs = self.q(sql, True)\n row = curs.fetchone()\n if curs._have_unread_result():\n curs.fetchall()\n curs.close()\n\n if row:\n return row[0]\n else:\n return None","def _query_and_fetchone(self, query):\n with self._connect() as conn:\n cur = conn.cursor()\n cur.execute(query)\n result = cur.fetchone()\n\n return result","def fetchone(self):\n self._check_closed()\n if self._result_set is None:\n raise Error(\"Previous execute did not produce any results or no call was issued yet\")\n self.rownumber += 1\n return self._result_set.fetchone(self.session)","def _query_one(self, sql, data=None):\n\n conn = psycopg2.connect(self.connect_args)\n cur = conn.cursor()\n cur.execute(sql, data)\n result = cur.fetchone()\n cur.close()\n conn.close()\n return result","def get_query_single_row(query, *args):\n conn = get_connection()\n cursor = conn.cursor()\n\n rows = cursor.execute(query, args).fetchall()\n\n result = map_row(cursor, rows[0])\n\n cursor.close()\n conn.close()\n\n return result","def fetch_one(q, *params):\n db = Database()\n db.cur.execute(q, params)\n ret = db.cur.fetchone()\n db.con.close()\n return ret","def fetchone(self):\n #if self._state == self._STATE_NONE:\n # raise exc.ProgrammingError(\"No query yet\")\n\n # Sleep until we're done or we have some data to return\n #self._fetch_while(lambda: not self._data and self._state != self._STATE_FINISHED)\n if not self._data:\n return None\n elif self._rownumber >= len( self._data.rows ):\n return None\n else:\n result = self._data.rows[self._rownumber]\n\n\n correctedData = OrderedDict()\n #Insure correct field order\n for column in self._data.columns:\n correctedData[column] = result[column]\n\n #result = tuple( result.values() )\n result = tuple( correctedData.values() )\n result = tuple( correctedData.values() )\n self._rownumber += 1\n return result","def fetchone(self, sql, val=()):\n cursor = self.__db.cursor()\n cursor.execute(sql, val)\n return cursor.fetchone()","def fetch_one(cls, sql, **kwargs):\n conn = kwargs['conn']\n\n cursor = conn.cursor(dictionary=True, buffered=False)\n for _ in cursor.execute(sql, kwargs.get('args'), multi=True):\n pass\n\n result = cursor.fetchone()\n cls.close(conn, cursor)\n\n return result","async def queryone(self, stmt, *args):\n results = await self.query(stmt, *args)\n if len(results) == 0:\n raise NoResultError()\n elif len(results) > 1:\n raise ValueError(\"Expected 1 result, got %d\" % len(results))\n return results[0]","async def first(\n self,\n statement: Executable,\n params: t.Optional[\n t.Union[t.Mapping[str, t.Any], t.Sequence[t.Mapping[str, t.Any]]]\n ] = None,\n execution_options: t.Optional[t.Mapping[str, t.Any]] = None,\n bind_arguments: t.Optional[t.Mapping[str, t.Any]] = None,\n ) -> t.Optional[t.Any]:\n result = await self.execute(\n statement, params, execution_options=execution_options, bind_arguments=bind_arguments\n )\n if isinstance(result, sa.engine.CursorResult):\n item = result.first()\n else:\n item = result.scalar()\n return item","def execute_fetchone(self, sql, sql_args=None, dictcursor=False):\n # Check that sql arguments have the correct type\n self._check_sql_args(sql_args)\n # Execute the query\n try:\n pgcursor = self.get_postgres_cursor(dictcursor)\n pgcursor.execute(sql, sql_args)\n result = pgcursor.fetchone()\n self._connection.commit()\n except PostgresError, e:\n self._connection.rollback()\n raise RuntimeError(\"Error running SQL query: %s\", str(e))\n finally:\n pgcursor.close()\n return result","def query_fetchone(self, q, param=None):\r\n try:\r\n c = self.connection.cursor()\r\n if param is None:\r\n c.execute(q)\r\n else:\r\n c.execute(q, param)\r\n self.logger.log(logger.LogLevel.DEBUG, 'database.fetchone: %s | %s' % (q, param)) \r\n return c.fetchone()\r\n except Exception as e:\r\n self.logger.log(logger.LogLevel.ERROR, 'database.fetchone: %s. %s | %s' % (e, q, param))\r\n return False","async def one(\n self,\n statement: Executable,\n params: t.Optional[\n t.Union[t.Mapping[str, t.Any], t.Sequence[t.Mapping[str, t.Any]]]\n ] = None,\n execution_options: t.Optional[t.Mapping[str, t.Any]] = None,\n bind_arguments: t.Optional[t.Mapping[str, t.Any]] = None,\n ) -> t.Any:\n result = await self.execute(\n statement, params, execution_options=execution_options, bind_arguments=bind_arguments\n )\n if isinstance(result, sa.engine.CursorResult):\n item = result.one()\n else:\n item = result.scalar_one()\n return item","def fetch_row(self, sql: str, values: Union[Tuple, Mapping] = None) -> Any:\n with self.cursor() as cursor:\n cursor.execute(sql, values)\n assert cursor.rowcount <= 1, f\"More than one result row for fetch_row({sql!r}, {values!r})\"\n return cursor.fetchone()","def returnOne(self):\n try:\n # self.checkValName()\n self.cursor.execute(self.query % self.val)\n self.results = self.conn.fetchone()\n except Exception as e:\n print \"Query failed: %s \" % e","def returnOne(self):\n try:\n # self.checkValName()\n self.dbc.execute(self.query, self.val)\n self.results = self.dbc.fetchone()\n except MySQLdb.Error, e:\n print \"Query failed: %s \" % e","async def fetch_single(self, query: str, args: Optional[Iterable]=None) -> Any:\n res = await self.fetch_row(query, args)\n ret = None\n if res and len(res) == 1:\n ret = res[0]\n return ret","def fetchone(cursor):\n\t# type: (Cursor, ) -> Any\n\n\trows = cursor.fetchall()\n\tif len(rows) == 0:\n\t\traise NoResult(\"No result found\")\n\telif len(rows) == 1:\n\t\treturn rows[0]\n\telse:\n\t\traise InconsistentState(\"More than one result found\")","def get(self, sql, *args):\n try:\n return next(self.run(sql, *args))\n\n except StopIteration:\n raise utils.NoResult()","def dbGetFirstRow(con, cmd, args=[], skipTrace=False):\n cursor = dbExecute(con, cmd, args, skipTrace)\n return cursor.fetchone()","def fetchone(self, autoConvert=True, format=None):\r\n if self._closed:\r\n raise Error('The cursor has been closed.')\r\n if self.connection._closed:\r\n raise Error('The connection to the database has been closed.')\r\n if self.info['results'] == None:\r\n raise Error('Previous call to execute() did not produce a result set. No results to fetch.')\r\n else:\r\n res = self.fetchmany(1, format)\r\n if res == ():\r\n return None\r\n else:\r\n return res[0]","def dbGetFirstRowOneValue(con, cmd, args=[], skipTrace=False):\n row = dbGetFirstRow(con, cmd, args, skipTrace)\n trace(\"row=\" + str(row))\n if row is not None and len(row) > 0:\n return row[0]\n return None","def fetch_first(self, tablename):\n\n query = 'select * from ' + tablename + \" ASC LIMIT 1\"\n try:\n self.__cur.execute(query)\n except Exception as e:\n self.__conn.rollback()\n raise e\n fetcheddata = self.__cur.fetchall()\n if fetcheddata:\n fetcheddata = fetcheddata[0]\n fetcheddata = self.__helper._functions__rowtodict([fetcheddata])\n return fetcheddata[0]\n return None","def fetchOne(self,query):\n# self.updateDBAndLog(query)\n# tup = self.cursor.fetchone()\n\tself.updateLog(query)\n\tcur = self.db.cursor()\n\tcur.execute(query)\n\ttup = cur.fetchone()\n\tcur.close()\n\treturn tup","async def fetchone(self, query, connection=None):\n async with self.connection(connection) as conn:\n r = await conn.execute(query)\n return await r.fetchone()","def query_one(self, *args, **kwargs):\n kwargs.setdefault('error_if_multiple', True)\n records = self.query(*args, **kwargs)\n return records[0] if records else None","def first(self):\n try:\n data = self.get_cursor()[0]\n return self.from_(**self.prepare_data(data))\n except IndexError:\n return None","def queryWithOneResult(self, query_str):\n try:\n conn = MySQLdb.connect(**self.connect)\n cursor = conn.cursor()\n cursor.execute(query_str)\n result = cursor.fetchone()\n conn.commit()\n conn.close()\n return result\n except Exception, e:\n logger.error(\"mysql util error:\" + str(e))","def query(query, args=(), one=False):\n cur = get_db().execute(query, args)\n result = cur.fetchall() # result is the answer to the query\n cur.close()\n return (result[0] if result else None) if one else result","def fetchone(self):\n try:\n return next(self._results)\n except StopIteration:\n return None","def fetch_single_res(db_conn, sql):\n try:\n with contextlib.closing(db_conn.cursor()) as curs:\n curs.execute(sql)\n return curs.fetchone()\n except RuntimeError as e:\n print(e.strerror)","def get_one(self, table, q_filter=None, fail_on_empty=True, fail_on_more=True):\n try:\n result = None\n with self.lock:\n for _, row in self._find(table, self._format_filter(q_filter)):\n if not fail_on_more:\n return deepcopy(row)\n if result:\n raise DbException(\"Found more than one entry with filter='{}'\".format(q_filter),\n HTTPStatus.CONFLICT.value)\n result = row\n if not result and fail_on_empty:\n raise DbException(\"Not found entry with filter='{}'\".format(q_filter), HTTPStatus.NOT_FOUND)\n return deepcopy(result)\n except Exception as e: # TODO refine\n raise DbException(str(e))","def fetchone(self) -> Any:\n return self.cursor.fetchone()","def get_one(self, sql_command, cmd_param=None):\n if cmd_param:\n count = self._mysql_cursor.execute(sql_command, cmd_param)\n else:\n count = self._mysql_cursor.execute(sql_command)\n\n if count:\n sql_result = self._mysql_cursor.fetchoneDict()\n else:\n sql_result = None\n\n return sql_result","def select_single(self, rownum):\n with self.connection:\n return self.cursor.execute(\n \"SELECT * FROM music WHERE id = ?\", (rownum,)\n ).fetchall()[0]","def find_one(self, select, tables, on={}, filter={}):\n query, args = self.__build_find_query(select, tables, on, filter)\n\n cur = self.conn.cursor()\n\n try:\n cur.execute(query, args)\n result = cur.fetchone()\n except Exception as e:\n # logger = Logger.getInstance()\n # logger.log(context=self.__class__.__name__,\n # method=\"fetch_all\",\n # msg=f\"query: {query}; Excpetion {e}\")\n print(e)\n result = None\n finally:\n cur.close()\n return result","def get_one(\n self, table=None, fields='*', where=None, order=None, limit=(0, 1)\n ):\n\n cur = self._select(table, fields, where, order, limit)\n result = cur.fetchone()\n\n row = None\n if result:\n Row = namedtuple('Row', [f[0] for f in cur.description])\n row = Row(*result)\n\n return row","def select_one(cls, pk):\n with sqlite3.connect(cls.dbpath) as conn:\n conn.row_factory = sqlite3.Row\n curs = conn.cursor()\n sql = f\"\"\"SELECT * FROM {cls.tablename} WHERE pk =?;\"\"\"\n curs.execute(sql, (pk,)) #don't forget to put a comma after single value inputs\n row = curs.fetchone()\n return cls(**row)","async def one_or_none(\n self,\n statement: Executable,\n params: t.Optional[\n t.Union[t.Mapping[str, t.Any], t.Sequence[t.Mapping[str, t.Any]]]\n ] = None,\n execution_options: t.Optional[t.Mapping[str, t.Any]] = None,\n bind_arguments: t.Optional[t.Mapping[str, t.Any]] = None,\n ) -> t.Optional[t.Any]:\n result = await self.execute(\n statement, params, execution_options=execution_options, bind_arguments=bind_arguments\n )\n if isinstance(result, sa.engine.CursorResult):\n item = result.one_or_none()\n else:\n item = result.scalar_one_or_none()\n return item","def select_one(self, table, column, value):\n select_row = \"SELECT * FROM {} WHERE {}='{}' AND delete_status = FALSE;\".format(table, column, value)\n self.cursor.execute(select_row)\n row = self.cursor.fetchone()\n return row","def find(self, primary_key):\n sql = '{} WHERE {} = %s'.format(self._select(), self.PRIMARY_KEY)\n cursor = yield self._pool.execute(sql, [primary_key])\n result = cursor.fetchmany(1)\n return self.convert_result_to_object(result)","def get_run(self, run_id: str) -> sqlite3.Row:\n with self.table_access_condition:\n conn = self._get_connection()\n c = conn.cursor()\n c.execute(\n \"\"\"\n SELECT * from runs\n WHERE run_id = ?;\n \"\"\",\n (run_id,),\n )\n results = c.fetchall()\n return results[0]","async def find_one(self, **query):\n\n return await self._expand(await self.db.get_one(**query))","def get(self, query: str, *args: List[str], **kwargs: Dict):\n rows = self.query(query, *args, **kwargs)\n if not rows:\n return None\n elif len(rows) > 1:\n raise Exception(\"Multiple rows returned for Database.get() query\")\n else:\n return rows[0]","def askOne(self, query, context):\n results = self.ask(query, context, True)\n return results.iterator().next() if (len(results) > 0) else None","def query_db(query, args=(), one=False):\n cur = get_db().execute(query, args)\n rv = cur.fetchall()\n return (rv[0] if rv else None) if one else rv","def query_db(query, args=(), one=False):\n cur = get_db().execute(query, args)\n rv = cur.fetchall()\n return (rv[0] if rv else None) if one else rv","def query_db(query, args=(), one=False):\n cur = get_db().execute(query, args)\n rv = cur.fetchall()\n return (rv[0] if rv else None) if one else rv","def query_db(query, args=(), one=False):\n cur = get_db().execute(query, args)\n rv = cur.fetchall()\n return (rv[0] if rv else None) if one else rv","def query_db(query, args=(), one=False):\n cur = get_db().execute(query, args)\n rv = cur.fetchall()\n return (rv[0] if rv else None) if one else rv","def select_scalar(self, *args, **kwargs):\n row = self.db_connection.execute(*args, **kwargs).fetchone()\n return None if row is None else row[0]","def _queryFetchOne(cols, tables, params):\n try:\n db = sqlite3.connect(DBPATH)\n cursor = db.cursor()\n\n cursor.execute('SELECT ' + cols\n + ' FROM ' + tables\n + ' WHERE ' + params\n )\n objectData = cursor.fetchone()\n\n except Exception as e:\n db.rollback()\n raise e\n\n finally:\n db.close()\n\n return objectData","def get_single_result(self, rows):\n if self.priority_sort_column:\n rows = rows.sort_values(by=self.priority_sort_column,\n ascending=self.priority_sort_ascending)\n return rows.iloc[0]","def get_one(self, object):\n self.lock.acquire()\n result = self.__Session.query(object).first()\n self.lock.release()\n return result","def single(self):\n records = list(self)\n num_records = len(records)\n if num_records == 0:\n raise ResultError(\"Cannot retrieve a single record, because this result is empty.\")\n elif num_records != 1:\n raise ResultError(\"Expected a result with a single record, but this result contains at least one more.\")\n else:\n return records[0]","def fetch_row(self, sql):\n curs = self.q(sql, True)\n cols = curs.column_names\n row = curs.fetchone()\n if curs._have_unread_result():\n curs.fetchall()\n curs.close()\n\n if row:\n result = {}\n for field in cols:\n k = cols.index(field)\n result[cols[k]] = row[k]\n return result\n else:\n return None","def find_one(self, criteria):\n return self.connection.find_one(criteria)","def get_first_item(cls):\n return cls.query.first()","def query_db(query, args=(), one=False):\n cur = g.db.execute(query, args)\n rv = cur.fetchall()\n cur.close()\n return (rv[0] if rv else None) if one else rv","def fetch_one(cls: Type[_T], session: Session, identifier: int) -> _T:\n return Query(cls, session=session).get(identifier)","def fetchone(self):\n row = self.cursor.fetchone()\n\n if row is None:\n return None\n\n if self.model.single:\n return self.__instance_from_db(self.model, row)\n else:\n return tuple(self.__instance_from_db(m, row) for m in self.model.models)","def query(query, parameters=(), single=False):\n cur = get_db().execute(query, parameters)\n cur.row_factory = dict_factory\n #cur = cur.cursor()\n if single:\n result = cur.fetchone()\n else:\n result = cur.fetchall()\n cur.close()\n return result","def get(self, query, *parameters, **kwparameters):\n cursor = self._cursor()\n try:\n cursor.execute(query, kwparameters or parameters)\n return cursor.fetchone()\n finally:\n cursor.close()","def get_row(self, pk):\n ans = self.execute(self.commands.get_row(\n cols=self._join_cols(self.columns),\n table=self.name,\n pk_col=self.primary_key_column,\n pk=pk\n ))\n if not ans:\n return None\n return self._dictify(self.columns, ans[0])","def one(self, except_all=False):\n\n length = len(self)\n if length > 1:\n raise MultipleResultsFound(\"%s results found.\" % length)\n\n result = self.first()\n if result is None and except_all:\n raise NoResultFound\n return result","def fetchone_query_and_close(self, query, values):\n self.cursor.execute(query, values)\n retval = self.cursor.fetchone()\n self.__close_db()\n return retval","def find_first(cls, **kwargs):\n return cls.query.filter_by(**kwargs).first()","def get(self, question, rows):\n con.query(question)\n result = con.use_result()\n return result.fetch_row(rows)","async def find_by_id(self, _id: int) -> Record:\n conn: Connection\n async with self.db_pool.acquire() as conn:\n return await conn.fetchrow(\n f\"SELECT * FROM {self.table_name} WHERE {self.primary_key}=$1\",\n _id,\n )","def get(cls, session: Session = None, **kwargs):\n sess = next(cls.session()) if not session else session\n query = sess.query(cls)\n for key, val in kwargs.items():\n col = getattr(cls, key)\n query = query.filter(col == val)\n\n if query.count() > 1:\n raise Exception(\"Only one row is supposed to be returned, but got more than one.\")\n result = query.first()\n if not session:\n sess.close()\n return result","def _select_single(self, disc):\n sqlstmt = \"SELECT h FROM %s WHERE d=?\" % self.VIEW\n pickup = self.cursor.execute(sqlstmt, (disc,))\n picked = pickup.fetchone()\n if picked is not None:\n # picked = (h,)\n return picked[0]\n else:\n raise KeyError(str(disc))","def findone(cls, *lst, **dct):\n query = cls.where(*lst, **dct).select()\n result = query.execute()\n return result.fetchone()","def first(self, query=False):\n self.set_action(\"select\")\n if query:\n return self.limit(1)\n result = self.connection.query(\n self.limit(1).to_qmark(), self._bindings, results=1\n )\n relations = self.eager_load_model(result)\n return self.owner.hydrate(result, relations=relations)","def _dictfetchone(self):\n data = self._dictfetchall()\n if data:\n return data[0]\n return {}","async def fetchrow(self, query, *args):\n return await self.conn.fetchrow(query, *args)","def execute(self, sql):\n with self.connection.cursor() as dbc:\n if sql[-1] != ';':\n sql += ';'\n dbc.execute(sql)\n self.last_row = dbc.lastrowid\n try:\n return dbc.fetchall()\n except:\n return","def get_raw_query(self, row_id):\n cursor = self.connection.cursor()\n cursor.execute(\"\"\"\n SELECT raw_query FROM queries WHERE rowid=(?);\n \"\"\", (row_id,))\n return cursor.fetchone()[0]","def SelectRow(\n self, cnxn, cols=None, default=None, where=None, **kwargs):\n rows = self.Select(cnxn, distinct=True, cols=cols, where=where, **kwargs)\n if len(rows) == 1:\n return rows[0]\n elif not rows:\n logging.info('SelectRow got 0 results, so using default %r', default)\n return default\n else:\n raise ValueError('SelectRow got %d results, expected only 1', len(rows))","def query_db(query, args=(), one=False):\n conn = get_db()\n cur = conn.cursor()\n cur.execute(query, args)\n rv = cur.fetchall()\n cur.close()\n return (rv[0] if rv else None) if one else rv","def fetch_one(cls, limiter=None):\n return cls.where(limiter).fetch_one()","def execute_sql_scalar(self, sql):\n msg_type, msg = self.protocol.build_execute_statement(\"sql\", sql)\n self.protocol.send_msg(msg_type, msg)\n result = RowResult(self)\n result.fetch_all()\n if result.count == 0:\n raise InterfaceError(\"No data found\")\n return result[0][0]","def Fetch(self, sql, params=(), limit=None):\n if self.SanityCheck(sql):\n try:\n self.cursor.execute(sql, params)\n row = self.cursor.fetchone()\n if not row: return None\n headings = tuple(row.keys())\n fetched = 1\n if limit is None or limit <= 0:\n rows = self.cursor.fetchall()\n else:\n rows = []\n while fetched < limit:\n row = self.cursor.fetchone()\n if not row: break\n rows.append(row)\n fetched += 1\n rows.insert(0, headings)\n rows.insert(1, row)\n return rows\n except sqlite3.Error as e:\n print >> sys.stderr, 'Failed to execute query: %s (%s)' % (sql, params)\n return None\n finally:\n self.cursor.close()\n self.cursor = None","def first_or_raise(self):\n res = super(CustomQuery, self).first()\n if not res:\n raise NotFoundException\n return res","def query(self, query):\n cursor = self.database.cursor()\n cursor.execute(query)\n # If it's a query that's expected to return a value (EG: SELECT)\n if query.strip().lower().startswith('select'): return cursor.fetchall()","def get(self, obj):\n obj = self._to_obj_tuple(obj)\n rows = self.query(object=obj)\n if rows:\n return rows[0]","def get_result_by_primary_key(self, pk):\n session = self.session_factory()\n result = session.query(PipelineRun).filter_by(id=pk).first()\n session.close()\n return result","def cud_operations(self, query=None, val=None):\n with self.conn:\n with self.conn.cursor() as curr:\n curr.execute(query, val)\n row = curr.fetchone()\n # self.conn.close()\n return row","def fetch_value(self, query, params=None):\n\n self.execute_query(query, params)\n result = self.cursor.fetchone()\n return list(result.values())[0]","def first(self, **kwargs):\n return self.find(**kwargs).first()","def _exec1(self, sql):\n result = self._exec(sql)\n return [row[0] for row in result]","def first(self, order_by: Optional[str] = '<created_at', **kwargs):\n kwargs = self._preprocess(**kwargs)\n with rconnect() as conn:\n try:\n query = self.q\n if order_by is not None:\n query = self._order_by(query, order_by)\n\n # NOTE: Always filter before limiting\n if len(kwargs) > 0:\n query = query.filter(kwargs)\n query = self._limit(query, 1)\n rv = query.run(conn)\n\n except ReqlOpFailedError as e:\n console.warn(e)\n raise\n except Exception as e:\n console.warn(e)\n raise\n else:\n try:\n data = [self.__model__(_, strict=False) for _ in rv]\n return data[0]\n except IndexError:\n return None","async def find(self, pk_value: Any) -> RowProxy:","def query_db(query, args=(), one=False):\n db = get_db()\n cur = db.cursor()\n cur.execute(query, args)\n rv = fetch_all(cur)\n cur.close()\n db.commit()\n return (rv[0] if rv else None) if one else rv","def _query(self, query, data, fetch='all'):\n rows = self.cursor.execute(query, data)\n try:\n if fetch == 'all':\n return rows.fetchall()\n elif fetch == 'one':\n return rows.fetchone()\n elif fetch == 'none':\n self.conn.commit()\n except Exception, e:\n self.log.warning(\"miss on the db for {}: {}\".format(q, e))\n raise Exception(\"SQLiteDB Fail on query: {}\".format(query) + \\\n \"excetion was {}\".format(e))","def get_first(cls, condition=None, async=True):\n\n result = cls.get(condition=condition, async=False)\n\n # for async result, return the future to be handled elsewhere\n if async:\n return result\n\n if not result:\n return None\n\n return result[0]","def find_one(self, collection, query):\n obj = getattr(self.db, collection)\n result = obj.find_one(query)\n return result","def first(self, limit=1, columns=None):\n if not columns:\n columns = ['*']\n\n results = self.take(limit).get(columns)\n\n if len(results) > 0:\n return results[0]\n\n return","def execute_and_fetch(db, query):\n rows = []\n\n db.query(query)\n res = db.use_result()\n row = res.fetch_row()\n while row != ():\n rows.append(row[0])\n row = res.fetch_row()\n return rows"],"string":"[\n \"def fetchone(self):\\n\\n self._check_executed()\\n\\n if self._query_id == -1:\\n msg = \\\"query didn't result in a resultset\\\"\\n self._exception_handler(ProgrammingError, msg)\\n\\n if self.rownumber >= self.rowcount:\\n return None\\n\\n if self.rownumber >= (self._offset + len(self._rows)):\\n self.nextset()\\n\\n result = self._rows[self.rownumber - self._offset]\\n self.rownumber += 1\\n return result\",\n \"def query_one(self, sql: str) -> Any:\\n with self.connection.cursor() as cursor:\\n self.connection.ping(reconnect=True)\\n cursor.execute(sql)\\n row = cursor.fetchone()\\n self.connection.commit()\\n return row\",\n \"def fetch_one(self, sql):\\n curs = self.q(sql, True)\\n row = curs.fetchone()\\n if curs._have_unread_result():\\n curs.fetchall()\\n curs.close()\\n\\n if row:\\n return row[0]\\n else:\\n return None\",\n \"def _query_and_fetchone(self, query):\\n with self._connect() as conn:\\n cur = conn.cursor()\\n cur.execute(query)\\n result = cur.fetchone()\\n\\n return result\",\n \"def fetchone(self):\\n self._check_closed()\\n if self._result_set is None:\\n raise Error(\\\"Previous execute did not produce any results or no call was issued yet\\\")\\n self.rownumber += 1\\n return self._result_set.fetchone(self.session)\",\n \"def _query_one(self, sql, data=None):\\n\\n conn = psycopg2.connect(self.connect_args)\\n cur = conn.cursor()\\n cur.execute(sql, data)\\n result = cur.fetchone()\\n cur.close()\\n conn.close()\\n return result\",\n \"def get_query_single_row(query, *args):\\n conn = get_connection()\\n cursor = conn.cursor()\\n\\n rows = cursor.execute(query, args).fetchall()\\n\\n result = map_row(cursor, rows[0])\\n\\n cursor.close()\\n conn.close()\\n\\n return result\",\n \"def fetch_one(q, *params):\\n db = Database()\\n db.cur.execute(q, params)\\n ret = db.cur.fetchone()\\n db.con.close()\\n return ret\",\n \"def fetchone(self):\\n #if self._state == self._STATE_NONE:\\n # raise exc.ProgrammingError(\\\"No query yet\\\")\\n\\n # Sleep until we're done or we have some data to return\\n #self._fetch_while(lambda: not self._data and self._state != self._STATE_FINISHED)\\n if not self._data:\\n return None\\n elif self._rownumber >= len( self._data.rows ):\\n return None\\n else:\\n result = self._data.rows[self._rownumber]\\n\\n\\n correctedData = OrderedDict()\\n #Insure correct field order\\n for column in self._data.columns:\\n correctedData[column] = result[column]\\n\\n #result = tuple( result.values() )\\n result = tuple( correctedData.values() )\\n result = tuple( correctedData.values() )\\n self._rownumber += 1\\n return result\",\n \"def fetchone(self, sql, val=()):\\n cursor = self.__db.cursor()\\n cursor.execute(sql, val)\\n return cursor.fetchone()\",\n \"def fetch_one(cls, sql, **kwargs):\\n conn = kwargs['conn']\\n\\n cursor = conn.cursor(dictionary=True, buffered=False)\\n for _ in cursor.execute(sql, kwargs.get('args'), multi=True):\\n pass\\n\\n result = cursor.fetchone()\\n cls.close(conn, cursor)\\n\\n return result\",\n \"async def queryone(self, stmt, *args):\\n results = await self.query(stmt, *args)\\n if len(results) == 0:\\n raise NoResultError()\\n elif len(results) > 1:\\n raise ValueError(\\\"Expected 1 result, got %d\\\" % len(results))\\n return results[0]\",\n \"async def first(\\n self,\\n statement: Executable,\\n params: t.Optional[\\n t.Union[t.Mapping[str, t.Any], t.Sequence[t.Mapping[str, t.Any]]]\\n ] = None,\\n execution_options: t.Optional[t.Mapping[str, t.Any]] = None,\\n bind_arguments: t.Optional[t.Mapping[str, t.Any]] = None,\\n ) -> t.Optional[t.Any]:\\n result = await self.execute(\\n statement, params, execution_options=execution_options, bind_arguments=bind_arguments\\n )\\n if isinstance(result, sa.engine.CursorResult):\\n item = result.first()\\n else:\\n item = result.scalar()\\n return item\",\n \"def execute_fetchone(self, sql, sql_args=None, dictcursor=False):\\n # Check that sql arguments have the correct type\\n self._check_sql_args(sql_args)\\n # Execute the query\\n try:\\n pgcursor = self.get_postgres_cursor(dictcursor)\\n pgcursor.execute(sql, sql_args)\\n result = pgcursor.fetchone()\\n self._connection.commit()\\n except PostgresError, e:\\n self._connection.rollback()\\n raise RuntimeError(\\\"Error running SQL query: %s\\\", str(e))\\n finally:\\n pgcursor.close()\\n return result\",\n \"def query_fetchone(self, q, param=None):\\r\\n try:\\r\\n c = self.connection.cursor()\\r\\n if param is None:\\r\\n c.execute(q)\\r\\n else:\\r\\n c.execute(q, param)\\r\\n self.logger.log(logger.LogLevel.DEBUG, 'database.fetchone: %s | %s' % (q, param)) \\r\\n return c.fetchone()\\r\\n except Exception as e:\\r\\n self.logger.log(logger.LogLevel.ERROR, 'database.fetchone: %s. %s | %s' % (e, q, param))\\r\\n return False\",\n \"async def one(\\n self,\\n statement: Executable,\\n params: t.Optional[\\n t.Union[t.Mapping[str, t.Any], t.Sequence[t.Mapping[str, t.Any]]]\\n ] = None,\\n execution_options: t.Optional[t.Mapping[str, t.Any]] = None,\\n bind_arguments: t.Optional[t.Mapping[str, t.Any]] = None,\\n ) -> t.Any:\\n result = await self.execute(\\n statement, params, execution_options=execution_options, bind_arguments=bind_arguments\\n )\\n if isinstance(result, sa.engine.CursorResult):\\n item = result.one()\\n else:\\n item = result.scalar_one()\\n return item\",\n \"def fetch_row(self, sql: str, values: Union[Tuple, Mapping] = None) -> Any:\\n with self.cursor() as cursor:\\n cursor.execute(sql, values)\\n assert cursor.rowcount <= 1, f\\\"More than one result row for fetch_row({sql!r}, {values!r})\\\"\\n return cursor.fetchone()\",\n \"def returnOne(self):\\n try:\\n # self.checkValName()\\n self.cursor.execute(self.query % self.val)\\n self.results = self.conn.fetchone()\\n except Exception as e:\\n print \\\"Query failed: %s \\\" % e\",\n \"def returnOne(self):\\n try:\\n # self.checkValName()\\n self.dbc.execute(self.query, self.val)\\n self.results = self.dbc.fetchone()\\n except MySQLdb.Error, e:\\n print \\\"Query failed: %s \\\" % e\",\n \"async def fetch_single(self, query: str, args: Optional[Iterable]=None) -> Any:\\n res = await self.fetch_row(query, args)\\n ret = None\\n if res and len(res) == 1:\\n ret = res[0]\\n return ret\",\n \"def fetchone(cursor):\\n\\t# type: (Cursor, ) -> Any\\n\\n\\trows = cursor.fetchall()\\n\\tif len(rows) == 0:\\n\\t\\traise NoResult(\\\"No result found\\\")\\n\\telif len(rows) == 1:\\n\\t\\treturn rows[0]\\n\\telse:\\n\\t\\traise InconsistentState(\\\"More than one result found\\\")\",\n \"def get(self, sql, *args):\\n try:\\n return next(self.run(sql, *args))\\n\\n except StopIteration:\\n raise utils.NoResult()\",\n \"def dbGetFirstRow(con, cmd, args=[], skipTrace=False):\\n cursor = dbExecute(con, cmd, args, skipTrace)\\n return cursor.fetchone()\",\n \"def fetchone(self, autoConvert=True, format=None):\\r\\n if self._closed:\\r\\n raise Error('The cursor has been closed.')\\r\\n if self.connection._closed:\\r\\n raise Error('The connection to the database has been closed.')\\r\\n if self.info['results'] == None:\\r\\n raise Error('Previous call to execute() did not produce a result set. No results to fetch.')\\r\\n else:\\r\\n res = self.fetchmany(1, format)\\r\\n if res == ():\\r\\n return None\\r\\n else:\\r\\n return res[0]\",\n \"def dbGetFirstRowOneValue(con, cmd, args=[], skipTrace=False):\\n row = dbGetFirstRow(con, cmd, args, skipTrace)\\n trace(\\\"row=\\\" + str(row))\\n if row is not None and len(row) > 0:\\n return row[0]\\n return None\",\n \"def fetch_first(self, tablename):\\n\\n query = 'select * from ' + tablename + \\\" ASC LIMIT 1\\\"\\n try:\\n self.__cur.execute(query)\\n except Exception as e:\\n self.__conn.rollback()\\n raise e\\n fetcheddata = self.__cur.fetchall()\\n if fetcheddata:\\n fetcheddata = fetcheddata[0]\\n fetcheddata = self.__helper._functions__rowtodict([fetcheddata])\\n return fetcheddata[0]\\n return None\",\n \"def fetchOne(self,query):\\n# self.updateDBAndLog(query)\\n# tup = self.cursor.fetchone()\\n\\tself.updateLog(query)\\n\\tcur = self.db.cursor()\\n\\tcur.execute(query)\\n\\ttup = cur.fetchone()\\n\\tcur.close()\\n\\treturn tup\",\n \"async def fetchone(self, query, connection=None):\\n async with self.connection(connection) as conn:\\n r = await conn.execute(query)\\n return await r.fetchone()\",\n \"def query_one(self, *args, **kwargs):\\n kwargs.setdefault('error_if_multiple', True)\\n records = self.query(*args, **kwargs)\\n return records[0] if records else None\",\n \"def first(self):\\n try:\\n data = self.get_cursor()[0]\\n return self.from_(**self.prepare_data(data))\\n except IndexError:\\n return None\",\n \"def queryWithOneResult(self, query_str):\\n try:\\n conn = MySQLdb.connect(**self.connect)\\n cursor = conn.cursor()\\n cursor.execute(query_str)\\n result = cursor.fetchone()\\n conn.commit()\\n conn.close()\\n return result\\n except Exception, e:\\n logger.error(\\\"mysql util error:\\\" + str(e))\",\n \"def query(query, args=(), one=False):\\n cur = get_db().execute(query, args)\\n result = cur.fetchall() # result is the answer to the query\\n cur.close()\\n return (result[0] if result else None) if one else result\",\n \"def fetchone(self):\\n try:\\n return next(self._results)\\n except StopIteration:\\n return None\",\n \"def fetch_single_res(db_conn, sql):\\n try:\\n with contextlib.closing(db_conn.cursor()) as curs:\\n curs.execute(sql)\\n return curs.fetchone()\\n except RuntimeError as e:\\n print(e.strerror)\",\n \"def get_one(self, table, q_filter=None, fail_on_empty=True, fail_on_more=True):\\n try:\\n result = None\\n with self.lock:\\n for _, row in self._find(table, self._format_filter(q_filter)):\\n if not fail_on_more:\\n return deepcopy(row)\\n if result:\\n raise DbException(\\\"Found more than one entry with filter='{}'\\\".format(q_filter),\\n HTTPStatus.CONFLICT.value)\\n result = row\\n if not result and fail_on_empty:\\n raise DbException(\\\"Not found entry with filter='{}'\\\".format(q_filter), HTTPStatus.NOT_FOUND)\\n return deepcopy(result)\\n except Exception as e: # TODO refine\\n raise DbException(str(e))\",\n \"def fetchone(self) -> Any:\\n return self.cursor.fetchone()\",\n \"def get_one(self, sql_command, cmd_param=None):\\n if cmd_param:\\n count = self._mysql_cursor.execute(sql_command, cmd_param)\\n else:\\n count = self._mysql_cursor.execute(sql_command)\\n\\n if count:\\n sql_result = self._mysql_cursor.fetchoneDict()\\n else:\\n sql_result = None\\n\\n return sql_result\",\n \"def select_single(self, rownum):\\n with self.connection:\\n return self.cursor.execute(\\n \\\"SELECT * FROM music WHERE id = ?\\\", (rownum,)\\n ).fetchall()[0]\",\n \"def find_one(self, select, tables, on={}, filter={}):\\n query, args = self.__build_find_query(select, tables, on, filter)\\n\\n cur = self.conn.cursor()\\n\\n try:\\n cur.execute(query, args)\\n result = cur.fetchone()\\n except Exception as e:\\n # logger = Logger.getInstance()\\n # logger.log(context=self.__class__.__name__,\\n # method=\\\"fetch_all\\\",\\n # msg=f\\\"query: {query}; Excpetion {e}\\\")\\n print(e)\\n result = None\\n finally:\\n cur.close()\\n return result\",\n \"def get_one(\\n self, table=None, fields='*', where=None, order=None, limit=(0, 1)\\n ):\\n\\n cur = self._select(table, fields, where, order, limit)\\n result = cur.fetchone()\\n\\n row = None\\n if result:\\n Row = namedtuple('Row', [f[0] for f in cur.description])\\n row = Row(*result)\\n\\n return row\",\n \"def select_one(cls, pk):\\n with sqlite3.connect(cls.dbpath) as conn:\\n conn.row_factory = sqlite3.Row\\n curs = conn.cursor()\\n sql = f\\\"\\\"\\\"SELECT * FROM {cls.tablename} WHERE pk =?;\\\"\\\"\\\"\\n curs.execute(sql, (pk,)) #don't forget to put a comma after single value inputs\\n row = curs.fetchone()\\n return cls(**row)\",\n \"async def one_or_none(\\n self,\\n statement: Executable,\\n params: t.Optional[\\n t.Union[t.Mapping[str, t.Any], t.Sequence[t.Mapping[str, t.Any]]]\\n ] = None,\\n execution_options: t.Optional[t.Mapping[str, t.Any]] = None,\\n bind_arguments: t.Optional[t.Mapping[str, t.Any]] = None,\\n ) -> t.Optional[t.Any]:\\n result = await self.execute(\\n statement, params, execution_options=execution_options, bind_arguments=bind_arguments\\n )\\n if isinstance(result, sa.engine.CursorResult):\\n item = result.one_or_none()\\n else:\\n item = result.scalar_one_or_none()\\n return item\",\n \"def select_one(self, table, column, value):\\n select_row = \\\"SELECT * FROM {} WHERE {}='{}' AND delete_status = FALSE;\\\".format(table, column, value)\\n self.cursor.execute(select_row)\\n row = self.cursor.fetchone()\\n return row\",\n \"def find(self, primary_key):\\n sql = '{} WHERE {} = %s'.format(self._select(), self.PRIMARY_KEY)\\n cursor = yield self._pool.execute(sql, [primary_key])\\n result = cursor.fetchmany(1)\\n return self.convert_result_to_object(result)\",\n \"def get_run(self, run_id: str) -> sqlite3.Row:\\n with self.table_access_condition:\\n conn = self._get_connection()\\n c = conn.cursor()\\n c.execute(\\n \\\"\\\"\\\"\\n SELECT * from runs\\n WHERE run_id = ?;\\n \\\"\\\"\\\",\\n (run_id,),\\n )\\n results = c.fetchall()\\n return results[0]\",\n \"async def find_one(self, **query):\\n\\n return await self._expand(await self.db.get_one(**query))\",\n \"def get(self, query: str, *args: List[str], **kwargs: Dict):\\n rows = self.query(query, *args, **kwargs)\\n if not rows:\\n return None\\n elif len(rows) > 1:\\n raise Exception(\\\"Multiple rows returned for Database.get() query\\\")\\n else:\\n return rows[0]\",\n \"def askOne(self, query, context):\\n results = self.ask(query, context, True)\\n return results.iterator().next() if (len(results) > 0) else None\",\n \"def query_db(query, args=(), one=False):\\n cur = get_db().execute(query, args)\\n rv = cur.fetchall()\\n return (rv[0] if rv else None) if one else rv\",\n \"def query_db(query, args=(), one=False):\\n cur = get_db().execute(query, args)\\n rv = cur.fetchall()\\n return (rv[0] if rv else None) if one else rv\",\n \"def query_db(query, args=(), one=False):\\n cur = get_db().execute(query, args)\\n rv = cur.fetchall()\\n return (rv[0] if rv else None) if one else rv\",\n \"def query_db(query, args=(), one=False):\\n cur = get_db().execute(query, args)\\n rv = cur.fetchall()\\n return (rv[0] if rv else None) if one else rv\",\n \"def query_db(query, args=(), one=False):\\n cur = get_db().execute(query, args)\\n rv = cur.fetchall()\\n return (rv[0] if rv else None) if one else rv\",\n \"def select_scalar(self, *args, **kwargs):\\n row = self.db_connection.execute(*args, **kwargs).fetchone()\\n return None if row is None else row[0]\",\n \"def _queryFetchOne(cols, tables, params):\\n try:\\n db = sqlite3.connect(DBPATH)\\n cursor = db.cursor()\\n\\n cursor.execute('SELECT ' + cols\\n + ' FROM ' + tables\\n + ' WHERE ' + params\\n )\\n objectData = cursor.fetchone()\\n\\n except Exception as e:\\n db.rollback()\\n raise e\\n\\n finally:\\n db.close()\\n\\n return objectData\",\n \"def get_single_result(self, rows):\\n if self.priority_sort_column:\\n rows = rows.sort_values(by=self.priority_sort_column,\\n ascending=self.priority_sort_ascending)\\n return rows.iloc[0]\",\n \"def get_one(self, object):\\n self.lock.acquire()\\n result = self.__Session.query(object).first()\\n self.lock.release()\\n return result\",\n \"def single(self):\\n records = list(self)\\n num_records = len(records)\\n if num_records == 0:\\n raise ResultError(\\\"Cannot retrieve a single record, because this result is empty.\\\")\\n elif num_records != 1:\\n raise ResultError(\\\"Expected a result with a single record, but this result contains at least one more.\\\")\\n else:\\n return records[0]\",\n \"def fetch_row(self, sql):\\n curs = self.q(sql, True)\\n cols = curs.column_names\\n row = curs.fetchone()\\n if curs._have_unread_result():\\n curs.fetchall()\\n curs.close()\\n\\n if row:\\n result = {}\\n for field in cols:\\n k = cols.index(field)\\n result[cols[k]] = row[k]\\n return result\\n else:\\n return None\",\n \"def find_one(self, criteria):\\n return self.connection.find_one(criteria)\",\n \"def get_first_item(cls):\\n return cls.query.first()\",\n \"def query_db(query, args=(), one=False):\\n cur = g.db.execute(query, args)\\n rv = cur.fetchall()\\n cur.close()\\n return (rv[0] if rv else None) if one else rv\",\n \"def fetch_one(cls: Type[_T], session: Session, identifier: int) -> _T:\\n return Query(cls, session=session).get(identifier)\",\n \"def fetchone(self):\\n row = self.cursor.fetchone()\\n\\n if row is None:\\n return None\\n\\n if self.model.single:\\n return self.__instance_from_db(self.model, row)\\n else:\\n return tuple(self.__instance_from_db(m, row) for m in self.model.models)\",\n \"def query(query, parameters=(), single=False):\\n cur = get_db().execute(query, parameters)\\n cur.row_factory = dict_factory\\n #cur = cur.cursor()\\n if single:\\n result = cur.fetchone()\\n else:\\n result = cur.fetchall()\\n cur.close()\\n return result\",\n \"def get(self, query, *parameters, **kwparameters):\\n cursor = self._cursor()\\n try:\\n cursor.execute(query, kwparameters or parameters)\\n return cursor.fetchone()\\n finally:\\n cursor.close()\",\n \"def get_row(self, pk):\\n ans = self.execute(self.commands.get_row(\\n cols=self._join_cols(self.columns),\\n table=self.name,\\n pk_col=self.primary_key_column,\\n pk=pk\\n ))\\n if not ans:\\n return None\\n return self._dictify(self.columns, ans[0])\",\n \"def one(self, except_all=False):\\n\\n length = len(self)\\n if length > 1:\\n raise MultipleResultsFound(\\\"%s results found.\\\" % length)\\n\\n result = self.first()\\n if result is None and except_all:\\n raise NoResultFound\\n return result\",\n \"def fetchone_query_and_close(self, query, values):\\n self.cursor.execute(query, values)\\n retval = self.cursor.fetchone()\\n self.__close_db()\\n return retval\",\n \"def find_first(cls, **kwargs):\\n return cls.query.filter_by(**kwargs).first()\",\n \"def get(self, question, rows):\\n con.query(question)\\n result = con.use_result()\\n return result.fetch_row(rows)\",\n \"async def find_by_id(self, _id: int) -> Record:\\n conn: Connection\\n async with self.db_pool.acquire() as conn:\\n return await conn.fetchrow(\\n f\\\"SELECT * FROM {self.table_name} WHERE {self.primary_key}=$1\\\",\\n _id,\\n )\",\n \"def get(cls, session: Session = None, **kwargs):\\n sess = next(cls.session()) if not session else session\\n query = sess.query(cls)\\n for key, val in kwargs.items():\\n col = getattr(cls, key)\\n query = query.filter(col == val)\\n\\n if query.count() > 1:\\n raise Exception(\\\"Only one row is supposed to be returned, but got more than one.\\\")\\n result = query.first()\\n if not session:\\n sess.close()\\n return result\",\n \"def _select_single(self, disc):\\n sqlstmt = \\\"SELECT h FROM %s WHERE d=?\\\" % self.VIEW\\n pickup = self.cursor.execute(sqlstmt, (disc,))\\n picked = pickup.fetchone()\\n if picked is not None:\\n # picked = (h,)\\n return picked[0]\\n else:\\n raise KeyError(str(disc))\",\n \"def findone(cls, *lst, **dct):\\n query = cls.where(*lst, **dct).select()\\n result = query.execute()\\n return result.fetchone()\",\n \"def first(self, query=False):\\n self.set_action(\\\"select\\\")\\n if query:\\n return self.limit(1)\\n result = self.connection.query(\\n self.limit(1).to_qmark(), self._bindings, results=1\\n )\\n relations = self.eager_load_model(result)\\n return self.owner.hydrate(result, relations=relations)\",\n \"def _dictfetchone(self):\\n data = self._dictfetchall()\\n if data:\\n return data[0]\\n return {}\",\n \"async def fetchrow(self, query, *args):\\n return await self.conn.fetchrow(query, *args)\",\n \"def execute(self, sql):\\n with self.connection.cursor() as dbc:\\n if sql[-1] != ';':\\n sql += ';'\\n dbc.execute(sql)\\n self.last_row = dbc.lastrowid\\n try:\\n return dbc.fetchall()\\n except:\\n return\",\n \"def get_raw_query(self, row_id):\\n cursor = self.connection.cursor()\\n cursor.execute(\\\"\\\"\\\"\\n SELECT raw_query FROM queries WHERE rowid=(?);\\n \\\"\\\"\\\", (row_id,))\\n return cursor.fetchone()[0]\",\n \"def SelectRow(\\n self, cnxn, cols=None, default=None, where=None, **kwargs):\\n rows = self.Select(cnxn, distinct=True, cols=cols, where=where, **kwargs)\\n if len(rows) == 1:\\n return rows[0]\\n elif not rows:\\n logging.info('SelectRow got 0 results, so using default %r', default)\\n return default\\n else:\\n raise ValueError('SelectRow got %d results, expected only 1', len(rows))\",\n \"def query_db(query, args=(), one=False):\\n conn = get_db()\\n cur = conn.cursor()\\n cur.execute(query, args)\\n rv = cur.fetchall()\\n cur.close()\\n return (rv[0] if rv else None) if one else rv\",\n \"def fetch_one(cls, limiter=None):\\n return cls.where(limiter).fetch_one()\",\n \"def execute_sql_scalar(self, sql):\\n msg_type, msg = self.protocol.build_execute_statement(\\\"sql\\\", sql)\\n self.protocol.send_msg(msg_type, msg)\\n result = RowResult(self)\\n result.fetch_all()\\n if result.count == 0:\\n raise InterfaceError(\\\"No data found\\\")\\n return result[0][0]\",\n \"def Fetch(self, sql, params=(), limit=None):\\n if self.SanityCheck(sql):\\n try:\\n self.cursor.execute(sql, params)\\n row = self.cursor.fetchone()\\n if not row: return None\\n headings = tuple(row.keys())\\n fetched = 1\\n if limit is None or limit <= 0:\\n rows = self.cursor.fetchall()\\n else:\\n rows = []\\n while fetched < limit:\\n row = self.cursor.fetchone()\\n if not row: break\\n rows.append(row)\\n fetched += 1\\n rows.insert(0, headings)\\n rows.insert(1, row)\\n return rows\\n except sqlite3.Error as e:\\n print >> sys.stderr, 'Failed to execute query: %s (%s)' % (sql, params)\\n return None\\n finally:\\n self.cursor.close()\\n self.cursor = None\",\n \"def first_or_raise(self):\\n res = super(CustomQuery, self).first()\\n if not res:\\n raise NotFoundException\\n return res\",\n \"def query(self, query):\\n cursor = self.database.cursor()\\n cursor.execute(query)\\n # If it's a query that's expected to return a value (EG: SELECT)\\n if query.strip().lower().startswith('select'): return cursor.fetchall()\",\n \"def get(self, obj):\\n obj = self._to_obj_tuple(obj)\\n rows = self.query(object=obj)\\n if rows:\\n return rows[0]\",\n \"def get_result_by_primary_key(self, pk):\\n session = self.session_factory()\\n result = session.query(PipelineRun).filter_by(id=pk).first()\\n session.close()\\n return result\",\n \"def cud_operations(self, query=None, val=None):\\n with self.conn:\\n with self.conn.cursor() as curr:\\n curr.execute(query, val)\\n row = curr.fetchone()\\n # self.conn.close()\\n return row\",\n \"def fetch_value(self, query, params=None):\\n\\n self.execute_query(query, params)\\n result = self.cursor.fetchone()\\n return list(result.values())[0]\",\n \"def first(self, **kwargs):\\n return self.find(**kwargs).first()\",\n \"def _exec1(self, sql):\\n result = self._exec(sql)\\n return [row[0] for row in result]\",\n \"def first(self, order_by: Optional[str] = '<created_at', **kwargs):\\n kwargs = self._preprocess(**kwargs)\\n with rconnect() as conn:\\n try:\\n query = self.q\\n if order_by is not None:\\n query = self._order_by(query, order_by)\\n\\n # NOTE: Always filter before limiting\\n if len(kwargs) > 0:\\n query = query.filter(kwargs)\\n query = self._limit(query, 1)\\n rv = query.run(conn)\\n\\n except ReqlOpFailedError as e:\\n console.warn(e)\\n raise\\n except Exception as e:\\n console.warn(e)\\n raise\\n else:\\n try:\\n data = [self.__model__(_, strict=False) for _ in rv]\\n return data[0]\\n except IndexError:\\n return None\",\n \"async def find(self, pk_value: Any) -> RowProxy:\",\n \"def query_db(query, args=(), one=False):\\n db = get_db()\\n cur = db.cursor()\\n cur.execute(query, args)\\n rv = fetch_all(cur)\\n cur.close()\\n db.commit()\\n return (rv[0] if rv else None) if one else rv\",\n \"def _query(self, query, data, fetch='all'):\\n rows = self.cursor.execute(query, data)\\n try:\\n if fetch == 'all':\\n return rows.fetchall()\\n elif fetch == 'one':\\n return rows.fetchone()\\n elif fetch == 'none':\\n self.conn.commit()\\n except Exception, e:\\n self.log.warning(\\\"miss on the db for {}: {}\\\".format(q, e))\\n raise Exception(\\\"SQLiteDB Fail on query: {}\\\".format(query) + \\\\\\n \\\"excetion was {}\\\".format(e))\",\n \"def get_first(cls, condition=None, async=True):\\n\\n result = cls.get(condition=condition, async=False)\\n\\n # for async result, return the future to be handled elsewhere\\n if async:\\n return result\\n\\n if not result:\\n return None\\n\\n return result[0]\",\n \"def find_one(self, collection, query):\\n obj = getattr(self.db, collection)\\n result = obj.find_one(query)\\n return result\",\n \"def first(self, limit=1, columns=None):\\n if not columns:\\n columns = ['*']\\n\\n results = self.take(limit).get(columns)\\n\\n if len(results) > 0:\\n return results[0]\\n\\n return\",\n \"def execute_and_fetch(db, query):\\n rows = []\\n\\n db.query(query)\\n res = db.use_result()\\n row = res.fetch_row()\\n while row != ():\\n rows.append(row[0])\\n row = res.fetch_row()\\n return rows\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8280298","0.80509466","0.79736346","0.7938769","0.79376507","0.78329694","0.7832246","0.76202947","0.7573742","0.75649405","0.7521718","0.75176406","0.7498476","0.74828917","0.7433074","0.7370562","0.72961265","0.72804594","0.7238881","0.72119147","0.7204466","0.7143122","0.7109367","0.71064556","0.7088819","0.70751756","0.7024199","0.7008461","0.70012945","0.69961876","0.6994135","0.69447845","0.6944754","0.6919039","0.69059765","0.6865538","0.6843003","0.68288785","0.6823704","0.6795375","0.67669684","0.67594934","0.67514646","0.66942126","0.66684157","0.66306245","0.66303146","0.6624741","0.66113955","0.66113955","0.66113955","0.66113955","0.66113955","0.660814","0.6605493","0.65804875","0.6578645","0.65678555","0.65559286","0.6555086","0.65485686","0.64947176","0.6479554","0.6471203","0.64657164","0.64657164","0.6454756","0.6441464","0.64391357","0.6404695","0.6402214","0.6397753","0.63874114","0.63728154","0.6344573","0.6339994","0.6339274","0.6335303","0.6326956","0.63252515","0.6322452","0.63150096","0.62935793","0.62896854","0.6286048","0.62428254","0.6242318","0.6218732","0.6210325","0.62065965","0.619338","0.6168437","0.61541456","0.6153028","0.61434025","0.6127558","0.6117554","0.61060774","0.6080316","0.6077795","0.6041381"],"string":"[\n \"0.8280298\",\n \"0.80509466\",\n \"0.79736346\",\n \"0.7938769\",\n \"0.79376507\",\n \"0.78329694\",\n \"0.7832246\",\n \"0.76202947\",\n \"0.7573742\",\n \"0.75649405\",\n \"0.7521718\",\n \"0.75176406\",\n \"0.7498476\",\n \"0.74828917\",\n \"0.7433074\",\n \"0.7370562\",\n \"0.72961265\",\n \"0.72804594\",\n \"0.7238881\",\n \"0.72119147\",\n \"0.7204466\",\n \"0.7143122\",\n \"0.7109367\",\n \"0.71064556\",\n \"0.7088819\",\n \"0.70751756\",\n \"0.7024199\",\n \"0.7008461\",\n \"0.70012945\",\n \"0.69961876\",\n \"0.6994135\",\n \"0.69447845\",\n \"0.6944754\",\n \"0.6919039\",\n \"0.69059765\",\n \"0.6865538\",\n \"0.6843003\",\n \"0.68288785\",\n \"0.6823704\",\n \"0.6795375\",\n \"0.67669684\",\n \"0.67594934\",\n \"0.67514646\",\n \"0.66942126\",\n \"0.66684157\",\n \"0.66306245\",\n \"0.66303146\",\n \"0.6624741\",\n \"0.66113955\",\n \"0.66113955\",\n \"0.66113955\",\n \"0.66113955\",\n \"0.66113955\",\n \"0.660814\",\n \"0.6605493\",\n \"0.65804875\",\n \"0.6578645\",\n \"0.65678555\",\n \"0.65559286\",\n \"0.6555086\",\n \"0.65485686\",\n \"0.64947176\",\n \"0.6479554\",\n \"0.6471203\",\n \"0.64657164\",\n \"0.64657164\",\n \"0.6454756\",\n \"0.6441464\",\n \"0.64391357\",\n \"0.6404695\",\n \"0.6402214\",\n \"0.6397753\",\n \"0.63874114\",\n \"0.63728154\",\n \"0.6344573\",\n \"0.6339994\",\n \"0.6339274\",\n \"0.6335303\",\n \"0.6326956\",\n \"0.63252515\",\n \"0.6322452\",\n \"0.63150096\",\n \"0.62935793\",\n \"0.62896854\",\n \"0.6286048\",\n \"0.62428254\",\n \"0.6242318\",\n \"0.6218732\",\n \"0.6210325\",\n \"0.62065965\",\n \"0.619338\",\n \"0.6168437\",\n \"0.61541456\",\n \"0.6153028\",\n \"0.61434025\",\n \"0.6127558\",\n \"0.6117554\",\n \"0.61060774\",\n \"0.6080316\",\n \"0.6077795\",\n \"0.6041381\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17775,"cells":{"query":{"kind":"string","value":"Executes the query and returns the number of matching rows."},"document":{"kind":"string","value":"def count(self):\n objects = self.all()\n return len(objects)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _execute_rowcount(self, query, *parameters, **kwparameters):\n result = -1\n cursor = self._cursor()\n ret = self._execute(cursor, query, parameters, kwparameters)\n if ret != -1:\n result = cursor.rowcount\n cursor.close()\n return result","def execute_rowcount(self, query, cs_type=None, *parameters, **kwparameters):\n cursor = self._cursor(cs_type)\n try:\n self._execute(cursor, query, parameters, kwparameters)\n return cursor.rowcount\n finally:\n cursor.close()","def execute(self, sql):\r\n try:\r\n with self.connection.cursor() as cursor:\r\n cursor.execute(sql)\r\n self.connection.commit()\r\n try:\r\n count = cursor.rowcount\r\n res = cursor.fetchall()\r\n except psycopg2.ProgrammingError:\r\n res = count\r\n return res\r\n except psycopg2.Error as e:\r\n # print(sql)\r\n raise e","def _executemany_rowcount(self, query, parameters):\n result = -1\n cursor = self._cursor()\n try:\n cursor.executemany(query, parameters)\n result = cursor.rowcount\n except Exception:\n logging.error(\"Error occurd in executemany_rowcount\")\n finally:\n cursor.close()\n return result","def count(self):\r\n if self._batch:\r\n raise CQLEngineException(\"Only inserts, updates, and deletes are available in batch mode\")\r\n\r\n if self._result_cache is None:\r\n query = self._select_query()\r\n query.count = True\r\n _, result = self._execute(query)\r\n return result[0][0]\r\n else:\r\n return len(self._result_cache)","def get_row_count(self, query=None):\n if query is not None:\n self.execute_query(query)\n return len(self.recordset.fetchall())","def get_row_count(self) :\n\n count_sql = 'SELECT count(*) FROM \"%s\"' % (self.name,)\n cur = self.con.cursor()\n cur.execute(count_sql)\n return cur.fetchone()[0]","def num_rows(curs, table_name):\n return curs.execute(\"\"\"SELECT COUNT(*)\n FROM {}\"\"\".format(table_name)).fetchone()[0]","def count_rows(self):\n with self.conn:\n return len(self.select_all_rows())","def rowCount(self):\n rows = self.db().execute(\"SELECT COUNT(*) FROM data\")\n return rows.fetchall()[0][0]","def query(self, query, *args, **kwargs):\n c = self._conn.cursor()\n easy_query(c.execute, query, *args, **kwargs)\n if c.description is None:\n return c.rowcount\n return Results(c.description, c.fetchall())","def query(self, query, params=()):\n with self.get_pool().connection() as conn:\n c = conn.cursor()\n try:\n c.execute(query, params)\n conn.commit()\n except IntegrityError:\n conn.rollback()\n raise\n if c.description is None:\n return c.rowcount\n return Results(c.description, c.fetchall())","def count_rows(self):\n with self.connection:\n result = self.cursor.execute(\"SELECT * FROM music\").fetchall()\n return len(result)","def count(query):\n cursor = db.execute_sql(query)\n result = cursor.fetchone()[0]\n return result","async def count_rows(self, query: str, args: Optional[Iterable]=None) -> float:\n res = await self.fetch_all(query, args)\n return len(res)","def _num_rows(cursor, table):\n cursor.execute('SELECT COUNT(*) AS rowcount FROM %s' % table)\n return cursor.fetchone()['rowcount']","def count(self, table, field, condition, *parameters, **kwparameters):\n table = self.prefix + table\n field = 'COUNT(' + field + ') AS rows_count'\n query = \"SELECT \" + field + \" FROM \" + table + \" \" + condition\n rows_count = self.query(query, *parameters, **kwparameters)\n if rows_count:\n return int(rows_count[0][\"rows_count\"])\n else:\n return 0","def query_count(query, params=None):\n count_query = 'SELECT COUNT(*) FROM (' + query + ') AS a;'\n response = database.get_engine().execute(count_query, params)\n count = response.fetchone()\n response.close()\n return count[0]","def executemany_rowcount(self, query, *parameters):\n cursor = self._cursor(None)\n try:\n cursor.executemany(query, *parameters)\n return cursor.rowcount\n finally:\n cursor.close()","def rpc_database_count_rows(self, *args):\n\t\targs = list(args)\n\t\tfields = self.path.split('/')[1:-2]\n\t\tassert len(fields) == len(args)\n\t\ttable = DATABASE_TABLE_OBJECTS.get(self.path.split('/')[-2])\n\t\tassert table\n\t\tsession = db_manager.Session()\n\t\tquery = session.query(table)\n\t\tquery = query.filter_by(**dict(zip((f + '_id' for f in fields), args)))\n\t\tresult = query.count()\n\t\tsession.close()\n\t\treturn result","def count(self):\n sql = u'SELECT count() FROM (%s)' % self.as_sql()\n raw = self._database.raw(sql)\n return int(raw) if raw else 0","def countPlayers():\n #gets the player column from the players table\n conn = DB().execute(\"SELECT COUNT(player) FROM players;\")\n #gets the result of the select statement\n count = conn[\"cursor\"].fetchone()[0]\n conn[\"cursor\"].close()\n return count","def get_count()->int:\n stmt = sqlalchemy.select([sqlalchemy.func.count()]).select_from(_READING_TABLE)\n return execute_command(stmt).fetchall()[0][0]","def execute(self, sql, *args):\r\n args = args or None\r\n with Executer(self) as cursor:\r\n cursor.execute(sql, args)\r\n return cursor.rowcount, cursor.lastrowid","def sql(self, sql, *args):\n cursor = self.connection.cursor()\n try:\n num_records = cursor.execute(sql, args)\n \n #Restart mysql if it's died for whatever reason, seems quite common\n except (pymysql.err.InterfaceError, pymysql.err.OperationalError):\n self.refresh()\n num_records = cursor.execute(sql, args)\n \n self.connection.commit()\n \n try:\n if sql.startswith('SELECT count(*) FROM'):\n return cursor.fetchall()[0][0]\n \n elif sql.startswith('SELECT'):\n return cursor.fetchall()\n \n elif sql.startswith('UPDATE'):\n return num_records\n \n elif sql.startswith('INSERT'):\n return cursor.lastrowid\n \n elif sql.startswith('DELETE'):\n return num_records\n finally:\n cursor.close()","def executeQuery(self):\n try:\n # self.checkValName()\n self.dbc.execute(self.query, self.val)\n self.cnt = self.dbc.rowcount\n except MySQLdb.Error as e:\n print \"Query failed: %s\" % e","def count(self):\n self._fetch_if_needed()\n return len(self._result_cache.get('rows', []))","def count(self, sql, params=None, error_message=None, connection=None):\n fresh_connection = False\n try:\n if not connection:\n connection = self.database.connection()\n fresh_connection = True\n # self.context.logger.debug(connection.cursor.mogrify(sql, params))\n result = single_value_sql(connection, sql, params)\n except psycopg2.Error, e:\n if error_message is None:\n error_message = \"Failed to execute count against PostgreSQL\"\n error_message = \"%s - %s\" % (error_message, str(e))\n logger.error(error_message, exc_info=True)\n raise DatabaseError(error_message)\n finally:\n if connection and fresh_connection:\n connection.close()\n\n return result","def count(self):\n with self.pdq:\n (count,)=self.pdq.cursor().execute('select count(*) from pdq').next()\n return count","def count(self, query):\n if len(query._where) == 0:\n url = '%s/%s' % (self.uri, query.table())\n else:\n url = '%s/%s/filter?%s' % (self.uri, query.table(), query.encode())\n data, resp = self.execute(method='HEAD', url=url)\n count = resp.getheader(\"X-Result-Count\")\n return int(count)","def countPlayers():\n # gets connection to tournament database in conn object\n conn = connect()\n # gets the cursor to execute queries\n c = conn.cursor()\n # executes select with count aggregate function query number of players\n # in PLAYER table\n c.execute(\"SELECT COUNT(*) FROM PLAYER;\")\n # retreives the result in count variable\n count = c.fetchone() [0]\n # closes the connection to tournament database\n conn.close()\n # returns the number of players in PLAYER table\n return count","def _get_num_searches(self):\n self._cursor.execute(f\"\"\"\n SELECT COALESCE(MAX(rowid), 0)\n FROM {self._table_name};\"\"\", {\"table\": self._table_name})\n\n num_searches = self._cursor.fetchone()[0]\n return num_searches","def count(self, table, q_filter=None):\n try:\n with self.lock:\n return sum(1 for x in self._find(table, self._format_filter(q_filter)))\n except DbException:\n raise\n except Exception as e: # TODO refine\n raise DbException(str(e))","def count(self):\n if self._result_cache is not None:\n return len(self._result_cache)\n\n return self.query.get_count(using=self.db)","def count(self):\n ans = self.execute(self.commands.table_count(self.name))\n return ans[0][0]","def countPlayers():\n\n count = 0\n query = (\"SELECT COUNT(id) FROM players;\")\n results = executeQuery({'dbname': 'tournament', 'query' : query, 'type' : 'find'})\n for row in results:\n count = row[0]\n return count","def count(self, query):","def get_num_rows(self):\n return self._num_rows","def query_database(conn, query, params=None, commit=False, returning=False):\n if params is None:\n params = []\n cur = conn.cursor()\n cur.execute(query, params)\n\n ret = None\n if commit:\n conn.commit()\n if returning:\n ret = cur.fetchall()\n else:\n ret = cur.rowcount\n\n else:\n conn.rollback()\n ret = cur.fetchall()\n cur.close()\n return ret","def execute_both(self, query, *parameters, **kwparameters):\n cursor = self._cursor()\n try:\n self._execute(cursor, query, parameters, kwparameters)\n return cursor.lastrowid, cursor.rowcount\n finally:\n cursor.close()","def execute(self, conn, rows: list) -> int:\n if not len(rows):\n raise ValueError('No data provided')\n if len(self.columns) != len(rows[0]):\n raise ValueError('Expecting {} columns, found {}'.format(\n len(self.columns), len(rows[0])))\n\n conn = conn()\n cursor = conn.cursor()\n try:\n cursor.execute(self.query + ', '.join(self.escape_rows(rows)))\n conn.commit()\n finally:\n cursor.close()\n conn.close()\n\n return len(rows)","def execute(self, query):\n with self.conn.cursor() as cur:\n # Execute the query\n try:\n cur.execute(query)\n except Exception as exc:\n print(\"Unable to execute query. Error was {0}\".format(str(exc)))\n exit()\n rows = cur.fetchall()\n return rows","def execute(self, begin: int, end: int) -> int:\n row_number = self.row_number\n self.row_number += 1\n return row_number","def get_response_record_count(self):\n if self.record_count is None:\n raise QueryNotExecuted(\"No query has been executed. Use the Execute Query keyword to retrieve records.\")\n else:\n return self.record_count","def test_count_all_rows_query(self):\n\n mock_connector = MagicMock()\n database = Database()\n database.connect(connector_impl=mock_connector)\n connection = mock_connector.connect()\n cursor = connection.cursor()\n cursor.__iter__.return_value = [(123,)]\n\n num = database.count_all_rows()\n\n self.assertEqual(num, 123)\n self.assertTrue(cursor.execute.called)\n\n sql = cursor.execute.call_args[0][0].lower()\n self.assertIn('select count(1)', sql)\n self.assertIn('from `covidcast`', sql)","def get_count(self):\n database = managers.database_manager.get_database(self.owner_id, self.database_id)\n cur = database.get_connection().cursor()\n cur.execute(\"SELECT COUNT(*) FROM `%s`\" % (self.name,))\n return cur.fetchone()[0]","def countPlayers():\n cur1 = conn.cursor()\n cur1.execute(\"\"\"SELECT * from players\"\"\")\n rows = cur1.fetchall()\n print \"\\t\\t\\tCount players; SELECT * from players:\\n\"\n #print (rows)\n count = 0\n for row in rows:\n print \"\\t\\t\\t\", row\n count = count+1\n print \"\\t\\t\\tCount:\", count\n print \"\\n\"\n return count","def rowcount(self):\n\t\treturn self._count","def executeQuery(self):\n try:\n # self.checkValName()\n self.cursor.execute(self.query, self.val)\n self.cnt = self.conn.total_changes\n except Exception as e:\n print \"Query failed: %s\" % e","def count(self):\n if not self.model:\n raise NameError('database model has not been set.')\n\n with self.session() as session:\n query = self.get_query(session)\n data = query.count()\n return data","def countPlayers():\n cursor.execute(\"\"\"select count(name) from players\"\"\")\n return cursor.fetchone()[0]","def count(self, query=None):\n return self.__db.count(query)","def count(cls):\n return DBSESSION.query(cls).count()","def countPlayers():\n conn, c = connect()\n\n q = \"select count(id) FROM PLAYERS;\"\n c.execute(q)\n res = c.fetchone()\n\n c.close()\n conn.commit()\n conn.close()\n return int(res[0])","def countPlayers():\n\n conn, c = main.connect()\n c.execute(\"SELECT count(*) FROM player\")\n\n return c.fetchone()[0]","def get_result_set_count(self):\n return self.db.zcount(\"soq_results\", 0, time())","def countPlayers():\n db_conn = connect()\n db_cursor = db_conn.cursor()\n db_cursor.execute(\"select count(player_id) from players;\")\n # fetchall returns a dict containing a tuples\n results = db_cursor.fetchall()\n #print \"Results: \" , repr(results), \" \", dir(results)\n player_count = results.pop()[0]\n #print \"player_count: \", player_count, \" \", player_count.__class__, \" \", dir(player_count)\n db_conn.commit()\n db_conn.close()\n return player_count","def countPlayers():\n with _connect_db() as (conn, cur):\n cur.execute(\"\"\"SELECT COUNT(*) from players;\"\"\")\n ret = int(cur.fetchone()[0])\n return ret","def count_records(self, conn, when='After insert') -> None:\n cur = conn.cursor()\n cur.execute(f\"SELECT COUNT(*) FROM {self.table}\")\n print(f\"{when}, the table {self.table} contains {cur.fetchall()[0][0]} records.\")","def _execute(self, query, params=None, retry=False):\n if self.settings.get('ENGINE') == 'sqlite':\n if '%s' in query and params:\n query = query.replace('%s', '?')\n elif '%(' in query and params:\n query = query.replace('%(', ':').replace(')s', '')\n if logger.getEffectiveLevel() == logging.DEBUG:\n _query = ' '.join([x.strip() for x in query.split()])\n logger.debug('QUERY: %s PARAMS: %s', _query, params)\n try:\n rowcount = self.cursor.execute(query, params)\n self._db.commit()\n return rowcount\n except (AttributeError, self.dbdriver.OperationalError) as err:\n if not retry:\n logger.debug('Database reconnect due to error: %s', err)\n self._connect()\n return self._execute(query, params, retry=True)\n else:\n raise\n except Exception as err:\n logger.exception('Database error: %s', err)\n raise","def countPlayers():\n\n db = connect()\n c = db.cursor()\n query = (\"SELECT count(players.id) AS count_player FROM players;\")\n c.execute(query)\n count_player = c.fetchone()[0]\n db.close()\n return count_player","def countPlayers():\n conn, cur = connect()\n query = \"SELECT count(*) AS player_count FROM players;\"\n try:\n cur.execute(query)\n except:\n print(\"Error encountered when selecting player count from players table\")\n num_players = cur.fetchone()\n conn.close()\n return num_players['player_count']","def get_rows_in_users(conn):\n cur = conn.cursor()\n cur.execute(\"SELECT * FROM users\")\n num_of_rows = len(cur.fetchall())\n\n return num_of_rows","def rowcount(self):\n return self._count","async def executemany(self, operation, seq_of_parameters):\n\n count = 0\n for parameters in seq_of_parameters:\n count += await self.execute(operation, parameters)\n self.rowcount = count\n return count","def countPlayers():\n conn = connect()\n c = conn.cursor()\n # Counts the number of entries in the \"players\" table.\n c.execute(\"select count(*) as num from players;\")\n num = c.fetchone()[0]\n conn.commit()\n conn.close()\n return num","def get_total_mkresults(self):\n\n self._logger.debug(\"Getting mk results count\")\n\n try:\n self.check_if_db_connected()\n cursor = self._db_conn.cursor()\n cursor.execute(\"SELECT COUNT(result_id) FROM mk_ind_result\")\n count = cursor.fetchone()[0]\n\n except MySQLdb.OperationalError:\n self._logger.error(\"MySQL operational error occured\")\n traceback.print_exc()\n raise exceptions.DBConnectionError(\"Cannot connect to MySQL server\")\n\n except MySQLdb.ProgrammingError:\n self._logger.error(\"MySQL programming error\")\n traceback.print_exc()\n raise exceptions.DBSyntaxError(\"MySQL syntax error\")\n\n else:\n return count","def count(self):\n if self._distinct or self._limits:\n # Use a subquery, since a simple count won't be accurate\n sql = u'SELECT count() FROM (%s)' % self.as_sql()\n raw = self._database.raw(sql)\n return int(raw) if raw else 0\n\n # Simple case\n conditions = (self._where_q & self._prewhere_q).to_sql(self._model_cls)\n return self._database.count(self._model_cls, conditions)","def countPlayers():\n conn = connect()\n cursor = conn.cursor()\n cursor.execute(\"SELECT count(*) FROM players\")\n playerCount = cursor.fetchone()[0]\n conn.close()\n return playerCount","def countPlayers():\n conn, c = connect()\n c.execute(\"SELECT COUNT(*) FROM players;\")\n return c.fetchone()[0]","def get_result_size(self) -> int:\n\n # Modifie the query to count the number of answer\n if self.query.strip().startswith(\"SELECT\") or self.query.strip().startswith(\"select\"):\n\n if self.verbose:\n print(tm.strftime(f\"[%H:%M:%S] Obtention du nombre de résultats avant exécuter la requête\"))\n\n start: int = 7 # We detect the position of the first variable after the select\n while self.query[start] != '?':\n start += 1\n end: int = start\n while self.query[end:end + 5] != \"WHERE\" and self.query[end:end + 5] != \"where\":\n end += 1\n\n mot: str = self.query[start: end - 1] # The name of the variables\n\n self.sparql.setQuery(self.query.replace(mot, f\"(COUNT (*) as ?cnt)\", 1))\n processed_results: dict = self.sparql.query().convert() # Do the query\n number_of_results: int = int(processed_results['results']['bindings'][0]['cnt']['value'])\n\n if self.verbose:\n print(tm.strftime(f\"[%H:%M:%S] Il y a {number_of_results} résultats...\"))\n\n return number_of_results\n return 1","def len(self):\n with self.session as session:\n\n return session.execute(select([func.count()]).select_from(IndexRecord)).scalar()","def count(self, query=None):\n return self.create_search(query).count()","def count(cls):\n return cls.query.count()","def countPlayers():\n\n conn = psycopg2.connect(\"dbname=tournament\")\n c = conn.cursor()\n c.execute(\"SELECT count(*) FROM player\")\n count = c.fetchall()[0][0]\n conn.close\n\n return count","def _get_total_record_count(self, conn, tblname):\n with conn.cursor() as cursor:\n cursor.execute(sql.SQL('SELECT COUNT(*) FROM {0}').format(sql.Identifier(tblname)))\n return cursor.fetchone()[0]","def count(self):\n return self.query.count(with_limit_and_skip = True)","def countPlayers():\n c.execute(\"SELECT count(*) FROM players\");\n num = c.fetchone()[0]\n print num\n if num == 1:\n print \"There is only %d player registered\" % (num)\n else:\n print \"There are %d players registered\" % (num)\n return num\n # except IndexError:\n # if num == 1:\n # print \"There is only %d player registered\" % (num)\n # else:\n # print \"There are %d players registered\" % (num)","def rowCount(self, index):\n return len(self._data)","def count(self, where=\"\", parameters={}):\n return self._backend.count(where, parameters)","def _raw_delete(self, using):\n query = self.query.clone()\n query.__class__ = sql.DeleteQuery\n cursor = query.get_compiler(using).execute_sql(CURSOR)\n if cursor:\n with cursor:\n return cursor.rowcount\n return 0","def count_players():\n DB = connect()\n c = DB.cursor()\n c.execute(\"SELECT * FROM count_players\")\n DB.commit()\n player_count = c.fetchall()[0][0]\n DB.close()\n return player_count","def executeQuery(query):\n c = db.cursor()\n c.execute(query)\n rows = c.fetchall()\n db.close()\n return rows","def countPlayers():\n db, cursor = connect()\n cursor.execute( \" SELECT count(*) as num FROM players \")\n count = cursor.fetchone()[0]\n return int(count)","def update(self, query: str, *args, **kwargs):\n cursor = self._cursor()\n try:\n self._execute(cursor, query, args, kwargs)\n return cursor.rowcount\n finally:\n cursor.close()","def row_count(self):\n return self.__row_count","def _count(self):\n if self._count_valid:\n return self._total_results\n\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\n results = result.get(\"results\", [])\n\n self._total_results = len(results)\n self._count_valid = True\n\n return self._total_results","def countPlayers():\n db = connect()\n db_cursor = db.cursor()\n query = \"SELECT COUNT(id) AS total_players FROM players\"\n db_cursor.execute(query)\n results = db_cursor.fetchone()\n db.close()\n if results:\n return results[0]\n else:\n return '0'","def _get_count(results):\n return len(results)","def row_count(self):\n return self._row_count","def get_rows(cur):\n\n # count all rows\n cur.execute('SELECT count(1) `num` FROM `flusurv`')\n for (num,) in cur:\n return num","def get_total_ssresults(self):\n\n self._logger.debug(\"Getting ss results count\")\n\n try:\n self.check_if_db_connected()\n cursor = self._db_conn.cursor()\n cursor.execute(\"SELECT COUNT(result_id) FROM ss_ind_result\")\n count = cursor.fetchone()[0]\n\n except MySQLdb.OperationalError:\n self._logger.error(\"MySQL operational error occured\")\n traceback.print_exc()\n raise exceptions.DBConnectionError(\"Cannot connect to MySQL server\")\n\n except MySQLdb.ProgrammingError:\n self._logger.error(\"MySQL programming error\")\n traceback.print_exc()\n raise exceptions.DBSyntaxError(\"MySQL syntax error\")\n\n else:\n return count","def countPlayers():\n DB = connect()\n c = DB.cursor()\n c.execute(\"SELECT count(id) FROM tournament\")\n result=c.fetchone()\n DB.close()\n return result[0]","def row_count_equal_to(self, expected_row_num, query=None):\n if query is not None:\n self.execute_query(query)\n row_num = len(self.recordset.fetchall())\n assert row_num == expected_row_num, f'The number of records {row_num} is not same as expected row num {expected_row_num}'\n return self","def num_rows(self):\n\n self.logger.debug('numrows looking for rows matching %s' \\\n % (self.locators['row']))\n rows = self.find_elements(self.locators['row'])\n self.logger.debug('rows = %s' % (rows))\n\n for r in rows:\n self.logger.debug('r.text() = %s' % r.get_attribute('innerHTML'))\n\n return len(rows)","def count_entries(self, tablename):\n query = \"Select count(*) from \" + tablename\n try:\n self.__cur.execute(query)\n except Exception as e:\n self.__conn.rollback()\n raise e\n fetcheddata = self.__cur.fetchone()\n return fetcheddata[0]","async def count(self, table_name: str, column: str, conditions_list=None):\n if conditions_list:\n conditions = LemkPgUtils.get_conditions(conditions_list)\n query = f\"\"\"SELECT COUNT({column}) FROM {table_name} WHERE {\" \".join(conditions)}\"\"\"\n else:\n query = f\"\"\"SELECT COUNT({column}) FROM {table_name}\"\"\"\n result = await LemkPgUtils.get_query_result(self.dsn, query)\n return result","def row_count(self) -> int:\n return len(self.rows)","def row_count(self) -> int:\n return len(self.rows)","def execute_total_dataframe_count_strategy(dataframe):\n\n logging.debug('>>>>>>>>> Using total count strategy <<<<<<<<<<<<')\n\n total_rows = len(dataframe.index)\n\n return total_rows","def test_rowcount_select(cursor: pyodbc.Cursor):\n cursor.execute(\"create table t1(i int)\")\n count = 4\n for i in range(count):\n cursor.execute(\"insert into t1 values (?)\", i)\n cursor.execute(\"select * from t1\")\n assert cursor.rowcount == -1\n\n rows = cursor.fetchall()\n assert len(rows) == count\n assert cursor.rowcount == -1"],"string":"[\n \"def _execute_rowcount(self, query, *parameters, **kwparameters):\\n result = -1\\n cursor = self._cursor()\\n ret = self._execute(cursor, query, parameters, kwparameters)\\n if ret != -1:\\n result = cursor.rowcount\\n cursor.close()\\n return result\",\n \"def execute_rowcount(self, query, cs_type=None, *parameters, **kwparameters):\\n cursor = self._cursor(cs_type)\\n try:\\n self._execute(cursor, query, parameters, kwparameters)\\n return cursor.rowcount\\n finally:\\n cursor.close()\",\n \"def execute(self, sql):\\r\\n try:\\r\\n with self.connection.cursor() as cursor:\\r\\n cursor.execute(sql)\\r\\n self.connection.commit()\\r\\n try:\\r\\n count = cursor.rowcount\\r\\n res = cursor.fetchall()\\r\\n except psycopg2.ProgrammingError:\\r\\n res = count\\r\\n return res\\r\\n except psycopg2.Error as e:\\r\\n # print(sql)\\r\\n raise e\",\n \"def _executemany_rowcount(self, query, parameters):\\n result = -1\\n cursor = self._cursor()\\n try:\\n cursor.executemany(query, parameters)\\n result = cursor.rowcount\\n except Exception:\\n logging.error(\\\"Error occurd in executemany_rowcount\\\")\\n finally:\\n cursor.close()\\n return result\",\n \"def count(self):\\r\\n if self._batch:\\r\\n raise CQLEngineException(\\\"Only inserts, updates, and deletes are available in batch mode\\\")\\r\\n\\r\\n if self._result_cache is None:\\r\\n query = self._select_query()\\r\\n query.count = True\\r\\n _, result = self._execute(query)\\r\\n return result[0][0]\\r\\n else:\\r\\n return len(self._result_cache)\",\n \"def get_row_count(self, query=None):\\n if query is not None:\\n self.execute_query(query)\\n return len(self.recordset.fetchall())\",\n \"def get_row_count(self) :\\n\\n count_sql = 'SELECT count(*) FROM \\\"%s\\\"' % (self.name,)\\n cur = self.con.cursor()\\n cur.execute(count_sql)\\n return cur.fetchone()[0]\",\n \"def num_rows(curs, table_name):\\n return curs.execute(\\\"\\\"\\\"SELECT COUNT(*)\\n FROM {}\\\"\\\"\\\".format(table_name)).fetchone()[0]\",\n \"def count_rows(self):\\n with self.conn:\\n return len(self.select_all_rows())\",\n \"def rowCount(self):\\n rows = self.db().execute(\\\"SELECT COUNT(*) FROM data\\\")\\n return rows.fetchall()[0][0]\",\n \"def query(self, query, *args, **kwargs):\\n c = self._conn.cursor()\\n easy_query(c.execute, query, *args, **kwargs)\\n if c.description is None:\\n return c.rowcount\\n return Results(c.description, c.fetchall())\",\n \"def query(self, query, params=()):\\n with self.get_pool().connection() as conn:\\n c = conn.cursor()\\n try:\\n c.execute(query, params)\\n conn.commit()\\n except IntegrityError:\\n conn.rollback()\\n raise\\n if c.description is None:\\n return c.rowcount\\n return Results(c.description, c.fetchall())\",\n \"def count_rows(self):\\n with self.connection:\\n result = self.cursor.execute(\\\"SELECT * FROM music\\\").fetchall()\\n return len(result)\",\n \"def count(query):\\n cursor = db.execute_sql(query)\\n result = cursor.fetchone()[0]\\n return result\",\n \"async def count_rows(self, query: str, args: Optional[Iterable]=None) -> float:\\n res = await self.fetch_all(query, args)\\n return len(res)\",\n \"def _num_rows(cursor, table):\\n cursor.execute('SELECT COUNT(*) AS rowcount FROM %s' % table)\\n return cursor.fetchone()['rowcount']\",\n \"def count(self, table, field, condition, *parameters, **kwparameters):\\n table = self.prefix + table\\n field = 'COUNT(' + field + ') AS rows_count'\\n query = \\\"SELECT \\\" + field + \\\" FROM \\\" + table + \\\" \\\" + condition\\n rows_count = self.query(query, *parameters, **kwparameters)\\n if rows_count:\\n return int(rows_count[0][\\\"rows_count\\\"])\\n else:\\n return 0\",\n \"def query_count(query, params=None):\\n count_query = 'SELECT COUNT(*) FROM (' + query + ') AS a;'\\n response = database.get_engine().execute(count_query, params)\\n count = response.fetchone()\\n response.close()\\n return count[0]\",\n \"def executemany_rowcount(self, query, *parameters):\\n cursor = self._cursor(None)\\n try:\\n cursor.executemany(query, *parameters)\\n return cursor.rowcount\\n finally:\\n cursor.close()\",\n \"def rpc_database_count_rows(self, *args):\\n\\t\\targs = list(args)\\n\\t\\tfields = self.path.split('/')[1:-2]\\n\\t\\tassert len(fields) == len(args)\\n\\t\\ttable = DATABASE_TABLE_OBJECTS.get(self.path.split('/')[-2])\\n\\t\\tassert table\\n\\t\\tsession = db_manager.Session()\\n\\t\\tquery = session.query(table)\\n\\t\\tquery = query.filter_by(**dict(zip((f + '_id' for f in fields), args)))\\n\\t\\tresult = query.count()\\n\\t\\tsession.close()\\n\\t\\treturn result\",\n \"def count(self):\\n sql = u'SELECT count() FROM (%s)' % self.as_sql()\\n raw = self._database.raw(sql)\\n return int(raw) if raw else 0\",\n \"def countPlayers():\\n #gets the player column from the players table\\n conn = DB().execute(\\\"SELECT COUNT(player) FROM players;\\\")\\n #gets the result of the select statement\\n count = conn[\\\"cursor\\\"].fetchone()[0]\\n conn[\\\"cursor\\\"].close()\\n return count\",\n \"def get_count()->int:\\n stmt = sqlalchemy.select([sqlalchemy.func.count()]).select_from(_READING_TABLE)\\n return execute_command(stmt).fetchall()[0][0]\",\n \"def execute(self, sql, *args):\\r\\n args = args or None\\r\\n with Executer(self) as cursor:\\r\\n cursor.execute(sql, args)\\r\\n return cursor.rowcount, cursor.lastrowid\",\n \"def sql(self, sql, *args):\\n cursor = self.connection.cursor()\\n try:\\n num_records = cursor.execute(sql, args)\\n \\n #Restart mysql if it's died for whatever reason, seems quite common\\n except (pymysql.err.InterfaceError, pymysql.err.OperationalError):\\n self.refresh()\\n num_records = cursor.execute(sql, args)\\n \\n self.connection.commit()\\n \\n try:\\n if sql.startswith('SELECT count(*) FROM'):\\n return cursor.fetchall()[0][0]\\n \\n elif sql.startswith('SELECT'):\\n return cursor.fetchall()\\n \\n elif sql.startswith('UPDATE'):\\n return num_records\\n \\n elif sql.startswith('INSERT'):\\n return cursor.lastrowid\\n \\n elif sql.startswith('DELETE'):\\n return num_records\\n finally:\\n cursor.close()\",\n \"def executeQuery(self):\\n try:\\n # self.checkValName()\\n self.dbc.execute(self.query, self.val)\\n self.cnt = self.dbc.rowcount\\n except MySQLdb.Error as e:\\n print \\\"Query failed: %s\\\" % e\",\n \"def count(self):\\n self._fetch_if_needed()\\n return len(self._result_cache.get('rows', []))\",\n \"def count(self, sql, params=None, error_message=None, connection=None):\\n fresh_connection = False\\n try:\\n if not connection:\\n connection = self.database.connection()\\n fresh_connection = True\\n # self.context.logger.debug(connection.cursor.mogrify(sql, params))\\n result = single_value_sql(connection, sql, params)\\n except psycopg2.Error, e:\\n if error_message is None:\\n error_message = \\\"Failed to execute count against PostgreSQL\\\"\\n error_message = \\\"%s - %s\\\" % (error_message, str(e))\\n logger.error(error_message, exc_info=True)\\n raise DatabaseError(error_message)\\n finally:\\n if connection and fresh_connection:\\n connection.close()\\n\\n return result\",\n \"def count(self):\\n with self.pdq:\\n (count,)=self.pdq.cursor().execute('select count(*) from pdq').next()\\n return count\",\n \"def count(self, query):\\n if len(query._where) == 0:\\n url = '%s/%s' % (self.uri, query.table())\\n else:\\n url = '%s/%s/filter?%s' % (self.uri, query.table(), query.encode())\\n data, resp = self.execute(method='HEAD', url=url)\\n count = resp.getheader(\\\"X-Result-Count\\\")\\n return int(count)\",\n \"def countPlayers():\\n # gets connection to tournament database in conn object\\n conn = connect()\\n # gets the cursor to execute queries\\n c = conn.cursor()\\n # executes select with count aggregate function query number of players\\n # in PLAYER table\\n c.execute(\\\"SELECT COUNT(*) FROM PLAYER;\\\")\\n # retreives the result in count variable\\n count = c.fetchone() [0]\\n # closes the connection to tournament database\\n conn.close()\\n # returns the number of players in PLAYER table\\n return count\",\n \"def _get_num_searches(self):\\n self._cursor.execute(f\\\"\\\"\\\"\\n SELECT COALESCE(MAX(rowid), 0)\\n FROM {self._table_name};\\\"\\\"\\\", {\\\"table\\\": self._table_name})\\n\\n num_searches = self._cursor.fetchone()[0]\\n return num_searches\",\n \"def count(self, table, q_filter=None):\\n try:\\n with self.lock:\\n return sum(1 for x in self._find(table, self._format_filter(q_filter)))\\n except DbException:\\n raise\\n except Exception as e: # TODO refine\\n raise DbException(str(e))\",\n \"def count(self):\\n if self._result_cache is not None:\\n return len(self._result_cache)\\n\\n return self.query.get_count(using=self.db)\",\n \"def count(self):\\n ans = self.execute(self.commands.table_count(self.name))\\n return ans[0][0]\",\n \"def countPlayers():\\n\\n count = 0\\n query = (\\\"SELECT COUNT(id) FROM players;\\\")\\n results = executeQuery({'dbname': 'tournament', 'query' : query, 'type' : 'find'})\\n for row in results:\\n count = row[0]\\n return count\",\n \"def count(self, query):\",\n \"def get_num_rows(self):\\n return self._num_rows\",\n \"def query_database(conn, query, params=None, commit=False, returning=False):\\n if params is None:\\n params = []\\n cur = conn.cursor()\\n cur.execute(query, params)\\n\\n ret = None\\n if commit:\\n conn.commit()\\n if returning:\\n ret = cur.fetchall()\\n else:\\n ret = cur.rowcount\\n\\n else:\\n conn.rollback()\\n ret = cur.fetchall()\\n cur.close()\\n return ret\",\n \"def execute_both(self, query, *parameters, **kwparameters):\\n cursor = self._cursor()\\n try:\\n self._execute(cursor, query, parameters, kwparameters)\\n return cursor.lastrowid, cursor.rowcount\\n finally:\\n cursor.close()\",\n \"def execute(self, conn, rows: list) -> int:\\n if not len(rows):\\n raise ValueError('No data provided')\\n if len(self.columns) != len(rows[0]):\\n raise ValueError('Expecting {} columns, found {}'.format(\\n len(self.columns), len(rows[0])))\\n\\n conn = conn()\\n cursor = conn.cursor()\\n try:\\n cursor.execute(self.query + ', '.join(self.escape_rows(rows)))\\n conn.commit()\\n finally:\\n cursor.close()\\n conn.close()\\n\\n return len(rows)\",\n \"def execute(self, query):\\n with self.conn.cursor() as cur:\\n # Execute the query\\n try:\\n cur.execute(query)\\n except Exception as exc:\\n print(\\\"Unable to execute query. Error was {0}\\\".format(str(exc)))\\n exit()\\n rows = cur.fetchall()\\n return rows\",\n \"def execute(self, begin: int, end: int) -> int:\\n row_number = self.row_number\\n self.row_number += 1\\n return row_number\",\n \"def get_response_record_count(self):\\n if self.record_count is None:\\n raise QueryNotExecuted(\\\"No query has been executed. Use the Execute Query keyword to retrieve records.\\\")\\n else:\\n return self.record_count\",\n \"def test_count_all_rows_query(self):\\n\\n mock_connector = MagicMock()\\n database = Database()\\n database.connect(connector_impl=mock_connector)\\n connection = mock_connector.connect()\\n cursor = connection.cursor()\\n cursor.__iter__.return_value = [(123,)]\\n\\n num = database.count_all_rows()\\n\\n self.assertEqual(num, 123)\\n self.assertTrue(cursor.execute.called)\\n\\n sql = cursor.execute.call_args[0][0].lower()\\n self.assertIn('select count(1)', sql)\\n self.assertIn('from `covidcast`', sql)\",\n \"def get_count(self):\\n database = managers.database_manager.get_database(self.owner_id, self.database_id)\\n cur = database.get_connection().cursor()\\n cur.execute(\\\"SELECT COUNT(*) FROM `%s`\\\" % (self.name,))\\n return cur.fetchone()[0]\",\n \"def countPlayers():\\n cur1 = conn.cursor()\\n cur1.execute(\\\"\\\"\\\"SELECT * from players\\\"\\\"\\\")\\n rows = cur1.fetchall()\\n print \\\"\\\\t\\\\t\\\\tCount players; SELECT * from players:\\\\n\\\"\\n #print (rows)\\n count = 0\\n for row in rows:\\n print \\\"\\\\t\\\\t\\\\t\\\", row\\n count = count+1\\n print \\\"\\\\t\\\\t\\\\tCount:\\\", count\\n print \\\"\\\\n\\\"\\n return count\",\n \"def rowcount(self):\\n\\t\\treturn self._count\",\n \"def executeQuery(self):\\n try:\\n # self.checkValName()\\n self.cursor.execute(self.query, self.val)\\n self.cnt = self.conn.total_changes\\n except Exception as e:\\n print \\\"Query failed: %s\\\" % e\",\n \"def count(self):\\n if not self.model:\\n raise NameError('database model has not been set.')\\n\\n with self.session() as session:\\n query = self.get_query(session)\\n data = query.count()\\n return data\",\n \"def countPlayers():\\n cursor.execute(\\\"\\\"\\\"select count(name) from players\\\"\\\"\\\")\\n return cursor.fetchone()[0]\",\n \"def count(self, query=None):\\n return self.__db.count(query)\",\n \"def count(cls):\\n return DBSESSION.query(cls).count()\",\n \"def countPlayers():\\n conn, c = connect()\\n\\n q = \\\"select count(id) FROM PLAYERS;\\\"\\n c.execute(q)\\n res = c.fetchone()\\n\\n c.close()\\n conn.commit()\\n conn.close()\\n return int(res[0])\",\n \"def countPlayers():\\n\\n conn, c = main.connect()\\n c.execute(\\\"SELECT count(*) FROM player\\\")\\n\\n return c.fetchone()[0]\",\n \"def get_result_set_count(self):\\n return self.db.zcount(\\\"soq_results\\\", 0, time())\",\n \"def countPlayers():\\n db_conn = connect()\\n db_cursor = db_conn.cursor()\\n db_cursor.execute(\\\"select count(player_id) from players;\\\")\\n # fetchall returns a dict containing a tuples\\n results = db_cursor.fetchall()\\n #print \\\"Results: \\\" , repr(results), \\\" \\\", dir(results)\\n player_count = results.pop()[0]\\n #print \\\"player_count: \\\", player_count, \\\" \\\", player_count.__class__, \\\" \\\", dir(player_count)\\n db_conn.commit()\\n db_conn.close()\\n return player_count\",\n \"def countPlayers():\\n with _connect_db() as (conn, cur):\\n cur.execute(\\\"\\\"\\\"SELECT COUNT(*) from players;\\\"\\\"\\\")\\n ret = int(cur.fetchone()[0])\\n return ret\",\n \"def count_records(self, conn, when='After insert') -> None:\\n cur = conn.cursor()\\n cur.execute(f\\\"SELECT COUNT(*) FROM {self.table}\\\")\\n print(f\\\"{when}, the table {self.table} contains {cur.fetchall()[0][0]} records.\\\")\",\n \"def _execute(self, query, params=None, retry=False):\\n if self.settings.get('ENGINE') == 'sqlite':\\n if '%s' in query and params:\\n query = query.replace('%s', '?')\\n elif '%(' in query and params:\\n query = query.replace('%(', ':').replace(')s', '')\\n if logger.getEffectiveLevel() == logging.DEBUG:\\n _query = ' '.join([x.strip() for x in query.split()])\\n logger.debug('QUERY: %s PARAMS: %s', _query, params)\\n try:\\n rowcount = self.cursor.execute(query, params)\\n self._db.commit()\\n return rowcount\\n except (AttributeError, self.dbdriver.OperationalError) as err:\\n if not retry:\\n logger.debug('Database reconnect due to error: %s', err)\\n self._connect()\\n return self._execute(query, params, retry=True)\\n else:\\n raise\\n except Exception as err:\\n logger.exception('Database error: %s', err)\\n raise\",\n \"def countPlayers():\\n\\n db = connect()\\n c = db.cursor()\\n query = (\\\"SELECT count(players.id) AS count_player FROM players;\\\")\\n c.execute(query)\\n count_player = c.fetchone()[0]\\n db.close()\\n return count_player\",\n \"def countPlayers():\\n conn, cur = connect()\\n query = \\\"SELECT count(*) AS player_count FROM players;\\\"\\n try:\\n cur.execute(query)\\n except:\\n print(\\\"Error encountered when selecting player count from players table\\\")\\n num_players = cur.fetchone()\\n conn.close()\\n return num_players['player_count']\",\n \"def get_rows_in_users(conn):\\n cur = conn.cursor()\\n cur.execute(\\\"SELECT * FROM users\\\")\\n num_of_rows = len(cur.fetchall())\\n\\n return num_of_rows\",\n \"def rowcount(self):\\n return self._count\",\n \"async def executemany(self, operation, seq_of_parameters):\\n\\n count = 0\\n for parameters in seq_of_parameters:\\n count += await self.execute(operation, parameters)\\n self.rowcount = count\\n return count\",\n \"def countPlayers():\\n conn = connect()\\n c = conn.cursor()\\n # Counts the number of entries in the \\\"players\\\" table.\\n c.execute(\\\"select count(*) as num from players;\\\")\\n num = c.fetchone()[0]\\n conn.commit()\\n conn.close()\\n return num\",\n \"def get_total_mkresults(self):\\n\\n self._logger.debug(\\\"Getting mk results count\\\")\\n\\n try:\\n self.check_if_db_connected()\\n cursor = self._db_conn.cursor()\\n cursor.execute(\\\"SELECT COUNT(result_id) FROM mk_ind_result\\\")\\n count = cursor.fetchone()[0]\\n\\n except MySQLdb.OperationalError:\\n self._logger.error(\\\"MySQL operational error occured\\\")\\n traceback.print_exc()\\n raise exceptions.DBConnectionError(\\\"Cannot connect to MySQL server\\\")\\n\\n except MySQLdb.ProgrammingError:\\n self._logger.error(\\\"MySQL programming error\\\")\\n traceback.print_exc()\\n raise exceptions.DBSyntaxError(\\\"MySQL syntax error\\\")\\n\\n else:\\n return count\",\n \"def count(self):\\n if self._distinct or self._limits:\\n # Use a subquery, since a simple count won't be accurate\\n sql = u'SELECT count() FROM (%s)' % self.as_sql()\\n raw = self._database.raw(sql)\\n return int(raw) if raw else 0\\n\\n # Simple case\\n conditions = (self._where_q & self._prewhere_q).to_sql(self._model_cls)\\n return self._database.count(self._model_cls, conditions)\",\n \"def countPlayers():\\n conn = connect()\\n cursor = conn.cursor()\\n cursor.execute(\\\"SELECT count(*) FROM players\\\")\\n playerCount = cursor.fetchone()[0]\\n conn.close()\\n return playerCount\",\n \"def countPlayers():\\n conn, c = connect()\\n c.execute(\\\"SELECT COUNT(*) FROM players;\\\")\\n return c.fetchone()[0]\",\n \"def get_result_size(self) -> int:\\n\\n # Modifie the query to count the number of answer\\n if self.query.strip().startswith(\\\"SELECT\\\") or self.query.strip().startswith(\\\"select\\\"):\\n\\n if self.verbose:\\n print(tm.strftime(f\\\"[%H:%M:%S] Obtention du nombre de résultats avant exécuter la requête\\\"))\\n\\n start: int = 7 # We detect the position of the first variable after the select\\n while self.query[start] != '?':\\n start += 1\\n end: int = start\\n while self.query[end:end + 5] != \\\"WHERE\\\" and self.query[end:end + 5] != \\\"where\\\":\\n end += 1\\n\\n mot: str = self.query[start: end - 1] # The name of the variables\\n\\n self.sparql.setQuery(self.query.replace(mot, f\\\"(COUNT (*) as ?cnt)\\\", 1))\\n processed_results: dict = self.sparql.query().convert() # Do the query\\n number_of_results: int = int(processed_results['results']['bindings'][0]['cnt']['value'])\\n\\n if self.verbose:\\n print(tm.strftime(f\\\"[%H:%M:%S] Il y a {number_of_results} résultats...\\\"))\\n\\n return number_of_results\\n return 1\",\n \"def len(self):\\n with self.session as session:\\n\\n return session.execute(select([func.count()]).select_from(IndexRecord)).scalar()\",\n \"def count(self, query=None):\\n return self.create_search(query).count()\",\n \"def count(cls):\\n return cls.query.count()\",\n \"def countPlayers():\\n\\n conn = psycopg2.connect(\\\"dbname=tournament\\\")\\n c = conn.cursor()\\n c.execute(\\\"SELECT count(*) FROM player\\\")\\n count = c.fetchall()[0][0]\\n conn.close\\n\\n return count\",\n \"def _get_total_record_count(self, conn, tblname):\\n with conn.cursor() as cursor:\\n cursor.execute(sql.SQL('SELECT COUNT(*) FROM {0}').format(sql.Identifier(tblname)))\\n return cursor.fetchone()[0]\",\n \"def count(self):\\n return self.query.count(with_limit_and_skip = True)\",\n \"def countPlayers():\\n c.execute(\\\"SELECT count(*) FROM players\\\");\\n num = c.fetchone()[0]\\n print num\\n if num == 1:\\n print \\\"There is only %d player registered\\\" % (num)\\n else:\\n print \\\"There are %d players registered\\\" % (num)\\n return num\\n # except IndexError:\\n # if num == 1:\\n # print \\\"There is only %d player registered\\\" % (num)\\n # else:\\n # print \\\"There are %d players registered\\\" % (num)\",\n \"def rowCount(self, index):\\n return len(self._data)\",\n \"def count(self, where=\\\"\\\", parameters={}):\\n return self._backend.count(where, parameters)\",\n \"def _raw_delete(self, using):\\n query = self.query.clone()\\n query.__class__ = sql.DeleteQuery\\n cursor = query.get_compiler(using).execute_sql(CURSOR)\\n if cursor:\\n with cursor:\\n return cursor.rowcount\\n return 0\",\n \"def count_players():\\n DB = connect()\\n c = DB.cursor()\\n c.execute(\\\"SELECT * FROM count_players\\\")\\n DB.commit()\\n player_count = c.fetchall()[0][0]\\n DB.close()\\n return player_count\",\n \"def executeQuery(query):\\n c = db.cursor()\\n c.execute(query)\\n rows = c.fetchall()\\n db.close()\\n return rows\",\n \"def countPlayers():\\n db, cursor = connect()\\n cursor.execute( \\\" SELECT count(*) as num FROM players \\\")\\n count = cursor.fetchone()[0]\\n return int(count)\",\n \"def update(self, query: str, *args, **kwargs):\\n cursor = self._cursor()\\n try:\\n self._execute(cursor, query, args, kwargs)\\n return cursor.rowcount\\n finally:\\n cursor.close()\",\n \"def row_count(self):\\n return self.__row_count\",\n \"def _count(self):\\n if self._count_valid:\\n return self._total_results\\n\\n result = self._cb.get_object(self._doc_class.urlobject.format(self._cb.credentials.org_key))\\n results = result.get(\\\"results\\\", [])\\n\\n self._total_results = len(results)\\n self._count_valid = True\\n\\n return self._total_results\",\n \"def countPlayers():\\n db = connect()\\n db_cursor = db.cursor()\\n query = \\\"SELECT COUNT(id) AS total_players FROM players\\\"\\n db_cursor.execute(query)\\n results = db_cursor.fetchone()\\n db.close()\\n if results:\\n return results[0]\\n else:\\n return '0'\",\n \"def _get_count(results):\\n return len(results)\",\n \"def row_count(self):\\n return self._row_count\",\n \"def get_rows(cur):\\n\\n # count all rows\\n cur.execute('SELECT count(1) `num` FROM `flusurv`')\\n for (num,) in cur:\\n return num\",\n \"def get_total_ssresults(self):\\n\\n self._logger.debug(\\\"Getting ss results count\\\")\\n\\n try:\\n self.check_if_db_connected()\\n cursor = self._db_conn.cursor()\\n cursor.execute(\\\"SELECT COUNT(result_id) FROM ss_ind_result\\\")\\n count = cursor.fetchone()[0]\\n\\n except MySQLdb.OperationalError:\\n self._logger.error(\\\"MySQL operational error occured\\\")\\n traceback.print_exc()\\n raise exceptions.DBConnectionError(\\\"Cannot connect to MySQL server\\\")\\n\\n except MySQLdb.ProgrammingError:\\n self._logger.error(\\\"MySQL programming error\\\")\\n traceback.print_exc()\\n raise exceptions.DBSyntaxError(\\\"MySQL syntax error\\\")\\n\\n else:\\n return count\",\n \"def countPlayers():\\n DB = connect()\\n c = DB.cursor()\\n c.execute(\\\"SELECT count(id) FROM tournament\\\")\\n result=c.fetchone()\\n DB.close()\\n return result[0]\",\n \"def row_count_equal_to(self, expected_row_num, query=None):\\n if query is not None:\\n self.execute_query(query)\\n row_num = len(self.recordset.fetchall())\\n assert row_num == expected_row_num, f'The number of records {row_num} is not same as expected row num {expected_row_num}'\\n return self\",\n \"def num_rows(self):\\n\\n self.logger.debug('numrows looking for rows matching %s' \\\\\\n % (self.locators['row']))\\n rows = self.find_elements(self.locators['row'])\\n self.logger.debug('rows = %s' % (rows))\\n\\n for r in rows:\\n self.logger.debug('r.text() = %s' % r.get_attribute('innerHTML'))\\n\\n return len(rows)\",\n \"def count_entries(self, tablename):\\n query = \\\"Select count(*) from \\\" + tablename\\n try:\\n self.__cur.execute(query)\\n except Exception as e:\\n self.__conn.rollback()\\n raise e\\n fetcheddata = self.__cur.fetchone()\\n return fetcheddata[0]\",\n \"async def count(self, table_name: str, column: str, conditions_list=None):\\n if conditions_list:\\n conditions = LemkPgUtils.get_conditions(conditions_list)\\n query = f\\\"\\\"\\\"SELECT COUNT({column}) FROM {table_name} WHERE {\\\" \\\".join(conditions)}\\\"\\\"\\\"\\n else:\\n query = f\\\"\\\"\\\"SELECT COUNT({column}) FROM {table_name}\\\"\\\"\\\"\\n result = await LemkPgUtils.get_query_result(self.dsn, query)\\n return result\",\n \"def row_count(self) -> int:\\n return len(self.rows)\",\n \"def row_count(self) -> int:\\n return len(self.rows)\",\n \"def execute_total_dataframe_count_strategy(dataframe):\\n\\n logging.debug('>>>>>>>>> Using total count strategy <<<<<<<<<<<<')\\n\\n total_rows = len(dataframe.index)\\n\\n return total_rows\",\n \"def test_rowcount_select(cursor: pyodbc.Cursor):\\n cursor.execute(\\\"create table t1(i int)\\\")\\n count = 4\\n for i in range(count):\\n cursor.execute(\\\"insert into t1 values (?)\\\", i)\\n cursor.execute(\\\"select * from t1\\\")\\n assert cursor.rowcount == -1\\n\\n rows = cursor.fetchall()\\n assert len(rows) == count\\n assert cursor.rowcount == -1\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8263714","0.75019467","0.74157685","0.74035823","0.73712915","0.73479736","0.7320812","0.7266392","0.7265946","0.72563446","0.72047913","0.7165406","0.71650726","0.71557415","0.71451116","0.71380395","0.7132593","0.701933","0.6974487","0.69694316","0.693505","0.6911388","0.6908528","0.6908382","0.6884893","0.68700314","0.68452567","0.6818769","0.6767212","0.6760972","0.66511095","0.6630395","0.66236997","0.65503705","0.6537169","0.65244275","0.6512996","0.6499868","0.64820445","0.6481997","0.6470249","0.64638865","0.6436845","0.64357185","0.64005476","0.63971376","0.6368562","0.63537","0.6350856","0.63453937","0.63431","0.6341723","0.63145137","0.62603545","0.62474597","0.62403715","0.62357223","0.62160844","0.62119234","0.6204799","0.6192307","0.617072","0.6165695","0.6159435","0.6152279","0.61453396","0.6144975","0.61414766","0.6133019","0.61167204","0.61054516","0.61026967","0.60984236","0.6096068","0.6093439","0.60891896","0.6087054","0.60725003","0.60716593","0.60702145","0.6069545","0.6067095","0.6065101","0.6059194","0.6055328","0.6053746","0.60506266","0.6045634","0.6034371","0.6025053","0.6023721","0.6022902","0.6020922","0.60174143","0.60094815","0.60017335","0.5994321","0.5969528","0.5969528","0.59625393","0.5947092"],"string":"[\n \"0.8263714\",\n \"0.75019467\",\n \"0.74157685\",\n \"0.74035823\",\n \"0.73712915\",\n \"0.73479736\",\n \"0.7320812\",\n \"0.7266392\",\n \"0.7265946\",\n \"0.72563446\",\n \"0.72047913\",\n \"0.7165406\",\n \"0.71650726\",\n \"0.71557415\",\n \"0.71451116\",\n \"0.71380395\",\n \"0.7132593\",\n \"0.701933\",\n \"0.6974487\",\n \"0.69694316\",\n \"0.693505\",\n \"0.6911388\",\n \"0.6908528\",\n \"0.6908382\",\n \"0.6884893\",\n \"0.68700314\",\n \"0.68452567\",\n \"0.6818769\",\n \"0.6767212\",\n \"0.6760972\",\n \"0.66511095\",\n \"0.6630395\",\n \"0.66236997\",\n \"0.65503705\",\n \"0.6537169\",\n \"0.65244275\",\n \"0.6512996\",\n \"0.6499868\",\n \"0.64820445\",\n \"0.6481997\",\n \"0.6470249\",\n \"0.64638865\",\n \"0.6436845\",\n \"0.64357185\",\n \"0.64005476\",\n \"0.63971376\",\n \"0.6368562\",\n \"0.63537\",\n \"0.6350856\",\n \"0.63453937\",\n \"0.63431\",\n \"0.6341723\",\n \"0.63145137\",\n \"0.62603545\",\n \"0.62474597\",\n \"0.62403715\",\n \"0.62357223\",\n \"0.62160844\",\n \"0.62119234\",\n \"0.6204799\",\n \"0.6192307\",\n \"0.617072\",\n \"0.6165695\",\n \"0.6159435\",\n \"0.6152279\",\n \"0.61453396\",\n \"0.6144975\",\n \"0.61414766\",\n \"0.6133019\",\n \"0.61167204\",\n \"0.61054516\",\n \"0.61026967\",\n \"0.60984236\",\n \"0.6096068\",\n \"0.6093439\",\n \"0.60891896\",\n \"0.6087054\",\n \"0.60725003\",\n \"0.60716593\",\n \"0.60702145\",\n \"0.6069545\",\n \"0.6067095\",\n \"0.6065101\",\n \"0.6059194\",\n \"0.6055328\",\n \"0.6053746\",\n \"0.60506266\",\n \"0.6045634\",\n \"0.6034371\",\n \"0.6025053\",\n \"0.6023721\",\n \"0.6022902\",\n \"0.6020922\",\n \"0.60174143\",\n \"0.60094815\",\n \"0.60017335\",\n \"0.5994321\",\n \"0.5969528\",\n \"0.5969528\",\n \"0.59625393\",\n \"0.5947092\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17776,"cells":{"query":{"kind":"string","value":"Handle only one batch, updating the internal state Params ======"},"document":{"kind":"string","value":"def train_one_batch(self, imgs: Tensor) -> None:\n with torch.no_grad():\n # b * c * w * h\n embeddings = self._embed_batch(imgs.to(self.device))\n if self.embedding_list is None:\n self.embedding_list = reshape_embedding(embeddings.cpu().detach().numpy())\n \n else:\n # very memory consuming (np.vstack not reshaping)\n self.embedding_list = np.vstack((self.embedding_list, reshape_embedding(embeddings.cpu().detach().numpy())))"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def update_batch(self, *args, **kwargs):\n pass","def process_state_batch(self, batch):\n return batch","def process_state_batch(self, batch):\n return batch","def handle_batch(self, batch: Mapping[str, Any]) -> None:\n self.batch = {**batch, **self.forward(batch)}","def on_batch_begin(self, batch, logs=None):","def index_batch(self,batch):\n pass","def batch(self, batch):\n\n self._batch = batch","def process_state_batch(self, batch):\n return np.squeeze(batch, axis=1)","def update(self, batch, batch_index):\n super(BayesianOptimization, self).update(batch, batch_index)\n self.state['n_evidence'] += self.batch_size\n\n params = batch_to_arr2d(batch, self.target_model.parameter_names)\n self._report_batch(batch_index, params, batch[self.target_name])\n\n optimize = self._should_optimize()\n self.target_model.update(params, batch[self.target_name], optimize)\n if optimize:\n self.state['last_GP_update'] = self.target_model.n_evidence","def fetch_batch(self, phase):\n pass","def process_state_batch(self, batch):\n # batch = np.squeeze(batch, axis=1)\n batch = np.array([np.concatenate(obs, axis=-1) for obs in batch])\n return batch","def init_batch(self):\n pass","def hook_store_batch_size(module):\n if self._batch_size == {}:\n batch_axis = 0\n batch_size = module.input0.shape[batch_axis]\n\n for group in param_groups:\n group_id = id(group)\n\n if self._verbose:\n print(f\"Group {group_id}: Store 'batch_size'\")\n\n self._batch_size[group_id] = batch_size","def on_batch_end(self, batch, logs=None):","def update(self, batch):\n assert self.is_training\n # process batch\n if self.warm_started:\n results = self.update_batch_multi_env(batch)\n else:\n results = self.update_batch_erm(batch)\n # log results\n self.update_log(results)\n return self.sanitize_dict(results)","def midbatch_hook(self, progress, logging_epoch):\n pass","def single_step(self):\n # Make a minibatch of training data by choosing \"batch_size\" elements with replacement\n num_train = self.X_train.shape[0]\n batch_mask = np.random.choice(num_train, self.batch_size) # random choice with replacement\n X_batch = self.X_train[batch_mask]\n y_batch = self.y_train[batch_mask]\n\n # Compute loss and gradient\n loss, grads = self.model.loss(X_batch, y_batch)\n self.latest_loss = loss\n\n # Perform a parameter update based on chosen optimiser\n for p, w in self.model.params.items():\n dw = grads[p] # current gradients\n config = self.optim_configs[p] # moments of gradients and learning rate till previous accuracy() call\n next_w, next_config = optimiser_type(self.optim_type, w, dw, config) # sent to choice of optimising technique\n self.model.params[p] = next_w # model params updated\n self.optim_configs[p] = next_config # # moments of gradients updated","def batchProcess(self,args:object,serial:bool=False,savePath:str='')->None:\n self.logger.debug(self.settingsReader)\n self.settingsReader.select(args.settings)\n self.dataReader.read(self.settingsReader, self.multiData,serial=serial)\n self.stats.descriptive(self.multiData,serial=serial,savePath=savePath)\n if not serial:\n sys.exit()","def _update_params(self):\n pass","def action_process(self):\n if not self.line_ids or len(self.line_ids) <= 0:\n raise ValidationError(\"Data detail tunjangan masih kosong!\")\n self.state = 'processed'","def get_batch(self):\n return self.batch","def forward(self, batch):\n raise NotImplementedError","def forward(self, batch):\n raise NotImplementedError","def post_randomize(self):\n super(BatchRandomizer, self).post_randomize()\n self.batch_idx += 1","def on_predict_batch_begin(self, batch, logs=None):","def __iter__(self):\n if not self.loading:\n self.reset_loading()\n self.current_batch_index = 0\n return self","def processBatchFunc(inputBatch): #this is redefined automatically, no need to do any special code here\n with tf.Session(graph=graph, config=config) as sess:\n sess.run(init)\n \n (outValue, ) = sess.run([outputs], feed_dict={inputs: inputBatch})\n \n #print \"count now is: %d\" % count\n\n return outValue","def run(self, batch):\n response = self.post(batch)\n log.info(\"< Discarding batch response\")\n response.close()","def process_batch(self, batch):\n # shapes are [time, ...original dims...]\n v_global = np.stack(batch[:,0]) # [time, agents, l_state_one_agent]\n # note that *_local objects have shape\n # [time, agents, ...original dim...]\n obs_others = np.stack(batch[:,1]) # [time,agents,h,w,c] or [time, agents, obs_others]\n v_local = np.stack(batch[:,2]) # [time,agents,l]\n actions = np.stack(batch[:,3]) # [time,agents]\n reward = np.stack(batch[:,4]) # [time]\n reward_local = np.stack(batch[:,5]) # [time,agents]\n v_global_next = np.stack(batch[:,6]) # [time, agents, l_state_one_agent]\n obs_others_next = np.stack(batch[:,7]) # [time,agents,h,w,c]\n v_local_next = np.stack(batch[:,8]) # [time,agents,l]\n done = np.stack(batch[:,9]) # [time]\n goals = np.stack(batch[:,10]) # [time, agents, l_goal]\n\n batch = None\n \n n_steps = v_global.shape[0]\n \n # For all global quantities, for each time step,\n # duplicate values <n_agents> times for\n # batch processing of all agents\n reward = np.repeat(reward, self.n_agents, axis=0)\n\n # In-place reshape for *_local quantities,\n # so that one time step for one agent is considered\n # one batch entry\n if self.experiment == 'sumo':\n obs_others.shape = (n_steps*self.n_agents, self.h_obs,\n self.w_obs, self.c_obs)\n obs_others_next.shape = (n_steps*self.n_agents, self.h_obs,\n self.w_obs, self.c_obs)\n elif self.experiment == 'particle':\n obs_others.shape = (n_steps*self.n_agents, self.l_obs_others)\n obs_others_next.shape = (n_steps*self.n_agents, self.l_obs_others)\n v_local.shape = (n_steps*self.n_agents, self.l_obs)\n reward_local.shape = (n_steps*self.n_agents)\n v_local_next.shape = (n_steps*self.n_agents, self.l_obs)\n\n actions_1hot, actions_others_1hot = self.process_actions(n_steps, actions)\n \n return n_steps, v_global, obs_others, v_local, actions_1hot, actions_others_1hot, reward, reward_local, v_global_next, obs_others_next, v_local_next, done, goals","def on_batch_end(self, batch, logs={}):\n if self.mode == 0:\n self.current_progress += logs.get(\"size\")\n self.total_progress += logs.get(\"size\")\n else:\n self.current_progress += 1\n self.total_progress += 1\n self.loss = logs.get(\"loss\")\n loss_item = {\"samples\": self.total_progress, \"loss\": self.loss}\n self.accuracy = logs.get(\"acc\")\n accuracy_item = {\"samples\": self.total_progress, \"accuracy\": self.accuracy}\n self.total_runtime = time() - self.start_time\n self.update_data(loss_item, accuracy_item)","def batch(self):\n return self._batch","def batch_start(self, batch_idx, batch_data):\n self.batch = batch_idx","def process(self, data_batch: Any, data_samples: Sequence[dict]) -> None:","def on_batch(self, x, y):","def on_test_batch_begin(self, batch, logs=None):","def _step(self):\n # Make a minibatch of training data\n num_train = self.X_train.shape[0]\n # random choose the samples\n batch_mask = np.random.choice(num_train, self.batch_size)\n X_batch = self.X_train[batch_mask]\n y_batch = self.y_train[batch_mask]\n\n # Compute loss and gradient\n loss, grads = self.model.loss(X_batch, y_batch)\n self.loss_history.append(loss)\n\n # Perform a parameter update\n for p, w in self.model.params.items():\n dw = grads[p]\n config = self.optim_configs[p]\n next_w, next_config = self.update_rule(w, dw, config)\n self.model.params[p] = next_w\n self.optim_configs[p] = next_config","def train_single_batch(self, batch_data):\n assert hasattr(self, \"model\"), \"Please specify the exact model !\"\n self.optimizer.zero_grad()\n norm_adj = self.norm_adj\n ua_embeddings, ia_embeddings = self.model.forward(norm_adj)\n\n batch_users, pos_items, neg_items = batch_data\n\n u_g_embeddings = ua_embeddings[batch_users]\n pos_i_g_embeddings = ia_embeddings[pos_items]\n neg_i_g_embeddings = ia_embeddings[neg_items]\n\n batch_mf_loss, batch_reg_loss = self.loss_comput(\n u_g_embeddings,\n pos_i_g_embeddings,\n neg_i_g_embeddings,\n batch_users,\n pos_items,\n neg_items,\n )\n\n batch_loss = batch_mf_loss + batch_reg_loss\n\n batch_loss.backward()\n self.optimizer.step()\n loss = batch_loss.item()\n return loss","def update_params(self):\n pass","def update(self, params):","def test_update_batch(self):\n self.batch_data['batch_id'] = self.batch_info.id\n resp = self.query_with_token(\n self.access_token_master,\n update_batch_info.format(**self.batch_data))\n\n self.assertIn('data', resp)\n self.assertEqual(\n resp['data']['updateBatchInfo']['batchInfo']['supplier']['name'],\n self.supplier.name)\n self.assertEqual(\n resp['data']['updateBatchInfo']['batchInfo']['batchNo'],\n self.batch_info.batch_no)","def validate_batch(self, *arg, **kwargs):\n pass","def batch_id(self, batch_id):\n\n self._batch_id = batch_id","def update(self, triplet_batch):\n feed_dict = {self.anchor: triplet_batch[0],\n self.pos: triplet_batch[1],\n self.neg: triplet_batch[2]}\n _, loss = self.sess.run([self.train_step, self.loss], feed_dict=feed_dict)\n return loss","def update(self, triplet_batch):\n feed_dict = {self.anchor: triplet_batch[0],\n self.pos: triplet_batch[1],\n self.neg: triplet_batch[2]}\n _, loss = self.sess.run([self.train_step, self.loss], feed_dict=feed_dict)\n return loss","def update(self, triplet_batch):\n feed_dict = {self.anchor: triplet_batch[0],\n self.pos: triplet_batch[1],\n self.neg: triplet_batch[2]}\n _, loss = self.sess.run([self.train_step, self.loss], feed_dict=feed_dict)\n return loss","def add(\n self,\n batch: RolloutBatchProtocol,\n buffer_ids: Optional[Union[np.ndarray, list[int]]] = None,\n ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n # preprocess batch\n new_batch = Batch()\n for key in set(self._reserved_keys).intersection(batch.keys()):\n new_batch.__dict__[key] = batch[key]\n batch = new_batch\n batch.__dict__[\"done\"] = np.logical_or(batch.terminated, batch.truncated)\n assert {\"obs\", \"act\", \"rew\", \"terminated\", \"truncated\", \"done\"}.issubset(batch.keys())\n if self._save_only_last_obs:\n batch.obs = batch.obs[:, -1]\n if not self._save_obs_next:\n batch.pop(\"obs_next\", None)\n elif self._save_only_last_obs:\n batch.obs_next = batch.obs_next[:, -1]\n # get index\n if buffer_ids is None:\n buffer_ids = np.arange(self.buffer_num)\n ptrs, ep_lens, ep_rews, ep_idxs = [], [], [], []\n for batch_idx, buffer_id in enumerate(buffer_ids):\n ptr, ep_rew, ep_len, ep_idx = self.buffers[buffer_id]._add_index(\n batch.rew[batch_idx],\n batch.done[batch_idx],\n )\n ptrs.append(ptr + self._offset[buffer_id])\n ep_lens.append(ep_len)\n ep_rews.append(ep_rew)\n ep_idxs.append(ep_idx + self._offset[buffer_id])\n self.last_index[buffer_id] = ptr + self._offset[buffer_id]\n self._lengths[buffer_id] = len(self.buffers[buffer_id])\n ptrs = np.array(ptrs)\n try:\n self._meta[ptrs] = batch\n except ValueError:\n batch.rew = batch.rew.astype(float)\n batch.done = batch.done.astype(bool)\n batch.terminated = batch.terminated.astype(bool)\n batch.truncated = batch.truncated.astype(bool)\n if self._meta.is_empty():\n self._meta = create_value(batch, self.maxsize, stack=False) # type: ignore\n else: # dynamic key pops up in batch\n alloc_by_keys_diff(self._meta, batch, self.maxsize, False)\n self._set_batch_for_children()\n self._meta[ptrs] = batch\n return ptrs, np.array(ep_rews), np.array(ep_lens), np.array(ep_idxs)","def handle_batch(self, transfer_batch, dilution_settings, robot_settings):\n # TODO: Shouldn't transfer_batch be an iterator?\n for transfer in transfer_batch.transfers:\n self.handle_transfer(transfer, dilution_settings, robot_settings)","def process(self, sess):\n\n sess.run(self.sync) # copy weights from shared to local\n rollout = self.pull_batch_from_queue()\n batch = process_rollout(rollout, gamma=0.99, lambda_=1.0)\n\n should_compute_summary = self.task == 0 and self.local_steps % 11 == 0\n\n if should_compute_summary:\n fetches = [self.summary_op, self.train_op, self.global_step]\n else:\n fetches = [self.train_op, self.global_step]\n\n feed_dict = {\n self.local_network.x: batch.si,\n self.ac: batch.a,\n self.adv: batch.adv,\n self.r: batch.r,\n self.local_network.state_in[0]: batch.features[0],\n self.local_network.state_in[1]: batch.features[1],\n }\n\n fetched = sess.run(fetches, feed_dict=feed_dict)\n\n if should_compute_summary:\n self.summary_writer.add_summary(tf.Summary.FromString(fetched[0]), fetched[-1])\n self.summary_writer.flush()\n self.local_steps += 1","def update(self, x_train_single, updated_h):\n # x_row = cp.array(x_train_single.toarray())\n # cp.cuda.Stream.null.synchronize()\n updater(x_train_single,updated_h,self.weights,self.num_features,self.num_models,self.learning_rate)\n # self.biases += updated_h * self.learning_rate","def process(self, sess):\n\n sess.run(self.sync) # copy weights from shared to local\n rollout = next(self.rollout_provider)\n batch = process_rollout(rollout, gamma=self.config.discount)\n\n should_compute_summary = (self.task == 0 \n and self.local_steps % self.config.summary_every == 0)\n\n if should_compute_summary:\n fetches = [self.summary_op, self.train_op, self.global_step]\n else:\n fetches = [self.train_op, self.global_step]\n\n feed_dict = {\n self.local_network.x: batch.si,\n self.r: batch.r,\n self.w: batch.w,\n self.local_network.state_in[0]: batch.features[0],\n self.local_network.state_in[1]: batch.features[1],\n }\n\n fetched = sess.run(fetches, feed_dict=feed_dict)\n\n if should_compute_summary:\n self.summary_writer.add_summary(\n tf.Summary.FromString(fetched[0]), fetched[-1])\n self.summary_writer.flush()\n self.local_steps += 1","def _update(self):\n if (len(self.buffer) < self.batch_size):\n return\n self.training_iter += 1\n # Make sure actor_target and critic_target are in eval mode\n assert not self.model.q_target_1.training\n assert not self.model.q_target_2.training\n\n assert self.model.q_1.training\n assert self.model.q_2.training\n transitions = self.buffer.sample(self.batch_size)\n batch = self.buffer.transition(*zip(*transitions))\n state_batch = torch.tensor(batch.state, device=self.device).float()\n action_batch = torch.tensor(batch.action,\n device=self.device).unsqueeze(-1).long()\n reward_batch = torch.tensor(batch.reward,\n device=self.device).unsqueeze(-1).float()\n next_state_batch = torch.tensor(batch.next_state,\n device=self.device).float()\n is_done_batch = torch.tensor(batch.done,\n device=self.device).unsqueeze(-1).bool()\n with torch.no_grad():\n Q_next_1 = ((~is_done_batch)\n * (self.model.q_target_1(next_state_batch).min(dim=-1)[0].unsqueeze(-1)))\n Q_next_2 = ((~is_done_batch)\n * (self.model.q_target_2(next_state_batch).min(dim=-1)[0].unsqueeze(-1)))\n\n # Use max want to avoid underestimation bias\n Q_next = torch.max(Q_next_1, Q_next_2)\n Q_expected = reward_batch + self.gamma * Q_next\n\n Q_1 = self.model.q_1(state_batch).gather(-1, action_batch)\n Q_2 = self.model.q_2(state_batch).gather(-1, action_batch)\n L_1 = nn.MSELoss()(Q_1, Q_expected)\n L_2 = nn.MSELoss()(Q_2, Q_expected)\n self.loss.append([L_1.item(), L_2.item()])\n self.model.q_optimizer_1.zero_grad()\n self.model.q_optimizer_2.zero_grad()\n L_1.backward()\n L_2.backward()\n self.model.q_optimizer_1.step()\n self.model.q_optimizer_2.step()\n self.store_Q.append([Q_1.tolist(), Q_2.tolist(), Q_expected.tolist()])\n if (self.training_iter % self.update_freq) == 0:\n self.model.update_target_nn()","def flush_batch(self, batch: Sequence[TResult]) -> None:\n pass","def _process(self, start_key, batch_size):\n\n query = self.MODEL.all()\n if start_key:\n query.filter('__key__ > ', start_key)\n\n try:\n entities = query.fetch(batch_size)\n\n if not entities:\n # all entities has already been processed\n return\n\n for entity in entities:\n for field in self.FIELDS_TO_UPDATE:\n old_reference = getattr(entity, field)\n\n if not old_reference:\n continue\n\n # check if the field has not been updated\n if isinstance(old_reference, self.PROFILE_MODEL):\n continue\n\n profile = _getProfileForRole(old_reference, self.PROFILE_MODEL)\n setattr(entity, field, profile)\n\n for list_property in self.LISTS_TO_UPDATE:\n l = getattr(entity, list_property)\n new_l = []\n for key in l:\n new_l.append(_getProfileKeyForRoleKey(key, self.PROFILE_MODEL))\n setattr(entity, list_property, new_l)\n\n db.put(entities)\n start_key = entities[-1].key()\n deferred.defer(self._process, start_key, batch_size)\n except DeadlineExceededError:\n # here we should probably be more careful\n deferred.defer(self._process, start_key, batch_size)","def on_predict_batch_begin(self, step, logs=None):","def train(self, batch):\n pass","def __call__(self, **kwargs):\r\n for item, value in kwargs.items():\r\n if item not in self._state[\"data\"]:\r\n self._state[\"data\"][item] = value","def __call__(self, **kwargs):\r\n for item, value in kwargs.items():\r\n if item not in self._state[\"data\"]:\r\n self._state[\"data\"][item] = value","def __getitem__(self, index):#get_batch(self, index=None, patient_list=None):\n\n #if patient_list is None:\n # Generate batch based on the provided index\n indices = self.indices[index]# * self.batch_size:(index + 1) * self.batch_size]\n #else:\n # Generate batch based on the request patients\n # indices = self.patient_to_index(patient_list)\n\n # Make a list of files to be loaded\n file_paths_to_load = [self.file_paths_list[indices]]#k] for k in\n\n # Load the requested files as a tensors\n loaded_data = self.load_data(file_paths_to_load)\n\n if self.trans != []:\n for transform in self.trans:\n if transform == 'flip':\n if np.random.random() >= 0.5:\n for key in loaded_data.keys():\n if not isinstance(loaded_data[key], list):\n if loaded_data[key].ndim == 4:\n\n loaded_data[key] = loaded_data[key][:, :, :, ::-1]\n if key == 'structure_masks':\n loaded_data[key][[3, 2]] = loaded_data[key][[2, 3]]\n\n elif isinstance(transform, int):\n for key in loaded_data.keys():\n if not isinstance(loaded_data[key], list):\n if loaded_data[key].ndim == 4:\n loaded_data[key] = torch.from_numpy(loaded_data[key].copy()).float()\n #loaded_data[key] = interpolate(torch.from_numpy(loaded_data[key].copy()).unsqueeze(0), scale_factor=transform/128).squeeze(0).float()\n\n\n elif transform == 'crop':\n\n seed_x = np.random.randint(0,10)\n seed_y = np.random.randint(0,10)\n seed_z = np.random.randint(0,10)\n for key in loaded_data.keys():\n if not isinstance(loaded_data[key], list):\n if loaded_data[key].ndim == 4:\n loaded_data[key] = pad(loaded_data[key], [5, 5, 5, 5, 5, 5], 'constant', 0)\n loaded_data[key] = loaded_data[key][:, seed_x:128+seed_x, seed_y:128+seed_y, seed_z:128+seed_z]\n\n loaded_data['ct'] = ((loaded_data['ct'].clamp(0, self.ct_scaling_factor) /self.ct_scaling_factor)) # -0.5)/0.5\n if self.mode_name == 'training_model':\n loaded_data['dose'] = ((loaded_data['dose'].clamp(0, self.dose_scaling_factor) / self.dose_scaling_factor)) # -0.5)/0.5\n\n return loaded_data","def update_batch_multi_env(self, batch):\n x, y_true, metadata = batch\n x = x.to(self.device)\n y_true = y_true.to(self.device)\n g = self.grouper.metadata_to_group(metadata).to(self.device)\n\n # Perform per group gradient computation and RD\n environments = torch.unique(g).tolist()\n sizes = []\n pos_acc = [] \n neg_acc = []\n total_grad = []\n\n self.pos_grad.fill_(0)\n self.neg_grad.fill_(0)\n self.total_grad.fill_(0)\n self.all_positive.fill_(1)\n self.all_negative.fill_(1)\n full_outputs = None\n for env_id, env_name in enumerate(environments):\n # get the ratio of size of env to batch\n env_ratio = torch.sum(g==env_name).item() / g.shape[0]\n self.model.zero_grad()\n\n if torch.is_tensor(x):\n outputs = self.model(x[g==env_name])\n partial_objective = self.partial_objective(outputs, y_true[g==env_name])\n if full_outputs is None:\n partial_shape = list(outputs.shape)\n partial_shape[0] = y_true.shape[0]\n full_outputs = torch.zeros(*partial_shape, dtype=outputs.dtype).to(torch.device(\"cuda\"))\n full_outputs[g==env_name] = outputs\n else:\n outputs = self.model(x)\n partial_objective = self.partial_objective(outputs[g==env_name], y_true[g==env_name])\n full_outputs = outputs\n if not partial_objective.requires_grad:\n print(\"Loss function returned 0 (possibly input is NaN\")\n else:\n partial_objective.backward()\n # Save the gradients here\n cur_pos = 0\n for i, param in enumerate(self.model.parameters()):\n p = param.grad.clone().detach()\n next_pos = cur_pos + np.prod(list(p.shape))\n\n p_neg = (p<0)*p\n p_pos = (p>0)*p\n\n self.all_positive[cur_pos:next_pos] = torch.logical_and(p.view(-1)>0, self.all_positive[cur_pos:next_pos])\n self.all_negative[cur_pos:next_pos] = torch.logical_and(p.view(-1)<0, self.all_negative[cur_pos:next_pos])\n \n self.dist_pos[env_id][0][cur_pos:next_pos] = -1.0 * p_neg.view(-1)\n self.dist_neg[env_id][0][cur_pos:next_pos] = p_pos.view(-1)\n\n self.pos_grad[cur_pos:next_pos] += p_pos.view(-1) * env_ratio\n self.neg_grad[cur_pos:next_pos] += p_neg.view(-1) * env_ratio\n self.total_grad[cur_pos:next_pos] += p.view(-1) * env_ratio\n cur_pos = next_pos\n \n if self._is_satisficing:\n for env_id, env_name in enumerate(environments):\n self.dist_pos[env_id][0] = self.dist_pos[env_id][0] * torch.abs(self.total_grad)\n self.dist_neg[env_id][0] = self.dist_neg[env_id][0] * torch.abs(self.total_grad)\n\n if self.and_mask:\n consistent_ones = torch.logical_or(self.all_positive, self.all_negative)\n final_grads = self.total_grad * consistent_ones\n else: \n marginals, _ = self.ba.optimize_rd(self.dist_pos, self.dist_neg, [self.dist_pos[0][0].shape])\n\n marginals[0][marginals[0]<0.0] = 1e-8\n marginals[0][marginals[0]>1.0] = 1.0 - 1e-8\n\n if self.without_sampling:\n pos_ones = marginals[0]\n else:\n # Sample the directions\n pos_ones = torch.bernoulli(marginals[0])\n\n if self.control_only_direction:\n magnitude = torch.abs(self.total_grad)\n direction = 2*pos_ones - 1\n final_rd_grads = magnitude * direction\n else:\n final_rd_grads = self.pos_grad*pos_ones + self.neg_grad*(1-pos_ones)\n\n\n if self.only_inconsistent:\n consistent_ones = torch.logical_or(self.all_positive, self.all_negative)\n final_grads = final_rd_grads * torch.logical_not(consistent_ones) + self.total_grad * consistent_ones\n else:\n final_grads = final_rd_grads\n\n self.model.zero_grad()\n \n results = {\n 'g': g,\n 'y_true': y_true,\n 'y_pred': full_outputs,\n 'metadata': metadata,\n }\n\n objective = self.objective(results)\n results['objective'] = objective.item()\n cur_pos = 0\n for i,p in enumerate(self.model.parameters()):\n next_pos = cur_pos + np.prod(list(p.shape))\n p.grad.data.copy_(final_grads[cur_pos:next_pos].data.view(p.shape))\n cur_pos = next_pos\n\n if self.max_grad_norm:\n clip_grad_norm_(self.model.parameters(), self.max_grad_norm)\n self.optimizer.step()\n self.step_schedulers(\n is_epoch=False,\n metrics=results,\n log_access=False)\n return results","def MutateBatchJob(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def __call__(self, **kwargs):\n for item, value in kwargs.items():\n if item not in self._state[\"data\"]:\n self._state[\"data\"][item] = value","def __call__(self, **kwargs):\n for item, value in kwargs.items():\n if item not in self._state[\"data\"]:\n self._state[\"data\"][item] = value","def __call__(self, **kwargs):\n for item, value in kwargs.items():\n if item not in self._state[\"data\"]:\n self._state[\"data\"][item] = value","def handle_batch(self, batch, batch_idx, phase=\"train\"):\n\n handler = self.batch_handlers[phase]\n return handler(batch, batch_idx)","def update(self, loss):\n self.st.batch_update(self.last_sampled, loss ** 2 / self.p)","def get_batch(self, batch_kwargs, batch_parameters=None) -> None:\n raise NotImplementedError","def process_batch(self, batch):\n # extend with current batch\n self._extend(batch)\n\n # unpack and compute bounds\n length = len(self.obs)\n c = self.c\n\n # normally we cannot compute samples for the last c elements, but\n # in the terminal case, we halluciante values where necessary\n end = length if batch.terminal else length - c\n\n # collect samples to return in a FeudalBatch\n feudal_batch = FeudalBatch()\n for t in range(c, end):\n\n # state difference\n s_diff = self.s[t + c] - self.s[t]\n\n # intrinsic reward\n ri = 0\n # note that this for loop considers s and g values\n # 1 timestep to c timesteps (inclusively) ago\n for i in range(1, c + 1):\n ri_s_diff = self.s[t] - self.s[t - i]\n if np.linalg.norm(ri_s_diff) != 0:\n ri += cosine_similarity(ri_s_diff, self.g[t - i])\n ri /= c\n\n # sum of g values used to derive w, input to the linear transform\n gsum = np.zeros_like(self.g[t - c])\n for i in range(t - c, t + 1):\n gsum += self.g[i]\n\n # add to the batch\n feudal_batch.add(self.obs[t], self.a[t], self.returns[t], s_diff,\n ri, gsum, self.features[t])\n\n # in the terminal case, set reset flag\n if batch.terminal:\n self.last_terminal = True\n # in the general case, forget all but the last 2 * c elements\n # reason being that the first c of those we have already computed\n # a batch for, and the second c need those first c\n else:\n twoc = 2 * self.c\n self.obs = self.obs[-twoc:]\n self.a = self.a[-twoc:]\n self.returns = self.returns[-twoc:]\n self.s = self.s[-twoc:]\n self.g = self.g[-twoc:]\n self.features = self.features[-twoc:]\n\n return feudal_batch.get_batch()","def _update_num_batches(self):\n # maximum possible number of batches is equal to number of whole times\n # batch_size divides in to the number of data points which can be\n # found using integer division\n possible_num_batches = self.inputs.shape[0] // self.batch_size\n if self.max_num_batches == -1:\n self.num_batches = possible_num_batches\n else:\n self.num_batches = min(self.max_num_batches, possible_num_batches)","def update_batch_of_entitiies(self, username, entity_batch_data):\n\t\treturn self._send_command_to_entity_server(us.SERVER_COMMAND_UPDATE_ENTITY_BATCH, username + '|' + str(entity_batch_data))","def is_batch():\n\n pass","def training_step(self, batch):\n return {}","def on_batch_mode(self, event):\n if self.parent is not None:\n wx.PostEvent(self.parent,\n NewBatchEvent(enable=True))","def _on_batch_cache(self, meta, batch):\n # 21/03/12 Boudewijn: we can not filter all packets this way. i.e. when multiple people\n # send us missing-identity messages some of them will be dropped\n #\n # def unique(batch):\n # unique = set()\n # for candidate, packet, conversion in batch:\n # assert isinstance(packet, str)\n # if packet in unique:\n # if __debug__:\n # dprint(\"drop a \", len(packet), \" byte packet (duplicate in batch) from \", candidate, level=\"warning\")\n # self._statistics.drop(\"_convert_packets_into_batch:duplicate in batch\", len(packet))\n # else:\n # unique.add(packet)\n # yield candidate, packet, conversion\n\n # # remove duplicated\n # # todo: make _convert_batch_into_messages accept iterator instead of list to avoid conversion\n # batch = list(unique(batch))\n\n # BEGIN = time()\n\n # convert binary packets into Message.Implementation instances\n messages = list(self._convert_batch_into_messages(batch))\n assert all(isinstance(message, Message.Implementation) for message in messages), \"_convert_batch_into_messages must return only Message.Implementation instances\"\n assert all(message.meta == meta for message in messages), \"All Message.Implementation instances must be in the same batch\"\n if __debug__: dprint(len(messages), \" \", meta.name, \" messages after conversion\")\n\n # handle the incoming messages\n if messages:\n self.on_message_batch(messages)\n\n # END = time()\n # dprint(\"%4.2f\" % (END-BEGIN), \" seconds for %2d\" % len(batch), \" \", meta.name, \" messages\", force=True)","def _batch_train(self, batch, training_step, step):\n lstm_size = (self.batch_size, self.Qmain.h_size)\n batch_mem = np.zeros(lstm_size)\n batch_carry = np.zeros(lstm_size)\n input_shape = (self.batch_size,\n self.trace_length,\n self.observation_size)\n m_data = np.vstack(batch[:, 0])\n m_data = m_data.reshape(input_shape)\n t_data = np.vstack(batch[:, 4])\n t_data = t_data.reshape(input_shape)\n q_input = [np.copy(batch_mem), np.copy(batch_carry), np.copy(m_data)]\n q1_input = [np.copy(batch_mem), np.copy(batch_carry), np.copy(t_data)]\n\n # Batch predict\n self.Qmain.trace_length.assign(self.trace_length)\n self.Qmain.dropout_rate.assign(0.0)\n self.Qtarget.trace_length.assign(self.trace_length)\n self.Qtarget.dropout_rate.assign(0.0)\n\n # Save the graph just the first time\n if training_step == 0:\n tf.summary.trace_on()\n\n # T batch predict\n pred = self.Qmain.model.predict(q_input,\n batch_size=self.batch_size)\n Q = pred[0]\n batch_bus = pred[1]\n batch_line = pred[2]\n batch_disp = pred[3]\n\n ## Log graph once and disable graph logging\n if training_step == 0:\n with self.tf_writer.as_default():\n tf.summary.trace_export(self.name + \"-graph\", step)\n\n # T+1 batch predict\n Qn, *_ = self.Qtarget.model.predict(q1_input,\n batch_size=self.batch_size)\n \n # Compute batch Q update to Qtarget\n for i in range(self.batch_size):\n idx = i * (self.trace_length - 1)\n a = batch[idx][1]\n grid = a[0]\n batch_bus[i][:] = a[1][:]\n batch_line[i][:] = a[2][:]\n batch_disp[i][:] = a[3][:]\n r = batch[idx][2]\n d = batch[idx][3]\n Q[i][grid] = r\n if d == False:\n Q[i][grid] += DISCOUNT_FACTOR * Qn[i][grid]\n\n # Batch train\n batch_x = [batch_mem, batch_carry, m_data]\n batch_y = [\n Q,\n batch_bus, batch_line, batch_disp,\n batch_mem, batch_carry\n ]\n loss = self.Qmain.model.train_on_batch(batch_x, batch_y)\n loss = loss[0]\n\n # Log to tensorboard\n self._tf_log_summary(loss, step)","def val_batch_start(self, batch_idx, batch_data):\n self.val_batch = batch_idx","def start_of_batch_hook(self, progress, logging_epoch):\n pass","def append(self, batch: Batch):","def validate_batch(self, *args, **kwargs):\n raise NotImplementedError()","def updatecalibBatch(calib, batchChange):\n\n if isinstance(calib, list):\n updatedcalib = list()\n for j in range(len(calib)):\n # all the same\n updatedcalib.append(calib[j])\n # modify batch number\n for batchNum in batchChange.keys():\n updatedcalib[j]['calibSampleMetadata'].loc[calib[j]['calibSampleMetadata']['Batch'] == batchNum, 'Batch'] = batchChange[batchNum]\n\n elif isinstance(calib, dict):\n updatedcalib = copy.deepcopy(calib)\n # modify batch number\n for batchNum in batchChange.keys():\n updatedcalib['calibSampleMetadata'].loc[calib['calibSampleMetadata']['Batch'] == batchNum, 'Batch'] = batchChange[batchNum]\n\n return updatedcalib","def __call__(self, graph):\n result = graph.sqs_message_dispatcher.handle_batch()\n if not result.message_count:\n raise SleepNow()","def update_job_state(self, job):","def _send(self, batch):\n return self.agent.emitBatch(batch)","def update_batch_erm(self, batch):\n x, y_true, metadata = batch\n x = x.to(self.device)\n y_true = y_true.to(self.device)\n g = self.grouper.metadata_to_group(metadata).to(self.device)\n outputs = self.model(x)\n results = {\n 'g': g,\n 'y_true': y_true,\n 'y_pred': outputs,\n 'metadata': metadata,\n }\n\n objective = self.objective(results)\n results['objective'] = objective.item()\n # update\n self.model.zero_grad()\n objective.backward()\n if self.max_grad_norm:\n clip_grad_norm_(self.model.parameters(), self.max_grad_norm)\n self.optimizer.step()\n self.step_schedulers(\n is_epoch=False,\n metrics=results,\n log_access=False)\n\n return results","def on_predict_batch_end(self, batch, logs=None):","def __call__(self, param):\n count = param.nbatch\n if self.last_count > count:\n self.init = False\n self.last_count = count\n\n if self.init:\n if count % self.frequent == 0:\n # #11504\n try:\n speed = self.frequent * self.batch_size / (time.time() - self.tic)\n except ZeroDivisionError:\n speed = float('inf')\n if param.eval_metric is not None:\n name_value = param.eval_metric.get_name_value()\n if self.auto_reset:\n param.eval_metric.reset()\n msg = 'Epoch[%d] Batch [%d-%d]\\tSpeed: %.2f samples/sec'\n msg += '\\t%s=%f'*len(name_value)\n logging.info(msg, param.epoch, count-self.frequent, count, speed, *sum(name_value, ()))\n else:\n msg = 'Epoch[%d] Batch [0-%d]\\tSpeed: %.2f samples/sec'\n msg += '\\t%s=%f'*len(name_value)\n logging.info(msg, param.epoch, count, speed, *sum(name_value, ()))\n else:\n logging.info(\"Iter[%d] Batch [%d]\\tSpeed: %.2f samples/sec\",\n param.epoch, count, speed)\n self.tic = time.time()\n else:\n self.init = True\n self.tic = time.time()","def __call__(self):\n if self.numbatches is None:\n pool = self.pooler()\n if self.batchsize is None:\n self.batchsize = self.pooler.nInPool()\n self.numbatches = self.pooler.nInPool()//self.batchsize\n for i in xrange(self.numbatches):\n pool = self.pooler()\n self._reset_batch()\n if self.samplemethod == 'balance' and len(self.keysamplers)>0:\n batchinds,keyids = self._samplebalanced(pool)\n elif self.samplemethod == 'uniform':\n batchinds,keyids = self._sampleuniform(pool)\n else:\n batchinds,keyids = self._samplesequential(i)\n batch = self._extractInds(pool,batchinds,keyids)\n for k in batch:\n batch[k][np.isnan(batch[k])] = self.nanreplacement\n yield batch","def _update(self, count=True, forced=False):","def pre_update(self, **values):\r\n pass","def on_batch_end(self, batch, logs=None):\r\n # Don't output batch_size and batch number as TensorBoard summaries\r\n logs = logs or {}\r\n self._samples_seen += logs.get('size', 1)\r\n samples_seen_since = self._samples_seen - self._samples_seen_at_last_write\r\n if self.update_freq != 'epoch' and samples_seen_since >= self.update_freq:\r\n self._log_metrics(logs, prefix='batch_', step=self._total_batches_seen)\r\n self._samples_seen_at_last_write = self._samples_seen\r\n self._total_batches_seen += 1\r\n if self._is_tracing:\r\n self._log_trace()\r\n elif (not self._is_tracing and\r\n self._total_batches_seen == self._profile_batch - 1):\r\n self._enable_trace()","def on_batch_(\n self,\n super_callback: \"AllennlpWandbCallback\",\n trainer: \"GradientDescentTrainer\",\n batch_inputs: List[TensorDict],\n batch_outputs: List[Dict[str, Any]],\n batch_metrics: Dict[str, Any],\n epoch: int,\n batch_number: int,\n is_training: bool,\n is_primary: bool = True,\n batch_grad_norm: Optional[float] = None,\n **kwargs: Any,\n ) -> None:\n pass","def forward_batch(self,batcher, phase=0):\n pass","def updateOne(self,ident):\n \tLOGGER.info(\"lazily updating {}\".format(ident))\n \tself.idToUpdate=ident\n \tself.newState=''\n \tself.save()","def batching_enabled(self) -> bool:\n ...","def batch_data(data, state_queue):\n while True:\n time.sleep(BATCH_SLEEP_TIME)\n state_queue.put((\"device_values\", [data]))","def sample_batch(pid, args, batch_queue, port_dict, device, actor_id_to_ip_dataport, local_size, cache_array):\n def recv_data(k, data_stream, actor_set, real_data_tasks_i):\n for real_data in data_stream:\n tmp = []\n tmp.append(real_data.state)\n tmp.append(real_data.action)\n tmp.append(real_data.reward)\n tmp.append(real_data.next_state)\n tmp.append(real_data.done)\n tmp.append(actor_set[k]['w'][real_data.idx])\n tmp.append(actor_set[k]['i'][real_data.idx])\n tmp.append(actor_set[k]['t'][real_data.idx])\n tmp.append(real_data.timestamp)\n local_dict[actor_set[k]['i'][real_data.idx]] = tmp\n cache_array[actor_set[k]['i'][real_data.idx]] |= 2**pid\n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.state), dtype=np.uint8).reshape((1, 4, 84, 84)))\n real_data_tasks_i['states'].append(decom_state) #.to(device))\n real_data_tasks_i['actions'].append(torch.LongTensor([real_data.action])) #.to(device))\n real_data_tasks_i['rewards'].append(torch.FloatTensor([real_data.reward])) #.to(device))\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.next_state), dtype=np.uint8).reshape((1, 4, 84, 84)))\n real_data_tasks_i['next_states'].append(decom_next_state) #.to(device))\n real_data_tasks_i['dones'].append(torch.FloatTensor([real_data.done])) #.to(device))\n real_data_tasks_i['batch_weights'].append(torch.FloatTensor([actor_set[k]['w'][real_data.idx]])) #.to(device))\n real_data_tasks_i['batch_idxes'].append(actor_set[k]['i'][real_data.idx])\n # is the data overwrited?\n real_data_tasks_i['batch_timestamp_store'].append(actor_set[k]['t'][real_data.idx])\n real_data_tasks_i['batch_timestamp_real'].append(real_data.timestamp)\n conn = grpc.insecure_channel(port_dict['replay_ip'] + ':' + port_dict['sampleDataPort'])\n client = apex_data_pb2_grpc.SampleDataStub(channel=conn)\n local_dict = {}\n while True:\n batch_timestamp_real = []\n batch_timestamp_store = []\n batch_weights = []\n batch_idxes = []\n\n states, actions, rewards, next_states, dones = [], [], [], [], []\n\n res_batch = client.Send(apex_data_pb2.SampleDataRequest(batch_size=args.batch_size, beta = args.beta))\n actor_ids, data_ids, timestamps, weights, idxes = res_batch.actor_ids, res_batch.data_ids, res_batch.timestamp, res_batch.weights, res_batch.idxes\n actor_set = {}\n cached_value = {'states':{},'actions':{},'rewards':{},'next_states':{},'dones':{},'batch_weights':{},'batch_idxes':{},'batch_timestamp_store':{},'batch_timestamp_real':{}}\n for i in range(len(actor_ids)):\n set_a = actor_set.get(actor_ids[i], False)\n if set_a == False:\n actor_set[actor_ids[i]] = {}\n set_a = actor_set[actor_ids[i]]\n set_a['d'] = []\n set_a['w'] = []\n set_a['i'] = []\n set_a['t'] = []\n cached_value['states'][actor_ids[i]] = []\n cached_value['actions'][actor_ids[i]] = []\n cached_value['rewards'][actor_ids[i]] = []\n cached_value['next_states'][actor_ids[i]] = []\n cached_value['dones'][actor_ids[i]] = []\n cached_value['batch_weights'][actor_ids[i]] = []\n cached_value['batch_idxes'][actor_ids[i]] = []\n cached_value['batch_timestamp_store'][actor_ids[i]] = []\n cached_value['batch_timestamp_real'][actor_ids[i]] = []\n cache_id = actor_ids[i]*local_size+data_ids[i]\n cache_trans = cache_array[cache_id]\n if cache_trans & 2**pid == 0:\n set_a['d'].append(data_ids[i])\n set_a['w'].append(weights[i])\n set_a['i'].append(idxes[i])\n set_a['t'].append(timestamps[i])\n if cache_trans == 0 and local_dict.get(cache_id, False) != False:\n del local_dict[cache_id]\n else:\n try:\n state_tmp = local_dict[cache_id][0]\n action_tmp = local_dict[cache_id][1]\n reward_tmp = local_dict[cache_id][2] \n next_state_tmp = local_dict[cache_id][3] \n done_tmp = local_dict[cache_id][4] \n batch_weight_tmp = local_dict[cache_id][5] \n batch_idx_tmp = local_dict[cache_id][6] \n batch_store_tmp = local_dict[cache_id][7] \n batch_real_tmp = local_dict[cache_id][8] \n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\n cached_value['states'][actor_ids[i]].append(decom_state)\n cached_value['actions'][actor_ids[i]].append(torch.LongTensor([action_tmp]))\n cached_value['rewards'][actor_ids[i]].append(torch.FloatTensor([reward_tmp]))\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(next_state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\n cached_value['next_states'][actor_ids[i]].append(decom_next_state)\n cached_value['dones'][actor_ids[i]].append(torch.FloatTensor([done_tmp]))\n cached_value['batch_weights'][actor_ids[i]].append(torch.FloatTensor([batch_weight_tmp]))\n cached_value['batch_idxes'][actor_ids[i]].append(batch_idx_tmp)\n cached_value['batch_timestamp_store'][actor_ids[i]].append(batch_store_tmp)\n cached_value['batch_timestamp_real'][actor_ids[i]].append(batch_real_tmp)\n except:\n set_a['d'].append(data_ids[i])\n set_a['w'].append(weights[i])\n set_a['i'].append(idxes[i])\n set_a['t'].append(timestamps[i])\n real_data_links = {}\n real_data_tasks = {}\n for k, v in actor_set.items():\n actor_ip, data_port = actor_id_to_ip_dataport[k]\n conn_actor = grpc.insecure_channel(actor_ip + ':' + data_port)\n client_actor = apex_data_pb2_grpc.SendRealDataStub(channel=conn_actor)\n real_data_links[k] = client_actor.Send(apex_data_pb2.RealBatchRequest(idxes=v['d']))\n real_data_tasks[k] = {}\n real_data_tasks[k]['states'] = cached_value['states'][k]\n real_data_tasks[k]['actions'] = cached_value['actions'][k]\n real_data_tasks[k]['rewards'] = cached_value['rewards'][k]\n real_data_tasks[k]['next_states'] = cached_value['next_states'][k]\n real_data_tasks[k]['dones'] = cached_value['dones'][k]\n real_data_tasks[k]['batch_weights'] = cached_value['batch_weights'][k]\n real_data_tasks[k]['batch_idxes'] = cached_value['batch_idxes'][k]\n real_data_tasks[k]['batch_timestamp_store'] = cached_value['batch_timestamp_store'][k]\n real_data_tasks[k]['batch_timestamp_real'] = cached_value['batch_timestamp_real'][k]\n threads = []\n for k, v in real_data_links.items():\n t = threading.Thread(target=recv_data, args=(k, v, actor_set, real_data_tasks[k],))\n threads.append(t)\n t.start()\n\n for t in threads:\n t.join()\n\n for k, v in real_data_tasks.items():\n states += v['states']\n actions += v['actions']\n rewards += v['rewards']\n next_states += v['next_states']\n dones += v['dones']\n batch_weights += v['batch_weights']\n batch_idxes += v['batch_idxes']\n batch_timestamp_real += v['batch_timestamp_real']\n batch_timestamp_store += v['batch_timestamp_store']\n\n states = torch.cat(states,0).to(device)\n actions = torch.cat(actions,0).to(device)\n rewards = torch.cat(rewards,0).to(device)\n next_states = torch.cat(next_states,0).to(device)\n dones = torch.cat(dones,0).to(device)\n batch_weights = torch.cat(batch_weights,0).to(device)\n\n batch = [states, actions, rewards, next_states, dones, batch_weights, batch_idxes]\n batch_queue.put(batch)\n data, batch = None, None","def update_policy(self):\n # this is update_policy \n # sample batch of 32 from the memory\n batch_of_samples = self.replay_memory.sample(batch_size=32)\n current_state_samples = batch_of_samples['current_state_samples']\n next_state_samples = batch_of_samples['next_state_samples']\n #print type(current_state_samples[0])\n #print current_state_samples\n\n # fetch stuff we need from samples 32*84*84*4\n current_state_images = np.zeros([1, 84, 84, 4])\n #print current_state_samples\n current_state_images[0,...] = np.dstack([sample.state for sample in current_state_samples])\n\n next_state_images = np.zeros([1, 84, 84, 4])\n next_state_images[0,...] = np.dstack([sample.state for sample in next_state_samples])\n\n # preprocess\n current_state_images = self.preprocessor.process_batch(current_state_images)\n next_state_images = self.preprocessor.process_batch(next_state_images)\n # print \"current_state_images {} max {} \".format(current_state_images.shape, np.max(current_state_images))\n #print current_state_images.shape\n q_current = self.q_network.predict(current_state_images,batch_size=self.batch_size) # 32*num_actions\n q_next = self.q_network.predict(next_state_images,batch_size=self.batch_size)\n\n # targets\n y_targets_all = q_current #1*num_actions\n #print y_targets_all.shape # [1,6]\n idx = 0 \n last_sample = current_state_samples[-1]\n if last_sample.is_terminal:\n y_targets_all[idx, last_sample.action] = last_sample.reward\n else:\n if self.mode == 'vanilla':\n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*np.max(q_next[idx])\n if self.mode == 'double': \n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*q_next[idx, np.argmax(q_current[idx])] \n\n loss = self.q_network.train_on_batch(current_state_images, np.float32(y_targets_all))\n\n with tf.name_scope('summaries'):\n self.tf_log_scaler(tag='train_loss', value=loss, step=self.iter_ctr)\n\n if not (self.iter_ctr % self.log_loss_every_nth):\n self.dump_train_loss(loss)\n\n # if (self.iter_ctr > (self.num_burn_in+1)) and not(self.iter_ctr%self.target_update_freq):\n # # copy weights\n # print \"Iter {} Updating target Q network\".format(self.iter_ctr)\n # self.target_q_network.set_weights(self.q_network.get_weights())\n # [self.target_q_network.trainable_weights[i].assign(self.q_network.trainable_weights[i]) \\\n # for i in range(len(self.target_q_network.trainable_weights))]","def on_train_batch_begin(self, step, logs=None):","def _during_execute(self, db, entity):\n pass","def batch_size(self) -> int:\n ...","def next_batch(self, batch_size):\r\n raise NotImplementedError","def priority_update(self, error,batch_index):\n pass"],"string":"[\n \"def update_batch(self, *args, **kwargs):\\n pass\",\n \"def process_state_batch(self, batch):\\n return batch\",\n \"def process_state_batch(self, batch):\\n return batch\",\n \"def handle_batch(self, batch: Mapping[str, Any]) -> None:\\n self.batch = {**batch, **self.forward(batch)}\",\n \"def on_batch_begin(self, batch, logs=None):\",\n \"def index_batch(self,batch):\\n pass\",\n \"def batch(self, batch):\\n\\n self._batch = batch\",\n \"def process_state_batch(self, batch):\\n return np.squeeze(batch, axis=1)\",\n \"def update(self, batch, batch_index):\\n super(BayesianOptimization, self).update(batch, batch_index)\\n self.state['n_evidence'] += self.batch_size\\n\\n params = batch_to_arr2d(batch, self.target_model.parameter_names)\\n self._report_batch(batch_index, params, batch[self.target_name])\\n\\n optimize = self._should_optimize()\\n self.target_model.update(params, batch[self.target_name], optimize)\\n if optimize:\\n self.state['last_GP_update'] = self.target_model.n_evidence\",\n \"def fetch_batch(self, phase):\\n pass\",\n \"def process_state_batch(self, batch):\\n # batch = np.squeeze(batch, axis=1)\\n batch = np.array([np.concatenate(obs, axis=-1) for obs in batch])\\n return batch\",\n \"def init_batch(self):\\n pass\",\n \"def hook_store_batch_size(module):\\n if self._batch_size == {}:\\n batch_axis = 0\\n batch_size = module.input0.shape[batch_axis]\\n\\n for group in param_groups:\\n group_id = id(group)\\n\\n if self._verbose:\\n print(f\\\"Group {group_id}: Store 'batch_size'\\\")\\n\\n self._batch_size[group_id] = batch_size\",\n \"def on_batch_end(self, batch, logs=None):\",\n \"def update(self, batch):\\n assert self.is_training\\n # process batch\\n if self.warm_started:\\n results = self.update_batch_multi_env(batch)\\n else:\\n results = self.update_batch_erm(batch)\\n # log results\\n self.update_log(results)\\n return self.sanitize_dict(results)\",\n \"def midbatch_hook(self, progress, logging_epoch):\\n pass\",\n \"def single_step(self):\\n # Make a minibatch of training data by choosing \\\"batch_size\\\" elements with replacement\\n num_train = self.X_train.shape[0]\\n batch_mask = np.random.choice(num_train, self.batch_size) # random choice with replacement\\n X_batch = self.X_train[batch_mask]\\n y_batch = self.y_train[batch_mask]\\n\\n # Compute loss and gradient\\n loss, grads = self.model.loss(X_batch, y_batch)\\n self.latest_loss = loss\\n\\n # Perform a parameter update based on chosen optimiser\\n for p, w in self.model.params.items():\\n dw = grads[p] # current gradients\\n config = self.optim_configs[p] # moments of gradients and learning rate till previous accuracy() call\\n next_w, next_config = optimiser_type(self.optim_type, w, dw, config) # sent to choice of optimising technique\\n self.model.params[p] = next_w # model params updated\\n self.optim_configs[p] = next_config # # moments of gradients updated\",\n \"def batchProcess(self,args:object,serial:bool=False,savePath:str='')->None:\\n self.logger.debug(self.settingsReader)\\n self.settingsReader.select(args.settings)\\n self.dataReader.read(self.settingsReader, self.multiData,serial=serial)\\n self.stats.descriptive(self.multiData,serial=serial,savePath=savePath)\\n if not serial:\\n sys.exit()\",\n \"def _update_params(self):\\n pass\",\n \"def action_process(self):\\n if not self.line_ids or len(self.line_ids) <= 0:\\n raise ValidationError(\\\"Data detail tunjangan masih kosong!\\\")\\n self.state = 'processed'\",\n \"def get_batch(self):\\n return self.batch\",\n \"def forward(self, batch):\\n raise NotImplementedError\",\n \"def forward(self, batch):\\n raise NotImplementedError\",\n \"def post_randomize(self):\\n super(BatchRandomizer, self).post_randomize()\\n self.batch_idx += 1\",\n \"def on_predict_batch_begin(self, batch, logs=None):\",\n \"def __iter__(self):\\n if not self.loading:\\n self.reset_loading()\\n self.current_batch_index = 0\\n return self\",\n \"def processBatchFunc(inputBatch): #this is redefined automatically, no need to do any special code here\\n with tf.Session(graph=graph, config=config) as sess:\\n sess.run(init)\\n \\n (outValue, ) = sess.run([outputs], feed_dict={inputs: inputBatch})\\n \\n #print \\\"count now is: %d\\\" % count\\n\\n return outValue\",\n \"def run(self, batch):\\n response = self.post(batch)\\n log.info(\\\"< Discarding batch response\\\")\\n response.close()\",\n \"def process_batch(self, batch):\\n # shapes are [time, ...original dims...]\\n v_global = np.stack(batch[:,0]) # [time, agents, l_state_one_agent]\\n # note that *_local objects have shape\\n # [time, agents, ...original dim...]\\n obs_others = np.stack(batch[:,1]) # [time,agents,h,w,c] or [time, agents, obs_others]\\n v_local = np.stack(batch[:,2]) # [time,agents,l]\\n actions = np.stack(batch[:,3]) # [time,agents]\\n reward = np.stack(batch[:,4]) # [time]\\n reward_local = np.stack(batch[:,5]) # [time,agents]\\n v_global_next = np.stack(batch[:,6]) # [time, agents, l_state_one_agent]\\n obs_others_next = np.stack(batch[:,7]) # [time,agents,h,w,c]\\n v_local_next = np.stack(batch[:,8]) # [time,agents,l]\\n done = np.stack(batch[:,9]) # [time]\\n goals = np.stack(batch[:,10]) # [time, agents, l_goal]\\n\\n batch = None\\n \\n n_steps = v_global.shape[0]\\n \\n # For all global quantities, for each time step,\\n # duplicate values <n_agents> times for\\n # batch processing of all agents\\n reward = np.repeat(reward, self.n_agents, axis=0)\\n\\n # In-place reshape for *_local quantities,\\n # so that one time step for one agent is considered\\n # one batch entry\\n if self.experiment == 'sumo':\\n obs_others.shape = (n_steps*self.n_agents, self.h_obs,\\n self.w_obs, self.c_obs)\\n obs_others_next.shape = (n_steps*self.n_agents, self.h_obs,\\n self.w_obs, self.c_obs)\\n elif self.experiment == 'particle':\\n obs_others.shape = (n_steps*self.n_agents, self.l_obs_others)\\n obs_others_next.shape = (n_steps*self.n_agents, self.l_obs_others)\\n v_local.shape = (n_steps*self.n_agents, self.l_obs)\\n reward_local.shape = (n_steps*self.n_agents)\\n v_local_next.shape = (n_steps*self.n_agents, self.l_obs)\\n\\n actions_1hot, actions_others_1hot = self.process_actions(n_steps, actions)\\n \\n return n_steps, v_global, obs_others, v_local, actions_1hot, actions_others_1hot, reward, reward_local, v_global_next, obs_others_next, v_local_next, done, goals\",\n \"def on_batch_end(self, batch, logs={}):\\n if self.mode == 0:\\n self.current_progress += logs.get(\\\"size\\\")\\n self.total_progress += logs.get(\\\"size\\\")\\n else:\\n self.current_progress += 1\\n self.total_progress += 1\\n self.loss = logs.get(\\\"loss\\\")\\n loss_item = {\\\"samples\\\": self.total_progress, \\\"loss\\\": self.loss}\\n self.accuracy = logs.get(\\\"acc\\\")\\n accuracy_item = {\\\"samples\\\": self.total_progress, \\\"accuracy\\\": self.accuracy}\\n self.total_runtime = time() - self.start_time\\n self.update_data(loss_item, accuracy_item)\",\n \"def batch(self):\\n return self._batch\",\n \"def batch_start(self, batch_idx, batch_data):\\n self.batch = batch_idx\",\n \"def process(self, data_batch: Any, data_samples: Sequence[dict]) -> None:\",\n \"def on_batch(self, x, y):\",\n \"def on_test_batch_begin(self, batch, logs=None):\",\n \"def _step(self):\\n # Make a minibatch of training data\\n num_train = self.X_train.shape[0]\\n # random choose the samples\\n batch_mask = np.random.choice(num_train, self.batch_size)\\n X_batch = self.X_train[batch_mask]\\n y_batch = self.y_train[batch_mask]\\n\\n # Compute loss and gradient\\n loss, grads = self.model.loss(X_batch, y_batch)\\n self.loss_history.append(loss)\\n\\n # Perform a parameter update\\n for p, w in self.model.params.items():\\n dw = grads[p]\\n config = self.optim_configs[p]\\n next_w, next_config = self.update_rule(w, dw, config)\\n self.model.params[p] = next_w\\n self.optim_configs[p] = next_config\",\n \"def train_single_batch(self, batch_data):\\n assert hasattr(self, \\\"model\\\"), \\\"Please specify the exact model !\\\"\\n self.optimizer.zero_grad()\\n norm_adj = self.norm_adj\\n ua_embeddings, ia_embeddings = self.model.forward(norm_adj)\\n\\n batch_users, pos_items, neg_items = batch_data\\n\\n u_g_embeddings = ua_embeddings[batch_users]\\n pos_i_g_embeddings = ia_embeddings[pos_items]\\n neg_i_g_embeddings = ia_embeddings[neg_items]\\n\\n batch_mf_loss, batch_reg_loss = self.loss_comput(\\n u_g_embeddings,\\n pos_i_g_embeddings,\\n neg_i_g_embeddings,\\n batch_users,\\n pos_items,\\n neg_items,\\n )\\n\\n batch_loss = batch_mf_loss + batch_reg_loss\\n\\n batch_loss.backward()\\n self.optimizer.step()\\n loss = batch_loss.item()\\n return loss\",\n \"def update_params(self):\\n pass\",\n \"def update(self, params):\",\n \"def test_update_batch(self):\\n self.batch_data['batch_id'] = self.batch_info.id\\n resp = self.query_with_token(\\n self.access_token_master,\\n update_batch_info.format(**self.batch_data))\\n\\n self.assertIn('data', resp)\\n self.assertEqual(\\n resp['data']['updateBatchInfo']['batchInfo']['supplier']['name'],\\n self.supplier.name)\\n self.assertEqual(\\n resp['data']['updateBatchInfo']['batchInfo']['batchNo'],\\n self.batch_info.batch_no)\",\n \"def validate_batch(self, *arg, **kwargs):\\n pass\",\n \"def batch_id(self, batch_id):\\n\\n self._batch_id = batch_id\",\n \"def update(self, triplet_batch):\\n feed_dict = {self.anchor: triplet_batch[0],\\n self.pos: triplet_batch[1],\\n self.neg: triplet_batch[2]}\\n _, loss = self.sess.run([self.train_step, self.loss], feed_dict=feed_dict)\\n return loss\",\n \"def update(self, triplet_batch):\\n feed_dict = {self.anchor: triplet_batch[0],\\n self.pos: triplet_batch[1],\\n self.neg: triplet_batch[2]}\\n _, loss = self.sess.run([self.train_step, self.loss], feed_dict=feed_dict)\\n return loss\",\n \"def update(self, triplet_batch):\\n feed_dict = {self.anchor: triplet_batch[0],\\n self.pos: triplet_batch[1],\\n self.neg: triplet_batch[2]}\\n _, loss = self.sess.run([self.train_step, self.loss], feed_dict=feed_dict)\\n return loss\",\n \"def add(\\n self,\\n batch: RolloutBatchProtocol,\\n buffer_ids: Optional[Union[np.ndarray, list[int]]] = None,\\n ) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\\n # preprocess batch\\n new_batch = Batch()\\n for key in set(self._reserved_keys).intersection(batch.keys()):\\n new_batch.__dict__[key] = batch[key]\\n batch = new_batch\\n batch.__dict__[\\\"done\\\"] = np.logical_or(batch.terminated, batch.truncated)\\n assert {\\\"obs\\\", \\\"act\\\", \\\"rew\\\", \\\"terminated\\\", \\\"truncated\\\", \\\"done\\\"}.issubset(batch.keys())\\n if self._save_only_last_obs:\\n batch.obs = batch.obs[:, -1]\\n if not self._save_obs_next:\\n batch.pop(\\\"obs_next\\\", None)\\n elif self._save_only_last_obs:\\n batch.obs_next = batch.obs_next[:, -1]\\n # get index\\n if buffer_ids is None:\\n buffer_ids = np.arange(self.buffer_num)\\n ptrs, ep_lens, ep_rews, ep_idxs = [], [], [], []\\n for batch_idx, buffer_id in enumerate(buffer_ids):\\n ptr, ep_rew, ep_len, ep_idx = self.buffers[buffer_id]._add_index(\\n batch.rew[batch_idx],\\n batch.done[batch_idx],\\n )\\n ptrs.append(ptr + self._offset[buffer_id])\\n ep_lens.append(ep_len)\\n ep_rews.append(ep_rew)\\n ep_idxs.append(ep_idx + self._offset[buffer_id])\\n self.last_index[buffer_id] = ptr + self._offset[buffer_id]\\n self._lengths[buffer_id] = len(self.buffers[buffer_id])\\n ptrs = np.array(ptrs)\\n try:\\n self._meta[ptrs] = batch\\n except ValueError:\\n batch.rew = batch.rew.astype(float)\\n batch.done = batch.done.astype(bool)\\n batch.terminated = batch.terminated.astype(bool)\\n batch.truncated = batch.truncated.astype(bool)\\n if self._meta.is_empty():\\n self._meta = create_value(batch, self.maxsize, stack=False) # type: ignore\\n else: # dynamic key pops up in batch\\n alloc_by_keys_diff(self._meta, batch, self.maxsize, False)\\n self._set_batch_for_children()\\n self._meta[ptrs] = batch\\n return ptrs, np.array(ep_rews), np.array(ep_lens), np.array(ep_idxs)\",\n \"def handle_batch(self, transfer_batch, dilution_settings, robot_settings):\\n # TODO: Shouldn't transfer_batch be an iterator?\\n for transfer in transfer_batch.transfers:\\n self.handle_transfer(transfer, dilution_settings, robot_settings)\",\n \"def process(self, sess):\\n\\n sess.run(self.sync) # copy weights from shared to local\\n rollout = self.pull_batch_from_queue()\\n batch = process_rollout(rollout, gamma=0.99, lambda_=1.0)\\n\\n should_compute_summary = self.task == 0 and self.local_steps % 11 == 0\\n\\n if should_compute_summary:\\n fetches = [self.summary_op, self.train_op, self.global_step]\\n else:\\n fetches = [self.train_op, self.global_step]\\n\\n feed_dict = {\\n self.local_network.x: batch.si,\\n self.ac: batch.a,\\n self.adv: batch.adv,\\n self.r: batch.r,\\n self.local_network.state_in[0]: batch.features[0],\\n self.local_network.state_in[1]: batch.features[1],\\n }\\n\\n fetched = sess.run(fetches, feed_dict=feed_dict)\\n\\n if should_compute_summary:\\n self.summary_writer.add_summary(tf.Summary.FromString(fetched[0]), fetched[-1])\\n self.summary_writer.flush()\\n self.local_steps += 1\",\n \"def update(self, x_train_single, updated_h):\\n # x_row = cp.array(x_train_single.toarray())\\n # cp.cuda.Stream.null.synchronize()\\n updater(x_train_single,updated_h,self.weights,self.num_features,self.num_models,self.learning_rate)\\n # self.biases += updated_h * self.learning_rate\",\n \"def process(self, sess):\\n\\n sess.run(self.sync) # copy weights from shared to local\\n rollout = next(self.rollout_provider)\\n batch = process_rollout(rollout, gamma=self.config.discount)\\n\\n should_compute_summary = (self.task == 0 \\n and self.local_steps % self.config.summary_every == 0)\\n\\n if should_compute_summary:\\n fetches = [self.summary_op, self.train_op, self.global_step]\\n else:\\n fetches = [self.train_op, self.global_step]\\n\\n feed_dict = {\\n self.local_network.x: batch.si,\\n self.r: batch.r,\\n self.w: batch.w,\\n self.local_network.state_in[0]: batch.features[0],\\n self.local_network.state_in[1]: batch.features[1],\\n }\\n\\n fetched = sess.run(fetches, feed_dict=feed_dict)\\n\\n if should_compute_summary:\\n self.summary_writer.add_summary(\\n tf.Summary.FromString(fetched[0]), fetched[-1])\\n self.summary_writer.flush()\\n self.local_steps += 1\",\n \"def _update(self):\\n if (len(self.buffer) < self.batch_size):\\n return\\n self.training_iter += 1\\n # Make sure actor_target and critic_target are in eval mode\\n assert not self.model.q_target_1.training\\n assert not self.model.q_target_2.training\\n\\n assert self.model.q_1.training\\n assert self.model.q_2.training\\n transitions = self.buffer.sample(self.batch_size)\\n batch = self.buffer.transition(*zip(*transitions))\\n state_batch = torch.tensor(batch.state, device=self.device).float()\\n action_batch = torch.tensor(batch.action,\\n device=self.device).unsqueeze(-1).long()\\n reward_batch = torch.tensor(batch.reward,\\n device=self.device).unsqueeze(-1).float()\\n next_state_batch = torch.tensor(batch.next_state,\\n device=self.device).float()\\n is_done_batch = torch.tensor(batch.done,\\n device=self.device).unsqueeze(-1).bool()\\n with torch.no_grad():\\n Q_next_1 = ((~is_done_batch)\\n * (self.model.q_target_1(next_state_batch).min(dim=-1)[0].unsqueeze(-1)))\\n Q_next_2 = ((~is_done_batch)\\n * (self.model.q_target_2(next_state_batch).min(dim=-1)[0].unsqueeze(-1)))\\n\\n # Use max want to avoid underestimation bias\\n Q_next = torch.max(Q_next_1, Q_next_2)\\n Q_expected = reward_batch + self.gamma * Q_next\\n\\n Q_1 = self.model.q_1(state_batch).gather(-1, action_batch)\\n Q_2 = self.model.q_2(state_batch).gather(-1, action_batch)\\n L_1 = nn.MSELoss()(Q_1, Q_expected)\\n L_2 = nn.MSELoss()(Q_2, Q_expected)\\n self.loss.append([L_1.item(), L_2.item()])\\n self.model.q_optimizer_1.zero_grad()\\n self.model.q_optimizer_2.zero_grad()\\n L_1.backward()\\n L_2.backward()\\n self.model.q_optimizer_1.step()\\n self.model.q_optimizer_2.step()\\n self.store_Q.append([Q_1.tolist(), Q_2.tolist(), Q_expected.tolist()])\\n if (self.training_iter % self.update_freq) == 0:\\n self.model.update_target_nn()\",\n \"def flush_batch(self, batch: Sequence[TResult]) -> None:\\n pass\",\n \"def _process(self, start_key, batch_size):\\n\\n query = self.MODEL.all()\\n if start_key:\\n query.filter('__key__ > ', start_key)\\n\\n try:\\n entities = query.fetch(batch_size)\\n\\n if not entities:\\n # all entities has already been processed\\n return\\n\\n for entity in entities:\\n for field in self.FIELDS_TO_UPDATE:\\n old_reference = getattr(entity, field)\\n\\n if not old_reference:\\n continue\\n\\n # check if the field has not been updated\\n if isinstance(old_reference, self.PROFILE_MODEL):\\n continue\\n\\n profile = _getProfileForRole(old_reference, self.PROFILE_MODEL)\\n setattr(entity, field, profile)\\n\\n for list_property in self.LISTS_TO_UPDATE:\\n l = getattr(entity, list_property)\\n new_l = []\\n for key in l:\\n new_l.append(_getProfileKeyForRoleKey(key, self.PROFILE_MODEL))\\n setattr(entity, list_property, new_l)\\n\\n db.put(entities)\\n start_key = entities[-1].key()\\n deferred.defer(self._process, start_key, batch_size)\\n except DeadlineExceededError:\\n # here we should probably be more careful\\n deferred.defer(self._process, start_key, batch_size)\",\n \"def on_predict_batch_begin(self, step, logs=None):\",\n \"def train(self, batch):\\n pass\",\n \"def __call__(self, **kwargs):\\r\\n for item, value in kwargs.items():\\r\\n if item not in self._state[\\\"data\\\"]:\\r\\n self._state[\\\"data\\\"][item] = value\",\n \"def __call__(self, **kwargs):\\r\\n for item, value in kwargs.items():\\r\\n if item not in self._state[\\\"data\\\"]:\\r\\n self._state[\\\"data\\\"][item] = value\",\n \"def __getitem__(self, index):#get_batch(self, index=None, patient_list=None):\\n\\n #if patient_list is None:\\n # Generate batch based on the provided index\\n indices = self.indices[index]# * self.batch_size:(index + 1) * self.batch_size]\\n #else:\\n # Generate batch based on the request patients\\n # indices = self.patient_to_index(patient_list)\\n\\n # Make a list of files to be loaded\\n file_paths_to_load = [self.file_paths_list[indices]]#k] for k in\\n\\n # Load the requested files as a tensors\\n loaded_data = self.load_data(file_paths_to_load)\\n\\n if self.trans != []:\\n for transform in self.trans:\\n if transform == 'flip':\\n if np.random.random() >= 0.5:\\n for key in loaded_data.keys():\\n if not isinstance(loaded_data[key], list):\\n if loaded_data[key].ndim == 4:\\n\\n loaded_data[key] = loaded_data[key][:, :, :, ::-1]\\n if key == 'structure_masks':\\n loaded_data[key][[3, 2]] = loaded_data[key][[2, 3]]\\n\\n elif isinstance(transform, int):\\n for key in loaded_data.keys():\\n if not isinstance(loaded_data[key], list):\\n if loaded_data[key].ndim == 4:\\n loaded_data[key] = torch.from_numpy(loaded_data[key].copy()).float()\\n #loaded_data[key] = interpolate(torch.from_numpy(loaded_data[key].copy()).unsqueeze(0), scale_factor=transform/128).squeeze(0).float()\\n\\n\\n elif transform == 'crop':\\n\\n seed_x = np.random.randint(0,10)\\n seed_y = np.random.randint(0,10)\\n seed_z = np.random.randint(0,10)\\n for key in loaded_data.keys():\\n if not isinstance(loaded_data[key], list):\\n if loaded_data[key].ndim == 4:\\n loaded_data[key] = pad(loaded_data[key], [5, 5, 5, 5, 5, 5], 'constant', 0)\\n loaded_data[key] = loaded_data[key][:, seed_x:128+seed_x, seed_y:128+seed_y, seed_z:128+seed_z]\\n\\n loaded_data['ct'] = ((loaded_data['ct'].clamp(0, self.ct_scaling_factor) /self.ct_scaling_factor)) # -0.5)/0.5\\n if self.mode_name == 'training_model':\\n loaded_data['dose'] = ((loaded_data['dose'].clamp(0, self.dose_scaling_factor) / self.dose_scaling_factor)) # -0.5)/0.5\\n\\n return loaded_data\",\n \"def update_batch_multi_env(self, batch):\\n x, y_true, metadata = batch\\n x = x.to(self.device)\\n y_true = y_true.to(self.device)\\n g = self.grouper.metadata_to_group(metadata).to(self.device)\\n\\n # Perform per group gradient computation and RD\\n environments = torch.unique(g).tolist()\\n sizes = []\\n pos_acc = [] \\n neg_acc = []\\n total_grad = []\\n\\n self.pos_grad.fill_(0)\\n self.neg_grad.fill_(0)\\n self.total_grad.fill_(0)\\n self.all_positive.fill_(1)\\n self.all_negative.fill_(1)\\n full_outputs = None\\n for env_id, env_name in enumerate(environments):\\n # get the ratio of size of env to batch\\n env_ratio = torch.sum(g==env_name).item() / g.shape[0]\\n self.model.zero_grad()\\n\\n if torch.is_tensor(x):\\n outputs = self.model(x[g==env_name])\\n partial_objective = self.partial_objective(outputs, y_true[g==env_name])\\n if full_outputs is None:\\n partial_shape = list(outputs.shape)\\n partial_shape[0] = y_true.shape[0]\\n full_outputs = torch.zeros(*partial_shape, dtype=outputs.dtype).to(torch.device(\\\"cuda\\\"))\\n full_outputs[g==env_name] = outputs\\n else:\\n outputs = self.model(x)\\n partial_objective = self.partial_objective(outputs[g==env_name], y_true[g==env_name])\\n full_outputs = outputs\\n if not partial_objective.requires_grad:\\n print(\\\"Loss function returned 0 (possibly input is NaN\\\")\\n else:\\n partial_objective.backward()\\n # Save the gradients here\\n cur_pos = 0\\n for i, param in enumerate(self.model.parameters()):\\n p = param.grad.clone().detach()\\n next_pos = cur_pos + np.prod(list(p.shape))\\n\\n p_neg = (p<0)*p\\n p_pos = (p>0)*p\\n\\n self.all_positive[cur_pos:next_pos] = torch.logical_and(p.view(-1)>0, self.all_positive[cur_pos:next_pos])\\n self.all_negative[cur_pos:next_pos] = torch.logical_and(p.view(-1)<0, self.all_negative[cur_pos:next_pos])\\n \\n self.dist_pos[env_id][0][cur_pos:next_pos] = -1.0 * p_neg.view(-1)\\n self.dist_neg[env_id][0][cur_pos:next_pos] = p_pos.view(-1)\\n\\n self.pos_grad[cur_pos:next_pos] += p_pos.view(-1) * env_ratio\\n self.neg_grad[cur_pos:next_pos] += p_neg.view(-1) * env_ratio\\n self.total_grad[cur_pos:next_pos] += p.view(-1) * env_ratio\\n cur_pos = next_pos\\n \\n if self._is_satisficing:\\n for env_id, env_name in enumerate(environments):\\n self.dist_pos[env_id][0] = self.dist_pos[env_id][0] * torch.abs(self.total_grad)\\n self.dist_neg[env_id][0] = self.dist_neg[env_id][0] * torch.abs(self.total_grad)\\n\\n if self.and_mask:\\n consistent_ones = torch.logical_or(self.all_positive, self.all_negative)\\n final_grads = self.total_grad * consistent_ones\\n else: \\n marginals, _ = self.ba.optimize_rd(self.dist_pos, self.dist_neg, [self.dist_pos[0][0].shape])\\n\\n marginals[0][marginals[0]<0.0] = 1e-8\\n marginals[0][marginals[0]>1.0] = 1.0 - 1e-8\\n\\n if self.without_sampling:\\n pos_ones = marginals[0]\\n else:\\n # Sample the directions\\n pos_ones = torch.bernoulli(marginals[0])\\n\\n if self.control_only_direction:\\n magnitude = torch.abs(self.total_grad)\\n direction = 2*pos_ones - 1\\n final_rd_grads = magnitude * direction\\n else:\\n final_rd_grads = self.pos_grad*pos_ones + self.neg_grad*(1-pos_ones)\\n\\n\\n if self.only_inconsistent:\\n consistent_ones = torch.logical_or(self.all_positive, self.all_negative)\\n final_grads = final_rd_grads * torch.logical_not(consistent_ones) + self.total_grad * consistent_ones\\n else:\\n final_grads = final_rd_grads\\n\\n self.model.zero_grad()\\n \\n results = {\\n 'g': g,\\n 'y_true': y_true,\\n 'y_pred': full_outputs,\\n 'metadata': metadata,\\n }\\n\\n objective = self.objective(results)\\n results['objective'] = objective.item()\\n cur_pos = 0\\n for i,p in enumerate(self.model.parameters()):\\n next_pos = cur_pos + np.prod(list(p.shape))\\n p.grad.data.copy_(final_grads[cur_pos:next_pos].data.view(p.shape))\\n cur_pos = next_pos\\n\\n if self.max_grad_norm:\\n clip_grad_norm_(self.model.parameters(), self.max_grad_norm)\\n self.optimizer.step()\\n self.step_schedulers(\\n is_epoch=False,\\n metrics=results,\\n log_access=False)\\n return results\",\n \"def MutateBatchJob(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def __call__(self, **kwargs):\\n for item, value in kwargs.items():\\n if item not in self._state[\\\"data\\\"]:\\n self._state[\\\"data\\\"][item] = value\",\n \"def __call__(self, **kwargs):\\n for item, value in kwargs.items():\\n if item not in self._state[\\\"data\\\"]:\\n self._state[\\\"data\\\"][item] = value\",\n \"def __call__(self, **kwargs):\\n for item, value in kwargs.items():\\n if item not in self._state[\\\"data\\\"]:\\n self._state[\\\"data\\\"][item] = value\",\n \"def handle_batch(self, batch, batch_idx, phase=\\\"train\\\"):\\n\\n handler = self.batch_handlers[phase]\\n return handler(batch, batch_idx)\",\n \"def update(self, loss):\\n self.st.batch_update(self.last_sampled, loss ** 2 / self.p)\",\n \"def get_batch(self, batch_kwargs, batch_parameters=None) -> None:\\n raise NotImplementedError\",\n \"def process_batch(self, batch):\\n # extend with current batch\\n self._extend(batch)\\n\\n # unpack and compute bounds\\n length = len(self.obs)\\n c = self.c\\n\\n # normally we cannot compute samples for the last c elements, but\\n # in the terminal case, we halluciante values where necessary\\n end = length if batch.terminal else length - c\\n\\n # collect samples to return in a FeudalBatch\\n feudal_batch = FeudalBatch()\\n for t in range(c, end):\\n\\n # state difference\\n s_diff = self.s[t + c] - self.s[t]\\n\\n # intrinsic reward\\n ri = 0\\n # note that this for loop considers s and g values\\n # 1 timestep to c timesteps (inclusively) ago\\n for i in range(1, c + 1):\\n ri_s_diff = self.s[t] - self.s[t - i]\\n if np.linalg.norm(ri_s_diff) != 0:\\n ri += cosine_similarity(ri_s_diff, self.g[t - i])\\n ri /= c\\n\\n # sum of g values used to derive w, input to the linear transform\\n gsum = np.zeros_like(self.g[t - c])\\n for i in range(t - c, t + 1):\\n gsum += self.g[i]\\n\\n # add to the batch\\n feudal_batch.add(self.obs[t], self.a[t], self.returns[t], s_diff,\\n ri, gsum, self.features[t])\\n\\n # in the terminal case, set reset flag\\n if batch.terminal:\\n self.last_terminal = True\\n # in the general case, forget all but the last 2 * c elements\\n # reason being that the first c of those we have already computed\\n # a batch for, and the second c need those first c\\n else:\\n twoc = 2 * self.c\\n self.obs = self.obs[-twoc:]\\n self.a = self.a[-twoc:]\\n self.returns = self.returns[-twoc:]\\n self.s = self.s[-twoc:]\\n self.g = self.g[-twoc:]\\n self.features = self.features[-twoc:]\\n\\n return feudal_batch.get_batch()\",\n \"def _update_num_batches(self):\\n # maximum possible number of batches is equal to number of whole times\\n # batch_size divides in to the number of data points which can be\\n # found using integer division\\n possible_num_batches = self.inputs.shape[0] // self.batch_size\\n if self.max_num_batches == -1:\\n self.num_batches = possible_num_batches\\n else:\\n self.num_batches = min(self.max_num_batches, possible_num_batches)\",\n \"def update_batch_of_entitiies(self, username, entity_batch_data):\\n\\t\\treturn self._send_command_to_entity_server(us.SERVER_COMMAND_UPDATE_ENTITY_BATCH, username + '|' + str(entity_batch_data))\",\n \"def is_batch():\\n\\n pass\",\n \"def training_step(self, batch):\\n return {}\",\n \"def on_batch_mode(self, event):\\n if self.parent is not None:\\n wx.PostEvent(self.parent,\\n NewBatchEvent(enable=True))\",\n \"def _on_batch_cache(self, meta, batch):\\n # 21/03/12 Boudewijn: we can not filter all packets this way. i.e. when multiple people\\n # send us missing-identity messages some of them will be dropped\\n #\\n # def unique(batch):\\n # unique = set()\\n # for candidate, packet, conversion in batch:\\n # assert isinstance(packet, str)\\n # if packet in unique:\\n # if __debug__:\\n # dprint(\\\"drop a \\\", len(packet), \\\" byte packet (duplicate in batch) from \\\", candidate, level=\\\"warning\\\")\\n # self._statistics.drop(\\\"_convert_packets_into_batch:duplicate in batch\\\", len(packet))\\n # else:\\n # unique.add(packet)\\n # yield candidate, packet, conversion\\n\\n # # remove duplicated\\n # # todo: make _convert_batch_into_messages accept iterator instead of list to avoid conversion\\n # batch = list(unique(batch))\\n\\n # BEGIN = time()\\n\\n # convert binary packets into Message.Implementation instances\\n messages = list(self._convert_batch_into_messages(batch))\\n assert all(isinstance(message, Message.Implementation) for message in messages), \\\"_convert_batch_into_messages must return only Message.Implementation instances\\\"\\n assert all(message.meta == meta for message in messages), \\\"All Message.Implementation instances must be in the same batch\\\"\\n if __debug__: dprint(len(messages), \\\" \\\", meta.name, \\\" messages after conversion\\\")\\n\\n # handle the incoming messages\\n if messages:\\n self.on_message_batch(messages)\\n\\n # END = time()\\n # dprint(\\\"%4.2f\\\" % (END-BEGIN), \\\" seconds for %2d\\\" % len(batch), \\\" \\\", meta.name, \\\" messages\\\", force=True)\",\n \"def _batch_train(self, batch, training_step, step):\\n lstm_size = (self.batch_size, self.Qmain.h_size)\\n batch_mem = np.zeros(lstm_size)\\n batch_carry = np.zeros(lstm_size)\\n input_shape = (self.batch_size,\\n self.trace_length,\\n self.observation_size)\\n m_data = np.vstack(batch[:, 0])\\n m_data = m_data.reshape(input_shape)\\n t_data = np.vstack(batch[:, 4])\\n t_data = t_data.reshape(input_shape)\\n q_input = [np.copy(batch_mem), np.copy(batch_carry), np.copy(m_data)]\\n q1_input = [np.copy(batch_mem), np.copy(batch_carry), np.copy(t_data)]\\n\\n # Batch predict\\n self.Qmain.trace_length.assign(self.trace_length)\\n self.Qmain.dropout_rate.assign(0.0)\\n self.Qtarget.trace_length.assign(self.trace_length)\\n self.Qtarget.dropout_rate.assign(0.0)\\n\\n # Save the graph just the first time\\n if training_step == 0:\\n tf.summary.trace_on()\\n\\n # T batch predict\\n pred = self.Qmain.model.predict(q_input,\\n batch_size=self.batch_size)\\n Q = pred[0]\\n batch_bus = pred[1]\\n batch_line = pred[2]\\n batch_disp = pred[3]\\n\\n ## Log graph once and disable graph logging\\n if training_step == 0:\\n with self.tf_writer.as_default():\\n tf.summary.trace_export(self.name + \\\"-graph\\\", step)\\n\\n # T+1 batch predict\\n Qn, *_ = self.Qtarget.model.predict(q1_input,\\n batch_size=self.batch_size)\\n \\n # Compute batch Q update to Qtarget\\n for i in range(self.batch_size):\\n idx = i * (self.trace_length - 1)\\n a = batch[idx][1]\\n grid = a[0]\\n batch_bus[i][:] = a[1][:]\\n batch_line[i][:] = a[2][:]\\n batch_disp[i][:] = a[3][:]\\n r = batch[idx][2]\\n d = batch[idx][3]\\n Q[i][grid] = r\\n if d == False:\\n Q[i][grid] += DISCOUNT_FACTOR * Qn[i][grid]\\n\\n # Batch train\\n batch_x = [batch_mem, batch_carry, m_data]\\n batch_y = [\\n Q,\\n batch_bus, batch_line, batch_disp,\\n batch_mem, batch_carry\\n ]\\n loss = self.Qmain.model.train_on_batch(batch_x, batch_y)\\n loss = loss[0]\\n\\n # Log to tensorboard\\n self._tf_log_summary(loss, step)\",\n \"def val_batch_start(self, batch_idx, batch_data):\\n self.val_batch = batch_idx\",\n \"def start_of_batch_hook(self, progress, logging_epoch):\\n pass\",\n \"def append(self, batch: Batch):\",\n \"def validate_batch(self, *args, **kwargs):\\n raise NotImplementedError()\",\n \"def updatecalibBatch(calib, batchChange):\\n\\n if isinstance(calib, list):\\n updatedcalib = list()\\n for j in range(len(calib)):\\n # all the same\\n updatedcalib.append(calib[j])\\n # modify batch number\\n for batchNum in batchChange.keys():\\n updatedcalib[j]['calibSampleMetadata'].loc[calib[j]['calibSampleMetadata']['Batch'] == batchNum, 'Batch'] = batchChange[batchNum]\\n\\n elif isinstance(calib, dict):\\n updatedcalib = copy.deepcopy(calib)\\n # modify batch number\\n for batchNum in batchChange.keys():\\n updatedcalib['calibSampleMetadata'].loc[calib['calibSampleMetadata']['Batch'] == batchNum, 'Batch'] = batchChange[batchNum]\\n\\n return updatedcalib\",\n \"def __call__(self, graph):\\n result = graph.sqs_message_dispatcher.handle_batch()\\n if not result.message_count:\\n raise SleepNow()\",\n \"def update_job_state(self, job):\",\n \"def _send(self, batch):\\n return self.agent.emitBatch(batch)\",\n \"def update_batch_erm(self, batch):\\n x, y_true, metadata = batch\\n x = x.to(self.device)\\n y_true = y_true.to(self.device)\\n g = self.grouper.metadata_to_group(metadata).to(self.device)\\n outputs = self.model(x)\\n results = {\\n 'g': g,\\n 'y_true': y_true,\\n 'y_pred': outputs,\\n 'metadata': metadata,\\n }\\n\\n objective = self.objective(results)\\n results['objective'] = objective.item()\\n # update\\n self.model.zero_grad()\\n objective.backward()\\n if self.max_grad_norm:\\n clip_grad_norm_(self.model.parameters(), self.max_grad_norm)\\n self.optimizer.step()\\n self.step_schedulers(\\n is_epoch=False,\\n metrics=results,\\n log_access=False)\\n\\n return results\",\n \"def on_predict_batch_end(self, batch, logs=None):\",\n \"def __call__(self, param):\\n count = param.nbatch\\n if self.last_count > count:\\n self.init = False\\n self.last_count = count\\n\\n if self.init:\\n if count % self.frequent == 0:\\n # #11504\\n try:\\n speed = self.frequent * self.batch_size / (time.time() - self.tic)\\n except ZeroDivisionError:\\n speed = float('inf')\\n if param.eval_metric is not None:\\n name_value = param.eval_metric.get_name_value()\\n if self.auto_reset:\\n param.eval_metric.reset()\\n msg = 'Epoch[%d] Batch [%d-%d]\\\\tSpeed: %.2f samples/sec'\\n msg += '\\\\t%s=%f'*len(name_value)\\n logging.info(msg, param.epoch, count-self.frequent, count, speed, *sum(name_value, ()))\\n else:\\n msg = 'Epoch[%d] Batch [0-%d]\\\\tSpeed: %.2f samples/sec'\\n msg += '\\\\t%s=%f'*len(name_value)\\n logging.info(msg, param.epoch, count, speed, *sum(name_value, ()))\\n else:\\n logging.info(\\\"Iter[%d] Batch [%d]\\\\tSpeed: %.2f samples/sec\\\",\\n param.epoch, count, speed)\\n self.tic = time.time()\\n else:\\n self.init = True\\n self.tic = time.time()\",\n \"def __call__(self):\\n if self.numbatches is None:\\n pool = self.pooler()\\n if self.batchsize is None:\\n self.batchsize = self.pooler.nInPool()\\n self.numbatches = self.pooler.nInPool()//self.batchsize\\n for i in xrange(self.numbatches):\\n pool = self.pooler()\\n self._reset_batch()\\n if self.samplemethod == 'balance' and len(self.keysamplers)>0:\\n batchinds,keyids = self._samplebalanced(pool)\\n elif self.samplemethod == 'uniform':\\n batchinds,keyids = self._sampleuniform(pool)\\n else:\\n batchinds,keyids = self._samplesequential(i)\\n batch = self._extractInds(pool,batchinds,keyids)\\n for k in batch:\\n batch[k][np.isnan(batch[k])] = self.nanreplacement\\n yield batch\",\n \"def _update(self, count=True, forced=False):\",\n \"def pre_update(self, **values):\\r\\n pass\",\n \"def on_batch_end(self, batch, logs=None):\\r\\n # Don't output batch_size and batch number as TensorBoard summaries\\r\\n logs = logs or {}\\r\\n self._samples_seen += logs.get('size', 1)\\r\\n samples_seen_since = self._samples_seen - self._samples_seen_at_last_write\\r\\n if self.update_freq != 'epoch' and samples_seen_since >= self.update_freq:\\r\\n self._log_metrics(logs, prefix='batch_', step=self._total_batches_seen)\\r\\n self._samples_seen_at_last_write = self._samples_seen\\r\\n self._total_batches_seen += 1\\r\\n if self._is_tracing:\\r\\n self._log_trace()\\r\\n elif (not self._is_tracing and\\r\\n self._total_batches_seen == self._profile_batch - 1):\\r\\n self._enable_trace()\",\n \"def on_batch_(\\n self,\\n super_callback: \\\"AllennlpWandbCallback\\\",\\n trainer: \\\"GradientDescentTrainer\\\",\\n batch_inputs: List[TensorDict],\\n batch_outputs: List[Dict[str, Any]],\\n batch_metrics: Dict[str, Any],\\n epoch: int,\\n batch_number: int,\\n is_training: bool,\\n is_primary: bool = True,\\n batch_grad_norm: Optional[float] = None,\\n **kwargs: Any,\\n ) -> None:\\n pass\",\n \"def forward_batch(self,batcher, phase=0):\\n pass\",\n \"def updateOne(self,ident):\\n \\tLOGGER.info(\\\"lazily updating {}\\\".format(ident))\\n \\tself.idToUpdate=ident\\n \\tself.newState=''\\n \\tself.save()\",\n \"def batching_enabled(self) -> bool:\\n ...\",\n \"def batch_data(data, state_queue):\\n while True:\\n time.sleep(BATCH_SLEEP_TIME)\\n state_queue.put((\\\"device_values\\\", [data]))\",\n \"def sample_batch(pid, args, batch_queue, port_dict, device, actor_id_to_ip_dataport, local_size, cache_array):\\n def recv_data(k, data_stream, actor_set, real_data_tasks_i):\\n for real_data in data_stream:\\n tmp = []\\n tmp.append(real_data.state)\\n tmp.append(real_data.action)\\n tmp.append(real_data.reward)\\n tmp.append(real_data.next_state)\\n tmp.append(real_data.done)\\n tmp.append(actor_set[k]['w'][real_data.idx])\\n tmp.append(actor_set[k]['i'][real_data.idx])\\n tmp.append(actor_set[k]['t'][real_data.idx])\\n tmp.append(real_data.timestamp)\\n local_dict[actor_set[k]['i'][real_data.idx]] = tmp\\n cache_array[actor_set[k]['i'][real_data.idx]] |= 2**pid\\n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.state), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n real_data_tasks_i['states'].append(decom_state) #.to(device))\\n real_data_tasks_i['actions'].append(torch.LongTensor([real_data.action])) #.to(device))\\n real_data_tasks_i['rewards'].append(torch.FloatTensor([real_data.reward])) #.to(device))\\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(real_data.next_state), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n real_data_tasks_i['next_states'].append(decom_next_state) #.to(device))\\n real_data_tasks_i['dones'].append(torch.FloatTensor([real_data.done])) #.to(device))\\n real_data_tasks_i['batch_weights'].append(torch.FloatTensor([actor_set[k]['w'][real_data.idx]])) #.to(device))\\n real_data_tasks_i['batch_idxes'].append(actor_set[k]['i'][real_data.idx])\\n # is the data overwrited?\\n real_data_tasks_i['batch_timestamp_store'].append(actor_set[k]['t'][real_data.idx])\\n real_data_tasks_i['batch_timestamp_real'].append(real_data.timestamp)\\n conn = grpc.insecure_channel(port_dict['replay_ip'] + ':' + port_dict['sampleDataPort'])\\n client = apex_data_pb2_grpc.SampleDataStub(channel=conn)\\n local_dict = {}\\n while True:\\n batch_timestamp_real = []\\n batch_timestamp_store = []\\n batch_weights = []\\n batch_idxes = []\\n\\n states, actions, rewards, next_states, dones = [], [], [], [], []\\n\\n res_batch = client.Send(apex_data_pb2.SampleDataRequest(batch_size=args.batch_size, beta = args.beta))\\n actor_ids, data_ids, timestamps, weights, idxes = res_batch.actor_ids, res_batch.data_ids, res_batch.timestamp, res_batch.weights, res_batch.idxes\\n actor_set = {}\\n cached_value = {'states':{},'actions':{},'rewards':{},'next_states':{},'dones':{},'batch_weights':{},'batch_idxes':{},'batch_timestamp_store':{},'batch_timestamp_real':{}}\\n for i in range(len(actor_ids)):\\n set_a = actor_set.get(actor_ids[i], False)\\n if set_a == False:\\n actor_set[actor_ids[i]] = {}\\n set_a = actor_set[actor_ids[i]]\\n set_a['d'] = []\\n set_a['w'] = []\\n set_a['i'] = []\\n set_a['t'] = []\\n cached_value['states'][actor_ids[i]] = []\\n cached_value['actions'][actor_ids[i]] = []\\n cached_value['rewards'][actor_ids[i]] = []\\n cached_value['next_states'][actor_ids[i]] = []\\n cached_value['dones'][actor_ids[i]] = []\\n cached_value['batch_weights'][actor_ids[i]] = []\\n cached_value['batch_idxes'][actor_ids[i]] = []\\n cached_value['batch_timestamp_store'][actor_ids[i]] = []\\n cached_value['batch_timestamp_real'][actor_ids[i]] = []\\n cache_id = actor_ids[i]*local_size+data_ids[i]\\n cache_trans = cache_array[cache_id]\\n if cache_trans & 2**pid == 0:\\n set_a['d'].append(data_ids[i])\\n set_a['w'].append(weights[i])\\n set_a['i'].append(idxes[i])\\n set_a['t'].append(timestamps[i])\\n if cache_trans == 0 and local_dict.get(cache_id, False) != False:\\n del local_dict[cache_id]\\n else:\\n try:\\n state_tmp = local_dict[cache_id][0]\\n action_tmp = local_dict[cache_id][1]\\n reward_tmp = local_dict[cache_id][2] \\n next_state_tmp = local_dict[cache_id][3] \\n done_tmp = local_dict[cache_id][4] \\n batch_weight_tmp = local_dict[cache_id][5] \\n batch_idx_tmp = local_dict[cache_id][6] \\n batch_store_tmp = local_dict[cache_id][7] \\n batch_real_tmp = local_dict[cache_id][8] \\n decom_state = torch.FloatTensor(np.frombuffer(zlib.decompress(state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n cached_value['states'][actor_ids[i]].append(decom_state)\\n cached_value['actions'][actor_ids[i]].append(torch.LongTensor([action_tmp]))\\n cached_value['rewards'][actor_ids[i]].append(torch.FloatTensor([reward_tmp]))\\n decom_next_state = torch.FloatTensor(np.frombuffer(zlib.decompress(next_state_tmp), dtype=np.uint8).reshape((1, 4, 84, 84)))\\n cached_value['next_states'][actor_ids[i]].append(decom_next_state)\\n cached_value['dones'][actor_ids[i]].append(torch.FloatTensor([done_tmp]))\\n cached_value['batch_weights'][actor_ids[i]].append(torch.FloatTensor([batch_weight_tmp]))\\n cached_value['batch_idxes'][actor_ids[i]].append(batch_idx_tmp)\\n cached_value['batch_timestamp_store'][actor_ids[i]].append(batch_store_tmp)\\n cached_value['batch_timestamp_real'][actor_ids[i]].append(batch_real_tmp)\\n except:\\n set_a['d'].append(data_ids[i])\\n set_a['w'].append(weights[i])\\n set_a['i'].append(idxes[i])\\n set_a['t'].append(timestamps[i])\\n real_data_links = {}\\n real_data_tasks = {}\\n for k, v in actor_set.items():\\n actor_ip, data_port = actor_id_to_ip_dataport[k]\\n conn_actor = grpc.insecure_channel(actor_ip + ':' + data_port)\\n client_actor = apex_data_pb2_grpc.SendRealDataStub(channel=conn_actor)\\n real_data_links[k] = client_actor.Send(apex_data_pb2.RealBatchRequest(idxes=v['d']))\\n real_data_tasks[k] = {}\\n real_data_tasks[k]['states'] = cached_value['states'][k]\\n real_data_tasks[k]['actions'] = cached_value['actions'][k]\\n real_data_tasks[k]['rewards'] = cached_value['rewards'][k]\\n real_data_tasks[k]['next_states'] = cached_value['next_states'][k]\\n real_data_tasks[k]['dones'] = cached_value['dones'][k]\\n real_data_tasks[k]['batch_weights'] = cached_value['batch_weights'][k]\\n real_data_tasks[k]['batch_idxes'] = cached_value['batch_idxes'][k]\\n real_data_tasks[k]['batch_timestamp_store'] = cached_value['batch_timestamp_store'][k]\\n real_data_tasks[k]['batch_timestamp_real'] = cached_value['batch_timestamp_real'][k]\\n threads = []\\n for k, v in real_data_links.items():\\n t = threading.Thread(target=recv_data, args=(k, v, actor_set, real_data_tasks[k],))\\n threads.append(t)\\n t.start()\\n\\n for t in threads:\\n t.join()\\n\\n for k, v in real_data_tasks.items():\\n states += v['states']\\n actions += v['actions']\\n rewards += v['rewards']\\n next_states += v['next_states']\\n dones += v['dones']\\n batch_weights += v['batch_weights']\\n batch_idxes += v['batch_idxes']\\n batch_timestamp_real += v['batch_timestamp_real']\\n batch_timestamp_store += v['batch_timestamp_store']\\n\\n states = torch.cat(states,0).to(device)\\n actions = torch.cat(actions,0).to(device)\\n rewards = torch.cat(rewards,0).to(device)\\n next_states = torch.cat(next_states,0).to(device)\\n dones = torch.cat(dones,0).to(device)\\n batch_weights = torch.cat(batch_weights,0).to(device)\\n\\n batch = [states, actions, rewards, next_states, dones, batch_weights, batch_idxes]\\n batch_queue.put(batch)\\n data, batch = None, None\",\n \"def update_policy(self):\\n # this is update_policy \\n # sample batch of 32 from the memory\\n batch_of_samples = self.replay_memory.sample(batch_size=32)\\n current_state_samples = batch_of_samples['current_state_samples']\\n next_state_samples = batch_of_samples['next_state_samples']\\n #print type(current_state_samples[0])\\n #print current_state_samples\\n\\n # fetch stuff we need from samples 32*84*84*4\\n current_state_images = np.zeros([1, 84, 84, 4])\\n #print current_state_samples\\n current_state_images[0,...] = np.dstack([sample.state for sample in current_state_samples])\\n\\n next_state_images = np.zeros([1, 84, 84, 4])\\n next_state_images[0,...] = np.dstack([sample.state for sample in next_state_samples])\\n\\n # preprocess\\n current_state_images = self.preprocessor.process_batch(current_state_images)\\n next_state_images = self.preprocessor.process_batch(next_state_images)\\n # print \\\"current_state_images {} max {} \\\".format(current_state_images.shape, np.max(current_state_images))\\n #print current_state_images.shape\\n q_current = self.q_network.predict(current_state_images,batch_size=self.batch_size) # 32*num_actions\\n q_next = self.q_network.predict(next_state_images,batch_size=self.batch_size)\\n\\n # targets\\n y_targets_all = q_current #1*num_actions\\n #print y_targets_all.shape # [1,6]\\n idx = 0 \\n last_sample = current_state_samples[-1]\\n if last_sample.is_terminal:\\n y_targets_all[idx, last_sample.action] = last_sample.reward\\n else:\\n if self.mode == 'vanilla':\\n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*np.max(q_next[idx])\\n if self.mode == 'double': \\n y_targets_all[idx, last_sample.action] = np.float32(last_sample.reward) + self.gamma*q_next[idx, np.argmax(q_current[idx])] \\n\\n loss = self.q_network.train_on_batch(current_state_images, np.float32(y_targets_all))\\n\\n with tf.name_scope('summaries'):\\n self.tf_log_scaler(tag='train_loss', value=loss, step=self.iter_ctr)\\n\\n if not (self.iter_ctr % self.log_loss_every_nth):\\n self.dump_train_loss(loss)\\n\\n # if (self.iter_ctr > (self.num_burn_in+1)) and not(self.iter_ctr%self.target_update_freq):\\n # # copy weights\\n # print \\\"Iter {} Updating target Q network\\\".format(self.iter_ctr)\\n # self.target_q_network.set_weights(self.q_network.get_weights())\\n # [self.target_q_network.trainable_weights[i].assign(self.q_network.trainable_weights[i]) \\\\\\n # for i in range(len(self.target_q_network.trainable_weights))]\",\n \"def on_train_batch_begin(self, step, logs=None):\",\n \"def _during_execute(self, db, entity):\\n pass\",\n \"def batch_size(self) -> int:\\n ...\",\n \"def next_batch(self, batch_size):\\r\\n raise NotImplementedError\",\n \"def priority_update(self, error,batch_index):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7484445","0.70731485","0.70731485","0.6753256","0.63523346","0.6343672","0.6236089","0.6083565","0.60835004","0.59652734","0.5964963","0.59062594","0.58258647","0.58248407","0.58213854","0.58161604","0.5768676","0.56665844","0.56641805","0.5637965","0.5614058","0.559843","0.559843","0.55974025","0.5579042","0.55708545","0.5559789","0.5552532","0.5552444","0.55441046","0.5528924","0.55148387","0.55079347","0.5500292","0.5500138","0.54902256","0.5487664","0.54834294","0.54749256","0.54675627","0.5465523","0.5464099","0.546303","0.546303","0.546303","0.5458278","0.5451163","0.54329544","0.54148835","0.540917","0.5401062","0.5395834","0.5395532","0.53947645","0.538806","0.5381803","0.5381803","0.53804404","0.5377168","0.5376344","0.53737664","0.53737664","0.53737664","0.5372704","0.53679895","0.5367277","0.5362607","0.5339388","0.5336917","0.53343815","0.5329632","0.53266364","0.5317473","0.5316884","0.5304906","0.5301781","0.53017056","0.53007895","0.52985984","0.5298592","0.5298556","0.5290642","0.5287863","0.52826506","0.5278398","0.5276322","0.5275049","0.5272018","0.52592","0.52583647","0.5256229","0.5253443","0.52532995","0.525233","0.52502304","0.5247323","0.5244549","0.52422225","0.5240776","0.52270776","0.5216467"],"string":"[\n \"0.7484445\",\n \"0.70731485\",\n \"0.70731485\",\n \"0.6753256\",\n \"0.63523346\",\n \"0.6343672\",\n \"0.6236089\",\n \"0.6083565\",\n \"0.60835004\",\n \"0.59652734\",\n \"0.5964963\",\n \"0.59062594\",\n \"0.58258647\",\n \"0.58248407\",\n \"0.58213854\",\n \"0.58161604\",\n \"0.5768676\",\n \"0.56665844\",\n \"0.56641805\",\n \"0.5637965\",\n \"0.5614058\",\n \"0.559843\",\n \"0.559843\",\n \"0.55974025\",\n \"0.5579042\",\n \"0.55708545\",\n \"0.5559789\",\n \"0.5552532\",\n \"0.5552444\",\n \"0.55441046\",\n \"0.5528924\",\n \"0.55148387\",\n \"0.55079347\",\n \"0.5500292\",\n \"0.5500138\",\n \"0.54902256\",\n \"0.5487664\",\n \"0.54834294\",\n \"0.54749256\",\n \"0.54675627\",\n \"0.5465523\",\n \"0.5464099\",\n \"0.546303\",\n \"0.546303\",\n \"0.546303\",\n \"0.5458278\",\n \"0.5451163\",\n \"0.54329544\",\n \"0.54148835\",\n \"0.540917\",\n \"0.5401062\",\n \"0.5395834\",\n \"0.5395532\",\n \"0.53947645\",\n \"0.538806\",\n \"0.5381803\",\n \"0.5381803\",\n \"0.53804404\",\n \"0.5377168\",\n \"0.5376344\",\n \"0.53737664\",\n \"0.53737664\",\n \"0.53737664\",\n \"0.5372704\",\n \"0.53679895\",\n \"0.5367277\",\n \"0.5362607\",\n \"0.5339388\",\n \"0.5336917\",\n \"0.53343815\",\n \"0.5329632\",\n \"0.53266364\",\n \"0.5317473\",\n \"0.5316884\",\n \"0.5304906\",\n \"0.5301781\",\n \"0.53017056\",\n \"0.53007895\",\n \"0.52985984\",\n \"0.5298592\",\n \"0.5298556\",\n \"0.5290642\",\n \"0.5287863\",\n \"0.52826506\",\n \"0.5278398\",\n \"0.5276322\",\n \"0.5275049\",\n \"0.5272018\",\n \"0.52592\",\n \"0.52583647\",\n \"0.5256229\",\n \"0.5253443\",\n \"0.52532995\",\n \"0.525233\",\n \"0.52502304\",\n \"0.5247323\",\n \"0.5244549\",\n \"0.52422225\",\n \"0.5240776\",\n \"0.52270776\",\n \"0.5216467\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17777,"cells":{"query":{"kind":"string","value":"Computes the distance matrix for each image patch Params ======"},"document":{"kind":"string","value":"def predict(self,\n new_imgs: Tensor,\n compare_all: bool = False) -> Tensor:\n embeddings = self._embed_batch(new_imgs)\n embeddings = reshape_embedding(embeddings.cpu().detach().numpy())\n #print(self.embedding_coreset.shape)\n #print(embeddings.shape)\n knn = KNN(X=self.embedding_coreset, k=self.cfg.n_neighbors, device=self.device)\n score_patches = knn(embeddings)\n #score_patches = torch.from_numpy(score_patches)\n #print(score_patches.shape)\n # score_patches == kNN distances -> shape = (num_patches, k)\n return score_patches"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def im_dist_mat(self):\n mat = np.zeros([self.I, self.M])\n for i in range(self.I):\n for m in range(self.M):\n mat[i, m] = distance(self.I_coords[i], self.M_coords[m])\n return mat","def _calculate_distances(boxes, homography):\n pos_markers = []\n pix_markers = []\n for box in boxes:\n (pt1_w, pt1_h), (pt2_w, pt2_h) = box\n\n pix_marker = ((pt1_w + pt2_w) // 2, max(pt1_h, pt2_h))\n pix_markers.append(pix_marker)\n\n pos_marker = np.array(pix_marker).reshape(\n 1, 1, 2).astype(\"float32\")\n pos_marker = cv2.perspectiveTransform(\n pos_marker, homography).squeeze()\n pos_markers.append(pos_marker)\n\n if len(pos_markers) <= 1:\n return np.array([]), np.array([])\n\n distances = pdist(np.array(pos_markers))\n return pix_markers, distances","def distances(self):","def point_distances(self, params=None):\n if params is None:\n params = self.collocation_points()\n with self.fix_evaluator():\n pts = np.array([self(la) for la in params])\n deltas = np.diff(pts, axis=0)\n distances = norm(deltas, axis=1)\n return distances","def calculate_dist_from_eqm(distance_from_eqm, variable_mask):","def all_distances(self):\n points = self.color_lookup_table_points\n\n red = np.repeat(np.expand_dims(points[0], axis=0), points[0].size, axis=0)\n green = np.repeat(np.expand_dims(points[1], axis=0), points[1].size, axis=0)\n blue = np.repeat(np.expand_dims(points[2], axis=0), points[2].size, axis=0)\n\n self.distances = np.sqrt(\n np.square(red - red.transpose())\n + np.square(green - green.transpose())\n + np.square(blue - blue.transpose()))","def cal_patches_norm(self):\n # norm of style image patches\n norm_array = torch.zeros(self.style_patches.shape[0])\n for i in range(self.style_patches.shape[0]):\n norm_array[i] = torch.pow(torch.sum(torch.pow(self.style_patches[i], 2)), 0.5)\n return norm_array.to(self.device)","def cal_dist(A, AA, B, BB, ay, ax, by, bx, patch_size):\r\n dx0 = dy0 = patch_size // 2\r\n dx1 = dy1 = patch_size // 2 + 1\r\n dx0 = min(ax, bx, dx0)\r\n dx1 = min(A.shape[0] - ax, B.shape[0] - bx, dx1)\r\n dy0 = min(ay, by, dy0)\r\n dy1 = min(A.shape[1] - ay, B.shape[1] - by, dy1)\r\n return np.sum(((A[ay - dy0:ay + dy1, ax - dx0:ax + dx1] - B[by - dy0:by + dy1, bx - dx0:bx + dx1]) ** 2) +\r\n ((AA[ay - dy0:ay + dy1, ax - dx0:ax + dx1] - BB[by - dy0:by + dy1, bx - dx0:bx + dx1]) ** 2)) / ( (dx1 + dx0) * (dy1 + dy0))","def calc_distances(self, templates_features=None, batch_size=50000, th=0.2, beta=1.1):\n if templates_features is None:\n templates_features = self.calc_templates()\n distances = np.empty((self.pairs.shape[0]), dtype=np.float32)\n start, end = 0, 0\n for batch in self.batches(self.pairs, batch_size):\n t1 = np.empty((len(batch), self.features_dim), dtype=np.float32)\n t2 = np.empty((len(batch), self.features_dim), dtype=np.float32)\n start = end\n end += len(batch)\n # attenuate = np.empty((len(batch)), dtype=np.bool)\n for i, pair in enumerate(batch):\n t1[i] = templates_features[pair[0]]\n t2[i] = templates_features[pair[1]]\n # lomax1 = np.max(self.quality_scores[pair[0]])\n # lomax2 = np.max(self.quality_scores[pair[1]])\n # attenuate[i] = lomax1 <= th or lomax2 <= th\n\n ## find cosine distance, assume template descriptors are normalized\n distances[start:end] = 1 - np.einsum(\"ij,ij->i\", t1, t2)\n # distances[start:end] = np.where(attenuate, distances[start:end], distances[start:end] / beta)\n return distances","def _calc_distance_features(self):\n d = ()\n for dx, dy in DIRECTIONS:\n if dx and dy:\n d += (list(self.__calc_distance(direction_x=dx, direction_y=dy)), )\n elif dx:\n tmp, _, _ = self.__calc_distance(direction_x=dx, direction_y=dy)\n d += (tmp, )\n elif dy:\n _, tmp, _ = self.__calc_distance(direction_x=dx, direction_y=dy)\n d += (tmp, )\n\n self.dist_features = d\n\n self.direc_dist = self.__calc_direc_distance()","def frame_to_distance_images(frame):\n \n # distance from ridge midlines\n frame = rgb2gray(frame)\n ridges = enhance_ridges(frame)\n thresh = filters.threshold_otsu(ridges)\n prominent_ridges = ridges > 0.8*thresh\n skeleton = morphology.skeletonize(prominent_ridges)\n edge_dist = ndimage.distance_transform_edt(~skeleton)\n edge_dist = filters.gaussian(edge_dist, sigma=2)\n\n # distance from skeleton branch points (ie, ridge intersections)\n blurred_skeleton = uniform_filter(skeleton.astype(float), size=3)\n corner_im = blurred_skeleton > 4./9\n corner_dist = ndimage.distance_transform_edt(~corner_im)\n \n return edge_dist, corner_dist","def cal_distances(embeddings):\n # calculate\n dist = np.zeros([len(embeddings), len(embeddings)], dtype=float)\n for ii in xrange(len(embeddings)):\n for jj in xrange(ii + 1, len(embeddings)):\n dist[ii, jj] = np.linalg.norm(embeddings[ii] - embeddings[jj])\n dist[jj, ii] = dist[ii, jj] \n \n # return\n return dist","def compute_distance(self):\n loc = np.extend_dims(self.state[:, :, Boids.Attr.LOC], axis=-1)\n m = np.tile(loc, (1, 1, self.num_boids))\n pos_diff = m-m.transpose(0, 2, 1)\n self.distance = np.linalg.norm(pos_diff, axis=0)","def compute_distances(self):\n if self.df is None:\n return\n\n self.origdist = []\n self.transdist = []\n for i in range(len(self.df)):\n for j in range(i+1, len(self.df)):\n self.origdist.append(distance(self.df['LPsol'].iloc[i], self.df['LPsol'].iloc[j]))\n self.transdist.append(distance(self.df[['x', 'y']].iloc[i], self.df[['x', 'y']].iloc[j]))","def image_distances(distance_function, image1, image2):\n \n shape1 = image1.shape\n shape2 = image2.shape\n \n assert shape1[2] == shape2[2]\n \n bands1 = [ image1[:, :, bidx] for bidx in range(shape1[2]) ]\n bands2 = [ image2[:, :, bidx] for bidx in range(shape2[2]) ]\n\n dist = []\n for band1, band2 in zip(bands1, bands2):\n dist.append(distance_function(\n ci.get_histogram(band1), ci.get_histogram(band2)))\n return dist","def __make_distance_matrix(self, src_image: Image, out_image: Image, padding: int, out_point: Point, causal: bool) -> np.ndarray:\n # Extract the reference window and for the neighbourhood matching.\n out_window = out_image.extract(out_point, padding, 'wrap')\n out_filled = out_image.filled(out_point, padding, 'wrap', causal)\n\n # Construct a 2D Gaussian kernel that matches the padding size.\n gaussian_1D = signal.gaussian(2 * padding + 1, std=padding)\n gaussian_2D = np.outer(gaussian_1D, gaussian_1D)\n gaussian_2X = np.extract(out_filled, gaussian_2D)\n\n # Return the weighted squared difference of each neighbourhood in the\n # source Image with respect to the reference window.\n return self._apply_distance_filter(src_image, out_window, out_filled, gaussian_2X)","def dist_train(imgdir, imgfmt):\r\n global square_width, row, col, content\r\n counter, loop_ctr = 0, 1\r\n\r\n # termination criteria\r\n criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, square_width, 0.001)\r\n\r\n # arrays to store object points and image points from all the images\r\n objpoints = [] # 3d point in real world space\r\n imgpoints = [] # 2d points in image plane\r\n images = glob.glob(imgdir+'*'+imgfmt)\r\n images.sort()\r\n\r\n gray = np.zeros((0, 0))\r\n for fname in images:\r\n img = cv2.imread(fname)\r\n gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\r\n objp = np.zeros((row*col, 3), np.float32)\r\n objp[:, :2] = np.mgrid[0:row, 0:col].T.reshape(-1, 2)\r\n ret, corners = cv2.findChessboardCorners(gray, (row, col), None)\r\n\r\n # if chessboard corners are found, add object points and image points to respective lists (after refining them)\r\n ind_line = fname.rindex(os.sep)\r\n ind_dot = fname.rindex('.')\r\n if ret == True:\r\n objpoints.append(objp)\r\n corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)\r\n imgpoints.append(corners2)\r\n # Draw and display the corners\r\n img = cv2.drawChessboardCorners(img, (row, col), corners2, ret)\r\n counter += 1\r\n save_image(fname[0:ind_line+1] + \"success/\", fname[ind_line+1:ind_dot],img)\r\n else:\r\n save_image(fname[0:ind_line + 1] + \"fail/\", fname[ind_line + 1:ind_dot], img)\r\n cv2.destroyAllWindows()\r\n loop_ctr += 1\r\n\r\n if len(images) > 0:\r\n ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)\r\n # get the calibration error\r\n error = calib_error(objpoints, imgpoints, rvecs, tvecs, mtx, dist)\r\n content += imgdir + ':\\n Number of qualified images = ' + str(counter) + '\\n Average calibration error = ' + str(np.round(error, 4)) + ' pixels\\n'\r\n return ret, mtx, dist, rvecs, tvecs, error\r\n else:\r\n content += imgdir + ':\\n No images in this folder. Cannot compute camera parameters.\\n'\r\n return 0, 0, 0, 0, 0, float('inf')","def pixel_distance(stars):\n distances = np.zeros((stars.shape[0], stars.shape[0]))\n logger.info('Start')\n for i in range(stars.shape[0]):\n if i < stars.shape[0] - 1:\n dis1 = stars[i, 0] - stars[i + 1:, 0]\n dis2 = stars[i, 1] - stars[i + 1:, 1]\n dis = np.sqrt(dis1 ** 2 + dis2 ** 2)\n distances[i][i + 1:] = dis\n logger.info('The End!')\n return distances","def _computeDistances(self) -> None:\n length = len(self.data)\n for i, sequenceOne in enumerate(self.data):\n print(f\"[SeqCluBaselineOffline] Computing distances is at iteration {i} of {length}.\")\n for j, sequenceTwo in enumerate(self.data):\n if i == j:\n self.distances[i][j] = 0\n continue\n distance = self.distanceMeasure.calculateDistance(sequenceOne, sequenceTwo)\n self.distances[i][j] = distance\n self.distances[j][i] = distance","def __build_distance_matrix(self):\n for i in range(0, len(self.__corpus)):\n doc_i = self.__corpus[i]\n for j in range(i + 1, len(self.__corpus)):\n doc_j = self.__corpus[j]\n distance = doc_i.calc_distance(doc_j)\n self.__distance_matrix.append(distance)","def get_general_distance_and_relationship_matrix(path_to_embeddings,image_level_embed_file_name, study=None):\n\n if study == None:\n images_info = pd.read_csv(os.path.join(DATA_DIR,STUDY,\"human_ISH_info.csv\"))\n else:\n images_info = pd.read_csv(os.path.join(DATA_DIR, study, \"human_ISH_info.csv\"))\n\n\n dist_matrix_df = build_distance_matrix(os.path.join(path_to_embeddings, image_level_embed_file_name))\n\n dist_matrix_rows = list(dist_matrix_df.index) # list of image IDs\n dist_matrix_columns = list(dist_matrix_df) # list of image IDs\n\n # --- sanity check -------------\n if dist_matrix_rows != dist_matrix_columns:\n print (\"Something is wrong, the number and order of image IDs in distance matrix's rows and columns should the same.\")\n return None\n # ------------------------------\n\n\n genes = images_info[images_info['image_id'].isin(dist_matrix_rows)]['gene_symbol']\n\n low_to_high_map = pd.DataFrame(list(zip(dist_matrix_rows, genes))) # create a 2-column df of image IDs and genes\n relationship_df = create_diagonal_mask(low_to_high_map, target_value=1)\n\n\n # --- check to see if rows and columns of dist matrix match the relationship matrix. ---------------------\n # if they don't re-arrange them in the relationship matrix to match the dist matrix\n\n dist_matrix_df, relationship_df = match_matrices(dist_matrix_df, relationship_df)\n\n # ---------------------------------------------------------------------------------------------------------\n\n return dist_matrix_df,relationship_df","def compute_distances(self, X):\n #print(X.shape, self.Xtr.shape)\n dists = np.zeros((X.shape[0], self.Xtr.shape[0]))\n for i in range(X.shape[0]):\n X_r = np.tile(X[i], (self.Xtr.shape[0], 1))\n dists[i] = np.sqrt(np.sum(np.square(self.Xtr - X_r), axis = 1))\n #print(dists.shape)\n return dists","def _get_distances(self):\n for molecule in self.values():\n molecule.get_distances()\n\n # for atom in self.atoms:\n # atom.get_distances()","def compute_distances(src):\n rr = np.vstack((src[0]['rr'][src[0]['inuse'].astype(np.bool)],\n src[1]['rr'][src[1]['inuse'].astype(np.bool)]))\n return distance.squareform(distance.pdist(rr))","def _build_dist(self):\n lamb = self.params['lamb']\n p = self.params['p']\n\n jac = self.jacobian\n # build D on grids\n xg, yg, mask = self._mask_grid()\n r_max = self._r_max(xg, yg, mask)\n d_mat = self._psf_grid(xg, yg, r_max=r_max)\n # E[yy^T]\n j_j_w = np.dot(jac, jac.transpose())\n r_mat = np.diag(np.diag(j_j_w) ** p)\n jac_inv = la.inv(j_j_w + lamb*r_mat)\n # RM = E[xx^T] / E[yy^T]\n h_mat = np.dot(np.dot(d_mat, jac.transpose()), jac_inv)\n return h_mat","def feature_dist(input_labels, struct=np.ones((3, 3))):\n # remove the pixels inside the coherent\n input_errosion = binary_erosion(input_labels, structure=struct)\n input_labels[input_errosion] = 0\n I, J = np.nonzero(input_labels)\n labels = input_labels[I, J]\n coords = np.column_stack((I, J))\n sorter = np.argsort(labels)\n labels = labels[sorter]\n coords = coords[sorter]\n I = I[sorter]\n J = J[sorter]\n sq_dists = cdist(coords, coords, 'sqeuclidean')\n start_idx = np.flatnonzero(np.r_[1, np.diff(labels)])\n nonzero_vs_feat = np.minimum.reduceat(sq_dists, start_idx, axis=1)\n feat_vs_feat = np.minimum.reduceat(nonzero_vs_feat, start_idx, axis=0)\n # calculate the distance between every two coherent areas\n # distance factor for one pixel to meter: 100\n distance_matrix = 100 * np.sqrt(feat_vs_feat)\n nRow, nCol = sq_dists.shape[0], start_idx.shape[0]\n # add the index of the final element to the slice array\n slice_indices = np.concatenate((start_idx, np.array([nRow])))\n col_args = np.zeros((nRow, nCol)).astype(int)\n row_index = np.zeros((nCol, nCol)).astype(int)\n '''\n How the following commands work:\n find closest pixel from label A to label B:\n Label A --> Label B\n row_index[A-1, B-1] = alpha\n col_index[A-1, B-1] = col_args[alpha, B-1] = beta\n index of pixel from Label A: input_labels[ I[alpha], J[alpha]]\n index of pixel from Label B: input_labels[ I[beta], J[beta]]\n '''\n for i in range(nCol):\n col_args[:, i] = start_idx[i] + \\\n np.argmin(sq_dists[:, slice_indices[i]:\n slice_indices[i+1]], axis=1)\n elements = sq_dists[np.arange(nRow).reshape((nRow, 1)), col_args]\n for i in range(nCol):\n row_index[i, :] = start_idx[i] + \\\n np.argmin(elements[slice_indices[i]:\n slice_indices[i+1], :], axis=0)\n col_index = col_args[row_index, np.arange(nCol).reshape((1, nCol))]\n # Change col_index and row_index to input array index.\n row_index_from_label = I[row_index]\n col_index_from_label = J[row_index]\n row_index_to_label = I[col_index]\n col_index_to_label = J[col_index]\n return distance_matrix, row_index_from_label, col_index_from_label, \\\n row_index_to_label, col_index_to_label","def _compute_distances(self, atoms: List[CellAtom]):\n muon = self._cell_atoms[self._muon_index]\n\n for atom in atoms:\n atom.distance_from_muon = np.linalg.norm(muon.position - atom.position)","def _derive_euclidean_dm(self, cat_mat, dim):\r\n res_mat = []\r\n\r\n for i in range(dim):\r\n res_mat.append([0 for k in range(dim)])\r\n for j in range(i):\r\n res_mat[i][j] = self._vector_dist(cat_mat[i], cat_mat[j])\r\n res_mat[j][i] = res_mat[i][j]\r\n\r\n return DistanceMatrix(res_mat, self.DistanceMatrices[0].ids)","def _calc_(self):\n self.data = []\n all_xyz_data = self.Var.data.get_xyz_data()\n all_cols = self.Var.data.get_xyz_cols()\n\n # Loop over all the xyz data and cols we have\n for xyz_data, cols in zip(all_xyz_data, all_cols):\n\n at_crds = np.array([i[cols[0] != 'Ne'] for i in xyz_data])\n self.natom = len(at_crds[0])\n self.nstep = len(at_crds)\n self.step_data = {}\n\n # Calculate the nearest neighbour lists for each step\n for step in range(self.nstep):\n self.step_data[step] = {}\n\n # Get coords\n crds = at_crds[step]\n\n # Get distances between neighbours\n self.get_distances(crds)\n\n # Get a sorted list of atom indices by distance\n self.get_nearest_atom_inds()\n\n # If we have some molecule metadata\n if 'atoms_per_molecule' in self.Var.metadata:\n self.at_per_mol = self.Var.metadata['atoms_per_molecule']\n self.nmol = mol_utils.get_nmol(self.natom, self.at_per_mol)\n self.reshape_at_dist()\n self.get_nearest_atom_inds_per_mol()\n self.step_data[step]['closest_atoms_mol_grouped'] = self.closest_at_per_mol\n self.step_data[step]['distances_mol_grouped'] = self.all_dist_per_mol\n\n # Save data in dict\n self.step_data[step]['distances'] = self.all_dist\n self.step_data[step]['closest_atom_indices'] = self.closest_ats\n\n self.data.append(self.step_data)\n\n return self.data","def calcDistance(self):\n # Initialize the distance matrix\n arr = np.repeat(0, self.num_col)\n result_mat = np.repeat(arr, self.num_col)\n result_mat = np.reshape(result_mat, (self.num_col, self.num_col))\n trinary_mat = self.df_trinary.values\n for left_val in TRINARY_VALUES:\n left_func = lambda v: 1 if v==left_val else 0\n left_mat = np.transpose(np.vectorize(left_func)(trinary_mat))\n for right_val in TRINARY_VALUES:\n if left_val == right_val:\n continue\n right_func = lambda v: 1 if v==right_val else 0\n right_mat = np.vectorize(right_func)(trinary_mat)\n # Count the number of occurrences of this combination of values\n # by doing a matrix multiply\n new_mat = np.matmul(left_mat, right_mat)\n # Multiply by the squared distance between the values\n squared_distance = (left_val - right_val)**2\n new_mat = new_mat*squared_distance\n # Accumulate the result\n result_mat = result_mat + new_mat\n # Convert to dataframe\n result_mat = np.vectorize(lambda v: np.sqrt(v)) (result_mat)\n self.df_distance = pd.DataFrame(result_mat, columns=self.columns,\n index=self.columns)","def _calculate_similarity(self):\n self._logger.info(\"Calculating the similarity between images.\")\n\n # Create a helper function to simplify the loops below:\n def get_wrapper(fdx, mdx):\n wrapper = pos_wrappers.image_similarity_wrapper(\n reference_image=self.f['src_gray'](idx=fdx),\n moving_image=self.f['src_gray'](idx=mdx),\n affine_transformation=self.f['part_transf'](mIdx=mdx, fIdx=fdx))\n return copy.copy(wrapper)\n\n commands = [] # Will hold commands for calculating the similarity\n\n # Will hold (moving, fixed) images partial_transforms basically: all\n # partial transformations array\n partial_transforms = []\n\n self._logger.debug(\"Generating similarity measure warppers.\")\n for moving_slice in self.options.slice_range:\n # Get all fixed images to which given moving slice will be aligned:\n tpair = list(flatten(self._get_slice_pair(moving_slice)))\n\n # Append partial transformations for given moving slice to the\n # global partial transformations array\n partial_transforms.append(tpair)\n\n # Generate wrapper for measuring similarity for a given partial\n # transformation.\n for mdx, fdx in tpair:\n commands.append(get_wrapper(fdx, mdx))\n\n # Execute and commands and workflow the similarity measurements.\n stdout, stderr = self.execute(commands)\n simmilarity = map(lambda x: float(x.strip()),\n stdout.strip().split(\"\\n\"))\n simmilarity = dict(zip(flatten(partial_transforms), simmilarity))\n\n self._logger.debug(\"Generating graph edges.\")\n graph_connections = []\n\n # Lambda defines slice skipping is preffered (lower l), or reluctant\n # to slice skipping (higher)\n l = self.options.graphEdgeLambda\n\n for (mdx, fdx), s in simmilarity.iteritems():\n w = (1.0 + s) * abs(mdx - fdx) * (1.0 + l) ** (abs(mdx - fdx))\n graph_connections.append((fdx, mdx, w))\n\n self._logger.info(\"Creating a graph based on image similarities.\")\n # Generate the graph basen on the weight of the edges\n self.G = nx.DiGraph()\n self.G.add_weighted_edges_from(graph_connections)\n\n self._logger.debug(\"Saving the graph to a file.\")\n # Save the edges for some further analysis.\n nx.write_weighted_edgelist(self.G,\n self.f['graph_edges'](sign=self.signature))\n\n # Also, save the individual similarity metrics:\n simm_fh = open(self.f['similarity'](sign=self.signature), 'w')\n for (mdx, fdx), s in sorted(simmilarity.iteritems()):\n simm_fh.write(\"%d %d %f\\n\" % (mdx, fdx, s))\n simm_fh.close()","def distance(dataset: Dataset) -> Dict[str, List[Tuple[int, float]]]:\n distances: Dict[str, List[Tuple[int, float]]] = {}\n\n images = dataset.images\n annotations = dataset.annotations\n\n for image in images:\n distances[image] = []\n image_area = img_area(image)\n\n for detection in annotations[image]:\n distances[image].append((detection.class_index, detection.bounds.area / image_area))\n\n return distances","def _calc_distances(preds, targets, mask, normalize):\n N, K, _ = preds.shape\n _mask = mask.copy()\n _mask[np.where((normalize == 0).sum(1))[0], :] = False\n distances = np.full((N, K), -1, dtype=np.float32)\n normalize[np.where(normalize <= 0)] = 1000000.0\n distances[_mask] = np.linalg.norm(((preds - targets) / normalize[:, None, :])[_mask], axis=-1)\n return distances.T","def compute_distmat(data, distfn):\n out = np.zeros((data.shape[0], data.shape[0]))\n for i in xrange(data.shape[0]):\n for j in xrange(data.shape[0]):\n if i == j: continue\n out[i,j] = distfn(data[i,:,:], data[j,:,:])\n return out","def generate_pairs(patches, constants):\n k_nearest = constants.K_NEAREST\n num_patches = constants.NUM_QUERY_PATCHES\n scaled_imgs = len(patches)\n\n pairs = []\n query_database = []\n candidate_database = []\n index_database = []\n length_database = []\n for k in range(scaled_imgs):\n qp = [patch.norm_patch for patch in patches[k] if 7 <= patch.bucket <= 9]\n qi = [index for index, patch in enumerate(patches[k]) if 7 <= patch.bucket <= 9]\n\n # Choose lesser query patches through random selection to improve speed\n if len(qi) > num_patches:\n np.random.seed(0)\n selection = np.random.choice(np.arange(len(qi)), num_patches, replace=False).tolist()\n selection.sort()\n query_patches = [qp[i] for i in selection]\n query_indices = [qi[i] for i in selection]\n else:\n query_patches = qp\n query_indices = qi\n\n query_database.append(\n np.vstack([query_patches])\n )\n index_database.append(query_indices)\n length_database.append(len(query_indices))\n candidate_database.append(\n np.vstack([[patch.norm_patch for i, patch in enumerate(patches[k]) if 0 <= patch.bucket <= 5]])\n )\n\n p1 = np.concatenate(candidate_database)\n kdt = KDTree(p1, leaf_size=30, metric='euclidean')\n\n # Find list of nearest neighbours for each patch\n # `total` is used to correct indices of queried patches for every iteration\n total = 0\n for k in range(scaled_imgs):\n nn = kdt.query(query_database[k], k=k_nearest, return_distance=False, sort_results=False)\n q = [total + index_database[k][i] for i in range(length_database[k])]\n for i in range(len(nn)):\n for j in range(k_nearest):\n pairs.append([q[i], nn[i][j]])\n total += len(patches[k])\n\n return pairs","def get_distances(self):\n N = len(self.cells) # Number of cells\n distances = np.zeros([N, N]) # distances between cells\n positions = self.position_matrix() # positions of cells \n \n # get distances between cells (exploit symmetry between upper and lower triangular form)\n for i, position in enumerate(positions[:-1, :]): # Iterate matrix except the last one\n directions = positions[i+1:, :] - position # direction from i to j > i\n distances[i, i+1:] = np.linalg.norm(directions, axis=1) # length of directions\n \n return distances + distances.T # Add lower triangle of matrix to upper ","def calculate_dist_mat(embeddings: np.ndarray, norm: int) -> np.ndarray:\n kwargs = {'p': norm}\n condensed_dist = pdist(embeddings, metric='minkowski', **kwargs)\n dist_mat = squareform(condensed_dist)\n return dist_mat","def calculate_distance_mesh(directory, metric='l2'):\n print('Starting mesh distance calculation.')\n shapes_files = glob(directory + '*.stl')\n shapes_files.extend(glob(directory + '*.ply'))\n shapes_files.extend(glob(directory + '*.obj'))\n\n shapes_files.sort(key=functools.cmp_to_key(sort_by_sample_id))\n shapes_list = []\n for shape_index, shape in enumerate(shapes_files):\n print('Opening shape %i / %i' % (shape_index, len(shapes_files)), end='\\r')\n shape_mesh = trimesh.load_mesh(shape, process=False)\n vertices = shape_mesh.vertices\n vertices = vertices.reshape((1, -1))\n shapes_list.append(vertices)\n all_shapes_array = np.row_stack(shapes_list)\n print('Calling pairwise distance.')\n return pairwise_distances(all_shapes_array, metric=metric)","def Distmatrix(self):\n self.Dismatrix = np.zeros((self.nodenum, self.nodenum))\n for i in range(len(self.Dismatrix)):\n for j in range(len(self.Dismatrix)):\n self.Dismatrix[i, j] = sf.dist(self.y[i], self.x[i], self.y[j], self.x[j])\n self.Dismatrix[j, i] = self.Dismatrix[i, j]","def calcDist(self):\n rhoOp = self.rhoOp\n s = np.array([[1,0,0],[0,-1,0],[0,0,1]])\n sAdj = s.conj().T \n symRhoOp = np.dot(s,np.dot(rhoOp,sAdj))\n self.dist = Node.S1(rhoOp, symRhoOp)","def _faceToCellDistanceRatio(self):\n XYdis = numerix.zeros((self.nx, self.ny, self.nz + 1), 'd')\n XYdis[:] = 0.5\n XYdis[..., 0] = 1\n XYdis[..., -1] = 1\n\n XZdis = numerix.zeros((self.nx, self.ny + 1, self.nz), 'd')\n XZdis[:] = 0.5\n XZdis[:, 0,:] = 1\n XZdis[:, -1,:] = 1\n\n YZdis = numerix.zeros((self.nx + 1, self.ny, self.nz), 'd')\n YZdis[:] = 0.5\n YZdis[ 0, ...] = 1\n YZdis[-1, ...] = 1\n\n return numerix.concatenate((numerix.ravel(XYdis.swapaxes(0, 2)),\n numerix.ravel(XZdis.swapaxes(0, 2)),\n numerix.ravel(YZdis.swapaxes(0, 2))))","def get_dist_mat(self):\n n_site = self.status.give(keyword=\"n_site\")\n sites = self.status.give(keyword=\"sites\")\n dist_mat = [[0.0 for j in xrange(n_site)] for i in xrange(n_site)]\n for i in xrange(n_site):\n for j in xrange(n_site):\n ri = sites[i].pos\n rj = sites[j].pos\n dist_mat[i][j] = np.linalg.norm(ri-rj)\n # print ri, rj\n return dist_mat","def mpd(distmat):\r\n return distmat.sum() / (distmat.size - distmat.shape[0])","def compute_wall_distance(self):\n phi = sp.ones(self.image_red.shape)\n if (len(self.mask_id[0])>0):\n phi[self.mask_id] = 0\n self.wall_distance = skfmm.distance(phi, dx=self.pixel_size)\n grad = sp.gradient(self.wall_distance,edge_order=2)\n grad_X = grad[1]/self.pixel_size\n grad_Y = grad[0]/self.pixel_size\n norm = sp.sqrt(grad_X**2+grad_Y**2)\n norm = (norm>0)*norm+(norm==0)*0.001\n self.wall_grad_X = grad_X/norm\n self.wall_grad_Y = grad_Y/norm\n else:\n self.wall_distance = 1.0e99*sp.ones(self.image_red.shape)","def _generate_distance_kernel_matrix(self):\n with self._rw_lock.read_lock():\n # Create matrix whose elements are the distances between all row\n # permutations\n fmat = self._feature_mat # shorter name\n num_rows = fmat.shape[0]\n\n # distance kernel is a square matrix based on feature samples\n dist_kernel = np.mat(np.ndarray((num_rows,)*2))\n self._log.info(\"Creating distance kernel with shape %s\",\n dist_kernel.shape)\n\n timer_log = logging.getLogger('.'.join((self.__module__,\n self.__class__.__name__,\n \"SimpleTimer\")))\n\n for i in xrange(num_rows - 1):\n with SimpleTimer('computing distances from row %d to [%d-%d]'\n % (i, i+1, num_rows-1), timer_log):\n dist_kernel[i, i] = 1.0\n for j in xrange(i + 1, num_rows):\n dist = self._histogram_intersection_distance(fmat[i],\n fmat[j])\n dist_kernel[i, j] = dist_kernel[j, i] = dist\n dist_kernel[-1, -1] = 1.0\n return dist_kernel","def create_dist_mat(predims, postdims, dist_metric):\n\n filename = './data/dist_matrix_' + dim2str(predims) + '_' + dim2str(postdims) + '_' + dist_metric.__name__ + '.npz'\n if os.path.isfile(filename):\n print('file existst: loading dist mat from file: ', filename)\n with np.load(filename) as loaded:\n dist_mat = loaded['dist_mat']\n else:\n xbins_pre = eq_dist_linspace(predims[0])\n ybins_pre = eq_dist_linspace(predims[1])\n x_pre, y_pre = np.meshgrid(xbins_pre, ybins_pre)\n\n xbins_post = eq_dist_linspace(postdims[0])\n ybins_post = eq_dist_linspace(postdims[1])\n x_post, y_post = np.meshgrid(xbins_post, ybins_post)\n\n pre_coordinates = np.asarray(list(zip(flatten(x_pre), flatten(y_pre))))\n post_coordinates = np.asarray(list(zip(flatten(x_post), flatten(y_post))))\n\n dist_metric = partial(dist_metric, weights=postdims)\n dist_mat = distance.cdist(post_coordinates, pre_coordinates, metric=dist_metric)\n # dist_mat = distance_matrix(pre_coordinates,post_coordinates)\n\n if not os.path.exists(os.path.dirname(filename)):\n os.makedirs(os.path.dirname(filename))\n print('created directory', os.path.dirname(filename), 'in', os.path.abspath(os.path.dirname(filename)))\n np.savez(filename, dist_mat=dist_mat)\n\n return dist_mat","def ni_dist_mat(self):\n mat = np.zeros([self.N, self.I])\n for n in range(self.N):\n for i in range(self.I):\n mat[n, i] = distance(self.N_coords[n], self.I_coords[i])\n return mat","def get_patches(rimage, gimage, mimage, num_patches=48, patch_size=80, patch_stride=80):\n num_FSpatches = 16\n num_RApatches = 32\n rpatches = []\n gpatches = []\n mpatches = []\n #R_imgs = ((rimage+1)*127.5).astype(np.uint8)\n #scipy.misc.imsave('results'+'/' + 'rainy.jpg', R_imgs[0,:,:,:])\n for i in range(int(math.sqrt(num_FSpatches))):\n for j in range(int(math.sqrt(num_FSpatches))):\n point_x = patch_stride*i\n point_y = patch_stride*j\n rpatch = rimage[:,(point_x):(point_x+patch_size), (point_y):(point_y+patch_size),:]\n #print(point_x)\n #print(point_y)\n #print(point_y+patch_size)\n #P_imgs = ((rpatch+1)*127.5).astype(np.uint8)\n #scipy.misc.imsave('results'+'/' + 'patch_%d_%d.jpg'%(i,j), P_imgs[0,:,:,:])\n #print(rpatch.shape)\n rpatches.append(rpatch)\n #print(np.array(rpatches).shape)\n gpatch = gimage[:,(point_x):(point_x+patch_size), (point_y):(point_y+patch_size),:]\n gpatches.append(gpatch)\n mpatch = mimage[:,(point_x):(point_x+patch_size), (point_y):(point_y+patch_size),:]\n mpatches.append(mpatch)\n\n for k in range(num_RApatches):\n point1 = random.randint(0,240) # 116 comes from the image source size (320) - the patch dimension (80)\n point2 = random.randint(0,240)\n #rpatch = tf.image.crop_to_bounding_box(rimage, point1, point2, patch_size, patch_size)\n rpatch = rimage[:,(point1):(point1+patch_size), (point2):(point2+patch_size),:]\n #P_imgs = ((rpatch+1)*127.5).astype(np.uint8)\n #scipy.misc.imsave('results'+'/' + 'patch_%d.jpg'%i, P_imgs[0,:,:,:])\n #print(rpatch.shape)\n rpatches.append(rpatch)\n #print(np.array(rpatches).shape)\n gpatch = gimage[:,(point1):(point1+patch_size), (point2):(point2+patch_size),:]\n gpatches.append(gpatch)\n mpatch = mimage[:,(point1):(point1+patch_size), (point2):(point2+patch_size),:]\n mpatches.append(mpatch)\n\n rpatches = np.array(rpatches)\n rpatches = np.squeeze(rpatches)\n #print(rpatches.shape)\n gpatches = np.array(gpatches)\n gpatches = np.squeeze(gpatches)\n mpatches = np.array(mpatches)\n mpatches = np.squeeze(mpatches)\n #assert rpatches.get_shape().dims == [num_patches, patch_size, patch_size, 3]\n assert rpatches.shape == (num_patches, patch_size, patch_size, 3)\n return rpatches, gpatches, mpatches","def nm_dist_mat(self):\n mat = np.zeros([self.N, self.M])\n for n in range(self.N):\n for m in range(self.M):\n mat[n, m] = distance(self.N_coords[n], self.M_coords[m])\n return mat","def ratio_cut_laplacian(affinity_mat):\n D_mat = np.zeros(affinity_mat.shape)\n d_trace = np.sum(affinity_mat, axis=0)\n D_mat.flat[::D_mat.shape[0]+1] = d_trace\n return D_mat - affinity_mat","def _distance_matrix(self):\n\n # Log the type of metric being used in Sequencing\n logger.info('Using {} Distance'.format(self.measure))\n\n # Convert the nodal coordinate tuples to a np.array\n coords = np.vstack(map(np.array, self.coords.values()))\n \n if self.measure == 'haversine':\n # Partially applied haversine function that takes a coord and computes the vector distances for all coords\n haversine = lambda coord: get_hav_distance(coords[:, 0], coords[:, 1], *coord) \n # Map the partially applied function over all coordinates, and stack to a matrix\n return np.vstack(map(haversine, coords))\n\n # Partially applied haversine function that takes a coord and computes the vector distances for all coords\n euclidean = lambda coord: get_euclidean_dist(coords, coord)\n # Map the partially applied function over all coordinates, and stack to a matrix\n return np.vstack(map(euclidean, coords))","def distancePairs(self):\n return spsd.squareform(spsd.pdist(self.coordinates()))","def distance(self, f):\n return [(f[0] + (f[2])*self.target[0])/self.im_w - self.tpw,\n (f[1] + (f[3])*self.target[1])/self.im_h - self.tph]","def _calc_distance(self, X):\n distances = np.zeros((X.shape[0], self.n_clusters))\n print(distances.shape)\n for i, centroid in enumerate(self.centroids):\n distances[:, i] = np.linalg.norm(X - centroid, axis=1)\n return distances","def test_poincare_distances_batch(self):\n vector_1 = self.vectors['dog.n.01']\n vectors_2 = self.vectors[['mammal.n.01', 'dog.n.01']]\n distances = self.vectors.vector_distance_batch(vector_1, vectors_2)\n self.assertTrue(np.allclose(distances, [4.5278745, 0]))","def getDistanceMatrix(self):\n return self.pointcloud.distmat","def get_patches(df, data_dir, img_type = \"ADC\", data_type = \"Train\"):\n print(\"Cleaning Output Directory\")\n os.system(\"rm -rf %s\" % (data_dir + \"/patches/\" + img_type ) )\n os.system(\"mkdir %s\" % (data_dir+\"/patches/\" + img_type) )\n os.system(\"mkdir %s\" % (data_dir+\"/patches/\" + img_type + \"/POS\") )\n os.system(\"mkdir %s\" % (data_dir+\"/patches/\" + img_type + \"/NEG\") )\n\n hlen = 113\n header = img_type + \" - \" + data_type\n prl = (hlen//2-len(header)//2) - 1\n prr = hlen - prl - len(header) - 2\n print(\"-\"*hlen)\n print(\"#\" + \" \"*prl + header + \" \"*prr + \"#\")\n print(\"-\"*hlen)\n\n print(\"{0:<15s}\\t{1:>5s}\\t{2:>10s}\\t{3:>10s}\\t{4:>6s}\\t{5:>6s}\\t{6:>6s}\\t{7:>6s}\"\\\n .format(\"PID\", \"#Find\", \"#Patch\", \"#Positive\", \"#Negative\", \"Load time\", \"Extract Time\", \"Save Time\"))\n overall = time.time()\n for pid in df.ProxID.unique():\n load_start = time.time()\n print(\"{0:<15s}\"\n .format(\"Loading\"), end = \"\\r\", flush = True)\n\n temp = df.loc[df.ProxID == pid]\n try:\n # Get ADC, BVAL Dirs\n names = temp.Name.values\n adc_dir = temp[[name.endswith(\"ADC0\") for name in names]].DCMSerUID.values[0]\n bval_dir = temp[[name.endswith(\"BVAL0\") for name in names]].DCMSerUID.values[0]\n dicom_find = list(temp[[name.endswith(\"BVAL0\") for name in names]].ijk.unique())\n dicom_find = [[int(coord) for coord in f.split(\" \")] for f in dicom_find]\n\n except:\n continue\n\n dicom_path = data_dir + \"/\".join([\"/raw/dicom\", pid])\n # ADC Processing\n if img_type == \"ADC\":\n imgs, findings = load_dicom(dicom_path, adc_dir, dicom_find)\n elif img_type == \"BVAL\":\n imgs, findings = load_dicom(dicom_path, bval_dir, dicom_find)\n elif img_type == \"KTRANS\":\n ktrans_path = data_dir + \"/\".join([\"/raw/ktrans\", pid, pid])\n imgs = load_ktrans(ktrans_path)\n findings = []\n else:\n print(\"image type not defined! ABORT\")\n return -1\n\n ltime = time.time() - load_start\n\n ext_start = time.time()\n fcount = len(dicom_find)\n print(\"{0:<15s}\\t{1:>5d}\".format(\"Extracting\", fcount), end = \"\\r\", flush = True)\n if img_type == \"ADC\" or img_type == \"BVAL\":\n # Patches without, Pathces with findings\n pwof, pwf = extract_patches(imgs, PATCH_SHAPE, findings)\n elif img_type == \"KTRANS\":\n print(\"KTRANS PROCESSING NOT YET IMPLEMENTED! ABORT\")\n return -1\n etime = time.time() - ext_start\n\n\n save_start = time.time()\n\n # Printing\n p = len(pwf)\n n = len(pwof)\n\n print(\"{0:<15s}\\t{1:>5d}\\t{2:>10s}\\t{3:>10s}\\t {4:>6s}\\t {5:>6s}\\t {6:>6s}\"\\\n .format(\"Saving\", fcount, str(p+n), str(p), str(n), str(ltime)[:6], str(etime)[:6]), end = \"\\r\", flush = True)\n\n pwf = np.array(pwf)\n pwof = np.array(pwof)\n n = save_patches(pid, fcount, data_dir, img_type, pwf, pwof)\n\n stime = time.time() - save_start\n print(\"{0:15s}\\t{1:>5d}\\t{2:>10s}\\t{3:>10s}\\t {4:>6s}\\t {5:>6s}\\t {6:>6s}\\t {7:>6s}\"\\\n .format(pid, fcount, str(n+n), str(n), str(n), str(ltime)[:6], str(etime)[:6], str(stime)[:6]))\n\n #break\n\n otime = time.time() - overall\n\n print(\"-\"*hlen)\n header = \"time taken:\" + \"{0:4.5f}\".format(otime)\n prl = (hlen//2-len(header)//2) - 1\n prr = hlen - prl - len(header) - 2\n print(\"#\" + \" \"*prl + header + \" \"*prr + \"#\")\n print(\"-\"*hlen)\n\n return 0","def distance_transform(img):\n dist_transform = cv2.distanceTransform(img, cv2.DIST_L2, 5)\n return dist_transform","def get_distance_matrix(self, points):\n return points[:, :, np.newaxis, :]-points[:, np.newaxis, :, :]","def _extract_patches(img, patch_s):\n def np_extract_patches(img):\n orig = np.array(img.shape[:2])\n new = patch_s[0] * np.ceil(orig / patch_s[0]).astype(int)\n points = new - orig\n img = np.pad(img, [(0, points[0]), (0, points[1]), (0, 0)],\n mode='constant')\n patches = view_as_blocks(img, tuple(patch_s)).astype(np.float32)\n patches = patches.reshape(-1, *patch_s)\n return patches\n\n patches = tf.numpy_function(np_extract_patches, [img], tf.float32)\n return patches","def tomatrix(self, ai_patch):\n V = self.space\n# print \"------------\"\n# print \"geo.npatchs : \", V.geometry.npatchs\n# print \"patch id : \", ai_patch\n# print \"dim : \", V.dim\n# print \"shape : \", V.geometry[ai_patch].shape\n if V.dim == 1 :\n [li_n_1] = V.geometry[ai_patch].shape\n return self.com.pyfem.field_to_matrix_1d ( self.id, ai_patch \\\n , li_n_1 )\n if V.dim == 2 :\n [li_n_1, li_n_2] = V.geometry[ai_patch].shape\n return self.com.pyfem.field_to_matrix_2d ( self.id, ai_patch \\\n , li_n_1, li_n_2 )\n if V.dim == 3 :\n [li_n_1, li_n_2, li_n_3] = V.geometry[ai_patch].shape\n return self.com.pyfem.field_to_matrix_3d ( self.id \\\n , ai_patch, li_n_1, li_n_2, li_n_3 )","def point_distances(src_points, gt_points):\n distances = EuclideanDistances(np.matrix(src_points), np.matrix(gt_points))\n return np.array(distances)","def _calculate_distances(self):\n all_dists = []\n for ref in range(len(self.atoms)):\n if self.atoms[ref].symbol in self.exclude:\n continue\n indices = list(range(ref+1, len(self.atoms)))\n indices = self._filter_excluded(indices)\n if len(indices) == 0:\n continue\n dists = self.atoms.get_distances(ref, indices, mic=True)\n all_dists += list(dists)\n \n # Normalize by the mean distance\n return np.array(all_dists)/np.mean(all_dists)","def compute_distances(Ls):\n if not isinstance(Ls, list):\n Ls = [Ls]\n\n dists = []\n for L in Ls:\n N,D = L.shape\n # 1xNxD - Nx1xD (L1 distance)\n dist = (np.abs(L[None,:,:] - L[:,None,:])).sum(axis=2)\n dists.append(dist)\n\n return dists","def distance_map(self, scaling='sum'):\n\n if scaling not in ['sum', 'mean']:\n raise ValueError(f'scaling should be either \"sum\" or \"mean\" ('\n f'\"{scaling}\" not valid)')\n\n um = nan * zeros((self._weights.shape[0],\n self._weights.shape[1],\n 8)) # 2 spots more for hexagonal topology\n\n ii = [[0, -1, -1, -1, 0, 1, 1, 1]]*2\n jj = [[-1, -1, 0, 1, 1, 1, 0, -1]]*2\n\n if self.topology == 'hexagonal':\n ii = [[1, 1, 1, 0, -1, 0], [0, 1, 0, -1, -1, -1]]\n jj = [[1, 0, -1, -1, 0, 1], [1, 0, -1, -1, 0, 1]]\n\n for x in range(self._weights.shape[0]):\n for y in range(self._weights.shape[1]):\n w_2 = self._weights[x, y]\n e = y % 2 == 0 # only used on hexagonal topology\n for k, (i, j) in enumerate(zip(ii[e], jj[e])):\n if (x+i >= 0 and x+i < self._weights.shape[0] and\n y+j >= 0 and y+j < self._weights.shape[1]):\n w_1 = self._weights[x+i, y+j]\n um[x, y, k] = fast_norm(w_2-w_1)\n\n if scaling == 'mean':\n um = nanmean(um, axis=2)\n if scaling == 'sum':\n um = nansum(um, axis=2)\n\n return um/um.max()","def __surface_distances(result, reference, voxelspacing=None, connectivity=1):\n result = np.atleast_1d(result.astype(np.bool))\n reference = np.atleast_1d(reference.astype(np.bool))\n if voxelspacing is not None:\n voxelspacing = _ni_support._normalize_sequence(voxelspacing, result.ndim)\n voxelspacing = np.asarray(voxelspacing, dtype=np.float64)\n if not voxelspacing.flags.contiguous:\n voxelspacing = voxelspacing.copy()\n \n # binary structure\n footprint = generate_binary_structure(result.ndim, connectivity)\n \n # test for emptiness\n if 0 == np.count_nonzero(result): \n raise RuntimeError('The first supplied array does not contain any binary object.')\n if 0 == np.count_nonzero(reference): \n raise RuntimeError('The second supplied array does not contain any binary object.') \n \n # extract only 1-pixel border line of objects\n result_border = result ^ binary_erosion(result, structure=footprint, iterations=1)\n # print(result_border+0)\n # result_border = (result_border+0).astype(np.float32)\n # imsave(\"./test_comp1/\" +\"truth.jpg\", result_border)\n reference_border = reference ^ binary_erosion(reference, structure=footprint, iterations=1)\n # print(reference_border)\n # reference_border = (reference_border + 0).astype(np.float32)\n # imsave(\"./test_comp1/\" +\"truth.jpg\", reference_border)\n # compute average surface distance \n # Note: scipys distance transform is calculated only inside the borders of the\n # foreground objects, therefore the input has to be reversed\n dt = distance_transform_edt(~reference_border, sampling=voxelspacing)\n # print(dt)\n reference_border = (reference_border + 0).astype(np.float32)\n # imsave(\"./test_comp1/\" +\"truth.jpg\", reference_border)\n sds = dt[result_border]\n \n return sds","def fun(x, *args):\n distance = a.normalized_distance(x.reshape(shape))\n return distance.value, distance.gradient.reshape(-1)","def compute_distance(self, data):\n logger.info('Computing Distances')\n self.num_points = len(data)\n # TODO: Maybe make this into a single mapping function and remove elif\n # ladder\n if self.distance == 'mahalanobis':\n data = np.exp(-1 * data / data.std())\n self.conden_dist_mat = pdist(data, self.distance)\n elif self.distance in ['euclidean', 'cosine']:\n self.conden_dist_mat = pdist(data, self.distance)\n elif self.distance == 'chisqr':\n self.conden_dist_mat = pdist(data, chisqr)\n elif self.distance == 'intersection':\n self.conden_dist_mat = pdist(data, intersection)\n else:\n raise ValueError(\"distance type not supported\")\n self._cleanse_dist_mat()","def calculate_and_set_propagation_distances(self):\n\n self.l_edge = self.calculate_distance_edge()\n self.l_int = self.calculate_distance_interaction()","def dist_matrix(self, group1, group2):\n \n tmps = []\n for i in group2:\n tmps.append([])\n for j in group1:\n mi, label = self.distance(i, j)\n tmps[-1].append(mi)\n return tmps","def measure_distance(self, mat):\n if len(mat) == 1:\n print(\"chain has only one CAatom\")\n return\n self.dists = []\n for num in range(0, len(mat)):\n if num + 1 <= len(mat) - 1:\n c1 = mat[num]\n c2 = mat[num + 1]\n d = c2 - c1\n self.dists.append(math.sqrt(np.sum(d * d)))\n return self.dists","def getDistanceRaster(input_img, input_mode, output_path=None):\n\n if input_mode == 'fp':\n np_img = loadImage(input_img)\n elif input_mode == 'np':\n np_img = input_img\n else:\n return (input_mode, \" is not a supported mode. Supported modes are 'np' or 'fp'.\")\n np_dist = ndimage.distance_transform_edt(np.logical_not(np_img))\n if output_path is not None:\n saveImage(np_dist, output_path, mode='dist_norm')\n return(np_dist)","def densMap(fit, metricArray, n, overlap, imageSize, imageName):\n \n if True: #Make true if you want to calculate densities (i.e. if you have a new fit/new density list) \n print 'calculating densities'\n if type(fit)==sklearn.gaussian_process.gaussian_process.GaussianProcess: #If the fit is Gaussian\n densities, MSE = fit.predict(metricArray, eval_MSE =True)\n sigma = numpy.sqrt(MSE)\n print(sigma)\n else:\n densities = fit.predict(metricArray) #Use the ML fit to predict the density for each tile \n #print len(densities)\n f = open('densities.txt', 'w')\n print >> f, list(densities)\n f.close()\n else: \n f = open('densities.txt', 'r')\n data = f.read()\n densities = eval(data)\n # print densities\n densities = numpy.array(densities)\n f.close()\n imageName = 'FirstStitchLong.jpg'\n overlap = 0.2 \n n = 50\n image = Image.open(imageName) #open the image\n imageSize = image.size #get the image size \n # print 'Image Size is ', imageSize\n \n\n width = imageSize[0] \n height = imageSize[1] \n # print 'width ', width \n #print 'height ', height\n overlapSize = int(overlap*n)\n rowTiles = int((width-n)/(overlapSize))+1\n print 'Row Tiles is ', rowTiles\n #colTiles = int(((height-n)/(overlapSize))+1)\n \n \n # newArray = numpy.zeros((int((width-n)/(overlapSize)+1),int(((height-n)/(overlapSize))+1)))\n \n #newArray = numpy.zeros(width, height)\n #print newArray\n points = []\n for i in range(len(densities)):\n x = (i%rowTiles)*overlapSize + n/2\n y = (i/rowTiles)*overlapSize + n/2\n points += [[x,y]]\n # newArray[i%(rowTiles),i/(rowTiles)] = densities[i]\n # newArray[(i%(rowTiles)+1)*(n/2) , (i/(rowTiles)+1)*(n/2)] = densities[i]\n # points += [[(i%(rowTiles)+1)*(n/2), (i/(rowTiles)+1)*(n/2)]] #Consider the density to be at the center of each tile \n # print points\n grid_x, grid_y = numpy.mgrid[0:width, 0:height] #create a meshgrid \n \n # print densities\n # print('Points is ', points) \n # print('Densities is ', densities)\n data = griddata(points, densities, (grid_x, grid_y), method = 'linear') #interpolate to get continuous function of points \n #can change interpolation method\n print data.size\n\n # print 'Max is ', numpy.amax(data)\n # min_max_scaler = sklearn.preprocessing.MinMaxScaler()\n #data_minmax = min_max_scaler.fit_transform(data) \n \n \n \n ##Plotting \n plt.figure(1)\n \n #plt.figure(figsize = (width, height))\n # print \"Hi Cassie\"\n v = numpy.linspace(min(densities), max(densities), 20, endpoint=True)\n fig = plt.contourf(grid_x, grid_y, data, levels = v, alpha = 0.4, antialiased = True)\n \n mapIm = Image.open(imageName)\n plt.imshow(mapIm)\n maxDens = max(densities)\n print maxDens \n x = plt.colorbar(fig) \n plt.savefig('ContourPlot.jpg')\n return data","def distance(self):\n try:\n import pdb\n pdb.set_trace()\n s = []\n x0,y0 = self.deriv.T\n for thing in everything:\n x1,y1 = thing.deriv.T\n r,p = pearsonr(y0,y1)\n s.append(( p,thing.label ))\n s.sort()\n #print s[-5:]\n print s\n except:\n return np.inf","def distance_matrix(data):\n D = numpy.zeros( (data.shape[0], data.shape[0]) )\n for i in xrange(data.shape[0]):\n for j in xrange(i):\n D[i,j] = numpy.linalg.norm(data[i,:]-data[j,:])\n D[j,i] = D[i,j]\n\n return D","def compute_distance(self, transpose=False):\n\n # Calculate distance matrix\n if transpose:\n distance_matrix = pdist(self.matrix.T, self.distance)\n else:\n distance_matrix = pdist(self.matrix, self.distance)\n\n # Remove NaNs\n distance_matrix[np.isnan(distance_matrix)] = 1.0\n\n return distance_matrix","def match3(img1, img2, coordinates1, coordinates2, PATCH_SIZE, threshold=0.7):\n\n\t#creating patches for all points from img1 and img2\n\tcoord1_patches = [make_patch(coordinate, PATCH_SIZE, img1) for coordinate in coordinates1]\n\tcoord2_patches = [make_patch(coordinate, PATCH_SIZE, img2) for coordinate in coordinates2]\n\n\t# creating a matrix with dissimilarity measures for all pairs\n\tall_matches = np.zeros((len(coordinates1), len(coordinates2)))\n\n\tfor (x, y), _ in np.ndenumerate(all_matches):\n\t\tall_matches[x,y] = count_difference(coord1_patches[x], coord2_patches[y])\n\n\t#looking for best left-to-right and right-to-left matches\n\tmatches = []\n\t#left-to-right\n\tfor i, coord1 in enumerate(coordinates1):\n\t\tbest_ltr_match = np.argmin(all_matches[i, :]) #best left-to-right match for coord1\n\t\tbest_rtl_match = np.argmin(all_matches[:, best_ltr_match]) #best match for a best match\n\t\tif (i == best_rtl_match): #hurray, there is a super match\n\n\t\t\tmatches.append([coord1, coordinates2[best_ltr_match], all_matches[i, best_ltr_match]])\n\t\n\treturn matches","def get_distances(self, crds):\n self.all_dist = np.zeros((self.natom, self.natom))\n # Loop over upper triangle of atom pairs\n for iat in range(self.natom-1):\n # Get the atom indices\n at_inds = np.arange(len(crds))\n\n # Calc distances between atoms (only upper triangle though)\n at_msk = at_inds > iat\n all_ut_dist = crds[at_msk] - crds[iat]\n all_ut_dist = np.linalg.norm(all_ut_dist, axis=1)\n\n self.all_dist[iat, iat+1:] = all_ut_dist\n\n # Get lower triangle indices\n self.all_dist = self.all_dist + self.all_dist.T","def dist_calc(self,Xtest):\n d,i = self.knn.kneighbors(Xtest)\n\n self.dist = d\n self.ind = i\n\n self.Xtest = Xtest","def dist_calc(self,Xtest):\n d,i = self.knn.kneighbors(Xtest)\n\n self.dist = d\n self.ind = i\n\n self.Xtest = Xtest","def train_patches(lmks, imgs, ref, psize, ssize, var=1.0, lmbda=1e-6, mu_init=1e-3, nsamples=1000):\n\n if isinstance(psize, int):\n psize = (psize, psize)\n if isinstance(ssize, int):\n ssize = (ssize, ssize)\n\n n = len(ref) // 2\n ximg = psize[1] + ssize[1]\n yimg = psize[0] + ssize[0]\n wsize = (yimg, ximg)\n\n patches = []\n\n # train each patch model\n for i in range(n):\n print('patch', i+1, 'of', n, '...')\n images = []\n for j in range(lmks.shape[1]):\n im = imgs[j]\n pt = lmks[:,j]\n S = calc_simil(pt, ref)\n A = np.empty((2,3))\n A[:2,:2] = S[:2,:2]\n A[0,2] = pt[2*i] - (A[0,0] * (ximg-1)/2 + A[0,1] * (yimg-1)/2)\n A[1,2] = pt[2*i+1] - (A[1,0] * (ximg-1)/2 + A[1,1] * (yimg-1)/2)\n I = cv2.warpAffine(im, A, wsize, flags=cv2.INTER_LINEAR+cv2.WARP_INVERSE_MAP)\n images.append(I)\n\n patch = train_patch(images, psize, var, lmbda, mu_init, nsamples)\n patches.append(patch)\n\n return np.array(patches)","def run(self):\n #calculate platescale of first input image\n try:\n det = np.linalg.det(wcs.WCS(self.datain[0].header).wcs.cd)\n pscale = np.sqrt(np.abs(det))*3600.\n except:\n try:\n det = np.linalg.det(wcs.WCS(self.datain[0].header).wcs.pc)\n pscale = np.sqrt(np.abs(det))*3600.\n except:\n pscale = self.datain[0].header['PIXSCAL']\n #filtering out images which are too far away from the others\n #passing images added to a list of (image, WCS) tuples\n '''\n image_centers = []\n for f in self.datain:\n image_centers.append((f.header['CRVAL1'], f.header['CRVAL2']))\n filtered_datain = []\n dist_list = [[[0]*(len(image_centers)-1)]*len(image_centers)]\n for i in range(len(image_centers)):\n for j in range(len(image_centers)-1):\n dist_list[i][j+1] = np.sqrt((image_)**2+()**2)\n '''\n #calculations necessary for updating wcs information\n px = []\n py = []\n \n #in order to avoid NaN interactions, creating weight map\n weights=[]\n for f in self.datain:\n weights.append((np.where(np.isnan(f.image) == True, 0, 1)))\n \n for f in self.datain:\n px.extend(wcs.WCS(f.header).calc_footprint()[:,0])\n py.extend(wcs.WCS(f.header).calc_footprint()[:,1])\n x0 = (max(px)+min(px))/2.\n y0 = (max(py)+min(py))/2.\n sx = (max(px)-min(px))*np.cos(y0/180*np.pi) # arcsec\n sy = (max(py)-min(py)) # arcsec\n size = (sx*3600+self.getarg('pad')*2, sy*3600+self.getarg('pad')*2)\n xpix = size[0]//pscale\n ypix = size[1]//pscale\n cdelt = [pscale/3600.]*2\n \n #create self.dataout and give it a copy of an input's header\n self.dataout = DataFits(config = self.config)\n self.dataout.header = self.datain[0].header.copy()\n \n #update header wcs information\n self.log.info('Creating new WCS header')\n \n self.dataout.header['CRPIX1'] = xpix/2\n self.dataout.header['CRPIX2'] = ypix/2\n self.dataout.header['CRVAL1'] = x0\n self.dataout.header['CRVAL2'] = y0\n self.dataout.header['CD1_1'] = -cdelt[0]\n self.dataout.header['CD1_2'] = self.dataout.header['CD2_1'] = 0.\n self.dataout.header['CD2_2'] = cdelt[1]\n self.dataout.header['NAXIS1'] = int(xpix)\n self.dataout.header['NAXIS2'] = int(ypix)\n self.dataout.header['CTYPE1'] = 'RA---TAN-SIP'\n self.dataout.header['CTYPE2'] = 'DEC--TAN-SIP'\n self.dataout.header['RADESYS'] = 'ICRS'\n self.dataout.header['EQUINOX'] = 2000\n self.dataout.header['LATPOLE'] = self.datain[0].header['CRVAL2']\n self.dataout.header['LONPOLE'] = 180\n self.dataout.header['PIXASEC'] = pscale\n \n theta_rad = np.deg2rad(self.getarg('outangle'))\n rot_matrix = np.array([[np.cos(theta_rad), -np.sin(theta_rad)], \n [np.sin(theta_rad), np.cos(theta_rad)]])\n rot_cd = np.dot(rot_matrix, np.array([[self.dataout.header['CD1_1'], 0.],[0., self.dataout.header['CD2_2']]]))\n for i in [0,1]:\n for j in [0,1]:\n self.dataout.header['CD{0:d}_{1:d}'.format(i+1, j+1)] = rot_cd[i,j]\n \n #check drizzle arguments\n if self.getarg('kernel') == 'smoothing':\n kernel = 'lanczos3'\n elif self.getarg('kernel') in ['square', 'point', 'gaussian', 'tophat']:\n kernel = self.getarg('kernel')\n else:\n self.log.error('Kernel name not recognized, using default')\n kernel = 'square'\n if self.getarg('drizzleweights') == 'uniform':\n driz_wt = ''\n elif self.getarg('drizzleweights') in ['exptime', 'expsq']:\n driz_wt = self.getarg('drizzleweights')\n else:\n self.log.error('Drizzle weighting not recognized, using default')\n driz_wt = ''\n \n #create drizzle object and add input images\n fullwcs = wcs.WCS(self.dataout.header)\n self.log.info('Starting drizzle')\n driz = drz.Drizzle(outwcs = fullwcs, pixfrac=self.getarg('pixfrac'), \\\n kernel=kernel, fillval='10000', wt_scl=driz_wt)\n for i,f in enumerate(self.datain):\n self.log.info('Adding %s to drizzle stack' % f.filename)\n driz.add_image(f.imgdata[0], wcs.WCS(f.header), inwht=weights[i])\n \n try:\n fillval=float(self.getarg('fillval'))\n except:\n fillval=np.nan\n self.log.error('Fillvalue not recognized or missing, using default')\n \n #creates output fits file from drizzle output\n self.dataout.imageset(np.where(driz.outsci == 10000, fillval, driz.outsci))\n self.dataout.imageset(driz.outwht,'OutWeight', self.dataout.header)\n self.dataout.filename = self.datain[0].filename\n\n #add history\n self.dataout.setheadval('HISTORY','Coadd: %d files combined with %s kernel, pixfrac %f at %f times resolution' \\\n % (len(self.datain), kernel, self.getarg('pixfrac'), self.getarg('resolution')))","def reshape_at_dist(self):\n self.all_dist_per_mol = np.zeros((self.nmol, self.at_per_mol, self.at_per_mol))\n for imol in range(self.nmol):\n start, end = self.at_per_mol*imol, (imol+1)*self.at_per_mol\n self.all_dist_per_mol[imol] = self.all_dist[start:end,\n start:end]","def extract_patches(data,patch_dim):\n \n m = data.shape[0]\n im_x = data.shape[1]\n im_y = data.shape[2]\n \n assert im_x%float(patch_dim)==0 and im_y%float(patch_dim)==0, \\\n \"patch_size must divide x and y dimensions of image\"\n\n numpatchs = m*(im_x/patch_dim)*(im_y/patch_dim)\n patch_size = patch_dim**2\n\n patches = np.empty((patch_size,numpatchs))\n p=0\n for i in range(data.shape[0]):\n image = data[i,...]\n for x in np.r_[0:im_x:patch_dim]:\n for y in np.r_[0:im_y:patch_dim]:\n patch = image[x:x+patch_dim,y:y+patch_dim]\n patches[:,p] = patch.ravel()\n p+=1\n \n return patches","def get_correct_distance_matrix(L):\n n = len(L)\n D = np.zeros((n,n))\n for i in range(n):\n for j in range(n):\n if i != j:\n D[i][j] = get_minor(L, [i, j], [i, j]) / get_minor(L, [i], [i])\n return D","def get_distance_matrix(self):\n names = self.get_named_leaves()\n num_names = len(names)\n dist_mat = np.zeros((num_names, num_names), dtype='float')\n for i, j in itertools.combinations(range(num_names), 2):\n node1, node2 = self.node_names[names[i]], self.node_names[names[j]]\n dist = self.node_distance(node1, node2)\n dist_mat[i,j] = dist\n dist_mat[j,i] = dist\n return names, dist_mat","def _get_distance_diff(self, input):\n nbatch = input.shape[0]\n in1 = input.unsqueeze(1).expand(\n nbatch, self.nelec, self.nelec, self.ndim)\n in2 = input.unsqueeze(2).expand(\n nbatch, self.nelec, self.nelec, self.ndim)\n dist = torch.pow(in1 - in2, 2).sum(3)\n return dist","def distance_image(self):\n return exclusion_distance(self.mask)","def calculateDistances(df):\n return","def dist_matrix(self):\n return self.__dist_matrix","def calc_dist_matrix(self):\n\n self.dist_matrix = spatial.distance.squareform(spatial.distance.pdist(self.data_vector,metric=\"hamming\"))\n\n self.dist_frame = pd.DataFrame(self.dist_matrix,\n index = self.seq_strings,\n columns = self.seq_strings)","def diffusion_mds(means, weights, d, diffusion_rounds=10):\n for i in range(diffusion_rounds):\n weights = weights*weights\n weights = weights/weights.sum(0)\n X = dim_reduce(means, weights, d)\n if X.shape[0]==2:\n return X.dot(weights)\n else:\n return X.T.dot(weights)","def distance_dmc(distances, Ks, points):\n doors = []\n for d in distances:\n dmc = []\n for k in Ks:\n print \"==========================\", k, \"==========================\"\n clusters = create_clusters(25, k)\n\n kmeans(points, clusters)\n # print \"Finished creating kmeans algorithm\"\n\n create_backbone_network(GRAPH, clusters, d)\n # print \"Finished creating backbone network\"\n\n find_all_shortest_paths(clusters, SP_TABLE, GRAPH)\n # print \"Finished finding all shortest paths\"\n\n for clst in clusters:\n clst.inter_cost = inter_cost(clst)\n clst.intra_cost = intra_cost(points, clst)\n clst.dm_cost = door_matt_cost(clusters, clst, SP_TABLE)\n\n ret = total_cost(clusters)\n dmc.append(ret[2])\n doors.append(sum(dmc))\n draw_door_matts(map(lambda d: float(format(d, \".4g\")), distances), doors)","def distance_from_pixels(args, preds, mesh_conversions, info_elem, mode):\n node2pix = json.load(open(args.image_dir + \"allScans_Node2pix.json\"))\n geodistance_nodes = json.load(open(args.geodistance_file))\n distances, episode_predictions = [], []\n dialogs, levels, scan_names, episode_ids, true_viewpoints = info_elem\n for pred, conversion, sn, tv, id in zip(\n preds, mesh_conversions, scan_names, true_viewpoints, episode_ids\n ):\n total_floors = len(set([v[2] for k, v in node2pix[sn].items()]))\n pred = nn.functional.interpolate(\n pred.unsqueeze(1), (700, 1200), mode=\"bilinear\"\n ).squeeze(1)[:total_floors]\n pred_coord = np.unravel_index(pred.argmax(), pred.size())\n convers = conversion.view(args.max_floors, 1, 1)[pred_coord[0].item()]\n pred_viewpoint = snap_to_grid(\n geodistance_nodes[sn],\n node2pix,\n sn,\n [pred_coord[1].item(), pred_coord[2].item()],\n convers,\n pred_coord[0].item(),\n )\n if mode != \"test\":\n dist = geodistance_nodes[sn][tv][pred_viewpoint]\n distances.append(dist)\n episode_predictions.append([id, pred_viewpoint])\n return distances, episode_predictions","def extract_patch(n, patch_size, imgs):\n # Extract patches from input images\n img_patches = [img_crop(imgs[i], patch_size, patch_size) for i in range(n)]\n #gt_patches = [img_crop(gt_imgs[i], patch_size, patch_size) for i in range(n)]\n\n # Linearize list of patches\n img_patches = np.asarray([img_patches[i][j] for i in range(len(img_patches)) for j in range(len(img_patches[i]))])\n #gt_patches = np.asarray([gt_patches[i][j] for i in range(len(gt_patches)) for j in range(len(gt_patches[i]))])\n \n return img_patches #,gt_patches","def predict_distances(self, inputs, features=None):\n outputs = dict()\n if self.args.pose_model_type == \"shared\":\n # If we are using a shared encoder for both norm and pose,\n # then all images are fed separately through the norm encoder.\n images = torch.cat([inputs[(\"color_aug\", frame_id, 0)] for frame_id in self.args.frame_idxs])\n all_features = self.models[\"encoder\"](images)\n all_features = [torch.split(f, self.args.batch_size) for f in all_features]\n features = dict()\n for i, frame_id in enumerate(self.args.frame_idxs):\n features[frame_id] = [f[i] for f in all_features]\n outputs.update(self.models[\"norm\"](features[0]))\n else:\n # Otherwise, we only feed the target image through the norm encoder\n features = self.models[\"encoder\"](inputs[\"color_aug\", 0, 0]) if features is None else features\n outputs.update(self.models[\"norm\"](features))\n\n return outputs, features","def calculateWeights(self):\n return self.distances #En lo que encontramos una funcion que represente","def distance_metric(seg_A, seg_B, dx):\n table_md = []\n table_hd = []\n X, Y, Z = seg_A.shape\n for z in range(Z):\n # Binary mask at this slice\n slice_A = seg_A[:, :, z].astype(np.uint8)\n slice_B = seg_B[:, :, z].astype(np.uint8)\n\n # The distance is defined only when both contours exist on this slice\n if np.sum(slice_A) > 0 and np.sum(slice_B) > 0:\n # Find contours and retrieve all the points\n _, contours, _ = cv2.findContours(cv2.inRange(slice_A, 1, 1),\n cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_NONE)\n pts_A = contours[0]\n for i in range(1, len(contours)):\n pts_A = np.vstack((pts_A, contours[i]))\n\n _, contours, _ = cv2.findContours(cv2.inRange(slice_B, 1, 1),\n cv2.RETR_EXTERNAL,\n cv2.CHAIN_APPROX_NONE)\n pts_B = contours[0]\n for i in range(1, len(contours)):\n pts_B = np.vstack((pts_B, contours[i]))\n\n # Distance matrix between point sets\n M = np.zeros((len(pts_A), len(pts_B)))\n for i in range(len(pts_A)):\n for j in range(len(pts_B)):\n M[i, j] = np.linalg.norm(pts_A[i, 0] - pts_B[j, 0])\n\n # Mean distance and hausdorff distance\n md = 0.5 * (np.mean(np.min(M, axis=0)) + np.mean(np.min(M, axis=1))) * dx\n hd = np.max([np.max(np.min(M, axis=0)), np.max(np.min(M, axis=1))]) * dx\n table_md += [md]\n table_hd += [hd]\n\n # Return the mean distance and Hausdorff distance across 2D slices\n mean_md = np.mean(table_md) if table_md else None\n mean_hd = np.mean(table_hd) if table_hd else None\n return mean_md, mean_hd","def align_images(self, translation_only=False):\n # Extract feature point locations and descriptors.\n # print('here')\n points_and_descriptors = []\n for file in self.files:\n # print('kaka')\n image = sol4_utils.read_image(file, 1)\n # print('bla',image)\n self.h, self.w = image.shape\n pyramid, _ = sol4_utils.build_gaussian_pyramid(image, 3, 7)\n points_and_descriptors.append(find_features(pyramid))\n\n # Compute homographies between successive pairs of images.\n Hs = []\n\n for i in range(len(points_and_descriptors) - 1):\n points1, points2 = points_and_descriptors[i][0], points_and_descriptors[i + 1][0]\n desc1, desc2 = points_and_descriptors[i][1], points_and_descriptors[i + 1][1]\n\n # Find matching feature points.\n ind1, ind2 = match_features(desc1, desc2, .7)\n points1, points2 = points1[ind1, :], points2[ind2, :]\n\n # Compute homography using RANSAC.\n # print(points1)\n # print(points2)\n H12, inliers = ransac_homography(points1, points2, 100, 6, translation_only)\n\n # Uncomment for debugging: display inliers and outliers among matching points.\n # In the submitted code this function should be commented out!\n # display_matches(self.images[i], self.images[i+1], points1 , points2, inliers)\n\n Hs.append(H12)\n\n # Compute composite homographies from the central coordinate system.\n # print(Hs)\n accumulated_homographies = accumulate_homographies(Hs, (len(Hs) - 1) // 2)\n self.homographies = np.stack(accumulated_homographies)\n self.frames_for_panoramas = filter_homographies_with_translation(self.homographies, minimum_right_translation=5)\n self.homographies = self.homographies[self.frames_for_panoramas]","def create_loss_weight_matrix(batch_size, patch_size, distance, min_value=0.2):\n w = min_value * torch.ones([patch_size, patch_size])\n for i in range(1, distance+1):\n value = (i + (distance-i) * min_value) / distance\n w[i:-i, i:-i] = value * torch.ones([patch_size - 2*i, patch_size - 2*i])\n w = w.expand(batch_size, 1, -1, -1).clone()\n w.requires_grad = False\n return w","def computeDistance(self):\n distance = 0.0\n height = self.heightField.getNumber()\n ratio = self.indexField.getNumber()\n numBounces = self.bouncesField.getNumber()\n\n for bounce in range(numBounces):\n bounceHeight = height * ratio\n distance += height + bounceHeight\n height = bounceHeight\n\n self.distanceField.setNumber(distance)"],"string":"[\n \"def im_dist_mat(self):\\n mat = np.zeros([self.I, self.M])\\n for i in range(self.I):\\n for m in range(self.M):\\n mat[i, m] = distance(self.I_coords[i], self.M_coords[m])\\n return mat\",\n \"def _calculate_distances(boxes, homography):\\n pos_markers = []\\n pix_markers = []\\n for box in boxes:\\n (pt1_w, pt1_h), (pt2_w, pt2_h) = box\\n\\n pix_marker = ((pt1_w + pt2_w) // 2, max(pt1_h, pt2_h))\\n pix_markers.append(pix_marker)\\n\\n pos_marker = np.array(pix_marker).reshape(\\n 1, 1, 2).astype(\\\"float32\\\")\\n pos_marker = cv2.perspectiveTransform(\\n pos_marker, homography).squeeze()\\n pos_markers.append(pos_marker)\\n\\n if len(pos_markers) <= 1:\\n return np.array([]), np.array([])\\n\\n distances = pdist(np.array(pos_markers))\\n return pix_markers, distances\",\n \"def distances(self):\",\n \"def point_distances(self, params=None):\\n if params is None:\\n params = self.collocation_points()\\n with self.fix_evaluator():\\n pts = np.array([self(la) for la in params])\\n deltas = np.diff(pts, axis=0)\\n distances = norm(deltas, axis=1)\\n return distances\",\n \"def calculate_dist_from_eqm(distance_from_eqm, variable_mask):\",\n \"def all_distances(self):\\n points = self.color_lookup_table_points\\n\\n red = np.repeat(np.expand_dims(points[0], axis=0), points[0].size, axis=0)\\n green = np.repeat(np.expand_dims(points[1], axis=0), points[1].size, axis=0)\\n blue = np.repeat(np.expand_dims(points[2], axis=0), points[2].size, axis=0)\\n\\n self.distances = np.sqrt(\\n np.square(red - red.transpose())\\n + np.square(green - green.transpose())\\n + np.square(blue - blue.transpose()))\",\n \"def cal_patches_norm(self):\\n # norm of style image patches\\n norm_array = torch.zeros(self.style_patches.shape[0])\\n for i in range(self.style_patches.shape[0]):\\n norm_array[i] = torch.pow(torch.sum(torch.pow(self.style_patches[i], 2)), 0.5)\\n return norm_array.to(self.device)\",\n \"def cal_dist(A, AA, B, BB, ay, ax, by, bx, patch_size):\\r\\n dx0 = dy0 = patch_size // 2\\r\\n dx1 = dy1 = patch_size // 2 + 1\\r\\n dx0 = min(ax, bx, dx0)\\r\\n dx1 = min(A.shape[0] - ax, B.shape[0] - bx, dx1)\\r\\n dy0 = min(ay, by, dy0)\\r\\n dy1 = min(A.shape[1] - ay, B.shape[1] - by, dy1)\\r\\n return np.sum(((A[ay - dy0:ay + dy1, ax - dx0:ax + dx1] - B[by - dy0:by + dy1, bx - dx0:bx + dx1]) ** 2) +\\r\\n ((AA[ay - dy0:ay + dy1, ax - dx0:ax + dx1] - BB[by - dy0:by + dy1, bx - dx0:bx + dx1]) ** 2)) / ( (dx1 + dx0) * (dy1 + dy0))\",\n \"def calc_distances(self, templates_features=None, batch_size=50000, th=0.2, beta=1.1):\\n if templates_features is None:\\n templates_features = self.calc_templates()\\n distances = np.empty((self.pairs.shape[0]), dtype=np.float32)\\n start, end = 0, 0\\n for batch in self.batches(self.pairs, batch_size):\\n t1 = np.empty((len(batch), self.features_dim), dtype=np.float32)\\n t2 = np.empty((len(batch), self.features_dim), dtype=np.float32)\\n start = end\\n end += len(batch)\\n # attenuate = np.empty((len(batch)), dtype=np.bool)\\n for i, pair in enumerate(batch):\\n t1[i] = templates_features[pair[0]]\\n t2[i] = templates_features[pair[1]]\\n # lomax1 = np.max(self.quality_scores[pair[0]])\\n # lomax2 = np.max(self.quality_scores[pair[1]])\\n # attenuate[i] = lomax1 <= th or lomax2 <= th\\n\\n ## find cosine distance, assume template descriptors are normalized\\n distances[start:end] = 1 - np.einsum(\\\"ij,ij->i\\\", t1, t2)\\n # distances[start:end] = np.where(attenuate, distances[start:end], distances[start:end] / beta)\\n return distances\",\n \"def _calc_distance_features(self):\\n d = ()\\n for dx, dy in DIRECTIONS:\\n if dx and dy:\\n d += (list(self.__calc_distance(direction_x=dx, direction_y=dy)), )\\n elif dx:\\n tmp, _, _ = self.__calc_distance(direction_x=dx, direction_y=dy)\\n d += (tmp, )\\n elif dy:\\n _, tmp, _ = self.__calc_distance(direction_x=dx, direction_y=dy)\\n d += (tmp, )\\n\\n self.dist_features = d\\n\\n self.direc_dist = self.__calc_direc_distance()\",\n \"def frame_to_distance_images(frame):\\n \\n # distance from ridge midlines\\n frame = rgb2gray(frame)\\n ridges = enhance_ridges(frame)\\n thresh = filters.threshold_otsu(ridges)\\n prominent_ridges = ridges > 0.8*thresh\\n skeleton = morphology.skeletonize(prominent_ridges)\\n edge_dist = ndimage.distance_transform_edt(~skeleton)\\n edge_dist = filters.gaussian(edge_dist, sigma=2)\\n\\n # distance from skeleton branch points (ie, ridge intersections)\\n blurred_skeleton = uniform_filter(skeleton.astype(float), size=3)\\n corner_im = blurred_skeleton > 4./9\\n corner_dist = ndimage.distance_transform_edt(~corner_im)\\n \\n return edge_dist, corner_dist\",\n \"def cal_distances(embeddings):\\n # calculate\\n dist = np.zeros([len(embeddings), len(embeddings)], dtype=float)\\n for ii in xrange(len(embeddings)):\\n for jj in xrange(ii + 1, len(embeddings)):\\n dist[ii, jj] = np.linalg.norm(embeddings[ii] - embeddings[jj])\\n dist[jj, ii] = dist[ii, jj] \\n \\n # return\\n return dist\",\n \"def compute_distance(self):\\n loc = np.extend_dims(self.state[:, :, Boids.Attr.LOC], axis=-1)\\n m = np.tile(loc, (1, 1, self.num_boids))\\n pos_diff = m-m.transpose(0, 2, 1)\\n self.distance = np.linalg.norm(pos_diff, axis=0)\",\n \"def compute_distances(self):\\n if self.df is None:\\n return\\n\\n self.origdist = []\\n self.transdist = []\\n for i in range(len(self.df)):\\n for j in range(i+1, len(self.df)):\\n self.origdist.append(distance(self.df['LPsol'].iloc[i], self.df['LPsol'].iloc[j]))\\n self.transdist.append(distance(self.df[['x', 'y']].iloc[i], self.df[['x', 'y']].iloc[j]))\",\n \"def image_distances(distance_function, image1, image2):\\n \\n shape1 = image1.shape\\n shape2 = image2.shape\\n \\n assert shape1[2] == shape2[2]\\n \\n bands1 = [ image1[:, :, bidx] for bidx in range(shape1[2]) ]\\n bands2 = [ image2[:, :, bidx] for bidx in range(shape2[2]) ]\\n\\n dist = []\\n for band1, band2 in zip(bands1, bands2):\\n dist.append(distance_function(\\n ci.get_histogram(band1), ci.get_histogram(band2)))\\n return dist\",\n \"def __make_distance_matrix(self, src_image: Image, out_image: Image, padding: int, out_point: Point, causal: bool) -> np.ndarray:\\n # Extract the reference window and for the neighbourhood matching.\\n out_window = out_image.extract(out_point, padding, 'wrap')\\n out_filled = out_image.filled(out_point, padding, 'wrap', causal)\\n\\n # Construct a 2D Gaussian kernel that matches the padding size.\\n gaussian_1D = signal.gaussian(2 * padding + 1, std=padding)\\n gaussian_2D = np.outer(gaussian_1D, gaussian_1D)\\n gaussian_2X = np.extract(out_filled, gaussian_2D)\\n\\n # Return the weighted squared difference of each neighbourhood in the\\n # source Image with respect to the reference window.\\n return self._apply_distance_filter(src_image, out_window, out_filled, gaussian_2X)\",\n \"def dist_train(imgdir, imgfmt):\\r\\n global square_width, row, col, content\\r\\n counter, loop_ctr = 0, 1\\r\\n\\r\\n # termination criteria\\r\\n criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, square_width, 0.001)\\r\\n\\r\\n # arrays to store object points and image points from all the images\\r\\n objpoints = [] # 3d point in real world space\\r\\n imgpoints = [] # 2d points in image plane\\r\\n images = glob.glob(imgdir+'*'+imgfmt)\\r\\n images.sort()\\r\\n\\r\\n gray = np.zeros((0, 0))\\r\\n for fname in images:\\r\\n img = cv2.imread(fname)\\r\\n gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\\r\\n objp = np.zeros((row*col, 3), np.float32)\\r\\n objp[:, :2] = np.mgrid[0:row, 0:col].T.reshape(-1, 2)\\r\\n ret, corners = cv2.findChessboardCorners(gray, (row, col), None)\\r\\n\\r\\n # if chessboard corners are found, add object points and image points to respective lists (after refining them)\\r\\n ind_line = fname.rindex(os.sep)\\r\\n ind_dot = fname.rindex('.')\\r\\n if ret == True:\\r\\n objpoints.append(objp)\\r\\n corners2 = cv2.cornerSubPix(gray,corners,(11,11),(-1,-1),criteria)\\r\\n imgpoints.append(corners2)\\r\\n # Draw and display the corners\\r\\n img = cv2.drawChessboardCorners(img, (row, col), corners2, ret)\\r\\n counter += 1\\r\\n save_image(fname[0:ind_line+1] + \\\"success/\\\", fname[ind_line+1:ind_dot],img)\\r\\n else:\\r\\n save_image(fname[0:ind_line + 1] + \\\"fail/\\\", fname[ind_line + 1:ind_dot], img)\\r\\n cv2.destroyAllWindows()\\r\\n loop_ctr += 1\\r\\n\\r\\n if len(images) > 0:\\r\\n ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)\\r\\n # get the calibration error\\r\\n error = calib_error(objpoints, imgpoints, rvecs, tvecs, mtx, dist)\\r\\n content += imgdir + ':\\\\n Number of qualified images = ' + str(counter) + '\\\\n Average calibration error = ' + str(np.round(error, 4)) + ' pixels\\\\n'\\r\\n return ret, mtx, dist, rvecs, tvecs, error\\r\\n else:\\r\\n content += imgdir + ':\\\\n No images in this folder. Cannot compute camera parameters.\\\\n'\\r\\n return 0, 0, 0, 0, 0, float('inf')\",\n \"def pixel_distance(stars):\\n distances = np.zeros((stars.shape[0], stars.shape[0]))\\n logger.info('Start')\\n for i in range(stars.shape[0]):\\n if i < stars.shape[0] - 1:\\n dis1 = stars[i, 0] - stars[i + 1:, 0]\\n dis2 = stars[i, 1] - stars[i + 1:, 1]\\n dis = np.sqrt(dis1 ** 2 + dis2 ** 2)\\n distances[i][i + 1:] = dis\\n logger.info('The End!')\\n return distances\",\n \"def _computeDistances(self) -> None:\\n length = len(self.data)\\n for i, sequenceOne in enumerate(self.data):\\n print(f\\\"[SeqCluBaselineOffline] Computing distances is at iteration {i} of {length}.\\\")\\n for j, sequenceTwo in enumerate(self.data):\\n if i == j:\\n self.distances[i][j] = 0\\n continue\\n distance = self.distanceMeasure.calculateDistance(sequenceOne, sequenceTwo)\\n self.distances[i][j] = distance\\n self.distances[j][i] = distance\",\n \"def __build_distance_matrix(self):\\n for i in range(0, len(self.__corpus)):\\n doc_i = self.__corpus[i]\\n for j in range(i + 1, len(self.__corpus)):\\n doc_j = self.__corpus[j]\\n distance = doc_i.calc_distance(doc_j)\\n self.__distance_matrix.append(distance)\",\n \"def get_general_distance_and_relationship_matrix(path_to_embeddings,image_level_embed_file_name, study=None):\\n\\n if study == None:\\n images_info = pd.read_csv(os.path.join(DATA_DIR,STUDY,\\\"human_ISH_info.csv\\\"))\\n else:\\n images_info = pd.read_csv(os.path.join(DATA_DIR, study, \\\"human_ISH_info.csv\\\"))\\n\\n\\n dist_matrix_df = build_distance_matrix(os.path.join(path_to_embeddings, image_level_embed_file_name))\\n\\n dist_matrix_rows = list(dist_matrix_df.index) # list of image IDs\\n dist_matrix_columns = list(dist_matrix_df) # list of image IDs\\n\\n # --- sanity check -------------\\n if dist_matrix_rows != dist_matrix_columns:\\n print (\\\"Something is wrong, the number and order of image IDs in distance matrix's rows and columns should the same.\\\")\\n return None\\n # ------------------------------\\n\\n\\n genes = images_info[images_info['image_id'].isin(dist_matrix_rows)]['gene_symbol']\\n\\n low_to_high_map = pd.DataFrame(list(zip(dist_matrix_rows, genes))) # create a 2-column df of image IDs and genes\\n relationship_df = create_diagonal_mask(low_to_high_map, target_value=1)\\n\\n\\n # --- check to see if rows and columns of dist matrix match the relationship matrix. ---------------------\\n # if they don't re-arrange them in the relationship matrix to match the dist matrix\\n\\n dist_matrix_df, relationship_df = match_matrices(dist_matrix_df, relationship_df)\\n\\n # ---------------------------------------------------------------------------------------------------------\\n\\n return dist_matrix_df,relationship_df\",\n \"def compute_distances(self, X):\\n #print(X.shape, self.Xtr.shape)\\n dists = np.zeros((X.shape[0], self.Xtr.shape[0]))\\n for i in range(X.shape[0]):\\n X_r = np.tile(X[i], (self.Xtr.shape[0], 1))\\n dists[i] = np.sqrt(np.sum(np.square(self.Xtr - X_r), axis = 1))\\n #print(dists.shape)\\n return dists\",\n \"def _get_distances(self):\\n for molecule in self.values():\\n molecule.get_distances()\\n\\n # for atom in self.atoms:\\n # atom.get_distances()\",\n \"def compute_distances(src):\\n rr = np.vstack((src[0]['rr'][src[0]['inuse'].astype(np.bool)],\\n src[1]['rr'][src[1]['inuse'].astype(np.bool)]))\\n return distance.squareform(distance.pdist(rr))\",\n \"def _build_dist(self):\\n lamb = self.params['lamb']\\n p = self.params['p']\\n\\n jac = self.jacobian\\n # build D on grids\\n xg, yg, mask = self._mask_grid()\\n r_max = self._r_max(xg, yg, mask)\\n d_mat = self._psf_grid(xg, yg, r_max=r_max)\\n # E[yy^T]\\n j_j_w = np.dot(jac, jac.transpose())\\n r_mat = np.diag(np.diag(j_j_w) ** p)\\n jac_inv = la.inv(j_j_w + lamb*r_mat)\\n # RM = E[xx^T] / E[yy^T]\\n h_mat = np.dot(np.dot(d_mat, jac.transpose()), jac_inv)\\n return h_mat\",\n \"def feature_dist(input_labels, struct=np.ones((3, 3))):\\n # remove the pixels inside the coherent\\n input_errosion = binary_erosion(input_labels, structure=struct)\\n input_labels[input_errosion] = 0\\n I, J = np.nonzero(input_labels)\\n labels = input_labels[I, J]\\n coords = np.column_stack((I, J))\\n sorter = np.argsort(labels)\\n labels = labels[sorter]\\n coords = coords[sorter]\\n I = I[sorter]\\n J = J[sorter]\\n sq_dists = cdist(coords, coords, 'sqeuclidean')\\n start_idx = np.flatnonzero(np.r_[1, np.diff(labels)])\\n nonzero_vs_feat = np.minimum.reduceat(sq_dists, start_idx, axis=1)\\n feat_vs_feat = np.minimum.reduceat(nonzero_vs_feat, start_idx, axis=0)\\n # calculate the distance between every two coherent areas\\n # distance factor for one pixel to meter: 100\\n distance_matrix = 100 * np.sqrt(feat_vs_feat)\\n nRow, nCol = sq_dists.shape[0], start_idx.shape[0]\\n # add the index of the final element to the slice array\\n slice_indices = np.concatenate((start_idx, np.array([nRow])))\\n col_args = np.zeros((nRow, nCol)).astype(int)\\n row_index = np.zeros((nCol, nCol)).astype(int)\\n '''\\n How the following commands work:\\n find closest pixel from label A to label B:\\n Label A --> Label B\\n row_index[A-1, B-1] = alpha\\n col_index[A-1, B-1] = col_args[alpha, B-1] = beta\\n index of pixel from Label A: input_labels[ I[alpha], J[alpha]]\\n index of pixel from Label B: input_labels[ I[beta], J[beta]]\\n '''\\n for i in range(nCol):\\n col_args[:, i] = start_idx[i] + \\\\\\n np.argmin(sq_dists[:, slice_indices[i]:\\n slice_indices[i+1]], axis=1)\\n elements = sq_dists[np.arange(nRow).reshape((nRow, 1)), col_args]\\n for i in range(nCol):\\n row_index[i, :] = start_idx[i] + \\\\\\n np.argmin(elements[slice_indices[i]:\\n slice_indices[i+1], :], axis=0)\\n col_index = col_args[row_index, np.arange(nCol).reshape((1, nCol))]\\n # Change col_index and row_index to input array index.\\n row_index_from_label = I[row_index]\\n col_index_from_label = J[row_index]\\n row_index_to_label = I[col_index]\\n col_index_to_label = J[col_index]\\n return distance_matrix, row_index_from_label, col_index_from_label, \\\\\\n row_index_to_label, col_index_to_label\",\n \"def _compute_distances(self, atoms: List[CellAtom]):\\n muon = self._cell_atoms[self._muon_index]\\n\\n for atom in atoms:\\n atom.distance_from_muon = np.linalg.norm(muon.position - atom.position)\",\n \"def _derive_euclidean_dm(self, cat_mat, dim):\\r\\n res_mat = []\\r\\n\\r\\n for i in range(dim):\\r\\n res_mat.append([0 for k in range(dim)])\\r\\n for j in range(i):\\r\\n res_mat[i][j] = self._vector_dist(cat_mat[i], cat_mat[j])\\r\\n res_mat[j][i] = res_mat[i][j]\\r\\n\\r\\n return DistanceMatrix(res_mat, self.DistanceMatrices[0].ids)\",\n \"def _calc_(self):\\n self.data = []\\n all_xyz_data = self.Var.data.get_xyz_data()\\n all_cols = self.Var.data.get_xyz_cols()\\n\\n # Loop over all the xyz data and cols we have\\n for xyz_data, cols in zip(all_xyz_data, all_cols):\\n\\n at_crds = np.array([i[cols[0] != 'Ne'] for i in xyz_data])\\n self.natom = len(at_crds[0])\\n self.nstep = len(at_crds)\\n self.step_data = {}\\n\\n # Calculate the nearest neighbour lists for each step\\n for step in range(self.nstep):\\n self.step_data[step] = {}\\n\\n # Get coords\\n crds = at_crds[step]\\n\\n # Get distances between neighbours\\n self.get_distances(crds)\\n\\n # Get a sorted list of atom indices by distance\\n self.get_nearest_atom_inds()\\n\\n # If we have some molecule metadata\\n if 'atoms_per_molecule' in self.Var.metadata:\\n self.at_per_mol = self.Var.metadata['atoms_per_molecule']\\n self.nmol = mol_utils.get_nmol(self.natom, self.at_per_mol)\\n self.reshape_at_dist()\\n self.get_nearest_atom_inds_per_mol()\\n self.step_data[step]['closest_atoms_mol_grouped'] = self.closest_at_per_mol\\n self.step_data[step]['distances_mol_grouped'] = self.all_dist_per_mol\\n\\n # Save data in dict\\n self.step_data[step]['distances'] = self.all_dist\\n self.step_data[step]['closest_atom_indices'] = self.closest_ats\\n\\n self.data.append(self.step_data)\\n\\n return self.data\",\n \"def calcDistance(self):\\n # Initialize the distance matrix\\n arr = np.repeat(0, self.num_col)\\n result_mat = np.repeat(arr, self.num_col)\\n result_mat = np.reshape(result_mat, (self.num_col, self.num_col))\\n trinary_mat = self.df_trinary.values\\n for left_val in TRINARY_VALUES:\\n left_func = lambda v: 1 if v==left_val else 0\\n left_mat = np.transpose(np.vectorize(left_func)(trinary_mat))\\n for right_val in TRINARY_VALUES:\\n if left_val == right_val:\\n continue\\n right_func = lambda v: 1 if v==right_val else 0\\n right_mat = np.vectorize(right_func)(trinary_mat)\\n # Count the number of occurrences of this combination of values\\n # by doing a matrix multiply\\n new_mat = np.matmul(left_mat, right_mat)\\n # Multiply by the squared distance between the values\\n squared_distance = (left_val - right_val)**2\\n new_mat = new_mat*squared_distance\\n # Accumulate the result\\n result_mat = result_mat + new_mat\\n # Convert to dataframe\\n result_mat = np.vectorize(lambda v: np.sqrt(v)) (result_mat)\\n self.df_distance = pd.DataFrame(result_mat, columns=self.columns,\\n index=self.columns)\",\n \"def _calculate_similarity(self):\\n self._logger.info(\\\"Calculating the similarity between images.\\\")\\n\\n # Create a helper function to simplify the loops below:\\n def get_wrapper(fdx, mdx):\\n wrapper = pos_wrappers.image_similarity_wrapper(\\n reference_image=self.f['src_gray'](idx=fdx),\\n moving_image=self.f['src_gray'](idx=mdx),\\n affine_transformation=self.f['part_transf'](mIdx=mdx, fIdx=fdx))\\n return copy.copy(wrapper)\\n\\n commands = [] # Will hold commands for calculating the similarity\\n\\n # Will hold (moving, fixed) images partial_transforms basically: all\\n # partial transformations array\\n partial_transforms = []\\n\\n self._logger.debug(\\\"Generating similarity measure warppers.\\\")\\n for moving_slice in self.options.slice_range:\\n # Get all fixed images to which given moving slice will be aligned:\\n tpair = list(flatten(self._get_slice_pair(moving_slice)))\\n\\n # Append partial transformations for given moving slice to the\\n # global partial transformations array\\n partial_transforms.append(tpair)\\n\\n # Generate wrapper for measuring similarity for a given partial\\n # transformation.\\n for mdx, fdx in tpair:\\n commands.append(get_wrapper(fdx, mdx))\\n\\n # Execute and commands and workflow the similarity measurements.\\n stdout, stderr = self.execute(commands)\\n simmilarity = map(lambda x: float(x.strip()),\\n stdout.strip().split(\\\"\\\\n\\\"))\\n simmilarity = dict(zip(flatten(partial_transforms), simmilarity))\\n\\n self._logger.debug(\\\"Generating graph edges.\\\")\\n graph_connections = []\\n\\n # Lambda defines slice skipping is preffered (lower l), or reluctant\\n # to slice skipping (higher)\\n l = self.options.graphEdgeLambda\\n\\n for (mdx, fdx), s in simmilarity.iteritems():\\n w = (1.0 + s) * abs(mdx - fdx) * (1.0 + l) ** (abs(mdx - fdx))\\n graph_connections.append((fdx, mdx, w))\\n\\n self._logger.info(\\\"Creating a graph based on image similarities.\\\")\\n # Generate the graph basen on the weight of the edges\\n self.G = nx.DiGraph()\\n self.G.add_weighted_edges_from(graph_connections)\\n\\n self._logger.debug(\\\"Saving the graph to a file.\\\")\\n # Save the edges for some further analysis.\\n nx.write_weighted_edgelist(self.G,\\n self.f['graph_edges'](sign=self.signature))\\n\\n # Also, save the individual similarity metrics:\\n simm_fh = open(self.f['similarity'](sign=self.signature), 'w')\\n for (mdx, fdx), s in sorted(simmilarity.iteritems()):\\n simm_fh.write(\\\"%d %d %f\\\\n\\\" % (mdx, fdx, s))\\n simm_fh.close()\",\n \"def distance(dataset: Dataset) -> Dict[str, List[Tuple[int, float]]]:\\n distances: Dict[str, List[Tuple[int, float]]] = {}\\n\\n images = dataset.images\\n annotations = dataset.annotations\\n\\n for image in images:\\n distances[image] = []\\n image_area = img_area(image)\\n\\n for detection in annotations[image]:\\n distances[image].append((detection.class_index, detection.bounds.area / image_area))\\n\\n return distances\",\n \"def _calc_distances(preds, targets, mask, normalize):\\n N, K, _ = preds.shape\\n _mask = mask.copy()\\n _mask[np.where((normalize == 0).sum(1))[0], :] = False\\n distances = np.full((N, K), -1, dtype=np.float32)\\n normalize[np.where(normalize <= 0)] = 1000000.0\\n distances[_mask] = np.linalg.norm(((preds - targets) / normalize[:, None, :])[_mask], axis=-1)\\n return distances.T\",\n \"def compute_distmat(data, distfn):\\n out = np.zeros((data.shape[0], data.shape[0]))\\n for i in xrange(data.shape[0]):\\n for j in xrange(data.shape[0]):\\n if i == j: continue\\n out[i,j] = distfn(data[i,:,:], data[j,:,:])\\n return out\",\n \"def generate_pairs(patches, constants):\\n k_nearest = constants.K_NEAREST\\n num_patches = constants.NUM_QUERY_PATCHES\\n scaled_imgs = len(patches)\\n\\n pairs = []\\n query_database = []\\n candidate_database = []\\n index_database = []\\n length_database = []\\n for k in range(scaled_imgs):\\n qp = [patch.norm_patch for patch in patches[k] if 7 <= patch.bucket <= 9]\\n qi = [index for index, patch in enumerate(patches[k]) if 7 <= patch.bucket <= 9]\\n\\n # Choose lesser query patches through random selection to improve speed\\n if len(qi) > num_patches:\\n np.random.seed(0)\\n selection = np.random.choice(np.arange(len(qi)), num_patches, replace=False).tolist()\\n selection.sort()\\n query_patches = [qp[i] for i in selection]\\n query_indices = [qi[i] for i in selection]\\n else:\\n query_patches = qp\\n query_indices = qi\\n\\n query_database.append(\\n np.vstack([query_patches])\\n )\\n index_database.append(query_indices)\\n length_database.append(len(query_indices))\\n candidate_database.append(\\n np.vstack([[patch.norm_patch for i, patch in enumerate(patches[k]) if 0 <= patch.bucket <= 5]])\\n )\\n\\n p1 = np.concatenate(candidate_database)\\n kdt = KDTree(p1, leaf_size=30, metric='euclidean')\\n\\n # Find list of nearest neighbours for each patch\\n # `total` is used to correct indices of queried patches for every iteration\\n total = 0\\n for k in range(scaled_imgs):\\n nn = kdt.query(query_database[k], k=k_nearest, return_distance=False, sort_results=False)\\n q = [total + index_database[k][i] for i in range(length_database[k])]\\n for i in range(len(nn)):\\n for j in range(k_nearest):\\n pairs.append([q[i], nn[i][j]])\\n total += len(patches[k])\\n\\n return pairs\",\n \"def get_distances(self):\\n N = len(self.cells) # Number of cells\\n distances = np.zeros([N, N]) # distances between cells\\n positions = self.position_matrix() # positions of cells \\n \\n # get distances between cells (exploit symmetry between upper and lower triangular form)\\n for i, position in enumerate(positions[:-1, :]): # Iterate matrix except the last one\\n directions = positions[i+1:, :] - position # direction from i to j > i\\n distances[i, i+1:] = np.linalg.norm(directions, axis=1) # length of directions\\n \\n return distances + distances.T # Add lower triangle of matrix to upper \",\n \"def calculate_dist_mat(embeddings: np.ndarray, norm: int) -> np.ndarray:\\n kwargs = {'p': norm}\\n condensed_dist = pdist(embeddings, metric='minkowski', **kwargs)\\n dist_mat = squareform(condensed_dist)\\n return dist_mat\",\n \"def calculate_distance_mesh(directory, metric='l2'):\\n print('Starting mesh distance calculation.')\\n shapes_files = glob(directory + '*.stl')\\n shapes_files.extend(glob(directory + '*.ply'))\\n shapes_files.extend(glob(directory + '*.obj'))\\n\\n shapes_files.sort(key=functools.cmp_to_key(sort_by_sample_id))\\n shapes_list = []\\n for shape_index, shape in enumerate(shapes_files):\\n print('Opening shape %i / %i' % (shape_index, len(shapes_files)), end='\\\\r')\\n shape_mesh = trimesh.load_mesh(shape, process=False)\\n vertices = shape_mesh.vertices\\n vertices = vertices.reshape((1, -1))\\n shapes_list.append(vertices)\\n all_shapes_array = np.row_stack(shapes_list)\\n print('Calling pairwise distance.')\\n return pairwise_distances(all_shapes_array, metric=metric)\",\n \"def Distmatrix(self):\\n self.Dismatrix = np.zeros((self.nodenum, self.nodenum))\\n for i in range(len(self.Dismatrix)):\\n for j in range(len(self.Dismatrix)):\\n self.Dismatrix[i, j] = sf.dist(self.y[i], self.x[i], self.y[j], self.x[j])\\n self.Dismatrix[j, i] = self.Dismatrix[i, j]\",\n \"def calcDist(self):\\n rhoOp = self.rhoOp\\n s = np.array([[1,0,0],[0,-1,0],[0,0,1]])\\n sAdj = s.conj().T \\n symRhoOp = np.dot(s,np.dot(rhoOp,sAdj))\\n self.dist = Node.S1(rhoOp, symRhoOp)\",\n \"def _faceToCellDistanceRatio(self):\\n XYdis = numerix.zeros((self.nx, self.ny, self.nz + 1), 'd')\\n XYdis[:] = 0.5\\n XYdis[..., 0] = 1\\n XYdis[..., -1] = 1\\n\\n XZdis = numerix.zeros((self.nx, self.ny + 1, self.nz), 'd')\\n XZdis[:] = 0.5\\n XZdis[:, 0,:] = 1\\n XZdis[:, -1,:] = 1\\n\\n YZdis = numerix.zeros((self.nx + 1, self.ny, self.nz), 'd')\\n YZdis[:] = 0.5\\n YZdis[ 0, ...] = 1\\n YZdis[-1, ...] = 1\\n\\n return numerix.concatenate((numerix.ravel(XYdis.swapaxes(0, 2)),\\n numerix.ravel(XZdis.swapaxes(0, 2)),\\n numerix.ravel(YZdis.swapaxes(0, 2))))\",\n \"def get_dist_mat(self):\\n n_site = self.status.give(keyword=\\\"n_site\\\")\\n sites = self.status.give(keyword=\\\"sites\\\")\\n dist_mat = [[0.0 for j in xrange(n_site)] for i in xrange(n_site)]\\n for i in xrange(n_site):\\n for j in xrange(n_site):\\n ri = sites[i].pos\\n rj = sites[j].pos\\n dist_mat[i][j] = np.linalg.norm(ri-rj)\\n # print ri, rj\\n return dist_mat\",\n \"def mpd(distmat):\\r\\n return distmat.sum() / (distmat.size - distmat.shape[0])\",\n \"def compute_wall_distance(self):\\n phi = sp.ones(self.image_red.shape)\\n if (len(self.mask_id[0])>0):\\n phi[self.mask_id] = 0\\n self.wall_distance = skfmm.distance(phi, dx=self.pixel_size)\\n grad = sp.gradient(self.wall_distance,edge_order=2)\\n grad_X = grad[1]/self.pixel_size\\n grad_Y = grad[0]/self.pixel_size\\n norm = sp.sqrt(grad_X**2+grad_Y**2)\\n norm = (norm>0)*norm+(norm==0)*0.001\\n self.wall_grad_X = grad_X/norm\\n self.wall_grad_Y = grad_Y/norm\\n else:\\n self.wall_distance = 1.0e99*sp.ones(self.image_red.shape)\",\n \"def _generate_distance_kernel_matrix(self):\\n with self._rw_lock.read_lock():\\n # Create matrix whose elements are the distances between all row\\n # permutations\\n fmat = self._feature_mat # shorter name\\n num_rows = fmat.shape[0]\\n\\n # distance kernel is a square matrix based on feature samples\\n dist_kernel = np.mat(np.ndarray((num_rows,)*2))\\n self._log.info(\\\"Creating distance kernel with shape %s\\\",\\n dist_kernel.shape)\\n\\n timer_log = logging.getLogger('.'.join((self.__module__,\\n self.__class__.__name__,\\n \\\"SimpleTimer\\\")))\\n\\n for i in xrange(num_rows - 1):\\n with SimpleTimer('computing distances from row %d to [%d-%d]'\\n % (i, i+1, num_rows-1), timer_log):\\n dist_kernel[i, i] = 1.0\\n for j in xrange(i + 1, num_rows):\\n dist = self._histogram_intersection_distance(fmat[i],\\n fmat[j])\\n dist_kernel[i, j] = dist_kernel[j, i] = dist\\n dist_kernel[-1, -1] = 1.0\\n return dist_kernel\",\n \"def create_dist_mat(predims, postdims, dist_metric):\\n\\n filename = './data/dist_matrix_' + dim2str(predims) + '_' + dim2str(postdims) + '_' + dist_metric.__name__ + '.npz'\\n if os.path.isfile(filename):\\n print('file existst: loading dist mat from file: ', filename)\\n with np.load(filename) as loaded:\\n dist_mat = loaded['dist_mat']\\n else:\\n xbins_pre = eq_dist_linspace(predims[0])\\n ybins_pre = eq_dist_linspace(predims[1])\\n x_pre, y_pre = np.meshgrid(xbins_pre, ybins_pre)\\n\\n xbins_post = eq_dist_linspace(postdims[0])\\n ybins_post = eq_dist_linspace(postdims[1])\\n x_post, y_post = np.meshgrid(xbins_post, ybins_post)\\n\\n pre_coordinates = np.asarray(list(zip(flatten(x_pre), flatten(y_pre))))\\n post_coordinates = np.asarray(list(zip(flatten(x_post), flatten(y_post))))\\n\\n dist_metric = partial(dist_metric, weights=postdims)\\n dist_mat = distance.cdist(post_coordinates, pre_coordinates, metric=dist_metric)\\n # dist_mat = distance_matrix(pre_coordinates,post_coordinates)\\n\\n if not os.path.exists(os.path.dirname(filename)):\\n os.makedirs(os.path.dirname(filename))\\n print('created directory', os.path.dirname(filename), 'in', os.path.abspath(os.path.dirname(filename)))\\n np.savez(filename, dist_mat=dist_mat)\\n\\n return dist_mat\",\n \"def ni_dist_mat(self):\\n mat = np.zeros([self.N, self.I])\\n for n in range(self.N):\\n for i in range(self.I):\\n mat[n, i] = distance(self.N_coords[n], self.I_coords[i])\\n return mat\",\n \"def get_patches(rimage, gimage, mimage, num_patches=48, patch_size=80, patch_stride=80):\\n num_FSpatches = 16\\n num_RApatches = 32\\n rpatches = []\\n gpatches = []\\n mpatches = []\\n #R_imgs = ((rimage+1)*127.5).astype(np.uint8)\\n #scipy.misc.imsave('results'+'/' + 'rainy.jpg', R_imgs[0,:,:,:])\\n for i in range(int(math.sqrt(num_FSpatches))):\\n for j in range(int(math.sqrt(num_FSpatches))):\\n point_x = patch_stride*i\\n point_y = patch_stride*j\\n rpatch = rimage[:,(point_x):(point_x+patch_size), (point_y):(point_y+patch_size),:]\\n #print(point_x)\\n #print(point_y)\\n #print(point_y+patch_size)\\n #P_imgs = ((rpatch+1)*127.5).astype(np.uint8)\\n #scipy.misc.imsave('results'+'/' + 'patch_%d_%d.jpg'%(i,j), P_imgs[0,:,:,:])\\n #print(rpatch.shape)\\n rpatches.append(rpatch)\\n #print(np.array(rpatches).shape)\\n gpatch = gimage[:,(point_x):(point_x+patch_size), (point_y):(point_y+patch_size),:]\\n gpatches.append(gpatch)\\n mpatch = mimage[:,(point_x):(point_x+patch_size), (point_y):(point_y+patch_size),:]\\n mpatches.append(mpatch)\\n\\n for k in range(num_RApatches):\\n point1 = random.randint(0,240) # 116 comes from the image source size (320) - the patch dimension (80)\\n point2 = random.randint(0,240)\\n #rpatch = tf.image.crop_to_bounding_box(rimage, point1, point2, patch_size, patch_size)\\n rpatch = rimage[:,(point1):(point1+patch_size), (point2):(point2+patch_size),:]\\n #P_imgs = ((rpatch+1)*127.5).astype(np.uint8)\\n #scipy.misc.imsave('results'+'/' + 'patch_%d.jpg'%i, P_imgs[0,:,:,:])\\n #print(rpatch.shape)\\n rpatches.append(rpatch)\\n #print(np.array(rpatches).shape)\\n gpatch = gimage[:,(point1):(point1+patch_size), (point2):(point2+patch_size),:]\\n gpatches.append(gpatch)\\n mpatch = mimage[:,(point1):(point1+patch_size), (point2):(point2+patch_size),:]\\n mpatches.append(mpatch)\\n\\n rpatches = np.array(rpatches)\\n rpatches = np.squeeze(rpatches)\\n #print(rpatches.shape)\\n gpatches = np.array(gpatches)\\n gpatches = np.squeeze(gpatches)\\n mpatches = np.array(mpatches)\\n mpatches = np.squeeze(mpatches)\\n #assert rpatches.get_shape().dims == [num_patches, patch_size, patch_size, 3]\\n assert rpatches.shape == (num_patches, patch_size, patch_size, 3)\\n return rpatches, gpatches, mpatches\",\n \"def nm_dist_mat(self):\\n mat = np.zeros([self.N, self.M])\\n for n in range(self.N):\\n for m in range(self.M):\\n mat[n, m] = distance(self.N_coords[n], self.M_coords[m])\\n return mat\",\n \"def ratio_cut_laplacian(affinity_mat):\\n D_mat = np.zeros(affinity_mat.shape)\\n d_trace = np.sum(affinity_mat, axis=0)\\n D_mat.flat[::D_mat.shape[0]+1] = d_trace\\n return D_mat - affinity_mat\",\n \"def _distance_matrix(self):\\n\\n # Log the type of metric being used in Sequencing\\n logger.info('Using {} Distance'.format(self.measure))\\n\\n # Convert the nodal coordinate tuples to a np.array\\n coords = np.vstack(map(np.array, self.coords.values()))\\n \\n if self.measure == 'haversine':\\n # Partially applied haversine function that takes a coord and computes the vector distances for all coords\\n haversine = lambda coord: get_hav_distance(coords[:, 0], coords[:, 1], *coord) \\n # Map the partially applied function over all coordinates, and stack to a matrix\\n return np.vstack(map(haversine, coords))\\n\\n # Partially applied haversine function that takes a coord and computes the vector distances for all coords\\n euclidean = lambda coord: get_euclidean_dist(coords, coord)\\n # Map the partially applied function over all coordinates, and stack to a matrix\\n return np.vstack(map(euclidean, coords))\",\n \"def distancePairs(self):\\n return spsd.squareform(spsd.pdist(self.coordinates()))\",\n \"def distance(self, f):\\n return [(f[0] + (f[2])*self.target[0])/self.im_w - self.tpw,\\n (f[1] + (f[3])*self.target[1])/self.im_h - self.tph]\",\n \"def _calc_distance(self, X):\\n distances = np.zeros((X.shape[0], self.n_clusters))\\n print(distances.shape)\\n for i, centroid in enumerate(self.centroids):\\n distances[:, i] = np.linalg.norm(X - centroid, axis=1)\\n return distances\",\n \"def test_poincare_distances_batch(self):\\n vector_1 = self.vectors['dog.n.01']\\n vectors_2 = self.vectors[['mammal.n.01', 'dog.n.01']]\\n distances = self.vectors.vector_distance_batch(vector_1, vectors_2)\\n self.assertTrue(np.allclose(distances, [4.5278745, 0]))\",\n \"def getDistanceMatrix(self):\\n return self.pointcloud.distmat\",\n \"def get_patches(df, data_dir, img_type = \\\"ADC\\\", data_type = \\\"Train\\\"):\\n print(\\\"Cleaning Output Directory\\\")\\n os.system(\\\"rm -rf %s\\\" % (data_dir + \\\"/patches/\\\" + img_type ) )\\n os.system(\\\"mkdir %s\\\" % (data_dir+\\\"/patches/\\\" + img_type) )\\n os.system(\\\"mkdir %s\\\" % (data_dir+\\\"/patches/\\\" + img_type + \\\"/POS\\\") )\\n os.system(\\\"mkdir %s\\\" % (data_dir+\\\"/patches/\\\" + img_type + \\\"/NEG\\\") )\\n\\n hlen = 113\\n header = img_type + \\\" - \\\" + data_type\\n prl = (hlen//2-len(header)//2) - 1\\n prr = hlen - prl - len(header) - 2\\n print(\\\"-\\\"*hlen)\\n print(\\\"#\\\" + \\\" \\\"*prl + header + \\\" \\\"*prr + \\\"#\\\")\\n print(\\\"-\\\"*hlen)\\n\\n print(\\\"{0:<15s}\\\\t{1:>5s}\\\\t{2:>10s}\\\\t{3:>10s}\\\\t{4:>6s}\\\\t{5:>6s}\\\\t{6:>6s}\\\\t{7:>6s}\\\"\\\\\\n .format(\\\"PID\\\", \\\"#Find\\\", \\\"#Patch\\\", \\\"#Positive\\\", \\\"#Negative\\\", \\\"Load time\\\", \\\"Extract Time\\\", \\\"Save Time\\\"))\\n overall = time.time()\\n for pid in df.ProxID.unique():\\n load_start = time.time()\\n print(\\\"{0:<15s}\\\"\\n .format(\\\"Loading\\\"), end = \\\"\\\\r\\\", flush = True)\\n\\n temp = df.loc[df.ProxID == pid]\\n try:\\n # Get ADC, BVAL Dirs\\n names = temp.Name.values\\n adc_dir = temp[[name.endswith(\\\"ADC0\\\") for name in names]].DCMSerUID.values[0]\\n bval_dir = temp[[name.endswith(\\\"BVAL0\\\") for name in names]].DCMSerUID.values[0]\\n dicom_find = list(temp[[name.endswith(\\\"BVAL0\\\") for name in names]].ijk.unique())\\n dicom_find = [[int(coord) for coord in f.split(\\\" \\\")] for f in dicom_find]\\n\\n except:\\n continue\\n\\n dicom_path = data_dir + \\\"/\\\".join([\\\"/raw/dicom\\\", pid])\\n # ADC Processing\\n if img_type == \\\"ADC\\\":\\n imgs, findings = load_dicom(dicom_path, adc_dir, dicom_find)\\n elif img_type == \\\"BVAL\\\":\\n imgs, findings = load_dicom(dicom_path, bval_dir, dicom_find)\\n elif img_type == \\\"KTRANS\\\":\\n ktrans_path = data_dir + \\\"/\\\".join([\\\"/raw/ktrans\\\", pid, pid])\\n imgs = load_ktrans(ktrans_path)\\n findings = []\\n else:\\n print(\\\"image type not defined! ABORT\\\")\\n return -1\\n\\n ltime = time.time() - load_start\\n\\n ext_start = time.time()\\n fcount = len(dicom_find)\\n print(\\\"{0:<15s}\\\\t{1:>5d}\\\".format(\\\"Extracting\\\", fcount), end = \\\"\\\\r\\\", flush = True)\\n if img_type == \\\"ADC\\\" or img_type == \\\"BVAL\\\":\\n # Patches without, Pathces with findings\\n pwof, pwf = extract_patches(imgs, PATCH_SHAPE, findings)\\n elif img_type == \\\"KTRANS\\\":\\n print(\\\"KTRANS PROCESSING NOT YET IMPLEMENTED! ABORT\\\")\\n return -1\\n etime = time.time() - ext_start\\n\\n\\n save_start = time.time()\\n\\n # Printing\\n p = len(pwf)\\n n = len(pwof)\\n\\n print(\\\"{0:<15s}\\\\t{1:>5d}\\\\t{2:>10s}\\\\t{3:>10s}\\\\t {4:>6s}\\\\t {5:>6s}\\\\t {6:>6s}\\\"\\\\\\n .format(\\\"Saving\\\", fcount, str(p+n), str(p), str(n), str(ltime)[:6], str(etime)[:6]), end = \\\"\\\\r\\\", flush = True)\\n\\n pwf = np.array(pwf)\\n pwof = np.array(pwof)\\n n = save_patches(pid, fcount, data_dir, img_type, pwf, pwof)\\n\\n stime = time.time() - save_start\\n print(\\\"{0:15s}\\\\t{1:>5d}\\\\t{2:>10s}\\\\t{3:>10s}\\\\t {4:>6s}\\\\t {5:>6s}\\\\t {6:>6s}\\\\t {7:>6s}\\\"\\\\\\n .format(pid, fcount, str(n+n), str(n), str(n), str(ltime)[:6], str(etime)[:6], str(stime)[:6]))\\n\\n #break\\n\\n otime = time.time() - overall\\n\\n print(\\\"-\\\"*hlen)\\n header = \\\"time taken:\\\" + \\\"{0:4.5f}\\\".format(otime)\\n prl = (hlen//2-len(header)//2) - 1\\n prr = hlen - prl - len(header) - 2\\n print(\\\"#\\\" + \\\" \\\"*prl + header + \\\" \\\"*prr + \\\"#\\\")\\n print(\\\"-\\\"*hlen)\\n\\n return 0\",\n \"def distance_transform(img):\\n dist_transform = cv2.distanceTransform(img, cv2.DIST_L2, 5)\\n return dist_transform\",\n \"def get_distance_matrix(self, points):\\n return points[:, :, np.newaxis, :]-points[:, np.newaxis, :, :]\",\n \"def _extract_patches(img, patch_s):\\n def np_extract_patches(img):\\n orig = np.array(img.shape[:2])\\n new = patch_s[0] * np.ceil(orig / patch_s[0]).astype(int)\\n points = new - orig\\n img = np.pad(img, [(0, points[0]), (0, points[1]), (0, 0)],\\n mode='constant')\\n patches = view_as_blocks(img, tuple(patch_s)).astype(np.float32)\\n patches = patches.reshape(-1, *patch_s)\\n return patches\\n\\n patches = tf.numpy_function(np_extract_patches, [img], tf.float32)\\n return patches\",\n \"def tomatrix(self, ai_patch):\\n V = self.space\\n# print \\\"------------\\\"\\n# print \\\"geo.npatchs : \\\", V.geometry.npatchs\\n# print \\\"patch id : \\\", ai_patch\\n# print \\\"dim : \\\", V.dim\\n# print \\\"shape : \\\", V.geometry[ai_patch].shape\\n if V.dim == 1 :\\n [li_n_1] = V.geometry[ai_patch].shape\\n return self.com.pyfem.field_to_matrix_1d ( self.id, ai_patch \\\\\\n , li_n_1 )\\n if V.dim == 2 :\\n [li_n_1, li_n_2] = V.geometry[ai_patch].shape\\n return self.com.pyfem.field_to_matrix_2d ( self.id, ai_patch \\\\\\n , li_n_1, li_n_2 )\\n if V.dim == 3 :\\n [li_n_1, li_n_2, li_n_3] = V.geometry[ai_patch].shape\\n return self.com.pyfem.field_to_matrix_3d ( self.id \\\\\\n , ai_patch, li_n_1, li_n_2, li_n_3 )\",\n \"def point_distances(src_points, gt_points):\\n distances = EuclideanDistances(np.matrix(src_points), np.matrix(gt_points))\\n return np.array(distances)\",\n \"def _calculate_distances(self):\\n all_dists = []\\n for ref in range(len(self.atoms)):\\n if self.atoms[ref].symbol in self.exclude:\\n continue\\n indices = list(range(ref+1, len(self.atoms)))\\n indices = self._filter_excluded(indices)\\n if len(indices) == 0:\\n continue\\n dists = self.atoms.get_distances(ref, indices, mic=True)\\n all_dists += list(dists)\\n \\n # Normalize by the mean distance\\n return np.array(all_dists)/np.mean(all_dists)\",\n \"def compute_distances(Ls):\\n if not isinstance(Ls, list):\\n Ls = [Ls]\\n\\n dists = []\\n for L in Ls:\\n N,D = L.shape\\n # 1xNxD - Nx1xD (L1 distance)\\n dist = (np.abs(L[None,:,:] - L[:,None,:])).sum(axis=2)\\n dists.append(dist)\\n\\n return dists\",\n \"def distance_map(self, scaling='sum'):\\n\\n if scaling not in ['sum', 'mean']:\\n raise ValueError(f'scaling should be either \\\"sum\\\" or \\\"mean\\\" ('\\n f'\\\"{scaling}\\\" not valid)')\\n\\n um = nan * zeros((self._weights.shape[0],\\n self._weights.shape[1],\\n 8)) # 2 spots more for hexagonal topology\\n\\n ii = [[0, -1, -1, -1, 0, 1, 1, 1]]*2\\n jj = [[-1, -1, 0, 1, 1, 1, 0, -1]]*2\\n\\n if self.topology == 'hexagonal':\\n ii = [[1, 1, 1, 0, -1, 0], [0, 1, 0, -1, -1, -1]]\\n jj = [[1, 0, -1, -1, 0, 1], [1, 0, -1, -1, 0, 1]]\\n\\n for x in range(self._weights.shape[0]):\\n for y in range(self._weights.shape[1]):\\n w_2 = self._weights[x, y]\\n e = y % 2 == 0 # only used on hexagonal topology\\n for k, (i, j) in enumerate(zip(ii[e], jj[e])):\\n if (x+i >= 0 and x+i < self._weights.shape[0] and\\n y+j >= 0 and y+j < self._weights.shape[1]):\\n w_1 = self._weights[x+i, y+j]\\n um[x, y, k] = fast_norm(w_2-w_1)\\n\\n if scaling == 'mean':\\n um = nanmean(um, axis=2)\\n if scaling == 'sum':\\n um = nansum(um, axis=2)\\n\\n return um/um.max()\",\n \"def __surface_distances(result, reference, voxelspacing=None, connectivity=1):\\n result = np.atleast_1d(result.astype(np.bool))\\n reference = np.atleast_1d(reference.astype(np.bool))\\n if voxelspacing is not None:\\n voxelspacing = _ni_support._normalize_sequence(voxelspacing, result.ndim)\\n voxelspacing = np.asarray(voxelspacing, dtype=np.float64)\\n if not voxelspacing.flags.contiguous:\\n voxelspacing = voxelspacing.copy()\\n \\n # binary structure\\n footprint = generate_binary_structure(result.ndim, connectivity)\\n \\n # test for emptiness\\n if 0 == np.count_nonzero(result): \\n raise RuntimeError('The first supplied array does not contain any binary object.')\\n if 0 == np.count_nonzero(reference): \\n raise RuntimeError('The second supplied array does not contain any binary object.') \\n \\n # extract only 1-pixel border line of objects\\n result_border = result ^ binary_erosion(result, structure=footprint, iterations=1)\\n # print(result_border+0)\\n # result_border = (result_border+0).astype(np.float32)\\n # imsave(\\\"./test_comp1/\\\" +\\\"truth.jpg\\\", result_border)\\n reference_border = reference ^ binary_erosion(reference, structure=footprint, iterations=1)\\n # print(reference_border)\\n # reference_border = (reference_border + 0).astype(np.float32)\\n # imsave(\\\"./test_comp1/\\\" +\\\"truth.jpg\\\", reference_border)\\n # compute average surface distance \\n # Note: scipys distance transform is calculated only inside the borders of the\\n # foreground objects, therefore the input has to be reversed\\n dt = distance_transform_edt(~reference_border, sampling=voxelspacing)\\n # print(dt)\\n reference_border = (reference_border + 0).astype(np.float32)\\n # imsave(\\\"./test_comp1/\\\" +\\\"truth.jpg\\\", reference_border)\\n sds = dt[result_border]\\n \\n return sds\",\n \"def fun(x, *args):\\n distance = a.normalized_distance(x.reshape(shape))\\n return distance.value, distance.gradient.reshape(-1)\",\n \"def compute_distance(self, data):\\n logger.info('Computing Distances')\\n self.num_points = len(data)\\n # TODO: Maybe make this into a single mapping function and remove elif\\n # ladder\\n if self.distance == 'mahalanobis':\\n data = np.exp(-1 * data / data.std())\\n self.conden_dist_mat = pdist(data, self.distance)\\n elif self.distance in ['euclidean', 'cosine']:\\n self.conden_dist_mat = pdist(data, self.distance)\\n elif self.distance == 'chisqr':\\n self.conden_dist_mat = pdist(data, chisqr)\\n elif self.distance == 'intersection':\\n self.conden_dist_mat = pdist(data, intersection)\\n else:\\n raise ValueError(\\\"distance type not supported\\\")\\n self._cleanse_dist_mat()\",\n \"def calculate_and_set_propagation_distances(self):\\n\\n self.l_edge = self.calculate_distance_edge()\\n self.l_int = self.calculate_distance_interaction()\",\n \"def dist_matrix(self, group1, group2):\\n \\n tmps = []\\n for i in group2:\\n tmps.append([])\\n for j in group1:\\n mi, label = self.distance(i, j)\\n tmps[-1].append(mi)\\n return tmps\",\n \"def measure_distance(self, mat):\\n if len(mat) == 1:\\n print(\\\"chain has only one CAatom\\\")\\n return\\n self.dists = []\\n for num in range(0, len(mat)):\\n if num + 1 <= len(mat) - 1:\\n c1 = mat[num]\\n c2 = mat[num + 1]\\n d = c2 - c1\\n self.dists.append(math.sqrt(np.sum(d * d)))\\n return self.dists\",\n \"def getDistanceRaster(input_img, input_mode, output_path=None):\\n\\n if input_mode == 'fp':\\n np_img = loadImage(input_img)\\n elif input_mode == 'np':\\n np_img = input_img\\n else:\\n return (input_mode, \\\" is not a supported mode. Supported modes are 'np' or 'fp'.\\\")\\n np_dist = ndimage.distance_transform_edt(np.logical_not(np_img))\\n if output_path is not None:\\n saveImage(np_dist, output_path, mode='dist_norm')\\n return(np_dist)\",\n \"def densMap(fit, metricArray, n, overlap, imageSize, imageName):\\n \\n if True: #Make true if you want to calculate densities (i.e. if you have a new fit/new density list) \\n print 'calculating densities'\\n if type(fit)==sklearn.gaussian_process.gaussian_process.GaussianProcess: #If the fit is Gaussian\\n densities, MSE = fit.predict(metricArray, eval_MSE =True)\\n sigma = numpy.sqrt(MSE)\\n print(sigma)\\n else:\\n densities = fit.predict(metricArray) #Use the ML fit to predict the density for each tile \\n #print len(densities)\\n f = open('densities.txt', 'w')\\n print >> f, list(densities)\\n f.close()\\n else: \\n f = open('densities.txt', 'r')\\n data = f.read()\\n densities = eval(data)\\n # print densities\\n densities = numpy.array(densities)\\n f.close()\\n imageName = 'FirstStitchLong.jpg'\\n overlap = 0.2 \\n n = 50\\n image = Image.open(imageName) #open the image\\n imageSize = image.size #get the image size \\n # print 'Image Size is ', imageSize\\n \\n\\n width = imageSize[0] \\n height = imageSize[1] \\n # print 'width ', width \\n #print 'height ', height\\n overlapSize = int(overlap*n)\\n rowTiles = int((width-n)/(overlapSize))+1\\n print 'Row Tiles is ', rowTiles\\n #colTiles = int(((height-n)/(overlapSize))+1)\\n \\n \\n # newArray = numpy.zeros((int((width-n)/(overlapSize)+1),int(((height-n)/(overlapSize))+1)))\\n \\n #newArray = numpy.zeros(width, height)\\n #print newArray\\n points = []\\n for i in range(len(densities)):\\n x = (i%rowTiles)*overlapSize + n/2\\n y = (i/rowTiles)*overlapSize + n/2\\n points += [[x,y]]\\n # newArray[i%(rowTiles),i/(rowTiles)] = densities[i]\\n # newArray[(i%(rowTiles)+1)*(n/2) , (i/(rowTiles)+1)*(n/2)] = densities[i]\\n # points += [[(i%(rowTiles)+1)*(n/2), (i/(rowTiles)+1)*(n/2)]] #Consider the density to be at the center of each tile \\n # print points\\n grid_x, grid_y = numpy.mgrid[0:width, 0:height] #create a meshgrid \\n \\n # print densities\\n # print('Points is ', points) \\n # print('Densities is ', densities)\\n data = griddata(points, densities, (grid_x, grid_y), method = 'linear') #interpolate to get continuous function of points \\n #can change interpolation method\\n print data.size\\n\\n # print 'Max is ', numpy.amax(data)\\n # min_max_scaler = sklearn.preprocessing.MinMaxScaler()\\n #data_minmax = min_max_scaler.fit_transform(data) \\n \\n \\n \\n ##Plotting \\n plt.figure(1)\\n \\n #plt.figure(figsize = (width, height))\\n # print \\\"Hi Cassie\\\"\\n v = numpy.linspace(min(densities), max(densities), 20, endpoint=True)\\n fig = plt.contourf(grid_x, grid_y, data, levels = v, alpha = 0.4, antialiased = True)\\n \\n mapIm = Image.open(imageName)\\n plt.imshow(mapIm)\\n maxDens = max(densities)\\n print maxDens \\n x = plt.colorbar(fig) \\n plt.savefig('ContourPlot.jpg')\\n return data\",\n \"def distance(self):\\n try:\\n import pdb\\n pdb.set_trace()\\n s = []\\n x0,y0 = self.deriv.T\\n for thing in everything:\\n x1,y1 = thing.deriv.T\\n r,p = pearsonr(y0,y1)\\n s.append(( p,thing.label ))\\n s.sort()\\n #print s[-5:]\\n print s\\n except:\\n return np.inf\",\n \"def distance_matrix(data):\\n D = numpy.zeros( (data.shape[0], data.shape[0]) )\\n for i in xrange(data.shape[0]):\\n for j in xrange(i):\\n D[i,j] = numpy.linalg.norm(data[i,:]-data[j,:])\\n D[j,i] = D[i,j]\\n\\n return D\",\n \"def compute_distance(self, transpose=False):\\n\\n # Calculate distance matrix\\n if transpose:\\n distance_matrix = pdist(self.matrix.T, self.distance)\\n else:\\n distance_matrix = pdist(self.matrix, self.distance)\\n\\n # Remove NaNs\\n distance_matrix[np.isnan(distance_matrix)] = 1.0\\n\\n return distance_matrix\",\n \"def match3(img1, img2, coordinates1, coordinates2, PATCH_SIZE, threshold=0.7):\\n\\n\\t#creating patches for all points from img1 and img2\\n\\tcoord1_patches = [make_patch(coordinate, PATCH_SIZE, img1) for coordinate in coordinates1]\\n\\tcoord2_patches = [make_patch(coordinate, PATCH_SIZE, img2) for coordinate in coordinates2]\\n\\n\\t# creating a matrix with dissimilarity measures for all pairs\\n\\tall_matches = np.zeros((len(coordinates1), len(coordinates2)))\\n\\n\\tfor (x, y), _ in np.ndenumerate(all_matches):\\n\\t\\tall_matches[x,y] = count_difference(coord1_patches[x], coord2_patches[y])\\n\\n\\t#looking for best left-to-right and right-to-left matches\\n\\tmatches = []\\n\\t#left-to-right\\n\\tfor i, coord1 in enumerate(coordinates1):\\n\\t\\tbest_ltr_match = np.argmin(all_matches[i, :]) #best left-to-right match for coord1\\n\\t\\tbest_rtl_match = np.argmin(all_matches[:, best_ltr_match]) #best match for a best match\\n\\t\\tif (i == best_rtl_match): #hurray, there is a super match\\n\\n\\t\\t\\tmatches.append([coord1, coordinates2[best_ltr_match], all_matches[i, best_ltr_match]])\\n\\t\\n\\treturn matches\",\n \"def get_distances(self, crds):\\n self.all_dist = np.zeros((self.natom, self.natom))\\n # Loop over upper triangle of atom pairs\\n for iat in range(self.natom-1):\\n # Get the atom indices\\n at_inds = np.arange(len(crds))\\n\\n # Calc distances between atoms (only upper triangle though)\\n at_msk = at_inds > iat\\n all_ut_dist = crds[at_msk] - crds[iat]\\n all_ut_dist = np.linalg.norm(all_ut_dist, axis=1)\\n\\n self.all_dist[iat, iat+1:] = all_ut_dist\\n\\n # Get lower triangle indices\\n self.all_dist = self.all_dist + self.all_dist.T\",\n \"def dist_calc(self,Xtest):\\n d,i = self.knn.kneighbors(Xtest)\\n\\n self.dist = d\\n self.ind = i\\n\\n self.Xtest = Xtest\",\n \"def dist_calc(self,Xtest):\\n d,i = self.knn.kneighbors(Xtest)\\n\\n self.dist = d\\n self.ind = i\\n\\n self.Xtest = Xtest\",\n \"def train_patches(lmks, imgs, ref, psize, ssize, var=1.0, lmbda=1e-6, mu_init=1e-3, nsamples=1000):\\n\\n if isinstance(psize, int):\\n psize = (psize, psize)\\n if isinstance(ssize, int):\\n ssize = (ssize, ssize)\\n\\n n = len(ref) // 2\\n ximg = psize[1] + ssize[1]\\n yimg = psize[0] + ssize[0]\\n wsize = (yimg, ximg)\\n\\n patches = []\\n\\n # train each patch model\\n for i in range(n):\\n print('patch', i+1, 'of', n, '...')\\n images = []\\n for j in range(lmks.shape[1]):\\n im = imgs[j]\\n pt = lmks[:,j]\\n S = calc_simil(pt, ref)\\n A = np.empty((2,3))\\n A[:2,:2] = S[:2,:2]\\n A[0,2] = pt[2*i] - (A[0,0] * (ximg-1)/2 + A[0,1] * (yimg-1)/2)\\n A[1,2] = pt[2*i+1] - (A[1,0] * (ximg-1)/2 + A[1,1] * (yimg-1)/2)\\n I = cv2.warpAffine(im, A, wsize, flags=cv2.INTER_LINEAR+cv2.WARP_INVERSE_MAP)\\n images.append(I)\\n\\n patch = train_patch(images, psize, var, lmbda, mu_init, nsamples)\\n patches.append(patch)\\n\\n return np.array(patches)\",\n \"def run(self):\\n #calculate platescale of first input image\\n try:\\n det = np.linalg.det(wcs.WCS(self.datain[0].header).wcs.cd)\\n pscale = np.sqrt(np.abs(det))*3600.\\n except:\\n try:\\n det = np.linalg.det(wcs.WCS(self.datain[0].header).wcs.pc)\\n pscale = np.sqrt(np.abs(det))*3600.\\n except:\\n pscale = self.datain[0].header['PIXSCAL']\\n #filtering out images which are too far away from the others\\n #passing images added to a list of (image, WCS) tuples\\n '''\\n image_centers = []\\n for f in self.datain:\\n image_centers.append((f.header['CRVAL1'], f.header['CRVAL2']))\\n filtered_datain = []\\n dist_list = [[[0]*(len(image_centers)-1)]*len(image_centers)]\\n for i in range(len(image_centers)):\\n for j in range(len(image_centers)-1):\\n dist_list[i][j+1] = np.sqrt((image_)**2+()**2)\\n '''\\n #calculations necessary for updating wcs information\\n px = []\\n py = []\\n \\n #in order to avoid NaN interactions, creating weight map\\n weights=[]\\n for f in self.datain:\\n weights.append((np.where(np.isnan(f.image) == True, 0, 1)))\\n \\n for f in self.datain:\\n px.extend(wcs.WCS(f.header).calc_footprint()[:,0])\\n py.extend(wcs.WCS(f.header).calc_footprint()[:,1])\\n x0 = (max(px)+min(px))/2.\\n y0 = (max(py)+min(py))/2.\\n sx = (max(px)-min(px))*np.cos(y0/180*np.pi) # arcsec\\n sy = (max(py)-min(py)) # arcsec\\n size = (sx*3600+self.getarg('pad')*2, sy*3600+self.getarg('pad')*2)\\n xpix = size[0]//pscale\\n ypix = size[1]//pscale\\n cdelt = [pscale/3600.]*2\\n \\n #create self.dataout and give it a copy of an input's header\\n self.dataout = DataFits(config = self.config)\\n self.dataout.header = self.datain[0].header.copy()\\n \\n #update header wcs information\\n self.log.info('Creating new WCS header')\\n \\n self.dataout.header['CRPIX1'] = xpix/2\\n self.dataout.header['CRPIX2'] = ypix/2\\n self.dataout.header['CRVAL1'] = x0\\n self.dataout.header['CRVAL2'] = y0\\n self.dataout.header['CD1_1'] = -cdelt[0]\\n self.dataout.header['CD1_2'] = self.dataout.header['CD2_1'] = 0.\\n self.dataout.header['CD2_2'] = cdelt[1]\\n self.dataout.header['NAXIS1'] = int(xpix)\\n self.dataout.header['NAXIS2'] = int(ypix)\\n self.dataout.header['CTYPE1'] = 'RA---TAN-SIP'\\n self.dataout.header['CTYPE2'] = 'DEC--TAN-SIP'\\n self.dataout.header['RADESYS'] = 'ICRS'\\n self.dataout.header['EQUINOX'] = 2000\\n self.dataout.header['LATPOLE'] = self.datain[0].header['CRVAL2']\\n self.dataout.header['LONPOLE'] = 180\\n self.dataout.header['PIXASEC'] = pscale\\n \\n theta_rad = np.deg2rad(self.getarg('outangle'))\\n rot_matrix = np.array([[np.cos(theta_rad), -np.sin(theta_rad)], \\n [np.sin(theta_rad), np.cos(theta_rad)]])\\n rot_cd = np.dot(rot_matrix, np.array([[self.dataout.header['CD1_1'], 0.],[0., self.dataout.header['CD2_2']]]))\\n for i in [0,1]:\\n for j in [0,1]:\\n self.dataout.header['CD{0:d}_{1:d}'.format(i+1, j+1)] = rot_cd[i,j]\\n \\n #check drizzle arguments\\n if self.getarg('kernel') == 'smoothing':\\n kernel = 'lanczos3'\\n elif self.getarg('kernel') in ['square', 'point', 'gaussian', 'tophat']:\\n kernel = self.getarg('kernel')\\n else:\\n self.log.error('Kernel name not recognized, using default')\\n kernel = 'square'\\n if self.getarg('drizzleweights') == 'uniform':\\n driz_wt = ''\\n elif self.getarg('drizzleweights') in ['exptime', 'expsq']:\\n driz_wt = self.getarg('drizzleweights')\\n else:\\n self.log.error('Drizzle weighting not recognized, using default')\\n driz_wt = ''\\n \\n #create drizzle object and add input images\\n fullwcs = wcs.WCS(self.dataout.header)\\n self.log.info('Starting drizzle')\\n driz = drz.Drizzle(outwcs = fullwcs, pixfrac=self.getarg('pixfrac'), \\\\\\n kernel=kernel, fillval='10000', wt_scl=driz_wt)\\n for i,f in enumerate(self.datain):\\n self.log.info('Adding %s to drizzle stack' % f.filename)\\n driz.add_image(f.imgdata[0], wcs.WCS(f.header), inwht=weights[i])\\n \\n try:\\n fillval=float(self.getarg('fillval'))\\n except:\\n fillval=np.nan\\n self.log.error('Fillvalue not recognized or missing, using default')\\n \\n #creates output fits file from drizzle output\\n self.dataout.imageset(np.where(driz.outsci == 10000, fillval, driz.outsci))\\n self.dataout.imageset(driz.outwht,'OutWeight', self.dataout.header)\\n self.dataout.filename = self.datain[0].filename\\n\\n #add history\\n self.dataout.setheadval('HISTORY','Coadd: %d files combined with %s kernel, pixfrac %f at %f times resolution' \\\\\\n % (len(self.datain), kernel, self.getarg('pixfrac'), self.getarg('resolution')))\",\n \"def reshape_at_dist(self):\\n self.all_dist_per_mol = np.zeros((self.nmol, self.at_per_mol, self.at_per_mol))\\n for imol in range(self.nmol):\\n start, end = self.at_per_mol*imol, (imol+1)*self.at_per_mol\\n self.all_dist_per_mol[imol] = self.all_dist[start:end,\\n start:end]\",\n \"def extract_patches(data,patch_dim):\\n \\n m = data.shape[0]\\n im_x = data.shape[1]\\n im_y = data.shape[2]\\n \\n assert im_x%float(patch_dim)==0 and im_y%float(patch_dim)==0, \\\\\\n \\\"patch_size must divide x and y dimensions of image\\\"\\n\\n numpatchs = m*(im_x/patch_dim)*(im_y/patch_dim)\\n patch_size = patch_dim**2\\n\\n patches = np.empty((patch_size,numpatchs))\\n p=0\\n for i in range(data.shape[0]):\\n image = data[i,...]\\n for x in np.r_[0:im_x:patch_dim]:\\n for y in np.r_[0:im_y:patch_dim]:\\n patch = image[x:x+patch_dim,y:y+patch_dim]\\n patches[:,p] = patch.ravel()\\n p+=1\\n \\n return patches\",\n \"def get_correct_distance_matrix(L):\\n n = len(L)\\n D = np.zeros((n,n))\\n for i in range(n):\\n for j in range(n):\\n if i != j:\\n D[i][j] = get_minor(L, [i, j], [i, j]) / get_minor(L, [i], [i])\\n return D\",\n \"def get_distance_matrix(self):\\n names = self.get_named_leaves()\\n num_names = len(names)\\n dist_mat = np.zeros((num_names, num_names), dtype='float')\\n for i, j in itertools.combinations(range(num_names), 2):\\n node1, node2 = self.node_names[names[i]], self.node_names[names[j]]\\n dist = self.node_distance(node1, node2)\\n dist_mat[i,j] = dist\\n dist_mat[j,i] = dist\\n return names, dist_mat\",\n \"def _get_distance_diff(self, input):\\n nbatch = input.shape[0]\\n in1 = input.unsqueeze(1).expand(\\n nbatch, self.nelec, self.nelec, self.ndim)\\n in2 = input.unsqueeze(2).expand(\\n nbatch, self.nelec, self.nelec, self.ndim)\\n dist = torch.pow(in1 - in2, 2).sum(3)\\n return dist\",\n \"def distance_image(self):\\n return exclusion_distance(self.mask)\",\n \"def calculateDistances(df):\\n return\",\n \"def dist_matrix(self):\\n return self.__dist_matrix\",\n \"def calc_dist_matrix(self):\\n\\n self.dist_matrix = spatial.distance.squareform(spatial.distance.pdist(self.data_vector,metric=\\\"hamming\\\"))\\n\\n self.dist_frame = pd.DataFrame(self.dist_matrix,\\n index = self.seq_strings,\\n columns = self.seq_strings)\",\n \"def diffusion_mds(means, weights, d, diffusion_rounds=10):\\n for i in range(diffusion_rounds):\\n weights = weights*weights\\n weights = weights/weights.sum(0)\\n X = dim_reduce(means, weights, d)\\n if X.shape[0]==2:\\n return X.dot(weights)\\n else:\\n return X.T.dot(weights)\",\n \"def distance_dmc(distances, Ks, points):\\n doors = []\\n for d in distances:\\n dmc = []\\n for k in Ks:\\n print \\\"==========================\\\", k, \\\"==========================\\\"\\n clusters = create_clusters(25, k)\\n\\n kmeans(points, clusters)\\n # print \\\"Finished creating kmeans algorithm\\\"\\n\\n create_backbone_network(GRAPH, clusters, d)\\n # print \\\"Finished creating backbone network\\\"\\n\\n find_all_shortest_paths(clusters, SP_TABLE, GRAPH)\\n # print \\\"Finished finding all shortest paths\\\"\\n\\n for clst in clusters:\\n clst.inter_cost = inter_cost(clst)\\n clst.intra_cost = intra_cost(points, clst)\\n clst.dm_cost = door_matt_cost(clusters, clst, SP_TABLE)\\n\\n ret = total_cost(clusters)\\n dmc.append(ret[2])\\n doors.append(sum(dmc))\\n draw_door_matts(map(lambda d: float(format(d, \\\".4g\\\")), distances), doors)\",\n \"def distance_from_pixels(args, preds, mesh_conversions, info_elem, mode):\\n node2pix = json.load(open(args.image_dir + \\\"allScans_Node2pix.json\\\"))\\n geodistance_nodes = json.load(open(args.geodistance_file))\\n distances, episode_predictions = [], []\\n dialogs, levels, scan_names, episode_ids, true_viewpoints = info_elem\\n for pred, conversion, sn, tv, id in zip(\\n preds, mesh_conversions, scan_names, true_viewpoints, episode_ids\\n ):\\n total_floors = len(set([v[2] for k, v in node2pix[sn].items()]))\\n pred = nn.functional.interpolate(\\n pred.unsqueeze(1), (700, 1200), mode=\\\"bilinear\\\"\\n ).squeeze(1)[:total_floors]\\n pred_coord = np.unravel_index(pred.argmax(), pred.size())\\n convers = conversion.view(args.max_floors, 1, 1)[pred_coord[0].item()]\\n pred_viewpoint = snap_to_grid(\\n geodistance_nodes[sn],\\n node2pix,\\n sn,\\n [pred_coord[1].item(), pred_coord[2].item()],\\n convers,\\n pred_coord[0].item(),\\n )\\n if mode != \\\"test\\\":\\n dist = geodistance_nodes[sn][tv][pred_viewpoint]\\n distances.append(dist)\\n episode_predictions.append([id, pred_viewpoint])\\n return distances, episode_predictions\",\n \"def extract_patch(n, patch_size, imgs):\\n # Extract patches from input images\\n img_patches = [img_crop(imgs[i], patch_size, patch_size) for i in range(n)]\\n #gt_patches = [img_crop(gt_imgs[i], patch_size, patch_size) for i in range(n)]\\n\\n # Linearize list of patches\\n img_patches = np.asarray([img_patches[i][j] for i in range(len(img_patches)) for j in range(len(img_patches[i]))])\\n #gt_patches = np.asarray([gt_patches[i][j] for i in range(len(gt_patches)) for j in range(len(gt_patches[i]))])\\n \\n return img_patches #,gt_patches\",\n \"def predict_distances(self, inputs, features=None):\\n outputs = dict()\\n if self.args.pose_model_type == \\\"shared\\\":\\n # If we are using a shared encoder for both norm and pose,\\n # then all images are fed separately through the norm encoder.\\n images = torch.cat([inputs[(\\\"color_aug\\\", frame_id, 0)] for frame_id in self.args.frame_idxs])\\n all_features = self.models[\\\"encoder\\\"](images)\\n all_features = [torch.split(f, self.args.batch_size) for f in all_features]\\n features = dict()\\n for i, frame_id in enumerate(self.args.frame_idxs):\\n features[frame_id] = [f[i] for f in all_features]\\n outputs.update(self.models[\\\"norm\\\"](features[0]))\\n else:\\n # Otherwise, we only feed the target image through the norm encoder\\n features = self.models[\\\"encoder\\\"](inputs[\\\"color_aug\\\", 0, 0]) if features is None else features\\n outputs.update(self.models[\\\"norm\\\"](features))\\n\\n return outputs, features\",\n \"def calculateWeights(self):\\n return self.distances #En lo que encontramos una funcion que represente\",\n \"def distance_metric(seg_A, seg_B, dx):\\n table_md = []\\n table_hd = []\\n X, Y, Z = seg_A.shape\\n for z in range(Z):\\n # Binary mask at this slice\\n slice_A = seg_A[:, :, z].astype(np.uint8)\\n slice_B = seg_B[:, :, z].astype(np.uint8)\\n\\n # The distance is defined only when both contours exist on this slice\\n if np.sum(slice_A) > 0 and np.sum(slice_B) > 0:\\n # Find contours and retrieve all the points\\n _, contours, _ = cv2.findContours(cv2.inRange(slice_A, 1, 1),\\n cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_NONE)\\n pts_A = contours[0]\\n for i in range(1, len(contours)):\\n pts_A = np.vstack((pts_A, contours[i]))\\n\\n _, contours, _ = cv2.findContours(cv2.inRange(slice_B, 1, 1),\\n cv2.RETR_EXTERNAL,\\n cv2.CHAIN_APPROX_NONE)\\n pts_B = contours[0]\\n for i in range(1, len(contours)):\\n pts_B = np.vstack((pts_B, contours[i]))\\n\\n # Distance matrix between point sets\\n M = np.zeros((len(pts_A), len(pts_B)))\\n for i in range(len(pts_A)):\\n for j in range(len(pts_B)):\\n M[i, j] = np.linalg.norm(pts_A[i, 0] - pts_B[j, 0])\\n\\n # Mean distance and hausdorff distance\\n md = 0.5 * (np.mean(np.min(M, axis=0)) + np.mean(np.min(M, axis=1))) * dx\\n hd = np.max([np.max(np.min(M, axis=0)), np.max(np.min(M, axis=1))]) * dx\\n table_md += [md]\\n table_hd += [hd]\\n\\n # Return the mean distance and Hausdorff distance across 2D slices\\n mean_md = np.mean(table_md) if table_md else None\\n mean_hd = np.mean(table_hd) if table_hd else None\\n return mean_md, mean_hd\",\n \"def align_images(self, translation_only=False):\\n # Extract feature point locations and descriptors.\\n # print('here')\\n points_and_descriptors = []\\n for file in self.files:\\n # print('kaka')\\n image = sol4_utils.read_image(file, 1)\\n # print('bla',image)\\n self.h, self.w = image.shape\\n pyramid, _ = sol4_utils.build_gaussian_pyramid(image, 3, 7)\\n points_and_descriptors.append(find_features(pyramid))\\n\\n # Compute homographies between successive pairs of images.\\n Hs = []\\n\\n for i in range(len(points_and_descriptors) - 1):\\n points1, points2 = points_and_descriptors[i][0], points_and_descriptors[i + 1][0]\\n desc1, desc2 = points_and_descriptors[i][1], points_and_descriptors[i + 1][1]\\n\\n # Find matching feature points.\\n ind1, ind2 = match_features(desc1, desc2, .7)\\n points1, points2 = points1[ind1, :], points2[ind2, :]\\n\\n # Compute homography using RANSAC.\\n # print(points1)\\n # print(points2)\\n H12, inliers = ransac_homography(points1, points2, 100, 6, translation_only)\\n\\n # Uncomment for debugging: display inliers and outliers among matching points.\\n # In the submitted code this function should be commented out!\\n # display_matches(self.images[i], self.images[i+1], points1 , points2, inliers)\\n\\n Hs.append(H12)\\n\\n # Compute composite homographies from the central coordinate system.\\n # print(Hs)\\n accumulated_homographies = accumulate_homographies(Hs, (len(Hs) - 1) // 2)\\n self.homographies = np.stack(accumulated_homographies)\\n self.frames_for_panoramas = filter_homographies_with_translation(self.homographies, minimum_right_translation=5)\\n self.homographies = self.homographies[self.frames_for_panoramas]\",\n \"def create_loss_weight_matrix(batch_size, patch_size, distance, min_value=0.2):\\n w = min_value * torch.ones([patch_size, patch_size])\\n for i in range(1, distance+1):\\n value = (i + (distance-i) * min_value) / distance\\n w[i:-i, i:-i] = value * torch.ones([patch_size - 2*i, patch_size - 2*i])\\n w = w.expand(batch_size, 1, -1, -1).clone()\\n w.requires_grad = False\\n return w\",\n \"def computeDistance(self):\\n distance = 0.0\\n height = self.heightField.getNumber()\\n ratio = self.indexField.getNumber()\\n numBounces = self.bouncesField.getNumber()\\n\\n for bounce in range(numBounces):\\n bounceHeight = height * ratio\\n distance += height + bounceHeight\\n height = bounceHeight\\n\\n self.distanceField.setNumber(distance)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62255037","0.6161878","0.61389506","0.59978783","0.59745884","0.593692","0.59346217","0.5861669","0.5828466","0.57970315","0.5744683","0.5730956","0.5708846","0.5702996","0.56961113","0.56669694","0.5647084","0.56355965","0.5611184","0.5610667","0.5594985","0.5593867","0.55651176","0.5544234","0.55398154","0.5522551","0.55024654","0.5483032","0.5446323","0.5434011","0.5433939","0.54329675","0.5428493","0.54274976","0.5427085","0.54215187","0.53733927","0.53729206","0.5372916","0.5361728","0.5357412","0.5338911","0.53213334","0.53135824","0.53083825","0.5307838","0.53074276","0.53055257","0.5305343","0.52935064","0.5282408","0.52778745","0.5260176","0.52578264","0.5257389","0.5248414","0.5246657","0.52411497","0.52282846","0.52268696","0.521303","0.5208563","0.52049553","0.5202658","0.5194769","0.51904273","0.51899296","0.5176037","0.51745844","0.51705474","0.51665866","0.5165766","0.51613945","0.5152846","0.5149246","0.5142735","0.51377404","0.5126611","0.51216507","0.51216507","0.5120963","0.5119271","0.5118897","0.510946","0.51062846","0.51045614","0.5103215","0.5101534","0.5101054","0.5094927","0.50944483","0.50910884","0.5090797","0.5080998","0.50684434","0.50673515","0.5062632","0.50535727","0.5046857","0.5046543","0.50437886"],"string":"[\n \"0.62255037\",\n \"0.6161878\",\n \"0.61389506\",\n \"0.59978783\",\n \"0.59745884\",\n \"0.593692\",\n \"0.59346217\",\n \"0.5861669\",\n \"0.5828466\",\n \"0.57970315\",\n \"0.5744683\",\n \"0.5730956\",\n \"0.5708846\",\n \"0.5702996\",\n \"0.56961113\",\n \"0.56669694\",\n \"0.5647084\",\n \"0.56355965\",\n \"0.5611184\",\n \"0.5610667\",\n \"0.5594985\",\n \"0.5593867\",\n \"0.55651176\",\n \"0.5544234\",\n \"0.55398154\",\n \"0.5522551\",\n \"0.55024654\",\n \"0.5483032\",\n \"0.5446323\",\n \"0.5434011\",\n \"0.5433939\",\n \"0.54329675\",\n \"0.5428493\",\n \"0.54274976\",\n \"0.5427085\",\n \"0.54215187\",\n \"0.53733927\",\n \"0.53729206\",\n \"0.5372916\",\n \"0.5361728\",\n \"0.5357412\",\n \"0.5338911\",\n \"0.53213334\",\n \"0.53135824\",\n \"0.53083825\",\n \"0.5307838\",\n \"0.53074276\",\n \"0.53055257\",\n \"0.5305343\",\n \"0.52935064\",\n \"0.5282408\",\n \"0.52778745\",\n \"0.5260176\",\n \"0.52578264\",\n \"0.5257389\",\n \"0.5248414\",\n \"0.5246657\",\n \"0.52411497\",\n \"0.52282846\",\n \"0.52268696\",\n \"0.521303\",\n \"0.5208563\",\n \"0.52049553\",\n \"0.5202658\",\n \"0.5194769\",\n \"0.51904273\",\n \"0.51899296\",\n \"0.5176037\",\n \"0.51745844\",\n \"0.51705474\",\n \"0.51665866\",\n \"0.5165766\",\n \"0.51613945\",\n \"0.5152846\",\n \"0.5149246\",\n \"0.5142735\",\n \"0.51377404\",\n \"0.5126611\",\n \"0.51216507\",\n \"0.51216507\",\n \"0.5120963\",\n \"0.5119271\",\n \"0.5118897\",\n \"0.510946\",\n \"0.51062846\",\n \"0.51045614\",\n \"0.5103215\",\n \"0.5101534\",\n \"0.5101054\",\n \"0.5094927\",\n \"0.50944483\",\n \"0.50910884\",\n \"0.5090797\",\n \"0.5080998\",\n \"0.50684434\",\n \"0.50673515\",\n \"0.5062632\",\n \"0.50535727\",\n \"0.5046857\",\n \"0.5046543\",\n \"0.50437886\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17778,"cells":{"query":{"kind":"string","value":"Call f on each item in seq, calling inter() in between."},"document":{"kind":"string","value":"def interleave(inter, f, seq):\n seq = iter(seq)\n try:\n f(next(seq))\n except StopIteration:\n pass\n else:\n for x in seq:\n inter()\n f(x)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def sequence(f, lst: list) -> list:\n ret = []\n for ele in lst:\n ret.append(f(ele))\n return ret","def intersperse(value, seq):\n seq = iter(seq)\n\n try:\n yield next(seq)\n except StopIteration:\n return\n\n for item in seq:\n yield value\n yield item","def applyToEach(L,f):\n for i in range(len(L)):\n L[i] = f(L[i])","def iterate(f, x):\n while True:\n yield x\n x = f(x)","def scanl(f, base, l):\n yield base\n for x in l:\n base = f(base, x)\n yield base","def intercept(iterable, function):\n\n def intercepting(iterable_):\n for item in iterable_:\n function(item)\n yield item\n\n return intercepting(iterable)","def iterate(func, x):\n while True:\n x = func(x)\n yield x","def intercalate(value, seq):\n return flatten(intersperse(value, seq))","def _fold_loop(cls, f, agg, next):\n\n while next is not None:\n (val, next) = next\n agg = f(val, agg)\n return agg","def each(self, func):\n\n for i in self._:\n func(i)\n return self","def sequence_side_effect(*args):\n seq = list(args)\n\n def rv_fun(*args, **kw):\n return seq.pop(0)\n return rv_fun","def process_list(_func, iterator, *args, **kwargs):\n return [_func(i, *args, **kwargs) for i in iterator]","def ireduce(f, it):\n acc = it.next()\n yield acc\n for x in it:\n acc = f(acc, x)\n yield acc","def imap_c(func):\n return functools.partial(imap, func)","def enumerate_list(seq):\n return zip(xrange(len(seq)), seq)","async def a_enumerate(seq, start=0):\n i = start\n async for val in seq:\n yield i, val\n i += 1","def mapf(f: Callable[[D_], R_], C: Iterable[D_]) -> Iterator[R_]:\n return (f(x) for x in C)","def scanl(func, start, itr):\n if not callable(func):\n raise TypeError(\"First argument to scanl must be callable\")\n itr = iter(itr)\n\n return _scanl(func, start, itr)","def coroutine(f, *a, **kw):\n i = f(*a, **kw)\n i.next()\n return i","def foreach(func, iterable):\n\n\tfor x in iterable:\n\t\tfunc(x)","def scan(func, iterable, start=_EMPTY, *, echo_start=True):\n it = iter(iterable)\n if start is _EMPTY:\n start = next(it)\n if echo_start:\n yield start\n for item in it:\n start = func(start, item)\n yield start","def foldl(func, start, itr):\n return _foldl(func, start, iter(itr))","def mapcatting(f):\n @coroutine\n def gen(target):\n while True:\n xs = yield\n for x in f(xs):\n target.send(x)\n\n return gen","def _call_n(x, f, n, *args, **kwargs):\n return [f(i, x, *args, **kwargs) for i in range(n)]","def apply_(self, function):\n self.sequences = [function(seq) for seq in self.sequences]\n return self","def progression(first_item:int, amount:int,func):\n item = first_item\n count = 0\n stop = False\n while count < amount and not stop:\n stop = yield item\n item = func(item)\n count += 1","def foreach(function):\n return partial(map, function)","def repeatedly(func, /, *args, **kwargs):\n func = to_callable(func)\n try:\n while True:\n yield func(*args, **kwargs)\n except StopIteration as e:\n yield from stop_seq(e)","def mapg(f: Callable[[D_], R_], C: Iterable[D_]) -> Iterator[R_]:\n for x in C:\n yield f(x)","def iter_sequence_infinite(seq):\n while True:\n for item in seq:\n yield item","def iterate(func: Callable[..., T], x: T, *args, index: Index = None):\n func = to_callable(func)\n index = to_index_seq(index)\n\n if index is None and not args:\n out = _iterate(func, x)\n elif index is None:\n out = _iterate_n(func, (x, *args))\n else:\n if not args:\n out = _iterate_indexed(func, index, x)\n else:\n out = _iterate_indexed_n(func, index, (x, *args))\n\n return Iter(out)","def apply(L, f):\n\n result = []\n for i in L:\n result.append(f(i))\n\n return result","def easy_parallize(f, sequence):\n pool = Pool(processes=NPROCESSORS) # depends on available cores\n result = pool.map(f, sequence) # for i in sequence: result[i] = f(i)\n cleaned = [x for x in result if not x is []] # getting results\n pool.close() # not optimal! but easy\n pool.join()\n return cleaned","def bind(self, f):\n return as_stream_iterator(y for x in self for y in f(x))","def simple_seq(seq):\n for i in seq:\n yield i","def make_func_repeater(f, x):\n\n def repeat(i, x=x):\n if i == 0:\n return x\n else:\n return repeat(i-1, f(x))\n return repeat","def fancier_uniquer(seq, f, p):\n bunches = {}\n for index, item in enumerate(seq):\n marker = f(item)\n bunches.setdefault(marker, []).append((index, item))\n auxlist = [p(candidates) for candidates in bunches.values()]\n auxlist.sort()\n return [item for index, item in auxlist]","def frange(start, stop, step):\n i = start\n while i < stop:\n yield i\n i += step","def map(iterable, function):\n for x in iterable:\n yield function(x)","async def aenumerate(asequence, start=0):\n n = start\n async for elem in asequence:\n yield n, elem\n n += 1","def mapping(f):\n @coroutine\n def gen(target):\n while True:\n x = yield\n target.send(f(x))\n return gen","def process(i):\n \n inlist.append(i)","def __call__ ( self , *x ) :\n return partial ( self.__index ,\n self.func ,\n x ,\n self.step ,\n self.order ,\n self.err )","def enumerate(context, data):\n items = ensure_list(context.params.get(\"items\"))\n for item in items:\n data[\"item\"] = item\n context.emit(data=data)","def enumerate_(start = 0):\n\n @filters\n def _dagpype_internal_fn_act(target):\n count = start \n try:\n while True:\n e = (yield)\n target.send((numpy.arange(count, count + len(e)), e))\n count += len(e)\n except GeneratorExit: \n target.close() \n\n return _dagpype_internal_fn_act","def apply_on_all(seq, method, *args, **kwargs):\n if seq:\n for obj in seq:\n getattr(obj, method)(*args, **kwargs)","def scanr(func, start, itr):\n if not callable(func):\n raise TypeError(\"First argument to scanr must be callable\")\n itr = iter(itr)\n \n return _scanr(func, start, itr)","def body(i, *args):\n del args\n fn_result = fn(ctx, iterator.get_next())\n flat_last_step_outputs = nest.flatten(ctx.last_step_outputs)\n with ops.control_dependencies([fn_result]):\n return [i + 1] + flat_last_step_outputs","def interleave(*seqs):\n queue = deque(iter(seq) for seq in seqs)\n\n while queue:\n seq = queue.popleft()\n\n try:\n yield next(seq)\n except StopIteration:\n pass\n else:\n queue.append(seq)","def mapg(f, C):\n for x in C:\n yield f(x)","def wrap_generator(generator, wrapper_function):\n for item in generator:\n yield wrapper_function(item)","def interpose(el, seq):\n return rest(mapcat(lambda x: [el, x], seq))","def func_wrapper():\n set_interval_sequence(functions[1:] + functions[:1], sec)\n functions[0]()","def foldr(func, start, itr):\n return _foldr(func, start, iter(itr))","def repeat(fun, n):\n for i in range(n):\n yield fun()","def fold(iterable, func, base):\n acc = base\n for element in iterable:\n acc = func(acc, element)\n return acc","def iwindow(seq, n):\n it = iter(seq)\n result = tuple(islice(it, n))\n\n if len(result) == n:\n yield result\n\n for elem in it:\n result = result[1:] + (elem,)\n yield result","def report_on_interval(\n iterable,\n message='processed {i} of {n} items',\n item_count=None,\n printer=print,\n get_deltas=compute_reporting_interval,\n report_at_end=False,\n extra_actions=[],\n extras_at_end=False,\n):\n if item_count is not None:\n n = item_count\n else:\n iterable = list(iterable)\n n = len(iterable)\n delta = get_deltas(n)\n for (i, item) in enumerate(iterable):\n yield item\n if (i + 1) % delta == 0:\n printer(message.format(i=i + 1, n=n))\n [a() for a in extra_actions]\n if n % delta != 0:\n if report_at_end:\n printer(message.format(i=n, n=n))\n if extras_at_end:\n [a() for a in extra_actions]","def map(function, iterable):\n\n return [function(x) for x in iterable]","def iseq(start=0, stop=None, inc=1):\n if stop is None: # allow isequence(3) to be 0, 1, 2, 3\n # take 1st arg as stop, start as 0, and inc=1\n stop = start; start = 0; inc = 1\n return range(start, stop+inc, inc)","def running_func(f, t, x, w, lims=None):\n if lims is None:\n lims = np.floor((t.jd.min() + w, t.jd.max() - w))\n t_func = np.arange(lims[0], lims[1], dtype='i')\n x_func = np.zeros_like(t_func, dtype='f')\n for i, jd in enumerate(t_func):\n sel = (t.jd >= (jd - w)) & (t.jd <= (jd + w))\n if np.sum(sel) == 0:\n x_func[i] = np.nan\n else:\n x_func[i] = f(x[sel])\n t_func = astropy.time.Time(t_func, format='jd')\n return t_func, x_func","def comap(function, iterator):\n results = []\n cfc = _CoFunCaller(function, resultCollector=results.append)\n d = cfc.coiterate(iterator)\n d.addCallback(lambda _: results)\n return d","def eat(seq, n=None):\n if n is None:\n collections.deque(seq, maxlen=0)\n else:\n next(itertools.islice(seq, n, n), None)","def react_on(*values):\n def wrapper(func):\n seq = tuple(map(int, values))\n actions[seq] = func\n def inner(*args, **kwargs):\n return func(*args, **kwargs)\n return inner\n return wrapper","def compute_over_actions(f, *args):\n\n '''\n # show the middle results\n for a in zip(*args):\n print(\"a:\", a)\n r = f(*a)\n print(\"r:\", r)\n '''\n\n return sum(f(*a) for a in zip(*args))","def interleave(seqs, pass_exceptions=()):\n iters = map(iter, seqs)\n while iters:\n newiters = []\n for itr in iters:\n try:\n yield next(itr)\n newiters.append(itr)\n except (StopIteration,) + tuple(pass_exceptions):\n pass\n iters = newiters","def map(iteratee, *seqs):\n return _map(fnc.iteratee(iteratee), *seqs)","def repeated_applications(f, n):\n\th = (f for i in range(n))\n\treturn itertools.reduce(combine, h)","def cofold(function, initial, iterator):\n acc = [initial]\n\n def handleAcc(newAcc):\n acc[0] = newAcc\n\n def dofold(item):\n return function(acc[0], item)\n\n d = _CoFunCaller(dofold, resultCollector=handleAcc).coiterate(iterator)\n d.addCallback(lambda _: acc[0])\n return d","def frange(start, stop, step=1.0):\n i = start\n while i <= stop:\n yield i\n i += step","def times(self, fn):\n for i in range(0, self._):\n fn()\n return self","def map(self, fn, *iterables, **kwargs):\n fn = self._prepare_fn(fn)\n return self._self.map(fn, *iterables, **kwargs)","def for_each(f: Callable[[A], Maybe[B]], iterable: Iterable[A]\n ) -> Maybe[Iterable[B]]:\n return cast(Maybe[Iterable[B]], map_m_(Just, f, iterable))","def multimap(funcs, iterable):\n\n for f in funcs:\n iterable = map(f, iterable)\n\n return iterable","def iterateInReactor(self, iterator):\n d = defer.Deferred()\n self.iterators.append((iterator, d))\n return d","def filter(iteratee, seq):\n return _filter(fnc.iteratee(iteratee), seq)","def map(self, func, *sequences):\n return self.mapper().map(func, *sequences)","def intervalselector(function):\n @wraps(function)\n @selector\n def wrapper(doc, selection, *args, selectmode=None, **kwargs):\n new_intervals = []\n for interval in selection:\n new_interval = function(doc, interval, *args, selectmode=selectmode, **kwargs)\n if new_interval == None:\n return\n new_intervals.append(new_interval)\n return Selection(new_intervals)\n return wrapper","def lift(f: Callable[..., Data]) -> LiftedFunc:\n def inner(*args: Result) -> Result:\n out = []\n for args1 in itertools.product(*args):\n val = f(*args1)\n out.append(val)\n return out\n return inner","def threadsafe(f):\n def g(*a, **kw):\n return threadsafe_iter(f(*a, **kw))\n return g","def sequential(self, func, args_dict=None):\n for uri, cf in self._cfs.items():\n args = self._process_args_dict(cf, uri, args_dict)\n func(*args)","def tmap(function, *sequences, **tqdm_kwargs):\n for i in tzip(*sequences, **tqdm_kwargs):\n yield function(*i)","def _pipe_and_accumulate(val, fns):\n for fn in fns:\n val = fn(val)\n yield val","def nextf(f, offset=1):\n def feature(s, i):\n i += offset\n return i < len(s) and f(s, i)\n return feature","def map(self, f):\n if self.is_empty():\n pass\n else:\n items = []\n items.append(f(self._first))\n map(f._rest)\n new_lst = LinkedListRec(items)","def callmany(self, allargs):\n seqs = []\n # add all inputs to queue\n for args, kw in allargs:\n t = time.time()\n seqs.append(t)\n self.inq.put((t, args, kw))\n # read all outputs\n outs = {}\n while len(seqs) > 0:\n t, ret = self.outq.get()\n # if we don't know this seq number, put it back on the queue\n if t not in seqs:\n self.outq.put((t, ret))\n time.sleep(0.01)\n continue\n # if this is the first item, yield it\n if not seqs: break\n if t == seqs[0]:\n seqs.pop(0)\n yield ret\n # also check if we have the next item(s) done\n while seqs and seqs[0] in outs:\n t = seqs.pop(0)\n ret = outs.pop(t)\n yield ret\n continue\n # else, save it for future use\n outs[t] = ret","def eval_f(f, xs):\n l = []\n for x in xs:\n l.append(f(x))\n return l","def map(self, function=lambda item: item):\n for i, row in enumerate(self):\n for j, item in enumerate(row):\n row[j] = function(item)","def mapf( f, C ):\n return (f(x) for x in C)","def wrap_execute_sequence(wrap_func, iterate_vals, nb_workers=NB_THREADS,\n desc='', ordered=False):\n iterate_vals = list(iterate_vals)\n nb_workers = 1 if not nb_workers else int(nb_workers)\n\n tqdm_bar = None\n if desc is not None:\n desc = '%r @%i-threads' % (desc, nb_workers)\n tqdm_bar = tqdm.tqdm(total=len(iterate_vals), desc=desc)\n\n if nb_workers > 1:\n logging.debug('perform parallel in %i threads', nb_workers)\n # Standard mproc.Pool created a demon processes which can be called\n # inside its children, cascade or multiprocessing\n # https://stackoverflow.com/questions/6974695/python-process-pool-non-daemonic\n pool = mproc.Pool(nb_workers)\n pooling = pool.imap if ordered else pool.imap_unordered\n\n for out in pooling(wrap_func, iterate_vals):\n tqdm_bar.update() if tqdm_bar is not None else None\n yield out\n pool.close()\n pool.join()\n else:\n logging.debug('perform sequential')\n for out in map(wrap_func, iterate_vals):\n tqdm_bar.update() if tqdm_bar is not None else None\n yield out\n\n tqdm_bar.close() if tqdm_bar is not None else None","def dispatch_next(self):\r\n self._dispatch_amount += 1\r\n while self._dispatch_amount:\r\n try:\r\n # XXX: possible race condition shuffling the order of\r\n # dispatches in the next two lines.\r\n func, args, kwargs = next(self._original_iterable)\r\n self.dispatch(func, args, kwargs)\r\n self._dispatch_amount -= 1\r\n except ValueError:\r\n \"\"\" Race condition in accessing a generator, we skip,\r\n the dispatch will be done later.\r\n \"\"\"\r\n except StopIteration:\r\n self._iterating = False\r\n self._original_iterable = None\r\n return","def find(f, seq):\n for item in seq:\n if f(item): \n return item","def _maplist_vm(vm, f, xs):\n def f_(*args):\n return vm.call(f, args)\n return list(map(f_, xs))","def mapcat(f, seqs):\n return concat(map(f, seqs))","def __iter__(self):\n for x in self.seq: yield x","def map(self, function=lambda value: value):\n for j, value in enumerate(self):\n self[j] = function(value)","def get_i(iterable: Iterable, i: int):\n for item in iterable:\n yield item[i]","def eager_map(func, iterable):\n for _ in map(func, iterable):\n continue","def loop(func, n):\n for i in range(n):\n func()","def __iterate(\n self,\n items: List[ClientWorklistItem],\n inc: Union[InitialIncClientWorklistData, IncClientWorklistData],\n ):\n if inc is None:\n return\n # append the items\n if inc.items_flat:\n items += inc.items_flat\n else:\n return\n # iterator is used up\n if inc.dropped:\n return\n\n # fetch next\n inc_cl: IncClientWorklistsApi = self.__service_provider.get_service(IncClientWorklistsApi)\n next_it: IncClientWorklistData = inc_cl.inc_client_wl_get_next(inc.inc_wl_id)\n self.__iterate(items, next_it)"],"string":"[\n \"def sequence(f, lst: list) -> list:\\n ret = []\\n for ele in lst:\\n ret.append(f(ele))\\n return ret\",\n \"def intersperse(value, seq):\\n seq = iter(seq)\\n\\n try:\\n yield next(seq)\\n except StopIteration:\\n return\\n\\n for item in seq:\\n yield value\\n yield item\",\n \"def applyToEach(L,f):\\n for i in range(len(L)):\\n L[i] = f(L[i])\",\n \"def iterate(f, x):\\n while True:\\n yield x\\n x = f(x)\",\n \"def scanl(f, base, l):\\n yield base\\n for x in l:\\n base = f(base, x)\\n yield base\",\n \"def intercept(iterable, function):\\n\\n def intercepting(iterable_):\\n for item in iterable_:\\n function(item)\\n yield item\\n\\n return intercepting(iterable)\",\n \"def iterate(func, x):\\n while True:\\n x = func(x)\\n yield x\",\n \"def intercalate(value, seq):\\n return flatten(intersperse(value, seq))\",\n \"def _fold_loop(cls, f, agg, next):\\n\\n while next is not None:\\n (val, next) = next\\n agg = f(val, agg)\\n return agg\",\n \"def each(self, func):\\n\\n for i in self._:\\n func(i)\\n return self\",\n \"def sequence_side_effect(*args):\\n seq = list(args)\\n\\n def rv_fun(*args, **kw):\\n return seq.pop(0)\\n return rv_fun\",\n \"def process_list(_func, iterator, *args, **kwargs):\\n return [_func(i, *args, **kwargs) for i in iterator]\",\n \"def ireduce(f, it):\\n acc = it.next()\\n yield acc\\n for x in it:\\n acc = f(acc, x)\\n yield acc\",\n \"def imap_c(func):\\n return functools.partial(imap, func)\",\n \"def enumerate_list(seq):\\n return zip(xrange(len(seq)), seq)\",\n \"async def a_enumerate(seq, start=0):\\n i = start\\n async for val in seq:\\n yield i, val\\n i += 1\",\n \"def mapf(f: Callable[[D_], R_], C: Iterable[D_]) -> Iterator[R_]:\\n return (f(x) for x in C)\",\n \"def scanl(func, start, itr):\\n if not callable(func):\\n raise TypeError(\\\"First argument to scanl must be callable\\\")\\n itr = iter(itr)\\n\\n return _scanl(func, start, itr)\",\n \"def coroutine(f, *a, **kw):\\n i = f(*a, **kw)\\n i.next()\\n return i\",\n \"def foreach(func, iterable):\\n\\n\\tfor x in iterable:\\n\\t\\tfunc(x)\",\n \"def scan(func, iterable, start=_EMPTY, *, echo_start=True):\\n it = iter(iterable)\\n if start is _EMPTY:\\n start = next(it)\\n if echo_start:\\n yield start\\n for item in it:\\n start = func(start, item)\\n yield start\",\n \"def foldl(func, start, itr):\\n return _foldl(func, start, iter(itr))\",\n \"def mapcatting(f):\\n @coroutine\\n def gen(target):\\n while True:\\n xs = yield\\n for x in f(xs):\\n target.send(x)\\n\\n return gen\",\n \"def _call_n(x, f, n, *args, **kwargs):\\n return [f(i, x, *args, **kwargs) for i in range(n)]\",\n \"def apply_(self, function):\\n self.sequences = [function(seq) for seq in self.sequences]\\n return self\",\n \"def progression(first_item:int, amount:int,func):\\n item = first_item\\n count = 0\\n stop = False\\n while count < amount and not stop:\\n stop = yield item\\n item = func(item)\\n count += 1\",\n \"def foreach(function):\\n return partial(map, function)\",\n \"def repeatedly(func, /, *args, **kwargs):\\n func = to_callable(func)\\n try:\\n while True:\\n yield func(*args, **kwargs)\\n except StopIteration as e:\\n yield from stop_seq(e)\",\n \"def mapg(f: Callable[[D_], R_], C: Iterable[D_]) -> Iterator[R_]:\\n for x in C:\\n yield f(x)\",\n \"def iter_sequence_infinite(seq):\\n while True:\\n for item in seq:\\n yield item\",\n \"def iterate(func: Callable[..., T], x: T, *args, index: Index = None):\\n func = to_callable(func)\\n index = to_index_seq(index)\\n\\n if index is None and not args:\\n out = _iterate(func, x)\\n elif index is None:\\n out = _iterate_n(func, (x, *args))\\n else:\\n if not args:\\n out = _iterate_indexed(func, index, x)\\n else:\\n out = _iterate_indexed_n(func, index, (x, *args))\\n\\n return Iter(out)\",\n \"def apply(L, f):\\n\\n result = []\\n for i in L:\\n result.append(f(i))\\n\\n return result\",\n \"def easy_parallize(f, sequence):\\n pool = Pool(processes=NPROCESSORS) # depends on available cores\\n result = pool.map(f, sequence) # for i in sequence: result[i] = f(i)\\n cleaned = [x for x in result if not x is []] # getting results\\n pool.close() # not optimal! but easy\\n pool.join()\\n return cleaned\",\n \"def bind(self, f):\\n return as_stream_iterator(y for x in self for y in f(x))\",\n \"def simple_seq(seq):\\n for i in seq:\\n yield i\",\n \"def make_func_repeater(f, x):\\n\\n def repeat(i, x=x):\\n if i == 0:\\n return x\\n else:\\n return repeat(i-1, f(x))\\n return repeat\",\n \"def fancier_uniquer(seq, f, p):\\n bunches = {}\\n for index, item in enumerate(seq):\\n marker = f(item)\\n bunches.setdefault(marker, []).append((index, item))\\n auxlist = [p(candidates) for candidates in bunches.values()]\\n auxlist.sort()\\n return [item for index, item in auxlist]\",\n \"def frange(start, stop, step):\\n i = start\\n while i < stop:\\n yield i\\n i += step\",\n \"def map(iterable, function):\\n for x in iterable:\\n yield function(x)\",\n \"async def aenumerate(asequence, start=0):\\n n = start\\n async for elem in asequence:\\n yield n, elem\\n n += 1\",\n \"def mapping(f):\\n @coroutine\\n def gen(target):\\n while True:\\n x = yield\\n target.send(f(x))\\n return gen\",\n \"def process(i):\\n \\n inlist.append(i)\",\n \"def __call__ ( self , *x ) :\\n return partial ( self.__index ,\\n self.func ,\\n x ,\\n self.step ,\\n self.order ,\\n self.err )\",\n \"def enumerate(context, data):\\n items = ensure_list(context.params.get(\\\"items\\\"))\\n for item in items:\\n data[\\\"item\\\"] = item\\n context.emit(data=data)\",\n \"def enumerate_(start = 0):\\n\\n @filters\\n def _dagpype_internal_fn_act(target):\\n count = start \\n try:\\n while True:\\n e = (yield)\\n target.send((numpy.arange(count, count + len(e)), e))\\n count += len(e)\\n except GeneratorExit: \\n target.close() \\n\\n return _dagpype_internal_fn_act\",\n \"def apply_on_all(seq, method, *args, **kwargs):\\n if seq:\\n for obj in seq:\\n getattr(obj, method)(*args, **kwargs)\",\n \"def scanr(func, start, itr):\\n if not callable(func):\\n raise TypeError(\\\"First argument to scanr must be callable\\\")\\n itr = iter(itr)\\n \\n return _scanr(func, start, itr)\",\n \"def body(i, *args):\\n del args\\n fn_result = fn(ctx, iterator.get_next())\\n flat_last_step_outputs = nest.flatten(ctx.last_step_outputs)\\n with ops.control_dependencies([fn_result]):\\n return [i + 1] + flat_last_step_outputs\",\n \"def interleave(*seqs):\\n queue = deque(iter(seq) for seq in seqs)\\n\\n while queue:\\n seq = queue.popleft()\\n\\n try:\\n yield next(seq)\\n except StopIteration:\\n pass\\n else:\\n queue.append(seq)\",\n \"def mapg(f, C):\\n for x in C:\\n yield f(x)\",\n \"def wrap_generator(generator, wrapper_function):\\n for item in generator:\\n yield wrapper_function(item)\",\n \"def interpose(el, seq):\\n return rest(mapcat(lambda x: [el, x], seq))\",\n \"def func_wrapper():\\n set_interval_sequence(functions[1:] + functions[:1], sec)\\n functions[0]()\",\n \"def foldr(func, start, itr):\\n return _foldr(func, start, iter(itr))\",\n \"def repeat(fun, n):\\n for i in range(n):\\n yield fun()\",\n \"def fold(iterable, func, base):\\n acc = base\\n for element in iterable:\\n acc = func(acc, element)\\n return acc\",\n \"def iwindow(seq, n):\\n it = iter(seq)\\n result = tuple(islice(it, n))\\n\\n if len(result) == n:\\n yield result\\n\\n for elem in it:\\n result = result[1:] + (elem,)\\n yield result\",\n \"def report_on_interval(\\n iterable,\\n message='processed {i} of {n} items',\\n item_count=None,\\n printer=print,\\n get_deltas=compute_reporting_interval,\\n report_at_end=False,\\n extra_actions=[],\\n extras_at_end=False,\\n):\\n if item_count is not None:\\n n = item_count\\n else:\\n iterable = list(iterable)\\n n = len(iterable)\\n delta = get_deltas(n)\\n for (i, item) in enumerate(iterable):\\n yield item\\n if (i + 1) % delta == 0:\\n printer(message.format(i=i + 1, n=n))\\n [a() for a in extra_actions]\\n if n % delta != 0:\\n if report_at_end:\\n printer(message.format(i=n, n=n))\\n if extras_at_end:\\n [a() for a in extra_actions]\",\n \"def map(function, iterable):\\n\\n return [function(x) for x in iterable]\",\n \"def iseq(start=0, stop=None, inc=1):\\n if stop is None: # allow isequence(3) to be 0, 1, 2, 3\\n # take 1st arg as stop, start as 0, and inc=1\\n stop = start; start = 0; inc = 1\\n return range(start, stop+inc, inc)\",\n \"def running_func(f, t, x, w, lims=None):\\n if lims is None:\\n lims = np.floor((t.jd.min() + w, t.jd.max() - w))\\n t_func = np.arange(lims[0], lims[1], dtype='i')\\n x_func = np.zeros_like(t_func, dtype='f')\\n for i, jd in enumerate(t_func):\\n sel = (t.jd >= (jd - w)) & (t.jd <= (jd + w))\\n if np.sum(sel) == 0:\\n x_func[i] = np.nan\\n else:\\n x_func[i] = f(x[sel])\\n t_func = astropy.time.Time(t_func, format='jd')\\n return t_func, x_func\",\n \"def comap(function, iterator):\\n results = []\\n cfc = _CoFunCaller(function, resultCollector=results.append)\\n d = cfc.coiterate(iterator)\\n d.addCallback(lambda _: results)\\n return d\",\n \"def eat(seq, n=None):\\n if n is None:\\n collections.deque(seq, maxlen=0)\\n else:\\n next(itertools.islice(seq, n, n), None)\",\n \"def react_on(*values):\\n def wrapper(func):\\n seq = tuple(map(int, values))\\n actions[seq] = func\\n def inner(*args, **kwargs):\\n return func(*args, **kwargs)\\n return inner\\n return wrapper\",\n \"def compute_over_actions(f, *args):\\n\\n '''\\n # show the middle results\\n for a in zip(*args):\\n print(\\\"a:\\\", a)\\n r = f(*a)\\n print(\\\"r:\\\", r)\\n '''\\n\\n return sum(f(*a) for a in zip(*args))\",\n \"def interleave(seqs, pass_exceptions=()):\\n iters = map(iter, seqs)\\n while iters:\\n newiters = []\\n for itr in iters:\\n try:\\n yield next(itr)\\n newiters.append(itr)\\n except (StopIteration,) + tuple(pass_exceptions):\\n pass\\n iters = newiters\",\n \"def map(iteratee, *seqs):\\n return _map(fnc.iteratee(iteratee), *seqs)\",\n \"def repeated_applications(f, n):\\n\\th = (f for i in range(n))\\n\\treturn itertools.reduce(combine, h)\",\n \"def cofold(function, initial, iterator):\\n acc = [initial]\\n\\n def handleAcc(newAcc):\\n acc[0] = newAcc\\n\\n def dofold(item):\\n return function(acc[0], item)\\n\\n d = _CoFunCaller(dofold, resultCollector=handleAcc).coiterate(iterator)\\n d.addCallback(lambda _: acc[0])\\n return d\",\n \"def frange(start, stop, step=1.0):\\n i = start\\n while i <= stop:\\n yield i\\n i += step\",\n \"def times(self, fn):\\n for i in range(0, self._):\\n fn()\\n return self\",\n \"def map(self, fn, *iterables, **kwargs):\\n fn = self._prepare_fn(fn)\\n return self._self.map(fn, *iterables, **kwargs)\",\n \"def for_each(f: Callable[[A], Maybe[B]], iterable: Iterable[A]\\n ) -> Maybe[Iterable[B]]:\\n return cast(Maybe[Iterable[B]], map_m_(Just, f, iterable))\",\n \"def multimap(funcs, iterable):\\n\\n for f in funcs:\\n iterable = map(f, iterable)\\n\\n return iterable\",\n \"def iterateInReactor(self, iterator):\\n d = defer.Deferred()\\n self.iterators.append((iterator, d))\\n return d\",\n \"def filter(iteratee, seq):\\n return _filter(fnc.iteratee(iteratee), seq)\",\n \"def map(self, func, *sequences):\\n return self.mapper().map(func, *sequences)\",\n \"def intervalselector(function):\\n @wraps(function)\\n @selector\\n def wrapper(doc, selection, *args, selectmode=None, **kwargs):\\n new_intervals = []\\n for interval in selection:\\n new_interval = function(doc, interval, *args, selectmode=selectmode, **kwargs)\\n if new_interval == None:\\n return\\n new_intervals.append(new_interval)\\n return Selection(new_intervals)\\n return wrapper\",\n \"def lift(f: Callable[..., Data]) -> LiftedFunc:\\n def inner(*args: Result) -> Result:\\n out = []\\n for args1 in itertools.product(*args):\\n val = f(*args1)\\n out.append(val)\\n return out\\n return inner\",\n \"def threadsafe(f):\\n def g(*a, **kw):\\n return threadsafe_iter(f(*a, **kw))\\n return g\",\n \"def sequential(self, func, args_dict=None):\\n for uri, cf in self._cfs.items():\\n args = self._process_args_dict(cf, uri, args_dict)\\n func(*args)\",\n \"def tmap(function, *sequences, **tqdm_kwargs):\\n for i in tzip(*sequences, **tqdm_kwargs):\\n yield function(*i)\",\n \"def _pipe_and_accumulate(val, fns):\\n for fn in fns:\\n val = fn(val)\\n yield val\",\n \"def nextf(f, offset=1):\\n def feature(s, i):\\n i += offset\\n return i < len(s) and f(s, i)\\n return feature\",\n \"def map(self, f):\\n if self.is_empty():\\n pass\\n else:\\n items = []\\n items.append(f(self._first))\\n map(f._rest)\\n new_lst = LinkedListRec(items)\",\n \"def callmany(self, allargs):\\n seqs = []\\n # add all inputs to queue\\n for args, kw in allargs:\\n t = time.time()\\n seqs.append(t)\\n self.inq.put((t, args, kw))\\n # read all outputs\\n outs = {}\\n while len(seqs) > 0:\\n t, ret = self.outq.get()\\n # if we don't know this seq number, put it back on the queue\\n if t not in seqs:\\n self.outq.put((t, ret))\\n time.sleep(0.01)\\n continue\\n # if this is the first item, yield it\\n if not seqs: break\\n if t == seqs[0]:\\n seqs.pop(0)\\n yield ret\\n # also check if we have the next item(s) done\\n while seqs and seqs[0] in outs:\\n t = seqs.pop(0)\\n ret = outs.pop(t)\\n yield ret\\n continue\\n # else, save it for future use\\n outs[t] = ret\",\n \"def eval_f(f, xs):\\n l = []\\n for x in xs:\\n l.append(f(x))\\n return l\",\n \"def map(self, function=lambda item: item):\\n for i, row in enumerate(self):\\n for j, item in enumerate(row):\\n row[j] = function(item)\",\n \"def mapf( f, C ):\\n return (f(x) for x in C)\",\n \"def wrap_execute_sequence(wrap_func, iterate_vals, nb_workers=NB_THREADS,\\n desc='', ordered=False):\\n iterate_vals = list(iterate_vals)\\n nb_workers = 1 if not nb_workers else int(nb_workers)\\n\\n tqdm_bar = None\\n if desc is not None:\\n desc = '%r @%i-threads' % (desc, nb_workers)\\n tqdm_bar = tqdm.tqdm(total=len(iterate_vals), desc=desc)\\n\\n if nb_workers > 1:\\n logging.debug('perform parallel in %i threads', nb_workers)\\n # Standard mproc.Pool created a demon processes which can be called\\n # inside its children, cascade or multiprocessing\\n # https://stackoverflow.com/questions/6974695/python-process-pool-non-daemonic\\n pool = mproc.Pool(nb_workers)\\n pooling = pool.imap if ordered else pool.imap_unordered\\n\\n for out in pooling(wrap_func, iterate_vals):\\n tqdm_bar.update() if tqdm_bar is not None else None\\n yield out\\n pool.close()\\n pool.join()\\n else:\\n logging.debug('perform sequential')\\n for out in map(wrap_func, iterate_vals):\\n tqdm_bar.update() if tqdm_bar is not None else None\\n yield out\\n\\n tqdm_bar.close() if tqdm_bar is not None else None\",\n \"def dispatch_next(self):\\r\\n self._dispatch_amount += 1\\r\\n while self._dispatch_amount:\\r\\n try:\\r\\n # XXX: possible race condition shuffling the order of\\r\\n # dispatches in the next two lines.\\r\\n func, args, kwargs = next(self._original_iterable)\\r\\n self.dispatch(func, args, kwargs)\\r\\n self._dispatch_amount -= 1\\r\\n except ValueError:\\r\\n \\\"\\\"\\\" Race condition in accessing a generator, we skip,\\r\\n the dispatch will be done later.\\r\\n \\\"\\\"\\\"\\r\\n except StopIteration:\\r\\n self._iterating = False\\r\\n self._original_iterable = None\\r\\n return\",\n \"def find(f, seq):\\n for item in seq:\\n if f(item): \\n return item\",\n \"def _maplist_vm(vm, f, xs):\\n def f_(*args):\\n return vm.call(f, args)\\n return list(map(f_, xs))\",\n \"def mapcat(f, seqs):\\n return concat(map(f, seqs))\",\n \"def __iter__(self):\\n for x in self.seq: yield x\",\n \"def map(self, function=lambda value: value):\\n for j, value in enumerate(self):\\n self[j] = function(value)\",\n \"def get_i(iterable: Iterable, i: int):\\n for item in iterable:\\n yield item[i]\",\n \"def eager_map(func, iterable):\\n for _ in map(func, iterable):\\n continue\",\n \"def loop(func, n):\\n for i in range(n):\\n func()\",\n \"def __iterate(\\n self,\\n items: List[ClientWorklistItem],\\n inc: Union[InitialIncClientWorklistData, IncClientWorklistData],\\n ):\\n if inc is None:\\n return\\n # append the items\\n if inc.items_flat:\\n items += inc.items_flat\\n else:\\n return\\n # iterator is used up\\n if inc.dropped:\\n return\\n\\n # fetch next\\n inc_cl: IncClientWorklistsApi = self.__service_provider.get_service(IncClientWorklistsApi)\\n next_it: IncClientWorklistData = inc_cl.inc_client_wl_get_next(inc.inc_wl_id)\\n self.__iterate(items, next_it)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61680573","0.6034688","0.6013112","0.5890083","0.5763176","0.5744706","0.5691687","0.5686545","0.56322396","0.5617056","0.5564194","0.55386853","0.55105126","0.5491585","0.5479231","0.5478667","0.5475974","0.54754245","0.5453396","0.5437749","0.5408432","0.53924096","0.53812087","0.53787","0.5364877","0.53592646","0.53576183","0.53561157","0.5329946","0.5317975","0.53158027","0.5300063","0.5258547","0.52339584","0.52212715","0.5199754","0.5197036","0.5182219","0.5160492","0.5157654","0.5138999","0.51252735","0.5115602","0.510817","0.510692","0.50967014","0.5084887","0.5069379","0.50624126","0.50547516","0.5041238","0.50286347","0.502672","0.5021081","0.50080866","0.50074446","0.4998349","0.4988849","0.49822238","0.49551782","0.49507898","0.4945075","0.49424675","0.49303603","0.49176678","0.49175543","0.49068055","0.49063668","0.48903206","0.48892817","0.48892123","0.48856407","0.48831874","0.48829028","0.4872215","0.48684755","0.48670053","0.48527598","0.485172","0.4845254","0.48417374","0.48275617","0.48239434","0.48200157","0.48135692","0.4811637","0.4801607","0.47964153","0.47934368","0.47878382","0.477799","0.4754264","0.47332317","0.47304317","0.47256744","0.47235453","0.47186917","0.4717489","0.4717131","0.47156212"],"string":"[\n \"0.61680573\",\n \"0.6034688\",\n \"0.6013112\",\n \"0.5890083\",\n \"0.5763176\",\n \"0.5744706\",\n \"0.5691687\",\n \"0.5686545\",\n \"0.56322396\",\n \"0.5617056\",\n \"0.5564194\",\n \"0.55386853\",\n \"0.55105126\",\n \"0.5491585\",\n \"0.5479231\",\n \"0.5478667\",\n \"0.5475974\",\n \"0.54754245\",\n \"0.5453396\",\n \"0.5437749\",\n \"0.5408432\",\n \"0.53924096\",\n \"0.53812087\",\n \"0.53787\",\n \"0.5364877\",\n \"0.53592646\",\n \"0.53576183\",\n \"0.53561157\",\n \"0.5329946\",\n \"0.5317975\",\n \"0.53158027\",\n \"0.5300063\",\n \"0.5258547\",\n \"0.52339584\",\n \"0.52212715\",\n \"0.5199754\",\n \"0.5197036\",\n \"0.5182219\",\n \"0.5160492\",\n \"0.5157654\",\n \"0.5138999\",\n \"0.51252735\",\n \"0.5115602\",\n \"0.510817\",\n \"0.510692\",\n \"0.50967014\",\n \"0.5084887\",\n \"0.5069379\",\n \"0.50624126\",\n \"0.50547516\",\n \"0.5041238\",\n \"0.50286347\",\n \"0.502672\",\n \"0.5021081\",\n \"0.50080866\",\n \"0.50074446\",\n \"0.4998349\",\n \"0.4988849\",\n \"0.49822238\",\n \"0.49551782\",\n \"0.49507898\",\n \"0.4945075\",\n \"0.49424675\",\n \"0.49303603\",\n \"0.49176678\",\n \"0.49175543\",\n \"0.49068055\",\n \"0.49063668\",\n \"0.48903206\",\n \"0.48892817\",\n \"0.48892123\",\n \"0.48856407\",\n \"0.48831874\",\n \"0.48829028\",\n \"0.4872215\",\n \"0.48684755\",\n \"0.48670053\",\n \"0.48527598\",\n \"0.485172\",\n \"0.4845254\",\n \"0.48417374\",\n \"0.48275617\",\n \"0.48239434\",\n \"0.48200157\",\n \"0.48135692\",\n \"0.4811637\",\n \"0.4801607\",\n \"0.47964153\",\n \"0.47934368\",\n \"0.47878382\",\n \"0.477799\",\n \"0.4754264\",\n \"0.47332317\",\n \"0.47304317\",\n \"0.47256744\",\n \"0.47235453\",\n \"0.47186917\",\n \"0.4717489\",\n \"0.4717131\",\n \"0.47156212\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77389354"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17779,"cells":{"query":{"kind":"string","value":"CodeGenerator(tree, file=sys.stdout) > None. Print the source for tree to file."},"document":{"kind":"string","value":"def __init__(self, tree, file = sys.stdout):\n self.f = file\n self.future_imports = []\n self._indent = 0\n # dict of locals used to determine if variable already exists in assignments\n self._locals = [\"pyflamegpu\"]\n self._device_functions = []\n self._message_iterator_var = None # default\n self._input_message_var = 'message_in' # default\n self._output_message_var = 'message_out' # default\n self.dispatch(tree)\n print(\"\", file=self.f)\n self.f.flush()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def generate(self):\n tree = self._generator.build_tree()\n\n for entry in tree:\n print(entry)","def log_tree(self, description, tree):\n if self.log:\n print(description)\n print_content(astor.to_source(tree), '.py')\n print()\n print()","def code_gen_tree(tree, evaluate_fn=EVALUATE_FN_NAME, gen=None):\n if gen is None:\n gen = CodeGenerator()\n\n def recur(node):\n if tree.children_left[node] == -1:\n assert tree.value[node].size == 1\n gen.write(\"return {0};\".format(tree.value[node].item()))\n return\n\n branch = \"if (f[{feature}] <= {threshold}) {{\".format(\n feature=tree.feature[node],\n threshold=tree.threshold[node])\n with gen.bracketed(branch, \"}\"):\n recur(tree.children_left[node])\n\n with gen.bracketed(\"else {\", \"}\"):\n recur(tree.children_right[node])\n\n with gen.bracketed('extern \"C\" {', \"}\"):\n fn_decl = \"{inline} float {name}(float* f) {{\".format(\n inline=ALWAYS_INLINE,\n name=evaluate_fn)\n with gen.bracketed(fn_decl, \"}\"):\n recur(0)\n return gen.lines","def generate_tree(tree, out_file='tree'):\n activities = [\n 'Working at Computer',\n 'Standing Up, Walking and Going up/down stairs',\n 'Standing',\n 'Walking',\n 'Going Up/Down Stairs',\n 'Walking and Talking with Someone',\n 'Talking while Standing',\n ]\n file_name = path.join('..', 'proposal', out_file)\n export_graphviz(tree, out_file=\"{}.dot\".format(file_name), class_names=activities, rounded=True)\n # system(\"dot -Tpng {0}.dot -o {0}.png\".format(out_file))","def writeCode(self,dir=\"\"):\n for codefile in self.codestructures:\n codefile.writeCode(self.codeformatter,dir)","def dump_code(self, reformat=True) -> str:\n code = utils.to_source(self._tree)\n if reformat:\n return utils.reformat(code)\n return code","def print_phpsyntax_tree(tree):\n import queue\n q = queue.Queue()\n indent = 10\n\n q.put(tree)\n while q.not_empty:\n item = q.get()\n s = \"\"\n for c in item.children:\n s = s + \" \" * indent + c.name\n q.put(c)\n print(s)\n indent -= 1","def print_output(tree):\n print_value(tree)\n print_tree(tree)","def printGeneration(tree):\n for mod in tree:\n if mod.param != []:\n print(str(mod.symbol) + str(mod.param).replace(\"[\",\"(\").replace(\"]\",\")\"),end=\"\")\n else:\n print(str(mod.symbol),end=\"\")\n print(\"\")","def codegen(self, indentation_level: int = 0) -> str:\n return f'{self.data}'","def print_tree(self):\n\t\tprint(self.__print_tree('', True, ''))","def gen(\n file: str,\n infer: bool = typer.Option(\n True, help=\"Whether to run type inference on code examples.\"\n ),\n exec: bool = typer.Option(\n False, help=\"Whether to attempt to execute doctring code.\"\n ),\n experimental: bool = typer.Option(False, help=\"Use experimental Ts parsing\"),\n debug: bool = False,\n dummy_progress: bool = typer.Option(False, help=\"Disable rich progress bar\"),\n):\n _intro()\n from papyri.gen import gen_main\n\n gen_main(\n infer=infer,\n exec_=exec,\n target_file=file,\n experimental=experimental,\n debug=debug,\n dummy_progress=dummy_progress,\n )","def export_call_trees_to_editor():\n\n def dump_tree_model(model, indent):\n values = []\n if model:\n for row in model:\n first = row[0]\n if first == 'computing...':\n return []\n if not row[1] or \\\n first.endswith(' called by ') or \\\n first.endswith('calls '):\n values.append(indent + first)\n else:\n values.append(indent + first + '\\t\\t{}'.format(row[1]))\n\n values.extend(\n dump_tree_model(row.iterchildren(), indent + \" \"))\n return values\n\n m = pygps.get_widget_by_name(\"Call Graph Tree\").get_model()\n text = '\\n'.join(dump_tree_model(m, \"\"))\n\n # Open an editor and write the contents\n\n GPS.execute_action(\"new file\")\n buf = GPS.EditorBuffer.get()\n buf.delete() # in case some template was inserted\n buf.insert(buf.at(1, 1), text)","def print_tree(self):\n return \"\"","def print_tree(self, parser=None):\n for pre, _, node in RenderTree(self):\n print(pre + node._self_string(parser))","def main():\n\n # Command Line Interface\n parse = command_line()\n args = parse.parse_args()\n if not os.path.isdir(args.directory):\n raise IOError\n\n # Abstract File Tree\n filetree = tree_walk(args.directory, args.replace, args.depth)\n jsontree = json.dumps(\n filetree,\n indent=4,\n sort_keys=True,\n separators=(', ', ': '),\n )\n\n # Pipe vs Redirection\n if sys.stdout.isatty():\n try: jsontree = highlight(\n jsontree,\n JsonLexer(),\n Terminal256Formatter(style='autumn'))\n except:\n pass\n\n print(jsontree)","def print_tree(self, tree, nodes):\n\t\tprint(self.display(tree, nodes, '', True, ''))","def genCode(self):\n # Init variables and environment.\n if 'Sys' in self.inputs:\n self.cg_bootstrap_sys()\n\n for ns, vm_code in self.inputs.items():\n input = Parser(vm_code).parse()\n self.ns = ns\n\n if input['errors']:\n # No meaningful output can be generated.\n # Print errors to stderr and return empty string.\n print(\"ERRORS\", file=sys.stderr)\n for err in input['errors']:\n print(\"Line {}: {}\".format(err.line, err.msg))\n return f\"// Encountered errors parsing {filename}\"\n\n # Translate input.\n self.asm(f\"// Input file: {ns}\")\n for cmd in input['commands']:\n # Print original VM command as a comment.\n self.asm('// ' + cmd.src)\n self.cg(cmd)\n self.asm(f\"// End of input file: {ns}\")\n\n # Add the stop loop.\n self.cg_stop()\n\n return '\\n'.join(self.text)","def PrintTree(self,num=0):\n self.ResetTarget()\n self.PrintTreeInt(num)\n return self.target","def main():\n load()\n\n print(generate())","def print_phpsyntax_tree(tree):\n for c in tree.children:\n print \"[\" + string.replace(c.name, \"_\", \".\"),\n if c.children != []:\n print_phpsyntax_tree(c),\n print \"]\",","def print_tree(node):\n print tree(node)","def codegen(self):\n\n try:\n import codegen_package\n except ImportError:\n # I.e.: add relative path (the cwd must be the directory containing this file).\n sys.path.append(\"codegen\")\n import codegen_package\n codegen_package.main()","def export_raw_tree(tree, filename): # Warning : Ugly, need to be improved\n # We save the tree\n print(tree, file=open(\"{:s}.tree\".format(filename), \"w\"), end=\"\\n\")\n\n #delete artefacts\n f = open(\"{:s}.tree\".format(filename), \"r\")\n lines = f.readlines()\n f.close()\n out = open(\"{:s}.tree\".format(filename), \"w\")\n\n reg = r\"Move\\.from_uci\\(\\'(\\w+)\\'\\)\"\n for l in lines:\n l = re.sub(reg, r\"\\1\", l)\n l = l.replace(\"(\", \"{\")\n l = l.replace(\")\", \"}\")\n l = l.replace(\"[\", \"{\")\n l = l.replace(\"]\", \"}\")\n out.write(l)","def compile(cls, node, filename):\n compiler = cls(filename)\n compiler.visit(node)\n code_ops = compiler.code_ops\n code = Code(\n code_ops, [], ['identifiers', 'toolkit'], False, False, True, \n node.name, filename, node.lineno, node.doc,\n )\n return code","def generate(pref_file, random, out_file=\"tree.obj\"):\n global writer\n writer = ObjWriter(out_file)\n global config\n config = configparser.RawConfigParser()\n global random_en\n random_en = random\n config.read(pref_file)\n _draw_tree(0, config.getfloat(\"params\", \"radius\"), config.getfloat(\"params\", \"length\"), deque([]))\n writer.write_all()\n writer.close()","def gen_code(self, filename, func_name):\n\n assert self.bits is not None\n\n vd_list = []\n n_vars = 0\n for tree in self.trees:\n vd_list.append(tree.gen_code(n_vars))\n n_vars += len(vd_list[-1])\n\n # checks the type by the suffix\n\n is_v = filename.split(\".\")[-1] == \"v\"\n\n assert self.inputs\n\n f = open(filename, \"w\")\n\n i_bits = np.sum(self.bits[:-1])\n o_bits = self.bits[-1]\n o_sign = self.is_neg[-1]\n\n if is_v:\n f.write(\"module {}(input [{}:0] i, output [{}:0] o);\\n\".format(\n func_name, i_bits-1, o_bits-1))\n else:\n f.write(\"#include<ac_int.h>\\n\\n\")\n f.write(\"void {}(ac_int<{},false> i, ac_int<{},{}> &o)\\n\".format(\n func_name, i_bits, o_bits, o_sign))\n f.write(\"{\\n\")\n\n\n # write function headline\n s_in_line = []\n\n i_bits = self.bits[0]\n i_sign = self.is_neg[0]\n\n if is_v:\n i_datatype = \" wire {}[{}:0] \".format(\n \"signed \" if i_sign else \"\", i_bits-1)\n else:\n i_datatype = \" ac_int<{},{}> \".format(i_bits, i_sign)\n\n len_s = len(i_datatype)\n\n for i in range(self.inputs):\n if is_v:\n s = (\n \"i_\" + str(i) + \" = \" + \"i[\" + str(i_bits*(i+1)-1) + \":\" +\n str(i_bits*i) + \"]\"\n )\n else:\n s = (\n \"i_\" + str(i) + \" = \" + \"i.slc<\" + str(i_bits) + \">(\" +\n str(i_bits*i) + \")\"\n )\n if (\n len_s + len(s) + 2 > 70 or i_bits != self.bits[i] or\n i_sign != self.is_neg[i]\n ):\n f.write(i_datatype + \", \".join(s_in_line) + \";\\n\")\n\n s_in_line = []\n if is_v:\n i_datatype = \" wire {}[{}:0] \".format(\n \"signed \" if i_sign else \"\", i_bits-1)\n else:\n i_datatype = \" ac_int<{},{}> \".format(i_bits, i_sign)\n\n len_s = len(i_datatype)\n\n s_in_line.append(s)\n len_s += len(s) + 2\n\n if s_in_line:\n f.write(i_datatype + \", \".join(s_in_line) + \";\\n\")\n\n if is_v:\n o_datatype = \" wire {}[{}:0] \".format(\n \"signed \" if o_sign else \"\", o_bits)\n else:\n o_datatype = \" ac_int<{},{}> \".format(o_bits, o_sign)\n\n o_list = []\n for i in range(len(vd_list)):\n for v in vd_list[i]:\n if is_v:\n f.write(o_datatype + v + \" = \" + vd_list[i][v] + \";\\n\")\n else:\n f.write(o_datatype + v + \" = \" + vd_list[i][v] + \";\\n\")\n f.write(\"\\n\")\n o_list.append(v)\n\n assert len(o_list) <= 3\n\n if is_v:\n f.write(\" assign \")\n else:\n f.write(\" \")\n\n if len(o_list) == 1:\n f.write(\"o = \" + o_list[0] + \";\")\n elif len(o_list) == 2:\n cond = \"( \" + o_list[0] + \" == \" + o_list[1] + \" ) \"\n n1 = o_list[0]\n n0 = \"( ( \" + \" + \".join(o_list) + \" ) >> 1 )\"\n f.write(\"o = \" + cond + \"? \" + n1 + \": \" + n0)\n elif len(o_list) == 3:\n cond = (\n \"( \" +\n \"( \" + \" == \".join(o_list[0:2]) + \" )?\" + o_list[0] + \":\" +\n \"( \" + \" == \".join(o_list[1:]) + \" )?\" + o_list[1] + \":\" +\n \"( \" + \" == \".join([o_list[0], o_list[2]]) + \" )?\" + o_list[0] +\n \":\" + \"( \" + \" < \".join(o_list[0:2]) + \" ) ?\" +\n \"( ( \" + \" < \".join(o_list[1:]) + \" ) ?\" + o_list[1] + \":\" +\n o_list[2] + \" ) : \" +\n \"( ( \" + \" < \".join([o_list[0], o_list[2]]) + \" ) ?\" + o_list[0] +\n \":\" + o_list[2] + \" )\"\n )\n f.write(\"o = \" + cond + \";\\n\")\n if is_v:\n f.write(\"endmodule\")\n else:\n f.write(\"}\")\n\n f.close()","def printTree(self):\n print(printTreeF(self, 0, self))","def prettyprint(self, _file):\n _file.write(\"Function %s returns %s\\n\" % (self.name, self.returnType))\n _file.write(\" local vars\\n\")\n for val in self.vars.values():\n _file.write(\" \")\n val.prettyprint(_file)\n _file.write(\" params\\n\")\n for val in self.params.values():\n _file.write(\" \")\n val.prettyprint(_file)\n _file.write(\" registers\\n\")\n for val in self.virtRegs.values():\n _file.write(\" \")\n val.prettyprint(_file)\n _file.write(\" code\\n\")\n for instr in self.instrs():\n if isinstance(instr, CLABEL):\n indent = \" \"\n else:\n indent = \" \"\n _file.write(indent + str(instr) + \"\\n\")","def make_tree(fname: str):\n\n tree = PDDL_Tree.create(fname)\n tree.print_tree()","def pytree(directory: Path, hidden: bool):\n d = Path(directory)\n printDirContents(d, show_hidden=hidden)","def main():\n # If you change this, use an odd number.\n SIZE = 21\n print(makeTree(SIZE))","def generate(module_name, code):\n try:\n ast_tree = ast.parse(code)\n except Exception:\n raise RuntimeError('Bad Python code')\n\n visitor = SearchSpaceGenerator(module_name)\n try:\n visitor.visit(ast_tree)\n except AssertionError as exc:\n raise RuntimeError('%d: %s' % (visitor.last_line, exc.args[0]))\n return visitor.search_space, astor.to_source(ast_tree)","def _write_sources(self, file, indent):\n file.write(indent)\n file.write('[')\n first = True\n for source in self._sources:\n if first:\n first = False\n file.write(source)\n else:\n file.write(',\\n')\n file.write(indent)\n file.write(' ')\n file.write(source)\n file.write(']')","def treefile(filename):\n nobv.visual_treefile(filename)","def generate(self):\n logger=self.logger\n outputter=self.OutputType()\n outputter.make_runner(parser=self.parser,dry_run=self.dry_run,\n setarith=self.setarith)\n con=fileless_context(\n scopes=[self.parse_result],verbose=self.verbose,logger=logger,\n run_mode=self.run_mode)\n self.make_more(self.parse_result,con)","def dump_iteration_tree(obj):\n def _dump_iteration_tree(obj, f, tablevel):\n if is_instance(obj, Driver):\n f.write(' ' * tablevel)\n f.write(obj.get_pathname())\n f.write('\\n')\n for comp in obj.workflow:\n if is_instance(comp, Driver) or is_instance(comp, Assembly):\n _dump_iteration_tree(comp, f, tablevel + 3)\n else:\n f.write(' ' * (tablevel + 3))\n f.write(comp.get_pathname())\n f.write('\\n')\n elif is_instance(obj, Assembly):\n f.write(' ' * tablevel)\n f.write(obj.get_pathname())\n f.write('\\n')\n _dump_iteration_tree(obj.driver, f, tablevel + 3)\n f = cStringIO.StringIO()\n _dump_iteration_tree(obj, f, 0)\n return f.getvalue()","def _write_model_code(self, file, indent = ''):\n file.write(indent)\n file.write('mdo --')\n file.write('\\n')\n indent2 = indent + ' '\n for action in self._actions:\n file.write(indent2)\n file.write(action)\n file.write('\\n')\n file.write(indent2)\n file.write('return $\\n')\n file.write(indent2)\n file.write(' results\\n')\n self._write_sources(file, indent2 + ' ')\n file.write('\\n')","def drawtree(self):\r\n\r\n Phylo.draw(self.tree)","def tree(self, depth_index=0):\r\n print(self.tree_str(depth_index))","def prettyprint(self, _file):\n for var in self.variables:\n var.prettyprint(_file)\n for fun in self.functions:\n fun.prettyprint(_file)","def compile_tree():\r\n vstr = '.'.join(map(str,sys.version_info[:2]))\r\n stat = os.system('python %s/lib/python%s/compileall.py .' %\r\n (sys.prefix,vstr))\r\n if stat:\r\n msg = '*** ERROR: Some Python files in tree do NOT compile! ***\\n'\r\n msg += 'See messages above for the actual file that produced it.\\n'\r\n raise SystemExit(msg)","def generate(self, tnode, indent=\" \", extra_indent=\" \"):\n\n return self.generator.generate(tnode, indent, extra_indent)","def compile(cls, node, filename):\n compiler = cls(filename)\n compiler.visit(node)\n return compiler.stack.pop()","def tree():\n nobv.visual_tree()","def print_code(func):\n print(inspect.getsource(func))","def GenerateCode(self):\n print \"Generating code...\"\n for type in self.getObjectTypes():\n generator = __import__(\"codegen.Cpp\" + type, globals(), locals(), [''])\n print \"Generating code for objects of type: %s\" % type\n generator.GenerateCode(self)","def code_print(code: str, file=None, print_function=None):\n str_io = StringIO()\n\n lines = code.splitlines()\n n_chars = int(np.floor(np.log10(len(lines))+1))\n\n for i, line in enumerate(lines):\n str_io.write(f\"{i+1:{n_chars}}| {line}\\n\")\n\n (print_function or print)(str_io.getvalue(), file=file)","def main(path):\n directory = []\n if os.path.isdir(path):\n code_writer = cw.CodeWriter(os.path.join(path, os.path.basename(path))+\".asm\")\n directory = glob.iglob(os.path.join(path, \"*.vm\"))\n else:\n file_name = path[:-3]\n code_writer = cw.CodeWriter(file_name + \".asm\")\n directory.append(path)\n\n for file in directory:\n # removing the file extension and send it to the setFileName\n f = os.path.basename(file)[:-3]\n code_writer.setFileName(f)\n # creating a relevant parser object\n parser = ps.Parser(file)\n while parser.hasMoreCommands():\n cmd = parser.commandType()\n if cmd == gc.C_PUSH or cmd == gc.C_POP:\n code_writer.writePushPop(cmd, parser.arg1(), parser.arg2())\n if cmd == gc.C_ARITHMETIC:\n code_writer.writeArithmetic(parser.arg1())\n parser.advance()\n code_writer.close()","def trace(tree, exact_src, **kw):\n ret = trace_walk_func(tree, exact_src)\n yield ret","def trace(tree, exact_src, **kw):\n ret = trace_walk_func(tree, exact_src)\n yield ret","def genCode(self, fileName, allowedTypes, genGraph = 1, isRootNode = 0, \r\n metaModelName = None, export = 0, newTypes = None, \r\n nodesToGenList = [], openModelStringList=[], attrGenFix=False):\r\n file = open(fileName, \"w+t\" )\r\n\r\n dir, fil = os.path.split(fileName)\r\n funcName = string.split (fil, \".\")\t\t\t\t\t# compose class name\r\n\r\n if export == 0:\r\n file.write('\"\"\"\\n')\r\n file.write(\"__\"+ fil +\"_____________________________________________________\\n\")\r\n file.write(\"\\n\") \r\n file.write(\"Automatically generated AToM3 Model File (Do not modify directly)\\n\")\r\n file.write(\"Author: \"+USER_NAME+\"\\n\")\r\n file.write(\"Modified: \"+time.asctime()+\"\\n\") \r\n file.write(\"__\"+ len(fil)*\"_\" +\"_____________________________________________________\\n\")\r\n file.write('\"\"\"\\n')\r\n #file.write('from graph_ASG_ERmetaMetaModel import *\\n')\t\t# just for the case!\r\n file.write('from stickylink import *\\n')\t\t\t\t# necessary if we describe some graphLinks...\r\n file.write('from widthXfillXdecoration import *\\n')\t\t\t# necessary if we describe some graphLinks...\r\n\r\n # import the subclass ...\r\n if( self.getClass() not in self.nodeTypes ):\r\n file.write('from '+self.getClass()+' import *\\n')\r\n \r\n # import all the node types...\r\n for nodetype in self.nodeTypes:\r\n if( self.listNodes[nodetype] != [] ): \r\n file.write('from '+nodetype+' import *\\n') \r\n \r\n # Import all the graphical appearences of the node types... that\r\n # are actually used! \r\n # Added by Denis Dube, last modified on Sept. 9, 2004\r\n if( genGraph ): \r\n # STEP 1: Find all graphObjects used in the model\r\n graph_objectDict = dict()\r\n for nodetype in self.listNodes.keys():\r\n for node in self.listNodes[nodetype]:\r\n if( node.graphClass_ ):\r\n graph_objectDict[ node.graphObject_.getGraphClassName() ]=1\r\n # STEP 2: Create the import statements for each graphObject\r\n for graphObject in graph_objectDict.keys():\r\n file.write('from '+graphObject+' import *\\n')\r\n # NOTE: I think the next two statements are caution overkill...\r\n #file.write('try: from '+graphObject+' import *\\n')\r\n #file.write('except: print \"WARNING: unable to load the graphical appearence file: '+graphObject+'.py\" \\n')\r\n \r\n # import the basic types...\r\n for typ in allowedTypes.keys():\r\n typeInstance, params = allowedTypes[typ]\r\n typeName = typeInstance.__name__\r\n file.write('from '+typeName+' import *\\n')\r\n \r\n # Generate the ASG constructor\r\n if( attrGenFix ):\r\n self.__genASGconstructor( file, funcName ) \r\n else:\r\n # Old way\r\n file.write('\\ndef '+funcName[0]+'(self, rootNode):\\n')\r\n \r\n # Generate code for the ASGroot attributes\r\n if( isRootNode ): \r\n # Should attrGenFix be always true? More testing required\r\n #todo: attrGenFix == True always?\r\n if( attrGenFix ): self.__genAttributesROOT( file )\r\n else: self.genAttributesCode(file, genGraph, \"rootNode\")\r\n\r\n self.writeGraph2File(file, genGraph, isRootNode, None, \" \", 1, funcName[0], nodesToGenList=nodesToGenList)\r\n\r\n # generate code for the sub-models\r\n counter = 0\r\n if( not nodesToGenList ):\r\n for nodetype in self.nodeTypes:\r\n for node in self.listNodes[nodetype]: \r\n newFile = funcName[0]+str(counter)\r\n res = node.genCode(os.path.join(dir, newFile+'.py'), allowedTypes, genGraph, 0)\r\n counter = counter + 1\r\n else: \r\n for node in nodesToGenList:\r\n newFile = funcName[0]+str(counter)\r\n res = node.genCode(os.path.join(dir, newFile+'.py'), allowedTypes, genGraph, 0)\r\n counter = counter + 1\r\n \r\n\r\n if isRootNode:\r\n hierarchical = self.isHierarchical()\r\n if export == 0:\r\n if hierarchical:\r\n file.write('def main'+funcName[0]+'(self, ASGroot):\\n')\r\n # file.write(' self.ASGroot = '+self.getClass()+'(self)\\n')\r\n file.write(' self.'+funcName[0]+'(self, ASGroot)\\n\\n')\r\n file.write(' self.'+funcName[0]+'_connections(self, ASGroot)\\n\\n')\r\n file.write('newfunction = main'+funcName[0]+'\\n\\n')\r\n else:\r\n file.write('newfunction = '+funcName[0]+'\\n\\n')\r\n if newTypes and len(newTypes)>0: # generate a list of newly added types\r\n file.write('loadedTypes = [')\r\n counter = 0\r\n for nt in newTypes:\r\n if counter > 0: file.write(',')\r\n file.write(str(nt))\r\n counter = counter + 1\r\n file.write(']\\n')\r\n \r\n self.genLoadedMMName( file )\r\n if( attrGenFix ): file.write( '\\natom3version = \\'0.3\\'\\n' )\r\n file.close()\r\n return funcName[0] \t\t\t\t# this indicates that we've done something\r","def source_code(obj):\n print(inspect.getsource(obj))","def testgen(self):\n self.parse()\n self.generate()","def print(self):\n dot = \"digraph G {\\nrankdir = UD\\n\"\n\n for i in range(len(self.allNodes)):\n if self.allNodes[i].left is not None:\n dot += str(self.allNodes[i].key) + \" -> \" + str(self.allNodes[i].left.key) + \"\\n\"\n if self.allNodes[i].right is not None:\n dot += str(self.allNodes[i].key) + \" -> \" + str(self.allNodes[i].right.key) + \"\\n\"\n\n dot += \"}\"\n\n file = open(\"outputfiles/BinTree.dot\", \"w\")\n file.write(dot)\n file.close()\n\n os.system(\"dot outputfiles/BinTree.dot -Tpng -o outputfiles/BinTree.png\")","def _compile(self,datafile):\n\t\ttemplate_py = 'def displayself(tpldata):\\n';\n\t\ttemplate_py += '\\tpage=[]\\n';\n\t\ttemplate_py += '\\tappend=page.append\\n';\n\t\tself.lineno = 1\n\t\tself.error_found = 0\n\t\tfor line in datafile:\n\t\t\tif self.error_found:\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tline=line.rstrip()\n\t\t\t\t#escape special chars\n\t\t\t\tline=line.replace(\"\\\\\",\"\\\\\\\\\").replace(\"'\",\"\\\\'\")\n\t\t\t\ttline = '\\t'*self.tab_dep + \"append('\" + line\n\t\t\t\ttemplate_py = template_py + self._compile_var_tags(tline)+'\\n'\n\t\t\t\tself.lineno += 1\n\t\t\t\n\t\ttemplate_py = template_py + '\\treturn page\\n'\n\t\treturn template_py","def get_source(node):\n return compiler.walk(node,visitor()).src","def list_code(self, ofile=sys.stdout):\r\n for i, line in enumerate(self.code().split('\\n')):\r\n print >> ofile, ('%4i' % (i + 1)), line\r\n ofile.flush()","def print_tree(self):\n out = \"\"\n for i in range(self.level):\n out += ' |'\n out += '___'\n out += str(self.action)\n if self.action is None:\n print \"None\"\n else:\n print out\n for child in self.children:\n child.print_tree()","def trace(self):\n with NamedTemporaryFile(delete=False, prefix=TRACER_FILE_PREFIX) as f:\n prog = self.transformed_module.code + '\\n'\n f.write(prog.encode('utf-8'))\n f.flush()\n self._fname = f.name\n\n should_trace = self.trace_lines or self.trace_reads\n try:\n prog_bytecode = compile(prog, f.name, 'exec')\n\n if should_trace:\n sys.settrace(self._trace_fn)\n\n # Can't seem to access local variables in a list comprehension?\n # import x; [x.foo() for _ in range(10)]\n # https://github.com/inducer/pudb/issues/103\n # For now, just only use globals\n exec(prog_bytecode, self.globls, self.globls)\n\n if should_trace:\n sys.settrace(None)\n except Exception:\n print(prog)\n raise\n\n return self","def write_code(self, code):\n\n self.save_text()\n self.save_ancestor(code['c'][1])","def print_xml(tree, file):\n tree.write(file, encoding=\"utf-8\", xml_declaration=True)","def compile_code():\n pyc_cmd = \"python3 ../course/common/compiler.py \"\n\n with open('log.out', 'w+', encoding=\"utf-8\") as f:\n subprocess.call(shlex.split(pyc_cmd + './student/'), universal_newlines=True, stderr=f)\n f.seek(0)\n out_student = f.read()\n\n if out_student != \"\":\n rawhtml = rst.get_codeblock(\"\", out_student)\n feedback.set_global_result('failed')\n feedback.set_global_feedback(_(\"Your program does not compile: \\n \") + rawhtml + \"\\n\")\n sys.exit(0)","def prettify(tree, indent=0):\n for key, value in six.iteritems(tree):\n if key == FILE_MARKER:\n if value:\n print((' ' * indent + str(value)))\n else:\n print((' ' * indent + str(key)))\n if isinstance(value, dict):\n prettify(value, indent+1)\n else:\n print((' ' * (indent+1) + str(value)))","def writeGraph2File(self, file, genGraph=1, isRootNode=0, rootNodeName = \"rootNode\", \\\r\n indent=\" \", genConstraints = 0, fileName = '', genGGcode = 0, parentName=\"self\", \\\r\n genImports = 0, depth = 1, nodesToGenList = [] ):\r\n\r\n # generate code for the nodes...\r\n counter =0\r\n if( not nodesToGenList ):\r\n for nodetype in self.nodeTypes:\t\t\t\t\t\t# iterate on all the node types...\r\n for node in self.listNodes[nodetype]:\t\t\t\t\t# Iterate on all the nodes of each type\r\n node.genAttributesCode(file, genGraph, None, isRootNode, rootNodeName, indent, genConstraints, 1, genGGcode, parentName, genImports, depth + 1 )\r\n if self.isSubclass(node, 'ASG'):\t\t\t\t\t# if it is a subclass of ASG, ws should include the file generated (hierarchical modeling)\r\n newFile = fileName+str(counter)\r\n file.write(indent+'exec \"from '+newFile+' import '+newFile+'\\\\n\" in self.__dict__, self.__dict__\\n')\r\n file.write(indent+'self.'+newFile+'(self, self.obj'+str(node.objectNumber)+') \\n\\n')\r\n counter = counter + 1\r\n else:\r\n for node in nodesToGenList:\t\t\t\t\r\n node.genAttributesCode(file, genGraph, None, isRootNode, rootNodeName, indent, genConstraints, 1, genGGcode, parentName, genImports, depth + 1 )\r\n if self.isSubclass(node, 'ASG'):\t\t\t\t\t# if it is a subclass of ASG, ws should include the file generated (hierarchical modeling)\r\n newFile = fileName+str(counter)\r\n file.write(indent+'exec \"from '+newFile+' import '+newFile+'\\\\n\" in self.__dict__, self.__dict__\\n')\r\n file.write(indent+'self.'+newFile+'(self, self.obj'+str(node.objectNumber)+') \\n\\n')\r\n counter = counter + 1\r\n \r\n \r\n # if fileName has a value, we are saving a model, we must generate a function to hold the connections...\r\n if fileName != '':\r\n # if we are not dealing with a hierarchical model, an extra method is not needed..\r\n hierarchical = self.isHierarchical()\r\n if hierarchical:\r\n file.write('\\ndef '+fileName+'_connections(self, rootNode):\\n')\r\n\r\n\r\n #-------- Modified by Ximeng Sun / Apr 9,2005 for large conn nums --------\r\n file.write('\\n')\r\n writed = 0\r\n # generate code for the connections...\r\n if( not nodesToGenList ):\r\n for nodetype in self.nodeTypes:\r\n for node in self.listNodes[nodetype]:\r\n if isRootNode: \r\n if(node.__dict__.has_key('name')):\r\n debugName = ' named ' + node.name.toString() + '\\n'\r\n else:\r\n debugName = ' of type ' + node.__class__.__name__ + '\\n'\r\n file.write(indent+'# Connections for obj'+str(node.objectNumber)\r\n +' (graphObject_: '+node.graphObject_.tag + ')' + debugName)\r\n file.write(indent+'self.drawConnections(\\n')\r\n res = node.genConnectionsCode(file, genGraph, isRootNode, \r\n indent, 1, writed)\r\n if isRootNode: \r\n file.write(' )\\n')\r\n else:\r\n for node in nodesToGenList:\r\n if isRootNode: file.write(indent+'self.drawConnections(')\r\n res = node.genConnectionsCode(file, genGraph, isRootNode, indent, 1, \r\n writed, nodesToGenList = nodesToGenList)\r\n if isRootNode: file.write(' )\\n')\r\n file.write('\\n')\r\n #------------ End of modification by Ximeng Sun / Apr 9,2005 -------------\r\n \r\n \r\n # if rootNode and I'm generating a function (filename != '')\r\n # then call subModel's functions for connections...\r\n if isRootNode and fileName != '': # if main model\r\n counter = 0\r\n if( not nodesToGenList ):\r\n for nodetype in self.nodeTypes: # iterate, to search for all submodels\r\n for node in self.listNodes[nodetype]:\r\n if self.isSubclass(node, 'ASG'): # found a submodel\r\n file.write(indent+'self.'+fileName+str(counter)+'_connections( self, self.obj'+str(node.objectNumber)+')\\n')\r\n writed = 1\r\n counter = counter + 1\r\n else:\r\n for node in nodesToGenList:\r\n if self.isSubclass(node, 'ASG'): # found a submodel\r\n file.write(indent+'self.'+fileName+str(counter)+'_connections( self, self.obj'+str(node.objectNumber)+')\\n')\r\n writed = 1\r\n counter = counter + 1\r\n \r\n \r\n if fileName != '' and (not writed) and hierarchical: # we must write 'pass', because nothing has been writed in the function!!\r\n file.write(indent+'pass\\n')","def dump(self):\n outputs = [\"Code object : %s\" % self.name]\n outputs.append(\" Type : %s\" % self.object_type)\n for source_line in self.source:\n # Each line is a (line_number, code) pair\n outputs.append('%d: %s' % source_line)\n return \"\".join(outputs)","def save_render(self, savefile=\"tree.gv\"):\n from graphviz import Source\n src = Source(self.get_dot())\n path = 'graphviz/{}'.format(savefile)\n src.render(path)","def to_source(node, comments, indent_width=' ' * 4):\n generator = SourceGenerator(indent_width)\n generator.visit(node)\n res = generator.result\n print;print\n import pprint\n pprint.pprint(res)\n res = generator.incorporateComments(comments)\n #print;print\n #pprint.pprint(res)\n #print;print\n return '\\n'.join(map(str, res))","def printTree(self):\n\t\tprint 'Tree:'\n\t\tprint self.root.toString(0)\n\t\tprint '\\n'","def build_page(self, fspath, outputpath, base):\r\n if fspath.check(ext='.py'):\r\n try:\r\n tag, nav = self.build_python_page(fspath)\r\n except (KeyboardInterrupt, SystemExit):\r\n raise\r\n except: # XXX strange stuff going wrong at times... need to fix\r\n raise\r\n exc, e, tb = py.std.sys.exc_info()\r\n print '%s - %s' % (exc, e)\r\n print\r\n print ''.join(py.std.traceback.format_tb(tb))\r\n print '-' * 79\r\n del tb\r\n tag, nav = self.build_nonpython_page(fspath)\r\n elif fspath.check(dir=True):\r\n tag, nav = self.build_dir_page(fspath)\r\n else:\r\n tag, nav = self.build_nonpython_page(fspath)\r\n title = 'sources for %s' % (fspath.basename,)\r\n rev = self.get_revision(fspath)\r\n if rev:\r\n title += ' [rev. %s]' % (rev,)\r\n reltargetpath = outputpath.relto(self.base).replace(os.path.sep,\r\n '/')\r\n self.write_page(title, reltargetpath, tag, nav)","def to_source(node, indent_with=' ' * 4, add_line_information=False):\r\n generator = SourceGenerator(indent_with, add_line_information)\r\n generator.visit(node)\r\n return ''.join(generator.result)","def print_tree(self):\n print(_Node.__print_tree(self))","def stream_code(latex_stream, node, depth):\n\n # Local module imports to avoid circular dependencies\n from stream_single_node import stream_single_node\n\n log_info(f\"{chr(32) * depth}code\")\n write_latex_stream(latex_stream, f\"\\\\texttt{{\")\n for child_node in node.children:\n stream_single_node(latex_stream, child_node, depth)\n write_latex_stream(latex_stream, f\"}}\")\n # Return the next node\n return node.next_sibling","def print_tree(self):\n\t\tself.root.print_recursive(0)","def atree(dir,\n print_files=True,\n outfile=None):\n\n nodes = dict()\n nodes[dir] = Node(dir)\n root_node = nodes[dir]\n for root, dirs, files in os.walk(dir, topdown=True):\n if root not in nodes:\n nodes[root] = Node(root)\n for name in files:\n if print_files:\n nodes[os.path.join(root, name)] = \\\n Node(name, parent=nodes[root])\n for name in dirs:\n nodes[os.path.join(root, name)] = Node(name, parent=nodes[root])\n\n output = []\n for pre, fill, node in RenderTree(root_node):\n output.append((f'{ pre }{ node.name }'))\n if outfile is not None:\n for line in output:\n print(line, file=outfile)\n else:\n return output","def compile(self):\n print \"+ Nothing to be done ;)\"","def generate():","def code():","def get_source(self) -> str:\n return unparse(self.tree)","def dump_tree(self) -> str:\n return utils.dump_tree(self._tree)","def generate(random, pid, autogen_tools, n):\n\n generator_path = autogen_tools.get_directory(__file__)\n\n template_path = path.join(generator_path, \"code.txt.template\")\n rendered_template_path = path.join(generator_path, \"code.txt\")\n\n autogen_tools.replace_source_tokens(\n template_path,\n {\"flag\": gen_code(n, \"Aviation House\")},\n rendered_template_path\n )\n\n code_link = autogen_tools.generate_resource_link(pid, \"code.txt\", title=\"Encrypted file\")\n\n return {\n \"resource_files\": {\n \"public\": [\n (rendered_template_path, \"code.txt\"),\n ],\n },\n \"static_files\": {\n },\n \"problem_updates\": {\n \"description\": \"<p>We've updated the system to AES. We heard that this is military grade encryption so that should fix everything</p><p>The team have stored the password in %s. Bet you can't get into it</p>\" % code_link\n }\n }","def parseprint(code, filename=\"<string>\", mode=\"exec\", **kwargs):\n node = parse(code, mode=mode) # An ode to the code\n print(dump(node, **kwargs))","def write_syntax_tree(self, compilation_engine):\n output_file = '{}ktCST.xml'.format(compilation_engine.filename[:-5])\n with open(output_file, 'w') as f:\n print 'writing syntax tree to {}'.format(output_file)\n f.write(''.join(compilation_engine.contents))","def printTree(self, tree, str):\n\n\t\tif type(tree) == dict:\n\t\t\tfor item in list(tree.values())[0].keys():\n\t\t\t\t\tprint(\"%s %s = %s \" % (str, list(tree.keys())[0], item))\n\t\t\t\t\tself.printTree(list(tree.values())[0][item], str + \"\\t\")\n\t\telse:\n\t\t\tprint(\"%s -> %s = %s\" % (str, self.targetName, tree))","def print_tree(tree):\n if not tree:\n print None\n return\n \n if tree.children:\n print 'Directory hash = {}'.format(base64.urlsafe_b64encode(tree.dmt_hash))\n print 'Contents:'\n for name, subtree in tree.children.iteritems():\n print\n print name\n print_tree(subtree)\n \n else:\n print 'File hash = {}'.format(base64.urlsafe_b64encode(tree.dmt_hash))","def printGeneTree(self):\n align = AlignIO.read(self.newPhylip, 'phylip') # Reads created .phy file containing the SeqRecord\n #print (align) # prints concatenated allignments\n calculator = DistanceCalculator('identity')\n dm = calculator.get_distance(align)# Calculate the distance matrix\n print('\\n======================================== DISTANCE MATRIX =======================================\\n')\n print(dm,\"\\n\\n\") # Print the distance Matrix\n constructor = DistanceTreeConstructor() # Construct the phylogenetic tree using UPGMA algorithm\n tree = constructor.upgma(dm)\n print('\\n========================================= GENE TREE ===========================================\\n')\n Phylo.draw(tree) # Draw the phylogenetic tree (must install matplotlib to use this formatting)\n Phylo.draw_ascii(tree) # Print the phylogenetic tree in terminal","def draw(self):\n for tree_idx, tree in enumerate(self.trees):\n print(\"==========================================\\nTree\",\n tree_idx)\n self._print_tree(tree)","def printShader(self):\n print self.file","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)","def generateCode(self, codeGenerator):\n return codeGenerator.generateCode(self)"],"string":"[\n \"def generate(self):\\n tree = self._generator.build_tree()\\n\\n for entry in tree:\\n print(entry)\",\n \"def log_tree(self, description, tree):\\n if self.log:\\n print(description)\\n print_content(astor.to_source(tree), '.py')\\n print()\\n print()\",\n \"def code_gen_tree(tree, evaluate_fn=EVALUATE_FN_NAME, gen=None):\\n if gen is None:\\n gen = CodeGenerator()\\n\\n def recur(node):\\n if tree.children_left[node] == -1:\\n assert tree.value[node].size == 1\\n gen.write(\\\"return {0};\\\".format(tree.value[node].item()))\\n return\\n\\n branch = \\\"if (f[{feature}] <= {threshold}) {{\\\".format(\\n feature=tree.feature[node],\\n threshold=tree.threshold[node])\\n with gen.bracketed(branch, \\\"}\\\"):\\n recur(tree.children_left[node])\\n\\n with gen.bracketed(\\\"else {\\\", \\\"}\\\"):\\n recur(tree.children_right[node])\\n\\n with gen.bracketed('extern \\\"C\\\" {', \\\"}\\\"):\\n fn_decl = \\\"{inline} float {name}(float* f) {{\\\".format(\\n inline=ALWAYS_INLINE,\\n name=evaluate_fn)\\n with gen.bracketed(fn_decl, \\\"}\\\"):\\n recur(0)\\n return gen.lines\",\n \"def generate_tree(tree, out_file='tree'):\\n activities = [\\n 'Working at Computer',\\n 'Standing Up, Walking and Going up/down stairs',\\n 'Standing',\\n 'Walking',\\n 'Going Up/Down Stairs',\\n 'Walking and Talking with Someone',\\n 'Talking while Standing',\\n ]\\n file_name = path.join('..', 'proposal', out_file)\\n export_graphviz(tree, out_file=\\\"{}.dot\\\".format(file_name), class_names=activities, rounded=True)\\n # system(\\\"dot -Tpng {0}.dot -o {0}.png\\\".format(out_file))\",\n \"def writeCode(self,dir=\\\"\\\"):\\n for codefile in self.codestructures:\\n codefile.writeCode(self.codeformatter,dir)\",\n \"def dump_code(self, reformat=True) -> str:\\n code = utils.to_source(self._tree)\\n if reformat:\\n return utils.reformat(code)\\n return code\",\n \"def print_phpsyntax_tree(tree):\\n import queue\\n q = queue.Queue()\\n indent = 10\\n\\n q.put(tree)\\n while q.not_empty:\\n item = q.get()\\n s = \\\"\\\"\\n for c in item.children:\\n s = s + \\\" \\\" * indent + c.name\\n q.put(c)\\n print(s)\\n indent -= 1\",\n \"def print_output(tree):\\n print_value(tree)\\n print_tree(tree)\",\n \"def printGeneration(tree):\\n for mod in tree:\\n if mod.param != []:\\n print(str(mod.symbol) + str(mod.param).replace(\\\"[\\\",\\\"(\\\").replace(\\\"]\\\",\\\")\\\"),end=\\\"\\\")\\n else:\\n print(str(mod.symbol),end=\\\"\\\")\\n print(\\\"\\\")\",\n \"def codegen(self, indentation_level: int = 0) -> str:\\n return f'{self.data}'\",\n \"def print_tree(self):\\n\\t\\tprint(self.__print_tree('', True, ''))\",\n \"def gen(\\n file: str,\\n infer: bool = typer.Option(\\n True, help=\\\"Whether to run type inference on code examples.\\\"\\n ),\\n exec: bool = typer.Option(\\n False, help=\\\"Whether to attempt to execute doctring code.\\\"\\n ),\\n experimental: bool = typer.Option(False, help=\\\"Use experimental Ts parsing\\\"),\\n debug: bool = False,\\n dummy_progress: bool = typer.Option(False, help=\\\"Disable rich progress bar\\\"),\\n):\\n _intro()\\n from papyri.gen import gen_main\\n\\n gen_main(\\n infer=infer,\\n exec_=exec,\\n target_file=file,\\n experimental=experimental,\\n debug=debug,\\n dummy_progress=dummy_progress,\\n )\",\n \"def export_call_trees_to_editor():\\n\\n def dump_tree_model(model, indent):\\n values = []\\n if model:\\n for row in model:\\n first = row[0]\\n if first == 'computing...':\\n return []\\n if not row[1] or \\\\\\n first.endswith(' called by ') or \\\\\\n first.endswith('calls '):\\n values.append(indent + first)\\n else:\\n values.append(indent + first + '\\\\t\\\\t{}'.format(row[1]))\\n\\n values.extend(\\n dump_tree_model(row.iterchildren(), indent + \\\" \\\"))\\n return values\\n\\n m = pygps.get_widget_by_name(\\\"Call Graph Tree\\\").get_model()\\n text = '\\\\n'.join(dump_tree_model(m, \\\"\\\"))\\n\\n # Open an editor and write the contents\\n\\n GPS.execute_action(\\\"new file\\\")\\n buf = GPS.EditorBuffer.get()\\n buf.delete() # in case some template was inserted\\n buf.insert(buf.at(1, 1), text)\",\n \"def print_tree(self):\\n return \\\"\\\"\",\n \"def print_tree(self, parser=None):\\n for pre, _, node in RenderTree(self):\\n print(pre + node._self_string(parser))\",\n \"def main():\\n\\n # Command Line Interface\\n parse = command_line()\\n args = parse.parse_args()\\n if not os.path.isdir(args.directory):\\n raise IOError\\n\\n # Abstract File Tree\\n filetree = tree_walk(args.directory, args.replace, args.depth)\\n jsontree = json.dumps(\\n filetree,\\n indent=4,\\n sort_keys=True,\\n separators=(', ', ': '),\\n )\\n\\n # Pipe vs Redirection\\n if sys.stdout.isatty():\\n try: jsontree = highlight(\\n jsontree,\\n JsonLexer(),\\n Terminal256Formatter(style='autumn'))\\n except:\\n pass\\n\\n print(jsontree)\",\n \"def print_tree(self, tree, nodes):\\n\\t\\tprint(self.display(tree, nodes, '', True, ''))\",\n \"def genCode(self):\\n # Init variables and environment.\\n if 'Sys' in self.inputs:\\n self.cg_bootstrap_sys()\\n\\n for ns, vm_code in self.inputs.items():\\n input = Parser(vm_code).parse()\\n self.ns = ns\\n\\n if input['errors']:\\n # No meaningful output can be generated.\\n # Print errors to stderr and return empty string.\\n print(\\\"ERRORS\\\", file=sys.stderr)\\n for err in input['errors']:\\n print(\\\"Line {}: {}\\\".format(err.line, err.msg))\\n return f\\\"// Encountered errors parsing {filename}\\\"\\n\\n # Translate input.\\n self.asm(f\\\"// Input file: {ns}\\\")\\n for cmd in input['commands']:\\n # Print original VM command as a comment.\\n self.asm('// ' + cmd.src)\\n self.cg(cmd)\\n self.asm(f\\\"// End of input file: {ns}\\\")\\n\\n # Add the stop loop.\\n self.cg_stop()\\n\\n return '\\\\n'.join(self.text)\",\n \"def PrintTree(self,num=0):\\n self.ResetTarget()\\n self.PrintTreeInt(num)\\n return self.target\",\n \"def main():\\n load()\\n\\n print(generate())\",\n \"def print_phpsyntax_tree(tree):\\n for c in tree.children:\\n print \\\"[\\\" + string.replace(c.name, \\\"_\\\", \\\".\\\"),\\n if c.children != []:\\n print_phpsyntax_tree(c),\\n print \\\"]\\\",\",\n \"def print_tree(node):\\n print tree(node)\",\n \"def codegen(self):\\n\\n try:\\n import codegen_package\\n except ImportError:\\n # I.e.: add relative path (the cwd must be the directory containing this file).\\n sys.path.append(\\\"codegen\\\")\\n import codegen_package\\n codegen_package.main()\",\n \"def export_raw_tree(tree, filename): # Warning : Ugly, need to be improved\\n # We save the tree\\n print(tree, file=open(\\\"{:s}.tree\\\".format(filename), \\\"w\\\"), end=\\\"\\\\n\\\")\\n\\n #delete artefacts\\n f = open(\\\"{:s}.tree\\\".format(filename), \\\"r\\\")\\n lines = f.readlines()\\n f.close()\\n out = open(\\\"{:s}.tree\\\".format(filename), \\\"w\\\")\\n\\n reg = r\\\"Move\\\\.from_uci\\\\(\\\\'(\\\\w+)\\\\'\\\\)\\\"\\n for l in lines:\\n l = re.sub(reg, r\\\"\\\\1\\\", l)\\n l = l.replace(\\\"(\\\", \\\"{\\\")\\n l = l.replace(\\\")\\\", \\\"}\\\")\\n l = l.replace(\\\"[\\\", \\\"{\\\")\\n l = l.replace(\\\"]\\\", \\\"}\\\")\\n out.write(l)\",\n \"def compile(cls, node, filename):\\n compiler = cls(filename)\\n compiler.visit(node)\\n code_ops = compiler.code_ops\\n code = Code(\\n code_ops, [], ['identifiers', 'toolkit'], False, False, True, \\n node.name, filename, node.lineno, node.doc,\\n )\\n return code\",\n \"def generate(pref_file, random, out_file=\\\"tree.obj\\\"):\\n global writer\\n writer = ObjWriter(out_file)\\n global config\\n config = configparser.RawConfigParser()\\n global random_en\\n random_en = random\\n config.read(pref_file)\\n _draw_tree(0, config.getfloat(\\\"params\\\", \\\"radius\\\"), config.getfloat(\\\"params\\\", \\\"length\\\"), deque([]))\\n writer.write_all()\\n writer.close()\",\n \"def gen_code(self, filename, func_name):\\n\\n assert self.bits is not None\\n\\n vd_list = []\\n n_vars = 0\\n for tree in self.trees:\\n vd_list.append(tree.gen_code(n_vars))\\n n_vars += len(vd_list[-1])\\n\\n # checks the type by the suffix\\n\\n is_v = filename.split(\\\".\\\")[-1] == \\\"v\\\"\\n\\n assert self.inputs\\n\\n f = open(filename, \\\"w\\\")\\n\\n i_bits = np.sum(self.bits[:-1])\\n o_bits = self.bits[-1]\\n o_sign = self.is_neg[-1]\\n\\n if is_v:\\n f.write(\\\"module {}(input [{}:0] i, output [{}:0] o);\\\\n\\\".format(\\n func_name, i_bits-1, o_bits-1))\\n else:\\n f.write(\\\"#include<ac_int.h>\\\\n\\\\n\\\")\\n f.write(\\\"void {}(ac_int<{},false> i, ac_int<{},{}> &o)\\\\n\\\".format(\\n func_name, i_bits, o_bits, o_sign))\\n f.write(\\\"{\\\\n\\\")\\n\\n\\n # write function headline\\n s_in_line = []\\n\\n i_bits = self.bits[0]\\n i_sign = self.is_neg[0]\\n\\n if is_v:\\n i_datatype = \\\" wire {}[{}:0] \\\".format(\\n \\\"signed \\\" if i_sign else \\\"\\\", i_bits-1)\\n else:\\n i_datatype = \\\" ac_int<{},{}> \\\".format(i_bits, i_sign)\\n\\n len_s = len(i_datatype)\\n\\n for i in range(self.inputs):\\n if is_v:\\n s = (\\n \\\"i_\\\" + str(i) + \\\" = \\\" + \\\"i[\\\" + str(i_bits*(i+1)-1) + \\\":\\\" +\\n str(i_bits*i) + \\\"]\\\"\\n )\\n else:\\n s = (\\n \\\"i_\\\" + str(i) + \\\" = \\\" + \\\"i.slc<\\\" + str(i_bits) + \\\">(\\\" +\\n str(i_bits*i) + \\\")\\\"\\n )\\n if (\\n len_s + len(s) + 2 > 70 or i_bits != self.bits[i] or\\n i_sign != self.is_neg[i]\\n ):\\n f.write(i_datatype + \\\", \\\".join(s_in_line) + \\\";\\\\n\\\")\\n\\n s_in_line = []\\n if is_v:\\n i_datatype = \\\" wire {}[{}:0] \\\".format(\\n \\\"signed \\\" if i_sign else \\\"\\\", i_bits-1)\\n else:\\n i_datatype = \\\" ac_int<{},{}> \\\".format(i_bits, i_sign)\\n\\n len_s = len(i_datatype)\\n\\n s_in_line.append(s)\\n len_s += len(s) + 2\\n\\n if s_in_line:\\n f.write(i_datatype + \\\", \\\".join(s_in_line) + \\\";\\\\n\\\")\\n\\n if is_v:\\n o_datatype = \\\" wire {}[{}:0] \\\".format(\\n \\\"signed \\\" if o_sign else \\\"\\\", o_bits)\\n else:\\n o_datatype = \\\" ac_int<{},{}> \\\".format(o_bits, o_sign)\\n\\n o_list = []\\n for i in range(len(vd_list)):\\n for v in vd_list[i]:\\n if is_v:\\n f.write(o_datatype + v + \\\" = \\\" + vd_list[i][v] + \\\";\\\\n\\\")\\n else:\\n f.write(o_datatype + v + \\\" = \\\" + vd_list[i][v] + \\\";\\\\n\\\")\\n f.write(\\\"\\\\n\\\")\\n o_list.append(v)\\n\\n assert len(o_list) <= 3\\n\\n if is_v:\\n f.write(\\\" assign \\\")\\n else:\\n f.write(\\\" \\\")\\n\\n if len(o_list) == 1:\\n f.write(\\\"o = \\\" + o_list[0] + \\\";\\\")\\n elif len(o_list) == 2:\\n cond = \\\"( \\\" + o_list[0] + \\\" == \\\" + o_list[1] + \\\" ) \\\"\\n n1 = o_list[0]\\n n0 = \\\"( ( \\\" + \\\" + \\\".join(o_list) + \\\" ) >> 1 )\\\"\\n f.write(\\\"o = \\\" + cond + \\\"? \\\" + n1 + \\\": \\\" + n0)\\n elif len(o_list) == 3:\\n cond = (\\n \\\"( \\\" +\\n \\\"( \\\" + \\\" == \\\".join(o_list[0:2]) + \\\" )?\\\" + o_list[0] + \\\":\\\" +\\n \\\"( \\\" + \\\" == \\\".join(o_list[1:]) + \\\" )?\\\" + o_list[1] + \\\":\\\" +\\n \\\"( \\\" + \\\" == \\\".join([o_list[0], o_list[2]]) + \\\" )?\\\" + o_list[0] +\\n \\\":\\\" + \\\"( \\\" + \\\" < \\\".join(o_list[0:2]) + \\\" ) ?\\\" +\\n \\\"( ( \\\" + \\\" < \\\".join(o_list[1:]) + \\\" ) ?\\\" + o_list[1] + \\\":\\\" +\\n o_list[2] + \\\" ) : \\\" +\\n \\\"( ( \\\" + \\\" < \\\".join([o_list[0], o_list[2]]) + \\\" ) ?\\\" + o_list[0] +\\n \\\":\\\" + o_list[2] + \\\" )\\\"\\n )\\n f.write(\\\"o = \\\" + cond + \\\";\\\\n\\\")\\n if is_v:\\n f.write(\\\"endmodule\\\")\\n else:\\n f.write(\\\"}\\\")\\n\\n f.close()\",\n \"def printTree(self):\\n print(printTreeF(self, 0, self))\",\n \"def prettyprint(self, _file):\\n _file.write(\\\"Function %s returns %s\\\\n\\\" % (self.name, self.returnType))\\n _file.write(\\\" local vars\\\\n\\\")\\n for val in self.vars.values():\\n _file.write(\\\" \\\")\\n val.prettyprint(_file)\\n _file.write(\\\" params\\\\n\\\")\\n for val in self.params.values():\\n _file.write(\\\" \\\")\\n val.prettyprint(_file)\\n _file.write(\\\" registers\\\\n\\\")\\n for val in self.virtRegs.values():\\n _file.write(\\\" \\\")\\n val.prettyprint(_file)\\n _file.write(\\\" code\\\\n\\\")\\n for instr in self.instrs():\\n if isinstance(instr, CLABEL):\\n indent = \\\" \\\"\\n else:\\n indent = \\\" \\\"\\n _file.write(indent + str(instr) + \\\"\\\\n\\\")\",\n \"def make_tree(fname: str):\\n\\n tree = PDDL_Tree.create(fname)\\n tree.print_tree()\",\n \"def pytree(directory: Path, hidden: bool):\\n d = Path(directory)\\n printDirContents(d, show_hidden=hidden)\",\n \"def main():\\n # If you change this, use an odd number.\\n SIZE = 21\\n print(makeTree(SIZE))\",\n \"def generate(module_name, code):\\n try:\\n ast_tree = ast.parse(code)\\n except Exception:\\n raise RuntimeError('Bad Python code')\\n\\n visitor = SearchSpaceGenerator(module_name)\\n try:\\n visitor.visit(ast_tree)\\n except AssertionError as exc:\\n raise RuntimeError('%d: %s' % (visitor.last_line, exc.args[0]))\\n return visitor.search_space, astor.to_source(ast_tree)\",\n \"def _write_sources(self, file, indent):\\n file.write(indent)\\n file.write('[')\\n first = True\\n for source in self._sources:\\n if first:\\n first = False\\n file.write(source)\\n else:\\n file.write(',\\\\n')\\n file.write(indent)\\n file.write(' ')\\n file.write(source)\\n file.write(']')\",\n \"def treefile(filename):\\n nobv.visual_treefile(filename)\",\n \"def generate(self):\\n logger=self.logger\\n outputter=self.OutputType()\\n outputter.make_runner(parser=self.parser,dry_run=self.dry_run,\\n setarith=self.setarith)\\n con=fileless_context(\\n scopes=[self.parse_result],verbose=self.verbose,logger=logger,\\n run_mode=self.run_mode)\\n self.make_more(self.parse_result,con)\",\n \"def dump_iteration_tree(obj):\\n def _dump_iteration_tree(obj, f, tablevel):\\n if is_instance(obj, Driver):\\n f.write(' ' * tablevel)\\n f.write(obj.get_pathname())\\n f.write('\\\\n')\\n for comp in obj.workflow:\\n if is_instance(comp, Driver) or is_instance(comp, Assembly):\\n _dump_iteration_tree(comp, f, tablevel + 3)\\n else:\\n f.write(' ' * (tablevel + 3))\\n f.write(comp.get_pathname())\\n f.write('\\\\n')\\n elif is_instance(obj, Assembly):\\n f.write(' ' * tablevel)\\n f.write(obj.get_pathname())\\n f.write('\\\\n')\\n _dump_iteration_tree(obj.driver, f, tablevel + 3)\\n f = cStringIO.StringIO()\\n _dump_iteration_tree(obj, f, 0)\\n return f.getvalue()\",\n \"def _write_model_code(self, file, indent = ''):\\n file.write(indent)\\n file.write('mdo --')\\n file.write('\\\\n')\\n indent2 = indent + ' '\\n for action in self._actions:\\n file.write(indent2)\\n file.write(action)\\n file.write('\\\\n')\\n file.write(indent2)\\n file.write('return $\\\\n')\\n file.write(indent2)\\n file.write(' results\\\\n')\\n self._write_sources(file, indent2 + ' ')\\n file.write('\\\\n')\",\n \"def drawtree(self):\\r\\n\\r\\n Phylo.draw(self.tree)\",\n \"def tree(self, depth_index=0):\\r\\n print(self.tree_str(depth_index))\",\n \"def prettyprint(self, _file):\\n for var in self.variables:\\n var.prettyprint(_file)\\n for fun in self.functions:\\n fun.prettyprint(_file)\",\n \"def compile_tree():\\r\\n vstr = '.'.join(map(str,sys.version_info[:2]))\\r\\n stat = os.system('python %s/lib/python%s/compileall.py .' %\\r\\n (sys.prefix,vstr))\\r\\n if stat:\\r\\n msg = '*** ERROR: Some Python files in tree do NOT compile! ***\\\\n'\\r\\n msg += 'See messages above for the actual file that produced it.\\\\n'\\r\\n raise SystemExit(msg)\",\n \"def generate(self, tnode, indent=\\\" \\\", extra_indent=\\\" \\\"):\\n\\n return self.generator.generate(tnode, indent, extra_indent)\",\n \"def compile(cls, node, filename):\\n compiler = cls(filename)\\n compiler.visit(node)\\n return compiler.stack.pop()\",\n \"def tree():\\n nobv.visual_tree()\",\n \"def print_code(func):\\n print(inspect.getsource(func))\",\n \"def GenerateCode(self):\\n print \\\"Generating code...\\\"\\n for type in self.getObjectTypes():\\n generator = __import__(\\\"codegen.Cpp\\\" + type, globals(), locals(), [''])\\n print \\\"Generating code for objects of type: %s\\\" % type\\n generator.GenerateCode(self)\",\n \"def code_print(code: str, file=None, print_function=None):\\n str_io = StringIO()\\n\\n lines = code.splitlines()\\n n_chars = int(np.floor(np.log10(len(lines))+1))\\n\\n for i, line in enumerate(lines):\\n str_io.write(f\\\"{i+1:{n_chars}}| {line}\\\\n\\\")\\n\\n (print_function or print)(str_io.getvalue(), file=file)\",\n \"def main(path):\\n directory = []\\n if os.path.isdir(path):\\n code_writer = cw.CodeWriter(os.path.join(path, os.path.basename(path))+\\\".asm\\\")\\n directory = glob.iglob(os.path.join(path, \\\"*.vm\\\"))\\n else:\\n file_name = path[:-3]\\n code_writer = cw.CodeWriter(file_name + \\\".asm\\\")\\n directory.append(path)\\n\\n for file in directory:\\n # removing the file extension and send it to the setFileName\\n f = os.path.basename(file)[:-3]\\n code_writer.setFileName(f)\\n # creating a relevant parser object\\n parser = ps.Parser(file)\\n while parser.hasMoreCommands():\\n cmd = parser.commandType()\\n if cmd == gc.C_PUSH or cmd == gc.C_POP:\\n code_writer.writePushPop(cmd, parser.arg1(), parser.arg2())\\n if cmd == gc.C_ARITHMETIC:\\n code_writer.writeArithmetic(parser.arg1())\\n parser.advance()\\n code_writer.close()\",\n \"def trace(tree, exact_src, **kw):\\n ret = trace_walk_func(tree, exact_src)\\n yield ret\",\n \"def trace(tree, exact_src, **kw):\\n ret = trace_walk_func(tree, exact_src)\\n yield ret\",\n \"def genCode(self, fileName, allowedTypes, genGraph = 1, isRootNode = 0, \\r\\n metaModelName = None, export = 0, newTypes = None, \\r\\n nodesToGenList = [], openModelStringList=[], attrGenFix=False):\\r\\n file = open(fileName, \\\"w+t\\\" )\\r\\n\\r\\n dir, fil = os.path.split(fileName)\\r\\n funcName = string.split (fil, \\\".\\\")\\t\\t\\t\\t\\t# compose class name\\r\\n\\r\\n if export == 0:\\r\\n file.write('\\\"\\\"\\\"\\\\n')\\r\\n file.write(\\\"__\\\"+ fil +\\\"_____________________________________________________\\\\n\\\")\\r\\n file.write(\\\"\\\\n\\\") \\r\\n file.write(\\\"Automatically generated AToM3 Model File (Do not modify directly)\\\\n\\\")\\r\\n file.write(\\\"Author: \\\"+USER_NAME+\\\"\\\\n\\\")\\r\\n file.write(\\\"Modified: \\\"+time.asctime()+\\\"\\\\n\\\") \\r\\n file.write(\\\"__\\\"+ len(fil)*\\\"_\\\" +\\\"_____________________________________________________\\\\n\\\")\\r\\n file.write('\\\"\\\"\\\"\\\\n')\\r\\n #file.write('from graph_ASG_ERmetaMetaModel import *\\\\n')\\t\\t# just for the case!\\r\\n file.write('from stickylink import *\\\\n')\\t\\t\\t\\t# necessary if we describe some graphLinks...\\r\\n file.write('from widthXfillXdecoration import *\\\\n')\\t\\t\\t# necessary if we describe some graphLinks...\\r\\n\\r\\n # import the subclass ...\\r\\n if( self.getClass() not in self.nodeTypes ):\\r\\n file.write('from '+self.getClass()+' import *\\\\n')\\r\\n \\r\\n # import all the node types...\\r\\n for nodetype in self.nodeTypes:\\r\\n if( self.listNodes[nodetype] != [] ): \\r\\n file.write('from '+nodetype+' import *\\\\n') \\r\\n \\r\\n # Import all the graphical appearences of the node types... that\\r\\n # are actually used! \\r\\n # Added by Denis Dube, last modified on Sept. 9, 2004\\r\\n if( genGraph ): \\r\\n # STEP 1: Find all graphObjects used in the model\\r\\n graph_objectDict = dict()\\r\\n for nodetype in self.listNodes.keys():\\r\\n for node in self.listNodes[nodetype]:\\r\\n if( node.graphClass_ ):\\r\\n graph_objectDict[ node.graphObject_.getGraphClassName() ]=1\\r\\n # STEP 2: Create the import statements for each graphObject\\r\\n for graphObject in graph_objectDict.keys():\\r\\n file.write('from '+graphObject+' import *\\\\n')\\r\\n # NOTE: I think the next two statements are caution overkill...\\r\\n #file.write('try: from '+graphObject+' import *\\\\n')\\r\\n #file.write('except: print \\\"WARNING: unable to load the graphical appearence file: '+graphObject+'.py\\\" \\\\n')\\r\\n \\r\\n # import the basic types...\\r\\n for typ in allowedTypes.keys():\\r\\n typeInstance, params = allowedTypes[typ]\\r\\n typeName = typeInstance.__name__\\r\\n file.write('from '+typeName+' import *\\\\n')\\r\\n \\r\\n # Generate the ASG constructor\\r\\n if( attrGenFix ):\\r\\n self.__genASGconstructor( file, funcName ) \\r\\n else:\\r\\n # Old way\\r\\n file.write('\\\\ndef '+funcName[0]+'(self, rootNode):\\\\n')\\r\\n \\r\\n # Generate code for the ASGroot attributes\\r\\n if( isRootNode ): \\r\\n # Should attrGenFix be always true? More testing required\\r\\n #todo: attrGenFix == True always?\\r\\n if( attrGenFix ): self.__genAttributesROOT( file )\\r\\n else: self.genAttributesCode(file, genGraph, \\\"rootNode\\\")\\r\\n\\r\\n self.writeGraph2File(file, genGraph, isRootNode, None, \\\" \\\", 1, funcName[0], nodesToGenList=nodesToGenList)\\r\\n\\r\\n # generate code for the sub-models\\r\\n counter = 0\\r\\n if( not nodesToGenList ):\\r\\n for nodetype in self.nodeTypes:\\r\\n for node in self.listNodes[nodetype]: \\r\\n newFile = funcName[0]+str(counter)\\r\\n res = node.genCode(os.path.join(dir, newFile+'.py'), allowedTypes, genGraph, 0)\\r\\n counter = counter + 1\\r\\n else: \\r\\n for node in nodesToGenList:\\r\\n newFile = funcName[0]+str(counter)\\r\\n res = node.genCode(os.path.join(dir, newFile+'.py'), allowedTypes, genGraph, 0)\\r\\n counter = counter + 1\\r\\n \\r\\n\\r\\n if isRootNode:\\r\\n hierarchical = self.isHierarchical()\\r\\n if export == 0:\\r\\n if hierarchical:\\r\\n file.write('def main'+funcName[0]+'(self, ASGroot):\\\\n')\\r\\n # file.write(' self.ASGroot = '+self.getClass()+'(self)\\\\n')\\r\\n file.write(' self.'+funcName[0]+'(self, ASGroot)\\\\n\\\\n')\\r\\n file.write(' self.'+funcName[0]+'_connections(self, ASGroot)\\\\n\\\\n')\\r\\n file.write('newfunction = main'+funcName[0]+'\\\\n\\\\n')\\r\\n else:\\r\\n file.write('newfunction = '+funcName[0]+'\\\\n\\\\n')\\r\\n if newTypes and len(newTypes)>0: # generate a list of newly added types\\r\\n file.write('loadedTypes = [')\\r\\n counter = 0\\r\\n for nt in newTypes:\\r\\n if counter > 0: file.write(',')\\r\\n file.write(str(nt))\\r\\n counter = counter + 1\\r\\n file.write(']\\\\n')\\r\\n \\r\\n self.genLoadedMMName( file )\\r\\n if( attrGenFix ): file.write( '\\\\natom3version = \\\\'0.3\\\\'\\\\n' )\\r\\n file.close()\\r\\n return funcName[0] \\t\\t\\t\\t# this indicates that we've done something\\r\",\n \"def source_code(obj):\\n print(inspect.getsource(obj))\",\n \"def testgen(self):\\n self.parse()\\n self.generate()\",\n \"def print(self):\\n dot = \\\"digraph G {\\\\nrankdir = UD\\\\n\\\"\\n\\n for i in range(len(self.allNodes)):\\n if self.allNodes[i].left is not None:\\n dot += str(self.allNodes[i].key) + \\\" -> \\\" + str(self.allNodes[i].left.key) + \\\"\\\\n\\\"\\n if self.allNodes[i].right is not None:\\n dot += str(self.allNodes[i].key) + \\\" -> \\\" + str(self.allNodes[i].right.key) + \\\"\\\\n\\\"\\n\\n dot += \\\"}\\\"\\n\\n file = open(\\\"outputfiles/BinTree.dot\\\", \\\"w\\\")\\n file.write(dot)\\n file.close()\\n\\n os.system(\\\"dot outputfiles/BinTree.dot -Tpng -o outputfiles/BinTree.png\\\")\",\n \"def _compile(self,datafile):\\n\\t\\ttemplate_py = 'def displayself(tpldata):\\\\n';\\n\\t\\ttemplate_py += '\\\\tpage=[]\\\\n';\\n\\t\\ttemplate_py += '\\\\tappend=page.append\\\\n';\\n\\t\\tself.lineno = 1\\n\\t\\tself.error_found = 0\\n\\t\\tfor line in datafile:\\n\\t\\t\\tif self.error_found:\\n\\t\\t\\t\\tbreak\\n\\t\\t\\telse:\\n\\t\\t\\t\\tline=line.rstrip()\\n\\t\\t\\t\\t#escape special chars\\n\\t\\t\\t\\tline=line.replace(\\\"\\\\\\\\\\\",\\\"\\\\\\\\\\\\\\\\\\\").replace(\\\"'\\\",\\\"\\\\\\\\'\\\")\\n\\t\\t\\t\\ttline = '\\\\t'*self.tab_dep + \\\"append('\\\" + line\\n\\t\\t\\t\\ttemplate_py = template_py + self._compile_var_tags(tline)+'\\\\n'\\n\\t\\t\\t\\tself.lineno += 1\\n\\t\\t\\t\\n\\t\\ttemplate_py = template_py + '\\\\treturn page\\\\n'\\n\\t\\treturn template_py\",\n \"def get_source(node):\\n return compiler.walk(node,visitor()).src\",\n \"def list_code(self, ofile=sys.stdout):\\r\\n for i, line in enumerate(self.code().split('\\\\n')):\\r\\n print >> ofile, ('%4i' % (i + 1)), line\\r\\n ofile.flush()\",\n \"def print_tree(self):\\n out = \\\"\\\"\\n for i in range(self.level):\\n out += ' |'\\n out += '___'\\n out += str(self.action)\\n if self.action is None:\\n print \\\"None\\\"\\n else:\\n print out\\n for child in self.children:\\n child.print_tree()\",\n \"def trace(self):\\n with NamedTemporaryFile(delete=False, prefix=TRACER_FILE_PREFIX) as f:\\n prog = self.transformed_module.code + '\\\\n'\\n f.write(prog.encode('utf-8'))\\n f.flush()\\n self._fname = f.name\\n\\n should_trace = self.trace_lines or self.trace_reads\\n try:\\n prog_bytecode = compile(prog, f.name, 'exec')\\n\\n if should_trace:\\n sys.settrace(self._trace_fn)\\n\\n # Can't seem to access local variables in a list comprehension?\\n # import x; [x.foo() for _ in range(10)]\\n # https://github.com/inducer/pudb/issues/103\\n # For now, just only use globals\\n exec(prog_bytecode, self.globls, self.globls)\\n\\n if should_trace:\\n sys.settrace(None)\\n except Exception:\\n print(prog)\\n raise\\n\\n return self\",\n \"def write_code(self, code):\\n\\n self.save_text()\\n self.save_ancestor(code['c'][1])\",\n \"def print_xml(tree, file):\\n tree.write(file, encoding=\\\"utf-8\\\", xml_declaration=True)\",\n \"def compile_code():\\n pyc_cmd = \\\"python3 ../course/common/compiler.py \\\"\\n\\n with open('log.out', 'w+', encoding=\\\"utf-8\\\") as f:\\n subprocess.call(shlex.split(pyc_cmd + './student/'), universal_newlines=True, stderr=f)\\n f.seek(0)\\n out_student = f.read()\\n\\n if out_student != \\\"\\\":\\n rawhtml = rst.get_codeblock(\\\"\\\", out_student)\\n feedback.set_global_result('failed')\\n feedback.set_global_feedback(_(\\\"Your program does not compile: \\\\n \\\") + rawhtml + \\\"\\\\n\\\")\\n sys.exit(0)\",\n \"def prettify(tree, indent=0):\\n for key, value in six.iteritems(tree):\\n if key == FILE_MARKER:\\n if value:\\n print((' ' * indent + str(value)))\\n else:\\n print((' ' * indent + str(key)))\\n if isinstance(value, dict):\\n prettify(value, indent+1)\\n else:\\n print((' ' * (indent+1) + str(value)))\",\n \"def writeGraph2File(self, file, genGraph=1, isRootNode=0, rootNodeName = \\\"rootNode\\\", \\\\\\r\\n indent=\\\" \\\", genConstraints = 0, fileName = '', genGGcode = 0, parentName=\\\"self\\\", \\\\\\r\\n genImports = 0, depth = 1, nodesToGenList = [] ):\\r\\n\\r\\n # generate code for the nodes...\\r\\n counter =0\\r\\n if( not nodesToGenList ):\\r\\n for nodetype in self.nodeTypes:\\t\\t\\t\\t\\t\\t# iterate on all the node types...\\r\\n for node in self.listNodes[nodetype]:\\t\\t\\t\\t\\t# Iterate on all the nodes of each type\\r\\n node.genAttributesCode(file, genGraph, None, isRootNode, rootNodeName, indent, genConstraints, 1, genGGcode, parentName, genImports, depth + 1 )\\r\\n if self.isSubclass(node, 'ASG'):\\t\\t\\t\\t\\t# if it is a subclass of ASG, ws should include the file generated (hierarchical modeling)\\r\\n newFile = fileName+str(counter)\\r\\n file.write(indent+'exec \\\"from '+newFile+' import '+newFile+'\\\\\\\\n\\\" in self.__dict__, self.__dict__\\\\n')\\r\\n file.write(indent+'self.'+newFile+'(self, self.obj'+str(node.objectNumber)+') \\\\n\\\\n')\\r\\n counter = counter + 1\\r\\n else:\\r\\n for node in nodesToGenList:\\t\\t\\t\\t\\r\\n node.genAttributesCode(file, genGraph, None, isRootNode, rootNodeName, indent, genConstraints, 1, genGGcode, parentName, genImports, depth + 1 )\\r\\n if self.isSubclass(node, 'ASG'):\\t\\t\\t\\t\\t# if it is a subclass of ASG, ws should include the file generated (hierarchical modeling)\\r\\n newFile = fileName+str(counter)\\r\\n file.write(indent+'exec \\\"from '+newFile+' import '+newFile+'\\\\\\\\n\\\" in self.__dict__, self.__dict__\\\\n')\\r\\n file.write(indent+'self.'+newFile+'(self, self.obj'+str(node.objectNumber)+') \\\\n\\\\n')\\r\\n counter = counter + 1\\r\\n \\r\\n \\r\\n # if fileName has a value, we are saving a model, we must generate a function to hold the connections...\\r\\n if fileName != '':\\r\\n # if we are not dealing with a hierarchical model, an extra method is not needed..\\r\\n hierarchical = self.isHierarchical()\\r\\n if hierarchical:\\r\\n file.write('\\\\ndef '+fileName+'_connections(self, rootNode):\\\\n')\\r\\n\\r\\n\\r\\n #-------- Modified by Ximeng Sun / Apr 9,2005 for large conn nums --------\\r\\n file.write('\\\\n')\\r\\n writed = 0\\r\\n # generate code for the connections...\\r\\n if( not nodesToGenList ):\\r\\n for nodetype in self.nodeTypes:\\r\\n for node in self.listNodes[nodetype]:\\r\\n if isRootNode: \\r\\n if(node.__dict__.has_key('name')):\\r\\n debugName = ' named ' + node.name.toString() + '\\\\n'\\r\\n else:\\r\\n debugName = ' of type ' + node.__class__.__name__ + '\\\\n'\\r\\n file.write(indent+'# Connections for obj'+str(node.objectNumber)\\r\\n +' (graphObject_: '+node.graphObject_.tag + ')' + debugName)\\r\\n file.write(indent+'self.drawConnections(\\\\n')\\r\\n res = node.genConnectionsCode(file, genGraph, isRootNode, \\r\\n indent, 1, writed)\\r\\n if isRootNode: \\r\\n file.write(' )\\\\n')\\r\\n else:\\r\\n for node in nodesToGenList:\\r\\n if isRootNode: file.write(indent+'self.drawConnections(')\\r\\n res = node.genConnectionsCode(file, genGraph, isRootNode, indent, 1, \\r\\n writed, nodesToGenList = nodesToGenList)\\r\\n if isRootNode: file.write(' )\\\\n')\\r\\n file.write('\\\\n')\\r\\n #------------ End of modification by Ximeng Sun / Apr 9,2005 -------------\\r\\n \\r\\n \\r\\n # if rootNode and I'm generating a function (filename != '')\\r\\n # then call subModel's functions for connections...\\r\\n if isRootNode and fileName != '': # if main model\\r\\n counter = 0\\r\\n if( not nodesToGenList ):\\r\\n for nodetype in self.nodeTypes: # iterate, to search for all submodels\\r\\n for node in self.listNodes[nodetype]:\\r\\n if self.isSubclass(node, 'ASG'): # found a submodel\\r\\n file.write(indent+'self.'+fileName+str(counter)+'_connections( self, self.obj'+str(node.objectNumber)+')\\\\n')\\r\\n writed = 1\\r\\n counter = counter + 1\\r\\n else:\\r\\n for node in nodesToGenList:\\r\\n if self.isSubclass(node, 'ASG'): # found a submodel\\r\\n file.write(indent+'self.'+fileName+str(counter)+'_connections( self, self.obj'+str(node.objectNumber)+')\\\\n')\\r\\n writed = 1\\r\\n counter = counter + 1\\r\\n \\r\\n \\r\\n if fileName != '' and (not writed) and hierarchical: # we must write 'pass', because nothing has been writed in the function!!\\r\\n file.write(indent+'pass\\\\n')\",\n \"def dump(self):\\n outputs = [\\\"Code object : %s\\\" % self.name]\\n outputs.append(\\\" Type : %s\\\" % self.object_type)\\n for source_line in self.source:\\n # Each line is a (line_number, code) pair\\n outputs.append('%d: %s' % source_line)\\n return \\\"\\\".join(outputs)\",\n \"def save_render(self, savefile=\\\"tree.gv\\\"):\\n from graphviz import Source\\n src = Source(self.get_dot())\\n path = 'graphviz/{}'.format(savefile)\\n src.render(path)\",\n \"def to_source(node, comments, indent_width=' ' * 4):\\n generator = SourceGenerator(indent_width)\\n generator.visit(node)\\n res = generator.result\\n print;print\\n import pprint\\n pprint.pprint(res)\\n res = generator.incorporateComments(comments)\\n #print;print\\n #pprint.pprint(res)\\n #print;print\\n return '\\\\n'.join(map(str, res))\",\n \"def printTree(self):\\n\\t\\tprint 'Tree:'\\n\\t\\tprint self.root.toString(0)\\n\\t\\tprint '\\\\n'\",\n \"def build_page(self, fspath, outputpath, base):\\r\\n if fspath.check(ext='.py'):\\r\\n try:\\r\\n tag, nav = self.build_python_page(fspath)\\r\\n except (KeyboardInterrupt, SystemExit):\\r\\n raise\\r\\n except: # XXX strange stuff going wrong at times... need to fix\\r\\n raise\\r\\n exc, e, tb = py.std.sys.exc_info()\\r\\n print '%s - %s' % (exc, e)\\r\\n print\\r\\n print ''.join(py.std.traceback.format_tb(tb))\\r\\n print '-' * 79\\r\\n del tb\\r\\n tag, nav = self.build_nonpython_page(fspath)\\r\\n elif fspath.check(dir=True):\\r\\n tag, nav = self.build_dir_page(fspath)\\r\\n else:\\r\\n tag, nav = self.build_nonpython_page(fspath)\\r\\n title = 'sources for %s' % (fspath.basename,)\\r\\n rev = self.get_revision(fspath)\\r\\n if rev:\\r\\n title += ' [rev. %s]' % (rev,)\\r\\n reltargetpath = outputpath.relto(self.base).replace(os.path.sep,\\r\\n '/')\\r\\n self.write_page(title, reltargetpath, tag, nav)\",\n \"def to_source(node, indent_with=' ' * 4, add_line_information=False):\\r\\n generator = SourceGenerator(indent_with, add_line_information)\\r\\n generator.visit(node)\\r\\n return ''.join(generator.result)\",\n \"def print_tree(self):\\n print(_Node.__print_tree(self))\",\n \"def stream_code(latex_stream, node, depth):\\n\\n # Local module imports to avoid circular dependencies\\n from stream_single_node import stream_single_node\\n\\n log_info(f\\\"{chr(32) * depth}code\\\")\\n write_latex_stream(latex_stream, f\\\"\\\\\\\\texttt{{\\\")\\n for child_node in node.children:\\n stream_single_node(latex_stream, child_node, depth)\\n write_latex_stream(latex_stream, f\\\"}}\\\")\\n # Return the next node\\n return node.next_sibling\",\n \"def print_tree(self):\\n\\t\\tself.root.print_recursive(0)\",\n \"def atree(dir,\\n print_files=True,\\n outfile=None):\\n\\n nodes = dict()\\n nodes[dir] = Node(dir)\\n root_node = nodes[dir]\\n for root, dirs, files in os.walk(dir, topdown=True):\\n if root not in nodes:\\n nodes[root] = Node(root)\\n for name in files:\\n if print_files:\\n nodes[os.path.join(root, name)] = \\\\\\n Node(name, parent=nodes[root])\\n for name in dirs:\\n nodes[os.path.join(root, name)] = Node(name, parent=nodes[root])\\n\\n output = []\\n for pre, fill, node in RenderTree(root_node):\\n output.append((f'{ pre }{ node.name }'))\\n if outfile is not None:\\n for line in output:\\n print(line, file=outfile)\\n else:\\n return output\",\n \"def compile(self):\\n print \\\"+ Nothing to be done ;)\\\"\",\n \"def generate():\",\n \"def code():\",\n \"def get_source(self) -> str:\\n return unparse(self.tree)\",\n \"def dump_tree(self) -> str:\\n return utils.dump_tree(self._tree)\",\n \"def generate(random, pid, autogen_tools, n):\\n\\n generator_path = autogen_tools.get_directory(__file__)\\n\\n template_path = path.join(generator_path, \\\"code.txt.template\\\")\\n rendered_template_path = path.join(generator_path, \\\"code.txt\\\")\\n\\n autogen_tools.replace_source_tokens(\\n template_path,\\n {\\\"flag\\\": gen_code(n, \\\"Aviation House\\\")},\\n rendered_template_path\\n )\\n\\n code_link = autogen_tools.generate_resource_link(pid, \\\"code.txt\\\", title=\\\"Encrypted file\\\")\\n\\n return {\\n \\\"resource_files\\\": {\\n \\\"public\\\": [\\n (rendered_template_path, \\\"code.txt\\\"),\\n ],\\n },\\n \\\"static_files\\\": {\\n },\\n \\\"problem_updates\\\": {\\n \\\"description\\\": \\\"<p>We've updated the system to AES. We heard that this is military grade encryption so that should fix everything</p><p>The team have stored the password in %s. Bet you can't get into it</p>\\\" % code_link\\n }\\n }\",\n \"def parseprint(code, filename=\\\"<string>\\\", mode=\\\"exec\\\", **kwargs):\\n node = parse(code, mode=mode) # An ode to the code\\n print(dump(node, **kwargs))\",\n \"def write_syntax_tree(self, compilation_engine):\\n output_file = '{}ktCST.xml'.format(compilation_engine.filename[:-5])\\n with open(output_file, 'w') as f:\\n print 'writing syntax tree to {}'.format(output_file)\\n f.write(''.join(compilation_engine.contents))\",\n \"def printTree(self, tree, str):\\n\\n\\t\\tif type(tree) == dict:\\n\\t\\t\\tfor item in list(tree.values())[0].keys():\\n\\t\\t\\t\\t\\tprint(\\\"%s %s = %s \\\" % (str, list(tree.keys())[0], item))\\n\\t\\t\\t\\t\\tself.printTree(list(tree.values())[0][item], str + \\\"\\\\t\\\")\\n\\t\\telse:\\n\\t\\t\\tprint(\\\"%s -> %s = %s\\\" % (str, self.targetName, tree))\",\n \"def print_tree(tree):\\n if not tree:\\n print None\\n return\\n \\n if tree.children:\\n print 'Directory hash = {}'.format(base64.urlsafe_b64encode(tree.dmt_hash))\\n print 'Contents:'\\n for name, subtree in tree.children.iteritems():\\n print\\n print name\\n print_tree(subtree)\\n \\n else:\\n print 'File hash = {}'.format(base64.urlsafe_b64encode(tree.dmt_hash))\",\n \"def printGeneTree(self):\\n align = AlignIO.read(self.newPhylip, 'phylip') # Reads created .phy file containing the SeqRecord\\n #print (align) # prints concatenated allignments\\n calculator = DistanceCalculator('identity')\\n dm = calculator.get_distance(align)# Calculate the distance matrix\\n print('\\\\n======================================== DISTANCE MATRIX =======================================\\\\n')\\n print(dm,\\\"\\\\n\\\\n\\\") # Print the distance Matrix\\n constructor = DistanceTreeConstructor() # Construct the phylogenetic tree using UPGMA algorithm\\n tree = constructor.upgma(dm)\\n print('\\\\n========================================= GENE TREE ===========================================\\\\n')\\n Phylo.draw(tree) # Draw the phylogenetic tree (must install matplotlib to use this formatting)\\n Phylo.draw_ascii(tree) # Print the phylogenetic tree in terminal\",\n \"def draw(self):\\n for tree_idx, tree in enumerate(self.trees):\\n print(\\\"==========================================\\\\nTree\\\",\\n tree_idx)\\n self._print_tree(tree)\",\n \"def printShader(self):\\n print self.file\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\",\n \"def generateCode(self, codeGenerator):\\n return codeGenerator.generateCode(self)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.64935875","0.61715996","0.6161561","0.6096719","0.5994618","0.5992754","0.5926395","0.5877472","0.5846541","0.5706362","0.56789345","0.5666667","0.56597686","0.56378835","0.5628827","0.56088674","0.558582","0.5557028","0.5552948","0.55473834","0.5543297","0.5527665","0.5521648","0.55140066","0.549379","0.5479612","0.5470216","0.5436203","0.54231673","0.5418352","0.5403666","0.5387963","0.53863454","0.5324167","0.52946997","0.52830756","0.5281511","0.52806485","0.5255019","0.5235609","0.52326745","0.5217424","0.52144957","0.5197233","0.51951915","0.5191265","0.5190351","0.5189717","0.5187377","0.51873344","0.51873344","0.51778144","0.517649","0.51738536","0.5160705","0.5154179","0.5149757","0.5149695","0.51486945","0.51348674","0.5134184","0.5131909","0.513073","0.51227677","0.5111687","0.51029223","0.5096148","0.5089488","0.50887537","0.5085902","0.50816584","0.50738597","0.50563174","0.5055968","0.50545084","0.50487196","0.5044836","0.5042892","0.502828","0.50250345","0.50179636","0.5011766","0.50105345","0.50065094","0.5005044","0.50008416","0.4997291","0.4994041","0.49853635","0.49853635","0.49853635","0.49853635","0.49853635","0.49853635","0.49853635","0.49853635","0.49853635","0.49853635","0.49853635","0.49853635","0.49853635"],"string":"[\n \"0.64935875\",\n \"0.61715996\",\n \"0.6161561\",\n \"0.6096719\",\n \"0.5994618\",\n \"0.5992754\",\n \"0.5926395\",\n \"0.5877472\",\n \"0.5846541\",\n \"0.5706362\",\n \"0.56789345\",\n \"0.5666667\",\n \"0.56597686\",\n \"0.56378835\",\n \"0.5628827\",\n \"0.56088674\",\n \"0.558582\",\n \"0.5557028\",\n \"0.5552948\",\n \"0.55473834\",\n \"0.5543297\",\n \"0.5527665\",\n \"0.5521648\",\n \"0.55140066\",\n \"0.549379\",\n \"0.5479612\",\n \"0.5470216\",\n \"0.5436203\",\n \"0.54231673\",\n \"0.5418352\",\n \"0.5403666\",\n \"0.5387963\",\n \"0.53863454\",\n \"0.5324167\",\n \"0.52946997\",\n \"0.52830756\",\n \"0.5281511\",\n \"0.52806485\",\n \"0.5255019\",\n \"0.5235609\",\n \"0.52326745\",\n \"0.5217424\",\n \"0.52144957\",\n \"0.5197233\",\n \"0.51951915\",\n \"0.5191265\",\n \"0.5190351\",\n \"0.5189717\",\n \"0.5187377\",\n \"0.51873344\",\n \"0.51873344\",\n \"0.51778144\",\n \"0.517649\",\n \"0.51738536\",\n \"0.5160705\",\n \"0.5154179\",\n \"0.5149757\",\n \"0.5149695\",\n \"0.51486945\",\n \"0.51348674\",\n \"0.5134184\",\n \"0.5131909\",\n \"0.513073\",\n \"0.51227677\",\n \"0.5111687\",\n \"0.51029223\",\n \"0.5096148\",\n \"0.5089488\",\n \"0.50887537\",\n \"0.5085902\",\n \"0.50816584\",\n \"0.50738597\",\n \"0.50563174\",\n \"0.5055968\",\n \"0.50545084\",\n \"0.50487196\",\n \"0.5044836\",\n \"0.5042892\",\n \"0.502828\",\n \"0.50250345\",\n \"0.50179636\",\n \"0.5011766\",\n \"0.50105345\",\n \"0.50065094\",\n \"0.5005044\",\n \"0.50008416\",\n \"0.4997291\",\n \"0.4994041\",\n \"0.49853635\",\n \"0.49853635\",\n \"0.49853635\",\n \"0.49853635\",\n \"0.49853635\",\n \"0.49853635\",\n \"0.49853635\",\n \"0.49853635\",\n \"0.49853635\",\n \"0.49853635\",\n \"0.49853635\",\n \"0.49853635\",\n \"0.49853635\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17780,"cells":{"query":{"kind":"string","value":"Gets the device function name by translating a typed Python version to a templated cpp version. Python functions looks like getVariableFloatArray6 and translate to getVariable This function will detect and test against a set of known types and also extract the Array length This function returns None if the string is invalid in format but only throws an error if the format is correct but the type is invalid."},"document":{"kind":"string","value":"def _deviceVariableFunctionName(self, tree, permitted_prefixes, allow_lengths = True):\n cpp_func_name = \"\"\n py_func = tree.attr\n # extract function name start\n for prefix in permitted_prefixes:\n if py_func.startswith(prefix):\n cpp_func_name = prefix\n py_func = py_func[len(prefix):]\n break # dont allow the else\n else:\n return None\n # check type and lengths\n if allow_lengths:\n #split to get type and Array Length (This could **potentially** be looked up from the model description but current syntax is consistent with swig bindings) \n type_and_length = py_func.split(\"Array\")\n if type_and_length[0] not in self._fgpu_types:\n self.RaiseError(tree, f\"'{type_and_length[0]}' is not a valid FLAME GPU type\")\n t = self._fgpu_types[type_and_length[0]]\n # generate template args\n if (len(type_and_length) == 1):\n cpp_func_name += f\"<{t}>\"\n elif (len(type_and_length) == 2):\n cpp_func_name += f\"<{t}, {type_and_length[1]}>\"\n else:\n return None\n else:\n if py_func not in self._fgpu_types:\n self.RaiseError(tree, f\"'{py_func}' is not a valid FLAME GPU type\")\n t = self._fgpu_types[py_func]\n cpp_func_name += f\"<{t}>\"\n # return \n return cpp_func_name"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def function_name_to_string(func):\n if func == statistical_parity_difference:\n return \"Statistical Parity Difference\"\n if func == theil_index:\n return \"Theil Index\"\n if func == equal_opportunity_difference:\n return \"Equal Opportunity Difference\"\n if func == disparate_impact:\n return \"Disparate Impact\"\n if func == average_odds_difference:\n return \"Average Odds Difference\"\n if func == auc:\n return \"AUC\"\n if func == binary_accuracy:\n return \"Binary Accuracy\"","def cython_functionname(self, t, cycyt=None):\n if cycyt is None:\n t = self.canon(t)\n if isinstance(t, basestring):\n return t, self.cython_functionnames[t]\n elif t[0] in self.base_types:\n return t, self.cython_functionnames[t[0]]\n return self.cython_functionname(t, self.cython_functionnames[t[0]])\n d = {}\n for key, x in zip(self.template_types[t[0]], t[1:-1]):\n if isinstance(x, basestring):\n val = self.cython_functionnames[x] if x in self.cython_functionnames \\\n else x\n elif isinstance(x, Number):\n val = str(x).replace('-', 'Neg').replace('+', 'Pos')\\\n .replace('.', 'point')\n elif x[0] in self.base_types:\n val = self.cython_functionnames[x[0]]\n else:\n _, val = self.cython_functionname(x, self.cython_functionnames[x[0]])\n d[key] = val\n return t, cycyt.format(**d)","def _get_source_type(version_string):\n # type: (str) -> Optional[str]\n if not version_string:\n return None\n\n if \"http://\" in version_string or \"https://\" in version_string:\n return \"url\"\n elif os.path.exists(version_string) and os.path.isfile(version_string):\n return \"file\"\n elif re.match(r\"\\d+\\.\\d+\\.\\d+\", version_string) or version_string == \"current\":\n return \"install_script\"\n else:\n raise ValueError(\n 'Invalid value \"%s\" for version_string. If it\\'s a path to a file, make'\n \"sure the file exists and if it's a URL, ensure URL exists.\"\n % (version_string)\n )","def get_type_from_str(type_str):\n try:\n # Assume the current language to be C/C++ and make a try.\n return gdb.parse_and_eval(\"(%s *)0\" % type_str).type.target()\n except RuntimeError:\n # If assumption of current language to be C/C++ was wrong, then\n # lookup the type using the API.\n try:\n return gdb.lookup_type(type_str)\n except RuntimeError:\n return None","def _get_uniform_function_name(varinfo):\n # NOTE: varinfo == dict(vartype=vartype, ndim=ndim, size=size)\n float_suffix = {True: 'f', False: 'i'}\n array_suffix = {True: 'v', False: ''}\n \n vartype = varinfo[\"vartype\"]\n ndim = varinfo[\"ndim\"]\n size = varinfo.get(\"size\", None)\n args = ()\n \n # scalar or vector uniform\n if type(ndim) == int or type(ndim) == long:\n # find function name\n funname = \"glUniform%d%s%s\" % (ndim, \\\n float_suffix[vartype == \"float\"], \\\n array_suffix[size is not None])\n\n # find function arguments\n if size is not None:\n args += (size,)\n \n # matrix uniform\n elif type(ndim) == tuple:\n # find function name\n funname = \"glUniformMatrix%dfv\" % (ndim[0])\n args += (1, False,)\n \n return funname, args","def _parse_function(proto_string):\n _, parsed_features = tf.parse_single_sequence_example(\n proto_string, sequence_features=_parsing_spec(input_size))\n lengths = tf.shape(parsed_features['inputs'])[0]\n # Type int32 is not supported by parsing spec, but int64 is not supported by\n # XLA. Here cast to int32. The parsing functions are executed by the C++\n # threads. So, casting op will not slow down the session.run call later.\n labels = tf.to_int32(parsed_features['labels'])\n return parsed_features['inputs'], lengths, labels","def src_get_name(converter_type):\n return ffi.string(_lib.src_get_name(converter_type)).decode()","def unparse_type(type_str):\n if not type_str.startswith('array'):\n return type_str\n arg_dim = type_str.lstrip('array')[0]\n data_type = type_str.lstrip('array')[1:]\n arg_type = \"vizgen.ndarray('\" + data_type + \"', \" + arg_dim + \")\"\n return arg_type","def formatLookup(format_str):\n pat = '(\\d+)([A-Z])'\n match = re.search(pat, format_str)\n #print match.group()\n \n data_len = int(match.group(1))\n data_fmt = str(match.group(2))\n np_fmt = fitsFormatLookup(data_fmt)\n np_dtype = '%i%s'%(data_len, np_fmt)\n \n return np_dtype, data_len, np_fmt","def find_type(token_string: str):\n if re.compile('\\d+').match(token_string):\n return 'number'\n elif re.compile('[a-zA-Z]').match(token_string):\n return 'id'\n elif re.compile('\\*|\\+|-|/').match(token_string):\n return 'op'\n else:\n return 'undefined'","def sample_type_str(t):\n if t == dsl.Type.NUMBER or t == dsl.Type.DIGIT:\n return get_number()\n elif t == dsl.Type.WORD:\n return get_word()\n elif t == dsl.Type.ALPHANUM or t == dsl.Type.CHAR:\n return get_alphanumeric()\n elif t == dsl.Type.ALL_CAPS:\n return get_caps()\n elif t == dsl.Type.PROP_CASE:\n return get_proper_case()\n elif t == dsl.Type.LOWER:\n return get_lower()\n else:\n raise ValueError('Unsupported type: {}'.format(t))","def _get_shader_type(varinfo):\n if type(varinfo[\"ndim\"]) == int or type(varinfo[\"ndim\"]) == long:\n if varinfo[\"ndim\"] == 1:\n shader_type = varinfo[\"vartype\"]\n elif varinfo[\"ndim\"] >= 2:\n shader_type = \"vec%d\" % varinfo[\"ndim\"]\n if varinfo[\"vartype\"] != \"float\":\n shader_type = \"i\" + shader_type\n # matrix: (2,2) or (3,3) or (4,4)\n elif type(varinfo[\"ndim\"]) == tuple:\n shader_type = \"mat%d\" % varinfo[\"ndim\"][0]\n return shader_type","def generate_definition(self):\n apientry = \"\"\n if self.__name[:2] == \"gl\":\n apientry = \"DNLOAD_APIENTRY \"\n params = \"void\"\n if self.__parameters:\n params = \", \".join(self.__parameters)\n return \"%s (%s*%s)(%s)\" % (self.__returntype, apientry, self.__name, params)","def generate_prototype(self):\n apientry = \"\"\n if self.__name[:2] == \"gl\":\n apientry = \"DNLOAD_APIENTRY \"\n params = \"void\"\n if self.__parameters:\n params = \", \".join(self.__parameters)\n return \"(%s (%s*)(%s))\" % (self.__returntype, apientry, params)","def compatible_firmware_version(self):\n identifier = self.firmware_version.split('compiled')[0]\n buf_size = self.MAX_BUF_SIZE\n buf = (ctypes.c_char * buf_size)()\n res = self._dll.JLINKARM_GetEmbeddedFWString(identifier.encode(), buf, buf_size)\n if res < 0:\n raise errors.JLinkException(res)\n\n return ctypes.string_at(buf).decode()","def get_jtype_string(c_type: str) -> str:\n m = STRING_PAT.match(c_type)\n if not m:\n return \"\"\n return \"jstring\"","def get_parsed_declaration(self) -> str:\n args = self._get_arguments()\n\n func = self.node\n tu = func.tu\n\n # For functions the extent encompasses the return value, and the\n # location is the beginning of the functions name. So we can consume\n # all tokens in between.\n end = cindex.SourceLocation.from_offset(\n tu, func.location.file, func.location.offset - 1\n )\n extent = cindex.SourceRange.from_locations(func.extent.start, end)\n\n return_type = \" \".join(\n t.spelling for t in cindex.TokenGroup.get_tokens(tu, extent=extent)\n )\n\n return f\"{return_type} {func.spelling}({args})\"","def _handle_string(\n *, artifacts: types.ColumnArtifacts\n) -> typing.Union[String, Binary, Date, DateTime]:\n if artifacts.open_api.format in {None, \"byte\", \"password\"}:\n if artifacts.open_api.max_length is None:\n return String\n return String(length=artifacts.open_api.max_length)\n if artifacts.open_api.format == \"binary\":\n if artifacts.open_api.max_length is None:\n return Binary\n return Binary(length=artifacts.open_api.max_length)\n if artifacts.open_api.format == \"date\":\n return Date\n if artifacts.open_api.format == \"date-time\":\n return DateTime\n raise exceptions.FeatureNotImplementedError(\n f\"{artifacts.open_api.format} format for string is not supported.\"\n )","def parse_typename(typename):\n if typename is None:\n raise ValueError(\"function type must be provided\")\n idx = typename.rfind(\"/\")\n if idx < 0:\n raise ValueError(\"function type must be of the from namespace/name\")\n namespace = typename[:idx]\n if not namespace:\n raise ValueError(\"function type's namespace must not be empty\")\n type = typename[idx + 1:]\n if not type:\n raise ValueError(\"function type's name must not be empty\")\n return namespace, type","def get_fun_name(line):\n match = re.match(r'(function|macro)\\s*\\((\\w+)', line)\n if not match:\n return\n return match.groups()[1]","def get_data_name(data_func, data_type, npoints, y_error_sigma, x_error_sigma):\n data_name = '{}_{}'.format(data_func.__name__, data_type)\n if data_func.__name__ != 'get_image':\n data_name += 'funcs'\n data_name += '_{}pts_{}ye'.format(npoints, y_error_sigma)\n if x_error_sigma is not None:\n data_name += '_{}xe'.format(x_error_sigma)\n return data_name.replace('.', '_')","def typeName (self, typecode):\n if typecode == qmf2.SCHEMA_DATA_VOID: return \"void\"\n elif typecode == qmf2.SCHEMA_DATA_BOOL: return \"bool\"\n elif typecode == qmf2.SCHEMA_DATA_INT: return \"int\"\n elif typecode == qmf2.SCHEMA_DATA_FLOAT: return \"float\"\n elif typecode == qmf2.SCHEMA_DATA_STRING: return \"string\"\n elif typecode == qmf2.SCHEMA_DATA_MAP: return \"map\"\n elif typecode == qmf2.SCHEMA_DATA_LIST: return \"list\"\n elif typecode == qmf2.SCHEMA_DATA_UUID: return \"uuid\"\n else:\n raise ValueError (\"Invalid type code: %s\" % str(typecode))","def _get_shader_type_noint(varinfo):\n if varinfo[\"ndim\"] == 1:\n shader_type = \"float\"\n elif varinfo[\"ndim\"] >= 2:\n shader_type = \"vec%d\" % varinfo[\"ndim\"]\n # matrix: (2,2) or (3,3) or (4,4)\n elif type(varinfo[\"ndim\"]) == tuple:\n shader_type = \"mat%d\" % varinfo[\"ndim\"][0]\n return shader_type","def GrabVariableInitializerType(line: str) -> (FClass.FiMVariableTypes,int):\n # Boolean\n if line.startswith( FGlobal.Methods['Variable Boolean Array'] ):\n length = len( _findFirst(FGlobal.Methods['Variable Boolean Array'], lambda x: line.startswith(x)) )\n return FClass.FiMVariableTypes.BOOL_ARRAY, length\n if line.startswith( FGlobal.Methods['Variable Boolean'] ):\n length = len( _findFirst(FGlobal.Methods['Variable Boolean'], lambda x: line.startswith(x)) )\n return FClass.FiMVariableTypes.BOOL, length\n # Number\n if line.startswith( FGlobal.Methods['Variable Number Array'] ):\n length = len( _findFirst(FGlobal.Methods['Variable Number Array'], lambda x: line.startswith(x)) )\n return FClass.FiMVariableTypes.NUMBER_ARRAY, length\n if line.startswith( FGlobal.Methods['Variable Number'] ):\n length = len( _findFirst(FGlobal.Methods['Variable Number'], lambda x: line.startswith(x)) )\n return FClass.FiMVariableTypes.NUMBER, length\n # String\n if line.startswith( FGlobal.Methods['Variable String Array'] ):\n length = len( _findFirst(FGlobal.Methods['Variable String Array'], lambda x: line.startswith(x)) )\n return FClass.FiMVariableTypes.STRING_ARRAY, length\n if line.startswith( FGlobal.Methods['Variable String'] ):\n length = len( _findFirst(FGlobal.Methods['Variable String'], lambda x: line.startswith(x)) )\n return FClass.FiMVariableTypes.STRING, length\n # Char\n if line.startswith( FGlobal.Methods['Variable Character'] ):\n length = len( _findFirst(FGlobal.Methods['Variable Character'], lambda x: line.startswith(x)) )\n return FClass.FiMVariableTypes.CHAR, length\n\n # Default value\n return FClass.FiMVariableTypes.UNKNOWN,-1","def firmware_version(self):\n buf = (ctypes.c_char * self.MAX_BUF_SIZE)()\n self._dll.JLINKARM_GetFirmwareString(buf, self.MAX_BUF_SIZE)\n return ctypes.string_at(buf).decode()","def get_function_raw_name_at(self, address):\n pass","def extract_function_name(maybe_function_str: str) -> Optional[str]:\n match = STACK_TRACE_LINE_RE.search(maybe_function_str)\n if match is not None:\n return match.group(2)\n return None","def funcstring(funcname):\n s = str(funcname)[10:] #chop off '<function '\n spi = s.index(' ')\n return s[:spi]","def config_gettype(function_name,\n config_name,\n param):\n config = configparser.ConfigParser()\n config.read(path_creator(config_name))\n #config.read(path_creator(config_name))\n if config[function_name][param].split(' ## ')[1] == 'str':\n return str(config.get(function_name,param).split(' ## ')[0])\n if config[function_name][param].split(' ## ')[1] == 'int':\n return int(config.get(function_name,param).split(' ## ')[0])\n if config[function_name][param].split(' ## ')[1] == 'float':\n return float(config.get(function_name,param).split(' ## ')[0])\n if config[function_name][param].split(' ## ')[1] == 'bool':\n return bool(config.get(function_name,param).split(' ## ')[0])\n if config[function_name][param].split(' ## ')[1] == 'path':\n return path_creator(str(config.get(function_name,param).split(' ## ')[0]))\n if config[function_name][param].split(' ## ')[1] == 'NoneType':\n return None","def get_func_type(header):\n func_type = header.functionType\n if func_type == SSE.SCALAR:\n return FunctionType.Scalar\n elif func_type == SSE.AGGREGATION:\n return FunctionType.Aggregation\n elif func_type == SSE.TENSOR:\n return FunctionType.Tensor","def get_process_func(dataset_type, dsname):\n\n dsformat = 'VOL'\n if dataset_type == 'RAW':\n func_name = process_raw\n elif dataset_type == 'NCVOL':\n func_name = process_save_radar\n elif dataset_type == 'PWR':\n func_name = 'process_signal_power'\n elif dataset_type == 'SNR':\n func_name = 'process_snr'\n elif dataset_type == 'RHOHV_CORRECTION':\n func_name = 'process_correct_noise_rhohv'\n elif dataset_type == 'BIAS_CORRECTION':\n func_name = 'process_correct_bias'\n elif dataset_type == 'L':\n func_name = 'process_l'\n elif dataset_type == 'CDR':\n func_name = 'process_cdr'\n elif dataset_type == 'SAN':\n func_name = 'process_echo_id'\n elif dataset_type == 'ECHO_FILTER':\n func_name = 'process_echo_filter'\n elif dataset_type == 'SNR_FILTER':\n func_name = 'process_filter_snr'\n elif dataset_type == 'VIS_FILTER':\n func_name = 'process_filter_visibility'\n elif dataset_type == 'OUTLIER_FILTER':\n func_name = 'process_outlier_filter'\n elif dataset_type == 'PHIDP0_CORRECTION':\n func_name = 'process_correct_phidp0'\n elif dataset_type == 'PHIDP_SMOOTH_1W':\n func_name = 'process_smooth_phidp_single_window'\n elif dataset_type == 'PHIDP_SMOOTH_2W':\n func_name = 'process_smooth_phidp_double_window'\n elif dataset_type == 'PHIDP_KDP_MAESAKA':\n func_name = 'process_phidp_kdp_Maesaka'\n elif dataset_type == 'PHIDP_KDP_LP':\n func_name = 'process_phidp_kdp_lp'\n elif dataset_type == 'KDP_LEASTSQUARE_1W':\n func_name = 'process_kdp_leastsquare_single_window'\n elif dataset_type == 'KDP_LEASTSQUARE_2W':\n func_name = 'process_kdp_leastsquare_double_window'\n elif dataset_type == 'ATTENUATION':\n func_name = 'process_attenuation'\n elif dataset_type == 'RAINRATE':\n func_name = 'process_rainrate'\n elif dataset_type == 'WIND_VEL':\n func_name = 'process_wind_vel'\n elif dataset_type == 'WINDSHEAR':\n func_name = 'process_windshear'\n elif dataset_type == 'HYDROCLASS':\n func_name = 'process_hydroclass'\n elif dataset_type == 'PHIDP0_ESTIMATE':\n func_name = 'process_estimate_phidp0'\n elif dataset_type == 'RHOHV_RAIN':\n func_name = 'process_rhohv_rain'\n elif dataset_type == 'ZDR_RAIN':\n func_name = 'process_zdr_rain'\n elif dataset_type == 'SELFCONSISTENCY_KDP_PHIDP':\n func_name = 'process_selfconsistency_kdp_phidp'\n elif dataset_type == 'SELFCONSISTENCY_BIAS':\n func_name = 'process_selfconsistency_bias'\n elif dataset_type == 'TIME_AVG':\n func_name = 'process_time_avg'\n dsformat = 'TIMEAVG'\n elif dataset_type == 'WEIGHTED_TIME_AVG':\n func_name = 'process_weighted_time_avg'\n dsformat = 'TIMEAVG'\n elif dataset_type == 'FLAG_TIME_AVG':\n func_name = 'process_time_avg_flag'\n dsformat = 'TIMEAVG'\n elif dataset_type == 'COLOCATED_GATES':\n func_name = 'process_colocated_gates'\n dsformat = 'COLOCATED_GATES'\n elif dataset_type == 'INTERCOMP':\n func_name = 'process_intercomp'\n dsformat = 'INTERCOMP'\n elif dataset_type == 'INTERCOMP_TIME_AVG':\n func_name = 'process_intercomp_time_avg'\n dsformat = 'INTERCOMP'\n elif dataset_type == 'MONITORING':\n func_name = 'process_monitoring'\n dsformat = 'MONITORING'\n elif dataset_type == 'SUN_HITS':\n func_name = 'process_sun_hits'\n dsformat = 'SUN_HITS'\n elif dataset_type == 'POINT_MEASUREMENT':\n func_name = process_point_measurement\n dsformat = 'TIMESERIES'\n elif dataset_type == 'TRAJ':\n func_name = process_trajectory\n dsformat = 'TRAJ_ONLY'\n elif dataset_type == 'TRAJ_ATPLANE':\n func_name = process_traj_atplane\n dsformat = 'TIMESERIES'\n elif dataset_type == 'TRAJ_ANTENNA_PATTERN':\n func_name = process_traj_antenna_pattern\n dsformat = 'TIMESERIES'\n else:\n raise ValueError(\"ERROR: Unknown dataset type '%s' of dataset '%s'\"\n % (dataset_type, dsname))\n\n return func_name, dsformat","def funcname(funcstr):\n ps = funcstr.find('(')\n return funcstr[:ps]","def get_type(args_str, entry_type):\r\n # The C-method-implementations accept self as the first argument,\r\n # so a one-argument method will be invoked with zero arguments in Python.\r\n no_args = 1 if entry_type == \"method\" else 0\r\n return (\"METH_NOARGS\" if len(args_str.split(\",\")) == no_args\r\n else \"METH_VARARGS\")","def get_class_decoder_function_name(name):\n name = get_class_functional_name(name)\n return 'decode_{0}'.format(name)","def typer(func):\n # get name of component from the name of the module, removing \"s.py\"\n component_name = os.path.basename(inspect.stack()[1][0].f_code.co_filename[:-4])\n\n def wrapper(s):\n out = func(s)\n if out is None:\n raise ArgumentTypeError(\"{} type not recognized\".format(component_name))\n return out\n\n wrapper.__doc__ = \"\"\"Validate a string identifying a {component} returned by get_{component}.\n\n Args:\n s (str): string specifying {article} {component}.\n Returns:\n (str): uniquely identifying string for a {component}.\n \"\"\".format(\n component=component_name,\n article=\"an\" if component_name[0] in (\"a\", \"e\", \"i\", \"o\", \"u\") else \"a\",\n )\n\n return wrapper","def cpp_funcname(self, name, argkinds=None):\n if isinstance(name, basestring):\n return name\n if argkinds is None:\n argkinds = [(Arg.NONE, None)] * (len(name) - 1)\n fname = name[0]\n cts = []\n for x, (argkind, argvalue) in zip(name[1:], argkinds):\n if argkind is Arg.TYPE:\n ct = self.cpp_type(x)\n elif argkind is Arg.LIT:\n ct = self.cpp_literal(x)\n elif isinstance(x, Number):\n ct = self.cpp_literal(x)\n else:\n try:\n ct = self.cpp_type(x) # guess it is a type\n except TypeError:\n ct = x # guess it is a variable\n cts.append(ct)\n fname += '' if 0 == len(cts) else \"< \" + \", \".join(cts) + \" >\"\n return fname","def get_type_string(data):\r\n data_type = type(data)\r\n\r\n if data_type in (int, long):\r\n return 'integer'\r\n elif data_type == float:\r\n return 'float'\r\n elif data_type == bool:\r\n return 'boolean'\r\n elif data_type in (list, tuple):\r\n return 'list'\r\n elif data_type == dict:\r\n return 'hash'\r\n elif data is None:\r\n return 'null'\r\n elif isinstance(data, basestring):\r\n return 'string'","def get_ctype_name(*args):\n return _ida_hexrays.get_ctype_name(*args)","def function_name(parameters):","def get_model(model: str) -> Any:\n try:\n model_function = eval(model)\n except (NameError, AttributeError) as err:\n sys.exit(f'{err}. Accepted models from {tf}, {sm}, {tfa}, {tfc}')\n return model_function","def _get_type(self, value: str) -> str:\n\n\t\tif value is None or not value:\n\t\t\treturn \"None\"\n\n\t\t# Get and return a single type\n\t\text: str = value[-4:].lower()\n\n\t\t# Check if value is File/Filepath\n\t\tif ext in self._VALID_FILE_EXT:\n\t\t\tif ext == \".xml\":\n\t\t\t\t# Add to filenames list\n\t\t\t\tfilename = os.path.basename(value.title())\n\t\t\t\tself.filenames.add(filename)\n\t\t\t# Check if file is directory\n\t\t\tif \"\\\\\" in value or \"/\" in value:\n\t\t\t\treturn \"Filepath\"\n\t\t\treturn \"File\"\n\n\t\t# Check if value is Dir\n\t\tif \"\\\\\" in value or \"/\" in value:\n\t\t\treturn \"Dir\"\n\n\t\t# Check if value is Floatf\n\t\tif value.endswith(\"f\"):\n\t\t\ttry:\n\t\t\t\t# This will raise a ValueError if it cannot convert to float\n\t\t\t\tfloat(value[0:-1])\n\t\t\t\treturn \"Floatf\"\n\t\t\texcept ValueError:\n\t\t\t\tpass\n\n\t\t# Check if value is Int\n\t\ttry:\n\t\t\t# This will raise a ValueError if it cannot convert to int\n\t\t\tint(value)\n\t\t\treturn \"Int\"\n\t\texcept ValueError:\n\t\t\tpass\n\n\t\t# Check if value is Float\n\t\tif \".\" in value:\n\t\t\ttry:\n\t\t\t\t# This will raise a ValueError if it cannot convert to float\n\t\t\t\tfloat(value)\n\t\t\t\treturn \"Float\"\n\t\t\texcept ValueError:\n\t\t\t\tpass\n\n\t\t# Check if value is Bool\n\t\telif value.lower() in self._VALID_BOOL:\n\t\t\t# Check if True/False or Yes/No instead of true/false or yes/no\n\t\t\treturn \"Bool\"\n\n\t\t# Give up, assume reference\n\t\treturn \"Ref\"","def __get_device_type_name(self, mps_db_session, device_type_id):\n device_type = mps_db_session.query(models.DeviceType).filter(models.DeviceType.id==device_type_id).all()\n\n if len(device_type) == 1:\n return device_type[0].name\n elif len(device_type) == 0:\n raise ValueError(\"Function \\\"__get_device_type_name(device_type_id={}). Not fault was found.\\\"\"\n .format(device_type_id))\n else:\n raise ValueError(\"Function \\\"__get_device_type_name(device_type_id={}). More than one device matches.\\\"\"\n .format(device_type_id))","def cfunc_type(self):\n tif = ida_typeinf.tinfo_t()\n result = self.get_func_type(tif)\n if not result:\n return\n return tif","def get_function(self, type: str) -> Optional[Callable]:\n function_str = getattr(self, type)\n if function_str:\n return import_string(f\"electricitymap.contrib.parsers.{function_str}\")","def str_to_type(name_type):\n if name_type == 'float' or name_type == 'Float':\n return float\n if name_type == 'bool':\n return bool\n if name_type == 'int':\n return lambda x: int(float(x))\n if name_type == 'list':\n return ast.literal_eval\n if name_type == 'date':\n return lambda x: dateutil.parser.parse(x).strftime('%Y-%m-%dT%H:%M:%SZ')\n if name_type == 'str':\n return str\n\n \n return None","def to_py_name(cpp_name, entry_type):\r\n if entry_type == 'function':\r\n return cpp_name\r\n first_underscore = cpp_name.find('_')\r\n assert(first_underscore != -1)\r\n return cpp_name[first_underscore + 1:]","def device_type(devices):\n num_of_types = len({type(device) for device in devices})\n if num_of_types == 1:\n return devices[0].list_type.replace(\"_\", \" \").title()\n elif num_of_types == 0:\n return None\n else:\n raise ValueError","def parse_function_line(line: str):\n without_call = (line.split(\"function\")[1]).strip()\n split_array = without_call.split()\n function = split_array[0]\n return function","def get_func_type(self, *args):\n return _ida_hexrays.cfunc_t_get_func_type(self, *args)","def basictypestring(\n dtype_char: ty.Text,\n length: int = -1,\n byteorder: ty.Text = \"=\"\n ) -> ty.Text:\n # only specify length if we have a variable width type\n lengthstr = str(length) if dtype_char in VARLENGTH_TYPE_CHARS else \"\"\n # only specify byte order if we need to\n byteorderchar = byteorder if byteorder not in (\"=\", \"|\") else \"\"\n return f\"{byteorderchar}{dtype_char}{lengthstr}\"","def get_jtype_raw(c_type: str) -> str:\n s = c_type.strip()\n if 'void' == s:\n return 'void'\n m = RAW_PAT.match(s)\n if not m:\n return \"\"\n return 'j{}'.format(m.group('type_name'))","def get_interface_type(interface):\n\n if interface is None:\n return None\n\n iftype = None\n\n if interface.upper().startswith('GE'):\n iftype = 'ge'\n elif interface.upper().startswith('10GE'):\n iftype = '10ge'\n elif interface.upper().startswith('25GE'):\n iftype = '25ge'\n elif interface.upper().startswith('40GE'):\n iftype = '40ge'\n elif interface.upper().startswith('100GE'):\n iftype = '100ge'\n elif interface.upper().startswith('PORT-GROUP'):\n iftype = 'stack-Port'\n elif interface.upper().startswith('NULL'):\n iftype = 'null'\n else:\n return None\n return iftype.lower()","def astType(cls, source):\n if source == '':\n return cls.BLANK\n if source == \"OPENQASM 2.0;\":\n return cls.DECLARATION_QASM_2_0\n x = QTRegEx.COMMENT.search(source)\n if x:\n return cls.COMMENT\n x = QTRegEx.INCLUDE.search(source)\n if x:\n return cls.INCLUDE\n x = QTRegEx.CTL_2.search(source)\n if x:\n if x.group(1) == 'if':\n return cls.CTL_2\n x = QTRegEx.QREG.search(source)\n if x:\n return cls.QREG\n x = QTRegEx.CREG.search(source)\n if x:\n return cls.CREG\n x = QTRegEx.MEASURE.search(source)\n if x:\n return cls.MEASURE\n x = QTRegEx.BARRIER.search(source)\n if x:\n return cls.BARRIER\n x = QTRegEx.GATE.search(source)\n if x:\n return cls.GATE\n x = QTRegEx.OP.search(source)\n if x:\n return cls.OP\n return cls.UNKNOWN","def device(arg):\n\tassert isinstance(arg, str)\n\treturn r\"(?P<%s>[\\w_\\-@\\' \\.]+)\" % (arg,)","def get_kernel_string(kernel_source, params=None):\n #logging.debug('get_kernel_string called with %s', str(kernel_source))\n logging.debug('get_kernel_string called')\n\n kernel_string = None\n if callable(kernel_source):\n kernel_string = kernel_source(params)\n elif isinstance(kernel_source, str):\n if looks_like_a_filename(kernel_source):\n kernel_string = read_file(kernel_source) or kernel_source\n else:\n kernel_string = kernel_source\n else:\n raise TypeError(\"Error kernel_source is not a string nor a callable function\")\n return kernel_string","def parse_function_str(function_str: str) -> Union[Callable[[object, dict], object], None]:\n\n if function_str is None:\n return None\n\n parsed_function = re.match(\"^([\\.\\w]+)(?:\\(([\\w|,%\\'-: ]*)\\))?$\", function_str)\n if not parsed_function:\n raise RuntimeError(f\"Invalid name for a transform function: '{function_str}'\")\n\n function_name, args = parsed_function.groups()\n args_list = str(args).split(\",\") if (args is not None and args != '') else []\n\n # Check if it is a built-in function\n builtin_function = FunctionBuilder.get_builtin_function(function_name, args_list)\n\n if builtin_function is not None:\n return builtin_function\n\n # Get it as custom function\n return FunctionBuilder.get_custom_function(function_name, args_list)","def data_ref_type_str(dref_enum):\n if dref_enum == 0x9000:\n return \"unknown\"\n elif dref_enum == 0x9001:\n return \"integer\"\n elif dref_enum == 0x9002:\n return \"fp\"\n elif dref_enum == 0x9003:\n return \"integer(store)\"\n else:\n return \"INVALID\"","def _get_python_prop_type(prop_type: Type[Variable]) -> str:\n if prop_type is VariableBool:\n return \"bool\"\n if prop_type in (VariableInt, VariableUInt):\n return \"int\"\n if prop_type is VariableFloat:\n return \"float\"\n if prop_type is VariableString:\n return \"bytes\"\n if prop_type is VariableVec2:\n return \"(float, float)\"\n if prop_type is VariableStruct:\n return \"dict[str, Variable]\"\n if prop_type is VariableArray:\n return \"MutableSequence\"\n raise TypeError(\"unexpected variable type\")","def get_variable_name(uuid: UUID, variable_index: int) -> Optional[str]:\n scenario: Optional[AoE2DEScenario] = store.get_scenario(uuid)\n if scenario:\n if gv := get_game_version(uuid) == \"DE\":\n variable = scenario.trigger_manager.get_variable(variable_index)\n if variable:\n return variable.name\n elif 0 <= variable_index <= 255:\n return f\"Variable {variable_index}\"\n else:\n raise ValueError(f\"Scenarios with the game version: {gv} do not support variables.\")\n return None","def get_tf_dtype(dtype_str: str):\n\n return getattr(tf, dtype_str)","def filename_type(filename):\n import re\n\n nii_re = re.compile(\".+(nii.gz)$|.+(nii)$\")\n npy_re = re.compile(\".+(npy)$|.+(npz)$\")\n\n\n if len(nii_re.findall(filename)):\n return 'nii'\n elif len(npy_re.findall(filename)):\n return 'npy'\n return None","def get(identifier):\n if identifier is None:\n return None\n if isinstance(identifier, str):\n identifier = str(identifier)\n return deserialize(identifier)\n if isinstance(identifier, dict):\n return deserialize(identifier)\n if callable(identifier):\n return identifier\n raise ValueError(\n f'Could not interpret loss function identifier: {identifier}')","def map_string2func(funcname, clss, compute_capability):\n if \"_get_\" + funcname not in globals():\n raise AttributeError(\"kernel type '\" + funcname + \"' not understood\")\n return globals()[\"_get_\" + funcname](clss, compute_capability)","def get_function_name_at(self, address):\n pass","def get_name_for(node: Union[str, cst.CSTNode]) -> Optional[str]:\n if isinstance(node, cst.Name):\n return node.value\n elif isinstance(node, str):\n return node\n elif isinstance(node, cst.Call):\n return _NameUtil.get_name_for(node.func)\n elif isinstance(node, cst.Subscript):\n return _NameUtil.get_name_for(node.value)\n elif isinstance(node, (cst.FunctionDef, cst.ClassDef)):\n return _NameUtil.get_name_for(node.name)\n return None","def detect_language(lang, kernel_source):\n if lang is None:\n if callable(kernel_source):\n raise TypeError(\"Please specify language when using a code generator function\")\n kernel_string = get_kernel_string(kernel_source)\n if \"__global__\" in kernel_string:\n lang = \"CUDA\"\n elif \"__kernel\" in kernel_string:\n lang = \"OpenCL\"\n else:\n lang = \"C\"\n return lang","def type_name(type_defn: JSON) -> str:\n rval = type_defn.get('code', '').replace('*', '').replace('@', '')\n return rval","def get_parsed_declaration(self) -> str:\n type_ = self.node.type.spelling\n\n # Libclang will return the type as `float [20]` when looking at\n # `float foo[20]`. We could look at the kind `TypeKind.CONSTANTARRAY`\n # but partitioning on the \"[\" just seems more straight forward.\n type_, *(array) = type_.partition(\"[\")\n array_contents = \"\".join(array)\n return f\"{type_} {self.name} {array_contents}\"","def _ptype(ptype_value, verbose=True):\n types = {\n 0:[\"PT_NULL\",\"Program header table entry unused\"],\n 1:[\"PT_LOAD\",\"Loadable program segment\"],\n 2:[\"PT_DYNAMIC\",\"Dynamic linking information\"],\n 3:[\"PT_INTERP\",\"Program interpreter\"],\n 4:[\"PT_NOTE\",\"Auxiliary information\"],\n 5:[\"PT_SHLIB\",\"Reserved\"],\n 6:[\"PT_PHDR\",\"Entry for header table itself\"],\n 7:[\"PT_TLS\",\"Thread-local storage segment\"],\n 8:[\"PT_NUM\",\"Number of defined types\"],\n 0x60000000:[\"PT_LOOS\",\"Start of OS-specific\"],\n 0x6474e550:[\"PT_GNU_EH_FRAME\",\"GCC .eh_frame_hdr segment\"],\n 0x6474e551:[\"PT_GNU_STACK\",\"Indicates stack executability\"],\n 0x6474e552:[\"PT_GNU_RELRO\",\"Read-only after relocation\"],\n 0x6ffffffa:[\"PT_SUNWBSS\",\"Sun Specific segment\"],\n 0x6ffffffb:[\"PT_SUNWSTACK\",\"Stack segment\"],\n 0x6fffffff:[\"PT_HIOS\",\"End of OS-specific\"],\n 0x70000000:[\"PT_LOPROC\",\"Start of processor-specific\"],\n 0x7fffffff:[\"PT_HIPROC\",\"End of processor-specific\"],\n }\n\n if types.get(ptype_value):\n value = types[ptype_value]\n if verbose:\n return \"%s (%s)\" % (value[0], value[1])\n else:\n return \"%s\" % value[0]\n else:\n return \"UNKNOWN\"","def GuessDataType(value, column_id=None):\n stripped_value = value.strip().replace('\"', '')\n\n if re.search('^-?[0-9]+$', stripped_value):\n if column_id == 'year':\n return 'date'\n else:\n return 'integer'\n elif re.search('^-?[0-9]+\\.[0-9]+$', stripped_value):\n return 'float'\n elif re.search('^[0-9]+(/|-)[0-9]+((/|-)[0-9]+){0,1}$', stripped_value):\n return 'date'\n else:\n return 'string'","def parse_dispatch_type(dispatch_string: str):\n if not dispatch_string:\n return None\n\n dispatch_string = dispatch_string.lower().strip()\n\n if dispatch_string == \"load\":\n return DispatchType.LOAD\n\n if dispatch_string == \"generating\":\n return DispatchType.GENERATOR\n\n if dispatch_string == \"generator\":\n return DispatchType.GENERATOR\n\n raise Exception(\"Unknown dispatch type: {}\".format(dispatch_string))","def name_from_dist(dist_func):\n return str(dist_func).split()[0].split('.')[-1][:-4]","def _find_extension(self, ext_type, types, values):\n counter = 0\n # For the APLN extension, grab the value in ASCII.\n if ext_type == b\"\\x00\\x10\":\n while counter < len(types):\n if types[counter] == ext_type:\n return ((values[counter][3:]).decode())\n counter += 1\n else:\n while counter < len(types):\n if types[counter] == ext_type:\n return values[counter].hex()\n counter += 1\n\n return \"\"","def parse_function_signature(code):\n m = re.search(\"^\\s*\" + re_func_decl + \"\\s*{\", code, re.M)\n if m is None:\n print(code)\n raise Exception(\"Failed to parse function signature. \"\n \"Full code is printed above.\")\n rtype, name, args = m.groups()[:3]\n if args == 'void' or args.strip() == '':\n args = []\n else:\n args = [tuple(arg.strip().split(' ')) for arg in args.split(',')]\n return name, args, rtype","def __Type(thing):\n from .native import (Vector, Color, Matrix, VectorField, ColorField,\n MatrixField, ScalarField)\n if isinstance(thing, (int, float, ScalarField)): return 'Scalar'\n if isinstance(thing, (Vector, VectorField)): return 'Vector'\n if isinstance(thing, (Color, ColorField)): return 'Color'\n if isinstance(thing, (Matrix, MatrixField)): return 'Matrix'\n raise ValueError(\"Could not guess type of: \" + thing)","def map_type(name):\n\n rv = MAPPINGS.get(name, None)\n if rv is not None:\n return rv\n\n name = name.replace(\"&\", \"*\")\n\n if name.startswith(\"const \"):\n rv = map_type(name[6:])\n\n elif name.endswith(\"const\"):\n rv = map_type(name[:-5])\n\n elif name.endswith(\" *\"):\n mapped = map_type(name[:-2])\n rv = f\"POINTER({mapped})\"\n\n elif name.endswith(\" **\"):\n mapped = map_type(name[:-1])\n rv = f\"POINTER({mapped})\"\n\n elif name.endswith(\"]\"):\n m = re.match(r\"(.*) \\[(\\d+)\\]\", name)\n if m is None:\n raise Exception(f\"Couldn't map type {name}\")\n\n mapped = map_type(m.group(1))\n count = m.group(2)\n\n rv = f\"({mapped} * {count})\"\n\n elif \"(*)\" in name:\n return \"c_void_p\"\n\n else:\n raise Exception(f\"Couldn't map type {name!r}\")\n\n MAPPINGS[name] = rv\n return rv","def get_func(name, argtypes=None, restype=c_int, lib=libDE):\n logger.debug(\"Getting NewWordFinder API function: 'name': '{}', 'argtypes': '{}',\"\n \" 'restype': '{}'.\".format(name, argtypes, restype))\n func = getattr(lib, name)\n if argtypes is not None:\n func.argtypes = argtypes\n if restype is not c_int:\n func.restype = restype\n logger.debug(\"NewWordFinder API function '{}' retrieved.\".format(name))\n return func","def src_get_version():\n return ffi.string(_lib.src_get_version()).decode()","def GetFormatType(format_):\n\n formattype = format_\n bitsize = 0\n if isinstance(format_, str):\n match = re.search(r'\\s*(\\D+)', format_)\n if match:\n formattype = match.group(0)\n bitsize = struct.calcsize(formattype) * 8\n return formattype, bitsize","def get_func_name(func, resolv_alias=True, win_characters=True):\r\n if hasattr(func, '__module__'):\r\n module = func.__module__\r\n else:\r\n try:\r\n module = inspect.getmodule(func)\r\n except TypeError:\r\n if hasattr(func, '__class__'):\r\n module = func.__class__.__module__\r\n else:\r\n module = 'unknown'\r\n if module is None:\r\n # Happens in doctests, eg\r\n module = ''\r\n if module == '__main__':\r\n try:\r\n filename = os.path.abspath(inspect.getsourcefile(func))\r\n except:\r\n filename = None\r\n if filename is not None:\r\n # mangling of full path to filename\r\n parts = filename.split(os.sep)\r\n if parts[-1].startswith('<ipython-input'):\r\n # function is defined in an IPython session. The filename\r\n # will change with every new kernel instance. This hack\r\n # always returns the same filename\r\n parts[-1] = '__ipython-input__'\r\n filename = '-'.join(parts)\r\n if filename.endswith('.py'):\r\n filename = filename[:-3]\r\n module = module + '-' + filename\r\n module = module.split('.')\r\n if hasattr(func, 'func_name'):\r\n name = func.func_name\r\n elif hasattr(func, '__name__'):\r\n name = func.__name__\r\n else:\r\n name = 'unknown'\r\n # Hack to detect functions not defined at the module-level\r\n if resolv_alias:\r\n # TODO: Maybe add a warning here?\r\n if hasattr(func, 'func_globals') and name in func.func_globals:\r\n if not func.func_globals[name] is func:\r\n name = '%s-alias' % name\r\n if inspect.ismethod(func):\r\n # We need to add the name of the class\r\n if hasattr(func, 'im_class'):\r\n klass = func.im_class\r\n module.append(klass.__name__)\r\n if os.name == 'nt' and win_characters:\r\n # Stupid windows can't encode certain characters in filenames\r\n name = _clean_win_chars(name)\r\n module = [_clean_win_chars(s) for s in module]\r\n return module, name","def get_func_type(self, *args):\n return _ida_hexrays.cfuncptr_t_get_func_type(self, *args)","def typeToName(type: int) -> unicode:\n ...","def discover_type(value) -> str:\n if isinstance(value, list):\n return \"array\"\n elif isinstance(value, bool):\n return \"bool\"\n elif isinstance(value, int):\n return \"int\"\n return \"string\"","def getStringData(self, addrString: unicode, dtName: unicode, dtId: unicode) -> ghidra.app.decompiler.DecompileCallback.StringData:\n ...","def data_type_str(self):\n data_type = self.data_type()\n if data_type == RegSZ:\n return \"RegSZ\"\n elif data_type == RegExpandSZ:\n return \"RegExpandSZ\"\n elif data_type == RegBin:\n return \"RegBin\"\n elif data_type == RegDWord:\n return \"RegDWord\"\n elif data_type == RegMultiSZ:\n return \"RegMultiSZ\"\n elif data_type == RegQWord:\n return \"RegQWord\"\n elif data_type == RegNone:\n return \"RegNone\"\n elif data_type == RegBigEndian:\n return \"RegBigEndian\"\n elif data_type == RegLink:\n return \"RegLink\"\n elif data_type == RegResourceList:\n return \"RegResourceList\"\n elif data_type == RegFullResourceDescriptor:\n return \"RegFullResourceDescriptor\"\n elif data_type == RegResourceRequirementsList:\n return \"RegResourceRequirementsList\"\n elif data_type == RegFileTime:\n return \"RegFileTime\"\n elif data_type == RegUint8:\n return \"RegUint8\"\n elif data_type == RegInt16:\n return \"RegInt16\"\n elif data_type == RegUint16:\n return \"RegUint16\"\n elif data_type == RegInt32:\n return \"RegInt32\"\n elif data_type == RegUint32:\n return \"RegUint32\"\n elif data_type == RegInt64:\n return \"RegInt64\"\n elif data_type == RegUint64:\n return \"RegUint64\"\n elif data_type == RegFloat:\n return \"RegFloat\"\n elif data_type == RegDouble:\n return \"RegDouble\"\n elif data_type == RegUnicodeChar:\n return \"RegUnicodeChar\"\n elif data_type == RegBoolean:\n return \"RegBoolean\"\n elif data_type == RegUnicodeString:\n return \"RegUnicodeString\"\n elif data_type == RegCompositeValue:\n return \"RegCompositeValue\"\n elif data_type == RegDateTimeOffset:\n return \"RegDateTimeOffset\"\n elif data_type == RegTimeSpan:\n return \"RegTimeSpan\"\n elif data_type == RegGUID:\n return \"RegGUID\"\n elif data_type == RegUnk111:\n return \"RegUnk111\"\n elif data_type == RegUnk112:\n return \"RegUnk112\"\n elif data_type == RegUnk113:\n return \"RegUnk113\"\n elif data_type == RegBytesArray:\n return \"RegBytesArray\"\n elif data_type == RegInt16Array:\n return \"RegInt16Array\"\n elif data_type == RegUint16Array:\n return \"RegUint16Array\"\n elif data_type == RegInt32Array:\n return \"RegInt32Array\"\n elif data_type == RegUInt32Array:\n return \"RegUInt32Array\"\n elif data_type == RegInt64Array:\n return \"RegInt64Array\"\n elif data_type == RegUInt64Array:\n return \"RegUInt64Array\"\n elif data_type == RegFloatArray:\n return \"RegFloatArray\"\n elif data_type == RegDoubleArray:\n return \"RegDoubleArray\"\n elif data_type == RegUnicodeCharArray:\n return \"RegUnicodeCharArray\"\n elif data_type == RegBooleanArray:\n return \"RegBooleanArray\"\n elif data_type == RegUnicodeStringArray:\n return \"RegUnicodeStringArray\"\n else:\n return \"Unknown type: %s\" % (hex(data_type))","def _iface_type(self, if_name):\n\n if if_name.lower().startswith('gi'):\n if_type = 'GigabitEthernet'\n elif if_name.lower().startswith('ten'):\n if_type = 'Ten-GigabitEthernet'\n elif if_name.lower().startswith('fo'):\n if_type = 'FortyGigE'\n elif if_name.lower().startswith('vl'):\n if_type = 'Vlan-interface'\n elif if_name.lower().startswith('lo'):\n if_type = 'LoopBack'\n elif if_name.lower().startswith('br'):\n if_type = 'Bridge-Aggregation'\n elif if_name.lower().startswith('ro'):\n if_type = 'Route-Aggregation'\n elif if_name.lower().startswith('tu'):\n if_type = 'Tunnel'\n elif if_name.lower().startswith('tw'):\n if_type = 'TwentyGigE'\n elif if_name.lower().startswith('hu'):\n if_type = 'HundredGigE'\n else:\n if_type = None\n\n number_list = if_name.split(' ')\n if len(number_list) == 2:\n number = number_list[-1].strip()\n else:\n number = self._get_number(if_name)\n\n if if_type:\n proper_interface = if_type + number\n else:\n proper_interface = if_name\n\n return proper_interface, if_type","def get_type_functional_name(type):\n name = type.name\n if type.is_simple:\n return name\n elif type.is_enum:\n return 'str'\n elif type.is_complex:\n return get_class_name(name)","def get_firmware_id_string(self):\n unpacked_string = False\n # Find the address of the string in memory\n id_string_addr = self._get_slt_entry(3)\n if id_string_addr is None:\n # maybe it's not packed in this slt\n id_string_addr = self._get_slt_entry(2)\n if id_string_addr is None:\n # Can't find it in the slt return None\n return None\n unpacked_string = True\n # parse the null terminated string\n last = build_string = \"\"\n # There is no reason for the build string to contain\n # any non ASCII character but do it like this to avoid\n # breaking support for Python 2.7\n try:\n char = unichr\n except NameError:\n char = chr\n while last != \"\\0\":\n word = self.get_data(id_string_addr)\n if unpacked_string:\n if Arch.addr_per_word == 4:\n # Two char per word\n build_string += char(word & 0x00FFFF)\n if build_string[-1] == \"\\0\":\n break\n last = char((word & 0xFFFF0000) >> 16)\n build_string += last\n else:\n # Only one char per word\n last = char(word)\n build_string += last\n else:\n # Four chars per word\n if Arch.addr_per_word == 4:\n string = cu.get_string_from_word(Arch.addr_per_word, word)\n stop_decoding = False\n for char in string:\n if char != '\\0':\n build_string += char\n else:\n stop_decoding = True\n break\n last = string[3:]\n\n if stop_decoding:\n break\n else:\n # Two chars per word\n build_string += char((word & 0xFF00) >> 8)\n if build_string[-1] == \"\\0\":\n break\n last = char(word & 0x00FF)\n build_string += last\n # Move to the next word in the string\n id_string_addr += Arch.addr_per_word\n\n # remove the \\0 we don't want the terminator\n if build_string[-1] == \"\\0\":\n build_string = build_string[:-1]\n\n return build_string.strip()","def getvarname(self,j_): # 3\n sizename_ = (1 + self.getvarnamelen((j_)))\n arr_name = array.array(\"b\",[0]*((sizename_)))\n memview_arr_name = memoryview(arr_name)\n res,resargs = self.__obj.getvarname(j_,sizename_,memview_arr_name)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)\n retarg_name = resargs\n retarg_name = arr_name.tobytes()[:-1].decode(\"utf-8\",errors=\"ignore\")\n return retarg_name","def _device_type_returner(self, symbol):\n if self.names.get_name_string(symbol.id) == \"AND\":\n return self.devices.AND\n if self.names.get_name_string(symbol.id) == \"OR\":\n return self.devices.OR\n if self.names.get_name_string(symbol.id) == \"NAND\":\n return self.devices.NAND\n if self.names.get_name_string(symbol.id) == \"NOR\":\n return self.devices.NOR\n if self.names.get_name_string(symbol.id) == \"XOR\":\n return self.devices.XOR\n if self.names.get_name_string(symbol.id) == \"CLOCK\":\n return self.devices.CLOCK\n if self.names.get_name_string(symbol.id) == \"SWITCH\":\n return self.devices.SWITCH\n if self.names.get_name_string(symbol.id) == \"DTYPE\":\n return self.devices.D_TYPE\n if self.names.get_name_string(symbol.id) == \"SIGGEN\":\n return self.devices.SIGGEN","def function_name(self) -> Optional[pulumi.Input[str]]:\n return pulumi.get(self, \"function_name\")","def sortof_type_str_of(x):\n if hasattr(x,\"_ServerProxy__host\"):\n return \"XML-RPC\"\n if hasattr(x,\"__setitem__\"):\n if hasattr(x,\"keys\"):\n return \"DICT\"\n if hasattr(x,\"append\"):\n return \"LIST\"\n if hasattr(x,\"join\" ):\n return \"STR\"\n if hasattr(x,\"__add__\") and hasattr(x,\"__sub__\"):\n if hasattr(x,\"__and__\"):\n return \"INT\"\n else:\n return \"FLOAT\"\n if hasattr(x,\"url\") and hasattr(x,\"start_pt\"):\n return \"REF\"","def Type(self, String):\r\n\r\n if (String == \"byte\") or (String == \"sbyte\"):\r\n return 1\r\n elif (String == \"word\") or (String == \"sword\"):\r\n return 2\r\n else:\r\n return 4","def get(identifier):\n if identifier is None:\n return None\n if isinstance(identifier, str):\n identifier = str(identifier)\n return deserialize(identifier)\n if isinstance(identifier, dict):\n return deserialize(identifier)\n elif callable(identifier):\n return identifier\n else:\n raise ValueError('Could not interpret '\n 'loss function identifier:', identifier)","def match_string_signature_old(self, file_name):\n\n pattern_msvc = 'This program cannot be run in DOS mode'\n pattern_delphi = 'This program must be run under Win32'\n p_vc = re.compile(pattern_msvc)\n p_delphi = re.compile(pattern_delphi)\n \n if platform.system() == 'Windows':\n # IF running on Windows, use jstrings.exe\n string_command = STRINGS_PATH + ' --ascii < ' + file_name\n elif platform.system() == 'Linux' or platform.system() == 'Darwin':\n # If running on Linux or MacOSX, use strings command\n string_command = 'strings ' + file_name\n else:\n print 'Unknown OS detected'\n sys.exit()\n \n string_result = subprocess.check_output(string_command, shell=True)\n if p_vc.search(string_result) is not None:\n compiler_name = 'Microsoft Visual C++ or MinGW'\n elif p_delphi.search(string_result) is not None:\n compiler_name = 'Borland C/C++ or Delphi'\n else:\n compiler_name = 'Unknown'\n return compiler_name","def parse_line(line):\n\tpattern = re.compile(\"\"\"\n\t\t[ \\t]*\n\t\t(private|package|remote|public)*[ ]*\n\t\t(any|string|array|numeric|boolean|component|struct|void)*[ ]*\n\t\t(function[ ]+|\\<cffunction[ ]+name[ ]*=[ ]*\\\"?)([_\\-a-z][a-z0-9_\\-]+)\n\t\t\"\"\", \n\t\tre.VERBOSE|re.MULTILINE|re.IGNORECASE)\n\tm = pattern.match(line)\n\t#return the 5th gouped regex, which should be the function name\n\tret_val = m.group(4) if m else ''\n\treturn ret_val","def get_func(self, name_str):\n return self.func.get(name_str)","def get_type_name(column_data: Dict) -> Text:\n column_type = str(column_data[\"db_type\"]).lower()\n if \"text\" in column_type:\n return \"text\"\n elif \"json\" in column_type:\n return \"json\"\n elif not isinstance(column_data[\"type\"], tuple):\n type_ = column_data[\"type\"].__name__\n else:\n type_ = f'array of {column_data[\"type\"][1]}'\n return type_","def r_type(byte_string):\n\n opcode = int(byte_string[:6], 2)\n rs = int(byte_string[6:11], 2)\n rt = int(byte_string[11:16], 2)\n rd = int(byte_string[16:21], 2)\n shift = int(byte_string[21:26], 2)\n func = int(byte_string[-6:], 2)\n print(OPCODES[opcode][func] + '\\t$' + REGISTERS[rd] + ', $' + REGISTERS[rs] + ', ' + str(shift) + '($' + REGISTERS[rt] + ')')\n \n # try:\n # state_from_r_type(rs, rt, rd, shift, func)\n # print_registers()\n # except Exception as err:\n # print(str(err))\n # pass","def chtype(var):\n return str(type(var)).split('\\'')[1]"],"string":"[\n \"def function_name_to_string(func):\\n if func == statistical_parity_difference:\\n return \\\"Statistical Parity Difference\\\"\\n if func == theil_index:\\n return \\\"Theil Index\\\"\\n if func == equal_opportunity_difference:\\n return \\\"Equal Opportunity Difference\\\"\\n if func == disparate_impact:\\n return \\\"Disparate Impact\\\"\\n if func == average_odds_difference:\\n return \\\"Average Odds Difference\\\"\\n if func == auc:\\n return \\\"AUC\\\"\\n if func == binary_accuracy:\\n return \\\"Binary Accuracy\\\"\",\n \"def cython_functionname(self, t, cycyt=None):\\n if cycyt is None:\\n t = self.canon(t)\\n if isinstance(t, basestring):\\n return t, self.cython_functionnames[t]\\n elif t[0] in self.base_types:\\n return t, self.cython_functionnames[t[0]]\\n return self.cython_functionname(t, self.cython_functionnames[t[0]])\\n d = {}\\n for key, x in zip(self.template_types[t[0]], t[1:-1]):\\n if isinstance(x, basestring):\\n val = self.cython_functionnames[x] if x in self.cython_functionnames \\\\\\n else x\\n elif isinstance(x, Number):\\n val = str(x).replace('-', 'Neg').replace('+', 'Pos')\\\\\\n .replace('.', 'point')\\n elif x[0] in self.base_types:\\n val = self.cython_functionnames[x[0]]\\n else:\\n _, val = self.cython_functionname(x, self.cython_functionnames[x[0]])\\n d[key] = val\\n return t, cycyt.format(**d)\",\n \"def _get_source_type(version_string):\\n # type: (str) -> Optional[str]\\n if not version_string:\\n return None\\n\\n if \\\"http://\\\" in version_string or \\\"https://\\\" in version_string:\\n return \\\"url\\\"\\n elif os.path.exists(version_string) and os.path.isfile(version_string):\\n return \\\"file\\\"\\n elif re.match(r\\\"\\\\d+\\\\.\\\\d+\\\\.\\\\d+\\\", version_string) or version_string == \\\"current\\\":\\n return \\\"install_script\\\"\\n else:\\n raise ValueError(\\n 'Invalid value \\\"%s\\\" for version_string. If it\\\\'s a path to a file, make'\\n \\\"sure the file exists and if it's a URL, ensure URL exists.\\\"\\n % (version_string)\\n )\",\n \"def get_type_from_str(type_str):\\n try:\\n # Assume the current language to be C/C++ and make a try.\\n return gdb.parse_and_eval(\\\"(%s *)0\\\" % type_str).type.target()\\n except RuntimeError:\\n # If assumption of current language to be C/C++ was wrong, then\\n # lookup the type using the API.\\n try:\\n return gdb.lookup_type(type_str)\\n except RuntimeError:\\n return None\",\n \"def _get_uniform_function_name(varinfo):\\n # NOTE: varinfo == dict(vartype=vartype, ndim=ndim, size=size)\\n float_suffix = {True: 'f', False: 'i'}\\n array_suffix = {True: 'v', False: ''}\\n \\n vartype = varinfo[\\\"vartype\\\"]\\n ndim = varinfo[\\\"ndim\\\"]\\n size = varinfo.get(\\\"size\\\", None)\\n args = ()\\n \\n # scalar or vector uniform\\n if type(ndim) == int or type(ndim) == long:\\n # find function name\\n funname = \\\"glUniform%d%s%s\\\" % (ndim, \\\\\\n float_suffix[vartype == \\\"float\\\"], \\\\\\n array_suffix[size is not None])\\n\\n # find function arguments\\n if size is not None:\\n args += (size,)\\n \\n # matrix uniform\\n elif type(ndim) == tuple:\\n # find function name\\n funname = \\\"glUniformMatrix%dfv\\\" % (ndim[0])\\n args += (1, False,)\\n \\n return funname, args\",\n \"def _parse_function(proto_string):\\n _, parsed_features = tf.parse_single_sequence_example(\\n proto_string, sequence_features=_parsing_spec(input_size))\\n lengths = tf.shape(parsed_features['inputs'])[0]\\n # Type int32 is not supported by parsing spec, but int64 is not supported by\\n # XLA. Here cast to int32. The parsing functions are executed by the C++\\n # threads. So, casting op will not slow down the session.run call later.\\n labels = tf.to_int32(parsed_features['labels'])\\n return parsed_features['inputs'], lengths, labels\",\n \"def src_get_name(converter_type):\\n return ffi.string(_lib.src_get_name(converter_type)).decode()\",\n \"def unparse_type(type_str):\\n if not type_str.startswith('array'):\\n return type_str\\n arg_dim = type_str.lstrip('array')[0]\\n data_type = type_str.lstrip('array')[1:]\\n arg_type = \\\"vizgen.ndarray('\\\" + data_type + \\\"', \\\" + arg_dim + \\\")\\\"\\n return arg_type\",\n \"def formatLookup(format_str):\\n pat = '(\\\\d+)([A-Z])'\\n match = re.search(pat, format_str)\\n #print match.group()\\n \\n data_len = int(match.group(1))\\n data_fmt = str(match.group(2))\\n np_fmt = fitsFormatLookup(data_fmt)\\n np_dtype = '%i%s'%(data_len, np_fmt)\\n \\n return np_dtype, data_len, np_fmt\",\n \"def find_type(token_string: str):\\n if re.compile('\\\\d+').match(token_string):\\n return 'number'\\n elif re.compile('[a-zA-Z]').match(token_string):\\n return 'id'\\n elif re.compile('\\\\*|\\\\+|-|/').match(token_string):\\n return 'op'\\n else:\\n return 'undefined'\",\n \"def sample_type_str(t):\\n if t == dsl.Type.NUMBER or t == dsl.Type.DIGIT:\\n return get_number()\\n elif t == dsl.Type.WORD:\\n return get_word()\\n elif t == dsl.Type.ALPHANUM or t == dsl.Type.CHAR:\\n return get_alphanumeric()\\n elif t == dsl.Type.ALL_CAPS:\\n return get_caps()\\n elif t == dsl.Type.PROP_CASE:\\n return get_proper_case()\\n elif t == dsl.Type.LOWER:\\n return get_lower()\\n else:\\n raise ValueError('Unsupported type: {}'.format(t))\",\n \"def _get_shader_type(varinfo):\\n if type(varinfo[\\\"ndim\\\"]) == int or type(varinfo[\\\"ndim\\\"]) == long:\\n if varinfo[\\\"ndim\\\"] == 1:\\n shader_type = varinfo[\\\"vartype\\\"]\\n elif varinfo[\\\"ndim\\\"] >= 2:\\n shader_type = \\\"vec%d\\\" % varinfo[\\\"ndim\\\"]\\n if varinfo[\\\"vartype\\\"] != \\\"float\\\":\\n shader_type = \\\"i\\\" + shader_type\\n # matrix: (2,2) or (3,3) or (4,4)\\n elif type(varinfo[\\\"ndim\\\"]) == tuple:\\n shader_type = \\\"mat%d\\\" % varinfo[\\\"ndim\\\"][0]\\n return shader_type\",\n \"def generate_definition(self):\\n apientry = \\\"\\\"\\n if self.__name[:2] == \\\"gl\\\":\\n apientry = \\\"DNLOAD_APIENTRY \\\"\\n params = \\\"void\\\"\\n if self.__parameters:\\n params = \\\", \\\".join(self.__parameters)\\n return \\\"%s (%s*%s)(%s)\\\" % (self.__returntype, apientry, self.__name, params)\",\n \"def generate_prototype(self):\\n apientry = \\\"\\\"\\n if self.__name[:2] == \\\"gl\\\":\\n apientry = \\\"DNLOAD_APIENTRY \\\"\\n params = \\\"void\\\"\\n if self.__parameters:\\n params = \\\", \\\".join(self.__parameters)\\n return \\\"(%s (%s*)(%s))\\\" % (self.__returntype, apientry, params)\",\n \"def compatible_firmware_version(self):\\n identifier = self.firmware_version.split('compiled')[0]\\n buf_size = self.MAX_BUF_SIZE\\n buf = (ctypes.c_char * buf_size)()\\n res = self._dll.JLINKARM_GetEmbeddedFWString(identifier.encode(), buf, buf_size)\\n if res < 0:\\n raise errors.JLinkException(res)\\n\\n return ctypes.string_at(buf).decode()\",\n \"def get_jtype_string(c_type: str) -> str:\\n m = STRING_PAT.match(c_type)\\n if not m:\\n return \\\"\\\"\\n return \\\"jstring\\\"\",\n \"def get_parsed_declaration(self) -> str:\\n args = self._get_arguments()\\n\\n func = self.node\\n tu = func.tu\\n\\n # For functions the extent encompasses the return value, and the\\n # location is the beginning of the functions name. So we can consume\\n # all tokens in between.\\n end = cindex.SourceLocation.from_offset(\\n tu, func.location.file, func.location.offset - 1\\n )\\n extent = cindex.SourceRange.from_locations(func.extent.start, end)\\n\\n return_type = \\\" \\\".join(\\n t.spelling for t in cindex.TokenGroup.get_tokens(tu, extent=extent)\\n )\\n\\n return f\\\"{return_type} {func.spelling}({args})\\\"\",\n \"def _handle_string(\\n *, artifacts: types.ColumnArtifacts\\n) -> typing.Union[String, Binary, Date, DateTime]:\\n if artifacts.open_api.format in {None, \\\"byte\\\", \\\"password\\\"}:\\n if artifacts.open_api.max_length is None:\\n return String\\n return String(length=artifacts.open_api.max_length)\\n if artifacts.open_api.format == \\\"binary\\\":\\n if artifacts.open_api.max_length is None:\\n return Binary\\n return Binary(length=artifacts.open_api.max_length)\\n if artifacts.open_api.format == \\\"date\\\":\\n return Date\\n if artifacts.open_api.format == \\\"date-time\\\":\\n return DateTime\\n raise exceptions.FeatureNotImplementedError(\\n f\\\"{artifacts.open_api.format} format for string is not supported.\\\"\\n )\",\n \"def parse_typename(typename):\\n if typename is None:\\n raise ValueError(\\\"function type must be provided\\\")\\n idx = typename.rfind(\\\"/\\\")\\n if idx < 0:\\n raise ValueError(\\\"function type must be of the from namespace/name\\\")\\n namespace = typename[:idx]\\n if not namespace:\\n raise ValueError(\\\"function type's namespace must not be empty\\\")\\n type = typename[idx + 1:]\\n if not type:\\n raise ValueError(\\\"function type's name must not be empty\\\")\\n return namespace, type\",\n \"def get_fun_name(line):\\n match = re.match(r'(function|macro)\\\\s*\\\\((\\\\w+)', line)\\n if not match:\\n return\\n return match.groups()[1]\",\n \"def get_data_name(data_func, data_type, npoints, y_error_sigma, x_error_sigma):\\n data_name = '{}_{}'.format(data_func.__name__, data_type)\\n if data_func.__name__ != 'get_image':\\n data_name += 'funcs'\\n data_name += '_{}pts_{}ye'.format(npoints, y_error_sigma)\\n if x_error_sigma is not None:\\n data_name += '_{}xe'.format(x_error_sigma)\\n return data_name.replace('.', '_')\",\n \"def typeName (self, typecode):\\n if typecode == qmf2.SCHEMA_DATA_VOID: return \\\"void\\\"\\n elif typecode == qmf2.SCHEMA_DATA_BOOL: return \\\"bool\\\"\\n elif typecode == qmf2.SCHEMA_DATA_INT: return \\\"int\\\"\\n elif typecode == qmf2.SCHEMA_DATA_FLOAT: return \\\"float\\\"\\n elif typecode == qmf2.SCHEMA_DATA_STRING: return \\\"string\\\"\\n elif typecode == qmf2.SCHEMA_DATA_MAP: return \\\"map\\\"\\n elif typecode == qmf2.SCHEMA_DATA_LIST: return \\\"list\\\"\\n elif typecode == qmf2.SCHEMA_DATA_UUID: return \\\"uuid\\\"\\n else:\\n raise ValueError (\\\"Invalid type code: %s\\\" % str(typecode))\",\n \"def _get_shader_type_noint(varinfo):\\n if varinfo[\\\"ndim\\\"] == 1:\\n shader_type = \\\"float\\\"\\n elif varinfo[\\\"ndim\\\"] >= 2:\\n shader_type = \\\"vec%d\\\" % varinfo[\\\"ndim\\\"]\\n # matrix: (2,2) or (3,3) or (4,4)\\n elif type(varinfo[\\\"ndim\\\"]) == tuple:\\n shader_type = \\\"mat%d\\\" % varinfo[\\\"ndim\\\"][0]\\n return shader_type\",\n \"def GrabVariableInitializerType(line: str) -> (FClass.FiMVariableTypes,int):\\n # Boolean\\n if line.startswith( FGlobal.Methods['Variable Boolean Array'] ):\\n length = len( _findFirst(FGlobal.Methods['Variable Boolean Array'], lambda x: line.startswith(x)) )\\n return FClass.FiMVariableTypes.BOOL_ARRAY, length\\n if line.startswith( FGlobal.Methods['Variable Boolean'] ):\\n length = len( _findFirst(FGlobal.Methods['Variable Boolean'], lambda x: line.startswith(x)) )\\n return FClass.FiMVariableTypes.BOOL, length\\n # Number\\n if line.startswith( FGlobal.Methods['Variable Number Array'] ):\\n length = len( _findFirst(FGlobal.Methods['Variable Number Array'], lambda x: line.startswith(x)) )\\n return FClass.FiMVariableTypes.NUMBER_ARRAY, length\\n if line.startswith( FGlobal.Methods['Variable Number'] ):\\n length = len( _findFirst(FGlobal.Methods['Variable Number'], lambda x: line.startswith(x)) )\\n return FClass.FiMVariableTypes.NUMBER, length\\n # String\\n if line.startswith( FGlobal.Methods['Variable String Array'] ):\\n length = len( _findFirst(FGlobal.Methods['Variable String Array'], lambda x: line.startswith(x)) )\\n return FClass.FiMVariableTypes.STRING_ARRAY, length\\n if line.startswith( FGlobal.Methods['Variable String'] ):\\n length = len( _findFirst(FGlobal.Methods['Variable String'], lambda x: line.startswith(x)) )\\n return FClass.FiMVariableTypes.STRING, length\\n # Char\\n if line.startswith( FGlobal.Methods['Variable Character'] ):\\n length = len( _findFirst(FGlobal.Methods['Variable Character'], lambda x: line.startswith(x)) )\\n return FClass.FiMVariableTypes.CHAR, length\\n\\n # Default value\\n return FClass.FiMVariableTypes.UNKNOWN,-1\",\n \"def firmware_version(self):\\n buf = (ctypes.c_char * self.MAX_BUF_SIZE)()\\n self._dll.JLINKARM_GetFirmwareString(buf, self.MAX_BUF_SIZE)\\n return ctypes.string_at(buf).decode()\",\n \"def get_function_raw_name_at(self, address):\\n pass\",\n \"def extract_function_name(maybe_function_str: str) -> Optional[str]:\\n match = STACK_TRACE_LINE_RE.search(maybe_function_str)\\n if match is not None:\\n return match.group(2)\\n return None\",\n \"def funcstring(funcname):\\n s = str(funcname)[10:] #chop off '<function '\\n spi = s.index(' ')\\n return s[:spi]\",\n \"def config_gettype(function_name,\\n config_name,\\n param):\\n config = configparser.ConfigParser()\\n config.read(path_creator(config_name))\\n #config.read(path_creator(config_name))\\n if config[function_name][param].split(' ## ')[1] == 'str':\\n return str(config.get(function_name,param).split(' ## ')[0])\\n if config[function_name][param].split(' ## ')[1] == 'int':\\n return int(config.get(function_name,param).split(' ## ')[0])\\n if config[function_name][param].split(' ## ')[1] == 'float':\\n return float(config.get(function_name,param).split(' ## ')[0])\\n if config[function_name][param].split(' ## ')[1] == 'bool':\\n return bool(config.get(function_name,param).split(' ## ')[0])\\n if config[function_name][param].split(' ## ')[1] == 'path':\\n return path_creator(str(config.get(function_name,param).split(' ## ')[0]))\\n if config[function_name][param].split(' ## ')[1] == 'NoneType':\\n return None\",\n \"def get_func_type(header):\\n func_type = header.functionType\\n if func_type == SSE.SCALAR:\\n return FunctionType.Scalar\\n elif func_type == SSE.AGGREGATION:\\n return FunctionType.Aggregation\\n elif func_type == SSE.TENSOR:\\n return FunctionType.Tensor\",\n \"def get_process_func(dataset_type, dsname):\\n\\n dsformat = 'VOL'\\n if dataset_type == 'RAW':\\n func_name = process_raw\\n elif dataset_type == 'NCVOL':\\n func_name = process_save_radar\\n elif dataset_type == 'PWR':\\n func_name = 'process_signal_power'\\n elif dataset_type == 'SNR':\\n func_name = 'process_snr'\\n elif dataset_type == 'RHOHV_CORRECTION':\\n func_name = 'process_correct_noise_rhohv'\\n elif dataset_type == 'BIAS_CORRECTION':\\n func_name = 'process_correct_bias'\\n elif dataset_type == 'L':\\n func_name = 'process_l'\\n elif dataset_type == 'CDR':\\n func_name = 'process_cdr'\\n elif dataset_type == 'SAN':\\n func_name = 'process_echo_id'\\n elif dataset_type == 'ECHO_FILTER':\\n func_name = 'process_echo_filter'\\n elif dataset_type == 'SNR_FILTER':\\n func_name = 'process_filter_snr'\\n elif dataset_type == 'VIS_FILTER':\\n func_name = 'process_filter_visibility'\\n elif dataset_type == 'OUTLIER_FILTER':\\n func_name = 'process_outlier_filter'\\n elif dataset_type == 'PHIDP0_CORRECTION':\\n func_name = 'process_correct_phidp0'\\n elif dataset_type == 'PHIDP_SMOOTH_1W':\\n func_name = 'process_smooth_phidp_single_window'\\n elif dataset_type == 'PHIDP_SMOOTH_2W':\\n func_name = 'process_smooth_phidp_double_window'\\n elif dataset_type == 'PHIDP_KDP_MAESAKA':\\n func_name = 'process_phidp_kdp_Maesaka'\\n elif dataset_type == 'PHIDP_KDP_LP':\\n func_name = 'process_phidp_kdp_lp'\\n elif dataset_type == 'KDP_LEASTSQUARE_1W':\\n func_name = 'process_kdp_leastsquare_single_window'\\n elif dataset_type == 'KDP_LEASTSQUARE_2W':\\n func_name = 'process_kdp_leastsquare_double_window'\\n elif dataset_type == 'ATTENUATION':\\n func_name = 'process_attenuation'\\n elif dataset_type == 'RAINRATE':\\n func_name = 'process_rainrate'\\n elif dataset_type == 'WIND_VEL':\\n func_name = 'process_wind_vel'\\n elif dataset_type == 'WINDSHEAR':\\n func_name = 'process_windshear'\\n elif dataset_type == 'HYDROCLASS':\\n func_name = 'process_hydroclass'\\n elif dataset_type == 'PHIDP0_ESTIMATE':\\n func_name = 'process_estimate_phidp0'\\n elif dataset_type == 'RHOHV_RAIN':\\n func_name = 'process_rhohv_rain'\\n elif dataset_type == 'ZDR_RAIN':\\n func_name = 'process_zdr_rain'\\n elif dataset_type == 'SELFCONSISTENCY_KDP_PHIDP':\\n func_name = 'process_selfconsistency_kdp_phidp'\\n elif dataset_type == 'SELFCONSISTENCY_BIAS':\\n func_name = 'process_selfconsistency_bias'\\n elif dataset_type == 'TIME_AVG':\\n func_name = 'process_time_avg'\\n dsformat = 'TIMEAVG'\\n elif dataset_type == 'WEIGHTED_TIME_AVG':\\n func_name = 'process_weighted_time_avg'\\n dsformat = 'TIMEAVG'\\n elif dataset_type == 'FLAG_TIME_AVG':\\n func_name = 'process_time_avg_flag'\\n dsformat = 'TIMEAVG'\\n elif dataset_type == 'COLOCATED_GATES':\\n func_name = 'process_colocated_gates'\\n dsformat = 'COLOCATED_GATES'\\n elif dataset_type == 'INTERCOMP':\\n func_name = 'process_intercomp'\\n dsformat = 'INTERCOMP'\\n elif dataset_type == 'INTERCOMP_TIME_AVG':\\n func_name = 'process_intercomp_time_avg'\\n dsformat = 'INTERCOMP'\\n elif dataset_type == 'MONITORING':\\n func_name = 'process_monitoring'\\n dsformat = 'MONITORING'\\n elif dataset_type == 'SUN_HITS':\\n func_name = 'process_sun_hits'\\n dsformat = 'SUN_HITS'\\n elif dataset_type == 'POINT_MEASUREMENT':\\n func_name = process_point_measurement\\n dsformat = 'TIMESERIES'\\n elif dataset_type == 'TRAJ':\\n func_name = process_trajectory\\n dsformat = 'TRAJ_ONLY'\\n elif dataset_type == 'TRAJ_ATPLANE':\\n func_name = process_traj_atplane\\n dsformat = 'TIMESERIES'\\n elif dataset_type == 'TRAJ_ANTENNA_PATTERN':\\n func_name = process_traj_antenna_pattern\\n dsformat = 'TIMESERIES'\\n else:\\n raise ValueError(\\\"ERROR: Unknown dataset type '%s' of dataset '%s'\\\"\\n % (dataset_type, dsname))\\n\\n return func_name, dsformat\",\n \"def funcname(funcstr):\\n ps = funcstr.find('(')\\n return funcstr[:ps]\",\n \"def get_type(args_str, entry_type):\\r\\n # The C-method-implementations accept self as the first argument,\\r\\n # so a one-argument method will be invoked with zero arguments in Python.\\r\\n no_args = 1 if entry_type == \\\"method\\\" else 0\\r\\n return (\\\"METH_NOARGS\\\" if len(args_str.split(\\\",\\\")) == no_args\\r\\n else \\\"METH_VARARGS\\\")\",\n \"def get_class_decoder_function_name(name):\\n name = get_class_functional_name(name)\\n return 'decode_{0}'.format(name)\",\n \"def typer(func):\\n # get name of component from the name of the module, removing \\\"s.py\\\"\\n component_name = os.path.basename(inspect.stack()[1][0].f_code.co_filename[:-4])\\n\\n def wrapper(s):\\n out = func(s)\\n if out is None:\\n raise ArgumentTypeError(\\\"{} type not recognized\\\".format(component_name))\\n return out\\n\\n wrapper.__doc__ = \\\"\\\"\\\"Validate a string identifying a {component} returned by get_{component}.\\n\\n Args:\\n s (str): string specifying {article} {component}.\\n Returns:\\n (str): uniquely identifying string for a {component}.\\n \\\"\\\"\\\".format(\\n component=component_name,\\n article=\\\"an\\\" if component_name[0] in (\\\"a\\\", \\\"e\\\", \\\"i\\\", \\\"o\\\", \\\"u\\\") else \\\"a\\\",\\n )\\n\\n return wrapper\",\n \"def cpp_funcname(self, name, argkinds=None):\\n if isinstance(name, basestring):\\n return name\\n if argkinds is None:\\n argkinds = [(Arg.NONE, None)] * (len(name) - 1)\\n fname = name[0]\\n cts = []\\n for x, (argkind, argvalue) in zip(name[1:], argkinds):\\n if argkind is Arg.TYPE:\\n ct = self.cpp_type(x)\\n elif argkind is Arg.LIT:\\n ct = self.cpp_literal(x)\\n elif isinstance(x, Number):\\n ct = self.cpp_literal(x)\\n else:\\n try:\\n ct = self.cpp_type(x) # guess it is a type\\n except TypeError:\\n ct = x # guess it is a variable\\n cts.append(ct)\\n fname += '' if 0 == len(cts) else \\\"< \\\" + \\\", \\\".join(cts) + \\\" >\\\"\\n return fname\",\n \"def get_type_string(data):\\r\\n data_type = type(data)\\r\\n\\r\\n if data_type in (int, long):\\r\\n return 'integer'\\r\\n elif data_type == float:\\r\\n return 'float'\\r\\n elif data_type == bool:\\r\\n return 'boolean'\\r\\n elif data_type in (list, tuple):\\r\\n return 'list'\\r\\n elif data_type == dict:\\r\\n return 'hash'\\r\\n elif data is None:\\r\\n return 'null'\\r\\n elif isinstance(data, basestring):\\r\\n return 'string'\",\n \"def get_ctype_name(*args):\\n return _ida_hexrays.get_ctype_name(*args)\",\n \"def function_name(parameters):\",\n \"def get_model(model: str) -> Any:\\n try:\\n model_function = eval(model)\\n except (NameError, AttributeError) as err:\\n sys.exit(f'{err}. Accepted models from {tf}, {sm}, {tfa}, {tfc}')\\n return model_function\",\n \"def _get_type(self, value: str) -> str:\\n\\n\\t\\tif value is None or not value:\\n\\t\\t\\treturn \\\"None\\\"\\n\\n\\t\\t# Get and return a single type\\n\\t\\text: str = value[-4:].lower()\\n\\n\\t\\t# Check if value is File/Filepath\\n\\t\\tif ext in self._VALID_FILE_EXT:\\n\\t\\t\\tif ext == \\\".xml\\\":\\n\\t\\t\\t\\t# Add to filenames list\\n\\t\\t\\t\\tfilename = os.path.basename(value.title())\\n\\t\\t\\t\\tself.filenames.add(filename)\\n\\t\\t\\t# Check if file is directory\\n\\t\\t\\tif \\\"\\\\\\\\\\\" in value or \\\"/\\\" in value:\\n\\t\\t\\t\\treturn \\\"Filepath\\\"\\n\\t\\t\\treturn \\\"File\\\"\\n\\n\\t\\t# Check if value is Dir\\n\\t\\tif \\\"\\\\\\\\\\\" in value or \\\"/\\\" in value:\\n\\t\\t\\treturn \\\"Dir\\\"\\n\\n\\t\\t# Check if value is Floatf\\n\\t\\tif value.endswith(\\\"f\\\"):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\t# This will raise a ValueError if it cannot convert to float\\n\\t\\t\\t\\tfloat(value[0:-1])\\n\\t\\t\\t\\treturn \\\"Floatf\\\"\\n\\t\\t\\texcept ValueError:\\n\\t\\t\\t\\tpass\\n\\n\\t\\t# Check if value is Int\\n\\t\\ttry:\\n\\t\\t\\t# This will raise a ValueError if it cannot convert to int\\n\\t\\t\\tint(value)\\n\\t\\t\\treturn \\\"Int\\\"\\n\\t\\texcept ValueError:\\n\\t\\t\\tpass\\n\\n\\t\\t# Check if value is Float\\n\\t\\tif \\\".\\\" in value:\\n\\t\\t\\ttry:\\n\\t\\t\\t\\t# This will raise a ValueError if it cannot convert to float\\n\\t\\t\\t\\tfloat(value)\\n\\t\\t\\t\\treturn \\\"Float\\\"\\n\\t\\t\\texcept ValueError:\\n\\t\\t\\t\\tpass\\n\\n\\t\\t# Check if value is Bool\\n\\t\\telif value.lower() in self._VALID_BOOL:\\n\\t\\t\\t# Check if True/False or Yes/No instead of true/false or yes/no\\n\\t\\t\\treturn \\\"Bool\\\"\\n\\n\\t\\t# Give up, assume reference\\n\\t\\treturn \\\"Ref\\\"\",\n \"def __get_device_type_name(self, mps_db_session, device_type_id):\\n device_type = mps_db_session.query(models.DeviceType).filter(models.DeviceType.id==device_type_id).all()\\n\\n if len(device_type) == 1:\\n return device_type[0].name\\n elif len(device_type) == 0:\\n raise ValueError(\\\"Function \\\\\\\"__get_device_type_name(device_type_id={}). Not fault was found.\\\\\\\"\\\"\\n .format(device_type_id))\\n else:\\n raise ValueError(\\\"Function \\\\\\\"__get_device_type_name(device_type_id={}). More than one device matches.\\\\\\\"\\\"\\n .format(device_type_id))\",\n \"def cfunc_type(self):\\n tif = ida_typeinf.tinfo_t()\\n result = self.get_func_type(tif)\\n if not result:\\n return\\n return tif\",\n \"def get_function(self, type: str) -> Optional[Callable]:\\n function_str = getattr(self, type)\\n if function_str:\\n return import_string(f\\\"electricitymap.contrib.parsers.{function_str}\\\")\",\n \"def str_to_type(name_type):\\n if name_type == 'float' or name_type == 'Float':\\n return float\\n if name_type == 'bool':\\n return bool\\n if name_type == 'int':\\n return lambda x: int(float(x))\\n if name_type == 'list':\\n return ast.literal_eval\\n if name_type == 'date':\\n return lambda x: dateutil.parser.parse(x).strftime('%Y-%m-%dT%H:%M:%SZ')\\n if name_type == 'str':\\n return str\\n\\n \\n return None\",\n \"def to_py_name(cpp_name, entry_type):\\r\\n if entry_type == 'function':\\r\\n return cpp_name\\r\\n first_underscore = cpp_name.find('_')\\r\\n assert(first_underscore != -1)\\r\\n return cpp_name[first_underscore + 1:]\",\n \"def device_type(devices):\\n num_of_types = len({type(device) for device in devices})\\n if num_of_types == 1:\\n return devices[0].list_type.replace(\\\"_\\\", \\\" \\\").title()\\n elif num_of_types == 0:\\n return None\\n else:\\n raise ValueError\",\n \"def parse_function_line(line: str):\\n without_call = (line.split(\\\"function\\\")[1]).strip()\\n split_array = without_call.split()\\n function = split_array[0]\\n return function\",\n \"def get_func_type(self, *args):\\n return _ida_hexrays.cfunc_t_get_func_type(self, *args)\",\n \"def basictypestring(\\n dtype_char: ty.Text,\\n length: int = -1,\\n byteorder: ty.Text = \\\"=\\\"\\n ) -> ty.Text:\\n # only specify length if we have a variable width type\\n lengthstr = str(length) if dtype_char in VARLENGTH_TYPE_CHARS else \\\"\\\"\\n # only specify byte order if we need to\\n byteorderchar = byteorder if byteorder not in (\\\"=\\\", \\\"|\\\") else \\\"\\\"\\n return f\\\"{byteorderchar}{dtype_char}{lengthstr}\\\"\",\n \"def get_jtype_raw(c_type: str) -> str:\\n s = c_type.strip()\\n if 'void' == s:\\n return 'void'\\n m = RAW_PAT.match(s)\\n if not m:\\n return \\\"\\\"\\n return 'j{}'.format(m.group('type_name'))\",\n \"def get_interface_type(interface):\\n\\n if interface is None:\\n return None\\n\\n iftype = None\\n\\n if interface.upper().startswith('GE'):\\n iftype = 'ge'\\n elif interface.upper().startswith('10GE'):\\n iftype = '10ge'\\n elif interface.upper().startswith('25GE'):\\n iftype = '25ge'\\n elif interface.upper().startswith('40GE'):\\n iftype = '40ge'\\n elif interface.upper().startswith('100GE'):\\n iftype = '100ge'\\n elif interface.upper().startswith('PORT-GROUP'):\\n iftype = 'stack-Port'\\n elif interface.upper().startswith('NULL'):\\n iftype = 'null'\\n else:\\n return None\\n return iftype.lower()\",\n \"def astType(cls, source):\\n if source == '':\\n return cls.BLANK\\n if source == \\\"OPENQASM 2.0;\\\":\\n return cls.DECLARATION_QASM_2_0\\n x = QTRegEx.COMMENT.search(source)\\n if x:\\n return cls.COMMENT\\n x = QTRegEx.INCLUDE.search(source)\\n if x:\\n return cls.INCLUDE\\n x = QTRegEx.CTL_2.search(source)\\n if x:\\n if x.group(1) == 'if':\\n return cls.CTL_2\\n x = QTRegEx.QREG.search(source)\\n if x:\\n return cls.QREG\\n x = QTRegEx.CREG.search(source)\\n if x:\\n return cls.CREG\\n x = QTRegEx.MEASURE.search(source)\\n if x:\\n return cls.MEASURE\\n x = QTRegEx.BARRIER.search(source)\\n if x:\\n return cls.BARRIER\\n x = QTRegEx.GATE.search(source)\\n if x:\\n return cls.GATE\\n x = QTRegEx.OP.search(source)\\n if x:\\n return cls.OP\\n return cls.UNKNOWN\",\n \"def device(arg):\\n\\tassert isinstance(arg, str)\\n\\treturn r\\\"(?P<%s>[\\\\w_\\\\-@\\\\' \\\\.]+)\\\" % (arg,)\",\n \"def get_kernel_string(kernel_source, params=None):\\n #logging.debug('get_kernel_string called with %s', str(kernel_source))\\n logging.debug('get_kernel_string called')\\n\\n kernel_string = None\\n if callable(kernel_source):\\n kernel_string = kernel_source(params)\\n elif isinstance(kernel_source, str):\\n if looks_like_a_filename(kernel_source):\\n kernel_string = read_file(kernel_source) or kernel_source\\n else:\\n kernel_string = kernel_source\\n else:\\n raise TypeError(\\\"Error kernel_source is not a string nor a callable function\\\")\\n return kernel_string\",\n \"def parse_function_str(function_str: str) -> Union[Callable[[object, dict], object], None]:\\n\\n if function_str is None:\\n return None\\n\\n parsed_function = re.match(\\\"^([\\\\.\\\\w]+)(?:\\\\(([\\\\w|,%\\\\'-: ]*)\\\\))?$\\\", function_str)\\n if not parsed_function:\\n raise RuntimeError(f\\\"Invalid name for a transform function: '{function_str}'\\\")\\n\\n function_name, args = parsed_function.groups()\\n args_list = str(args).split(\\\",\\\") if (args is not None and args != '') else []\\n\\n # Check if it is a built-in function\\n builtin_function = FunctionBuilder.get_builtin_function(function_name, args_list)\\n\\n if builtin_function is not None:\\n return builtin_function\\n\\n # Get it as custom function\\n return FunctionBuilder.get_custom_function(function_name, args_list)\",\n \"def data_ref_type_str(dref_enum):\\n if dref_enum == 0x9000:\\n return \\\"unknown\\\"\\n elif dref_enum == 0x9001:\\n return \\\"integer\\\"\\n elif dref_enum == 0x9002:\\n return \\\"fp\\\"\\n elif dref_enum == 0x9003:\\n return \\\"integer(store)\\\"\\n else:\\n return \\\"INVALID\\\"\",\n \"def _get_python_prop_type(prop_type: Type[Variable]) -> str:\\n if prop_type is VariableBool:\\n return \\\"bool\\\"\\n if prop_type in (VariableInt, VariableUInt):\\n return \\\"int\\\"\\n if prop_type is VariableFloat:\\n return \\\"float\\\"\\n if prop_type is VariableString:\\n return \\\"bytes\\\"\\n if prop_type is VariableVec2:\\n return \\\"(float, float)\\\"\\n if prop_type is VariableStruct:\\n return \\\"dict[str, Variable]\\\"\\n if prop_type is VariableArray:\\n return \\\"MutableSequence\\\"\\n raise TypeError(\\\"unexpected variable type\\\")\",\n \"def get_variable_name(uuid: UUID, variable_index: int) -> Optional[str]:\\n scenario: Optional[AoE2DEScenario] = store.get_scenario(uuid)\\n if scenario:\\n if gv := get_game_version(uuid) == \\\"DE\\\":\\n variable = scenario.trigger_manager.get_variable(variable_index)\\n if variable:\\n return variable.name\\n elif 0 <= variable_index <= 255:\\n return f\\\"Variable {variable_index}\\\"\\n else:\\n raise ValueError(f\\\"Scenarios with the game version: {gv} do not support variables.\\\")\\n return None\",\n \"def get_tf_dtype(dtype_str: str):\\n\\n return getattr(tf, dtype_str)\",\n \"def filename_type(filename):\\n import re\\n\\n nii_re = re.compile(\\\".+(nii.gz)$|.+(nii)$\\\")\\n npy_re = re.compile(\\\".+(npy)$|.+(npz)$\\\")\\n\\n\\n if len(nii_re.findall(filename)):\\n return 'nii'\\n elif len(npy_re.findall(filename)):\\n return 'npy'\\n return None\",\n \"def get(identifier):\\n if identifier is None:\\n return None\\n if isinstance(identifier, str):\\n identifier = str(identifier)\\n return deserialize(identifier)\\n if isinstance(identifier, dict):\\n return deserialize(identifier)\\n if callable(identifier):\\n return identifier\\n raise ValueError(\\n f'Could not interpret loss function identifier: {identifier}')\",\n \"def map_string2func(funcname, clss, compute_capability):\\n if \\\"_get_\\\" + funcname not in globals():\\n raise AttributeError(\\\"kernel type '\\\" + funcname + \\\"' not understood\\\")\\n return globals()[\\\"_get_\\\" + funcname](clss, compute_capability)\",\n \"def get_function_name_at(self, address):\\n pass\",\n \"def get_name_for(node: Union[str, cst.CSTNode]) -> Optional[str]:\\n if isinstance(node, cst.Name):\\n return node.value\\n elif isinstance(node, str):\\n return node\\n elif isinstance(node, cst.Call):\\n return _NameUtil.get_name_for(node.func)\\n elif isinstance(node, cst.Subscript):\\n return _NameUtil.get_name_for(node.value)\\n elif isinstance(node, (cst.FunctionDef, cst.ClassDef)):\\n return _NameUtil.get_name_for(node.name)\\n return None\",\n \"def detect_language(lang, kernel_source):\\n if lang is None:\\n if callable(kernel_source):\\n raise TypeError(\\\"Please specify language when using a code generator function\\\")\\n kernel_string = get_kernel_string(kernel_source)\\n if \\\"__global__\\\" in kernel_string:\\n lang = \\\"CUDA\\\"\\n elif \\\"__kernel\\\" in kernel_string:\\n lang = \\\"OpenCL\\\"\\n else:\\n lang = \\\"C\\\"\\n return lang\",\n \"def type_name(type_defn: JSON) -> str:\\n rval = type_defn.get('code', '').replace('*', '').replace('@', '')\\n return rval\",\n \"def get_parsed_declaration(self) -> str:\\n type_ = self.node.type.spelling\\n\\n # Libclang will return the type as `float [20]` when looking at\\n # `float foo[20]`. We could look at the kind `TypeKind.CONSTANTARRAY`\\n # but partitioning on the \\\"[\\\" just seems more straight forward.\\n type_, *(array) = type_.partition(\\\"[\\\")\\n array_contents = \\\"\\\".join(array)\\n return f\\\"{type_} {self.name} {array_contents}\\\"\",\n \"def _ptype(ptype_value, verbose=True):\\n types = {\\n 0:[\\\"PT_NULL\\\",\\\"Program header table entry unused\\\"],\\n 1:[\\\"PT_LOAD\\\",\\\"Loadable program segment\\\"],\\n 2:[\\\"PT_DYNAMIC\\\",\\\"Dynamic linking information\\\"],\\n 3:[\\\"PT_INTERP\\\",\\\"Program interpreter\\\"],\\n 4:[\\\"PT_NOTE\\\",\\\"Auxiliary information\\\"],\\n 5:[\\\"PT_SHLIB\\\",\\\"Reserved\\\"],\\n 6:[\\\"PT_PHDR\\\",\\\"Entry for header table itself\\\"],\\n 7:[\\\"PT_TLS\\\",\\\"Thread-local storage segment\\\"],\\n 8:[\\\"PT_NUM\\\",\\\"Number of defined types\\\"],\\n 0x60000000:[\\\"PT_LOOS\\\",\\\"Start of OS-specific\\\"],\\n 0x6474e550:[\\\"PT_GNU_EH_FRAME\\\",\\\"GCC .eh_frame_hdr segment\\\"],\\n 0x6474e551:[\\\"PT_GNU_STACK\\\",\\\"Indicates stack executability\\\"],\\n 0x6474e552:[\\\"PT_GNU_RELRO\\\",\\\"Read-only after relocation\\\"],\\n 0x6ffffffa:[\\\"PT_SUNWBSS\\\",\\\"Sun Specific segment\\\"],\\n 0x6ffffffb:[\\\"PT_SUNWSTACK\\\",\\\"Stack segment\\\"],\\n 0x6fffffff:[\\\"PT_HIOS\\\",\\\"End of OS-specific\\\"],\\n 0x70000000:[\\\"PT_LOPROC\\\",\\\"Start of processor-specific\\\"],\\n 0x7fffffff:[\\\"PT_HIPROC\\\",\\\"End of processor-specific\\\"],\\n }\\n\\n if types.get(ptype_value):\\n value = types[ptype_value]\\n if verbose:\\n return \\\"%s (%s)\\\" % (value[0], value[1])\\n else:\\n return \\\"%s\\\" % value[0]\\n else:\\n return \\\"UNKNOWN\\\"\",\n \"def GuessDataType(value, column_id=None):\\n stripped_value = value.strip().replace('\\\"', '')\\n\\n if re.search('^-?[0-9]+$', stripped_value):\\n if column_id == 'year':\\n return 'date'\\n else:\\n return 'integer'\\n elif re.search('^-?[0-9]+\\\\.[0-9]+$', stripped_value):\\n return 'float'\\n elif re.search('^[0-9]+(/|-)[0-9]+((/|-)[0-9]+){0,1}$', stripped_value):\\n return 'date'\\n else:\\n return 'string'\",\n \"def parse_dispatch_type(dispatch_string: str):\\n if not dispatch_string:\\n return None\\n\\n dispatch_string = dispatch_string.lower().strip()\\n\\n if dispatch_string == \\\"load\\\":\\n return DispatchType.LOAD\\n\\n if dispatch_string == \\\"generating\\\":\\n return DispatchType.GENERATOR\\n\\n if dispatch_string == \\\"generator\\\":\\n return DispatchType.GENERATOR\\n\\n raise Exception(\\\"Unknown dispatch type: {}\\\".format(dispatch_string))\",\n \"def name_from_dist(dist_func):\\n return str(dist_func).split()[0].split('.')[-1][:-4]\",\n \"def _find_extension(self, ext_type, types, values):\\n counter = 0\\n # For the APLN extension, grab the value in ASCII.\\n if ext_type == b\\\"\\\\x00\\\\x10\\\":\\n while counter < len(types):\\n if types[counter] == ext_type:\\n return ((values[counter][3:]).decode())\\n counter += 1\\n else:\\n while counter < len(types):\\n if types[counter] == ext_type:\\n return values[counter].hex()\\n counter += 1\\n\\n return \\\"\\\"\",\n \"def parse_function_signature(code):\\n m = re.search(\\\"^\\\\s*\\\" + re_func_decl + \\\"\\\\s*{\\\", code, re.M)\\n if m is None:\\n print(code)\\n raise Exception(\\\"Failed to parse function signature. \\\"\\n \\\"Full code is printed above.\\\")\\n rtype, name, args = m.groups()[:3]\\n if args == 'void' or args.strip() == '':\\n args = []\\n else:\\n args = [tuple(arg.strip().split(' ')) for arg in args.split(',')]\\n return name, args, rtype\",\n \"def __Type(thing):\\n from .native import (Vector, Color, Matrix, VectorField, ColorField,\\n MatrixField, ScalarField)\\n if isinstance(thing, (int, float, ScalarField)): return 'Scalar'\\n if isinstance(thing, (Vector, VectorField)): return 'Vector'\\n if isinstance(thing, (Color, ColorField)): return 'Color'\\n if isinstance(thing, (Matrix, MatrixField)): return 'Matrix'\\n raise ValueError(\\\"Could not guess type of: \\\" + thing)\",\n \"def map_type(name):\\n\\n rv = MAPPINGS.get(name, None)\\n if rv is not None:\\n return rv\\n\\n name = name.replace(\\\"&\\\", \\\"*\\\")\\n\\n if name.startswith(\\\"const \\\"):\\n rv = map_type(name[6:])\\n\\n elif name.endswith(\\\"const\\\"):\\n rv = map_type(name[:-5])\\n\\n elif name.endswith(\\\" *\\\"):\\n mapped = map_type(name[:-2])\\n rv = f\\\"POINTER({mapped})\\\"\\n\\n elif name.endswith(\\\" **\\\"):\\n mapped = map_type(name[:-1])\\n rv = f\\\"POINTER({mapped})\\\"\\n\\n elif name.endswith(\\\"]\\\"):\\n m = re.match(r\\\"(.*) \\\\[(\\\\d+)\\\\]\\\", name)\\n if m is None:\\n raise Exception(f\\\"Couldn't map type {name}\\\")\\n\\n mapped = map_type(m.group(1))\\n count = m.group(2)\\n\\n rv = f\\\"({mapped} * {count})\\\"\\n\\n elif \\\"(*)\\\" in name:\\n return \\\"c_void_p\\\"\\n\\n else:\\n raise Exception(f\\\"Couldn't map type {name!r}\\\")\\n\\n MAPPINGS[name] = rv\\n return rv\",\n \"def get_func(name, argtypes=None, restype=c_int, lib=libDE):\\n logger.debug(\\\"Getting NewWordFinder API function: 'name': '{}', 'argtypes': '{}',\\\"\\n \\\" 'restype': '{}'.\\\".format(name, argtypes, restype))\\n func = getattr(lib, name)\\n if argtypes is not None:\\n func.argtypes = argtypes\\n if restype is not c_int:\\n func.restype = restype\\n logger.debug(\\\"NewWordFinder API function '{}' retrieved.\\\".format(name))\\n return func\",\n \"def src_get_version():\\n return ffi.string(_lib.src_get_version()).decode()\",\n \"def GetFormatType(format_):\\n\\n formattype = format_\\n bitsize = 0\\n if isinstance(format_, str):\\n match = re.search(r'\\\\s*(\\\\D+)', format_)\\n if match:\\n formattype = match.group(0)\\n bitsize = struct.calcsize(formattype) * 8\\n return formattype, bitsize\",\n \"def get_func_name(func, resolv_alias=True, win_characters=True):\\r\\n if hasattr(func, '__module__'):\\r\\n module = func.__module__\\r\\n else:\\r\\n try:\\r\\n module = inspect.getmodule(func)\\r\\n except TypeError:\\r\\n if hasattr(func, '__class__'):\\r\\n module = func.__class__.__module__\\r\\n else:\\r\\n module = 'unknown'\\r\\n if module is None:\\r\\n # Happens in doctests, eg\\r\\n module = ''\\r\\n if module == '__main__':\\r\\n try:\\r\\n filename = os.path.abspath(inspect.getsourcefile(func))\\r\\n except:\\r\\n filename = None\\r\\n if filename is not None:\\r\\n # mangling of full path to filename\\r\\n parts = filename.split(os.sep)\\r\\n if parts[-1].startswith('<ipython-input'):\\r\\n # function is defined in an IPython session. The filename\\r\\n # will change with every new kernel instance. This hack\\r\\n # always returns the same filename\\r\\n parts[-1] = '__ipython-input__'\\r\\n filename = '-'.join(parts)\\r\\n if filename.endswith('.py'):\\r\\n filename = filename[:-3]\\r\\n module = module + '-' + filename\\r\\n module = module.split('.')\\r\\n if hasattr(func, 'func_name'):\\r\\n name = func.func_name\\r\\n elif hasattr(func, '__name__'):\\r\\n name = func.__name__\\r\\n else:\\r\\n name = 'unknown'\\r\\n # Hack to detect functions not defined at the module-level\\r\\n if resolv_alias:\\r\\n # TODO: Maybe add a warning here?\\r\\n if hasattr(func, 'func_globals') and name in func.func_globals:\\r\\n if not func.func_globals[name] is func:\\r\\n name = '%s-alias' % name\\r\\n if inspect.ismethod(func):\\r\\n # We need to add the name of the class\\r\\n if hasattr(func, 'im_class'):\\r\\n klass = func.im_class\\r\\n module.append(klass.__name__)\\r\\n if os.name == 'nt' and win_characters:\\r\\n # Stupid windows can't encode certain characters in filenames\\r\\n name = _clean_win_chars(name)\\r\\n module = [_clean_win_chars(s) for s in module]\\r\\n return module, name\",\n \"def get_func_type(self, *args):\\n return _ida_hexrays.cfuncptr_t_get_func_type(self, *args)\",\n \"def typeToName(type: int) -> unicode:\\n ...\",\n \"def discover_type(value) -> str:\\n if isinstance(value, list):\\n return \\\"array\\\"\\n elif isinstance(value, bool):\\n return \\\"bool\\\"\\n elif isinstance(value, int):\\n return \\\"int\\\"\\n return \\\"string\\\"\",\n \"def getStringData(self, addrString: unicode, dtName: unicode, dtId: unicode) -> ghidra.app.decompiler.DecompileCallback.StringData:\\n ...\",\n \"def data_type_str(self):\\n data_type = self.data_type()\\n if data_type == RegSZ:\\n return \\\"RegSZ\\\"\\n elif data_type == RegExpandSZ:\\n return \\\"RegExpandSZ\\\"\\n elif data_type == RegBin:\\n return \\\"RegBin\\\"\\n elif data_type == RegDWord:\\n return \\\"RegDWord\\\"\\n elif data_type == RegMultiSZ:\\n return \\\"RegMultiSZ\\\"\\n elif data_type == RegQWord:\\n return \\\"RegQWord\\\"\\n elif data_type == RegNone:\\n return \\\"RegNone\\\"\\n elif data_type == RegBigEndian:\\n return \\\"RegBigEndian\\\"\\n elif data_type == RegLink:\\n return \\\"RegLink\\\"\\n elif data_type == RegResourceList:\\n return \\\"RegResourceList\\\"\\n elif data_type == RegFullResourceDescriptor:\\n return \\\"RegFullResourceDescriptor\\\"\\n elif data_type == RegResourceRequirementsList:\\n return \\\"RegResourceRequirementsList\\\"\\n elif data_type == RegFileTime:\\n return \\\"RegFileTime\\\"\\n elif data_type == RegUint8:\\n return \\\"RegUint8\\\"\\n elif data_type == RegInt16:\\n return \\\"RegInt16\\\"\\n elif data_type == RegUint16:\\n return \\\"RegUint16\\\"\\n elif data_type == RegInt32:\\n return \\\"RegInt32\\\"\\n elif data_type == RegUint32:\\n return \\\"RegUint32\\\"\\n elif data_type == RegInt64:\\n return \\\"RegInt64\\\"\\n elif data_type == RegUint64:\\n return \\\"RegUint64\\\"\\n elif data_type == RegFloat:\\n return \\\"RegFloat\\\"\\n elif data_type == RegDouble:\\n return \\\"RegDouble\\\"\\n elif data_type == RegUnicodeChar:\\n return \\\"RegUnicodeChar\\\"\\n elif data_type == RegBoolean:\\n return \\\"RegBoolean\\\"\\n elif data_type == RegUnicodeString:\\n return \\\"RegUnicodeString\\\"\\n elif data_type == RegCompositeValue:\\n return \\\"RegCompositeValue\\\"\\n elif data_type == RegDateTimeOffset:\\n return \\\"RegDateTimeOffset\\\"\\n elif data_type == RegTimeSpan:\\n return \\\"RegTimeSpan\\\"\\n elif data_type == RegGUID:\\n return \\\"RegGUID\\\"\\n elif data_type == RegUnk111:\\n return \\\"RegUnk111\\\"\\n elif data_type == RegUnk112:\\n return \\\"RegUnk112\\\"\\n elif data_type == RegUnk113:\\n return \\\"RegUnk113\\\"\\n elif data_type == RegBytesArray:\\n return \\\"RegBytesArray\\\"\\n elif data_type == RegInt16Array:\\n return \\\"RegInt16Array\\\"\\n elif data_type == RegUint16Array:\\n return \\\"RegUint16Array\\\"\\n elif data_type == RegInt32Array:\\n return \\\"RegInt32Array\\\"\\n elif data_type == RegUInt32Array:\\n return \\\"RegUInt32Array\\\"\\n elif data_type == RegInt64Array:\\n return \\\"RegInt64Array\\\"\\n elif data_type == RegUInt64Array:\\n return \\\"RegUInt64Array\\\"\\n elif data_type == RegFloatArray:\\n return \\\"RegFloatArray\\\"\\n elif data_type == RegDoubleArray:\\n return \\\"RegDoubleArray\\\"\\n elif data_type == RegUnicodeCharArray:\\n return \\\"RegUnicodeCharArray\\\"\\n elif data_type == RegBooleanArray:\\n return \\\"RegBooleanArray\\\"\\n elif data_type == RegUnicodeStringArray:\\n return \\\"RegUnicodeStringArray\\\"\\n else:\\n return \\\"Unknown type: %s\\\" % (hex(data_type))\",\n \"def _iface_type(self, if_name):\\n\\n if if_name.lower().startswith('gi'):\\n if_type = 'GigabitEthernet'\\n elif if_name.lower().startswith('ten'):\\n if_type = 'Ten-GigabitEthernet'\\n elif if_name.lower().startswith('fo'):\\n if_type = 'FortyGigE'\\n elif if_name.lower().startswith('vl'):\\n if_type = 'Vlan-interface'\\n elif if_name.lower().startswith('lo'):\\n if_type = 'LoopBack'\\n elif if_name.lower().startswith('br'):\\n if_type = 'Bridge-Aggregation'\\n elif if_name.lower().startswith('ro'):\\n if_type = 'Route-Aggregation'\\n elif if_name.lower().startswith('tu'):\\n if_type = 'Tunnel'\\n elif if_name.lower().startswith('tw'):\\n if_type = 'TwentyGigE'\\n elif if_name.lower().startswith('hu'):\\n if_type = 'HundredGigE'\\n else:\\n if_type = None\\n\\n number_list = if_name.split(' ')\\n if len(number_list) == 2:\\n number = number_list[-1].strip()\\n else:\\n number = self._get_number(if_name)\\n\\n if if_type:\\n proper_interface = if_type + number\\n else:\\n proper_interface = if_name\\n\\n return proper_interface, if_type\",\n \"def get_type_functional_name(type):\\n name = type.name\\n if type.is_simple:\\n return name\\n elif type.is_enum:\\n return 'str'\\n elif type.is_complex:\\n return get_class_name(name)\",\n \"def get_firmware_id_string(self):\\n unpacked_string = False\\n # Find the address of the string in memory\\n id_string_addr = self._get_slt_entry(3)\\n if id_string_addr is None:\\n # maybe it's not packed in this slt\\n id_string_addr = self._get_slt_entry(2)\\n if id_string_addr is None:\\n # Can't find it in the slt return None\\n return None\\n unpacked_string = True\\n # parse the null terminated string\\n last = build_string = \\\"\\\"\\n # There is no reason for the build string to contain\\n # any non ASCII character but do it like this to avoid\\n # breaking support for Python 2.7\\n try:\\n char = unichr\\n except NameError:\\n char = chr\\n while last != \\\"\\\\0\\\":\\n word = self.get_data(id_string_addr)\\n if unpacked_string:\\n if Arch.addr_per_word == 4:\\n # Two char per word\\n build_string += char(word & 0x00FFFF)\\n if build_string[-1] == \\\"\\\\0\\\":\\n break\\n last = char((word & 0xFFFF0000) >> 16)\\n build_string += last\\n else:\\n # Only one char per word\\n last = char(word)\\n build_string += last\\n else:\\n # Four chars per word\\n if Arch.addr_per_word == 4:\\n string = cu.get_string_from_word(Arch.addr_per_word, word)\\n stop_decoding = False\\n for char in string:\\n if char != '\\\\0':\\n build_string += char\\n else:\\n stop_decoding = True\\n break\\n last = string[3:]\\n\\n if stop_decoding:\\n break\\n else:\\n # Two chars per word\\n build_string += char((word & 0xFF00) >> 8)\\n if build_string[-1] == \\\"\\\\0\\\":\\n break\\n last = char(word & 0x00FF)\\n build_string += last\\n # Move to the next word in the string\\n id_string_addr += Arch.addr_per_word\\n\\n # remove the \\\\0 we don't want the terminator\\n if build_string[-1] == \\\"\\\\0\\\":\\n build_string = build_string[:-1]\\n\\n return build_string.strip()\",\n \"def getvarname(self,j_): # 3\\n sizename_ = (1 + self.getvarnamelen((j_)))\\n arr_name = array.array(\\\"b\\\",[0]*((sizename_)))\\n memview_arr_name = memoryview(arr_name)\\n res,resargs = self.__obj.getvarname(j_,sizename_,memview_arr_name)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\\n retarg_name = resargs\\n retarg_name = arr_name.tobytes()[:-1].decode(\\\"utf-8\\\",errors=\\\"ignore\\\")\\n return retarg_name\",\n \"def _device_type_returner(self, symbol):\\n if self.names.get_name_string(symbol.id) == \\\"AND\\\":\\n return self.devices.AND\\n if self.names.get_name_string(symbol.id) == \\\"OR\\\":\\n return self.devices.OR\\n if self.names.get_name_string(symbol.id) == \\\"NAND\\\":\\n return self.devices.NAND\\n if self.names.get_name_string(symbol.id) == \\\"NOR\\\":\\n return self.devices.NOR\\n if self.names.get_name_string(symbol.id) == \\\"XOR\\\":\\n return self.devices.XOR\\n if self.names.get_name_string(symbol.id) == \\\"CLOCK\\\":\\n return self.devices.CLOCK\\n if self.names.get_name_string(symbol.id) == \\\"SWITCH\\\":\\n return self.devices.SWITCH\\n if self.names.get_name_string(symbol.id) == \\\"DTYPE\\\":\\n return self.devices.D_TYPE\\n if self.names.get_name_string(symbol.id) == \\\"SIGGEN\\\":\\n return self.devices.SIGGEN\",\n \"def function_name(self) -> Optional[pulumi.Input[str]]:\\n return pulumi.get(self, \\\"function_name\\\")\",\n \"def sortof_type_str_of(x):\\n if hasattr(x,\\\"_ServerProxy__host\\\"):\\n return \\\"XML-RPC\\\"\\n if hasattr(x,\\\"__setitem__\\\"):\\n if hasattr(x,\\\"keys\\\"):\\n return \\\"DICT\\\"\\n if hasattr(x,\\\"append\\\"):\\n return \\\"LIST\\\"\\n if hasattr(x,\\\"join\\\" ):\\n return \\\"STR\\\"\\n if hasattr(x,\\\"__add__\\\") and hasattr(x,\\\"__sub__\\\"):\\n if hasattr(x,\\\"__and__\\\"):\\n return \\\"INT\\\"\\n else:\\n return \\\"FLOAT\\\"\\n if hasattr(x,\\\"url\\\") and hasattr(x,\\\"start_pt\\\"):\\n return \\\"REF\\\"\",\n \"def Type(self, String):\\r\\n\\r\\n if (String == \\\"byte\\\") or (String == \\\"sbyte\\\"):\\r\\n return 1\\r\\n elif (String == \\\"word\\\") or (String == \\\"sword\\\"):\\r\\n return 2\\r\\n else:\\r\\n return 4\",\n \"def get(identifier):\\n if identifier is None:\\n return None\\n if isinstance(identifier, str):\\n identifier = str(identifier)\\n return deserialize(identifier)\\n if isinstance(identifier, dict):\\n return deserialize(identifier)\\n elif callable(identifier):\\n return identifier\\n else:\\n raise ValueError('Could not interpret '\\n 'loss function identifier:', identifier)\",\n \"def match_string_signature_old(self, file_name):\\n\\n pattern_msvc = 'This program cannot be run in DOS mode'\\n pattern_delphi = 'This program must be run under Win32'\\n p_vc = re.compile(pattern_msvc)\\n p_delphi = re.compile(pattern_delphi)\\n \\n if platform.system() == 'Windows':\\n # IF running on Windows, use jstrings.exe\\n string_command = STRINGS_PATH + ' --ascii < ' + file_name\\n elif platform.system() == 'Linux' or platform.system() == 'Darwin':\\n # If running on Linux or MacOSX, use strings command\\n string_command = 'strings ' + file_name\\n else:\\n print 'Unknown OS detected'\\n sys.exit()\\n \\n string_result = subprocess.check_output(string_command, shell=True)\\n if p_vc.search(string_result) is not None:\\n compiler_name = 'Microsoft Visual C++ or MinGW'\\n elif p_delphi.search(string_result) is not None:\\n compiler_name = 'Borland C/C++ or Delphi'\\n else:\\n compiler_name = 'Unknown'\\n return compiler_name\",\n \"def parse_line(line):\\n\\tpattern = re.compile(\\\"\\\"\\\"\\n\\t\\t[ \\\\t]*\\n\\t\\t(private|package|remote|public)*[ ]*\\n\\t\\t(any|string|array|numeric|boolean|component|struct|void)*[ ]*\\n\\t\\t(function[ ]+|\\\\<cffunction[ ]+name[ ]*=[ ]*\\\\\\\"?)([_\\\\-a-z][a-z0-9_\\\\-]+)\\n\\t\\t\\\"\\\"\\\", \\n\\t\\tre.VERBOSE|re.MULTILINE|re.IGNORECASE)\\n\\tm = pattern.match(line)\\n\\t#return the 5th gouped regex, which should be the function name\\n\\tret_val = m.group(4) if m else ''\\n\\treturn ret_val\",\n \"def get_func(self, name_str):\\n return self.func.get(name_str)\",\n \"def get_type_name(column_data: Dict) -> Text:\\n column_type = str(column_data[\\\"db_type\\\"]).lower()\\n if \\\"text\\\" in column_type:\\n return \\\"text\\\"\\n elif \\\"json\\\" in column_type:\\n return \\\"json\\\"\\n elif not isinstance(column_data[\\\"type\\\"], tuple):\\n type_ = column_data[\\\"type\\\"].__name__\\n else:\\n type_ = f'array of {column_data[\\\"type\\\"][1]}'\\n return type_\",\n \"def r_type(byte_string):\\n\\n opcode = int(byte_string[:6], 2)\\n rs = int(byte_string[6:11], 2)\\n rt = int(byte_string[11:16], 2)\\n rd = int(byte_string[16:21], 2)\\n shift = int(byte_string[21:26], 2)\\n func = int(byte_string[-6:], 2)\\n print(OPCODES[opcode][func] + '\\\\t$' + REGISTERS[rd] + ', $' + REGISTERS[rs] + ', ' + str(shift) + '($' + REGISTERS[rt] + ')')\\n \\n # try:\\n # state_from_r_type(rs, rt, rd, shift, func)\\n # print_registers()\\n # except Exception as err:\\n # print(str(err))\\n # pass\",\n \"def chtype(var):\\n return str(type(var)).split('\\\\'')[1]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5464446","0.5418039","0.53736573","0.5311592","0.53082407","0.52509713","0.52308387","0.52233297","0.5139834","0.5137596","0.5136655","0.51061374","0.50472605","0.50168824","0.5014381","0.49955937","0.4979121","0.49771407","0.49526176","0.4947558","0.4932718","0.49298552","0.4911541","0.49067682","0.49021164","0.48970383","0.48911554","0.4874435","0.48539236","0.48426372","0.48370716","0.48341304","0.4828932","0.48135713","0.48129025","0.47940245","0.47929138","0.47836134","0.47762233","0.47732937","0.4763746","0.4760739","0.47571608","0.4750213","0.47366557","0.47356027","0.47334528","0.4730498","0.47221017","0.47202414","0.4720124","0.47178978","0.4710332","0.47091326","0.47084704","0.47080767","0.47054055","0.47038186","0.47009507","0.46982265","0.46931204","0.46873435","0.46865305","0.46707082","0.4669125","0.46669254","0.46616316","0.46602598","0.4660066","0.4656261","0.46559644","0.46534154","0.4649223","0.46484154","0.46469954","0.46467042","0.46398738","0.46387494","0.46361628","0.46291476","0.46276006","0.46243757","0.46121365","0.46115363","0.46102324","0.46016324","0.45948666","0.4587545","0.45851547","0.45817316","0.45814","0.45803475","0.45712262","0.45697182","0.45697123","0.45678943","0.45672825","0.4551388","0.45499915","0.45453116"],"string":"[\n \"0.5464446\",\n \"0.5418039\",\n \"0.53736573\",\n \"0.5311592\",\n \"0.53082407\",\n \"0.52509713\",\n \"0.52308387\",\n \"0.52233297\",\n \"0.5139834\",\n \"0.5137596\",\n \"0.5136655\",\n \"0.51061374\",\n \"0.50472605\",\n \"0.50168824\",\n \"0.5014381\",\n \"0.49955937\",\n \"0.4979121\",\n \"0.49771407\",\n \"0.49526176\",\n \"0.4947558\",\n \"0.4932718\",\n \"0.49298552\",\n \"0.4911541\",\n \"0.49067682\",\n \"0.49021164\",\n \"0.48970383\",\n \"0.48911554\",\n \"0.4874435\",\n \"0.48539236\",\n \"0.48426372\",\n \"0.48370716\",\n \"0.48341304\",\n \"0.4828932\",\n \"0.48135713\",\n \"0.48129025\",\n \"0.47940245\",\n \"0.47929138\",\n \"0.47836134\",\n \"0.47762233\",\n \"0.47732937\",\n \"0.4763746\",\n \"0.4760739\",\n \"0.47571608\",\n \"0.4750213\",\n \"0.47366557\",\n \"0.47356027\",\n \"0.47334528\",\n \"0.4730498\",\n \"0.47221017\",\n \"0.47202414\",\n \"0.4720124\",\n \"0.47178978\",\n \"0.4710332\",\n \"0.47091326\",\n \"0.47084704\",\n \"0.47080767\",\n \"0.47054055\",\n \"0.47038186\",\n \"0.47009507\",\n \"0.46982265\",\n \"0.46931204\",\n \"0.46873435\",\n \"0.46865305\",\n \"0.46707082\",\n \"0.4669125\",\n \"0.46669254\",\n \"0.46616316\",\n \"0.46602598\",\n \"0.4660066\",\n \"0.4656261\",\n \"0.46559644\",\n \"0.46534154\",\n \"0.4649223\",\n \"0.46484154\",\n \"0.46469954\",\n \"0.46467042\",\n \"0.46398738\",\n \"0.46387494\",\n \"0.46361628\",\n \"0.46291476\",\n \"0.46276006\",\n \"0.46243757\",\n \"0.46121365\",\n \"0.46115363\",\n \"0.46102324\",\n \"0.46016324\",\n \"0.45948666\",\n \"0.4587545\",\n \"0.45851547\",\n \"0.45817316\",\n \"0.45814\",\n \"0.45803475\",\n \"0.45712262\",\n \"0.45697182\",\n \"0.45697123\",\n \"0.45678943\",\n \"0.45672825\",\n \"0.4551388\",\n \"0.45499915\",\n \"0.45453116\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.69010454"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17781,"cells":{"query":{"kind":"string","value":"Function will handle a getMacroEnvironment function (assuming it is correctly formatted (by checking with _deviceVariableFunctionName first))"},"document":{"kind":"string","value":"def dispatchMacroEnvFunction(self, tree, tree_parent):\n cpp_func_name = \"getMacroProperty\"\n py_func = tree.attr\n # extract type from function name\n py_type = py_func[len(cpp_func_name):]\n if py_type not in self._fgpu_types:\n self.RaiseError(tree, f\"'{py_type}' is not a valid FLAME GPU type\")\n # get cpp type\n t = self._fgpu_types[py_type]\n cpp_func_name += f\"<{t}\"\n # mess with the parent to extract (and remove arguments so they dont end up in the argument list)\n if not tree_parent.args :\n self.RaiseError(tree, f\" Macro environment function '{py_func}' is expected to have some arguments.\")\n # if more than one arg then the rest are bounds to translate\n if len(tree_parent.args) > 1:\n bounds = tree_parent.args[1:]\n # process bounds by appending to cpp function template arguments\n for i in bounds:\n if isinstance(i, ast.Num): # num required for python 3.7\n if not isinstance(i.n, int):\n self.RaiseError(tree, f\" Macro environment function argument '{i}' should be an integer value.\")\n cpp_func_name += f\", {i.n}\"\n else: # all Python > 3.7 \n if not isinstance(i, ast.Constant):\n self.RaiseError(tree, f\" Macro environment function argument '{i}' should be an constant value (or Num in Python <3.8).\")\n if not isinstance(i.value, int):\n self.RaiseError(tree, f\" Macro environment function argument '{i}' should be an integer value.\")\n cpp_func_name += f\", {i.value}\"\n # remove bounds from argument list (in place)\n del tree_parent.args[1:]\n cpp_func_name += \">\"\n self.write(cpp_func_name)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_get_environment_string(self):\n pass","def __MakeEnvironment(self):\n environment= os.environ.copy()\n\n for key, value in self.__context.items():\n if type(value) is str:\n name = \"QMV_\" + key.replace(\".\", \"__\")\n environment[name]= value\n\n return environment","def getenv(device, variable_name):\n command = 'getenv \"%s\" \"%s\"' % (device.udid, variable_name)\n variable = _run_command(command)\n # The variable has an extra new line at the end, so remove it when returning\n return variable[:-1]","def test_DDSim_getEnvScript_vars( self ):\n\n platform = \"Windows\"\n appname = \"ddsim\"\n appversion = \"Vista\"\n self.ddsim.ops.getOptionsDict = Mock( return_value = S_OK( dict(KNIGHTSWORD=\"Ni\",\n WHEN=\"Always\",\n G4LEDATA=\"/dev/camelot\",\n )\n )\n )\n res = self.ddsim.getEnvScript( platform, appname, appversion )\n self.assertEqual( res['Value'], os.path.abspath(\"DDSimEnv.sh\") )\n self.assertTrue( os.path.exists(os.path.abspath(\"DDSimEnv.sh\")) )\n with open(\"DDSimEnv.sh\") as script:\n scriptLines = \"\".join(script.readlines())\n self.assertIn( \"declare -x KNIGHTSWORD=Ni\", scriptLines )\n self.assertIn( \"declare -x WHEN=Always\", scriptLines )\n self.assertIn( \"declare -x G4LEDATA=/dev/camelot\", scriptLines )\n self.assertNotIn( \"declare -x G4LEDATA=$(ls -d $G4DATA/\", scriptLines )\n self.assertIn( \"declare -x G4LEVELGAMMADATA=$(ls -d $G4DATA/\", scriptLines )","def _get_environment():\n namespace = current_app.config.get('POD_NAMESPACE').lower()\n if namespace.endswith('dev'):\n return 'DEV'\n if namespace.endswith('test'):\n return 'TEST'\n if namespace.endswith('tools'):\n return 'SANDBOX'\n return ''","def _get_environmentdef():\n if 'environmentdef' not in env:\n abort(\"Environment needs to be configured\")\n\n environmentdef = env.environmentdef\n\n # If we're running via `fab`, we should restrict the environment\n # to the current host.\n if env.host_string:\n environmentdef = environmentdef.with_hosts(env.host_string)\n\n return environmentdef","def getenv(space, var):\n e = os.environ.get(var)\n if e is None:\n return space.w_False\n return space.newstr(e)","def get_os_env():\n env = os.environ\n# print(\"env \\n\" , env)\n return env","def substitute_macros(text):\n f_text = text\n for (pattern,replacement) in context.environment.items():\n replacement = replacement.replace(os.path.sep,'/')\n f_text = f_text.replace('$(%s)' % pattern.upper(), replacement)\n return f_text","def _GetEnvironmentVars(benchmark_spec):\n return ' '.join([\n 'NUM_GPUS=%s' % benchmark_spec.total_gpus,\n 'OMP_NUM_THREADS=%s' % benchmark_spec.cpus_per_rank\n ])","def _env_switch(environment: str, prod_value: T, qa_value: T) -> T:\n if environment == PROD:\n return prod_value\n return qa_value","def _readenv(name, ctor, default):\n value = os.environ.get(name)\n if value is None:\n return default() if callable(default) else default\n try:\n return ctor(value)\n except Exception:\n import warnings\n\n warnings.warn(\n \"environ %s defined but failed to parse '%s'\" % (name, value),\n RuntimeWarning,\n )\n return default","def collect_env():\n env_info = mmengine_collect_env()\n\n # MMEngine does not add the hipcc compiler information when collecting\n # environment information, so it is added here. When MMEngine v0.3.0 is\n # released, the code here can be removed.\n cuda_available = torch.cuda.is_available()\n if cuda_available and env_info.get('NVCC') == 'Not Available':\n CUDA_HOME = env_info['CUDA_HOME']\n if CUDA_HOME is not None and osp.isdir(CUDA_HOME):\n if CUDA_HOME == '/opt/rocm':\n try:\n nvcc = osp.join(CUDA_HOME, 'hip/bin/hipcc')\n nvcc = subprocess.check_output(\n f'\"{nvcc}\" --version', shell=True)\n nvcc = nvcc.decode('utf-8').strip()\n release = nvcc.rfind('HIP version:')\n build = nvcc.rfind('')\n nvcc = nvcc[release:build].strip()\n except subprocess.SubprocessError:\n nvcc = 'Not Available'\n else:\n try:\n nvcc = osp.join(CUDA_HOME, 'bin/nvcc')\n nvcc = subprocess.check_output(f'\"{nvcc}\" -V', shell=True)\n nvcc = nvcc.decode('utf-8').strip()\n release = nvcc.rfind('Cuda compilation tools')\n build = nvcc.rfind('Build ')\n nvcc = nvcc[release:build].strip()\n except subprocess.SubprocessError:\n nvcc = 'Not Available'\n env_info['NVCC'] = nvcc\n\n env_info['MMCV'] = mmcv.__version__\n\n try:\n from mmcv.ops import get_compiler_version, get_compiling_cuda_version\n except ModuleNotFoundError:\n env_info['MMCV Compiler'] = 'n/a'\n env_info['MMCV CUDA Compiler'] = 'n/a'\n else:\n env_info['MMCV Compiler'] = get_compiler_version()\n env_info['MMCV CUDA Compiler'] = get_compiling_cuda_version()\n\n return env_info","def parse(env, platform_name=None):\n\n platform_name = platform_name or PLATFORM\n\n result = {}\n for variable, value in env.items():\n\n # Platform specific values\n if isinstance(value, dict):\n value = value.get(platform_name, \"\")\n\n if not value:\n continue\n\n # Allow to have lists as values in the tool data\n if isinstance(value, (list, tuple)):\n value = \";\".join(value)\n\n result[variable] = value\n\n return result","def getEnvironment(self):\n pass","def _get_interpreter_values(token='ZEPPELIN_INTERPRETER') -> dict:\n env_variables = {}\n for env_variable in os.environ:\n if env_variable.startswith(token):\n env_variables[env_variable] = os.environ[env_variable]\n\n LOG.info('%s-based token environment variables: %s', token, env_variables)\n\n return env_variables","def _default_getter(environ, metadata, prefix, name):\n ce = metadata[CNF_KEY]\n var = ce.name if ce.name is not None else \"_\".join((*prefix, name)).upper()\n log.debug(\"looking for env var '%s'.\", var)\n try:\n return environ[var]\n except KeyError:\n raise MissingEnvValueError(var) from None","def convert_macro(macro):\n convert_dict = {\n 'CONTACTNAME' : 'user.name',\n 'CONTACTALIAS' : 'user.display_name',\n 'CONTACTEMAIL' : 'user.email',\n 'CONTACTPAGER' : 'user.pager',\n 'SERVICEDESC' : 'service.name',\n 'SERVICEDISPLAYNAME' : 'service.display_name',\n 'SERVICECHECKCOMMAND' : 'service.check_command',\n 'SERVICESTATE' : 'service.state',\n 'SERVICESTATEID' : 'service.state_id',\n 'SERVICESTATETYPE' : 'service.state_type',\n 'SERVICEATTEMPT' : 'service.check_attempt',\n 'MAXSERVICEATTEMPT' : 'service.max_check_attempts',\n 'LASTSERVICESTATE' : 'service.last_state',\n 'LASTSERVICESTATEID' : 'service.last_state_id',\n 'LASTSERVICESTATETYPE' : 'service.last_state_type',\n 'LASTSERVICESTATECHANGE' : 'service.last_state_change',\n 'SERVICEDOWNTIME' : 'service.downtime_depth',\n 'SERVICEDURATIONSEC' : 'service.duration_sec',\n 'SERVICELATENCY' : 'service.latency',\n 'SERVICEEXECUTIONTIME' : 'service.execution_time',\n 'SERVICEOUTPUT' : 'service.output',\n 'SERVICEPERFDATA' : 'service.perfdata',\n 'LASTSERVICECHECK' : 'service.last_check',\n 'SERVICENOTES' : 'service.notes',\n 'SERVICENOTESURL' : 'service.notes_url',\n 'SERVICEACTIONURL' : 'service.action_url',\n 'HOSTNAME' : 'host.name',\n 'HOSTADDRESS' : 'host.address',\n 'HOSTADDRESS6' : 'host.address6',\n 'HOSTDISPLAYNAME' : 'host.display_name',\n 'HOSTALIAS' : 'host.display_name',\n 'HOSTCHECKCOMMAND' : 'host.check_command',\n 'HOSTSTATE' : 'host.state',\n 'HOSTSTATEID' : 'host.state_id',\n 'HOSTSTATETYPE' : 'host.state_type',\n 'HOSTATTEMPT' : 'host.check_attempt',\n 'MAXHOSTATTEMPT' : 'host.max_check_attempts',\n 'LASTHOSTSTATE' : 'host.last_state',\n 'LASTHOSTSTATEID' : 'host.last_state_id',\n 'LASTHOSTSTATETYPE' : 'host.last_state_type',\n 'LASTHOSTSTATECHANGE' : 'host.last_state_change',\n 'HOSTDOWNTIME' : 'host.downtime_depth',\n 'HOSTDURATIONSEC' : 'host.duration_sec',\n 'HOSTLATENCY' : 'host.latency',\n 'HOSTEXECUTIONTIME' : 'host.execution_time',\n 'HOSTOUTPUT' : 'host.output',\n 'HOSTPERFDATA' : 'host.perfdata',\n 'LASTHOSTCHECK' : 'host.last_check',\n 'HOSTNOTES' : 'host.notes',\n 'HOSTNOTESURL' : 'host.notes_url',\n 'HOSTACTIONURL' : 'host.action_url',\n 'TOTALSERVICES' : 'host.num_services',\n 'TOTALSERVICESOK' : 'host.num_services_ok',\n 'TOTALSERVICESWARNING' : 'host.num_services_warning',\n 'TOTALSERVICESUNKNOWN' : 'host.num_services_unknown',\n 'TOTALSERVICESCRITICAL' : 'host.num_services_critical',\n 'COMMANDNAME' : 'command.name',\n 'NOTIFICATIONTYPE' : 'notification.type',\n 'NOTIFICATIONAUTHOR' : 'notification.author',\n 'NOTIFICATIONCOMMENT' : 'notification.comment',\n 'NOTIFICATIONAUTHORNAME' : 'notification.author',\n 'NOTIFICATIONAUTHORALIAS' : 'notification.author',\n 'TIMET' : 'icinga.timet',\n 'LONGDATETIME' : 'icinga.long_date_time',\n 'SHORTDATETIME' : 'icinga.short_date_time',\n 'DATE' : 'icinga.date',\n 'TIME' : 'icinga.time',\n 'PROCESSSTARTTIME' : 'icinga.uptime',\n 'TOTALHOSTSUP' : 'icinga.num_hosts_up',\n 'TOTALHOSTSDOWN' : 'icinga.num_hosts_down',\n 'TOTALHOSTSUNREACHABLE' : 'icinga.num_hosts_unreachable',\n 'TOTALHOSTSDOWNUNHANDLED' : '-',\n 'TOTALHOSTSUNREACHABLEUNHANDLED' : '-',\n 'TOTALHOSTPROBLEMS' : 'down',\n 'TOTALHOSTPROBLEMSUNHANDLED' : 'down-(downtime+acknowledged)',\n 'TOTALSERVICESOK' : 'icinga.num_services_ok',\n 'TOTALSERVICESWARNING' : 'icinga.num_services_warning',\n 'TOTALSERVICESCRITICAL' : 'icinga.num_services_critical',\n 'TOTALSERVICESUNKNOWN' : 'icinga.num_services_unknown',\n 'TOTALSERVICESWARNINGUNHANDLED' : '-',\n 'TOTALSERVICESCRITICALUNHANDLED' : '-',\n 'TOTALSERVICESUNKNOWNUNHANDLED' : '-',\n 'TOTALSERVICEPROBLEMS' : 'ok+warning+critical+unknown',\n 'TOTALSERVICEPROBLEMSUNHANDLED' : 'warning+critical+unknown-(downtime+acknowledged)',\n 'CHANGE_CUSTOM_CONTACT_VAR' : 'CHANGE_CUSTOM_USER_VAR',\n }\n\n #The following external commands are not supported:\n unsupported = [\n 'CHANGE_*MODATTR',\n 'CHANGE_CONTACT_HOST_NOTIFICATION_TIMEPERIOD',\n 'CHANGE_HOST_NOTIFICATION_TIMEPERIOD',\n 'CHANGE_SVC_NOTIFICATION_TIMEPERIOD',\n 'DEL_DOWNTIME_BY_HOSTGROUP_NAME',\n 'DEL_DOWNTIME_BY_START_TIME_COMMENT',\n 'DISABLE_ALL_NOTIFICATIONS_BEYOND_HOST',\n 'DISABLE_CONTACT_HOST_NOTIFICATIONS',\n 'DISABLE_CONTACT_SVC_NOTIFICATIONS',\n 'DISABLE_CONTACTGROUP_HOST_NOTIFICATIONS',\n 'DISABLE_CONTACTGROUP_SVC_NOTIFICATIONS',\n 'DISABLE_FAILURE_PREDICTION',\n 'DISABLE_HOST_AND_CHILD_NOTIFICATIONS',\n 'DISABLE_HOST_FRESHNESS_CHECKS',\n 'DISABLE_NOTIFICATIONS_EXPIRE_TIME',\n 'DISABLE_SERVICE_FRESHNESS_CHECKS',\n 'ENABLE_ALL_NOTIFICATIONS_BEYOND_HOST',\n 'ENABLE_CONTACT_HOST_NOTIFICATIONS',\n 'ENABLE_CONTACT_SVC_NOTIFICATIONS',\n 'ENABLE_CONTACTGROUP_HOST_NOTIFICATIONS',\n 'ENABLE_CONTACTGROUP_SVC_NOTIFICATIONS',\n 'ENABLE_FAILURE_PREDICTION',\n 'ENABLE_HOST_AND_CHILD_NOTIFICATIONS',\n 'ENABLE_HOST_FRESHNESS_CHECKS',\n 'ENABLE_SERVICE_FRESHNESS_CHECKS',\n 'READ_STATE_INFORMATION',\n 'SAVE_STATE_INFORMATION',\n 'SET_HOST_NOTIFICATION_NUMBER',\n 'SET_SVC_NOTIFICATION_NUMBER',\n 'START_ACCEPTING_PASSIVE_HOST_CHECKS',\n 'START_ACCEPTING_PASSIVE_SVC_CHECKS',\n 'START_OBSESSING_OVER_HOST',\n 'START_OBSESSING_OVER_HOST_CHECKS',\n 'START_OBSESSING_OVER_SVC',\n 'START_OBSESSING_OVER_SVC_CHECKS',\n 'STOP_ACCEPTING_PASSIVE_HOST_CHECKS',\n 'STOP_ACCEPTING_PASSIVE_SVC_CHECKS',\n 'STOP_OBSESSING_OVER_HOST',\n 'STOP_OBSESSING_OVER_HOST_CHECKS',\n 'STOP_OBSESSING_OVER_SVC',\n 'STOP_OBSESSING_OVER_SVC_CHECKS',\n]\n # Strip the $$\n sane_macro = macro.translate(None, '$')\n #debug(sane_macro)\n # Return true when it's a global parameter\n if sane_macro in convert_dict:\n return False, '$' + convert_dict[sane_macro] + '$'\n elif sane_macro in unsupported:\n return False, 'UNSUPPORTED'\n elif not sane_macro in ['ARG1','ARG2','ARG3','ARG4','ARG5','ARG6']:\n return True, sane_macro.title()\n else:\n return False, ''","def _setenv(self):\n tokens = {}\n tokens[\"CT_TIMESTAMP\"] = self._timestamp\n tokens[\"CT_SUBMITTER\"] = self._node.name()\n # tokens[\"CT_HIPBASE\"] = self._file[\"hipbase\"]\n tokens[\"CT_SCENE\"] = self._scene\n tokens[\"CT_PROJECT\"] = self.project_name\n\n for token in tokens:\n hou.putenv(token, tokens[token])\n\n return tokens","def get_environment(self):\r\n return self.mcas[0].get_environment()","def MakeEnvironment(self, context):\n\n # Start with any environment variables that are already present\n # in the environment.\n environment = os.environ.copy()\n # Copy context variables into the environment.\n for key, value in context.items():\n name = \"QMV_\" + key\n environment[name] = value\n # Extract additional environment variable assignments from the\n # 'Environment' field.\n for assignment in self.environment:\n if \"=\" in assignment:\n # Break the assignment at the first equals sign.\n variable, value = string.split(assignment, \"=\", 1)\n environment[variable] = value\n else:\n raise ValueError, \\\n qm.error(\"invalid environment assignment\",\n assignment=assignment)\n return environment","def MakeEnvironment(self, context):\n\n # Start with any environment variables that are already present\n # in the environment.\n environment = os.environ.copy()\n # Copy context variables into the environment.\n for key, value in context.items():\n name = \"QMV_\" + key\n environment[name] = value\n return environment","def get(self):\n if not self.__name in g_platform_variables:\n raise RuntimeError(\"unknown platform variable '%s'\" % (self.__name))\n current_var = g_platform_variables[self.__name]\n combinations = get_platform_combinations()\n for ii in combinations:\n if ii in current_var:\n return current_var[ii]\n raise RuntimeError(\"current platform %s not supported for variable '%s'\" % (str(combinations), self.__name))","def evaluateMacro(compiled_expression):","def constructor_env_variables(loader, node):\n value = loader.construct_scalar(node)\n match = pattern.findall(value) # to find all env variables in line\n if match:\n full_value = value\n for g in match:\n full_value = full_value.replace(\n f'${{{g}}}', os.environ.get(g, g)\n )\n return full_value\n return value","def handle_software_environment(cwl_env: Dict[str, str], script: str) -> Dict[str, str]:\n exec_env = cwl_env.copy()\n exec_env[\"_CWLTOOL\"] = \"1\"\n res = subprocess.run([\"bash\", script], shell=False, env=exec_env) # nosec\n if res.returncode != 0:\n sys.stderr.write(\"Error while using SoftwareRequirements to modify environment\\n\")\n return cwl_env\n\n env = cwl_env.copy()\n with open(\"output_environment.dat\") as _:\n data = _.read().strip(\"\\0\")\n for line in data.split(\"\\0\"):\n key, val = line.split(\"=\", 1)\n if key in (\"_\", \"PWD\", \"SHLVL\", \"TMPDIR\", \"HOME\", \"_CWLTOOL\"):\n # Skip some variables that are meaningful to the shell or set\n # specifically by the CWL runtime environment.\n continue\n env[key] = val\n return env","def testGetPortageEnvVar(self):\n stage = self.ConstructStage()\n board = self._current_board\n\n envvar = 'EXAMPLE'\n rc_mock = self.StartPatcher(cros_build_lib_unittest.RunCommandMock())\n rc_mock.AddCmdResult(['portageq-%s' % board, 'envvar', envvar],\n output='RESULT\\n')\n\n result = stage._GetPortageEnvVar(envvar, board)\n self.assertEqual(result, 'RESULT')","def retrieve_environment(env_config, filter_config):\n global path, filter_list_from_config\n\n arena_data_server = acm.FDhDatabase[\"ADM\"].ADSNameAndPort().upper()\n configuration = acm.GetDefaultValueFromName(\n acm.GetDefaultContext(), acm.FObject, env_config)\n\n filter_list_from_config = acm.GetDefaultValueFromName(\n acm.GetDefaultContext(), acm.FList, filter_config).split(';')\n\n dom_xml = xml.parseString(configuration)\n tags = dom_xml.getElementsByTagName(\"Host\")\n for element in tags: \n if element.getAttribute(\"Name\") == arena_data_server:\n path = element.getElementsByTagName(\n \"output_path\")[0].childNodes[0].data\n print(\" path found: \", path)","def test_DDSim_getEnvScript_noVars( self ):\n\n platform = \"Windows\"\n appname = \"ddsim\"\n appversion = \"Vista\"\n self.ddsim.ops.getOptionsDict = Mock( return_value = S_ERROR( \"No Variables Set\" ) )\n self.ddsim.ops.getValue = Mock( return_value = None )\n res = self.ddsim.getEnvScript( platform, appname, appversion )\n self.ddsim.ops.getValue.assert_called_once_with(\"/AvailableTarBalls/Windows/ddsim/Vista/InitScript\", None)\n self.assertEqual( res['Value'], os.path.abspath(\"DDSimEnv.sh\") )\n self.assertTrue( os.path.exists(os.path.abspath(\"DDSimEnv.sh\")) )\n with open(\"DDSimEnv.sh\") as script:\n scriptLines = \"\".join(script.readlines())\n self.assertIn( \"declare -x G4LEDATA=$(ls -d $G4DATA/\", scriptLines )\n self.assertNotIn( \"source \", scriptLines )","def _Get_Env_Key(self, line0):\n #line0 = chunk[0]\n #code = '\\n'.join(chunk)\n q2 = self.p2.match(line0)\n if q2:\n envkey = q2.group(1)\n else:\n envkey = 'body'\n #codelist = code.split('\\n')\n return envkey#, code","def register_environment_functions(self):\n self.environment.globals['path_join'] = path_join\n self.environment.globals['path_normalize'] = path_normalize","def parse_st_cmd_env(st_cmd_filepath):\n re_env = re.compile(\"^epicsEnvSet[ \\t\\\"\\(]*([\\w]+)[, \\t\\\"\\)]*([^\\\"\\n]*)\")\n env = {}\n with open(st_cmd_filepath, \"r\") as f:\n for line in f:\n match = re_env.search(line)\n if match:\n env[match.group(1)] = match.group(2)\n return env","def _generate_environment(self):\n envvars = {}\n for key in self.envvars:\n try:\n envvars[key] = os.environ[key]\n except KeyError:\n continue\n\n # Warn the user that we cannot support secrets\n if envvars:\n logger.warning(\"This API does not support environment secrets.\")\n return envvars","def __getitem__(self, name):\n return os.environ[name]","def env_loop(environment):\n def scan(vars, vals):\n \"\"\"\n scans variables in a frame\n \"\"\"\n if isNull(vars):\n return env_loop(enclosing_env(environment)) # 5-4: env -> environment\n elif isEq(var, car(vars)) == TRUE:\n return car(vals)\n else:\n return scan(cdr(vars), cdr(vals))\n if environment is the_empty_environment:\n raise UnboundLocalError(\"lookup_variable\")\n frame = first_frame(environment)\n return scan(frame_variables(frame), frame_values(frame))","def function_macro(self, platform, stack):\n initial_pos = self.pos\n try:\n identifier = self.match_type(Identifier)\n if identifier in stack:\n raise ParseError('Cannot expand token')\n\n self.match_value(Punctuator, \"(\")\n args = self.__arg_list()\n self.match_value(Punctuator, \")\")\n except ParseError:\n self.pos = initial_pos\n raise ParseError(\"Not a function-like macro.\")\n\n macro = platform.get_macro(str(identifier))\n if not macro:\n raise ParseError(\"Not a function-like macro.\")\n\n # Argument pre-scan\n # Macro arguments are macro-expanded before substitution\n expanded_args = [MacroExpander(arg).expand(platform) for arg in args]\n\n # Combine variadic arguments into one, separated by commas\n va_args = None\n if macro.is_variadic():\n va_args = util.interleave(expanded_args[len(macro.args) - 1:],\n Punctuator(\"EXPANSION\", -1, False, \",\"))\n va_args = va_args[:-1] # Remove final comma\n\n # Substitute each occurrence of an argument in the expansion\n substituted_tokens = []\n for token in macro.expansion:\n\n substitution = []\n\n # If a token matches an argument, it is substituted;\n # otherwise it passes through\n try:\n substitution = expanded_args[macro.args.index(token.token)]\n except (ValueError, ParseError):\n substitution = token\n\n # If a token matches the variable argument, substitute\n # precomputed va_args\n if macro.is_variadic() and token.token == macro.variable_argument() and va_args:\n\n # Whether the token was preceded by whitespace should be\n # tracked through substitution\n for t in va_args[0]:\n t.prev_white = token.prev_white\n substitution = va_args\n\n self.grow_token_list(substituted_tokens, substitution)\n\n # Check the expansion for macros to expand\n stack.push(identifier)\n expansion = MacroExpander(substituted_tokens).expand(platform, stack)\n stack.pop()\n return expansion","def env(var):\n return os.environ[var]","def _get_input_from_env():\n try:\n params = {\n 'repo': {\n 'owner': os.environ['DRONE_REPO_OWNER'],\n 'name': os.environ['DRONE_REPO_NAME'],\n 'full_name': os.environ['DRONE_REPO'],\n 'link_url': os.environ['DRONE_REPO_LINK'],\n 'clone_url': os.environ['DRONE_REMOTE_URL']\n },\n 'build': {\n 'number': os.environ['DRONE_BUILD_NUMBER'],\n 'event': os.environ['DRONE_BUILD_EVENT'],\n 'branch': os.environ['DRONE_BRANCH'],\n 'commit': os.environ['DRONE_COMMIT'],\n 'ref': os.environ['DRONE_COMMIT_REF'],\n 'author': os.environ['DRONE_COMMIT_AUTHOR'],\n 'author_email': os.environ['DRONE_COMMIT_AUTHOR_EMAIL']\n },\n 'workspace': {\n 'root': os.environ['DRONE_WORKSPACE'],\n 'path': os.environ['DRONE_WORKSPACE']\n },\n 'vargs': {\n key[7:].lower(): value\n for key, value in os.environ.items()\n if key.startswith('PLUGIN_')\n }\n }\n except KeyError:\n raise ValueError(\n \"Envronment variables were misconfigured.\")\n return json.dumps(params)","def _process_environment_cvterm(self):\n\n line_counter = 0\n raw = '/'.join((self.rawdir, 'environment_cvterm'))\n logger.info(\"processing environment to cvterm mappings\")\n\n with open(raw, 'r') as f:\n f.readline() # read the header row; skip\n filereader = csv.reader(f, delimiter='\\t', quotechar='\\\"')\n for line in filereader:\n line_counter += 1\n\n (environment_cvterm_id, environment_id, cvterm_id) = line\n # 1\t1\t60468\n\n environment_key = environment_id\n\n cvterm_key = cvterm_id\n cvterm_id = self.idhash['cvterm'][cvterm_key]\n\n # look up the dbxref_id for the cvterm\n # hopefully it's one-to-one\n self.idhash['environment'][environment_key] = cvterm_id\n\n return","def env_loop(environment):\n def scan(vars, vals):\n \"\"\"\n scans variables in a frame\n \"\"\"\n if isNull(vars):\n return env_loop(enclosing_env(environment)) # 5-4: env -> environment\n elif var == car(vars):\n return set_car(vals, val) #4-15\n else:\n return scan(cdr(vars), cdr(vals)) # 4-15\n if environment is the_empty_environment:\n raise UnboundLocalError(\"lookup_variable\")\n frame = first_frame(environment)\n return scan(frame_variables(frame), frame_values(frame)) # 4-15","def guess_machine_parameter () :\n val = [ \"COMPUTERNAME\", \"NUMBER_OF_PROCESSORS\", \"OS\",\n \"PATH\", \"USERDOMAIN\", \"USERNAME\", \"USERPROFILE\",\n \"windir\", \"TEMP\" ]\n res = { }\n for v in val :\n if v == \"PATH\" :\n x = os.getenv (v)\n x = x.split (\";\")\n res [v] = x\n else : res [v] = os.getenv (v)\n \n if not sys.platform.startswith(\"win\") :\n if \"TEMP\" not in res or res[\"TEMP\"] is None :\n res[\"TEMP\"] = \"/tmp\"\n \n return res","def read_environment(self):\n # Setup credentials\n if os.getenv(\"DO_API_TOKEN\"):\n self.api_token = os.getenv(\"DO_API_TOKEN\")\n if os.getenv(\"DO_API_KEY\"):\n self.api_token = os.getenv(\"DO_API_KEY\")","def _deviceVariableFunctionName(self, tree, permitted_prefixes, allow_lengths = True):\n cpp_func_name = \"\"\n py_func = tree.attr\n # extract function name start\n for prefix in permitted_prefixes:\n if py_func.startswith(prefix):\n cpp_func_name = prefix\n py_func = py_func[len(prefix):]\n break # dont allow the else\n else:\n return None\n # check type and lengths\n if allow_lengths:\n #split to get type and Array Length (This could **potentially** be looked up from the model description but current syntax is consistent with swig bindings) \n type_and_length = py_func.split(\"Array\")\n if type_and_length[0] not in self._fgpu_types:\n self.RaiseError(tree, f\"'{type_and_length[0]}' is not a valid FLAME GPU type\")\n t = self._fgpu_types[type_and_length[0]]\n # generate template args\n if (len(type_and_length) == 1):\n cpp_func_name += f\"<{t}>\"\n elif (len(type_and_length) == 2):\n cpp_func_name += f\"<{t}, {type_and_length[1]}>\"\n else:\n return None\n else:\n if py_func not in self._fgpu_types:\n self.RaiseError(tree, f\"'{py_func}' is not a valid FLAME GPU type\")\n t = self._fgpu_types[py_func]\n cpp_func_name += f\"<{t}>\"\n # return \n return cpp_func_name","def _get_env(key: str) -> str:\n value = os.getenv(key)\n assert isinstance(value, str), (\n f\"the {key} environment variable must be set and a string, \" f\"{value=}\"\n )\n return value","def _parse(self, inval):\n regex = re.compile(r'^os\\.environ\\[(.*)\\]$')\n for val in inval:\n if val is None:\n continue\n # split on \\n\n cmd = val.split('\\n')\n for v2 in cmd:\n if not v2:\n continue\n dict_call, pth = v2.split(' = ')\n m = re.match(regex, dict_call)\n if m:\n key = m.groups()[0]\n self.env[key] = pth","def _get_test_variables():\n if __name__ == \"__main__\":\n return _MOCKUP_TEST_VARIABLES\n else:\n return BuiltIn().get_variables()","def env(*vars, **kwargs):\r\n for v in vars:\r\n value = os.environ.get(v)\r\n if value:\r\n return value\r\n return kwargs.get('default', '')","def env(*vars, **kwargs):\n for v in vars:\n value = os.environ.get(v)\n if value:\n return value\n return kwargs.get('default', '')","def getEnvironmentVars(self):\n my_env = os.environ.copy()\n my_env[\"MODE\"] = self.model.mode\n my_env[\"ALGORITHM\"] = self.model.algorithm\n my_env[\"INPUT_FILE\"] = self.model.inputFile\n my_env[\"TARGET_FILE\"] = self.model.targetFile\n my_env[\"DEVICE\"] = self.model.device\n my_env[\"SIZE\"] = str(self.model.size)\n my_env[\"ROLE\"] = self.model.role\n my_env[\"WAIT_SIZE\"] = str(self.model.waitSize)\n my_env[\"STEP_SIZE\"] = str(self.model.stepSize)\n my_env[\"TUI_CONNECTION\"] = str(True)\n return my_env","def env(*vars, **kwargs):\n for v in vars:\n value = os.environ.get(v, None)\n if value:\n return value\n return kwargs.get('default', '')","def env(*vars, **kwargs):\n for v in vars:\n value = os.environ.get(v, None)\n if value:\n return value\n return kwargs.get('default', '')","def get_environment() -> dict:\n go_path = subprocess.check_output([\"go\", \"env\", \"GOPATH\"]).decode().strip()\n setup_script_path = f\"{go_path}/src/github.com/harmony-one/harmony/scripts/setup_bls_build_flags.sh\"\n response = subprocess.check_output([\"bash\", setup_script_path, \"-v\"], timeout=5)\n environment = json.loads(response)\n environment[\"HOME\"] = os.environ.get(\"HOME\")\n return environment","def _create_environment(config):\n if isinstance(config.env, str):\n env = gym.make(config.env)\n else:\n env = config.env()\n if config.max_length:\n env = tools.wrappers.LimitDuration(env, config.max_length)\n env = tools.wrappers.RangeNormalize(env)\n env = tools.wrappers.ClipAction(env)\n env = tools.wrappers.ConvertTo32Bit(env)\n return env","def current_config():\n if os.environ[\"ENVIRONMENT\"] == \"production\":\n return Production()\n elif os.environ[\"ENVIRONMENT\"] == \"staging\":\n return Staging()\n elif os.environ[\"ENVIRONMENT\"] == \"testing\":\n return Testing()\n elif os.environ[\"ENVIRONMENT\"] == \"development\":\n return Development()\n else:\n raise KeyError(f\"Unknown environment '{os.environ['ENVIRONMENT']}'\")","def __getitem__(self, key):\n return os.environ[key]","def parse_env():\n os_env = {}\n try:\n os_env[\"github_token\"] = os.environ[\"GITHUB_TOKEN\"]\n os_env[\"github_repository\"] = os.environ[\"GITHUB_REPOSITORY\"]\n os_env[\"threshold\"] = int(os.environ[\"THRESHOLD\"])\n os_env[\"slack_channel\"] = os.environ[\"SLACK_CHANNEL\"]\n os_env[\"slack_token\"] = os.environ[\"SLACK_TOKEN\"]\n os_env[\"sleep_interval\"] = int(os.environ[\"SLEEP_INTERVAL\"])\n return os_env\n except KeyError as e:\n logging.error(f\"failed to retrieve variable from env: {e}\")\n return {}","def get_env(self, *args):\n m = module(*args)\n return m.env","def get_var(self, tag):\n if not tag in self.env:\n print(\"ERROR: value {} is not defined yet\".format(tag))\n elif callable(self.env[tag]):\n print(\"ERROR: tried to access callable {} was a value\".format(tag))\n else:\n return self.env[tag]","def _get_environment(cls):\n return cls.__name__.lower()","def __getitem__(self, item):\n\n check_name = self._varPrefix + item.upper()\n\n return self._environmentVariables.get(check_name)","def getEnvironment(self, environment = {}):\r\n e = dict(environment, **self.environment)\r\n e.update(locals(), **globals())\r\n for x in dir(self):\r\n if not x.startswith('_'):\r\n e[x] = getattr(self, x)\r\n return e","def env(self): # type: () -> t.List[str]\n return self.config['Env']","def _expand_macros(tokdict,macrodict):\r\n def macro_value(m):\r\n return '(?:%s)' % macrodict[m.groupdict()['macro']]\r\n for key, value in tokdict.items():\r\n while re.search(r'{[a-z][a-z0-9-]*}', value):\r\n value = re.sub(r'{(?P<macro>[a-z][a-z0-9-]*)}',\r\n macro_value, value)\r\n tokdict[key] = value\r\n return tokdict","def _get_device():\n return context.get_context('device_target')","def environment_variables(\n self,\n ) -> typing.Optional[\n typing.Mapping[str, aws_cdk.aws_codebuild.BuildEnvironmentVariable]\n ]:\n return self._values.get(\"environment_variables\")","def environment_variables(\n self,\n ) -> typing.Optional[\n typing.Mapping[str, aws_cdk.aws_codebuild.BuildEnvironmentVariable]\n ]:\n return self._values.get(\"environment_variables\")","def environment_variables(\n self,\n ) -> typing.Optional[\n typing.Mapping[str, aws_cdk.aws_codebuild.BuildEnvironmentVariable]\n ]:\n return self._values.get(\"environment_variables\")","def environment_variables(\n self,\n ) -> typing.Optional[\n typing.Mapping[str, aws_cdk.aws_codebuild.BuildEnvironmentVariable]\n ]:\n return self._values.get(\"environment_variables\")","def get_environment():\n return GenericGymEnv(id=\"real-time-gym-v1\", gym_kwargs={\"config\": CONFIG_DICT})","def get_current_environment():\n # Search for the environment variable set by the hutch python setup\n env = os.getenv('CONDA_ENVNAME')\n # Otherwise look for built-in Conda environment variables\n if not env:\n env = os.getenv('CONDA_DEFAULT_ENV')\n # Check the top level PYTHONPATH to see if we have packages installed in\n # development mode\n dev = os.getenv('PYTHONPATH')\n if dev:\n try:\n dev_pkgs = os.listdir(dev)\n except FileNotFoundError:\n logger.debug(\"No dev folder found\")\n dev_pkgs = list()\n else:\n dev_pkgs = list()\n return env, dev_pkgs","def get_elinos_environment():\n result = {}\n for key in (\"project\", \"cdk\", \"target\"):\n var = \"ELINOS_\" + key.upper()\n if var in os.environ:\n result[key] = os.environ[var]\n else:\n warn(\"%s not set\" % var)\n result[key] = None\n\n if result[\"project\"] is not None:\n result[\"xenomai\"] = glob.glob(result[\"project\"] + \"/xenomai-[0-9.]*\")\n else:\n result[\"xenomai\"] = []\n\n return result","def get_environment_actions(self, init_act):\n # Set initial values for environment variables\n time_of_day = \"2020-10-23T06:00:00\"\n time_animation = \"false\"\n cloud_state = \"free\"\n fog_range = \"100000\"\n sun_intensity = \"0.85\"\n sun_azimuth = \"0\"\n sun_elevation = \"1.31\"\n percip_type = \"dry\"\n percip_intensity = \"0\"\n\n try:\n env_layer = QgsProject.instance().mapLayersByName(\"Environment\")[0]\n for feature in env_layer.getFeatures():\n time_of_day = feature[\"Datetime\"]\n time_animation = str(feature[\"Datetime Animation\"]).lower()\n cloud_state = feature[\"Cloud State\"]\n fog_range = str(feature[\"Fog Visual Range\"])\n sun_intensity = str(feature[\"Sun Intensity\"])\n sun_azimuth = str(feature[\"Sun Azimuth\"])\n sun_elevation = str(feature[\"Sun Elevation\"])\n percip_type = feature[\"Precipitation Type\"]\n percip_intensity = str(feature[\"Precipitation Intensity\"])\n except IndexError:\n error_message = \"No environment variables detected, using defaults\"\n iface.messageBar().pushMessage(\"Info\", error_message, level=Qgis.Info)\n QgsMessageLog.logMessage(error_message, level=Qgis.Info)\n self._warning_message.append(f\"Info: {error_message}\")\n\n time_of_day = \"2020-10-23T06:00:00\"\n time_animation = \"false\"\n cloud_state = \"free\"\n fog_range = \"100000\"\n sun_intensity = \"0.85\"\n sun_azimuth = \"0\"\n sun_elevation = \"1.31\"\n percip_type = \"dry\"\n percip_intensity = \"0\"\n\n global_act = etree.SubElement(init_act, \"GlobalAction\")\n env_act = etree.SubElement(global_act, \"EnvironmentAction\")\n environ = etree.SubElement(env_act, \"Environment\")\n environ.set(\"name\", \"Environment1\")\n\n env_time = etree.SubElement(environ, \"TimeOfDay\")\n env_time.set(\"animation\", time_animation)\n env_time.set(\"dateTime\", time_of_day)\n\n weather = etree.SubElement(environ, \"Weather\")\n weather.set(\"cloudState\", cloud_state)\n weather_sun = etree.SubElement(weather, \"Sun\")\n weather_sun.set(\"intensity\", sun_intensity)\n weather_sun.set(\"azimuth\", sun_azimuth)\n weather_sun.set(\"elevation\", sun_elevation)\n weather_fog = etree.SubElement(weather, \"Fog\")\n weather_fog.set(\"visualRange\", fog_range)\n weather_percip = etree.SubElement(weather, \"Precipitation\")\n weather_percip.set(\"precipitationType\", percip_type)\n weather_percip.set(\"intensity\", percip_intensity)\n\n env_road = etree.SubElement(environ, \"RoadCondition\")\n env_road.set(\"frictionScaleFactor\", \"1.0\")","def get_system():\n if 'google.colab' in sys.modules:\n return Constant.SYS_GOOGLE_COLAB\n if os.name == 'posix':\n return Constant.SYS_LINUX\n if os.name == 'nt':\n return Constant.SYS_WINDOWS\n\n raise EnvironmentError('Unsupported environment')","def getCDefinesAsString( targetPlatform, targetName ):\n Any.requireIsTextNonEmpty( targetPlatform )\n Any.requireIsTextNonEmpty( targetName )\n\n fileName = os.path.join( 'build/%s/CMakeFiles/%s.dir/flags.make' %\n ( targetPlatform, targetName ) )\n\n Any.requireIsDirNonEmpty( 'build/%s' % targetPlatform )\n Any.requireIsFileNonEmpty( fileName )\n\n # read-in ground truth information\n logging.debug( 'parsing %s' % fileName )\n content = FastScript.getFileContent( fileName, splitLines=True )\n raw_C = ''\n raw_CPP = ''\n raw_C_CFLAGS = ''\n raw_CPP_CFLAGS = ''\n regexp_C = re.compile( '^C_DEFINES\\s=\\s+(.*)$' )\n regexp_CPP = re.compile( '^CXX_DEFINES\\s=\\s+(.*)$' )\n regexp_C_CFLAGS = re.compile( '^C_FLAGS\\s=\\s+(.*)$' )\n regexp_CPP_CFLAGS = re.compile( '^CXX_FLAGS\\s=\\s+(.*)$' )\n result = ''\n\n for line in content:\n tmp = regexp_C.search( line )\n\n if tmp:\n raw_C = tmp.group( 1 )\n # logging.debug( 'raw C defines: %s' % raw_C )\n\n tmp = regexp_CPP.search( line )\n\n if tmp:\n raw_CPP = tmp.group( 1 )\n # logging.debug( 'raw CPP defines: %s' % raw_CPP )\n\n tmp = regexp_C_CFLAGS.search(line)\n\n if tmp:\n raw_C_CFLAGS = tmp.group(1)\n\n tmp = regexp_CPP_CFLAGS.search(line)\n\n if tmp:\n raw_CPP_CFLAGS = tmp.group(1)\n\n candidates = ( shlex.split( raw_C ) +\n shlex.split( raw_CPP ) +\n shlex.split( raw_C_CFLAGS ) +\n shlex.split( raw_CPP_CFLAGS ) )\n\n for candidate in candidates:\n if candidate.startswith( '-D' ):\n result += candidate + ' '\n\n return result","def get(self, var):\n # pylint: disable=no-self-use\n return os.getenv('ASTRID_RTM_{0}'.format(var.upper()))","def _get_environment_variables(cluster):\n # Make environment variables for the shell commands\n shell_env = {}\n shell_env[\"OMP_NUM_THREADS\"] = cluster.num_vcores\n shell_env[\"GRAPHLAB_MEMORY_LIMIT_IN_MB\"] = cluster.container_size * .8\n shell_env[\"TURI_PORT_START\"] = cluster.start_port\n shell_env[\"TURI_PORT_END\"] = cluster.end_port\n # Overwrite environment for SFrame cache capacity. Being conservative, we use default 1/5 of the container size.\n for key in ['GRAPHLAB_FILEIO_MAXIMUM_CACHE_CAPACITY', 'GRAPHLAB_FILEIO_MAXIMUM_CACHE_CAPACITY_PER_FILE']:\n if key in os.environ:\n shell_env[key] = os.environ[key]\n else:\n shell_env[key] = cluster.container_size * 1024 * 1024 * .2\n\n # Overwrite environment for SFrame cache file location.\n if cluster.node_tmp_dir:\n shell_env['GRAPHLAB_CACHE_FILE_LOCATIONS'] = cluster.node_tmp_dir\n if cluster.hdfs_tmp_dir:\n shell_env['GRAPHLAB_CACHE_FILE_HDFS_LOCATION'] = cluster.hdfs_tmp_dir\n return shell_env","def default_environment():\n return dict(_VARS)","def windows_get_env_value(var_name: str) -> str:\n if var_name in os.environ.keys():\n return os.environ[var_name]","def collect_env():\n env_info = {}\n env_info['sys.platform'] = sys.platform\n env_info['Python'] = sys.version.replace('\\n', '')\n\n cuda_available = torch.cuda.is_available()\n env_info['CUDA available'] = cuda_available\n\n if cuda_available:\n devices = defaultdict(list)\n for k in range(torch.cuda.device_count()):\n devices[torch.cuda.get_device_name(k)].append(str(k))\n for name, device_ids in devices.items():\n env_info['GPU ' + ','.join(device_ids)] = name\n\n from mmcv.utils.parrots_wrapper import _get_cuda_home\n CUDA_HOME = _get_cuda_home()\n env_info['CUDA_HOME'] = CUDA_HOME\n\n if CUDA_HOME is not None and osp.isdir(CUDA_HOME):\n try:\n nvcc = osp.join(CUDA_HOME, 'bin/nvcc')\n nvcc = subprocess.check_output(f'\"{nvcc}\" -V', shell=True)\n nvcc = nvcc.decode('utf-8').strip()\n release = nvcc.rfind('Cuda compilation tools')\n build = nvcc.rfind('Build ')\n nvcc = nvcc[release:build].strip()\n except subprocess.SubprocessError:\n nvcc = 'Not Available'\n env_info['NVCC'] = nvcc\n\n try:\n # Check C++ Compiler.\n # For Unix-like, sysconfig has 'CC' variable like 'gcc -pthread ...',\n # indicating the compiler used, we use this to get the compiler name\n import sysconfig\n cc = sysconfig.get_config_var('CC')\n if cc:\n cc = osp.basename(cc.split()[0])\n cc_info = subprocess.check_output(f'{cc} --version', shell=True)\n env_info['GCC'] = cc_info.decode('utf-8').partition(\n '\\n')[0].strip()\n else:\n # on Windows, cl.exe is not in PATH. We need to find the path.\n # distutils.ccompiler.new_compiler() returns a msvccompiler\n # object and after initialization, path to cl.exe is found.\n import locale\n import os\n from distutils.ccompiler import new_compiler\n ccompiler = new_compiler()\n ccompiler.initialize()\n cc = subprocess.check_output(\n f'{ccompiler.cc}', stderr=subprocess.STDOUT, shell=True)\n encoding = os.device_encoding(\n sys.stdout.fileno()) or locale.getpreferredencoding()\n env_info['MSVC'] = cc.decode(encoding).partition('\\n')[0].strip()\n env_info['GCC'] = 'n/a'\n except subprocess.CalledProcessError:\n env_info['GCC'] = 'n/a'\n\n env_info['PyTorch'] = torch.__version__\n env_info['PyTorch compiling details'] = get_build_config()\n\n try:\n import torchvision\n env_info['TorchVision'] = torchvision.__version__\n except ModuleNotFoundError:\n pass\n\n env_info['OpenCV'] = cv2.__version__\n\n env_info['MMCV'] = mmcv.__version__\n\n try:\n from mmcv.ops import get_compiler_version, get_compiling_cuda_version\n except ModuleNotFoundError:\n env_info['MMCV Compiler'] = 'n/a'\n env_info['MMCV CUDA Compiler'] = 'n/a'\n else:\n env_info['MMCV Compiler'] = get_compiler_version()\n env_info['MMCV CUDA Compiler'] = get_compiling_cuda_version()\n\n return env_info","def _parse_jenkins_env_vars():\n # TODO find a way to get the socket number of\n os_cpu_socket = 'true'\n\n metadata = {\n 'jenkins': {\n 'jenkins_job_id': os.environ['BUILD_ID'],\n },\n 'github': {\n 'git_branch': os.environ['GIT_BRANCH'],\n 'git_commit_id': os.environ['GIT_COMMIT'],\n },\n 'environment': {\n 'os_version': ' '.join(distro.linux_distribution()),\n 'cpu_number': os.cpu_count(),\n 'cpu_socket': os_cpu_socket\n }\n }\n return metadata","def get_functions():\n\treturn [f for f in globals() if f.startswith('make_')]","def env(*_vars, **kwargs):\n for v in _vars:\n value = os.environ.get(v, None)\n if value:\n return value\n return kwargs.get('default', '')","def constructor_env_variables(loader, node) -> Any: # type: ignore\n value = loader.construct_scalar(node)\n match = pattern.findall(value) # to find all env variables in line\n if match:\n full_value = value\n for g in match:\n (env_var, default_val) = g.split(\"::\")\n value = os.environ.get(env_var, default_val)\n full_value = full_value.replace(f\"${{{g}}}\", value)\n if not full_value:\n full_value = None\n _logger.debug(f\"injected ENV parameter {env_var} resolved to {value}\")\n return full_value\n return value","def get_env_type ( base_name ) :\n return base_name.split( '-', 1 )[ 0 ]","def getEnvironmentVars(var):\n \n if var['pbs-jobid'] is None:\n var['pbs-jobid'] = os.getenv(\"PBS_JOBID\")\n if var['pbs-jobid'] is None:\n logger.error(\"ERROR: Environment Variable PBS_JOBID not found!\")\n exit(1)\n\n return var","def env(*_vars, **kwargs):\r\n for v in _vars:\r\n value = os.environ.get(v, None)\r\n if value:\r\n return value\r\n return kwargs.get('default', '')","def environments(self):\n envs = self.config[\"tox\"][\"envlist\"]\n #result = re.split(\"[^a-zA-Z0-9]\", envs)\n result = re.split(r'\\n| ,|,', envs)\n #print ([string for string in result if string != \"\"])\n result = (([string.strip() for string in result if string != \"\"]))\n print(list(dict.fromkeys(result)))\n return ((list(dict.fromkeys(result))))","def retrieve_variables(content):\n variables = []\n in_var_section = False\n for line in content.splitlines():\n #print line\n if in_var_section:\n var_def = re.split(' +', line)\n if len(var_def) > 1:\n #print var_def[0], ':', var_def[1]\n var_name = var_def[0]\n def_value = var_def[1]\n if not def_value.startswith('%'): #not environment variable which would be directly passed to robot\n variables.append([var_name.strip('${').strip('}'), def_value])\n if '*** Variables ***' in line:\n in_var_section = True\n elif in_var_section and '*** ' in line:\n #end of Variables section\n break\n return variables","def env_callback(args):\n return {\n \"MY_CUSTOM_FLAG\": args.my_custom_flag\n }","def check_for_env_vars(value: Union[str, dict]):\n if type(value) is dict and \"env_var\" in value:\n var_name = value[\"env_var\"]\n try:\n return get_var(var_name)\n except KeyError:\n raise KeyError(f\"missing env var: {value['env_var']}\")\n if type(value) is dict and \"enc_env_var\" in value:\n var_name = value[\"enc_env_var\"]\n try:\n return get_kms_var(var_name)\n except KeyError:\n raise KeyError(f\"missing enc env var: {value['enc_env_var']}\")\n return value","def setup_matrix_env(config, env):\n envs = config.get('env', {})\n if isinstance(envs, dict):\n matrix = listify(envs.get('matrix', []))\n else:\n matrix = listify(envs)\n\n return ([env[:] + ['export {}'.format(val)] for val in matrix]\n if matrix else [env[:]])","def set_envdata():\n\n\t\tenv = ArgvParserObject.get_environment()\n\n\t\t# reads data for selected enviroment\n\t\tif env == 'DEV':\n\t\t\tenvdata = DevData\n\t\t# elif env == 'PREPROD':\n\t\t#\tenvdata = PreprodData\n\t\telse:\n\t\t\traise NameError('Wrong enviroment value. ')\n\n\t\treturn envdata","def env(parser, args):\n action = subcommand_functions[args.env_command]\n action(args)","def __getattr__(self, config_property):\n\n is_build_var = config_property in self._directVariables.keys()\n is_runtime_var = config_property in self._directVariablesRuntime.keys()\n\n # For now, all unprefixed variables are also runtime variables. If that ever changes this logic will change\n # with it.\n is_unprefixed_var = config_property in self._unPrefixedVariablesRuntime.keys()\n\n if is_build_var:\n value = self[self._directVariables[config_property]]\n elif is_runtime_var:\n value = self[self._directVariablesRuntime[config_property]]\n elif is_unprefixed_var:\n value = self._environmentVariables.get(self._unPrefixedVariablesRuntime[config_property])\n else:\n raise AttributeError('No such variable defined: {}'.format(config_property))\n\n if not value:\n if self.in_build() and (is_runtime_var or is_unprefixed_var):\n raise BuildTimeVariableAccessException(\n 'The {} variable is not available during build time.'.format(config_property)\n )\n raise NotValidPlatformException(\n 'The {} variable is not defined. Are you sure you\\'re running on Platform.sh?'.format(config_property)\n )\n\n return value","def global_jinja_env():\n return _ENV","def getValue(self, value=None):\n if self.data and self.source & COMMANDLINE:\n return self.data\n\n if self.environ and str(self.environ) in os.environ:\n self.source = ENVIRONMENT\n self.file = None\n return self.cast(os.environ[str(self.environ)])\n\n if self.data:\n return self.data\n\n if self.default:\n self.source = BUILTIN\n self.file = None\n return self.default\n\n self.source = CODE\n self.file = None\n\n if value is None:\n return []\n\n return value","def run(self, context):\n i = context.skip_ws(0)\n if len(context.history) > 1 and context.history[-2] == \"IsFuncDeclaration\":\n self.check_function_declaration(context)\n if type(context.scope) is not GlobalScope:\n if type(context.scope) == Function and context.scope.multiline == False:\n pass\n else:\n context.new_error(\"PREPROC_GLOBAL\", context.peek_token(0))\n if context.check_token(i, \"DEFINE\") is False:\n return False, 0\n val = context.peek_token(i).value.split(\"define\", 1)[1]\n content = Lexer(val, context.peek_token(i).pos[0])\n tkns = content.get_tokens()\n i = 0\n identifiers = []\n protection = context.filename.upper().split(\"/\")[-1].replace(\".\", \"_\")\n for tkn in tkns:\n if tkn.type == \"ESCAPED_NEWLINE\":\n context.new_error(\"NEWLINE_DEFINE\", tkn)\n elif tkn.type in [\"TAB\", \"SPACE\"]:\n i += 1\n continue\n elif tkn.type == \"IDENTIFIER\" and len(identifiers) == 0:\n if tkn.value.isupper() is False:\n context.new_error(\"MACRO_NAME_CAPITAL\", tkn)\n identifiers.append(tkn)\n tmp = i\n while tmp < len(tkns) - 1 and tkns[tmp].type in [\n \"SPACE\",\n \"TAB\",\n \"IDENTIFIER\",\n ]:\n tmp += 1\n if tmp == (len(tkns) - 1) and context.filetype == \"h\":\n if context.scope.header_protection == 0:\n if identifiers[0].value == protection:\n context.scope.header_protection = 1\n elif identifiers[0].value != protection:\n context.new_error(\"HEADER_PROT_NAME\", tkns[1])\n elif (\n context.filetype == \"c\"\n and context.scope.include_allowed == True\n and (\n len(tkns) > tmp + 1\n or (\n len(tkns) == tmp + 1\n and identifiers[0].value != protection\n and context.scope.header_protection == -1\n )\n )\n ):\n context.scope.include_allowed = False\n\n elif tkn.type in [\"IDENTIFIER\", \"STRING\", \"CONSTANT\"]:\n if context.skip_define_error == True:\n continue\n if len(identifiers) == 1:\n if tkn.type == \"IDENTIFIER\" and tkn.value.isupper() is False:\n context.new_error(\"PREPROC_CONSTANT\", tkn)\n identifiers.append(tkn)\n elif len(identifiers) == 0:\n context.new_error(\"INCORRECT_DEFINE\", tkn)\n else:\n context.new_error(\"TOO_MANY_VALS\", tkn)\n elif tkn.type == \"LPARENTHESIS\":\n if context.skip_define_error == True:\n continue\n if len(identifiers) == 0:\n continue\n elif len(identifiers) == 1 and tkns[i - 1].type in [\"SPACE\", \"TAB\"]:\n continue\n else:\n context.new_error(\"PREPROC_CONSTANT\", tkn)\n elif tkn.type in [\"LBRACKET\", \"LBRACE\"]:\n if context.skip_define_error == True:\n continue\n context.new_error(\"PREPROC_CONSTANT\", tkn)\n\n i += 1\n if context.filetype == \"h\" and context.scope.header_protection != 1:\n context.new_error(\"HEADER_PROT_ALL\", context.peek_token(0))\n return False, 0","def __init__(self, environment_variables=None, var_prefix='PLATFORM_'):\n\n self._environmentVariables = os.environ if environment_variables is None else environment_variables\n self._varPrefix = var_prefix\n\n if self['ROUTES']:\n routes = self['ROUTES']\n self._routesDef = self.decode(routes)\n if self['RELATIONSHIPS']:\n relationships = self['RELATIONSHIPS']\n self._relationshipsDef = self.decode(relationships)\n self.register_formatter('pymongo', pymongo_formatter)\n self.register_formatter('pysolr', pysolr_formatter)\n self.register_formatter('postgresql_dsn', posgresql_dsn_formatter)\n\n if self['VARIABLES']:\n variables = self['VARIABLES']\n self._variablesDef = self.decode(variables)\n if self['APPLICATION']:\n application = self['APPLICATION']\n self._applicationDef = self.decode(application)","def evaluate_for_platform(self, **kwargs):\n macro = Macro(self.identifier, self.args, self.value)\n kwargs['platform'].define(self.identifier.as_str(), macro)\n return False","def generateEnvList( self, index ):\n EnvList = [ \n (\"GLOBUS_DUROC_SUBJOB_INDEX\", \"%d\" % index),\n (\"LD_LIBRARY_PATH\", \"/usr/local/globus/globus-3.2/lib/\") \n ]\n return EnvList"],"string":"[\n \"def test_get_environment_string(self):\\n pass\",\n \"def __MakeEnvironment(self):\\n environment= os.environ.copy()\\n\\n for key, value in self.__context.items():\\n if type(value) is str:\\n name = \\\"QMV_\\\" + key.replace(\\\".\\\", \\\"__\\\")\\n environment[name]= value\\n\\n return environment\",\n \"def getenv(device, variable_name):\\n command = 'getenv \\\"%s\\\" \\\"%s\\\"' % (device.udid, variable_name)\\n variable = _run_command(command)\\n # The variable has an extra new line at the end, so remove it when returning\\n return variable[:-1]\",\n \"def test_DDSim_getEnvScript_vars( self ):\\n\\n platform = \\\"Windows\\\"\\n appname = \\\"ddsim\\\"\\n appversion = \\\"Vista\\\"\\n self.ddsim.ops.getOptionsDict = Mock( return_value = S_OK( dict(KNIGHTSWORD=\\\"Ni\\\",\\n WHEN=\\\"Always\\\",\\n G4LEDATA=\\\"/dev/camelot\\\",\\n )\\n )\\n )\\n res = self.ddsim.getEnvScript( platform, appname, appversion )\\n self.assertEqual( res['Value'], os.path.abspath(\\\"DDSimEnv.sh\\\") )\\n self.assertTrue( os.path.exists(os.path.abspath(\\\"DDSimEnv.sh\\\")) )\\n with open(\\\"DDSimEnv.sh\\\") as script:\\n scriptLines = \\\"\\\".join(script.readlines())\\n self.assertIn( \\\"declare -x KNIGHTSWORD=Ni\\\", scriptLines )\\n self.assertIn( \\\"declare -x WHEN=Always\\\", scriptLines )\\n self.assertIn( \\\"declare -x G4LEDATA=/dev/camelot\\\", scriptLines )\\n self.assertNotIn( \\\"declare -x G4LEDATA=$(ls -d $G4DATA/\\\", scriptLines )\\n self.assertIn( \\\"declare -x G4LEVELGAMMADATA=$(ls -d $G4DATA/\\\", scriptLines )\",\n \"def _get_environment():\\n namespace = current_app.config.get('POD_NAMESPACE').lower()\\n if namespace.endswith('dev'):\\n return 'DEV'\\n if namespace.endswith('test'):\\n return 'TEST'\\n if namespace.endswith('tools'):\\n return 'SANDBOX'\\n return ''\",\n \"def _get_environmentdef():\\n if 'environmentdef' not in env:\\n abort(\\\"Environment needs to be configured\\\")\\n\\n environmentdef = env.environmentdef\\n\\n # If we're running via `fab`, we should restrict the environment\\n # to the current host.\\n if env.host_string:\\n environmentdef = environmentdef.with_hosts(env.host_string)\\n\\n return environmentdef\",\n \"def getenv(space, var):\\n e = os.environ.get(var)\\n if e is None:\\n return space.w_False\\n return space.newstr(e)\",\n \"def get_os_env():\\n env = os.environ\\n# print(\\\"env \\\\n\\\" , env)\\n return env\",\n \"def substitute_macros(text):\\n f_text = text\\n for (pattern,replacement) in context.environment.items():\\n replacement = replacement.replace(os.path.sep,'/')\\n f_text = f_text.replace('$(%s)' % pattern.upper(), replacement)\\n return f_text\",\n \"def _GetEnvironmentVars(benchmark_spec):\\n return ' '.join([\\n 'NUM_GPUS=%s' % benchmark_spec.total_gpus,\\n 'OMP_NUM_THREADS=%s' % benchmark_spec.cpus_per_rank\\n ])\",\n \"def _env_switch(environment: str, prod_value: T, qa_value: T) -> T:\\n if environment == PROD:\\n return prod_value\\n return qa_value\",\n \"def _readenv(name, ctor, default):\\n value = os.environ.get(name)\\n if value is None:\\n return default() if callable(default) else default\\n try:\\n return ctor(value)\\n except Exception:\\n import warnings\\n\\n warnings.warn(\\n \\\"environ %s defined but failed to parse '%s'\\\" % (name, value),\\n RuntimeWarning,\\n )\\n return default\",\n \"def collect_env():\\n env_info = mmengine_collect_env()\\n\\n # MMEngine does not add the hipcc compiler information when collecting\\n # environment information, so it is added here. When MMEngine v0.3.0 is\\n # released, the code here can be removed.\\n cuda_available = torch.cuda.is_available()\\n if cuda_available and env_info.get('NVCC') == 'Not Available':\\n CUDA_HOME = env_info['CUDA_HOME']\\n if CUDA_HOME is not None and osp.isdir(CUDA_HOME):\\n if CUDA_HOME == '/opt/rocm':\\n try:\\n nvcc = osp.join(CUDA_HOME, 'hip/bin/hipcc')\\n nvcc = subprocess.check_output(\\n f'\\\"{nvcc}\\\" --version', shell=True)\\n nvcc = nvcc.decode('utf-8').strip()\\n release = nvcc.rfind('HIP version:')\\n build = nvcc.rfind('')\\n nvcc = nvcc[release:build].strip()\\n except subprocess.SubprocessError:\\n nvcc = 'Not Available'\\n else:\\n try:\\n nvcc = osp.join(CUDA_HOME, 'bin/nvcc')\\n nvcc = subprocess.check_output(f'\\\"{nvcc}\\\" -V', shell=True)\\n nvcc = nvcc.decode('utf-8').strip()\\n release = nvcc.rfind('Cuda compilation tools')\\n build = nvcc.rfind('Build ')\\n nvcc = nvcc[release:build].strip()\\n except subprocess.SubprocessError:\\n nvcc = 'Not Available'\\n env_info['NVCC'] = nvcc\\n\\n env_info['MMCV'] = mmcv.__version__\\n\\n try:\\n from mmcv.ops import get_compiler_version, get_compiling_cuda_version\\n except ModuleNotFoundError:\\n env_info['MMCV Compiler'] = 'n/a'\\n env_info['MMCV CUDA Compiler'] = 'n/a'\\n else:\\n env_info['MMCV Compiler'] = get_compiler_version()\\n env_info['MMCV CUDA Compiler'] = get_compiling_cuda_version()\\n\\n return env_info\",\n \"def parse(env, platform_name=None):\\n\\n platform_name = platform_name or PLATFORM\\n\\n result = {}\\n for variable, value in env.items():\\n\\n # Platform specific values\\n if isinstance(value, dict):\\n value = value.get(platform_name, \\\"\\\")\\n\\n if not value:\\n continue\\n\\n # Allow to have lists as values in the tool data\\n if isinstance(value, (list, tuple)):\\n value = \\\";\\\".join(value)\\n\\n result[variable] = value\\n\\n return result\",\n \"def getEnvironment(self):\\n pass\",\n \"def _get_interpreter_values(token='ZEPPELIN_INTERPRETER') -> dict:\\n env_variables = {}\\n for env_variable in os.environ:\\n if env_variable.startswith(token):\\n env_variables[env_variable] = os.environ[env_variable]\\n\\n LOG.info('%s-based token environment variables: %s', token, env_variables)\\n\\n return env_variables\",\n \"def _default_getter(environ, metadata, prefix, name):\\n ce = metadata[CNF_KEY]\\n var = ce.name if ce.name is not None else \\\"_\\\".join((*prefix, name)).upper()\\n log.debug(\\\"looking for env var '%s'.\\\", var)\\n try:\\n return environ[var]\\n except KeyError:\\n raise MissingEnvValueError(var) from None\",\n \"def convert_macro(macro):\\n convert_dict = {\\n 'CONTACTNAME' : 'user.name',\\n 'CONTACTALIAS' : 'user.display_name',\\n 'CONTACTEMAIL' : 'user.email',\\n 'CONTACTPAGER' : 'user.pager',\\n 'SERVICEDESC' : 'service.name',\\n 'SERVICEDISPLAYNAME' : 'service.display_name',\\n 'SERVICECHECKCOMMAND' : 'service.check_command',\\n 'SERVICESTATE' : 'service.state',\\n 'SERVICESTATEID' : 'service.state_id',\\n 'SERVICESTATETYPE' : 'service.state_type',\\n 'SERVICEATTEMPT' : 'service.check_attempt',\\n 'MAXSERVICEATTEMPT' : 'service.max_check_attempts',\\n 'LASTSERVICESTATE' : 'service.last_state',\\n 'LASTSERVICESTATEID' : 'service.last_state_id',\\n 'LASTSERVICESTATETYPE' : 'service.last_state_type',\\n 'LASTSERVICESTATECHANGE' : 'service.last_state_change',\\n 'SERVICEDOWNTIME' : 'service.downtime_depth',\\n 'SERVICEDURATIONSEC' : 'service.duration_sec',\\n 'SERVICELATENCY' : 'service.latency',\\n 'SERVICEEXECUTIONTIME' : 'service.execution_time',\\n 'SERVICEOUTPUT' : 'service.output',\\n 'SERVICEPERFDATA' : 'service.perfdata',\\n 'LASTSERVICECHECK' : 'service.last_check',\\n 'SERVICENOTES' : 'service.notes',\\n 'SERVICENOTESURL' : 'service.notes_url',\\n 'SERVICEACTIONURL' : 'service.action_url',\\n 'HOSTNAME' : 'host.name',\\n 'HOSTADDRESS' : 'host.address',\\n 'HOSTADDRESS6' : 'host.address6',\\n 'HOSTDISPLAYNAME' : 'host.display_name',\\n 'HOSTALIAS' : 'host.display_name',\\n 'HOSTCHECKCOMMAND' : 'host.check_command',\\n 'HOSTSTATE' : 'host.state',\\n 'HOSTSTATEID' : 'host.state_id',\\n 'HOSTSTATETYPE' : 'host.state_type',\\n 'HOSTATTEMPT' : 'host.check_attempt',\\n 'MAXHOSTATTEMPT' : 'host.max_check_attempts',\\n 'LASTHOSTSTATE' : 'host.last_state',\\n 'LASTHOSTSTATEID' : 'host.last_state_id',\\n 'LASTHOSTSTATETYPE' : 'host.last_state_type',\\n 'LASTHOSTSTATECHANGE' : 'host.last_state_change',\\n 'HOSTDOWNTIME' : 'host.downtime_depth',\\n 'HOSTDURATIONSEC' : 'host.duration_sec',\\n 'HOSTLATENCY' : 'host.latency',\\n 'HOSTEXECUTIONTIME' : 'host.execution_time',\\n 'HOSTOUTPUT' : 'host.output',\\n 'HOSTPERFDATA' : 'host.perfdata',\\n 'LASTHOSTCHECK' : 'host.last_check',\\n 'HOSTNOTES' : 'host.notes',\\n 'HOSTNOTESURL' : 'host.notes_url',\\n 'HOSTACTIONURL' : 'host.action_url',\\n 'TOTALSERVICES' : 'host.num_services',\\n 'TOTALSERVICESOK' : 'host.num_services_ok',\\n 'TOTALSERVICESWARNING' : 'host.num_services_warning',\\n 'TOTALSERVICESUNKNOWN' : 'host.num_services_unknown',\\n 'TOTALSERVICESCRITICAL' : 'host.num_services_critical',\\n 'COMMANDNAME' : 'command.name',\\n 'NOTIFICATIONTYPE' : 'notification.type',\\n 'NOTIFICATIONAUTHOR' : 'notification.author',\\n 'NOTIFICATIONCOMMENT' : 'notification.comment',\\n 'NOTIFICATIONAUTHORNAME' : 'notification.author',\\n 'NOTIFICATIONAUTHORALIAS' : 'notification.author',\\n 'TIMET' : 'icinga.timet',\\n 'LONGDATETIME' : 'icinga.long_date_time',\\n 'SHORTDATETIME' : 'icinga.short_date_time',\\n 'DATE' : 'icinga.date',\\n 'TIME' : 'icinga.time',\\n 'PROCESSSTARTTIME' : 'icinga.uptime',\\n 'TOTALHOSTSUP' : 'icinga.num_hosts_up',\\n 'TOTALHOSTSDOWN' : 'icinga.num_hosts_down',\\n 'TOTALHOSTSUNREACHABLE' : 'icinga.num_hosts_unreachable',\\n 'TOTALHOSTSDOWNUNHANDLED' : '-',\\n 'TOTALHOSTSUNREACHABLEUNHANDLED' : '-',\\n 'TOTALHOSTPROBLEMS' : 'down',\\n 'TOTALHOSTPROBLEMSUNHANDLED' : 'down-(downtime+acknowledged)',\\n 'TOTALSERVICESOK' : 'icinga.num_services_ok',\\n 'TOTALSERVICESWARNING' : 'icinga.num_services_warning',\\n 'TOTALSERVICESCRITICAL' : 'icinga.num_services_critical',\\n 'TOTALSERVICESUNKNOWN' : 'icinga.num_services_unknown',\\n 'TOTALSERVICESWARNINGUNHANDLED' : '-',\\n 'TOTALSERVICESCRITICALUNHANDLED' : '-',\\n 'TOTALSERVICESUNKNOWNUNHANDLED' : '-',\\n 'TOTALSERVICEPROBLEMS' : 'ok+warning+critical+unknown',\\n 'TOTALSERVICEPROBLEMSUNHANDLED' : 'warning+critical+unknown-(downtime+acknowledged)',\\n 'CHANGE_CUSTOM_CONTACT_VAR' : 'CHANGE_CUSTOM_USER_VAR',\\n }\\n\\n #The following external commands are not supported:\\n unsupported = [\\n 'CHANGE_*MODATTR',\\n 'CHANGE_CONTACT_HOST_NOTIFICATION_TIMEPERIOD',\\n 'CHANGE_HOST_NOTIFICATION_TIMEPERIOD',\\n 'CHANGE_SVC_NOTIFICATION_TIMEPERIOD',\\n 'DEL_DOWNTIME_BY_HOSTGROUP_NAME',\\n 'DEL_DOWNTIME_BY_START_TIME_COMMENT',\\n 'DISABLE_ALL_NOTIFICATIONS_BEYOND_HOST',\\n 'DISABLE_CONTACT_HOST_NOTIFICATIONS',\\n 'DISABLE_CONTACT_SVC_NOTIFICATIONS',\\n 'DISABLE_CONTACTGROUP_HOST_NOTIFICATIONS',\\n 'DISABLE_CONTACTGROUP_SVC_NOTIFICATIONS',\\n 'DISABLE_FAILURE_PREDICTION',\\n 'DISABLE_HOST_AND_CHILD_NOTIFICATIONS',\\n 'DISABLE_HOST_FRESHNESS_CHECKS',\\n 'DISABLE_NOTIFICATIONS_EXPIRE_TIME',\\n 'DISABLE_SERVICE_FRESHNESS_CHECKS',\\n 'ENABLE_ALL_NOTIFICATIONS_BEYOND_HOST',\\n 'ENABLE_CONTACT_HOST_NOTIFICATIONS',\\n 'ENABLE_CONTACT_SVC_NOTIFICATIONS',\\n 'ENABLE_CONTACTGROUP_HOST_NOTIFICATIONS',\\n 'ENABLE_CONTACTGROUP_SVC_NOTIFICATIONS',\\n 'ENABLE_FAILURE_PREDICTION',\\n 'ENABLE_HOST_AND_CHILD_NOTIFICATIONS',\\n 'ENABLE_HOST_FRESHNESS_CHECKS',\\n 'ENABLE_SERVICE_FRESHNESS_CHECKS',\\n 'READ_STATE_INFORMATION',\\n 'SAVE_STATE_INFORMATION',\\n 'SET_HOST_NOTIFICATION_NUMBER',\\n 'SET_SVC_NOTIFICATION_NUMBER',\\n 'START_ACCEPTING_PASSIVE_HOST_CHECKS',\\n 'START_ACCEPTING_PASSIVE_SVC_CHECKS',\\n 'START_OBSESSING_OVER_HOST',\\n 'START_OBSESSING_OVER_HOST_CHECKS',\\n 'START_OBSESSING_OVER_SVC',\\n 'START_OBSESSING_OVER_SVC_CHECKS',\\n 'STOP_ACCEPTING_PASSIVE_HOST_CHECKS',\\n 'STOP_ACCEPTING_PASSIVE_SVC_CHECKS',\\n 'STOP_OBSESSING_OVER_HOST',\\n 'STOP_OBSESSING_OVER_HOST_CHECKS',\\n 'STOP_OBSESSING_OVER_SVC',\\n 'STOP_OBSESSING_OVER_SVC_CHECKS',\\n]\\n # Strip the $$\\n sane_macro = macro.translate(None, '$')\\n #debug(sane_macro)\\n # Return true when it's a global parameter\\n if sane_macro in convert_dict:\\n return False, '$' + convert_dict[sane_macro] + '$'\\n elif sane_macro in unsupported:\\n return False, 'UNSUPPORTED'\\n elif not sane_macro in ['ARG1','ARG2','ARG3','ARG4','ARG5','ARG6']:\\n return True, sane_macro.title()\\n else:\\n return False, ''\",\n \"def _setenv(self):\\n tokens = {}\\n tokens[\\\"CT_TIMESTAMP\\\"] = self._timestamp\\n tokens[\\\"CT_SUBMITTER\\\"] = self._node.name()\\n # tokens[\\\"CT_HIPBASE\\\"] = self._file[\\\"hipbase\\\"]\\n tokens[\\\"CT_SCENE\\\"] = self._scene\\n tokens[\\\"CT_PROJECT\\\"] = self.project_name\\n\\n for token in tokens:\\n hou.putenv(token, tokens[token])\\n\\n return tokens\",\n \"def get_environment(self):\\r\\n return self.mcas[0].get_environment()\",\n \"def MakeEnvironment(self, context):\\n\\n # Start with any environment variables that are already present\\n # in the environment.\\n environment = os.environ.copy()\\n # Copy context variables into the environment.\\n for key, value in context.items():\\n name = \\\"QMV_\\\" + key\\n environment[name] = value\\n # Extract additional environment variable assignments from the\\n # 'Environment' field.\\n for assignment in self.environment:\\n if \\\"=\\\" in assignment:\\n # Break the assignment at the first equals sign.\\n variable, value = string.split(assignment, \\\"=\\\", 1)\\n environment[variable] = value\\n else:\\n raise ValueError, \\\\\\n qm.error(\\\"invalid environment assignment\\\",\\n assignment=assignment)\\n return environment\",\n \"def MakeEnvironment(self, context):\\n\\n # Start with any environment variables that are already present\\n # in the environment.\\n environment = os.environ.copy()\\n # Copy context variables into the environment.\\n for key, value in context.items():\\n name = \\\"QMV_\\\" + key\\n environment[name] = value\\n return environment\",\n \"def get(self):\\n if not self.__name in g_platform_variables:\\n raise RuntimeError(\\\"unknown platform variable '%s'\\\" % (self.__name))\\n current_var = g_platform_variables[self.__name]\\n combinations = get_platform_combinations()\\n for ii in combinations:\\n if ii in current_var:\\n return current_var[ii]\\n raise RuntimeError(\\\"current platform %s not supported for variable '%s'\\\" % (str(combinations), self.__name))\",\n \"def evaluateMacro(compiled_expression):\",\n \"def constructor_env_variables(loader, node):\\n value = loader.construct_scalar(node)\\n match = pattern.findall(value) # to find all env variables in line\\n if match:\\n full_value = value\\n for g in match:\\n full_value = full_value.replace(\\n f'${{{g}}}', os.environ.get(g, g)\\n )\\n return full_value\\n return value\",\n \"def handle_software_environment(cwl_env: Dict[str, str], script: str) -> Dict[str, str]:\\n exec_env = cwl_env.copy()\\n exec_env[\\\"_CWLTOOL\\\"] = \\\"1\\\"\\n res = subprocess.run([\\\"bash\\\", script], shell=False, env=exec_env) # nosec\\n if res.returncode != 0:\\n sys.stderr.write(\\\"Error while using SoftwareRequirements to modify environment\\\\n\\\")\\n return cwl_env\\n\\n env = cwl_env.copy()\\n with open(\\\"output_environment.dat\\\") as _:\\n data = _.read().strip(\\\"\\\\0\\\")\\n for line in data.split(\\\"\\\\0\\\"):\\n key, val = line.split(\\\"=\\\", 1)\\n if key in (\\\"_\\\", \\\"PWD\\\", \\\"SHLVL\\\", \\\"TMPDIR\\\", \\\"HOME\\\", \\\"_CWLTOOL\\\"):\\n # Skip some variables that are meaningful to the shell or set\\n # specifically by the CWL runtime environment.\\n continue\\n env[key] = val\\n return env\",\n \"def testGetPortageEnvVar(self):\\n stage = self.ConstructStage()\\n board = self._current_board\\n\\n envvar = 'EXAMPLE'\\n rc_mock = self.StartPatcher(cros_build_lib_unittest.RunCommandMock())\\n rc_mock.AddCmdResult(['portageq-%s' % board, 'envvar', envvar],\\n output='RESULT\\\\n')\\n\\n result = stage._GetPortageEnvVar(envvar, board)\\n self.assertEqual(result, 'RESULT')\",\n \"def retrieve_environment(env_config, filter_config):\\n global path, filter_list_from_config\\n\\n arena_data_server = acm.FDhDatabase[\\\"ADM\\\"].ADSNameAndPort().upper()\\n configuration = acm.GetDefaultValueFromName(\\n acm.GetDefaultContext(), acm.FObject, env_config)\\n\\n filter_list_from_config = acm.GetDefaultValueFromName(\\n acm.GetDefaultContext(), acm.FList, filter_config).split(';')\\n\\n dom_xml = xml.parseString(configuration)\\n tags = dom_xml.getElementsByTagName(\\\"Host\\\")\\n for element in tags: \\n if element.getAttribute(\\\"Name\\\") == arena_data_server:\\n path = element.getElementsByTagName(\\n \\\"output_path\\\")[0].childNodes[0].data\\n print(\\\" path found: \\\", path)\",\n \"def test_DDSim_getEnvScript_noVars( self ):\\n\\n platform = \\\"Windows\\\"\\n appname = \\\"ddsim\\\"\\n appversion = \\\"Vista\\\"\\n self.ddsim.ops.getOptionsDict = Mock( return_value = S_ERROR( \\\"No Variables Set\\\" ) )\\n self.ddsim.ops.getValue = Mock( return_value = None )\\n res = self.ddsim.getEnvScript( platform, appname, appversion )\\n self.ddsim.ops.getValue.assert_called_once_with(\\\"/AvailableTarBalls/Windows/ddsim/Vista/InitScript\\\", None)\\n self.assertEqual( res['Value'], os.path.abspath(\\\"DDSimEnv.sh\\\") )\\n self.assertTrue( os.path.exists(os.path.abspath(\\\"DDSimEnv.sh\\\")) )\\n with open(\\\"DDSimEnv.sh\\\") as script:\\n scriptLines = \\\"\\\".join(script.readlines())\\n self.assertIn( \\\"declare -x G4LEDATA=$(ls -d $G4DATA/\\\", scriptLines )\\n self.assertNotIn( \\\"source \\\", scriptLines )\",\n \"def _Get_Env_Key(self, line0):\\n #line0 = chunk[0]\\n #code = '\\\\n'.join(chunk)\\n q2 = self.p2.match(line0)\\n if q2:\\n envkey = q2.group(1)\\n else:\\n envkey = 'body'\\n #codelist = code.split('\\\\n')\\n return envkey#, code\",\n \"def register_environment_functions(self):\\n self.environment.globals['path_join'] = path_join\\n self.environment.globals['path_normalize'] = path_normalize\",\n \"def parse_st_cmd_env(st_cmd_filepath):\\n re_env = re.compile(\\\"^epicsEnvSet[ \\\\t\\\\\\\"\\\\(]*([\\\\w]+)[, \\\\t\\\\\\\"\\\\)]*([^\\\\\\\"\\\\n]*)\\\")\\n env = {}\\n with open(st_cmd_filepath, \\\"r\\\") as f:\\n for line in f:\\n match = re_env.search(line)\\n if match:\\n env[match.group(1)] = match.group(2)\\n return env\",\n \"def _generate_environment(self):\\n envvars = {}\\n for key in self.envvars:\\n try:\\n envvars[key] = os.environ[key]\\n except KeyError:\\n continue\\n\\n # Warn the user that we cannot support secrets\\n if envvars:\\n logger.warning(\\\"This API does not support environment secrets.\\\")\\n return envvars\",\n \"def __getitem__(self, name):\\n return os.environ[name]\",\n \"def env_loop(environment):\\n def scan(vars, vals):\\n \\\"\\\"\\\"\\n scans variables in a frame\\n \\\"\\\"\\\"\\n if isNull(vars):\\n return env_loop(enclosing_env(environment)) # 5-4: env -> environment\\n elif isEq(var, car(vars)) == TRUE:\\n return car(vals)\\n else:\\n return scan(cdr(vars), cdr(vals))\\n if environment is the_empty_environment:\\n raise UnboundLocalError(\\\"lookup_variable\\\")\\n frame = first_frame(environment)\\n return scan(frame_variables(frame), frame_values(frame))\",\n \"def function_macro(self, platform, stack):\\n initial_pos = self.pos\\n try:\\n identifier = self.match_type(Identifier)\\n if identifier in stack:\\n raise ParseError('Cannot expand token')\\n\\n self.match_value(Punctuator, \\\"(\\\")\\n args = self.__arg_list()\\n self.match_value(Punctuator, \\\")\\\")\\n except ParseError:\\n self.pos = initial_pos\\n raise ParseError(\\\"Not a function-like macro.\\\")\\n\\n macro = platform.get_macro(str(identifier))\\n if not macro:\\n raise ParseError(\\\"Not a function-like macro.\\\")\\n\\n # Argument pre-scan\\n # Macro arguments are macro-expanded before substitution\\n expanded_args = [MacroExpander(arg).expand(platform) for arg in args]\\n\\n # Combine variadic arguments into one, separated by commas\\n va_args = None\\n if macro.is_variadic():\\n va_args = util.interleave(expanded_args[len(macro.args) - 1:],\\n Punctuator(\\\"EXPANSION\\\", -1, False, \\\",\\\"))\\n va_args = va_args[:-1] # Remove final comma\\n\\n # Substitute each occurrence of an argument in the expansion\\n substituted_tokens = []\\n for token in macro.expansion:\\n\\n substitution = []\\n\\n # If a token matches an argument, it is substituted;\\n # otherwise it passes through\\n try:\\n substitution = expanded_args[macro.args.index(token.token)]\\n except (ValueError, ParseError):\\n substitution = token\\n\\n # If a token matches the variable argument, substitute\\n # precomputed va_args\\n if macro.is_variadic() and token.token == macro.variable_argument() and va_args:\\n\\n # Whether the token was preceded by whitespace should be\\n # tracked through substitution\\n for t in va_args[0]:\\n t.prev_white = token.prev_white\\n substitution = va_args\\n\\n self.grow_token_list(substituted_tokens, substitution)\\n\\n # Check the expansion for macros to expand\\n stack.push(identifier)\\n expansion = MacroExpander(substituted_tokens).expand(platform, stack)\\n stack.pop()\\n return expansion\",\n \"def env(var):\\n return os.environ[var]\",\n \"def _get_input_from_env():\\n try:\\n params = {\\n 'repo': {\\n 'owner': os.environ['DRONE_REPO_OWNER'],\\n 'name': os.environ['DRONE_REPO_NAME'],\\n 'full_name': os.environ['DRONE_REPO'],\\n 'link_url': os.environ['DRONE_REPO_LINK'],\\n 'clone_url': os.environ['DRONE_REMOTE_URL']\\n },\\n 'build': {\\n 'number': os.environ['DRONE_BUILD_NUMBER'],\\n 'event': os.environ['DRONE_BUILD_EVENT'],\\n 'branch': os.environ['DRONE_BRANCH'],\\n 'commit': os.environ['DRONE_COMMIT'],\\n 'ref': os.environ['DRONE_COMMIT_REF'],\\n 'author': os.environ['DRONE_COMMIT_AUTHOR'],\\n 'author_email': os.environ['DRONE_COMMIT_AUTHOR_EMAIL']\\n },\\n 'workspace': {\\n 'root': os.environ['DRONE_WORKSPACE'],\\n 'path': os.environ['DRONE_WORKSPACE']\\n },\\n 'vargs': {\\n key[7:].lower(): value\\n for key, value in os.environ.items()\\n if key.startswith('PLUGIN_')\\n }\\n }\\n except KeyError:\\n raise ValueError(\\n \\\"Envronment variables were misconfigured.\\\")\\n return json.dumps(params)\",\n \"def _process_environment_cvterm(self):\\n\\n line_counter = 0\\n raw = '/'.join((self.rawdir, 'environment_cvterm'))\\n logger.info(\\\"processing environment to cvterm mappings\\\")\\n\\n with open(raw, 'r') as f:\\n f.readline() # read the header row; skip\\n filereader = csv.reader(f, delimiter='\\\\t', quotechar='\\\\\\\"')\\n for line in filereader:\\n line_counter += 1\\n\\n (environment_cvterm_id, environment_id, cvterm_id) = line\\n # 1\\t1\\t60468\\n\\n environment_key = environment_id\\n\\n cvterm_key = cvterm_id\\n cvterm_id = self.idhash['cvterm'][cvterm_key]\\n\\n # look up the dbxref_id for the cvterm\\n # hopefully it's one-to-one\\n self.idhash['environment'][environment_key] = cvterm_id\\n\\n return\",\n \"def env_loop(environment):\\n def scan(vars, vals):\\n \\\"\\\"\\\"\\n scans variables in a frame\\n \\\"\\\"\\\"\\n if isNull(vars):\\n return env_loop(enclosing_env(environment)) # 5-4: env -> environment\\n elif var == car(vars):\\n return set_car(vals, val) #4-15\\n else:\\n return scan(cdr(vars), cdr(vals)) # 4-15\\n if environment is the_empty_environment:\\n raise UnboundLocalError(\\\"lookup_variable\\\")\\n frame = first_frame(environment)\\n return scan(frame_variables(frame), frame_values(frame)) # 4-15\",\n \"def guess_machine_parameter () :\\n val = [ \\\"COMPUTERNAME\\\", \\\"NUMBER_OF_PROCESSORS\\\", \\\"OS\\\",\\n \\\"PATH\\\", \\\"USERDOMAIN\\\", \\\"USERNAME\\\", \\\"USERPROFILE\\\",\\n \\\"windir\\\", \\\"TEMP\\\" ]\\n res = { }\\n for v in val :\\n if v == \\\"PATH\\\" :\\n x = os.getenv (v)\\n x = x.split (\\\";\\\")\\n res [v] = x\\n else : res [v] = os.getenv (v)\\n \\n if not sys.platform.startswith(\\\"win\\\") :\\n if \\\"TEMP\\\" not in res or res[\\\"TEMP\\\"] is None :\\n res[\\\"TEMP\\\"] = \\\"/tmp\\\"\\n \\n return res\",\n \"def read_environment(self):\\n # Setup credentials\\n if os.getenv(\\\"DO_API_TOKEN\\\"):\\n self.api_token = os.getenv(\\\"DO_API_TOKEN\\\")\\n if os.getenv(\\\"DO_API_KEY\\\"):\\n self.api_token = os.getenv(\\\"DO_API_KEY\\\")\",\n \"def _deviceVariableFunctionName(self, tree, permitted_prefixes, allow_lengths = True):\\n cpp_func_name = \\\"\\\"\\n py_func = tree.attr\\n # extract function name start\\n for prefix in permitted_prefixes:\\n if py_func.startswith(prefix):\\n cpp_func_name = prefix\\n py_func = py_func[len(prefix):]\\n break # dont allow the else\\n else:\\n return None\\n # check type and lengths\\n if allow_lengths:\\n #split to get type and Array Length (This could **potentially** be looked up from the model description but current syntax is consistent with swig bindings) \\n type_and_length = py_func.split(\\\"Array\\\")\\n if type_and_length[0] not in self._fgpu_types:\\n self.RaiseError(tree, f\\\"'{type_and_length[0]}' is not a valid FLAME GPU type\\\")\\n t = self._fgpu_types[type_and_length[0]]\\n # generate template args\\n if (len(type_and_length) == 1):\\n cpp_func_name += f\\\"<{t}>\\\"\\n elif (len(type_and_length) == 2):\\n cpp_func_name += f\\\"<{t}, {type_and_length[1]}>\\\"\\n else:\\n return None\\n else:\\n if py_func not in self._fgpu_types:\\n self.RaiseError(tree, f\\\"'{py_func}' is not a valid FLAME GPU type\\\")\\n t = self._fgpu_types[py_func]\\n cpp_func_name += f\\\"<{t}>\\\"\\n # return \\n return cpp_func_name\",\n \"def _get_env(key: str) -> str:\\n value = os.getenv(key)\\n assert isinstance(value, str), (\\n f\\\"the {key} environment variable must be set and a string, \\\" f\\\"{value=}\\\"\\n )\\n return value\",\n \"def _parse(self, inval):\\n regex = re.compile(r'^os\\\\.environ\\\\[(.*)\\\\]$')\\n for val in inval:\\n if val is None:\\n continue\\n # split on \\\\n\\n cmd = val.split('\\\\n')\\n for v2 in cmd:\\n if not v2:\\n continue\\n dict_call, pth = v2.split(' = ')\\n m = re.match(regex, dict_call)\\n if m:\\n key = m.groups()[0]\\n self.env[key] = pth\",\n \"def _get_test_variables():\\n if __name__ == \\\"__main__\\\":\\n return _MOCKUP_TEST_VARIABLES\\n else:\\n return BuiltIn().get_variables()\",\n \"def env(*vars, **kwargs):\\r\\n for v in vars:\\r\\n value = os.environ.get(v)\\r\\n if value:\\r\\n return value\\r\\n return kwargs.get('default', '')\",\n \"def env(*vars, **kwargs):\\n for v in vars:\\n value = os.environ.get(v)\\n if value:\\n return value\\n return kwargs.get('default', '')\",\n \"def getEnvironmentVars(self):\\n my_env = os.environ.copy()\\n my_env[\\\"MODE\\\"] = self.model.mode\\n my_env[\\\"ALGORITHM\\\"] = self.model.algorithm\\n my_env[\\\"INPUT_FILE\\\"] = self.model.inputFile\\n my_env[\\\"TARGET_FILE\\\"] = self.model.targetFile\\n my_env[\\\"DEVICE\\\"] = self.model.device\\n my_env[\\\"SIZE\\\"] = str(self.model.size)\\n my_env[\\\"ROLE\\\"] = self.model.role\\n my_env[\\\"WAIT_SIZE\\\"] = str(self.model.waitSize)\\n my_env[\\\"STEP_SIZE\\\"] = str(self.model.stepSize)\\n my_env[\\\"TUI_CONNECTION\\\"] = str(True)\\n return my_env\",\n \"def env(*vars, **kwargs):\\n for v in vars:\\n value = os.environ.get(v, None)\\n if value:\\n return value\\n return kwargs.get('default', '')\",\n \"def env(*vars, **kwargs):\\n for v in vars:\\n value = os.environ.get(v, None)\\n if value:\\n return value\\n return kwargs.get('default', '')\",\n \"def get_environment() -> dict:\\n go_path = subprocess.check_output([\\\"go\\\", \\\"env\\\", \\\"GOPATH\\\"]).decode().strip()\\n setup_script_path = f\\\"{go_path}/src/github.com/harmony-one/harmony/scripts/setup_bls_build_flags.sh\\\"\\n response = subprocess.check_output([\\\"bash\\\", setup_script_path, \\\"-v\\\"], timeout=5)\\n environment = json.loads(response)\\n environment[\\\"HOME\\\"] = os.environ.get(\\\"HOME\\\")\\n return environment\",\n \"def _create_environment(config):\\n if isinstance(config.env, str):\\n env = gym.make(config.env)\\n else:\\n env = config.env()\\n if config.max_length:\\n env = tools.wrappers.LimitDuration(env, config.max_length)\\n env = tools.wrappers.RangeNormalize(env)\\n env = tools.wrappers.ClipAction(env)\\n env = tools.wrappers.ConvertTo32Bit(env)\\n return env\",\n \"def current_config():\\n if os.environ[\\\"ENVIRONMENT\\\"] == \\\"production\\\":\\n return Production()\\n elif os.environ[\\\"ENVIRONMENT\\\"] == \\\"staging\\\":\\n return Staging()\\n elif os.environ[\\\"ENVIRONMENT\\\"] == \\\"testing\\\":\\n return Testing()\\n elif os.environ[\\\"ENVIRONMENT\\\"] == \\\"development\\\":\\n return Development()\\n else:\\n raise KeyError(f\\\"Unknown environment '{os.environ['ENVIRONMENT']}'\\\")\",\n \"def __getitem__(self, key):\\n return os.environ[key]\",\n \"def parse_env():\\n os_env = {}\\n try:\\n os_env[\\\"github_token\\\"] = os.environ[\\\"GITHUB_TOKEN\\\"]\\n os_env[\\\"github_repository\\\"] = os.environ[\\\"GITHUB_REPOSITORY\\\"]\\n os_env[\\\"threshold\\\"] = int(os.environ[\\\"THRESHOLD\\\"])\\n os_env[\\\"slack_channel\\\"] = os.environ[\\\"SLACK_CHANNEL\\\"]\\n os_env[\\\"slack_token\\\"] = os.environ[\\\"SLACK_TOKEN\\\"]\\n os_env[\\\"sleep_interval\\\"] = int(os.environ[\\\"SLEEP_INTERVAL\\\"])\\n return os_env\\n except KeyError as e:\\n logging.error(f\\\"failed to retrieve variable from env: {e}\\\")\\n return {}\",\n \"def get_env(self, *args):\\n m = module(*args)\\n return m.env\",\n \"def get_var(self, tag):\\n if not tag in self.env:\\n print(\\\"ERROR: value {} is not defined yet\\\".format(tag))\\n elif callable(self.env[tag]):\\n print(\\\"ERROR: tried to access callable {} was a value\\\".format(tag))\\n else:\\n return self.env[tag]\",\n \"def _get_environment(cls):\\n return cls.__name__.lower()\",\n \"def __getitem__(self, item):\\n\\n check_name = self._varPrefix + item.upper()\\n\\n return self._environmentVariables.get(check_name)\",\n \"def getEnvironment(self, environment = {}):\\r\\n e = dict(environment, **self.environment)\\r\\n e.update(locals(), **globals())\\r\\n for x in dir(self):\\r\\n if not x.startswith('_'):\\r\\n e[x] = getattr(self, x)\\r\\n return e\",\n \"def env(self): # type: () -> t.List[str]\\n return self.config['Env']\",\n \"def _expand_macros(tokdict,macrodict):\\r\\n def macro_value(m):\\r\\n return '(?:%s)' % macrodict[m.groupdict()['macro']]\\r\\n for key, value in tokdict.items():\\r\\n while re.search(r'{[a-z][a-z0-9-]*}', value):\\r\\n value = re.sub(r'{(?P<macro>[a-z][a-z0-9-]*)}',\\r\\n macro_value, value)\\r\\n tokdict[key] = value\\r\\n return tokdict\",\n \"def _get_device():\\n return context.get_context('device_target')\",\n \"def environment_variables(\\n self,\\n ) -> typing.Optional[\\n typing.Mapping[str, aws_cdk.aws_codebuild.BuildEnvironmentVariable]\\n ]:\\n return self._values.get(\\\"environment_variables\\\")\",\n \"def environment_variables(\\n self,\\n ) -> typing.Optional[\\n typing.Mapping[str, aws_cdk.aws_codebuild.BuildEnvironmentVariable]\\n ]:\\n return self._values.get(\\\"environment_variables\\\")\",\n \"def environment_variables(\\n self,\\n ) -> typing.Optional[\\n typing.Mapping[str, aws_cdk.aws_codebuild.BuildEnvironmentVariable]\\n ]:\\n return self._values.get(\\\"environment_variables\\\")\",\n \"def environment_variables(\\n self,\\n ) -> typing.Optional[\\n typing.Mapping[str, aws_cdk.aws_codebuild.BuildEnvironmentVariable]\\n ]:\\n return self._values.get(\\\"environment_variables\\\")\",\n \"def get_environment():\\n return GenericGymEnv(id=\\\"real-time-gym-v1\\\", gym_kwargs={\\\"config\\\": CONFIG_DICT})\",\n \"def get_current_environment():\\n # Search for the environment variable set by the hutch python setup\\n env = os.getenv('CONDA_ENVNAME')\\n # Otherwise look for built-in Conda environment variables\\n if not env:\\n env = os.getenv('CONDA_DEFAULT_ENV')\\n # Check the top level PYTHONPATH to see if we have packages installed in\\n # development mode\\n dev = os.getenv('PYTHONPATH')\\n if dev:\\n try:\\n dev_pkgs = os.listdir(dev)\\n except FileNotFoundError:\\n logger.debug(\\\"No dev folder found\\\")\\n dev_pkgs = list()\\n else:\\n dev_pkgs = list()\\n return env, dev_pkgs\",\n \"def get_elinos_environment():\\n result = {}\\n for key in (\\\"project\\\", \\\"cdk\\\", \\\"target\\\"):\\n var = \\\"ELINOS_\\\" + key.upper()\\n if var in os.environ:\\n result[key] = os.environ[var]\\n else:\\n warn(\\\"%s not set\\\" % var)\\n result[key] = None\\n\\n if result[\\\"project\\\"] is not None:\\n result[\\\"xenomai\\\"] = glob.glob(result[\\\"project\\\"] + \\\"/xenomai-[0-9.]*\\\")\\n else:\\n result[\\\"xenomai\\\"] = []\\n\\n return result\",\n \"def get_environment_actions(self, init_act):\\n # Set initial values for environment variables\\n time_of_day = \\\"2020-10-23T06:00:00\\\"\\n time_animation = \\\"false\\\"\\n cloud_state = \\\"free\\\"\\n fog_range = \\\"100000\\\"\\n sun_intensity = \\\"0.85\\\"\\n sun_azimuth = \\\"0\\\"\\n sun_elevation = \\\"1.31\\\"\\n percip_type = \\\"dry\\\"\\n percip_intensity = \\\"0\\\"\\n\\n try:\\n env_layer = QgsProject.instance().mapLayersByName(\\\"Environment\\\")[0]\\n for feature in env_layer.getFeatures():\\n time_of_day = feature[\\\"Datetime\\\"]\\n time_animation = str(feature[\\\"Datetime Animation\\\"]).lower()\\n cloud_state = feature[\\\"Cloud State\\\"]\\n fog_range = str(feature[\\\"Fog Visual Range\\\"])\\n sun_intensity = str(feature[\\\"Sun Intensity\\\"])\\n sun_azimuth = str(feature[\\\"Sun Azimuth\\\"])\\n sun_elevation = str(feature[\\\"Sun Elevation\\\"])\\n percip_type = feature[\\\"Precipitation Type\\\"]\\n percip_intensity = str(feature[\\\"Precipitation Intensity\\\"])\\n except IndexError:\\n error_message = \\\"No environment variables detected, using defaults\\\"\\n iface.messageBar().pushMessage(\\\"Info\\\", error_message, level=Qgis.Info)\\n QgsMessageLog.logMessage(error_message, level=Qgis.Info)\\n self._warning_message.append(f\\\"Info: {error_message}\\\")\\n\\n time_of_day = \\\"2020-10-23T06:00:00\\\"\\n time_animation = \\\"false\\\"\\n cloud_state = \\\"free\\\"\\n fog_range = \\\"100000\\\"\\n sun_intensity = \\\"0.85\\\"\\n sun_azimuth = \\\"0\\\"\\n sun_elevation = \\\"1.31\\\"\\n percip_type = \\\"dry\\\"\\n percip_intensity = \\\"0\\\"\\n\\n global_act = etree.SubElement(init_act, \\\"GlobalAction\\\")\\n env_act = etree.SubElement(global_act, \\\"EnvironmentAction\\\")\\n environ = etree.SubElement(env_act, \\\"Environment\\\")\\n environ.set(\\\"name\\\", \\\"Environment1\\\")\\n\\n env_time = etree.SubElement(environ, \\\"TimeOfDay\\\")\\n env_time.set(\\\"animation\\\", time_animation)\\n env_time.set(\\\"dateTime\\\", time_of_day)\\n\\n weather = etree.SubElement(environ, \\\"Weather\\\")\\n weather.set(\\\"cloudState\\\", cloud_state)\\n weather_sun = etree.SubElement(weather, \\\"Sun\\\")\\n weather_sun.set(\\\"intensity\\\", sun_intensity)\\n weather_sun.set(\\\"azimuth\\\", sun_azimuth)\\n weather_sun.set(\\\"elevation\\\", sun_elevation)\\n weather_fog = etree.SubElement(weather, \\\"Fog\\\")\\n weather_fog.set(\\\"visualRange\\\", fog_range)\\n weather_percip = etree.SubElement(weather, \\\"Precipitation\\\")\\n weather_percip.set(\\\"precipitationType\\\", percip_type)\\n weather_percip.set(\\\"intensity\\\", percip_intensity)\\n\\n env_road = etree.SubElement(environ, \\\"RoadCondition\\\")\\n env_road.set(\\\"frictionScaleFactor\\\", \\\"1.0\\\")\",\n \"def get_system():\\n if 'google.colab' in sys.modules:\\n return Constant.SYS_GOOGLE_COLAB\\n if os.name == 'posix':\\n return Constant.SYS_LINUX\\n if os.name == 'nt':\\n return Constant.SYS_WINDOWS\\n\\n raise EnvironmentError('Unsupported environment')\",\n \"def getCDefinesAsString( targetPlatform, targetName ):\\n Any.requireIsTextNonEmpty( targetPlatform )\\n Any.requireIsTextNonEmpty( targetName )\\n\\n fileName = os.path.join( 'build/%s/CMakeFiles/%s.dir/flags.make' %\\n ( targetPlatform, targetName ) )\\n\\n Any.requireIsDirNonEmpty( 'build/%s' % targetPlatform )\\n Any.requireIsFileNonEmpty( fileName )\\n\\n # read-in ground truth information\\n logging.debug( 'parsing %s' % fileName )\\n content = FastScript.getFileContent( fileName, splitLines=True )\\n raw_C = ''\\n raw_CPP = ''\\n raw_C_CFLAGS = ''\\n raw_CPP_CFLAGS = ''\\n regexp_C = re.compile( '^C_DEFINES\\\\s=\\\\s+(.*)$' )\\n regexp_CPP = re.compile( '^CXX_DEFINES\\\\s=\\\\s+(.*)$' )\\n regexp_C_CFLAGS = re.compile( '^C_FLAGS\\\\s=\\\\s+(.*)$' )\\n regexp_CPP_CFLAGS = re.compile( '^CXX_FLAGS\\\\s=\\\\s+(.*)$' )\\n result = ''\\n\\n for line in content:\\n tmp = regexp_C.search( line )\\n\\n if tmp:\\n raw_C = tmp.group( 1 )\\n # logging.debug( 'raw C defines: %s' % raw_C )\\n\\n tmp = regexp_CPP.search( line )\\n\\n if tmp:\\n raw_CPP = tmp.group( 1 )\\n # logging.debug( 'raw CPP defines: %s' % raw_CPP )\\n\\n tmp = regexp_C_CFLAGS.search(line)\\n\\n if tmp:\\n raw_C_CFLAGS = tmp.group(1)\\n\\n tmp = regexp_CPP_CFLAGS.search(line)\\n\\n if tmp:\\n raw_CPP_CFLAGS = tmp.group(1)\\n\\n candidates = ( shlex.split( raw_C ) +\\n shlex.split( raw_CPP ) +\\n shlex.split( raw_C_CFLAGS ) +\\n shlex.split( raw_CPP_CFLAGS ) )\\n\\n for candidate in candidates:\\n if candidate.startswith( '-D' ):\\n result += candidate + ' '\\n\\n return result\",\n \"def get(self, var):\\n # pylint: disable=no-self-use\\n return os.getenv('ASTRID_RTM_{0}'.format(var.upper()))\",\n \"def _get_environment_variables(cluster):\\n # Make environment variables for the shell commands\\n shell_env = {}\\n shell_env[\\\"OMP_NUM_THREADS\\\"] = cluster.num_vcores\\n shell_env[\\\"GRAPHLAB_MEMORY_LIMIT_IN_MB\\\"] = cluster.container_size * .8\\n shell_env[\\\"TURI_PORT_START\\\"] = cluster.start_port\\n shell_env[\\\"TURI_PORT_END\\\"] = cluster.end_port\\n # Overwrite environment for SFrame cache capacity. Being conservative, we use default 1/5 of the container size.\\n for key in ['GRAPHLAB_FILEIO_MAXIMUM_CACHE_CAPACITY', 'GRAPHLAB_FILEIO_MAXIMUM_CACHE_CAPACITY_PER_FILE']:\\n if key in os.environ:\\n shell_env[key] = os.environ[key]\\n else:\\n shell_env[key] = cluster.container_size * 1024 * 1024 * .2\\n\\n # Overwrite environment for SFrame cache file location.\\n if cluster.node_tmp_dir:\\n shell_env['GRAPHLAB_CACHE_FILE_LOCATIONS'] = cluster.node_tmp_dir\\n if cluster.hdfs_tmp_dir:\\n shell_env['GRAPHLAB_CACHE_FILE_HDFS_LOCATION'] = cluster.hdfs_tmp_dir\\n return shell_env\",\n \"def default_environment():\\n return dict(_VARS)\",\n \"def windows_get_env_value(var_name: str) -> str:\\n if var_name in os.environ.keys():\\n return os.environ[var_name]\",\n \"def collect_env():\\n env_info = {}\\n env_info['sys.platform'] = sys.platform\\n env_info['Python'] = sys.version.replace('\\\\n', '')\\n\\n cuda_available = torch.cuda.is_available()\\n env_info['CUDA available'] = cuda_available\\n\\n if cuda_available:\\n devices = defaultdict(list)\\n for k in range(torch.cuda.device_count()):\\n devices[torch.cuda.get_device_name(k)].append(str(k))\\n for name, device_ids in devices.items():\\n env_info['GPU ' + ','.join(device_ids)] = name\\n\\n from mmcv.utils.parrots_wrapper import _get_cuda_home\\n CUDA_HOME = _get_cuda_home()\\n env_info['CUDA_HOME'] = CUDA_HOME\\n\\n if CUDA_HOME is not None and osp.isdir(CUDA_HOME):\\n try:\\n nvcc = osp.join(CUDA_HOME, 'bin/nvcc')\\n nvcc = subprocess.check_output(f'\\\"{nvcc}\\\" -V', shell=True)\\n nvcc = nvcc.decode('utf-8').strip()\\n release = nvcc.rfind('Cuda compilation tools')\\n build = nvcc.rfind('Build ')\\n nvcc = nvcc[release:build].strip()\\n except subprocess.SubprocessError:\\n nvcc = 'Not Available'\\n env_info['NVCC'] = nvcc\\n\\n try:\\n # Check C++ Compiler.\\n # For Unix-like, sysconfig has 'CC' variable like 'gcc -pthread ...',\\n # indicating the compiler used, we use this to get the compiler name\\n import sysconfig\\n cc = sysconfig.get_config_var('CC')\\n if cc:\\n cc = osp.basename(cc.split()[0])\\n cc_info = subprocess.check_output(f'{cc} --version', shell=True)\\n env_info['GCC'] = cc_info.decode('utf-8').partition(\\n '\\\\n')[0].strip()\\n else:\\n # on Windows, cl.exe is not in PATH. We need to find the path.\\n # distutils.ccompiler.new_compiler() returns a msvccompiler\\n # object and after initialization, path to cl.exe is found.\\n import locale\\n import os\\n from distutils.ccompiler import new_compiler\\n ccompiler = new_compiler()\\n ccompiler.initialize()\\n cc = subprocess.check_output(\\n f'{ccompiler.cc}', stderr=subprocess.STDOUT, shell=True)\\n encoding = os.device_encoding(\\n sys.stdout.fileno()) or locale.getpreferredencoding()\\n env_info['MSVC'] = cc.decode(encoding).partition('\\\\n')[0].strip()\\n env_info['GCC'] = 'n/a'\\n except subprocess.CalledProcessError:\\n env_info['GCC'] = 'n/a'\\n\\n env_info['PyTorch'] = torch.__version__\\n env_info['PyTorch compiling details'] = get_build_config()\\n\\n try:\\n import torchvision\\n env_info['TorchVision'] = torchvision.__version__\\n except ModuleNotFoundError:\\n pass\\n\\n env_info['OpenCV'] = cv2.__version__\\n\\n env_info['MMCV'] = mmcv.__version__\\n\\n try:\\n from mmcv.ops import get_compiler_version, get_compiling_cuda_version\\n except ModuleNotFoundError:\\n env_info['MMCV Compiler'] = 'n/a'\\n env_info['MMCV CUDA Compiler'] = 'n/a'\\n else:\\n env_info['MMCV Compiler'] = get_compiler_version()\\n env_info['MMCV CUDA Compiler'] = get_compiling_cuda_version()\\n\\n return env_info\",\n \"def _parse_jenkins_env_vars():\\n # TODO find a way to get the socket number of\\n os_cpu_socket = 'true'\\n\\n metadata = {\\n 'jenkins': {\\n 'jenkins_job_id': os.environ['BUILD_ID'],\\n },\\n 'github': {\\n 'git_branch': os.environ['GIT_BRANCH'],\\n 'git_commit_id': os.environ['GIT_COMMIT'],\\n },\\n 'environment': {\\n 'os_version': ' '.join(distro.linux_distribution()),\\n 'cpu_number': os.cpu_count(),\\n 'cpu_socket': os_cpu_socket\\n }\\n }\\n return metadata\",\n \"def get_functions():\\n\\treturn [f for f in globals() if f.startswith('make_')]\",\n \"def env(*_vars, **kwargs):\\n for v in _vars:\\n value = os.environ.get(v, None)\\n if value:\\n return value\\n return kwargs.get('default', '')\",\n \"def constructor_env_variables(loader, node) -> Any: # type: ignore\\n value = loader.construct_scalar(node)\\n match = pattern.findall(value) # to find all env variables in line\\n if match:\\n full_value = value\\n for g in match:\\n (env_var, default_val) = g.split(\\\"::\\\")\\n value = os.environ.get(env_var, default_val)\\n full_value = full_value.replace(f\\\"${{{g}}}\\\", value)\\n if not full_value:\\n full_value = None\\n _logger.debug(f\\\"injected ENV parameter {env_var} resolved to {value}\\\")\\n return full_value\\n return value\",\n \"def get_env_type ( base_name ) :\\n return base_name.split( '-', 1 )[ 0 ]\",\n \"def getEnvironmentVars(var):\\n \\n if var['pbs-jobid'] is None:\\n var['pbs-jobid'] = os.getenv(\\\"PBS_JOBID\\\")\\n if var['pbs-jobid'] is None:\\n logger.error(\\\"ERROR: Environment Variable PBS_JOBID not found!\\\")\\n exit(1)\\n\\n return var\",\n \"def env(*_vars, **kwargs):\\r\\n for v in _vars:\\r\\n value = os.environ.get(v, None)\\r\\n if value:\\r\\n return value\\r\\n return kwargs.get('default', '')\",\n \"def environments(self):\\n envs = self.config[\\\"tox\\\"][\\\"envlist\\\"]\\n #result = re.split(\\\"[^a-zA-Z0-9]\\\", envs)\\n result = re.split(r'\\\\n| ,|,', envs)\\n #print ([string for string in result if string != \\\"\\\"])\\n result = (([string.strip() for string in result if string != \\\"\\\"]))\\n print(list(dict.fromkeys(result)))\\n return ((list(dict.fromkeys(result))))\",\n \"def retrieve_variables(content):\\n variables = []\\n in_var_section = False\\n for line in content.splitlines():\\n #print line\\n if in_var_section:\\n var_def = re.split(' +', line)\\n if len(var_def) > 1:\\n #print var_def[0], ':', var_def[1]\\n var_name = var_def[0]\\n def_value = var_def[1]\\n if not def_value.startswith('%'): #not environment variable which would be directly passed to robot\\n variables.append([var_name.strip('${').strip('}'), def_value])\\n if '*** Variables ***' in line:\\n in_var_section = True\\n elif in_var_section and '*** ' in line:\\n #end of Variables section\\n break\\n return variables\",\n \"def env_callback(args):\\n return {\\n \\\"MY_CUSTOM_FLAG\\\": args.my_custom_flag\\n }\",\n \"def check_for_env_vars(value: Union[str, dict]):\\n if type(value) is dict and \\\"env_var\\\" in value:\\n var_name = value[\\\"env_var\\\"]\\n try:\\n return get_var(var_name)\\n except KeyError:\\n raise KeyError(f\\\"missing env var: {value['env_var']}\\\")\\n if type(value) is dict and \\\"enc_env_var\\\" in value:\\n var_name = value[\\\"enc_env_var\\\"]\\n try:\\n return get_kms_var(var_name)\\n except KeyError:\\n raise KeyError(f\\\"missing enc env var: {value['enc_env_var']}\\\")\\n return value\",\n \"def setup_matrix_env(config, env):\\n envs = config.get('env', {})\\n if isinstance(envs, dict):\\n matrix = listify(envs.get('matrix', []))\\n else:\\n matrix = listify(envs)\\n\\n return ([env[:] + ['export {}'.format(val)] for val in matrix]\\n if matrix else [env[:]])\",\n \"def set_envdata():\\n\\n\\t\\tenv = ArgvParserObject.get_environment()\\n\\n\\t\\t# reads data for selected enviroment\\n\\t\\tif env == 'DEV':\\n\\t\\t\\tenvdata = DevData\\n\\t\\t# elif env == 'PREPROD':\\n\\t\\t#\\tenvdata = PreprodData\\n\\t\\telse:\\n\\t\\t\\traise NameError('Wrong enviroment value. ')\\n\\n\\t\\treturn envdata\",\n \"def env(parser, args):\\n action = subcommand_functions[args.env_command]\\n action(args)\",\n \"def __getattr__(self, config_property):\\n\\n is_build_var = config_property in self._directVariables.keys()\\n is_runtime_var = config_property in self._directVariablesRuntime.keys()\\n\\n # For now, all unprefixed variables are also runtime variables. If that ever changes this logic will change\\n # with it.\\n is_unprefixed_var = config_property in self._unPrefixedVariablesRuntime.keys()\\n\\n if is_build_var:\\n value = self[self._directVariables[config_property]]\\n elif is_runtime_var:\\n value = self[self._directVariablesRuntime[config_property]]\\n elif is_unprefixed_var:\\n value = self._environmentVariables.get(self._unPrefixedVariablesRuntime[config_property])\\n else:\\n raise AttributeError('No such variable defined: {}'.format(config_property))\\n\\n if not value:\\n if self.in_build() and (is_runtime_var or is_unprefixed_var):\\n raise BuildTimeVariableAccessException(\\n 'The {} variable is not available during build time.'.format(config_property)\\n )\\n raise NotValidPlatformException(\\n 'The {} variable is not defined. Are you sure you\\\\'re running on Platform.sh?'.format(config_property)\\n )\\n\\n return value\",\n \"def global_jinja_env():\\n return _ENV\",\n \"def getValue(self, value=None):\\n if self.data and self.source & COMMANDLINE:\\n return self.data\\n\\n if self.environ and str(self.environ) in os.environ:\\n self.source = ENVIRONMENT\\n self.file = None\\n return self.cast(os.environ[str(self.environ)])\\n\\n if self.data:\\n return self.data\\n\\n if self.default:\\n self.source = BUILTIN\\n self.file = None\\n return self.default\\n\\n self.source = CODE\\n self.file = None\\n\\n if value is None:\\n return []\\n\\n return value\",\n \"def run(self, context):\\n i = context.skip_ws(0)\\n if len(context.history) > 1 and context.history[-2] == \\\"IsFuncDeclaration\\\":\\n self.check_function_declaration(context)\\n if type(context.scope) is not GlobalScope:\\n if type(context.scope) == Function and context.scope.multiline == False:\\n pass\\n else:\\n context.new_error(\\\"PREPROC_GLOBAL\\\", context.peek_token(0))\\n if context.check_token(i, \\\"DEFINE\\\") is False:\\n return False, 0\\n val = context.peek_token(i).value.split(\\\"define\\\", 1)[1]\\n content = Lexer(val, context.peek_token(i).pos[0])\\n tkns = content.get_tokens()\\n i = 0\\n identifiers = []\\n protection = context.filename.upper().split(\\\"/\\\")[-1].replace(\\\".\\\", \\\"_\\\")\\n for tkn in tkns:\\n if tkn.type == \\\"ESCAPED_NEWLINE\\\":\\n context.new_error(\\\"NEWLINE_DEFINE\\\", tkn)\\n elif tkn.type in [\\\"TAB\\\", \\\"SPACE\\\"]:\\n i += 1\\n continue\\n elif tkn.type == \\\"IDENTIFIER\\\" and len(identifiers) == 0:\\n if tkn.value.isupper() is False:\\n context.new_error(\\\"MACRO_NAME_CAPITAL\\\", tkn)\\n identifiers.append(tkn)\\n tmp = i\\n while tmp < len(tkns) - 1 and tkns[tmp].type in [\\n \\\"SPACE\\\",\\n \\\"TAB\\\",\\n \\\"IDENTIFIER\\\",\\n ]:\\n tmp += 1\\n if tmp == (len(tkns) - 1) and context.filetype == \\\"h\\\":\\n if context.scope.header_protection == 0:\\n if identifiers[0].value == protection:\\n context.scope.header_protection = 1\\n elif identifiers[0].value != protection:\\n context.new_error(\\\"HEADER_PROT_NAME\\\", tkns[1])\\n elif (\\n context.filetype == \\\"c\\\"\\n and context.scope.include_allowed == True\\n and (\\n len(tkns) > tmp + 1\\n or (\\n len(tkns) == tmp + 1\\n and identifiers[0].value != protection\\n and context.scope.header_protection == -1\\n )\\n )\\n ):\\n context.scope.include_allowed = False\\n\\n elif tkn.type in [\\\"IDENTIFIER\\\", \\\"STRING\\\", \\\"CONSTANT\\\"]:\\n if context.skip_define_error == True:\\n continue\\n if len(identifiers) == 1:\\n if tkn.type == \\\"IDENTIFIER\\\" and tkn.value.isupper() is False:\\n context.new_error(\\\"PREPROC_CONSTANT\\\", tkn)\\n identifiers.append(tkn)\\n elif len(identifiers) == 0:\\n context.new_error(\\\"INCORRECT_DEFINE\\\", tkn)\\n else:\\n context.new_error(\\\"TOO_MANY_VALS\\\", tkn)\\n elif tkn.type == \\\"LPARENTHESIS\\\":\\n if context.skip_define_error == True:\\n continue\\n if len(identifiers) == 0:\\n continue\\n elif len(identifiers) == 1 and tkns[i - 1].type in [\\\"SPACE\\\", \\\"TAB\\\"]:\\n continue\\n else:\\n context.new_error(\\\"PREPROC_CONSTANT\\\", tkn)\\n elif tkn.type in [\\\"LBRACKET\\\", \\\"LBRACE\\\"]:\\n if context.skip_define_error == True:\\n continue\\n context.new_error(\\\"PREPROC_CONSTANT\\\", tkn)\\n\\n i += 1\\n if context.filetype == \\\"h\\\" and context.scope.header_protection != 1:\\n context.new_error(\\\"HEADER_PROT_ALL\\\", context.peek_token(0))\\n return False, 0\",\n \"def __init__(self, environment_variables=None, var_prefix='PLATFORM_'):\\n\\n self._environmentVariables = os.environ if environment_variables is None else environment_variables\\n self._varPrefix = var_prefix\\n\\n if self['ROUTES']:\\n routes = self['ROUTES']\\n self._routesDef = self.decode(routes)\\n if self['RELATIONSHIPS']:\\n relationships = self['RELATIONSHIPS']\\n self._relationshipsDef = self.decode(relationships)\\n self.register_formatter('pymongo', pymongo_formatter)\\n self.register_formatter('pysolr', pysolr_formatter)\\n self.register_formatter('postgresql_dsn', posgresql_dsn_formatter)\\n\\n if self['VARIABLES']:\\n variables = self['VARIABLES']\\n self._variablesDef = self.decode(variables)\\n if self['APPLICATION']:\\n application = self['APPLICATION']\\n self._applicationDef = self.decode(application)\",\n \"def evaluate_for_platform(self, **kwargs):\\n macro = Macro(self.identifier, self.args, self.value)\\n kwargs['platform'].define(self.identifier.as_str(), macro)\\n return False\",\n \"def generateEnvList( self, index ):\\n EnvList = [ \\n (\\\"GLOBUS_DUROC_SUBJOB_INDEX\\\", \\\"%d\\\" % index),\\n (\\\"LD_LIBRARY_PATH\\\", \\\"/usr/local/globus/globus-3.2/lib/\\\") \\n ]\\n return EnvList\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.58264863","0.5568216","0.53334945","0.5327967","0.51543343","0.51488876","0.51430243","0.5069374","0.50373465","0.5026348","0.50191504","0.5002579","0.49759382","0.49720997","0.49513227","0.49360543","0.4913783","0.49026003","0.48999316","0.4882077","0.48813444","0.4872327","0.4866534","0.48442066","0.48220772","0.4811985","0.48090705","0.48069423","0.47924456","0.47458562","0.47243866","0.4713377","0.47093886","0.470711","0.47050568","0.47004843","0.4699758","0.46990386","0.4698482","0.4692353","0.46647525","0.46629184","0.46608558","0.46567816","0.46514663","0.46457404","0.46398386","0.4639367","0.46300447","0.4629752","0.4629752","0.4624875","0.4620017","0.46138266","0.46007556","0.4599181","0.4597541","0.45970854","0.45856336","0.45804784","0.4573579","0.45702907","0.45687962","0.45670396","0.45645323","0.45645323","0.45645323","0.45645323","0.45567995","0.45452988","0.45446324","0.45290506","0.45250678","0.45207074","0.4520517","0.4514818","0.4510791","0.45088893","0.45077747","0.4502866","0.45026737","0.44953662","0.44934645","0.4486844","0.44828948","0.4480035","0.44758278","0.44732213","0.44696128","0.4462309","0.4460405","0.4458579","0.44551018","0.44510648","0.44440293","0.44334513","0.4430659","0.44304204","0.4425778","0.4423764"],"string":"[\n \"0.58264863\",\n \"0.5568216\",\n \"0.53334945\",\n \"0.5327967\",\n \"0.51543343\",\n \"0.51488876\",\n \"0.51430243\",\n \"0.5069374\",\n \"0.50373465\",\n \"0.5026348\",\n \"0.50191504\",\n \"0.5002579\",\n \"0.49759382\",\n \"0.49720997\",\n \"0.49513227\",\n \"0.49360543\",\n \"0.4913783\",\n \"0.49026003\",\n \"0.48999316\",\n \"0.4882077\",\n \"0.48813444\",\n \"0.4872327\",\n \"0.4866534\",\n \"0.48442066\",\n \"0.48220772\",\n \"0.4811985\",\n \"0.48090705\",\n \"0.48069423\",\n \"0.47924456\",\n \"0.47458562\",\n \"0.47243866\",\n \"0.4713377\",\n \"0.47093886\",\n \"0.470711\",\n \"0.47050568\",\n \"0.47004843\",\n \"0.4699758\",\n \"0.46990386\",\n \"0.4698482\",\n \"0.4692353\",\n \"0.46647525\",\n \"0.46629184\",\n \"0.46608558\",\n \"0.46567816\",\n \"0.46514663\",\n \"0.46457404\",\n \"0.46398386\",\n \"0.4639367\",\n \"0.46300447\",\n \"0.4629752\",\n \"0.4629752\",\n \"0.4624875\",\n \"0.4620017\",\n \"0.46138266\",\n \"0.46007556\",\n \"0.4599181\",\n \"0.4597541\",\n \"0.45970854\",\n \"0.45856336\",\n \"0.45804784\",\n \"0.4573579\",\n \"0.45702907\",\n \"0.45687962\",\n \"0.45670396\",\n \"0.45645323\",\n \"0.45645323\",\n \"0.45645323\",\n \"0.45645323\",\n \"0.45567995\",\n \"0.45452988\",\n \"0.45446324\",\n \"0.45290506\",\n \"0.45250678\",\n \"0.45207074\",\n \"0.4520517\",\n \"0.4514818\",\n \"0.4510791\",\n \"0.45088893\",\n \"0.45077747\",\n \"0.4502866\",\n \"0.45026737\",\n \"0.44953662\",\n \"0.44934645\",\n \"0.4486844\",\n \"0.44828948\",\n \"0.4480035\",\n \"0.44758278\",\n \"0.44732213\",\n \"0.44696128\",\n \"0.4462309\",\n \"0.4460405\",\n \"0.4458579\",\n \"0.44551018\",\n \"0.44510648\",\n \"0.44440293\",\n \"0.44334513\",\n \"0.4430659\",\n \"0.44304204\",\n \"0.4425778\",\n \"0.4423764\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61382043"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17782,"cells":{"query":{"kind":"string","value":"There is a limited set of types and formats of type description supported. Types can be either; 1) A python built in type of int or float, or 2) A subset of numpy types prefixed with either numpy or np. e.g. np.int16 This function translates and a catches unsupported types but does not translate a function call (i.e. cast)"},"document":{"kind":"string","value":"def dispatchType(self, tree):\n if isinstance(tree, ast.Name):\n if tree.id not in self.basic_arg_types:\n self.RaiseError(tree, \"Not a supported type\")\n self.write(tree.id)\n elif isinstance(tree, ast.Attribute):\n if not isinstance(tree.value, ast.Name) :\n self.RaiseError(tree, \"Not a supported type\")\n if not (tree.value.id == \"numpy\" or tree.value.id == \"np\"):\n self.RaiseError(tree, \"Not a supported type\")\n if tree.attr not in self.numpytypes:\n self.RaiseError(tree, \"Not a supported numpy type\")\n self.write(self.numpytypes[tree.attr])\n else:\n self.RaiseError(tree, \"Not a supported type\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _type_translate(p_type, default_v=None):\n translate_dict = {'float': 'float',\n 'double': 'real',\n 'int': 'int',\n 'gr_complex': 'complex',\n 'char': 'byte',\n 'unsigned char': 'byte',\n 'std::string': 'string',\n 'std::vector<int>': 'int_vector',\n 'std::vector<float>': 'real_vector',\n 'std::vector<gr_complex>': 'complex_vector',\n }\n if p_type in ('int',) and default_v[:2].lower() == '0x':\n return 'hex'\n try:\n return translate_dict[p_type]\n except KeyError:\n return 'raw'","def data_types():\n\n return ...","def __Type(thing):\n from .native import (Vector, Color, Matrix, VectorField, ColorField,\n MatrixField, ScalarField)\n if isinstance(thing, (int, float, ScalarField)): return 'Scalar'\n if isinstance(thing, (Vector, VectorField)): return 'Vector'\n if isinstance(thing, (Color, ColorField)): return 'Color'\n if isinstance(thing, (Matrix, MatrixField)): return 'Matrix'\n raise ValueError(\"Could not guess type of: \" + thing)","def conversion_type(alg, parameters):\n parameters = translation_parameters(parameters)\n if alg.upper() in INLINE_ALGORITHMS.keys():\n param = getfullargspec(INLINE_ALGORITHMS[alg.upper()].__init__)\n if param is not None:\n if param.defaults:\n for key, value in parameters.items():\n if key != 'input_dim' and key != 'rank':\n index = list(reversed(param.args)).index(key)\n data_type = type(list(reversed(param.defaults))[index])(value)\n else:\n if value is None:\n data_type = None\n else:\n data_type = int(value)\n parameters[key] = data_type\n return parameters","def _to_type_desc(input):\n if type(input) is bool:\n return \"bool\"\n if type(input) is int:\n return \"int32\"\n if type(input) is float:\n return \"float32\" # TODO(klecki): current DALI limitation\n if isinstance(input, _ScalarConstant):\n dtype_to_desc = {\n _DALIDataType.BOOL: \"bool\",\n _DALIDataType.INT8: \"int8\",\n _DALIDataType.INT16: \"int16\",\n _DALIDataType.INT32: \"int32\",\n _DALIDataType.INT64: \"int64\",\n _DALIDataType.UINT8: \"uint8\",\n _DALIDataType.UINT16: \"uint16\",\n _DALIDataType.UINT32: \"uint32\",\n _DALIDataType.UINT64: \"uint64\",\n _DALIDataType.FLOAT16: \"float16\",\n _DALIDataType.FLOAT: \"float32\",\n _DALIDataType.FLOAT64: \"float64\",\n }\n return dtype_to_desc[input.dtype]\n\n raise TypeError(\n f\"Constant argument to arithmetic operation not supported. \"\n f\"Got {str(type(input))}, expected \"\n f\"a constant value of type 'bool', 'int', 'float' or 'nvidia.dali.types.Constant'.\")","def convert_type(self, value, schema_type, **kwargs):","def test_types(self):\n assert types.typeClass(\"str\") == str\n\n assert types.isBuiltinType(\"str\")\n\n assert types.isCollectionType(\"map\")\n assert types.isCollectionType(\"seq\")\n assert not types.isCollectionType(\"str\")\n\n assert types.isScalarType(\"str\")\n assert not types.isScalarType(\"seq\")\n assert not types.isScalarType(\"map\")\n\n assert types.isCollection([])\n assert types.isCollection({})\n assert not types.isCollection(\"foo\")\n\n assert types.isScalar(\"\")\n assert types.isScalar(True)\n assert not types.isScalar([])\n\n assert types.isCorrectType(\"\", str)\n assert types.isCorrectType({}, dict)\n\n assert types.isString(\"foo\")\n assert not types.isString([])\n\n assert types.isInt(1)\n assert not types.isInt(\"foo\")\n\n assert types.isBool(True)\n assert not types.isBool(1)\n assert not types.isBool(\"true\")\n\n assert types.isFloat(1.0)\n assert not types.isFloat(\"foo\")\n\n assert types.isNumber(1)\n assert types.isNumber(1.0)\n assert not types.isNumber(\"foo\")\n\n assert types.isText(\"foo\")\n assert types.isText(1)\n assert types.isText(1.0)\n assert not types.isText([])\n assert not types.isText(True)\n\n assert types.isAny(\"foo\")\n assert types.isAny(True)\n assert types.isAny(1)\n assert types.isAny(1.0)\n assert types.isAny({})\n assert types.isAny([])\n\n assert types.isEnum(\"foo\")\n assert not types.isEnum(1)\n\n assert types.isNone(None)\n assert not types.isNone(\"foo\")","def datatype_map(dtype):\n # TODO: add datetype conversion\n if 'float' in dtype:\n return 'numeric'\n elif 'int' in dtype:\n return 'int'\n elif 'bool' in dtype:\n return 'boolean'\n else:\n return 'text'","def typecast(dtype: Any) -> str:\n if dtype is int:\n return \"Int64\"\n elif dtype is float:\n return \"Float64\"\n elif dtype is bool:\n return \"bool\"\n return \"string\"","def _cast_types(self, input_dict):\n return cast_types(input_dict, self.params['dtype'])","def datatype(f):\n from numpy import bool, uint8, uint16, int32\n code = f.dtype\n if code == bool: type='binary'\n elif code == uint8: type='uint8'\n elif code == uint16: type='uint16'\n elif code == int32: type='int32'\n else:\n assert 0,'Does not accept this typecode: %s' % code\n return type","def _infer_dtype(val):\n if re.match(r'\\d{4}-\\d{2}(?:-\\d{2})?', val):\n return 'date'\n elif re.match(r'[+-]?\\d+$', val):\n return 'int'\n elif re.match(r'[+-]?\\d+%$', val):\n return 'pct'\n elif re.match(r'[a-zA-Z ]+', val):\n return 'text'\n else:\n msg = \"val={0} dtype not recognized\".format(val)\n raise ValueError(msg)","def data_types(self):","def test_as_python_types(self):\n obs = _as_python_types(self.metadata_map, self.headers)\n exp = [[2.1, 3.1, 3],\n ['str1', '200', 'string30'],\n [1, 2, 3]]\n self.assertEqual(obs, exp)","def _map_primitive_type(_type: str) -> str:\n _type = _type.lower()\n if _type in [\"double\", \"float\"]:\n return InputTypes.NUMBER\n return _type","def __convToTyped(index, value, dtypes):\n\t#print(index, value)\n\tdtype = dtypes[index]\n\ttvalue = value\n\tif dtype == \"int\":\n\t\ttvalue = int(value)\n\telif dtype == \"float\":\n\t\ttvalue = float(value)\n\treturn tvalue","def _convert_dtype_value(val):\n\n convert_dtype_map = {\n 21: \"int8\",\n 20: \"uint8\",\n 6: \"float64\",\n 5: \"float32\",\n 4: \"float16\",\n 3: \"int64\",\n 2: \"int32\",\n 1: \"int16\",\n 0: \"bool\",\n }\n if val not in convert_dtype_map:\n msg = f\"Paddle data type value {val} is not handled yet.\"\n raise NotImplementedError(msg)\n return convert_dtype_map[val]","def _convertType(self, s):\n for func in (int, float):\n try:\n n = func(s)\n return n\n except:\n pass\n return s","def fix_param_type(value, dtype):\n if dtype == 'bool':\n if type(value) is not bool:\n sval = str(value).lower().strip()\n if sval in FALSY:\n value = False\n else:\n value = True\n elif dtype == 'int':\n if type(value) is not int:\n try:\n value = int(value)\n except (TypeError, ValueError):\n # allow it to be a non-number -- initial\n # dtype may be not be broad enough\n value = str(value)\n elif dtype == 'float':\n if type(value) is not float:\n try:\n value = float(value)\n except (TypeError, ValueError):\n value = str(value)\n elif dtype == 'strlist':\n if type(value) is not list:\n value = [re.sub(r'[\\'\"\\[\\]]', '', v).strip()\n for v in str(value).split(',')]\n else:\n value = [str(v).strip() for v in value]\n elif dtype == 'intlist':\n if type(value) is not list:\n try:\n value = [int(re.sub(r'[\\s\\'\"\\[\\]]', '', v))\n for v in str(value).split(',')]\n except (TypeError, ValueError):\n # warn for this one -- it will likely not be\n # interpreted correctly\n log.warning('Found data type {}; '\n 'expected {}'.format(type(value), dtype))\n value = str(value)\n else:\n try:\n value = [int(v) for v in value]\n except (TypeError, ValueError):\n # allow this case -- where some elements\n # may be ints, some not -- since initial\n # dtype guess may not be broad enough\n pass\n elif dtype == 'floatlist':\n if type(value) is not list:\n try:\n value = [float(re.sub(r'[\\s\\'\"\\[\\]]', '', v))\n for v in str(value).split(',')]\n except (TypeError, ValueError):\n log.warning('Found data type {}; '\n 'expected {}'.format(type(value), dtype))\n value = str(value)\n else:\n try:\n value = [float(v) for v in value]\n except (TypeError, ValueError):\n # allow this case -- where some elements\n # may be floats, some not -- since initial\n # dtype guess may not be broad enough\n pass\n\n elif dtype == 'boollist':\n if type(value) is not list:\n value = [re.sub(r'[\\s\\'\"\\[\\]]', '', v)\n for v in str(value).split(',')]\n for i, v in enumerate(value):\n if type(v) is bool:\n continue\n sval = str(v).lower().strip()\n if sval in FALSY:\n v = False\n else:\n v = True\n value[i] = v\n else:\n value = str(value)\n\n return value","def _map_dtypes(type_names, field_widths):\n dtypes = []\n for i, name in enumerate(type_names):\n if name == 'int':\n dtypes.append('i8')\n elif name == 'double':\n dtypes.append('f8')\n elif name == 'char':\n dtypes.append('a{0}'.format(field_widths[i]))\n else:\n raise ValueError('Unexpected type name: {0}.'.format(name))\n return dtypes","def to_internal(value):\n if isinstance(value, bool):\n return types.Bool((value,))\n if isinstance(value, int):\n return types.Int((value,))\n if isinstance(value, float):\n return types.Float((value,))\n if isinstance(value, six.string_types):\n return types.String((value,))","def test__specification_type_to_python_type_unsupported_type(self):\n with self.assertRaises(TypeError):\n _specification_type_to_python_type(\"unsupported_type\")","def initTypes(self):\n self.types = [ty.NoneType]*self.numcols()\n for k,row in enumerate(self.data):\n for i in range(self.numcols()):\n val = row[i]\n typ = self.types[i]\n if not val is None:\n if typ in [ty.NoneType,ty.IntType]:\n if val.isdigit():\n row[i] = int(val)\n if val.startswith('-') and val[1:].isdigit():\n row[i] = -int(val[1:])\n self.types[i] = ty.IntType\n continue\n if typ in [ty.NoneType,ty.IntType,ty.FloatType]:\n try:\n row[i] = float(val)\n if not typ == ty.FloatType:\n self.types[i] = ty.FloatType\n # Convert already existing values\n for j in range(k):\n elt = self.data[j][i]\n self.data[j][i] = None if elt is None else float(elt)\n continue\n except ValueError:\n pass\n if typ in [ty.NoneType,utils.Date]:\n try:\n row[i] = utils.Date(val)\n self.types[i] = utils.Date\n continue\n except ValueError:\n pass\n row[i] = unicode(val)\n if not typ == ty.UnicodeType:\n self.types[i] = ty.UnicodeType\n # Convert already existing values\n for j in range(k):\n elt = self.data[j][i]\n self.data[j][i] = None if elt is None else unicode(elt)","def dataType(self, data):\n if isinstance(data,str):\n return STRING\n elif isinstance(data,dict):\n return ASSOC\n elif isinstance(data,int) or isinstance(data,float):\n return STRING\n elif is_python2() and isinstance(data,long):\n return STRING\n elif isinstance(data, SpecArray.SpecArrayData):\n self.rows, self.cols = data.shape\n return data.type","def convert_data_types(fields, src_db='mysql', dest_db='postgres'):\n\n data_type_map = {\n 'mysql': {\n 'postgres': {\n 'date': 'date',\n 'tinyint': 'smallint',\n 'smallint': 'smallint',\n 'mediumint': 'integer',\n 'int': 'bigint',\n 'bigint': 'numeric',\n 'float': 'real',\n 'double': 'double precision',\n 'tinytext': 'varchar',\n 'mediumtext': 'varchar',\n 'longtext': 'varchar',\n 'varchar': 'varchar',\n 'text': 'varchar',\n 'char': 'char',\n 'binary': 'bytea',\n 'varbinary': 'bytea',\n 'tinyblob': 'bytea',\n 'blob': 'bytea',\n 'mediumblob': 'bytea',\n 'longblob': 'bytea',\n 'datetime': 'timestamp',\n 'time': 'time',\n 'decimal': 'decimal',\n 'json': 'jsonb'\n }\n }\n }\n\n for elem in fields:\n elem['data_type'] = data_type_map[src_db][dest_db][elem['data_type']]\n\n if elem['data_type'] == 'decimal':\n elem['data_type'] += f'''{int(elem['numeric_precision']), int(elem['numeric_scale'])}'''\n\n fields = {e['column_name']: e['data_type'] for e in fields}\n\n return fields","def _infer_variable_types_from_data(raw_data):\n raise NotImplementedError()","def map_latencydata_types(type_name):\n\n if type_name in ['0', '1']:\n return Unknown(type_name)\n\n # cl is register that is used for certain instructions\n if type_name == 'cl':\n return Register(type_name)\n\n # TODO make this its own type?\n if type_name == \"stack pointer\":\n return Register(type_name)\n\n if type_name == \"[r+s*x]\" or type_name == \"[r+s*y]\":\n return Unknown(type_name)\n\n if type_name[:1] == 'r':\n if type_name[-1] == 'l' or type_name[-1] == 'h':\n # h, l refer to high, low? get rid of these and continnue as normally\n type_name = type_name[:-1] # mistake in the document? get rid of the trailing l?\n size = int(type_name[1:]) if len(type_name) > 1 else None\n return Register(type_name, size)\n\n # vector registers (I think)\n if type_name in [\"xmm\", \"mmx\", \"ymm\", \"mmy\"]:\n return Register(type_name)\n\n if type_name == 'i':\n return Immediate(type_name)\n if type_name == \"v\":\n return Register(type_name)\n\n if type_name[:3] == \"xmm\":\n return Register(type_name)\n\n if type_name[:2] == 'mm':\n size = int(type_name[2:]) if len(type_name) > 2 else None\n return Memory(type_name, size)\n\n if type_name[0] == 'm':\n size = int(type_name[1:]) if len(type_name) > 1 else None\n return Memory(type_name, size)\n\n if type_name == \"x\":\n return Register(type_name)\n\n if type_name == \"y\":\n return Register(type_name)\n\n if type_name == \"near\" or type_name == \"short\":\n return Unknown(type_name)\n raise ValueError(f\"uknown type {type_name}\")","def missing_types():\n\n return ...","def convert_type(atype, btype):\n # type + type => type\n if atype == btype:\n return atype\n\n # [Q]BOOLEAN + [Q]BOOLEAN => BOOLEAN\n if atype == 'BOOLEAN' and btype == 'QBOOLEAN':\n return 'BOOLEAN'\n if atype == 'QBOOLEAN' and btype == 'BOOLEAN':\n return 'BOOLEAN'\n\n # [Q]INTEGER + [Q]INTEGER => INTEGER\n if atype == 'QINTEGER' and btype == 'INTEGER':\n return 'INTEGER'\n if atype == 'INTEGER' and btype == 'QINTEGER':\n return 'INTEGER'\n\n # [Q]FLOAT + [Q]FLOAT => FLOAT\n if atype == 'QFLOAT' and btype == 'FLOAT':\n return 'FLOAT'\n if atype == 'FLOAT' and btype == 'QFLOAT':\n return 'FLOAT'\n\n # QINTEGER + QFLOAT => QFLOAT\n if atype == 'QINTEGER' and btype == 'QFLOAT':\n return 'QFLOAT'\n\n # QFLOAT + QINTEGER => QFLOAT\n if atype == 'QFLOAT' and btype == 'QINTEGER':\n return 'QFLOAT'\n\n # [Q]INTEGER + [Q]FLOAT => FLOAT (except QINTEGER + QFLOAT => QFLOAT)\n if atype == 'INTEGER' and btype == 'FLOAT':\n return 'FLOAT'\n if atype == 'INTEGER' and btype == 'QFLOAT':\n return 'FLOAT'\n if atype == 'QINTEGER' and btype == 'FLOAT':\n return 'FLOAT'\n\n # [Q]FLOAT + [Q]INTEGER => FLOAT (except # QFLOAT + QINTEGER => QFLOAT)\n if atype == 'FLOAT' and btype == 'INTEGER':\n return 'FLOAT'\n if atype == 'FLOAT' and btype == 'QINTEGER':\n return 'FLOAT'\n if atype == 'QFLOAT' and btype == 'INTEGER':\n return 'FLOAT'\n\n # All remaining combination of:\n # (DATE, TIME, TIMESTAMP, QBOOLEAN, QINTEGER, QFLOAT, STRING) +\n # (DATE, TIME, TIMESTAMP, QBOOLEAN, QINTEGER, QFLOAT, STRING) => STRING\n if is_string_type(atype) and is_string_type(btype):\n return 'STRING'\n\n return None","def map_dtype(dtype):\n item_idx = int(math.log(dtype.itemsize, 2))\n if dtype.kind == 'i':\n return [int8, int16, int32, int64][item_idx]\n elif dtype.kind == 'u':\n return [uint8, uint16, uint32, uint64][item_idx]\n elif dtype.kind == 'f':\n if dtype.itemsize == 2:\n pass # half floats not supported yet\n elif dtype.itemsize == 4:\n return float32\n elif dtype.itemsize == 8:\n return float64\n elif dtype.itemsize == 16:\n return float128\n elif dtype.kind == 'b':\n return int8\n elif dtype.kind == 'c':\n if dtype.itemsize == 8:\n return complex64\n elif dtype.itemsize == 16:\n return complex128\n elif dtype.itemsize == 32:\n return complex256\n elif dtype.kind == 'O':\n return object_","def _type(string, has_invisible=True, numparse=True):\n\n if has_invisible and isinstance(string, (str, bytes)):\n string = _strip_ansi(string)\n\n if string is None:\n return type(None)\n elif hasattr(string, \"isoformat\"): # datetime.datetime, date, and time\n return str\n elif _isbool(string):\n return bool\n elif _isint(string) and numparse:\n return int\n elif _isnumber(string) and numparse:\n return float\n elif isinstance(string, bytes):\n return bytes\n else:\n return str","def type_(type_):\n\n try:\n type_ = float(type_)\n if type_.is_integer():\n return int\n if not type_.is_integer():\n return float\n except ValueError:\n return str","def cast_type(cdm_column_type, value):\n if cdm_column_type in ('integer', 'int64'):\n # Regex check only relevant if submission dtype is 'object'\n if not re.match(SCIENTIFIC_NOTATION_REGEX, str(value)):\n return int(value)\n if cdm_column_type in ('character varying', 'text', 'string'):\n return str(value)\n if cdm_column_type == 'numeric':\n return float(value)\n if cdm_column_type == 'float' and isinstance(value, float):\n return value\n if cdm_column_type == 'date' and isinstance(value, datetime.date):\n return value\n if cdm_column_type == 'timestamp' and isinstance(\n value, datetime.datetime): # do not do datetime.datetime\n return value","def py_to_clips_type(ptype):\n\n ctype = None\n if ptype == int:\n ctype = \"INTEGER\"\n elif ptype == float:\n ctype = \"FLOAT\"\n elif ptype == str:\n ctype = \"STRING\"\n elif ptype == bool:\n ctype = \"BOOLEAN\"\n return ctype","def test_datatype(self):\n with Pandas() as pd:\n if pd is None:\n return\n with Numpy() as np: # noqa\n if numpy is None:\n return\n sys.stderr.write(\"\\n\")\n\n df, hist1, hist2, hist3 = get_test_histograms1()\n\n assert hist1.datatype == str\n np.testing.assert_array_equal(hist2.datatype, [numpy.number, str])\n np.testing.assert_array_equal(hist3.datatype, [numpy.datetime64, numpy.number, str])","def parse_type(fobj, data_type):\n if data_type == \"Boolean\": ## False if 0x00 else True\n return bool(fobj.read(1))\n elif data_type == \"Byte\": ## 1 byte int\n return fobj.read(1)[0]\n elif data_type == \"DateTime\": ## 8 bytes signed int\n return struct.unpack(\"<q\", fobj.read(8))[0]\n elif data_type == \"Double\": ## 8 bytes floating point\n return struct.unpack(\"<d\", fobj.read(8))[0]\n elif data_type == \"Int\": ## 4 bytes unsigned int\n return struct.unpack(\"<I\", fobj.read(4))[0]\n elif data_type == \"Int-Double pair\": ## 0x08-Int-0x0d-Double with AssertionError\n bb = fobj.read(1)[0]\n if bb != 0x08:\n raise AssertionError('parse_type(fobj, data_type): '\n '1st byte(%s) of \"Int-Double pair\" != 0x08' % bb)\n first_int = parse_type(fobj, \"Int\")\n bb = fobj.read(1)[0]\n if bb != 0x0d:\n raise AssertionError('parse_type(fobj, data_type): '\n '6th byte(%s) of \"Int-Double pair\" != 0x0d' % bb)\n return [first_int, parse_type(fobj, \"Double\")]\n elif data_type == \"Int-Double pair*\": ## int(n) - \"Int-Double pair\"*n\n return [parse_type(fobj, \"Int-Double pair\") for i in range(parse_type(fobj, \"Int\"))]\n elif data_type == \"Long\": ## 8 bytes unsigned int\n return struct.unpack(\"<Q\", fobj.read(8))[0]\n elif data_type == \"Short\": ## 2 bytes unsigned int\n return struct.unpack(\"<H\", fobj.read(2))[0]\n elif data_type == \"Single\": ## 4 bytes floating point\n return struct.unpack(\"<f\", fobj.read(4))[0]\n elif data_type == \"String\": ## 0x00 or 0x0b - ULE128(n) - UTF-8(length=n)\n bb = fobj.read(1)[0]\n if bb == 0x00:\n return None\n elif bb != 0x0b:\n ## TODO: show integers in assertion error in hexadecimal and decimal\n ## to make debug more convenient (cause I may inspect the file in a byte reader.\n raise AssertionError('parse_type(fobj, data_type): '\n '1st byte(%s) of \"String\" not in {0x00, 0x0b}' % bb)\n strlen = parse_type(fobj, \"ULEB128\")\n return fobj.read(strlen).decode(\"utf-8\")\n elif data_type == \"ULEB128\": ## https://en.wikipedia.org/wiki/LEB128#Decode_unsigned_integer\n i = 0 ## derived from the wiki psuedo code\n res = 0\n shift = 0\n while True:\n bb = fobj.read(1)[0]\n i += 1\n res |= ((bb & 0b1111111) << shift)\n if (bb & 0b10000000) == 0:\n break\n shift += 7\n return res\n elif data_type == \"Timing point\": ## Double - Double - Boolean\n return parse_types(fobj, [\"Double\", \"Double\", \"Boolean\"])\n elif data_type == \"Timing point+\": ## int(n) - \"Timing point\"*n\n return [parse_type(fobj, \"Timing point\") for i in range(parse_type(fobj, \"Int\"))]\n else:\n raise NotImplementedError('parse_type(fobj, data_type): Unknown data type: \"%s\".' % data_type)","def discover_type(value) -> str:\n if isinstance(value, list):\n return \"array\"\n elif isinstance(value, bool):\n return \"bool\"\n elif isinstance(value, int):\n return \"int\"\n return \"string\"","def _convert(string, type, message):\n try:\n return type(string)\n except ValueError as e:\n print(e)\n raise CharmmPSFError('Could not convert %s' % message)","def datatype(parameter):\n return type(parameter)","def ttrans(cdmtype, kinds=kinds): \n nptype=numpy.float32\n try:\n nptype=kinds[cdmtype.strip()]\n except:\n pass\n #print(cdmtype,'not found, using numpy.float32') \n return nptype","def convert_type(self, name, type):\n\t\t#\t\tprint 'Called with name = %s and type = %s' %(name, type)\n\t\tname = ''.join(name.split())\n\t\ttype = ''.join(type.split())\n\n\t\tif re.match(r'\\w+', type): #It's a concrete type\n\t\t\treturn self.TYPES_DICT.get(type,type) + ' ' + name\n\n\t\tarrow = type.rfind('->')\n\t\tassert arrow != -1, \"If it's not a primitive, it must be a function\"\n\t\tparams, return_type = type[:arrow], type[arrow+2:]\n\t\tassert params[0] == '(' and params[-1] == ')'\n\t\tparams = params[1:-1]\n\n\t\tparams_tokenized = Lexer(params).get_tokens()\n\t\tparam_list = self.split_params(params_tokenized)\n\t\tcpp_params = map(lambda n: self.convert_type('', n), param_list)\n\t\treturn_type = self.convert_type('', return_type)\n\t\treturn return_type + '(*' + name + ')(' + ','.join(cpp_params) + ')'","def guess_type_value_type (none = True) :\n return [ None, str, int, float ] if none else [ str, int, float ]","def test_get_type():\n formatter = TabularOutputFormatter()\n\n tests = (\n (1, int),\n (2.0, float),\n (b\"binary\", binary_type),\n (\"text\", text_type),\n (None, type(None)),\n ((), text_type),\n )\n\n for value, data_type in tests:\n assert data_type is formatter._get_type(value)","def field_type_converter(self, old_type):\n\n if old_type == 'String':\n new_type = 'Text'\n elif old_type == 'Integer':\n new_type = 'Short'\n elif old_type == 'Date':\n new_type = 'Date'\n elif old_type == 'GlobalID':\n new_type = 'GUID'\n else:\n new_type = 'Double'\n return new_type","def numpy_type(sqltype):\n m = re.match(\"char\\(([0-9]+)\\)\", sqltype.strip())\n if m is not None:\n # It's a string\n return np.dtype(\"|S\"+m.group(1))\n else:\n # It's a numeric type\n if sqltype == \"integer\" or sqltype == \"int\":\n return np.int32\n elif sqltype == \"bigint\":\n return np.int64\n elif sqltype == \"real\":\n return np.float32\n elif sqltype == \"float\":\n return np.float64\n else:\n raise ValueError(\"Unsupported data type \"+sqltype)","def _translate_in_type(self, type_conversion: type, *args) -> Any:\n try:\n return type_conversion(self.string)\n except (ValueError, TypeError):\n return type_conversion(*args)","def __new__(self, *types):\r\n conv = {int8: float32,\r\n int16: float32,\r\n int32: float64,\r\n int64: float64,\r\n uint8: float32,\r\n uint16: float32,\r\n uint32: float64,\r\n uint64: float64}\r\n return get_scalar_type(Scalar.upcast(*[conv.get(type, type)\r\n for type in types])),","def argument_type(arg):\n types = (int, float)\n \n for t in types:\n try:\n return type(t(arg))\n except ValueError:\n continue\n \n return str","def get_check_types():","def test_basic_numpy_dtypes():\n assert np.int != np.int8\n assert np.int != np.int16\n assert np.int != np.int32\n assert np.int != np.int64\n\n assert np.int == int\n assert np.int8 != int\n assert np.int16 != int\n assert np.int32 != int\n assert np.int64 != int\n\n assert np.dtype(np.int) == np.dtype('int') == np.dtype(int)\n assert np.dtype(np.int8) == np.dtype('int8') == np.int8\n assert np.dtype(np.int16) == np.dtype('int16') == np.int16\n assert np.dtype(np.int32) == np.dtype('int32') == np.int32\n assert np.dtype(np.int64) == np.dtype('int64') == np.int64","def _cast_unsupported_dtypes(tensor):\n\n if tensor.dtype.__eq__(dtypes.int64):\n # outside-compilation doesn't support int64 input yet.\n return math_ops.cast(tensor, dtypes.int32)\n if tensor.dtype.__eq__(dtypes.bfloat16) or tensor.dtype.__eq__(\n dtypes.float16):\n # Since host can't handle bf16, convert tensor to f32.\n return math_ops.cast(tensor, dtypes.float32)\n return tensor","def _cast_types(args):\n\targs.x_val = None if args.x_val == 'None' else int(args.x_val)\n\targs.test_size = float(args.test_size)\n\targs.alpha = float(args.alpha)\n\targs.fit_prior = (args.fit_prior in ['True', \"True\", 'true', \"true\"])\n\n\t# class_prior - array like type (problem to convert)\n\tif args.class_prior == \"None\" or args.class_prior == 'None':\n\t\targs.class_prior = None\n\n\t# --------- #\n\treturn args","def dtype_to_type(dtype) -> Type:\n if dtype == np.object:\n return str\n else:\n return type(np.zeros(1, dtype).item())","def _parse_dtypes(data, table_meta):\n for name, field in table_meta['fields'].items():\n field_type = field['type']\n if field_type == 'datetime':\n datetime_format = field.get('format')\n data[name] = pd.to_datetime(data[name], format=datetime_format, exact=False)\n elif field_type == 'numerical' and field.get('subtype') == 'integer':\n data[name] = data[name].dropna().astype(np.int64)\n elif field_type == 'id' and field.get('subtype', 'integer') == 'integer':\n data[name] = data[name].dropna().astype(np.int64)\n\n return data","def get_pytype(self, c_arg, parse_arg):\n if isinstance(c_arg, FunctionAddress):\n return 'O'\n else:\n try:\n return pytype_parse_registry[(parse_arg.dtype, parse_arg.precision)]\n except KeyError as e:\n raise NotImplementedError(\"Type not implemented for argument collection : \"+str(type(parse_arg))) from e","def python_type_to_sql_type(_python_type):\n if _python_type == str:\n return 'string'\n elif _python_type == bytes:\n return \"blob\"\n elif _python_type == float:\n return \"float\"\n elif _python_type == int:\n return \"integer\"\n elif _python_type == datetime:\n return \"datetime\"\n elif _python_type == bool:\n return \"boolean\"\n else:\n raise Exception(\"python_type_to_sql_type: _type_code \\\"\" + str(_python_type) + \"\\\"not supported\")","def str_to_type(name_type):\n if name_type == 'float' or name_type == 'Float':\n return float\n if name_type == 'bool':\n return bool\n if name_type == 'int':\n return lambda x: int(float(x))\n if name_type == 'list':\n return ast.literal_eval\n if name_type == 'date':\n return lambda x: dateutil.parser.parse(x).strftime('%Y-%m-%dT%H:%M:%SZ')\n if name_type == 'str':\n return str\n\n \n return None","def _map_v1_io_type(output_type: str) -> str:\n\n # TODO: put it in a common place\n def _map_primitive_type(_type: str) -> str:\n \"\"\"Convert double and float to number type.\n\n :param _type: A primitive v1 IO type\n :type _type: str\n :return:\n * InputTypes.NUMBER if _type is \"double\" or \"float\"\n * The provided type otherwise\n :rtype: str\n \"\"\"\n _type = _type.lower()\n if _type in [\"double\", \"float\"]:\n return InputTypes.NUMBER\n return _type\n\n if output_type in list(get_all_enum_values_iter(AssetTypes)):\n return output_type\n if output_type in SUPPORTED_INTERNAL_PARAM_TYPES:\n return _map_primitive_type(output_type)\n if output_type in [\"AnyFile\"]:\n return AssetTypes.URI_FILE\n # Handle AnyDirectory and the other types.\n return AssetTypes.URI_FOLDER","def str2type(raw_val):\n try:\n return int(raw_val)\n except ValueError:\n pass\n\n try:\n return float(raw_val)\n except ValueError:\n return raw_val","def get_type_string(data):\r\n data_type = type(data)\r\n\r\n if data_type in (int, long):\r\n return 'integer'\r\n elif data_type == float:\r\n return 'float'\r\n elif data_type == bool:\r\n return 'boolean'\r\n elif data_type in (list, tuple):\r\n return 'list'\r\n elif data_type == dict:\r\n return 'hash'\r\n elif data is None:\r\n return 'null'\r\n elif isinstance(data, basestring):\r\n return 'string'","def convert_numpy_type(cls, dtype):\n\n import numpy as np\n\n m = {\n 'int64': cls.DATATYPE_INTEGER64,\n 'float64': cls.DATATYPE_FLOAT,\n 'object': cls.DATATYPE_TEXT # Hack. Pandas makes strings into object.\n\n }\n\n t = m.get(dtype.name, None)\n\n if not t:\n raise TypeError(\n \"Failed to convert numpy type: '{}' \".format(\n dtype.name))\n\n return t","def dtype_to_pytype(type_):\n\n return {\n bool_: bool,\n int8: int,\n int16: int,\n int32: int,\n int64: int,\n uint8: int,\n uint16: int,\n uint32: int,\n uint64: int,\n float16: float,\n float32: float,\n float64: float,\n list_: list,\n tuple_: tuple,\n string: str,\n type_none: type(None)\n }[type_]","def test_get_datatypes(self):\n obs = _get_datatypes(self.metadata_map.ix[:, self.headers])\n exp = ['float8', 'varchar', 'integer']\n self.assertEqual(obs, exp)","def datatype_conversion(self):\n\n category_cols = self.FEATURE_TYPES[\"category_cols\"]\n integer_cols = self.FEATURE_TYPES[\"integer_cols\"]\n float_cols = self.FEATURE_TYPES[\"float_cols\"]\n datetime_cols = self.FEATURE_TYPES[\"datetime_cols\"]\n string_cols = self.FEATURE_TYPES[\"string_cols\"]\n bool_cols = self.FEATURE_TYPES[\"bool_cols\"]\n data = self.data\n \n data[category_cols] = data[category_cols].astype('category',copy=False) \n data[integer_cols] = data[integer_cols].astype('int64',copy=False)\n data[float_cols] = data[float_cols].astype('float64',copy=False)\n data[datetime_cols] = data[datetime_cols].astype('datetime64[ns]',copy=False)\n data[string_cols] = data[string_cols].astype('str',copy=False)\n data[bool_cols] = data[bool_cols].astype('bool', copy=False)\n\n return data","def inspect_dtype(series: pd.Series) -> str:\n\n mapping = {pd_types.is_bool_dtype: \"bool\",\n pd_types.is_integer_dtype: \"int\",\n pd_types.is_float_dtype: \"float\",\n pd_types.is_datetime64_any_dtype: \"datetime\"}\n\n for check, result in mapping.items():\n if check(series):\n return result\n\n raise TypeError(\"Type is not understand for column '{}'. Allowed \"\n \"types are bool, int, float, str and datetime.\"\n .format(series.name))","def _type_coerce(value, typ):\n from . import world\n\n if value is None:\n return None\n if typ is None:\n return value\n elif typ in (str, float, int, uuid.UUID):\n return typ(value)\n elif typ in (np.ndarray,):\n # Not ideal to assume 'd' type\n return np.array(value, dtype='d')\n if isinstance(value, dict) and '__type__' in value and '__namespace__' in value:\n if value['__type__'] in PROXY_TYPES:\n typ = PROXY_TYPES[value['__type__']]\n # return type_coerce(value, typ)\n elif '.' in value['__type__'] and value['__type__'].split('.') in PROXY_TYPES:\n typ = PROXY_TYPES[value['__type__'].split('.')]\n elif value['__namespace__'] in PROXY_TYPES:\n typ = PROXY_TYPES[value['__namespace__']]\n # return type_coerce(value, typ)\n else:\n log.warning(\"No type for dictionary: %s\", value)\n\n if isinstance(typ, type):\n if issubclass(typ, typing.List):\n if typ.__args__:\n sub_type = typ.__args__[0] # YUCK!\n return [type_coerce(item, sub_type) for item in value]\n else:\n log.warning(\"No sub-type on %s\", typ)\n return value\n elif issubclass(typ, typing.Dict):\n key_typ, value_typ = typ.__args__\n result = {}\n for key, value in value.items():\n result[type_coerce(key, key_typ)] = type_coerce(value, value_typ)\n return result\n\n else:\n if hasattr(typ, 'from_server'):\n return typ.from_server(value)\n else:\n return typ(value)\n if hasattr(typ, '__args__'):\n # WTF? Why can't I test for \"is this a union\"\n for possible in typ.__args__:\n if isinstance(value, possible):\n return value\n for possible in typ.__args__:\n try:\n return type_coerce(value, possible)\n except (ValueError, TypeError) as err:\n log.exception(\"Failed to convert\")\n raise ValueError(\n \"Union type %s we don't know how to convert %r\"\n % (\n typ,\n value,\n )\n )\n\n raise ValueError(\n \"Do not know how to convert type %s with value %r\"\n % (\n typ,\n value,\n )\n )","def feast_value_type_to_python_type(field_value_proto: ProtoValue) -> Any:\n field_value_dict = MessageToDict(field_value_proto)\n\n for k, v in field_value_dict.items():\n if k == \"int64Val\":\n return int(v)\n if k == \"bytesVal\":\n return bytes(v)\n if (k == \"int64ListVal\") or (k == \"int32ListVal\"):\n return [int(item) for item in v[\"val\"]]\n if (k == \"floatListVal\") or (k == \"doubleListVal\"):\n return [float(item) for item in v[\"val\"]]\n if k == \"stringListVal\":\n return [str(item) for item in v[\"val\"]]\n if k == \"bytesListVal\":\n return [bytes(item) for item in v[\"val\"]]\n if k == \"boolListVal\":\n return [bool(item) for item in v[\"val\"]]\n\n if k in [\"int32Val\", \"floatVal\", \"doubleVal\", \"stringVal\", \"boolVal\"]:\n return v\n else:\n raise TypeError(\n f\"Casting to Python native type for type {k} failed. \"\n f\"Type {k} not found\"\n )","def assign_type(s):\n if type(s) == list:\n return s\n else:\n s = str(s)\n try:\n return int(s)\n except ValueError:\n try:\n return float(s)\n except ValueError:\n return format_string(s)","def info(obj):\n if type(obj) is tuple:\n return '({})'.format(', '.join(map(TypeTool.info,obj)))\n elif type(obj) is list:\n return 'List[{}]'.format(TypeTool.info(obj[0]))\n else:\n ctype_name = type(obj).__name__\n if ctype_name == 'ndarray': return '{}[{}]{}'.format(ctype_name,obj.dtype, obj.shape)\n elif ctype_name == 'str': return 'string'\n elif ctype_name == 'bytes': return 'List[byte]'\n else: return ctype_name","def __set_utils_types(self):\n self.__arrayt = type(self.c_byte * 1)\n # self.__cfuncptrt = type(type(self.memmove))\n # class _p(self.Structure):\n # pass\n # self.__ptrt = type(self.POINTER(_p))\n self.__basic_types_name = {\n 'c_bool': '?',\n 'c_char': 'c',\n 'c_byte': 'b',\n 'c_ubyte': 'B',\n 'c_short': 'h',\n 'c_ushort': 'H',\n 'c_int': 'i', # c_int is c_long\n 'c_uint': 'I',\n 'int': 'i',\n 'c_longlong': 'q',\n 'c_ulonglong': 'Q',\n 'c_float': 'f',\n 'c_double': 'd',\n 'c_longdouble': 'g',\n 'c_char_p': 's',\n 'c_void_p': 'P',\n # 'c_void': 'P', ## void in array is void_p ##DEBUG\n }\n if self.__longsize == 4:\n # long == int\n self.__basic_types_name.update({'c_long': 'i',\n 'c_ulong': 'I',\n 'long': 'i',\n 'c_void': 'I'})\n elif self.__longsize == 8:\n # long == longlong\n self.__basic_types_name.update({'c_long': 'q',\n 'c_ulong': 'Q',\n 'long': 'q',\n 'c_void': 'Q'})\n # we need to account for the possible changes in c_longdouble\n self.__basic_types = set([getattr(self, k) for k in self.__basic_types_name.keys() if hasattr(self, k)])\n return","def type_map(cls, mysql_type):\n d = {\n FIELD_TYPE.INT24: 'INTEGER',\n FIELD_TYPE.TINY: 'INTEGER',\n FIELD_TYPE.BIT: 'INTEGER',\n FIELD_TYPE.DATETIME: 'TIMESTAMP',\n FIELD_TYPE.DATE: 'TIMESTAMP',\n FIELD_TYPE.DECIMAL: 'FLOAT',\n FIELD_TYPE.NEWDECIMAL: 'FLOAT',\n FIELD_TYPE.DOUBLE: 'FLOAT',\n FIELD_TYPE.FLOAT: 'FLOAT',\n FIELD_TYPE.INT24: 'INTEGER',\n FIELD_TYPE.LONG: 'INTEGER',\n FIELD_TYPE.LONGLONG: 'INTEGER',\n FIELD_TYPE.SHORT: 'INTEGER',\n FIELD_TYPE.TIMESTAMP: 'TIMESTAMP',\n FIELD_TYPE.YEAR: 'INTEGER',\n }\n return d[mysql_type] if mysql_type in d else 'STRING'","def javatype(s):\n return {\n 'int': 'int',\n 'String': 'String',\n 'Long': 'Long',\n 'boolean': 'boolean'\n }.get(s.name, s.name)","def process_ir_type(typ: Type) -> Union[BaseCType, VectorCType, OptionalCType, ListCType]:\n if isinstance(typ, BaseType):\n if typ.name == BaseTy.Tensor:\n return BaseCType(valueT)\n elif typ.name == BaseTy.Scalar:\n # at::scalar has special handling,\n # and is wrapped in an IR value just like at::tensor\n return BaseCType(valueT)\n elif typ.name == BaseTy.ScalarType:\n return BaseCType(scalarTypeT)\n elif typ.name == BaseTy.int:\n return BaseCType(longT)\n elif typ.name == BaseTy.bool:\n return BaseCType(boolT)\n elif typ.name == BaseTy.float:\n return BaseCType(doubleT)\n elif typ.name == BaseTy.str:\n return BaseCType(stringT)\n elif typ.name == BaseTy.Device:\n return BaseCType(deviceT)\n elif typ.name == BaseTy.Layout:\n return BaseCType(layoutT)\n else:\n raise AssertionError(f\"TODO add support for type {repr(typ)}\")\n elif isinstance(typ, OptionalType):\n return OptionalCType(process_ir_type(typ.elem))\n elif isinstance(typ, ListType):\n if str(typ.elem) == 'Tensor?':\n # TODO(whc) is this actually correct? or should it use a Vector like above\n return ListCType(OptionalCType(BaseCType(valueT)))\n else:\n return VectorCType(process_ir_type(typ.elem))\n else:\n raise AssertionError(f\"unrecognized type {repr(typ)}\")","def unparse_type(type_str):\n if not type_str.startswith('array'):\n return type_str\n arg_dim = type_str.lstrip('array')[0]\n data_type = type_str.lstrip('array')[1:]\n arg_type = \"vizgen.ndarray('\" + data_type + \"', \" + arg_dim + \")\"\n return arg_type","def change_object_type(obj, new_type):\n # type: (Union[str, SupportsInt, SupportsFloat], str) -> Union[str, int, float]\n if new_type == 'str':\n return str(obj)\n elif new_type == 'int':\n return int(obj)\n elif new_type == 'float':\n return float(obj)\n else:\n raise IOError('expected_type \"{}\" is not supported in this function.'.format(new_type))","def get_param_type(value):\n dtype = type(value).__name__\n if dtype not in ['str', 'float', 'int', 'bool', 'list']:\n dtype = 'str'\n if dtype == 'list':\n try:\n eltype = type(value[0]).__name__\n except IndexError:\n eltype = 'str'\n if eltype not in ['str', 'float', 'int', 'bool']:\n eltype = 'str'\n dtype = eltype + dtype\n return dtype","def test_get_types(self):\n pass","def TYPE(value):\n raise NotImplementedError()","def test_match_types(self):\n f = lws.match_types\n # assert f(str, u'test') is True\n assert f(str, 'test') is True\n assert f(int, 123) is True\n assert f(int, 123.00) is False\n assert f(bool, [1, 2, 3]) is False","def test_datatype_detection():\n\n grammar = \"\"\"\n IsObjectDatatype: INT | STRING | ID;\n IsIntDatatype: INT;\n IsIdDatatype: ID;\n IsAlsoDatatype: SubDT1 | SubDT2;\n SubDT1: INT;\n SubDT2: STRING;\n \"\"\"\n\n mm = metamodel_from_str(grammar)\n\n IsObjectDatatype = mm['IsObjectDatatype']\n assert isinstance(IsObjectDatatype, ecore.EDataType)\n assert IsObjectDatatype.name == 'IsObjectDatatype'\n assert IsObjectDatatype.eType == object\n\n IsIntDatatype = mm['IsIntDatatype']\n assert isinstance(IsIntDatatype, ecore.EDataType)\n assert IsIntDatatype.name == 'IsIntDatatype'\n assert IsIntDatatype.eType == int\n\n IsIdDatatype = mm['IsIdDatatype']\n assert isinstance(IsIdDatatype, ecore.EDataType)\n assert IsIdDatatype.name == 'IsIdDatatype'\n assert IsIdDatatype.eType == str\n\n IsAlsoDatatype = mm['IsAlsoDatatype']\n assert isinstance(IsAlsoDatatype, ecore.EDataType)\n IsAlsoDatatype = mm['IsAlsoDatatype']\n assert IsAlsoDatatype.eType == object","def decode_itype(self):\n self.instr.imm = self.instr.binary[0:12]\n self.instr.rs1 = self.instr.binary[12:17]\n self.instr.funct3 = self.instr.binary[17:20]\n self.instr.operation = itype_funct3_decode[self.instr.funct3]\n self.instr.source1RegValue = self.simulator.registers[int(self.instr.rs1, 2)][1]\n self.instr.source2RegValue = int(self.instr.imm, 2)","def infer_value_type(self, value):\n if isinstance(value, str):\n if self.TIMESTAMP_MATCHER.match(value):\n return 'TIMESTAMP'\n elif self.DATE_MATCHER.match(value):\n return 'DATE'\n elif self.TIME_MATCHER.match(value):\n return 'TIME'\n elif not self.quoted_values_are_strings:\n # Implement the same type inference algorithm as 'bq load' for\n # quoted values that look like ints, floats or bools.\n if self.INTEGER_MATCHER.match(value):\n if (int(value) < self.INTEGER_MIN_VALUE\n or self.INTEGER_MAX_VALUE < int(value)):\n return 'QFLOAT' # quoted float\n else:\n return 'QINTEGER' # quoted integer\n elif self.FLOAT_MATCHER.match(value):\n return 'QFLOAT' # quoted float\n elif value.lower() in ['true', 'false']:\n return 'QBOOLEAN' # quoted boolean\n else:\n return 'STRING'\n else:\n return 'STRING'\n # Python 'bool' is a subclass of 'int' so we must check it first\n elif isinstance(value, bool):\n return 'BOOLEAN'\n elif isinstance(value, int):\n if value < self.INTEGER_MIN_VALUE or self.INTEGER_MAX_VALUE < value:\n return 'FLOAT'\n else:\n return 'INTEGER'\n elif isinstance(value, float):\n return 'FLOAT'\n elif value is None:\n return '__null__'\n elif isinstance(value, dict):\n if value:\n return 'RECORD'\n else:\n return '__empty_record__'\n elif isinstance(value, list):\n if value:\n return '__array__'\n else:\n return '__empty_array__'\n else:\n raise Exception(\n f'Unsupported node type: {type(value)} (should not happen)'\n )","def _typecheck(name, value, *types):\n if not types:\n raise ValueError('expected one or more types, maybe use _textcheck?')\n if not isinstance(value, types):\n raise TypeError(\"expected %s for %s, got %r\"\n % (\" or \".join([t.__name__ for t in types]),\n name, value))\n return value","def ftype(obj):\n if isinstance(obj, np.ndarray) or isinstance(obj, torch.Tensor):\n types = \" \".join([str(x) for x in [type(obj), obj.dtype, list(obj.shape)]])\n if isinstance(obj, torch.Tensor):\n types = \" \".join([obj.device.type, types])\n else:\n types = str(type(obj))\n\n for s in [\"class \", \"'\", \"<\", \">\", \"(\", \")\", \"torch.\", \"numpy.\" ]:\n types = types.replace(s, \"\")\n return types","def isdatatype(object):\n return isinstance(object, (str, int, bool, float, type(None)))","def _ptype(ptype_value, verbose=True):\n types = {\n 0:[\"PT_NULL\",\"Program header table entry unused\"],\n 1:[\"PT_LOAD\",\"Loadable program segment\"],\n 2:[\"PT_DYNAMIC\",\"Dynamic linking information\"],\n 3:[\"PT_INTERP\",\"Program interpreter\"],\n 4:[\"PT_NOTE\",\"Auxiliary information\"],\n 5:[\"PT_SHLIB\",\"Reserved\"],\n 6:[\"PT_PHDR\",\"Entry for header table itself\"],\n 7:[\"PT_TLS\",\"Thread-local storage segment\"],\n 8:[\"PT_NUM\",\"Number of defined types\"],\n 0x60000000:[\"PT_LOOS\",\"Start of OS-specific\"],\n 0x6474e550:[\"PT_GNU_EH_FRAME\",\"GCC .eh_frame_hdr segment\"],\n 0x6474e551:[\"PT_GNU_STACK\",\"Indicates stack executability\"],\n 0x6474e552:[\"PT_GNU_RELRO\",\"Read-only after relocation\"],\n 0x6ffffffa:[\"PT_SUNWBSS\",\"Sun Specific segment\"],\n 0x6ffffffb:[\"PT_SUNWSTACK\",\"Stack segment\"],\n 0x6fffffff:[\"PT_HIOS\",\"End of OS-specific\"],\n 0x70000000:[\"PT_LOPROC\",\"Start of processor-specific\"],\n 0x7fffffff:[\"PT_HIPROC\",\"End of processor-specific\"],\n }\n\n if types.get(ptype_value):\n value = types[ptype_value]\n if verbose:\n return \"%s (%s)\" % (value[0], value[1])\n else:\n return \"%s\" % value[0]\n else:\n return \"UNKNOWN\"","def data_type(value):\n if type(value) == type(None):\n return 'no value'\n elif type(value) == list:\n if len(value) >= 3:\n return value[2]\n else:\n return None\n elif type(value) == bool:\n return value\n elif type(value) == int:\n if value < 100:\n return 'less than 100'\n elif value > 100:\n return 'more than 100'\n else:\n return 'equal to 100'\n elif type(value) == str:\n return len(value)\n else:\n return value","def map_dtypes(pgtype):\n # may not be a complete list, could not find CARTO SQL API documentation\n # about data types\n dtypes = {'number': 'float64',\n 'date': 'datetime64',\n 'string': 'object',\n 'geometry': 'object',\n 'boolean': 'bool'}\n try:\n return dtypes[pgtype]\n except KeyError:\n # make it a string if not in dict above\n return 'object'","def translate_type(resolved_type):\n\n # Another model\n if resolved_type.container == ContainerType.MODEL:\n return \"*\" + resolved_type.type\n\n # Primitive type\n if resolved_type.container == ContainerType.PRIMITIVE:\n return \"*\" + PRIMITIVE_TYPES[resolved_type.type]\n\n # Composite type\n item_type = translate_item_type(resolved_type.type)\n\n # A dict\n if resolved_type.container == ContainerType.DICT:\n return f\"map[string]{item_type}\"\n\n # A list\n if resolved_type.container in (ContainerType.LIST, ContainerType.SET):\n return f\"[]{item_type}\"\n\n raise ValueError(f\"Unknown container type {resolved_type.container}\")","def convert_dtypes(rows):\n dtype_map = {pd.Timestamp: lambda x: x.to_pydatetime(),\n np.int8: lambda x: int(x),\n np.int16: lambda x: int(x),\n np.int32: lambda x: int(x),\n np.int64: lambda x: int(x),\n np.float16: lambda x: float(x),\n np.float32: lambda x: float(x),\n np.float64: lambda x: float(x),\n np.float128: lambda x: float(x)}\n for row in rows:\n yield [dtype_map.get(type(elem), lambda x: x)(elem) for elem in row]","def clips_to_py_type(ctype):\n\n ptype = None\n if ctype == \"INTEGER\":\n ptype = int\n elif ctype == \"FLOAT\":\n ptype = float\n elif ctype == \"STRING\":\n ptype = str\n elif ctype == \"BOOLEAN\":\n ptype = bool\n return ptype","def test_type(self):\n geometric = [x for x in generators.geometric(1, 10, 0.5)]\n type_of_geometric = type(geometric[0])\n self.assertTrue(type_of_geometric == int or type_of_geometric == float)","def _convert_field_type(self, row):\n try:\n return [row[0], int(row[1]), int(row[2]),\n self._format_float_data(float(row[3]) * self._ms_to_us),\n self._format_float_data(float(row[4]) * self._ms_to_us),\n self._format_float_data(float(row[5])*100)]\n except IndexError as err:\n log.exception(err)\n raise ProfilerRawFileException('failed to get HOST CPU operator type data.')","def cython_pytype(self, t):\n if isinstance(t, Number):\n return str(t)\n t = self.canon(t)\n if t in self.cython_pytypes:\n return self.cython_pytypes[t]\n if isinstance(t, basestring):\n if t in self.base_types:\n return self.cython_pytypes[t]\n # must be tuple below this line\n tlen = len(t)\n if 2 == tlen:\n if 0 == t[1]:\n return self.cython_pytype(t[0])\n elif self.isrefinement(t[1]):\n return self.cython_pytype(t[0])\n else:\n # FIXME last is ignored for strings, but what about other types?\n #last = '[{0}]'.format(t[-1]) if isinstance(t[-1], int) else t[-1]\n #return cython_pytype(t[0]) + ' {0}'.format(last)\n return self.cython_pytype(t[0])\n elif 3 <= tlen:\n if t in self.cython_pytypes:\n return self.cython_pytypes[t]\n assert t[0] in self.template_types\n assert len(t) == len(self.template_types[t[0]]) + 2\n template_name = self.cython_pytypes[t[0]]\n assert template_name is not NotImplemented\n cypyt = self.cython_pytypes[t[0]]\n cypyt, t = self._fill_cypyt(cypyt, t)\n # FIXME last is ignored for strings, but what about other types?\n #if 0 != t[-1]:\n # last = '[{0}]'.format(t[-1]) if isinstance(t[-1], int) else t[-1]\n # cypyt += ' {0}'.format(last)\n return cypyt","def cast_tensor_type(inputs, src_type, dst_type):\n if isinstance(inputs, torch.Tensor):\n return inputs\n elif isinstance(inputs, str):\n return inputs\n elif isinstance(inputs, np.ndarray):\n return inputs\n elif isinstance(inputs, abc.Mapping):\n return type(inputs)({k: cast_tensor_type(v, src_type, dst_type) for k, v in inputs.items()})\n elif isinstance(inputs, abc.Iterable):\n return type(inputs)(cast_tensor_type(item, src_type, dst_type) for item in inputs)\n return inputs","def convert_type(param: str, target: str):\n converted = param\n if target == \"boolean\":\n converted = bool(strtobool(param))\n if target == \"integer\":\n converted = int(param)\n return converted","def as_type(module, x, y):\n _import_modules()\n if module in [np, ma, jnp]:\n return x.astype(y.dtype)\n elif module == tf:\n return tf.cast(x, y.dtype)\n elif module == torch:\n return x.to(dtype=y.dtype, device=y.device)\n raise UnknownModuleException(f\"Module {module.__name__} not supported.\")","def getconversiontype(self, *args, **kwargs):\n return _coordsys.coordsys_getconversiontype(self, *args, **kwargs)","def variable_types(self, data_key, only_type=None):\r\n if self[data_key].meta['columns'] is None:\r\n return 'No meta attached to data_key: %s' %(data_key)\r\n else:\r\n types = {\r\n 'int': [],\r\n 'float': [],\r\n 'single': [],\r\n 'delimited set': [],\r\n 'string': [],\r\n 'date': [],\r\n 'time': [],\r\n 'array': []\r\n }\r\n not_found = []\r\n for col in self[data_key].data.columns:\r\n if not col in ['@1', 'id_L1', 'id_L1.1']: \r\n try:\r\n types[\r\n self[data_key].meta['columns'][col]['type']\r\n ].append(col)\r\n except:\r\n not_found.append(col) \r\n for mask in self[data_key].meta['masks'].keys():\r\n types[self[data_key].meta['masks'][mask]['type']].append(mask)\r\n if not_found:\r\n print '%s not found in meta file. Ignored.' %(not_found)\r\n if only_type:\r\n return types[only_type]\r\n else:\r\n return types","def _column_type(strings, has_invisible=True, numparse=True):\n types = [_type(s, has_invisible, numparse) for s in strings]\n return reduce(_more_generic, types, bool)"],"string":"[\n \"def _type_translate(p_type, default_v=None):\\n translate_dict = {'float': 'float',\\n 'double': 'real',\\n 'int': 'int',\\n 'gr_complex': 'complex',\\n 'char': 'byte',\\n 'unsigned char': 'byte',\\n 'std::string': 'string',\\n 'std::vector<int>': 'int_vector',\\n 'std::vector<float>': 'real_vector',\\n 'std::vector<gr_complex>': 'complex_vector',\\n }\\n if p_type in ('int',) and default_v[:2].lower() == '0x':\\n return 'hex'\\n try:\\n return translate_dict[p_type]\\n except KeyError:\\n return 'raw'\",\n \"def data_types():\\n\\n return ...\",\n \"def __Type(thing):\\n from .native import (Vector, Color, Matrix, VectorField, ColorField,\\n MatrixField, ScalarField)\\n if isinstance(thing, (int, float, ScalarField)): return 'Scalar'\\n if isinstance(thing, (Vector, VectorField)): return 'Vector'\\n if isinstance(thing, (Color, ColorField)): return 'Color'\\n if isinstance(thing, (Matrix, MatrixField)): return 'Matrix'\\n raise ValueError(\\\"Could not guess type of: \\\" + thing)\",\n \"def conversion_type(alg, parameters):\\n parameters = translation_parameters(parameters)\\n if alg.upper() in INLINE_ALGORITHMS.keys():\\n param = getfullargspec(INLINE_ALGORITHMS[alg.upper()].__init__)\\n if param is not None:\\n if param.defaults:\\n for key, value in parameters.items():\\n if key != 'input_dim' and key != 'rank':\\n index = list(reversed(param.args)).index(key)\\n data_type = type(list(reversed(param.defaults))[index])(value)\\n else:\\n if value is None:\\n data_type = None\\n else:\\n data_type = int(value)\\n parameters[key] = data_type\\n return parameters\",\n \"def _to_type_desc(input):\\n if type(input) is bool:\\n return \\\"bool\\\"\\n if type(input) is int:\\n return \\\"int32\\\"\\n if type(input) is float:\\n return \\\"float32\\\" # TODO(klecki): current DALI limitation\\n if isinstance(input, _ScalarConstant):\\n dtype_to_desc = {\\n _DALIDataType.BOOL: \\\"bool\\\",\\n _DALIDataType.INT8: \\\"int8\\\",\\n _DALIDataType.INT16: \\\"int16\\\",\\n _DALIDataType.INT32: \\\"int32\\\",\\n _DALIDataType.INT64: \\\"int64\\\",\\n _DALIDataType.UINT8: \\\"uint8\\\",\\n _DALIDataType.UINT16: \\\"uint16\\\",\\n _DALIDataType.UINT32: \\\"uint32\\\",\\n _DALIDataType.UINT64: \\\"uint64\\\",\\n _DALIDataType.FLOAT16: \\\"float16\\\",\\n _DALIDataType.FLOAT: \\\"float32\\\",\\n _DALIDataType.FLOAT64: \\\"float64\\\",\\n }\\n return dtype_to_desc[input.dtype]\\n\\n raise TypeError(\\n f\\\"Constant argument to arithmetic operation not supported. \\\"\\n f\\\"Got {str(type(input))}, expected \\\"\\n f\\\"a constant value of type 'bool', 'int', 'float' or 'nvidia.dali.types.Constant'.\\\")\",\n \"def convert_type(self, value, schema_type, **kwargs):\",\n \"def test_types(self):\\n assert types.typeClass(\\\"str\\\") == str\\n\\n assert types.isBuiltinType(\\\"str\\\")\\n\\n assert types.isCollectionType(\\\"map\\\")\\n assert types.isCollectionType(\\\"seq\\\")\\n assert not types.isCollectionType(\\\"str\\\")\\n\\n assert types.isScalarType(\\\"str\\\")\\n assert not types.isScalarType(\\\"seq\\\")\\n assert not types.isScalarType(\\\"map\\\")\\n\\n assert types.isCollection([])\\n assert types.isCollection({})\\n assert not types.isCollection(\\\"foo\\\")\\n\\n assert types.isScalar(\\\"\\\")\\n assert types.isScalar(True)\\n assert not types.isScalar([])\\n\\n assert types.isCorrectType(\\\"\\\", str)\\n assert types.isCorrectType({}, dict)\\n\\n assert types.isString(\\\"foo\\\")\\n assert not types.isString([])\\n\\n assert types.isInt(1)\\n assert not types.isInt(\\\"foo\\\")\\n\\n assert types.isBool(True)\\n assert not types.isBool(1)\\n assert not types.isBool(\\\"true\\\")\\n\\n assert types.isFloat(1.0)\\n assert not types.isFloat(\\\"foo\\\")\\n\\n assert types.isNumber(1)\\n assert types.isNumber(1.0)\\n assert not types.isNumber(\\\"foo\\\")\\n\\n assert types.isText(\\\"foo\\\")\\n assert types.isText(1)\\n assert types.isText(1.0)\\n assert not types.isText([])\\n assert not types.isText(True)\\n\\n assert types.isAny(\\\"foo\\\")\\n assert types.isAny(True)\\n assert types.isAny(1)\\n assert types.isAny(1.0)\\n assert types.isAny({})\\n assert types.isAny([])\\n\\n assert types.isEnum(\\\"foo\\\")\\n assert not types.isEnum(1)\\n\\n assert types.isNone(None)\\n assert not types.isNone(\\\"foo\\\")\",\n \"def datatype_map(dtype):\\n # TODO: add datetype conversion\\n if 'float' in dtype:\\n return 'numeric'\\n elif 'int' in dtype:\\n return 'int'\\n elif 'bool' in dtype:\\n return 'boolean'\\n else:\\n return 'text'\",\n \"def typecast(dtype: Any) -> str:\\n if dtype is int:\\n return \\\"Int64\\\"\\n elif dtype is float:\\n return \\\"Float64\\\"\\n elif dtype is bool:\\n return \\\"bool\\\"\\n return \\\"string\\\"\",\n \"def _cast_types(self, input_dict):\\n return cast_types(input_dict, self.params['dtype'])\",\n \"def datatype(f):\\n from numpy import bool, uint8, uint16, int32\\n code = f.dtype\\n if code == bool: type='binary'\\n elif code == uint8: type='uint8'\\n elif code == uint16: type='uint16'\\n elif code == int32: type='int32'\\n else:\\n assert 0,'Does not accept this typecode: %s' % code\\n return type\",\n \"def _infer_dtype(val):\\n if re.match(r'\\\\d{4}-\\\\d{2}(?:-\\\\d{2})?', val):\\n return 'date'\\n elif re.match(r'[+-]?\\\\d+$', val):\\n return 'int'\\n elif re.match(r'[+-]?\\\\d+%$', val):\\n return 'pct'\\n elif re.match(r'[a-zA-Z ]+', val):\\n return 'text'\\n else:\\n msg = \\\"val={0} dtype not recognized\\\".format(val)\\n raise ValueError(msg)\",\n \"def data_types(self):\",\n \"def test_as_python_types(self):\\n obs = _as_python_types(self.metadata_map, self.headers)\\n exp = [[2.1, 3.1, 3],\\n ['str1', '200', 'string30'],\\n [1, 2, 3]]\\n self.assertEqual(obs, exp)\",\n \"def _map_primitive_type(_type: str) -> str:\\n _type = _type.lower()\\n if _type in [\\\"double\\\", \\\"float\\\"]:\\n return InputTypes.NUMBER\\n return _type\",\n \"def __convToTyped(index, value, dtypes):\\n\\t#print(index, value)\\n\\tdtype = dtypes[index]\\n\\ttvalue = value\\n\\tif dtype == \\\"int\\\":\\n\\t\\ttvalue = int(value)\\n\\telif dtype == \\\"float\\\":\\n\\t\\ttvalue = float(value)\\n\\treturn tvalue\",\n \"def _convert_dtype_value(val):\\n\\n convert_dtype_map = {\\n 21: \\\"int8\\\",\\n 20: \\\"uint8\\\",\\n 6: \\\"float64\\\",\\n 5: \\\"float32\\\",\\n 4: \\\"float16\\\",\\n 3: \\\"int64\\\",\\n 2: \\\"int32\\\",\\n 1: \\\"int16\\\",\\n 0: \\\"bool\\\",\\n }\\n if val not in convert_dtype_map:\\n msg = f\\\"Paddle data type value {val} is not handled yet.\\\"\\n raise NotImplementedError(msg)\\n return convert_dtype_map[val]\",\n \"def _convertType(self, s):\\n for func in (int, float):\\n try:\\n n = func(s)\\n return n\\n except:\\n pass\\n return s\",\n \"def fix_param_type(value, dtype):\\n if dtype == 'bool':\\n if type(value) is not bool:\\n sval = str(value).lower().strip()\\n if sval in FALSY:\\n value = False\\n else:\\n value = True\\n elif dtype == 'int':\\n if type(value) is not int:\\n try:\\n value = int(value)\\n except (TypeError, ValueError):\\n # allow it to be a non-number -- initial\\n # dtype may be not be broad enough\\n value = str(value)\\n elif dtype == 'float':\\n if type(value) is not float:\\n try:\\n value = float(value)\\n except (TypeError, ValueError):\\n value = str(value)\\n elif dtype == 'strlist':\\n if type(value) is not list:\\n value = [re.sub(r'[\\\\'\\\"\\\\[\\\\]]', '', v).strip()\\n for v in str(value).split(',')]\\n else:\\n value = [str(v).strip() for v in value]\\n elif dtype == 'intlist':\\n if type(value) is not list:\\n try:\\n value = [int(re.sub(r'[\\\\s\\\\'\\\"\\\\[\\\\]]', '', v))\\n for v in str(value).split(',')]\\n except (TypeError, ValueError):\\n # warn for this one -- it will likely not be\\n # interpreted correctly\\n log.warning('Found data type {}; '\\n 'expected {}'.format(type(value), dtype))\\n value = str(value)\\n else:\\n try:\\n value = [int(v) for v in value]\\n except (TypeError, ValueError):\\n # allow this case -- where some elements\\n # may be ints, some not -- since initial\\n # dtype guess may not be broad enough\\n pass\\n elif dtype == 'floatlist':\\n if type(value) is not list:\\n try:\\n value = [float(re.sub(r'[\\\\s\\\\'\\\"\\\\[\\\\]]', '', v))\\n for v in str(value).split(',')]\\n except (TypeError, ValueError):\\n log.warning('Found data type {}; '\\n 'expected {}'.format(type(value), dtype))\\n value = str(value)\\n else:\\n try:\\n value = [float(v) for v in value]\\n except (TypeError, ValueError):\\n # allow this case -- where some elements\\n # may be floats, some not -- since initial\\n # dtype guess may not be broad enough\\n pass\\n\\n elif dtype == 'boollist':\\n if type(value) is not list:\\n value = [re.sub(r'[\\\\s\\\\'\\\"\\\\[\\\\]]', '', v)\\n for v in str(value).split(',')]\\n for i, v in enumerate(value):\\n if type(v) is bool:\\n continue\\n sval = str(v).lower().strip()\\n if sval in FALSY:\\n v = False\\n else:\\n v = True\\n value[i] = v\\n else:\\n value = str(value)\\n\\n return value\",\n \"def _map_dtypes(type_names, field_widths):\\n dtypes = []\\n for i, name in enumerate(type_names):\\n if name == 'int':\\n dtypes.append('i8')\\n elif name == 'double':\\n dtypes.append('f8')\\n elif name == 'char':\\n dtypes.append('a{0}'.format(field_widths[i]))\\n else:\\n raise ValueError('Unexpected type name: {0}.'.format(name))\\n return dtypes\",\n \"def to_internal(value):\\n if isinstance(value, bool):\\n return types.Bool((value,))\\n if isinstance(value, int):\\n return types.Int((value,))\\n if isinstance(value, float):\\n return types.Float((value,))\\n if isinstance(value, six.string_types):\\n return types.String((value,))\",\n \"def test__specification_type_to_python_type_unsupported_type(self):\\n with self.assertRaises(TypeError):\\n _specification_type_to_python_type(\\\"unsupported_type\\\")\",\n \"def initTypes(self):\\n self.types = [ty.NoneType]*self.numcols()\\n for k,row in enumerate(self.data):\\n for i in range(self.numcols()):\\n val = row[i]\\n typ = self.types[i]\\n if not val is None:\\n if typ in [ty.NoneType,ty.IntType]:\\n if val.isdigit():\\n row[i] = int(val)\\n if val.startswith('-') and val[1:].isdigit():\\n row[i] = -int(val[1:])\\n self.types[i] = ty.IntType\\n continue\\n if typ in [ty.NoneType,ty.IntType,ty.FloatType]:\\n try:\\n row[i] = float(val)\\n if not typ == ty.FloatType:\\n self.types[i] = ty.FloatType\\n # Convert already existing values\\n for j in range(k):\\n elt = self.data[j][i]\\n self.data[j][i] = None if elt is None else float(elt)\\n continue\\n except ValueError:\\n pass\\n if typ in [ty.NoneType,utils.Date]:\\n try:\\n row[i] = utils.Date(val)\\n self.types[i] = utils.Date\\n continue\\n except ValueError:\\n pass\\n row[i] = unicode(val)\\n if not typ == ty.UnicodeType:\\n self.types[i] = ty.UnicodeType\\n # Convert already existing values\\n for j in range(k):\\n elt = self.data[j][i]\\n self.data[j][i] = None if elt is None else unicode(elt)\",\n \"def dataType(self, data):\\n if isinstance(data,str):\\n return STRING\\n elif isinstance(data,dict):\\n return ASSOC\\n elif isinstance(data,int) or isinstance(data,float):\\n return STRING\\n elif is_python2() and isinstance(data,long):\\n return STRING\\n elif isinstance(data, SpecArray.SpecArrayData):\\n self.rows, self.cols = data.shape\\n return data.type\",\n \"def convert_data_types(fields, src_db='mysql', dest_db='postgres'):\\n\\n data_type_map = {\\n 'mysql': {\\n 'postgres': {\\n 'date': 'date',\\n 'tinyint': 'smallint',\\n 'smallint': 'smallint',\\n 'mediumint': 'integer',\\n 'int': 'bigint',\\n 'bigint': 'numeric',\\n 'float': 'real',\\n 'double': 'double precision',\\n 'tinytext': 'varchar',\\n 'mediumtext': 'varchar',\\n 'longtext': 'varchar',\\n 'varchar': 'varchar',\\n 'text': 'varchar',\\n 'char': 'char',\\n 'binary': 'bytea',\\n 'varbinary': 'bytea',\\n 'tinyblob': 'bytea',\\n 'blob': 'bytea',\\n 'mediumblob': 'bytea',\\n 'longblob': 'bytea',\\n 'datetime': 'timestamp',\\n 'time': 'time',\\n 'decimal': 'decimal',\\n 'json': 'jsonb'\\n }\\n }\\n }\\n\\n for elem in fields:\\n elem['data_type'] = data_type_map[src_db][dest_db][elem['data_type']]\\n\\n if elem['data_type'] == 'decimal':\\n elem['data_type'] += f'''{int(elem['numeric_precision']), int(elem['numeric_scale'])}'''\\n\\n fields = {e['column_name']: e['data_type'] for e in fields}\\n\\n return fields\",\n \"def _infer_variable_types_from_data(raw_data):\\n raise NotImplementedError()\",\n \"def map_latencydata_types(type_name):\\n\\n if type_name in ['0', '1']:\\n return Unknown(type_name)\\n\\n # cl is register that is used for certain instructions\\n if type_name == 'cl':\\n return Register(type_name)\\n\\n # TODO make this its own type?\\n if type_name == \\\"stack pointer\\\":\\n return Register(type_name)\\n\\n if type_name == \\\"[r+s*x]\\\" or type_name == \\\"[r+s*y]\\\":\\n return Unknown(type_name)\\n\\n if type_name[:1] == 'r':\\n if type_name[-1] == 'l' or type_name[-1] == 'h':\\n # h, l refer to high, low? get rid of these and continnue as normally\\n type_name = type_name[:-1] # mistake in the document? get rid of the trailing l?\\n size = int(type_name[1:]) if len(type_name) > 1 else None\\n return Register(type_name, size)\\n\\n # vector registers (I think)\\n if type_name in [\\\"xmm\\\", \\\"mmx\\\", \\\"ymm\\\", \\\"mmy\\\"]:\\n return Register(type_name)\\n\\n if type_name == 'i':\\n return Immediate(type_name)\\n if type_name == \\\"v\\\":\\n return Register(type_name)\\n\\n if type_name[:3] == \\\"xmm\\\":\\n return Register(type_name)\\n\\n if type_name[:2] == 'mm':\\n size = int(type_name[2:]) if len(type_name) > 2 else None\\n return Memory(type_name, size)\\n\\n if type_name[0] == 'm':\\n size = int(type_name[1:]) if len(type_name) > 1 else None\\n return Memory(type_name, size)\\n\\n if type_name == \\\"x\\\":\\n return Register(type_name)\\n\\n if type_name == \\\"y\\\":\\n return Register(type_name)\\n\\n if type_name == \\\"near\\\" or type_name == \\\"short\\\":\\n return Unknown(type_name)\\n raise ValueError(f\\\"uknown type {type_name}\\\")\",\n \"def missing_types():\\n\\n return ...\",\n \"def convert_type(atype, btype):\\n # type + type => type\\n if atype == btype:\\n return atype\\n\\n # [Q]BOOLEAN + [Q]BOOLEAN => BOOLEAN\\n if atype == 'BOOLEAN' and btype == 'QBOOLEAN':\\n return 'BOOLEAN'\\n if atype == 'QBOOLEAN' and btype == 'BOOLEAN':\\n return 'BOOLEAN'\\n\\n # [Q]INTEGER + [Q]INTEGER => INTEGER\\n if atype == 'QINTEGER' and btype == 'INTEGER':\\n return 'INTEGER'\\n if atype == 'INTEGER' and btype == 'QINTEGER':\\n return 'INTEGER'\\n\\n # [Q]FLOAT + [Q]FLOAT => FLOAT\\n if atype == 'QFLOAT' and btype == 'FLOAT':\\n return 'FLOAT'\\n if atype == 'FLOAT' and btype == 'QFLOAT':\\n return 'FLOAT'\\n\\n # QINTEGER + QFLOAT => QFLOAT\\n if atype == 'QINTEGER' and btype == 'QFLOAT':\\n return 'QFLOAT'\\n\\n # QFLOAT + QINTEGER => QFLOAT\\n if atype == 'QFLOAT' and btype == 'QINTEGER':\\n return 'QFLOAT'\\n\\n # [Q]INTEGER + [Q]FLOAT => FLOAT (except QINTEGER + QFLOAT => QFLOAT)\\n if atype == 'INTEGER' and btype == 'FLOAT':\\n return 'FLOAT'\\n if atype == 'INTEGER' and btype == 'QFLOAT':\\n return 'FLOAT'\\n if atype == 'QINTEGER' and btype == 'FLOAT':\\n return 'FLOAT'\\n\\n # [Q]FLOAT + [Q]INTEGER => FLOAT (except # QFLOAT + QINTEGER => QFLOAT)\\n if atype == 'FLOAT' and btype == 'INTEGER':\\n return 'FLOAT'\\n if atype == 'FLOAT' and btype == 'QINTEGER':\\n return 'FLOAT'\\n if atype == 'QFLOAT' and btype == 'INTEGER':\\n return 'FLOAT'\\n\\n # All remaining combination of:\\n # (DATE, TIME, TIMESTAMP, QBOOLEAN, QINTEGER, QFLOAT, STRING) +\\n # (DATE, TIME, TIMESTAMP, QBOOLEAN, QINTEGER, QFLOAT, STRING) => STRING\\n if is_string_type(atype) and is_string_type(btype):\\n return 'STRING'\\n\\n return None\",\n \"def map_dtype(dtype):\\n item_idx = int(math.log(dtype.itemsize, 2))\\n if dtype.kind == 'i':\\n return [int8, int16, int32, int64][item_idx]\\n elif dtype.kind == 'u':\\n return [uint8, uint16, uint32, uint64][item_idx]\\n elif dtype.kind == 'f':\\n if dtype.itemsize == 2:\\n pass # half floats not supported yet\\n elif dtype.itemsize == 4:\\n return float32\\n elif dtype.itemsize == 8:\\n return float64\\n elif dtype.itemsize == 16:\\n return float128\\n elif dtype.kind == 'b':\\n return int8\\n elif dtype.kind == 'c':\\n if dtype.itemsize == 8:\\n return complex64\\n elif dtype.itemsize == 16:\\n return complex128\\n elif dtype.itemsize == 32:\\n return complex256\\n elif dtype.kind == 'O':\\n return object_\",\n \"def _type(string, has_invisible=True, numparse=True):\\n\\n if has_invisible and isinstance(string, (str, bytes)):\\n string = _strip_ansi(string)\\n\\n if string is None:\\n return type(None)\\n elif hasattr(string, \\\"isoformat\\\"): # datetime.datetime, date, and time\\n return str\\n elif _isbool(string):\\n return bool\\n elif _isint(string) and numparse:\\n return int\\n elif _isnumber(string) and numparse:\\n return float\\n elif isinstance(string, bytes):\\n return bytes\\n else:\\n return str\",\n \"def type_(type_):\\n\\n try:\\n type_ = float(type_)\\n if type_.is_integer():\\n return int\\n if not type_.is_integer():\\n return float\\n except ValueError:\\n return str\",\n \"def cast_type(cdm_column_type, value):\\n if cdm_column_type in ('integer', 'int64'):\\n # Regex check only relevant if submission dtype is 'object'\\n if not re.match(SCIENTIFIC_NOTATION_REGEX, str(value)):\\n return int(value)\\n if cdm_column_type in ('character varying', 'text', 'string'):\\n return str(value)\\n if cdm_column_type == 'numeric':\\n return float(value)\\n if cdm_column_type == 'float' and isinstance(value, float):\\n return value\\n if cdm_column_type == 'date' and isinstance(value, datetime.date):\\n return value\\n if cdm_column_type == 'timestamp' and isinstance(\\n value, datetime.datetime): # do not do datetime.datetime\\n return value\",\n \"def py_to_clips_type(ptype):\\n\\n ctype = None\\n if ptype == int:\\n ctype = \\\"INTEGER\\\"\\n elif ptype == float:\\n ctype = \\\"FLOAT\\\"\\n elif ptype == str:\\n ctype = \\\"STRING\\\"\\n elif ptype == bool:\\n ctype = \\\"BOOLEAN\\\"\\n return ctype\",\n \"def test_datatype(self):\\n with Pandas() as pd:\\n if pd is None:\\n return\\n with Numpy() as np: # noqa\\n if numpy is None:\\n return\\n sys.stderr.write(\\\"\\\\n\\\")\\n\\n df, hist1, hist2, hist3 = get_test_histograms1()\\n\\n assert hist1.datatype == str\\n np.testing.assert_array_equal(hist2.datatype, [numpy.number, str])\\n np.testing.assert_array_equal(hist3.datatype, [numpy.datetime64, numpy.number, str])\",\n \"def parse_type(fobj, data_type):\\n if data_type == \\\"Boolean\\\": ## False if 0x00 else True\\n return bool(fobj.read(1))\\n elif data_type == \\\"Byte\\\": ## 1 byte int\\n return fobj.read(1)[0]\\n elif data_type == \\\"DateTime\\\": ## 8 bytes signed int\\n return struct.unpack(\\\"<q\\\", fobj.read(8))[0]\\n elif data_type == \\\"Double\\\": ## 8 bytes floating point\\n return struct.unpack(\\\"<d\\\", fobj.read(8))[0]\\n elif data_type == \\\"Int\\\": ## 4 bytes unsigned int\\n return struct.unpack(\\\"<I\\\", fobj.read(4))[0]\\n elif data_type == \\\"Int-Double pair\\\": ## 0x08-Int-0x0d-Double with AssertionError\\n bb = fobj.read(1)[0]\\n if bb != 0x08:\\n raise AssertionError('parse_type(fobj, data_type): '\\n '1st byte(%s) of \\\"Int-Double pair\\\" != 0x08' % bb)\\n first_int = parse_type(fobj, \\\"Int\\\")\\n bb = fobj.read(1)[0]\\n if bb != 0x0d:\\n raise AssertionError('parse_type(fobj, data_type): '\\n '6th byte(%s) of \\\"Int-Double pair\\\" != 0x0d' % bb)\\n return [first_int, parse_type(fobj, \\\"Double\\\")]\\n elif data_type == \\\"Int-Double pair*\\\": ## int(n) - \\\"Int-Double pair\\\"*n\\n return [parse_type(fobj, \\\"Int-Double pair\\\") for i in range(parse_type(fobj, \\\"Int\\\"))]\\n elif data_type == \\\"Long\\\": ## 8 bytes unsigned int\\n return struct.unpack(\\\"<Q\\\", fobj.read(8))[0]\\n elif data_type == \\\"Short\\\": ## 2 bytes unsigned int\\n return struct.unpack(\\\"<H\\\", fobj.read(2))[0]\\n elif data_type == \\\"Single\\\": ## 4 bytes floating point\\n return struct.unpack(\\\"<f\\\", fobj.read(4))[0]\\n elif data_type == \\\"String\\\": ## 0x00 or 0x0b - ULE128(n) - UTF-8(length=n)\\n bb = fobj.read(1)[0]\\n if bb == 0x00:\\n return None\\n elif bb != 0x0b:\\n ## TODO: show integers in assertion error in hexadecimal and decimal\\n ## to make debug more convenient (cause I may inspect the file in a byte reader.\\n raise AssertionError('parse_type(fobj, data_type): '\\n '1st byte(%s) of \\\"String\\\" not in {0x00, 0x0b}' % bb)\\n strlen = parse_type(fobj, \\\"ULEB128\\\")\\n return fobj.read(strlen).decode(\\\"utf-8\\\")\\n elif data_type == \\\"ULEB128\\\": ## https://en.wikipedia.org/wiki/LEB128#Decode_unsigned_integer\\n i = 0 ## derived from the wiki psuedo code\\n res = 0\\n shift = 0\\n while True:\\n bb = fobj.read(1)[0]\\n i += 1\\n res |= ((bb & 0b1111111) << shift)\\n if (bb & 0b10000000) == 0:\\n break\\n shift += 7\\n return res\\n elif data_type == \\\"Timing point\\\": ## Double - Double - Boolean\\n return parse_types(fobj, [\\\"Double\\\", \\\"Double\\\", \\\"Boolean\\\"])\\n elif data_type == \\\"Timing point+\\\": ## int(n) - \\\"Timing point\\\"*n\\n return [parse_type(fobj, \\\"Timing point\\\") for i in range(parse_type(fobj, \\\"Int\\\"))]\\n else:\\n raise NotImplementedError('parse_type(fobj, data_type): Unknown data type: \\\"%s\\\".' % data_type)\",\n \"def discover_type(value) -> str:\\n if isinstance(value, list):\\n return \\\"array\\\"\\n elif isinstance(value, bool):\\n return \\\"bool\\\"\\n elif isinstance(value, int):\\n return \\\"int\\\"\\n return \\\"string\\\"\",\n \"def _convert(string, type, message):\\n try:\\n return type(string)\\n except ValueError as e:\\n print(e)\\n raise CharmmPSFError('Could not convert %s' % message)\",\n \"def datatype(parameter):\\n return type(parameter)\",\n \"def ttrans(cdmtype, kinds=kinds): \\n nptype=numpy.float32\\n try:\\n nptype=kinds[cdmtype.strip()]\\n except:\\n pass\\n #print(cdmtype,'not found, using numpy.float32') \\n return nptype\",\n \"def convert_type(self, name, type):\\n\\t\\t#\\t\\tprint 'Called with name = %s and type = %s' %(name, type)\\n\\t\\tname = ''.join(name.split())\\n\\t\\ttype = ''.join(type.split())\\n\\n\\t\\tif re.match(r'\\\\w+', type): #It's a concrete type\\n\\t\\t\\treturn self.TYPES_DICT.get(type,type) + ' ' + name\\n\\n\\t\\tarrow = type.rfind('->')\\n\\t\\tassert arrow != -1, \\\"If it's not a primitive, it must be a function\\\"\\n\\t\\tparams, return_type = type[:arrow], type[arrow+2:]\\n\\t\\tassert params[0] == '(' and params[-1] == ')'\\n\\t\\tparams = params[1:-1]\\n\\n\\t\\tparams_tokenized = Lexer(params).get_tokens()\\n\\t\\tparam_list = self.split_params(params_tokenized)\\n\\t\\tcpp_params = map(lambda n: self.convert_type('', n), param_list)\\n\\t\\treturn_type = self.convert_type('', return_type)\\n\\t\\treturn return_type + '(*' + name + ')(' + ','.join(cpp_params) + ')'\",\n \"def guess_type_value_type (none = True) :\\n return [ None, str, int, float ] if none else [ str, int, float ]\",\n \"def test_get_type():\\n formatter = TabularOutputFormatter()\\n\\n tests = (\\n (1, int),\\n (2.0, float),\\n (b\\\"binary\\\", binary_type),\\n (\\\"text\\\", text_type),\\n (None, type(None)),\\n ((), text_type),\\n )\\n\\n for value, data_type in tests:\\n assert data_type is formatter._get_type(value)\",\n \"def field_type_converter(self, old_type):\\n\\n if old_type == 'String':\\n new_type = 'Text'\\n elif old_type == 'Integer':\\n new_type = 'Short'\\n elif old_type == 'Date':\\n new_type = 'Date'\\n elif old_type == 'GlobalID':\\n new_type = 'GUID'\\n else:\\n new_type = 'Double'\\n return new_type\",\n \"def numpy_type(sqltype):\\n m = re.match(\\\"char\\\\(([0-9]+)\\\\)\\\", sqltype.strip())\\n if m is not None:\\n # It's a string\\n return np.dtype(\\\"|S\\\"+m.group(1))\\n else:\\n # It's a numeric type\\n if sqltype == \\\"integer\\\" or sqltype == \\\"int\\\":\\n return np.int32\\n elif sqltype == \\\"bigint\\\":\\n return np.int64\\n elif sqltype == \\\"real\\\":\\n return np.float32\\n elif sqltype == \\\"float\\\":\\n return np.float64\\n else:\\n raise ValueError(\\\"Unsupported data type \\\"+sqltype)\",\n \"def _translate_in_type(self, type_conversion: type, *args) -> Any:\\n try:\\n return type_conversion(self.string)\\n except (ValueError, TypeError):\\n return type_conversion(*args)\",\n \"def __new__(self, *types):\\r\\n conv = {int8: float32,\\r\\n int16: float32,\\r\\n int32: float64,\\r\\n int64: float64,\\r\\n uint8: float32,\\r\\n uint16: float32,\\r\\n uint32: float64,\\r\\n uint64: float64}\\r\\n return get_scalar_type(Scalar.upcast(*[conv.get(type, type)\\r\\n for type in types])),\",\n \"def argument_type(arg):\\n types = (int, float)\\n \\n for t in types:\\n try:\\n return type(t(arg))\\n except ValueError:\\n continue\\n \\n return str\",\n \"def get_check_types():\",\n \"def test_basic_numpy_dtypes():\\n assert np.int != np.int8\\n assert np.int != np.int16\\n assert np.int != np.int32\\n assert np.int != np.int64\\n\\n assert np.int == int\\n assert np.int8 != int\\n assert np.int16 != int\\n assert np.int32 != int\\n assert np.int64 != int\\n\\n assert np.dtype(np.int) == np.dtype('int') == np.dtype(int)\\n assert np.dtype(np.int8) == np.dtype('int8') == np.int8\\n assert np.dtype(np.int16) == np.dtype('int16') == np.int16\\n assert np.dtype(np.int32) == np.dtype('int32') == np.int32\\n assert np.dtype(np.int64) == np.dtype('int64') == np.int64\",\n \"def _cast_unsupported_dtypes(tensor):\\n\\n if tensor.dtype.__eq__(dtypes.int64):\\n # outside-compilation doesn't support int64 input yet.\\n return math_ops.cast(tensor, dtypes.int32)\\n if tensor.dtype.__eq__(dtypes.bfloat16) or tensor.dtype.__eq__(\\n dtypes.float16):\\n # Since host can't handle bf16, convert tensor to f32.\\n return math_ops.cast(tensor, dtypes.float32)\\n return tensor\",\n \"def _cast_types(args):\\n\\targs.x_val = None if args.x_val == 'None' else int(args.x_val)\\n\\targs.test_size = float(args.test_size)\\n\\targs.alpha = float(args.alpha)\\n\\targs.fit_prior = (args.fit_prior in ['True', \\\"True\\\", 'true', \\\"true\\\"])\\n\\n\\t# class_prior - array like type (problem to convert)\\n\\tif args.class_prior == \\\"None\\\" or args.class_prior == 'None':\\n\\t\\targs.class_prior = None\\n\\n\\t# --------- #\\n\\treturn args\",\n \"def dtype_to_type(dtype) -> Type:\\n if dtype == np.object:\\n return str\\n else:\\n return type(np.zeros(1, dtype).item())\",\n \"def _parse_dtypes(data, table_meta):\\n for name, field in table_meta['fields'].items():\\n field_type = field['type']\\n if field_type == 'datetime':\\n datetime_format = field.get('format')\\n data[name] = pd.to_datetime(data[name], format=datetime_format, exact=False)\\n elif field_type == 'numerical' and field.get('subtype') == 'integer':\\n data[name] = data[name].dropna().astype(np.int64)\\n elif field_type == 'id' and field.get('subtype', 'integer') == 'integer':\\n data[name] = data[name].dropna().astype(np.int64)\\n\\n return data\",\n \"def get_pytype(self, c_arg, parse_arg):\\n if isinstance(c_arg, FunctionAddress):\\n return 'O'\\n else:\\n try:\\n return pytype_parse_registry[(parse_arg.dtype, parse_arg.precision)]\\n except KeyError as e:\\n raise NotImplementedError(\\\"Type not implemented for argument collection : \\\"+str(type(parse_arg))) from e\",\n \"def python_type_to_sql_type(_python_type):\\n if _python_type == str:\\n return 'string'\\n elif _python_type == bytes:\\n return \\\"blob\\\"\\n elif _python_type == float:\\n return \\\"float\\\"\\n elif _python_type == int:\\n return \\\"integer\\\"\\n elif _python_type == datetime:\\n return \\\"datetime\\\"\\n elif _python_type == bool:\\n return \\\"boolean\\\"\\n else:\\n raise Exception(\\\"python_type_to_sql_type: _type_code \\\\\\\"\\\" + str(_python_type) + \\\"\\\\\\\"not supported\\\")\",\n \"def str_to_type(name_type):\\n if name_type == 'float' or name_type == 'Float':\\n return float\\n if name_type == 'bool':\\n return bool\\n if name_type == 'int':\\n return lambda x: int(float(x))\\n if name_type == 'list':\\n return ast.literal_eval\\n if name_type == 'date':\\n return lambda x: dateutil.parser.parse(x).strftime('%Y-%m-%dT%H:%M:%SZ')\\n if name_type == 'str':\\n return str\\n\\n \\n return None\",\n \"def _map_v1_io_type(output_type: str) -> str:\\n\\n # TODO: put it in a common place\\n def _map_primitive_type(_type: str) -> str:\\n \\\"\\\"\\\"Convert double and float to number type.\\n\\n :param _type: A primitive v1 IO type\\n :type _type: str\\n :return:\\n * InputTypes.NUMBER if _type is \\\"double\\\" or \\\"float\\\"\\n * The provided type otherwise\\n :rtype: str\\n \\\"\\\"\\\"\\n _type = _type.lower()\\n if _type in [\\\"double\\\", \\\"float\\\"]:\\n return InputTypes.NUMBER\\n return _type\\n\\n if output_type in list(get_all_enum_values_iter(AssetTypes)):\\n return output_type\\n if output_type in SUPPORTED_INTERNAL_PARAM_TYPES:\\n return _map_primitive_type(output_type)\\n if output_type in [\\\"AnyFile\\\"]:\\n return AssetTypes.URI_FILE\\n # Handle AnyDirectory and the other types.\\n return AssetTypes.URI_FOLDER\",\n \"def str2type(raw_val):\\n try:\\n return int(raw_val)\\n except ValueError:\\n pass\\n\\n try:\\n return float(raw_val)\\n except ValueError:\\n return raw_val\",\n \"def get_type_string(data):\\r\\n data_type = type(data)\\r\\n\\r\\n if data_type in (int, long):\\r\\n return 'integer'\\r\\n elif data_type == float:\\r\\n return 'float'\\r\\n elif data_type == bool:\\r\\n return 'boolean'\\r\\n elif data_type in (list, tuple):\\r\\n return 'list'\\r\\n elif data_type == dict:\\r\\n return 'hash'\\r\\n elif data is None:\\r\\n return 'null'\\r\\n elif isinstance(data, basestring):\\r\\n return 'string'\",\n \"def convert_numpy_type(cls, dtype):\\n\\n import numpy as np\\n\\n m = {\\n 'int64': cls.DATATYPE_INTEGER64,\\n 'float64': cls.DATATYPE_FLOAT,\\n 'object': cls.DATATYPE_TEXT # Hack. Pandas makes strings into object.\\n\\n }\\n\\n t = m.get(dtype.name, None)\\n\\n if not t:\\n raise TypeError(\\n \\\"Failed to convert numpy type: '{}' \\\".format(\\n dtype.name))\\n\\n return t\",\n \"def dtype_to_pytype(type_):\\n\\n return {\\n bool_: bool,\\n int8: int,\\n int16: int,\\n int32: int,\\n int64: int,\\n uint8: int,\\n uint16: int,\\n uint32: int,\\n uint64: int,\\n float16: float,\\n float32: float,\\n float64: float,\\n list_: list,\\n tuple_: tuple,\\n string: str,\\n type_none: type(None)\\n }[type_]\",\n \"def test_get_datatypes(self):\\n obs = _get_datatypes(self.metadata_map.ix[:, self.headers])\\n exp = ['float8', 'varchar', 'integer']\\n self.assertEqual(obs, exp)\",\n \"def datatype_conversion(self):\\n\\n category_cols = self.FEATURE_TYPES[\\\"category_cols\\\"]\\n integer_cols = self.FEATURE_TYPES[\\\"integer_cols\\\"]\\n float_cols = self.FEATURE_TYPES[\\\"float_cols\\\"]\\n datetime_cols = self.FEATURE_TYPES[\\\"datetime_cols\\\"]\\n string_cols = self.FEATURE_TYPES[\\\"string_cols\\\"]\\n bool_cols = self.FEATURE_TYPES[\\\"bool_cols\\\"]\\n data = self.data\\n \\n data[category_cols] = data[category_cols].astype('category',copy=False) \\n data[integer_cols] = data[integer_cols].astype('int64',copy=False)\\n data[float_cols] = data[float_cols].astype('float64',copy=False)\\n data[datetime_cols] = data[datetime_cols].astype('datetime64[ns]',copy=False)\\n data[string_cols] = data[string_cols].astype('str',copy=False)\\n data[bool_cols] = data[bool_cols].astype('bool', copy=False)\\n\\n return data\",\n \"def inspect_dtype(series: pd.Series) -> str:\\n\\n mapping = {pd_types.is_bool_dtype: \\\"bool\\\",\\n pd_types.is_integer_dtype: \\\"int\\\",\\n pd_types.is_float_dtype: \\\"float\\\",\\n pd_types.is_datetime64_any_dtype: \\\"datetime\\\"}\\n\\n for check, result in mapping.items():\\n if check(series):\\n return result\\n\\n raise TypeError(\\\"Type is not understand for column '{}'. Allowed \\\"\\n \\\"types are bool, int, float, str and datetime.\\\"\\n .format(series.name))\",\n \"def _type_coerce(value, typ):\\n from . import world\\n\\n if value is None:\\n return None\\n if typ is None:\\n return value\\n elif typ in (str, float, int, uuid.UUID):\\n return typ(value)\\n elif typ in (np.ndarray,):\\n # Not ideal to assume 'd' type\\n return np.array(value, dtype='d')\\n if isinstance(value, dict) and '__type__' in value and '__namespace__' in value:\\n if value['__type__'] in PROXY_TYPES:\\n typ = PROXY_TYPES[value['__type__']]\\n # return type_coerce(value, typ)\\n elif '.' in value['__type__'] and value['__type__'].split('.') in PROXY_TYPES:\\n typ = PROXY_TYPES[value['__type__'].split('.')]\\n elif value['__namespace__'] in PROXY_TYPES:\\n typ = PROXY_TYPES[value['__namespace__']]\\n # return type_coerce(value, typ)\\n else:\\n log.warning(\\\"No type for dictionary: %s\\\", value)\\n\\n if isinstance(typ, type):\\n if issubclass(typ, typing.List):\\n if typ.__args__:\\n sub_type = typ.__args__[0] # YUCK!\\n return [type_coerce(item, sub_type) for item in value]\\n else:\\n log.warning(\\\"No sub-type on %s\\\", typ)\\n return value\\n elif issubclass(typ, typing.Dict):\\n key_typ, value_typ = typ.__args__\\n result = {}\\n for key, value in value.items():\\n result[type_coerce(key, key_typ)] = type_coerce(value, value_typ)\\n return result\\n\\n else:\\n if hasattr(typ, 'from_server'):\\n return typ.from_server(value)\\n else:\\n return typ(value)\\n if hasattr(typ, '__args__'):\\n # WTF? Why can't I test for \\\"is this a union\\\"\\n for possible in typ.__args__:\\n if isinstance(value, possible):\\n return value\\n for possible in typ.__args__:\\n try:\\n return type_coerce(value, possible)\\n except (ValueError, TypeError) as err:\\n log.exception(\\\"Failed to convert\\\")\\n raise ValueError(\\n \\\"Union type %s we don't know how to convert %r\\\"\\n % (\\n typ,\\n value,\\n )\\n )\\n\\n raise ValueError(\\n \\\"Do not know how to convert type %s with value %r\\\"\\n % (\\n typ,\\n value,\\n )\\n )\",\n \"def feast_value_type_to_python_type(field_value_proto: ProtoValue) -> Any:\\n field_value_dict = MessageToDict(field_value_proto)\\n\\n for k, v in field_value_dict.items():\\n if k == \\\"int64Val\\\":\\n return int(v)\\n if k == \\\"bytesVal\\\":\\n return bytes(v)\\n if (k == \\\"int64ListVal\\\") or (k == \\\"int32ListVal\\\"):\\n return [int(item) for item in v[\\\"val\\\"]]\\n if (k == \\\"floatListVal\\\") or (k == \\\"doubleListVal\\\"):\\n return [float(item) for item in v[\\\"val\\\"]]\\n if k == \\\"stringListVal\\\":\\n return [str(item) for item in v[\\\"val\\\"]]\\n if k == \\\"bytesListVal\\\":\\n return [bytes(item) for item in v[\\\"val\\\"]]\\n if k == \\\"boolListVal\\\":\\n return [bool(item) for item in v[\\\"val\\\"]]\\n\\n if k in [\\\"int32Val\\\", \\\"floatVal\\\", \\\"doubleVal\\\", \\\"stringVal\\\", \\\"boolVal\\\"]:\\n return v\\n else:\\n raise TypeError(\\n f\\\"Casting to Python native type for type {k} failed. \\\"\\n f\\\"Type {k} not found\\\"\\n )\",\n \"def assign_type(s):\\n if type(s) == list:\\n return s\\n else:\\n s = str(s)\\n try:\\n return int(s)\\n except ValueError:\\n try:\\n return float(s)\\n except ValueError:\\n return format_string(s)\",\n \"def info(obj):\\n if type(obj) is tuple:\\n return '({})'.format(', '.join(map(TypeTool.info,obj)))\\n elif type(obj) is list:\\n return 'List[{}]'.format(TypeTool.info(obj[0]))\\n else:\\n ctype_name = type(obj).__name__\\n if ctype_name == 'ndarray': return '{}[{}]{}'.format(ctype_name,obj.dtype, obj.shape)\\n elif ctype_name == 'str': return 'string'\\n elif ctype_name == 'bytes': return 'List[byte]'\\n else: return ctype_name\",\n \"def __set_utils_types(self):\\n self.__arrayt = type(self.c_byte * 1)\\n # self.__cfuncptrt = type(type(self.memmove))\\n # class _p(self.Structure):\\n # pass\\n # self.__ptrt = type(self.POINTER(_p))\\n self.__basic_types_name = {\\n 'c_bool': '?',\\n 'c_char': 'c',\\n 'c_byte': 'b',\\n 'c_ubyte': 'B',\\n 'c_short': 'h',\\n 'c_ushort': 'H',\\n 'c_int': 'i', # c_int is c_long\\n 'c_uint': 'I',\\n 'int': 'i',\\n 'c_longlong': 'q',\\n 'c_ulonglong': 'Q',\\n 'c_float': 'f',\\n 'c_double': 'd',\\n 'c_longdouble': 'g',\\n 'c_char_p': 's',\\n 'c_void_p': 'P',\\n # 'c_void': 'P', ## void in array is void_p ##DEBUG\\n }\\n if self.__longsize == 4:\\n # long == int\\n self.__basic_types_name.update({'c_long': 'i',\\n 'c_ulong': 'I',\\n 'long': 'i',\\n 'c_void': 'I'})\\n elif self.__longsize == 8:\\n # long == longlong\\n self.__basic_types_name.update({'c_long': 'q',\\n 'c_ulong': 'Q',\\n 'long': 'q',\\n 'c_void': 'Q'})\\n # we need to account for the possible changes in c_longdouble\\n self.__basic_types = set([getattr(self, k) for k in self.__basic_types_name.keys() if hasattr(self, k)])\\n return\",\n \"def type_map(cls, mysql_type):\\n d = {\\n FIELD_TYPE.INT24: 'INTEGER',\\n FIELD_TYPE.TINY: 'INTEGER',\\n FIELD_TYPE.BIT: 'INTEGER',\\n FIELD_TYPE.DATETIME: 'TIMESTAMP',\\n FIELD_TYPE.DATE: 'TIMESTAMP',\\n FIELD_TYPE.DECIMAL: 'FLOAT',\\n FIELD_TYPE.NEWDECIMAL: 'FLOAT',\\n FIELD_TYPE.DOUBLE: 'FLOAT',\\n FIELD_TYPE.FLOAT: 'FLOAT',\\n FIELD_TYPE.INT24: 'INTEGER',\\n FIELD_TYPE.LONG: 'INTEGER',\\n FIELD_TYPE.LONGLONG: 'INTEGER',\\n FIELD_TYPE.SHORT: 'INTEGER',\\n FIELD_TYPE.TIMESTAMP: 'TIMESTAMP',\\n FIELD_TYPE.YEAR: 'INTEGER',\\n }\\n return d[mysql_type] if mysql_type in d else 'STRING'\",\n \"def javatype(s):\\n return {\\n 'int': 'int',\\n 'String': 'String',\\n 'Long': 'Long',\\n 'boolean': 'boolean'\\n }.get(s.name, s.name)\",\n \"def process_ir_type(typ: Type) -> Union[BaseCType, VectorCType, OptionalCType, ListCType]:\\n if isinstance(typ, BaseType):\\n if typ.name == BaseTy.Tensor:\\n return BaseCType(valueT)\\n elif typ.name == BaseTy.Scalar:\\n # at::scalar has special handling,\\n # and is wrapped in an IR value just like at::tensor\\n return BaseCType(valueT)\\n elif typ.name == BaseTy.ScalarType:\\n return BaseCType(scalarTypeT)\\n elif typ.name == BaseTy.int:\\n return BaseCType(longT)\\n elif typ.name == BaseTy.bool:\\n return BaseCType(boolT)\\n elif typ.name == BaseTy.float:\\n return BaseCType(doubleT)\\n elif typ.name == BaseTy.str:\\n return BaseCType(stringT)\\n elif typ.name == BaseTy.Device:\\n return BaseCType(deviceT)\\n elif typ.name == BaseTy.Layout:\\n return BaseCType(layoutT)\\n else:\\n raise AssertionError(f\\\"TODO add support for type {repr(typ)}\\\")\\n elif isinstance(typ, OptionalType):\\n return OptionalCType(process_ir_type(typ.elem))\\n elif isinstance(typ, ListType):\\n if str(typ.elem) == 'Tensor?':\\n # TODO(whc) is this actually correct? or should it use a Vector like above\\n return ListCType(OptionalCType(BaseCType(valueT)))\\n else:\\n return VectorCType(process_ir_type(typ.elem))\\n else:\\n raise AssertionError(f\\\"unrecognized type {repr(typ)}\\\")\",\n \"def unparse_type(type_str):\\n if not type_str.startswith('array'):\\n return type_str\\n arg_dim = type_str.lstrip('array')[0]\\n data_type = type_str.lstrip('array')[1:]\\n arg_type = \\\"vizgen.ndarray('\\\" + data_type + \\\"', \\\" + arg_dim + \\\")\\\"\\n return arg_type\",\n \"def change_object_type(obj, new_type):\\n # type: (Union[str, SupportsInt, SupportsFloat], str) -> Union[str, int, float]\\n if new_type == 'str':\\n return str(obj)\\n elif new_type == 'int':\\n return int(obj)\\n elif new_type == 'float':\\n return float(obj)\\n else:\\n raise IOError('expected_type \\\"{}\\\" is not supported in this function.'.format(new_type))\",\n \"def get_param_type(value):\\n dtype = type(value).__name__\\n if dtype not in ['str', 'float', 'int', 'bool', 'list']:\\n dtype = 'str'\\n if dtype == 'list':\\n try:\\n eltype = type(value[0]).__name__\\n except IndexError:\\n eltype = 'str'\\n if eltype not in ['str', 'float', 'int', 'bool']:\\n eltype = 'str'\\n dtype = eltype + dtype\\n return dtype\",\n \"def test_get_types(self):\\n pass\",\n \"def TYPE(value):\\n raise NotImplementedError()\",\n \"def test_match_types(self):\\n f = lws.match_types\\n # assert f(str, u'test') is True\\n assert f(str, 'test') is True\\n assert f(int, 123) is True\\n assert f(int, 123.00) is False\\n assert f(bool, [1, 2, 3]) is False\",\n \"def test_datatype_detection():\\n\\n grammar = \\\"\\\"\\\"\\n IsObjectDatatype: INT | STRING | ID;\\n IsIntDatatype: INT;\\n IsIdDatatype: ID;\\n IsAlsoDatatype: SubDT1 | SubDT2;\\n SubDT1: INT;\\n SubDT2: STRING;\\n \\\"\\\"\\\"\\n\\n mm = metamodel_from_str(grammar)\\n\\n IsObjectDatatype = mm['IsObjectDatatype']\\n assert isinstance(IsObjectDatatype, ecore.EDataType)\\n assert IsObjectDatatype.name == 'IsObjectDatatype'\\n assert IsObjectDatatype.eType == object\\n\\n IsIntDatatype = mm['IsIntDatatype']\\n assert isinstance(IsIntDatatype, ecore.EDataType)\\n assert IsIntDatatype.name == 'IsIntDatatype'\\n assert IsIntDatatype.eType == int\\n\\n IsIdDatatype = mm['IsIdDatatype']\\n assert isinstance(IsIdDatatype, ecore.EDataType)\\n assert IsIdDatatype.name == 'IsIdDatatype'\\n assert IsIdDatatype.eType == str\\n\\n IsAlsoDatatype = mm['IsAlsoDatatype']\\n assert isinstance(IsAlsoDatatype, ecore.EDataType)\\n IsAlsoDatatype = mm['IsAlsoDatatype']\\n assert IsAlsoDatatype.eType == object\",\n \"def decode_itype(self):\\n self.instr.imm = self.instr.binary[0:12]\\n self.instr.rs1 = self.instr.binary[12:17]\\n self.instr.funct3 = self.instr.binary[17:20]\\n self.instr.operation = itype_funct3_decode[self.instr.funct3]\\n self.instr.source1RegValue = self.simulator.registers[int(self.instr.rs1, 2)][1]\\n self.instr.source2RegValue = int(self.instr.imm, 2)\",\n \"def infer_value_type(self, value):\\n if isinstance(value, str):\\n if self.TIMESTAMP_MATCHER.match(value):\\n return 'TIMESTAMP'\\n elif self.DATE_MATCHER.match(value):\\n return 'DATE'\\n elif self.TIME_MATCHER.match(value):\\n return 'TIME'\\n elif not self.quoted_values_are_strings:\\n # Implement the same type inference algorithm as 'bq load' for\\n # quoted values that look like ints, floats or bools.\\n if self.INTEGER_MATCHER.match(value):\\n if (int(value) < self.INTEGER_MIN_VALUE\\n or self.INTEGER_MAX_VALUE < int(value)):\\n return 'QFLOAT' # quoted float\\n else:\\n return 'QINTEGER' # quoted integer\\n elif self.FLOAT_MATCHER.match(value):\\n return 'QFLOAT' # quoted float\\n elif value.lower() in ['true', 'false']:\\n return 'QBOOLEAN' # quoted boolean\\n else:\\n return 'STRING'\\n else:\\n return 'STRING'\\n # Python 'bool' is a subclass of 'int' so we must check it first\\n elif isinstance(value, bool):\\n return 'BOOLEAN'\\n elif isinstance(value, int):\\n if value < self.INTEGER_MIN_VALUE or self.INTEGER_MAX_VALUE < value:\\n return 'FLOAT'\\n else:\\n return 'INTEGER'\\n elif isinstance(value, float):\\n return 'FLOAT'\\n elif value is None:\\n return '__null__'\\n elif isinstance(value, dict):\\n if value:\\n return 'RECORD'\\n else:\\n return '__empty_record__'\\n elif isinstance(value, list):\\n if value:\\n return '__array__'\\n else:\\n return '__empty_array__'\\n else:\\n raise Exception(\\n f'Unsupported node type: {type(value)} (should not happen)'\\n )\",\n \"def _typecheck(name, value, *types):\\n if not types:\\n raise ValueError('expected one or more types, maybe use _textcheck?')\\n if not isinstance(value, types):\\n raise TypeError(\\\"expected %s for %s, got %r\\\"\\n % (\\\" or \\\".join([t.__name__ for t in types]),\\n name, value))\\n return value\",\n \"def ftype(obj):\\n if isinstance(obj, np.ndarray) or isinstance(obj, torch.Tensor):\\n types = \\\" \\\".join([str(x) for x in [type(obj), obj.dtype, list(obj.shape)]])\\n if isinstance(obj, torch.Tensor):\\n types = \\\" \\\".join([obj.device.type, types])\\n else:\\n types = str(type(obj))\\n\\n for s in [\\\"class \\\", \\\"'\\\", \\\"<\\\", \\\">\\\", \\\"(\\\", \\\")\\\", \\\"torch.\\\", \\\"numpy.\\\" ]:\\n types = types.replace(s, \\\"\\\")\\n return types\",\n \"def isdatatype(object):\\n return isinstance(object, (str, int, bool, float, type(None)))\",\n \"def _ptype(ptype_value, verbose=True):\\n types = {\\n 0:[\\\"PT_NULL\\\",\\\"Program header table entry unused\\\"],\\n 1:[\\\"PT_LOAD\\\",\\\"Loadable program segment\\\"],\\n 2:[\\\"PT_DYNAMIC\\\",\\\"Dynamic linking information\\\"],\\n 3:[\\\"PT_INTERP\\\",\\\"Program interpreter\\\"],\\n 4:[\\\"PT_NOTE\\\",\\\"Auxiliary information\\\"],\\n 5:[\\\"PT_SHLIB\\\",\\\"Reserved\\\"],\\n 6:[\\\"PT_PHDR\\\",\\\"Entry for header table itself\\\"],\\n 7:[\\\"PT_TLS\\\",\\\"Thread-local storage segment\\\"],\\n 8:[\\\"PT_NUM\\\",\\\"Number of defined types\\\"],\\n 0x60000000:[\\\"PT_LOOS\\\",\\\"Start of OS-specific\\\"],\\n 0x6474e550:[\\\"PT_GNU_EH_FRAME\\\",\\\"GCC .eh_frame_hdr segment\\\"],\\n 0x6474e551:[\\\"PT_GNU_STACK\\\",\\\"Indicates stack executability\\\"],\\n 0x6474e552:[\\\"PT_GNU_RELRO\\\",\\\"Read-only after relocation\\\"],\\n 0x6ffffffa:[\\\"PT_SUNWBSS\\\",\\\"Sun Specific segment\\\"],\\n 0x6ffffffb:[\\\"PT_SUNWSTACK\\\",\\\"Stack segment\\\"],\\n 0x6fffffff:[\\\"PT_HIOS\\\",\\\"End of OS-specific\\\"],\\n 0x70000000:[\\\"PT_LOPROC\\\",\\\"Start of processor-specific\\\"],\\n 0x7fffffff:[\\\"PT_HIPROC\\\",\\\"End of processor-specific\\\"],\\n }\\n\\n if types.get(ptype_value):\\n value = types[ptype_value]\\n if verbose:\\n return \\\"%s (%s)\\\" % (value[0], value[1])\\n else:\\n return \\\"%s\\\" % value[0]\\n else:\\n return \\\"UNKNOWN\\\"\",\n \"def data_type(value):\\n if type(value) == type(None):\\n return 'no value'\\n elif type(value) == list:\\n if len(value) >= 3:\\n return value[2]\\n else:\\n return None\\n elif type(value) == bool:\\n return value\\n elif type(value) == int:\\n if value < 100:\\n return 'less than 100'\\n elif value > 100:\\n return 'more than 100'\\n else:\\n return 'equal to 100'\\n elif type(value) == str:\\n return len(value)\\n else:\\n return value\",\n \"def map_dtypes(pgtype):\\n # may not be a complete list, could not find CARTO SQL API documentation\\n # about data types\\n dtypes = {'number': 'float64',\\n 'date': 'datetime64',\\n 'string': 'object',\\n 'geometry': 'object',\\n 'boolean': 'bool'}\\n try:\\n return dtypes[pgtype]\\n except KeyError:\\n # make it a string if not in dict above\\n return 'object'\",\n \"def translate_type(resolved_type):\\n\\n # Another model\\n if resolved_type.container == ContainerType.MODEL:\\n return \\\"*\\\" + resolved_type.type\\n\\n # Primitive type\\n if resolved_type.container == ContainerType.PRIMITIVE:\\n return \\\"*\\\" + PRIMITIVE_TYPES[resolved_type.type]\\n\\n # Composite type\\n item_type = translate_item_type(resolved_type.type)\\n\\n # A dict\\n if resolved_type.container == ContainerType.DICT:\\n return f\\\"map[string]{item_type}\\\"\\n\\n # A list\\n if resolved_type.container in (ContainerType.LIST, ContainerType.SET):\\n return f\\\"[]{item_type}\\\"\\n\\n raise ValueError(f\\\"Unknown container type {resolved_type.container}\\\")\",\n \"def convert_dtypes(rows):\\n dtype_map = {pd.Timestamp: lambda x: x.to_pydatetime(),\\n np.int8: lambda x: int(x),\\n np.int16: lambda x: int(x),\\n np.int32: lambda x: int(x),\\n np.int64: lambda x: int(x),\\n np.float16: lambda x: float(x),\\n np.float32: lambda x: float(x),\\n np.float64: lambda x: float(x),\\n np.float128: lambda x: float(x)}\\n for row in rows:\\n yield [dtype_map.get(type(elem), lambda x: x)(elem) for elem in row]\",\n \"def clips_to_py_type(ctype):\\n\\n ptype = None\\n if ctype == \\\"INTEGER\\\":\\n ptype = int\\n elif ctype == \\\"FLOAT\\\":\\n ptype = float\\n elif ctype == \\\"STRING\\\":\\n ptype = str\\n elif ctype == \\\"BOOLEAN\\\":\\n ptype = bool\\n return ptype\",\n \"def test_type(self):\\n geometric = [x for x in generators.geometric(1, 10, 0.5)]\\n type_of_geometric = type(geometric[0])\\n self.assertTrue(type_of_geometric == int or type_of_geometric == float)\",\n \"def _convert_field_type(self, row):\\n try:\\n return [row[0], int(row[1]), int(row[2]),\\n self._format_float_data(float(row[3]) * self._ms_to_us),\\n self._format_float_data(float(row[4]) * self._ms_to_us),\\n self._format_float_data(float(row[5])*100)]\\n except IndexError as err:\\n log.exception(err)\\n raise ProfilerRawFileException('failed to get HOST CPU operator type data.')\",\n \"def cython_pytype(self, t):\\n if isinstance(t, Number):\\n return str(t)\\n t = self.canon(t)\\n if t in self.cython_pytypes:\\n return self.cython_pytypes[t]\\n if isinstance(t, basestring):\\n if t in self.base_types:\\n return self.cython_pytypes[t]\\n # must be tuple below this line\\n tlen = len(t)\\n if 2 == tlen:\\n if 0 == t[1]:\\n return self.cython_pytype(t[0])\\n elif self.isrefinement(t[1]):\\n return self.cython_pytype(t[0])\\n else:\\n # FIXME last is ignored for strings, but what about other types?\\n #last = '[{0}]'.format(t[-1]) if isinstance(t[-1], int) else t[-1]\\n #return cython_pytype(t[0]) + ' {0}'.format(last)\\n return self.cython_pytype(t[0])\\n elif 3 <= tlen:\\n if t in self.cython_pytypes:\\n return self.cython_pytypes[t]\\n assert t[0] in self.template_types\\n assert len(t) == len(self.template_types[t[0]]) + 2\\n template_name = self.cython_pytypes[t[0]]\\n assert template_name is not NotImplemented\\n cypyt = self.cython_pytypes[t[0]]\\n cypyt, t = self._fill_cypyt(cypyt, t)\\n # FIXME last is ignored for strings, but what about other types?\\n #if 0 != t[-1]:\\n # last = '[{0}]'.format(t[-1]) if isinstance(t[-1], int) else t[-1]\\n # cypyt += ' {0}'.format(last)\\n return cypyt\",\n \"def cast_tensor_type(inputs, src_type, dst_type):\\n if isinstance(inputs, torch.Tensor):\\n return inputs\\n elif isinstance(inputs, str):\\n return inputs\\n elif isinstance(inputs, np.ndarray):\\n return inputs\\n elif isinstance(inputs, abc.Mapping):\\n return type(inputs)({k: cast_tensor_type(v, src_type, dst_type) for k, v in inputs.items()})\\n elif isinstance(inputs, abc.Iterable):\\n return type(inputs)(cast_tensor_type(item, src_type, dst_type) for item in inputs)\\n return inputs\",\n \"def convert_type(param: str, target: str):\\n converted = param\\n if target == \\\"boolean\\\":\\n converted = bool(strtobool(param))\\n if target == \\\"integer\\\":\\n converted = int(param)\\n return converted\",\n \"def as_type(module, x, y):\\n _import_modules()\\n if module in [np, ma, jnp]:\\n return x.astype(y.dtype)\\n elif module == tf:\\n return tf.cast(x, y.dtype)\\n elif module == torch:\\n return x.to(dtype=y.dtype, device=y.device)\\n raise UnknownModuleException(f\\\"Module {module.__name__} not supported.\\\")\",\n \"def getconversiontype(self, *args, **kwargs):\\n return _coordsys.coordsys_getconversiontype(self, *args, **kwargs)\",\n \"def variable_types(self, data_key, only_type=None):\\r\\n if self[data_key].meta['columns'] is None:\\r\\n return 'No meta attached to data_key: %s' %(data_key)\\r\\n else:\\r\\n types = {\\r\\n 'int': [],\\r\\n 'float': [],\\r\\n 'single': [],\\r\\n 'delimited set': [],\\r\\n 'string': [],\\r\\n 'date': [],\\r\\n 'time': [],\\r\\n 'array': []\\r\\n }\\r\\n not_found = []\\r\\n for col in self[data_key].data.columns:\\r\\n if not col in ['@1', 'id_L1', 'id_L1.1']: \\r\\n try:\\r\\n types[\\r\\n self[data_key].meta['columns'][col]['type']\\r\\n ].append(col)\\r\\n except:\\r\\n not_found.append(col) \\r\\n for mask in self[data_key].meta['masks'].keys():\\r\\n types[self[data_key].meta['masks'][mask]['type']].append(mask)\\r\\n if not_found:\\r\\n print '%s not found in meta file. Ignored.' %(not_found)\\r\\n if only_type:\\r\\n return types[only_type]\\r\\n else:\\r\\n return types\",\n \"def _column_type(strings, has_invisible=True, numparse=True):\\n types = [_type(s, has_invisible, numparse) for s in strings]\\n return reduce(_more_generic, types, bool)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65132457","0.6380109","0.63286304","0.6217381","0.6156143","0.6123783","0.6113949","0.6086107","0.6069828","0.60447073","0.602957","0.600259","0.5983749","0.597207","0.5966418","0.5907714","0.58719164","0.58526105","0.5849916","0.5831822","0.5815611","0.580958","0.5797013","0.57838154","0.57752776","0.5772184","0.5754938","0.57526827","0.57492983","0.57409734","0.5740008","0.5723973","0.5714971","0.5711341","0.5706189","0.570308","0.5693902","0.56866336","0.5678858","0.5676002","0.5675387","0.56574076","0.56496686","0.5639001","0.563806","0.56366473","0.56276596","0.5627267","0.5623481","0.5619789","0.56132215","0.56100374","0.5606676","0.5594541","0.5590461","0.5587763","0.558282","0.55779636","0.55752975","0.5574127","0.5563121","0.5545388","0.55249155","0.5518464","0.54995114","0.54982173","0.54972047","0.54860145","0.54837954","0.5475289","0.54717654","0.54686916","0.54678345","0.5465648","0.54607975","0.54500884","0.5438655","0.5437271","0.54368633","0.5435789","0.5433903","0.5430938","0.54260844","0.5424714","0.5415449","0.5414067","0.5410411","0.54058516","0.54014146","0.5398006","0.53943914","0.539107","0.5386673","0.53828055","0.5377454","0.5376411","0.537427","0.53720236","0.5371918","0.5362505"],"string":"[\n \"0.65132457\",\n \"0.6380109\",\n \"0.63286304\",\n \"0.6217381\",\n \"0.6156143\",\n \"0.6123783\",\n \"0.6113949\",\n \"0.6086107\",\n \"0.6069828\",\n \"0.60447073\",\n \"0.602957\",\n \"0.600259\",\n \"0.5983749\",\n \"0.597207\",\n \"0.5966418\",\n \"0.5907714\",\n \"0.58719164\",\n \"0.58526105\",\n \"0.5849916\",\n \"0.5831822\",\n \"0.5815611\",\n \"0.580958\",\n \"0.5797013\",\n \"0.57838154\",\n \"0.57752776\",\n \"0.5772184\",\n \"0.5754938\",\n \"0.57526827\",\n \"0.57492983\",\n \"0.57409734\",\n \"0.5740008\",\n \"0.5723973\",\n \"0.5714971\",\n \"0.5711341\",\n \"0.5706189\",\n \"0.570308\",\n \"0.5693902\",\n \"0.56866336\",\n \"0.5678858\",\n \"0.5676002\",\n \"0.5675387\",\n \"0.56574076\",\n \"0.56496686\",\n \"0.5639001\",\n \"0.563806\",\n \"0.56366473\",\n \"0.56276596\",\n \"0.5627267\",\n \"0.5623481\",\n \"0.5619789\",\n \"0.56132215\",\n \"0.56100374\",\n \"0.5606676\",\n \"0.5594541\",\n \"0.5590461\",\n \"0.5587763\",\n \"0.558282\",\n \"0.55779636\",\n \"0.55752975\",\n \"0.5574127\",\n \"0.5563121\",\n \"0.5545388\",\n \"0.55249155\",\n \"0.5518464\",\n \"0.54995114\",\n \"0.54982173\",\n \"0.54972047\",\n \"0.54860145\",\n \"0.54837954\",\n \"0.5475289\",\n \"0.54717654\",\n \"0.54686916\",\n \"0.54678345\",\n \"0.5465648\",\n \"0.54607975\",\n \"0.54500884\",\n \"0.5438655\",\n \"0.5437271\",\n \"0.54368633\",\n \"0.5435789\",\n \"0.5433903\",\n \"0.5430938\",\n \"0.54260844\",\n \"0.5424714\",\n \"0.5415449\",\n \"0.5414067\",\n \"0.5410411\",\n \"0.54058516\",\n \"0.54014146\",\n \"0.5398006\",\n \"0.53943914\",\n \"0.539107\",\n \"0.5386673\",\n \"0.53828055\",\n \"0.5377454\",\n \"0.5376411\",\n \"0.537427\",\n \"0.53720236\",\n \"0.5371918\",\n \"0.5362505\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5697489"},"document_rank":{"kind":"string","value":"36"}}},{"rowIdx":17783,"cells":{"query":{"kind":"string","value":"Handles arguments for a FLAME GPU device function. Arguments must use type hinting to be translated to cpp."},"document":{"kind":"string","value":"def dispatchFGPUDeviceFunctionArgs(self, tree):\n # reset the locals variable stack\n self._locals = [\"pyflamegpu\"]\n # input message\n first = True\n annotation = None\n for arg in tree.args.args:\n # ensure that there is a type annotation\n if not arg.annotation:\n self.RaiseError(arg, \"Device function argument requires type annotation\")\n # comma if not first\n if not first:\n self.write(\", \")\n self.dispatchType(arg.annotation)\n self.write(f\" {arg.arg}\")\n # add arg to local variable stack\n self._locals.append(arg.arg)\n first = False"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _deviceVariableFunctionName(self, tree, permitted_prefixes, allow_lengths = True):\n cpp_func_name = \"\"\n py_func = tree.attr\n # extract function name start\n for prefix in permitted_prefixes:\n if py_func.startswith(prefix):\n cpp_func_name = prefix\n py_func = py_func[len(prefix):]\n break # dont allow the else\n else:\n return None\n # check type and lengths\n if allow_lengths:\n #split to get type and Array Length (This could **potentially** be looked up from the model description but current syntax is consistent with swig bindings) \n type_and_length = py_func.split(\"Array\")\n if type_and_length[0] not in self._fgpu_types:\n self.RaiseError(tree, f\"'{type_and_length[0]}' is not a valid FLAME GPU type\")\n t = self._fgpu_types[type_and_length[0]]\n # generate template args\n if (len(type_and_length) == 1):\n cpp_func_name += f\"<{t}>\"\n elif (len(type_and_length) == 2):\n cpp_func_name += f\"<{t}, {type_and_length[1]}>\"\n else:\n return None\n else:\n if py_func not in self._fgpu_types:\n self.RaiseError(tree, f\"'{py_func}' is not a valid FLAME GPU type\")\n t = self._fgpu_types[py_func]\n cpp_func_name += f\"<{t}>\"\n # return \n return cpp_func_name","def _FunctionDef(self, t):\n self.write(\"\\n\")\n # check decorators\n if len(t.decorator_list) != 1 or not isinstance(t.decorator_list[0], ast.Attribute):\n self.RaiseError(t, \"Function definitions require a single pyflamegpu decorator of either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'\") \n # FLAMEGPU_AGENT_FUNCTION\n if t.decorator_list[0].attr == 'agent_function' and t.decorator_list[0].value.id == 'pyflamegpu':\n if getattr(t, \"returns\", False):\n self.RaiseWarning(t, \"Function definition return type not supported on 'pyflamegpu.agent_function'\")\n self.fill(f\"FLAMEGPU_AGENT_FUNCTION({t.name}, \")\n self.dispatchFGPUFunctionArgs(t)\n self.write(\")\")\n # FLAMEGPU_DEVICE_FUNCTION\n elif t.decorator_list[0].attr == 'device_function' and t.decorator_list[0].value.id == 'pyflamegpu':\n self.fill(f\"FLAMEGPU_DEVICE_FUNCTION \")\n if t.returns:\n self.dispatchType(t.returns)\n else:\n self.write(\"void\")\n self.write(f\" {t.name}(\")\n self.dispatchFGPUDeviceFunctionArgs(t)\n self.write(\")\")\n # add to list of defined functions that can be called\n self._device_functions.append(t.name)\n # FLAMEGPU_DEVICE_FUNCTION\n elif t.decorator_list[0].attr == 'agent_function_condition' and t.decorator_list[0].value.id == 'pyflamegpu':\n # check for return annotation\n if not hasattr(t, \"returns\"):\n self.RaiseError(t, \"Agent function conditions must have a 'bool' return type specified as a return type annotation\")\n # check for return annotation type\n if not isinstance(t.returns, ast.Name):\n self.RaiseError(t, \"Agent function conditions return type must be 'bool'\")\n if t.returns.id is not 'bool':\n self.RaiseError(t, \"Agent function conditions return type must be 'bool'\")\n # check to ensure no arguments (discard any with a warning)\n if t.args.args:\n self.RaiseWarning(t, \"Agent function conditions does not support arguments. These will be discarded.\")\n # write the agent function macro\n self.fill(f\"FLAMEGPU_AGENT_FUNCTION_CONDITION({t.name})\")\n else:\n self.RaiseError(t, \"Function definition uses an unsupported decorator. Must use either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'\")\n self.enter()\n self.dispatch(t.body)\n self.leave()","def inner(*args, **kwargs):\n\n s = f(*args, **kwargs)\n\n if torch.cuda.is_available():\n return torch.from_numpy(s).cuda().type(dtype)\n\n return torch.from_numpy(s).type(dtype)","def inner(*args, **kwargs):\n\n s, r, d, p = f(*args, **kwargs)\n\n if torch.cuda.is_available():\n s = torch.from_numpy(s).cuda().type(dtype)\n\n else:\n s = torch.from_numpy(s).type(dtype)\n\n return s, r, d, p","def set_arg_types( self ):\n if self.mode == 'grad':\n self.function = terms.dw_grad\n use_method_with_name( self, self.get_fargs_grad, 'get_fargs' )\n elif self.mode == 'div':\n self.function = terms.dw_div\n use_method_with_name( self, self.get_fargs_div, 'get_fargs' )\n else:\n self.function = self.d_eval\n use_method_with_name( self, self.get_fargs_eval, 'get_fargs' )\n self.use_caches = {'state_in_volume_qp' : [['parameter_s']],\n 'div_vector' : [['parameter_v']]}","def arg_to_CFI(self, node, ordered_functions):\n options = node.options\n fmt_func = node.fmtdict\n\n if options.wrap_fortran is False:\n # The buffer function is intended to be called by Fortran.\n # No Fortran, no need for buffer function.\n return\n\n ast = node.ast\n declarator = ast.declarator\n result_typemap = ast.typemap\n # shadow classes have not been added yet.\n # Only care about string, vector here.\n result_is_ptr = declarator.is_indirect()\n if (\n result_typemap\n and result_typemap.base in [\"string\", \"vector\"]\n and result_typemap.name != \"char\"\n and not result_is_ptr\n ):\n node.wrap.c = False\n # node.wrap.fortran = False\n self.config.log.write(\n \"Skipping {}, unable to create C wrapper \"\n \"for function returning {} instance\"\n \" (must return a pointer or reference).\"\n \" Bufferify version will still be created.\\n\".format(\n result_typemap.cxx_type, declarator.user_name\n )\n )\n \n cfi_args = {}\n for arg in ast.declarator.params:\n declarator = arg.declarator\n name = declarator.user_name\n attrs = declarator.attrs\n meta = declarator.metaattrs\n cfi_args[name] = False\n arg_typemap = arg.typemap\n if meta[\"api\"]:\n # API explicitly set by user.\n continue\n elif meta[\"assumed-rank\"]:\n cfi_args[name] = True\n elif attrs[\"rank\"]:\n cfi_args[name] = True\n elif arg_typemap.sgroup == \"string\":\n cfi_args[name] = True\n elif arg_typemap.sgroup == \"char\":\n if declarator.is_indirect():\n cfi_args[name] = True\n elif meta[\"deref\"] in [\"allocatable\", \"pointer\"]:\n cfi_args[name] = True\n has_cfi_arg = any(cfi_args.values())\n\n # Function result.\n need_buf_result = None\n\n result_as_arg = \"\" # Only applies to string functions\n # when the result is added as an argument to the Fortran api.\n\n # Check if result needs to be an argument.\n declarator = ast.declarator\n attrs = declarator.attrs\n meta = declarator.metaattrs\n if meta[\"deref\"] == \"raw\":\n # No bufferify required for raw pointer result.\n pass\n elif result_typemap.sgroup == \"string\":\n need_buf_result = \"cfi\"\n result_as_arg = fmt_func.F_string_result_as_arg\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\n elif result_typemap.sgroup == \"char\" and result_is_ptr:\n need_buf_result = \"cfi\"\n result_as_arg = fmt_func.F_string_result_as_arg\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\n elif meta[\"deref\"] in [\"allocatable\", \"pointer\"]:\n need_buf_result = \"cfi\"\n\n if not (need_buf_result or\n has_cfi_arg):\n return False\n\n options.wrap_fortran = False\n\n # Create a new C function and change arguments\n # and add attributes.\n C_new = node.clone()\n ordered_functions.append(C_new)\n self.append_function_index(C_new)\n\n generated_suffix = \"cfi\"\n C_new._generated = \"arg_to_cfi\"\n C_new.splicer_group = \"cfi\"\n if need_buf_result:\n C_new.ast.declarator.metaattrs[\"api\"] = need_buf_result\n fmt_func = C_new.fmtdict\n fmt_func.function_suffix = fmt_func.function_suffix + fmt_func.C_cfi_suffix\n\n C_new.wrap.assign(c=True)#, fortran=True)\n C_new._PTR_C_CXX_index = node._function_index\n\n for arg in C_new.ast.declarator.params:\n name = arg.declarator.user_name\n if cfi_args[name]:\n arg.declarator.metaattrs[\"api\"] = generated_suffix\n\n ast = C_new.ast\n if True: # preserve to avoid changing indention for now.\n f_attrs = node.ast.declarator.attrs # Fortran function attributes\n f_meta = node.ast.declarator.metaattrs # Fortran function attributes\n if result_as_arg:\n # decl: const char * getCharPtr2() +len(30)\n # +len implies copying into users buffer.\n result_as_string = ast.result_as_arg(result_name)\n result_as_string.const = False # must be writeable\n attrs = result_as_string.declarator.attrs\n # Special case for wrapf.py to override \"allocatable\"\n f_meta[\"deref\"] = None\n result_as_string.declarator.metaattrs[\"api\"] = \"cfi\"\n result_as_string.declarator.metaattrs[\"deref\"] = \"result\"\n result_as_string.declarator.metaattrs[\"is_result\"] = True\n C_new.ast.declarator.metaattrs[\"api\"] = None\n C_new.ast.declarator.metaattrs[\"intent\"] = \"subroutine\"\n C_new.ast.declarator.metaattrs[\"deref\"] = None\n\n if result_as_arg:\n F_new = self.result_as_arg(node, C_new)\n ordered_functions.append(F_new)\n self.append_function_index(F_new)\n else:\n if node._generated in [\"result_to_arg\", \"fortran_generic\", \"getter/setter\"]:\n node.wrap.c = False\n # Fortran function may call C subroutine if string/vector result\n # Fortran function calls bufferify function.\n node._PTR_F_C_index = C_new._function_index\n return True","def __init__(self, *args, **kwargs):\n super(MadryEtAlMultiGPU, self).__init__(*args, **kwargs)\n self.structural_kwargs += ['ngpu']","def _handle_arg(obj, arg):\n if isinstance(arg, PythonTensor):\n if arg.has_init:\n arg.init_data()\n if not arg.const_arg:\n return arg\n elif isinstance(arg, (Tensor, CSRTensor, COOTensor)):\n return arg\n elif hasattr(arg, \"__ms_mutable__\") and getattr(arg, \"__ms_mutable__\"):\n # mutable([]) will be eliminated by FuncGraphSpecializer, and empty list is not supported by backend.\n if isinstance(arg, list) and not arg:\n return None\n return arg\n elif context.get_context(\"grad_for_scalar\") and isinstance(arg, (int, float)):\n return arg\n elif hasattr(obj, \"enable_tuple_broaden\") and obj.enable_tuple_broaden and isinstance(arg, tuple) and \\\n _check_all_tensor(arg):\n return arg\n return None","def build_func_body(func_name, arg_dict, return_type):\n body = \"\"\n arg_list = \"\"\n\n # the following are pointers to scalar outputs\n # Note: pBufferSize was renamed pBufferSizeInBytes in v6.5\n scalar_ptr_outputs = ['nnzTotalDevHostPtr',\n 'pBufferSize',\n 'pBufferSizeInBytes',\n 'resultDevHostPtr']\n\n is_creator = 'cusparseCreate' in func_name\n is_getter = 'cusparseGet' in func_name\n\n if return_type == 'cusparseStatus_t' and not (is_creator or is_getter):\n is_return = False\n else:\n is_return = True\n\n # else:\n return_str = ''\n for k, v in arg_dict.items():\n\n \"\"\"\n set some flags based on the name/type of the argument\n will use these flags to determine whether and how to call ffi.new or\n ffi.cast on each variable\n \"\"\"\n is_ptr = '*' in v\n is_cusparse_type = '_t' in v\n is_cusparse_ptr = is_ptr and is_cusparse_type\n is_output_scalar = k in scalar_ptr_outputs\n if k in ['alpha', 'beta']:\n is_scalar = True\n else:\n is_scalar = False\n if is_getter:\n is_gpu_array = False\n else:\n is_gpu_array = is_ptr and (not is_cusparse_ptr) and (not is_scalar)\n if 'Complex' in v:\n is_complex = True\n else:\n is_complex = False\n\n # convert variable to appropriate type for the FFI\n if is_output_scalar:\n # for scalar outputs make a new pointer\n body += \"%s = ffi.cast('%s', %s)\\n\" % (k, v, k)\n elif is_getter and is_ptr and (return_type == 'cusparseStatus_t'):\n # any pointers in cusparseGet* are new outputs to be created\n body += \"%s = ffi.new('%s')\\n\" % (k, v)\n elif is_gpu_array:\n # pass pointer to GPU array data (use either .ptr or .gpudata)\n body += \"%s = ffi.cast('%s', %s.ptr)\\n\" % (k, v, k)\n elif is_cusparse_ptr:\n if is_creator:\n # generate custom cusparse type\n body += \"%s = ffi.new('%s')\\n\" % (k, v)\n else:\n # cast to the custom cusparse type\n body += \"%s = ffi.cast('%s', %s)\\n\" % (k, v, k)\n elif is_ptr and is_scalar:\n # create new pointer, with value initialized to scalar\n if is_complex:\n # complex case is a bit tricky. requires ffi.buffer\n body += \"%sffi = ffi.new('%s')\\n\" % (k, v)\n if 'cusparseC' in func_name:\n body += \"ffi.buffer(%sffi)[:] = \\\n np.complex64(%s).tostring()\\n\" % (k, k)\n elif 'cusparseZ' in func_name:\n body += \"ffi.buffer(%sffi)[:] = \\\n np.complex128(%s).tostring()\\n\" % (k, k)\n else:\n body += \"%s = ffi.new('%s', %s)\\n\" % (k, v, k)\n elif is_ptr or v == 'cudaStream_t':\n # case non-scalar pointer to appropriate type\n body += \"%s = ffi.cast('%s', %s)\\n\" % (k, v, k)\n else:\n # don't need explicit cast for plain int, float, etc\n pass\n\n # build the list of arguments to pass to the API\n if is_ptr and is_scalar and is_complex:\n # take into account modified argument name for complex scalars\n arg_list += \"%sffi, \" % k\n else:\n arg_list += \"%s, \" % k\n\n # add the function call and optionally return the result\n last_key = k\n arg_list = arg_list[:-2] # remove trailing \", \"\n if is_getter and return_type != 'cusparseStatus_t':\n body += \"return ffi_lib.%s(%s)\\n\" % (func_name, arg_list)\n else:\n # check cusparseStatus_t state before returning\n call_str = \"status = ffi_lib.%s(%s)\\n\" % (func_name, arg_list)\n body += split_line(call_str, break_pattern=', ', nmax=76)\n body += \"cusparseCheckStatus(status)\\n\"\n if is_return:\n # len(arg_dict) == 2) is to avoid return for cusparseGetLevelInfo\n if is_creator or (is_getter and (len(arg_dict) == 2)):\n body += \"return %s[0]\\n\" % last_key\n else:\n body += \"#TODO: return the appropriate result\"\n body += '\\n\\n'\n return reindent(body, numSpaces=4, lstrip=False)","def get_helper_c_code_args(self):\r\n return {'c_prefix': 'PyGpuArray',\r\n 'strides_mul': 1\r\n }","def apply(op_type, device, inputs, **kwargs):\n cache = ExecutionCache.get_cache(op_type)\n run_config = cache.get_config(device, **kwargs)\n return FunctionLib._forward(inputs, run_config, **kwargs)","def fn(*args, **kwargs):\n pass","def function(args):\n pass","def handle_args():\n parser = argparse.ArgumentParser(description=\"Faster-RCNN Implementation\")\n parser.add_argument(\"-handle-gpu\", action=\"store_true\", help=\"Tensorflow 2 GPU compatibility flag\")\n args = parser.parse_args()\n return args","def parse_params(self, ngpu=1, **kwargs):\n\n return_status = super(MadryEtAlMultiGPU, self).parse_params(**kwargs)\n self.ngpu = ngpu\n\n return return_status","def arg_to_buffer(self, node, ordered_functions):\n options = node.options\n fmt_func = node.fmtdict\n\n if node.wrap.c is False:\n# if options.wrap_c is False: # XXX cdesc.yaml GetScalar2\n # The user does not require a C wrapper.\n # This can be the case if the Fortran wrapper is doing all\n # the work via splicer or fstatements.\n return\n\n # If a C++ function returns a std::string instance,\n # the default wrapper will not compile since the wrapper\n # will be declared as char. It will also want to return the\n # c_str of a stack variable. Warn and turn off the wrapper.\n ast = node.ast\n declarator = ast.declarator\n result_typemap = ast.typemap\n # shadow classes have not been added yet.\n # Only care about string, vector here.\n result_is_ptr = ast.declarator.is_indirect()\n if (\n result_typemap\n and result_typemap.base in [\"string\", \"vector\"]\n and result_typemap.name != \"char\"\n and not result_is_ptr\n ):\n node.wrap.c = False\n # node.wrap.fortran = False\n self.config.log.write(\n \"Skipping {}, unable to create C wrapper \"\n \"for function returning {} instance\"\n \" (must return a pointer or reference).\"\n \" Bufferify version will still be created.\\n\".format(\n result_typemap.cxx_type, declarator.user_name\n )\n )\n\n if node.wrap.fortran is False:\n # The buffer function is intended to be called by Fortran.\n # No Fortran, no need for buffer function.\n return\n if options.F_string_len_trim is False: # XXX what about vector?\n return\n\n # Arguments.\n # Is result or any argument a string or vector?\n # If so, additional arguments will be passed down so\n # create buffer version of function.\n buf_args = {}\n for arg in declarator.params:\n has_buf_arg = None\n arg_typemap = arg.typemap\n declarator = arg.declarator\n attrs = declarator.attrs\n meta = declarator.metaattrs\n if meta[\"api\"]:\n # API explicitly set by user.\n continue\n elif attrs[\"cdesc\"]:\n # User requested cdesc.\n has_buf_arg = \"cdesc\"\n elif arg_typemap.sgroup == \"string\":\n if meta[\"deref\"] in [\"allocatable\", \"pointer\", \"copy\"]:\n has_buf_arg = \"cdesc\"\n # XXX - this is not tested\n # XXX - tested with string **arg+intent(out)+dimension(ndim)\n else:\n has_buf_arg = \"buf\"\n elif arg_typemap.sgroup == \"char\":\n if arg.ftrim_char_in:\n pass\n elif declarator.is_indirect():\n if meta[\"deref\"] in [\"allocatable\", \"pointer\"]:\n has_buf_arg = \"cdesc\"\n else:\n has_buf_arg = \"buf\"\n elif arg_typemap.sgroup == \"vector\":\n if meta[\"intent\"] == \"in\":\n # Pass SIZE.\n has_buf_arg = \"buf\"\n else:\n has_buf_arg = \"cdesc\"\n elif (arg_typemap.sgroup == \"native\" and\n meta[\"intent\"] == \"out\" and\n meta[\"deref\"] != \"raw\" and\n declarator.get_indirect_stmt() in [\"**\", \"*&\"]):\n # double **values +intent(out) +deref(pointer)\n has_buf_arg = \"cdesc\"\n #has_buf_arg = \"buf\" # XXX - for scalar?\n buf_args[declarator.user_name] = has_buf_arg\n # --- End loop over function parameters\n has_buf_arg = any(buf_args.values())\n\n # Function result.\n need_buf_result = None\n\n result_as_arg = \"\" # Only applies to string functions\n # when the result is added as an argument to the Fortran api.\n\n # Check if result needs to be an argument.\n attrs = ast.declarator.attrs\n meta = ast.declarator.metaattrs\n if meta[\"deref\"] == \"raw\":\n # No bufferify required for raw pointer result.\n pass\n elif result_typemap.sgroup == \"string\":\n if meta[\"deref\"] in [\"allocatable\", \"pointer\"]:\n need_buf_result = \"cdesc\"\n else:\n need_buf_result = \"buf\"\n result_as_arg = fmt_func.F_string_result_as_arg\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\n elif result_typemap.sgroup == \"char\" and result_is_ptr:\n if meta[\"deref\"] in [\"allocatable\", \"pointer\"]:\n # Result default to \"allocatable\".\n need_buf_result = \"cdesc\"\n else:\n need_buf_result = \"buf\"\n result_as_arg = fmt_func.F_string_result_as_arg\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\n elif result_typemap.base == \"vector\":\n need_buf_result = \"cdesc\"\n elif result_is_ptr:\n if meta[\"deref\"] in [\"allocatable\", \"pointer\"]:\n if meta[\"dimension\"]:\n # int *get_array() +deref(pointer)+dimension(10)\n need_buf_result = \"cdesc\"\n\n # Functions with these results need wrappers.\n if not (need_buf_result or\n has_buf_arg):\n return\n\n # XXX node.wrap.fortran = False\n # Preserve wrap.c.\n # This keep a version which accepts char * arguments.\n\n # Create a new C function and change arguments\n # and add attributes.\n C_new = node.clone()\n ordered_functions.append(C_new)\n self.append_function_index(C_new)\n\n generated_suffix = \"buf\"\n C_new._generated = \"arg_to_buffer\"\n C_new.splicer_group = \"buf\"\n if need_buf_result:\n C_new.ast.declarator.metaattrs[\"api\"] = need_buf_result\n \n fmt_func = C_new.fmtdict\n fmt_func.function_suffix = fmt_func.function_suffix + fmt_func.C_bufferify_suffix\n\n options = C_new.options\n C_new.wrap.assign(c=node.options.wrap_c)\n C_new._PTR_C_CXX_index = node._function_index\n\n for arg in C_new.ast.declarator.params:\n declarator = arg.declarator\n attrs = declarator.attrs\n meta = declarator.metaattrs\n if buf_args[declarator.user_name]:\n meta[\"api\"] = buf_args[declarator.user_name]\n if arg.ftrim_char_in:\n continue\n arg_typemap = arg.typemap\n if arg_typemap.base == \"vector\":\n # Do not wrap the orignal C function with vector argument.\n # Meaningless to call without the size argument.\n # TODO: add an option where char** length is determined by looking\n # for trailing NULL pointer. { \"foo\", \"bar\", NULL };\n node.wrap.c = False\n node.wrap.lua = False # NotImplemented\n\n ast = C_new.ast\n if True: # preserve to avoid changing indention for now.\n # Add additional argument to hold result.\n # This will allocate a new character variable to hold the\n # results of the C++ function.\n f_attrs = node.ast.declarator.attrs # Fortran function attributes\n f_meta = node.ast.declarator.metaattrs # Fortran function attributes\n\n if result_as_arg:\n # decl: const char * getCharPtr2() +len(30)\n # +len implies copying into users buffer.\n result_as_string = ast.result_as_arg(result_name)\n result_as_string.const = False # must be writeable\n attrs = result_as_string.declarator.attrs\n # Special case for wrapf.py to override \"allocatable\"\n f_meta[\"deref\"] = None\n # We've added an argument to fill, use api=buf.\n result_as_string.declarator.metaattrs[\"api\"] = \"buf\"\n result_as_string.declarator.metaattrs[\"deref\"] = \"result\"\n result_as_string.declarator.metaattrs[\"is_result\"] = True\n C_new.ast.declarator.metaattrs[\"api\"] = None\n C_new.ast.declarator.metaattrs[\"intent\"] = \"subroutine\"\n C_new.ast.declarator.metaattrs[\"deref\"] = None\n\n if result_as_arg:\n F_new = self.result_as_arg(node, C_new)\n ordered_functions.append(F_new)\n self.append_function_index(F_new)\n else:\n if node._generated in [\"result_to_arg\", \"fortran_generic\", \"getter/setter\"]:\n node.wrap.c = False\n \n # Fortran function may call C subroutine if string/vector result\n node._PTR_F_C_index = C_new._function_index","def handle_args(args):\n\n if args.id and len(args.id) > MAX_ID_LENGTH:\n raise ValueError\n if args.payload and (len(args.payload) % 2 != 0 or len(args.payload) > MAX_PAYLOAD_LENGTH):\n raise ValueError\n\n if args.id_bitmap:\n if len(args.id_bitmap) > MAX_ID_LENGTH:\n raise ValueError\n for i in range(len(args.id_bitmap)):\n args.id_bitmap[i] = string_to_bool(args.id_bitmap[i])\n if args.payload_bitmap:\n if len(args.payload_bitmap) > MAX_PAYLOAD_LENGTH:\n raise ValueError\n for i in range(len(args.payload_bitmap)):\n args.payload_bitmap[i] = string_to_bool(args.payload_bitmap[i])\n\n if args.alg == \"random\":\n __handle_random(args)\n elif args.alg == \"linear\":\n __handle_linear(args)\n elif args.alg == \"replay\":\n __handle_replay(args)\n elif args.alg == \"ring_bf\":\n __handle_ring_bf(args)\n elif args.alg == \"mutate\":\n __handle_mutate(args)\n else:\n raise ValueError","def call_function(self):\n try:\n arg_list = self.argument_list()\n function_dict = {}\n info = []\n for name_arg in arg_list:\n type_arg = self.arguments_type[name_arg]\n function_dict[name_arg] = utils.value_from_rpc(self.argument(name_arg)[1])\n info.append('{0}({1}): {2}'.format(name_arg, type_arg, function_dict[name_arg]))\n\n log.info('Execute command \\'{0}\\' with arguments [{1}] from device \\'{2}\\''\n .format(self.name(), '; '.join(info), self.device.id))\n self.function(self.device, **function_dict)\n\n except Exception as err:\n t = traceback.format_exc()\n log.error('Command \\'{0}\\' raise exception: {1}'.format(self.name(), decode_string(t)))","def __call__(self, *args, backend=\"auto\", device_id=-1, ranges=None, out=None):\n\n dtype = args[0].dtype.__str__().split(\".\")[1]\n\n nx, ny = get_sizes(self.aliases, *args)\n nout, nred = (nx, ny) if self.axis == 1 else (ny, nx)\n\n if \"Arg\" in self.reduction_op:\n # when using Arg type reductions,\n # if nred is greater than 16 millions and dtype=float32, the result is not reliable\n # because we encode indices as floats, so we raise an exception ;\n # same with float16 type and nred>2048\n if nred > 1.6e7 and dtype in (\"float32\", \"float\"):\n raise ValueError(\n \"size of input array is too large for Arg type reduction with single precision. Use double precision.\"\n )\n elif nred > 2048 and dtype in (\"float16\", \"half\"):\n raise ValueError(\n \"size of input array is too large for Arg type reduction with float16 dtype..\"\n )\n\n out = GenredAutograd.apply(\n self.formula,\n self.aliases,\n backend,\n dtype,\n device_id,\n ranges,\n self.optional_flags,\n self.rec_multVar_highdim,\n nx,\n ny,\n out,\n *args\n )\n\n return postprocess(out, \"torch\", self.reduction_op, nout, self.opt_arg, dtype)","def handle_input(self, data, feature_names, feature_types):\n interface = data.__cuda_array_interface__\n if 'mask' in interface:\n interface['mask'] = interface['mask'].__cuda_array_interface__\n interface_str = bytes(json.dumps(interface, indent=2), 'utf-8')\n\n handle = ctypes.c_void_p()\n _check_call(\n _LIB.XGDMatrixCreateFromArrayInterface(\n interface_str,\n ctypes.c_float(self.missing),\n ctypes.c_int(self.nthread),\n ctypes.byref(handle)))\n return handle, feature_names, feature_types","def compile_function(self, function, arguments):","def _set_function_parameters(self, p_args=None) -> bool:\n if self.get_type() == self.C_UNIT_CONV_LENGTH:\n self.units = {\n 'fm' : 1000000000000000,\n 'pm' : 1000000000000,\n 'nm' : 1000000000,\n 'um' : 1000000,\n 'mm' : 1000,\n 'cm' : 100,\n 'm' : 1.0,\n 'dam' : 0.1,\n 'hm' : 0.01,\n 'km' : 0.001,\n 'Mm' : 0.000001,\n 'Gm' : 0.000000001,\n 'Tm' : 0.000000000001,\n 'Pm' : 0.000000000000001,\n 'inch' : 39.3701,\n 'ft' : 3.28084,\n 'yd' : 1.09361,\n 'mi' : 0.000621371,\n 'nautMi' : 1.0/1852.0,\n 'lightYear' : 1.0/(9.4607304725808*(10**15))\n }\n \n elif self.get_type() == self.C_UNIT_CONV_PRESSURE:\n self.units = {\n 'Pa' : 100000.0,\n 'hPa' : 1000.0,\n 'kPa' : 100.0,\n 'MPa' : 0.1,\n 'bar' : 1.0,\n 'mbar' : 1000.0,\n 'ubar' : 1000000.0,\n 'kgcm2' : 1.01972,\n 'atm' : 0.986923,\n 'mmHg' : 750.062,\n 'mmH2O' : 10197.162129779,\n 'mH2O' : 10.197162129779,\n 'ftH2O' : 33.455256555148,\n 'inH2O' : 401.865,\n 'inHg' : 29.53,\n 'psi' : 14.5038\n }\n \n elif self.get_type() == self.C_UNIT_CONV_CURRENT:\n self.units = {\n 'fA' : 1000000000000000,\n 'pA' : 1000000000000,\n 'nA' : 1000000000,\n 'uA' : 1000000,\n 'mA' : 1000,\n 'cA' : 100,\n 'A' : 1.0,\n 'daA' : 0.1,\n 'hA' : 0.01,\n 'kA' : 0.001,\n 'MA' : 0.000001,\n 'GA' : 0.000000001,\n 'TA' : 0.000000000001,\n 'PA' : 0.000000000000001,\n }\n \n elif self.get_type() == self.C_UNIT_CONV_FORCE:\n self.units = {\n 'fN' : 1000000000000000,\n 'pN' : 1000000000000,\n 'nN' : 1000000000,\n 'uN' : 1000000,\n 'mN' : 1000,\n 'cN' : 100,\n 'N' : 1.0,\n 'daN' : 0.1,\n 'hN' : 0.01,\n 'kN' : 0.001,\n 'MN' : 0.000001,\n 'GN' : 0.000000001,\n 'TN' : 0.000000000001,\n 'PN' : 0.000000000000001,\n 'shortTonF' : 1.124045e-4,\n 'longTonF' : 1.003611e-4,\n 'kipf' : 2.248089e-4,\n 'lbf' : 2.248089431e-1,\n 'ozf' : 3.5969430896,\n 'pdf' : 7.2330138512,\n 'gf' : 1.019716213e+2,\n 'kgf' : 1.019716213e-1,\n 'dyn' : 1e+5,\n 'J/m' : 1.0,\n 'J/cm' : 100.0\n }\n \n elif self.get_type() == self.C_UNIT_CONV_POWER:\n self.units = {\n 'fW' : 1000000000000000*1e3,\n 'pW' : 1000000000000*1e3,\n 'nW' : 1000000000*1e3,\n 'uW' : 1000000*1e3,\n 'mW' : 1000*1e3,\n 'cW' : 100*1e3,\n 'W' : 1.0*1e3,\n 'daW' : 0.1*1e3,\n 'hW' : 0.01*1e3,\n 'kW' : 0.001*1e3,\n 'MW' : 0.000001*1e3,\n 'GW' : 0.000000001*1e3,\n 'TW' : 0.000000000001*1e3,\n 'PW' : 0.000000000000001*1e3,\n 'BTU/hr' : 3412.14,\n 'BTU/min' : 56.869,\n 'BTU/sec' : 0.94781666666,\n 'cal/sec' : 238.85,\n 'cal/min' : 238.85*60,\n 'cal/hr' : 238.85*60*60,\n 'erg/sec' : 10e9,\n 'erg/min' : 10e9*60,\n 'erg/hr' : 10e9*60*60,\n 'ftlb/sec' : 737.56,\n 'kCal/sec' : 0.24,\n 'kCal/min' : 0.24*60,\n 'kCal/hr' : 0.24*60*60,\n 'VA' : 1e3,\n 'metric_ton_ref' : 0.259,\n 'US_ton_ref' : 0.2843451361,\n 'J/sec' : 1000.0,\n 'J/min' : 1000.0*60,\n 'J/hr' : 1000.0*60*60,\n 'kgf-m/sec' : 101.97162129779,\n 'hp_mech' : 1.3410220888,\n 'hp_ele' : 1.3404825737,\n 'hp_metric' : 1.359621617304\n }\n \n elif self.get_type() == self.C_UNIT_CONV_MASS:\n self.units = {\n 'fg' : 1000000000000000*1e3,\n 'pg' : 1000000000000*1e3,\n 'ng' : 1000000000*1e3,\n 'ug' : 1000000*1e3,\n 'mg' : 1000*1e3,\n 'cg' : 100*1e3,\n 'g' : 1.0*1e3,\n 'dag' : 0.1*1e3,\n 'hg' : 0.01*1e3,\n 'kg' : 0.001*1e3,\n 'Mg' : 0.000001*1e3,\n 'Gg' : 0.000000001*1e3,\n 'Tg' : 0.000000000001*1e3,\n 'Pg' : 0.000000000000001*1e3,\n 'metricTon' : 1.0/1000.0,\n 'shortTon' : 1.0/907.185,\n 'longTon' : 1.0/1016.047,\n 'slug' : 1.0/14.5939029,\n 'lb' : 2.2046226218,\n 'oz' : 35.274,\n 'grain' : 2.2046226218*7000.0\n }\n \n elif self.get_type() == self.C_UNIT_CONV_TIME:\n self.units = {\n 'fs' : 1000000000000000,\n 'ps' : 1000000000000,\n 'ns' : 1000000000,\n 'us' : 1000000,\n 'ms' : 1000,\n 'cs' : 100,\n 's' : 1.0,\n 'das' : 0.1,\n 'hs' : 0.01,\n 'ks' : 0.001,\n 'Ms' : 0.000001,\n 'Gs' : 0.000000001,\n 'Ts' : 0.000000000001,\n 'Ps' : 0.000000000000001,\n 'min' : 1.0/60.0,\n 'hr' : 1.0/60.0/60.0,\n 'day' : 1.0/60.0/60.0/24.0\n }\n \n elif self.get_type() == self.C_UNIT_CONV_TEMPERATURE:\n self.units = {\n 'K' : 'Kelvin',\n 'R' : 'Rankine',\n 'F' : 'Fahrenheit',\n 'C' : 'Celcius',\n }\n \n if self.units.get(self._unit_in) is not None and self.units.get(self._unit_out) is not None:\n return True\n else:\n raise NotImplementedError('The input and/or output units do not exist!')","def run(self, fn, args=(), kwargs=None, options=None): # pylint: disable=useless-super-delegation\n return super(OneDeviceStrategy, self).run(fn, args, kwargs, options)","def ubercam(*args, **kwargs)->AnyStr:\n pass","def __call__(self, *args, **kwargs):\n dprint(2, \"FunctionMetadata::__call__\", self.func.__name__, args, kwargs, self.numba_args)\n atypes = tuple([type(x) for x in args])\n try_again = True\n count = 0\n if not self.numba_pfunc:\n if len(self.numba_args) == 0 and not self.no_global_cache:\n self.numba_pfunc = get_fm(FillerFunc(self.func), True)\n self.numba_func = get_fm(FillerFunc(self.func), False)\n else:\n self.numba_pfunc = numba.njit(parallel=True, **self.numba_args)(self.func)\n self.numba_func = numba.njit(**self.numba_args)(self.func)\n\n if gpu_present:\n dprint(1, \"using gpu context\")\n\n with dpctl.device_context(\"level0:gpu\"):\n while try_again and count < 2:\n count += 1\n try_again = False\n if self.ngfunc.get(atypes, True):\n try:\n ret = self.numba_pfunc(*args, **kwargs)\n self.ngfunc[atypes] = True\n return ret\n except numba.core.errors.TypingError as te:\n tetxt = str(te)\n tesplit = tetxt.splitlines()\n for teline in tesplit:\n if \"Untyped global name\" in teline and \"ramba.StencilMetadata\" in teline:\n try_again = True\n # Name of global that is of type ramba.StencilMetadata\n tes = teline[21:].split()[0][:-2]\n outer_globals = self.func.__globals__\n outer_locals = {}\n etes = eval(tes, outer_globals, outer_locals)\n etes.compile() # Converts to a Numba StencilFunc\n outer_globals[tes] = etes.sfunc # Rewrite the global to the Numba StencilFunc\n self.numba_pfunc = numba.njit(parallel=True, **self.numba_args)(self.func)\n self.numba_func = numba.njit(**self.numba_args)(self.func)\n if not try_again:\n self.ngfunc[atypes] = False\n dprint(1, \"Numba GPU ParallelAccelerator attempt failed.\")\n except:\n self.ngfunc[atypes] = False\n dprint(1, \"Numba GPU ParallelAccelerator attempt failed.\")\n\n while try_again and count < 2:\n count += 1\n try_again = False\n if self.npfunc.get(atypes, True):\n try:\n ret = self.numba_pfunc(*args, **kwargs)\n self.npfunc[atypes] = True\n return ret\n except numba.core.errors.TypingError as te:\n tetxt = str(te)\n tesplit = tetxt.splitlines()\n for teline in tesplit:\n if \"Untyped global name\" in teline and \"ramba.StencilMetadata\" in teline:\n try_again = True\n # Name of global that is of type ramba.StencilMetadata\n tes = teline[21:].split()[0][:-2]\n outer_globals = self.func.__globals__\n outer_locals = {}\n etes = eval(tes, outer_globals, outer_locals)\n etes.compile() # Converts to a Numba StencilFunc\n outer_globals[tes] = etes.sfunc # Rewrite the global to the Numba StencilFunc\n self.numba_pfunc = numba.njit(parallel=True, **self.numba_args)(self.func)\n self.numba_func = numba.njit(**self.numba_args)(self.func)\n if not try_again:\n self.npfunc[atypes] = False\n dprint(1, \"Numba ParallelAccelerator attempt failed.\")\n except:\n self.npfunc[atypes] = False\n dprint(1, \"Numba ParallelAccelerator attempt failed.\")\n\n if self.nfunc.get(atypes, True):\n try:\n ret = self.numba_func(*args, **kwargs)\n self.nfunc[atypes] = True\n dprint(3, \"Numba attempt succeeded.\")\n return ret\n except numba.core.errors.TypingError as te:\n print(\"Ramba TypingError:\", te, type(te))\n self.npfunc[atypes] = False\n dprint(1, \"Numba attempt failed.\")\n except:\n self.nfunc[atypes] = False\n dprint(1, \"Numba attempt failed.\")\n raise\n\n return self.func(*args, **kwargs)","def run(fn, *input_values, **kwds):\n \n ee = kwds.get('ee', shared_exec_engine)\n input_types = [arg.type for arg in fn.args]\n gv_inputs = [gv_from_python(x, t) \n for (x,t) in \n zip(input_values, input_types)]\n \n return run_with_generic_values(fn, gv_inputs, ee)","def cudify(fn):\n\n @functools.wraps(fn)\n def wrapper(*args, **kwargs):\n result = fn(*args, **kwargs)\n return cuda_if_gpu(result)\n\n return wrapper","def fsig(arg_types: ArgTypes, name: Text, span: Span, ctx: DeduceCtx,\n _: Optional[ParametricBindings]) -> ConcreteType:\n checker = _Checker(arg_types, name, span).len(2).is_bits(0).is_array(1)\n\n arg0 = arg_types[0]\n arg1 = arg_types[1]\n assert isinstance(arg1, ArrayType), arg1\n assert isinstance(arg1.size.value, int), arg1\n return_type = arg1.element_type\n checker.check_is_bits(return_type,\n 'Want arg 1 element type to be bits; got {0}')\n checker.check_is_len(arg1, arg0.size,\n 'bit width {target} must match {t} array size {t.size}')\n return FunctionType(arg_types, return_type)","def feed_arg(self, ws, name, value, dtype):\n ws.FeedTensor(name, numpy.array(value, dtype), self._arg_device)","def handle_func_command(cls, command):\n cmd, _, args, kwargs = command\n\n try: # will work if tensors are wrappers\n\n # Replace all TensorFlow tensor with their child attribute\n # Note that we return also args_type which helps handling case 3 in the docstring\n new_args, new_kwargs, new_type, args_type = hook_args.unwrap_args_from_function(\n cmd, args, kwargs, return_args_type=True\n )\n # This handles case 3: it redirects the command to the appropriate class depending\n # of the syft type of the arguments and returns\n if args_type not in FrameworkTensor:\n return args_type.handle_func_command(command)\n\n # build the new command\n new_command = (cmd, None, new_args, new_kwargs)\n # Send it to the appropriate class and get the response\n response = new_type.handle_func_command(new_command)\n # Put back the wrappers where needed\n response = hook_args.hook_response(cmd, response, wrap_type=args_type)\n except PureFrameworkTensorFoundError: # means that it's not a wrapper but a pure tensor\n\n # Check that the function has not been overwritten\n try:\n # Try to get recursively the attributes in cmd = \"<attr1>.<attr2>.<attr3>...\"\n command = cls.rgetattr(cls, cmd)\n return command(*args, **kwargs)\n except AttributeError:\n pass\n\n # TODO: clean this line\n cmd_split = cmd.split(\".\")\n cmd_path = cmd_split[:-1]\n cmd_name = cmd_split[-1]\n cmd = \"syft.local_worker.hook.\" + \".\".join(cmd_path) + \".native_\" + cmd_name\n\n # Run the native function with the new args\n # Note the the cmd should already be checked upon reception by the worker\n # in the execute_command function\n if isinstance(args, tuple):\n response = eval(cmd)(*args, **kwargs)\n else:\n response = eval(cmd)(args, **kwargs)\n\n return response","def _Call(self, t):\n # check calls but let attributes check in their own dispatcher\n funcs = self._device_functions + self.pythonbuiltins + [self._input_message_var] # message_input variable is a valid function name as certain message types have arguments on iterator\n if isinstance(t.func, ast.Name):\n if (t.func.id not in funcs):\n self.RaiseWarning(t, \"Function call is not a defined FLAME GPU device function or a supported python built in.\")\n # dispatch even if warning raised\n self.dispatch(t.func)\n elif isinstance(t.func, ast.Lambda):\n self.dispatch(t.func) # not supported\n else:\n # special handler for dispatching member function calls\n # This would otherwise be an attribute\n self.dispatchMemberFunction(t.func, t) \n self.write(\"(\")\n self._CallArguments(t)\n self.write(\")\")","def handle_input(self, data, feature_names, feature_types):\n if not hasattr(data, '__cuda_array_interface__') and hasattr(\n data, '__array__'):\n import cupy # pylint: disable=import-error\n data = cupy.array(data, copy=False)\n\n interface = data.__cuda_array_interface__\n if 'mask' in interface:\n interface['mask'] = interface['mask'].__cuda_array_interface__\n interface_str = bytes(json.dumps(interface, indent=2), 'utf-8')\n\n handle = ctypes.c_void_p()\n _check_call(\n _LIB.XGDeviceQuantileDMatrixCreateFromArrayInterface(\n interface_str,\n ctypes.c_float(self.missing), ctypes.c_int(self.nthread),\n ctypes.c_int(self.max_bin), ctypes.byref(handle)))\n return handle, feature_names, feature_types","def _unpack_argument(self, ty, val, sycl_queue, retr, kernelargs,\n device_arrs, access_type):\n\n device_arrs.append(None)\n\n if isinstance(ty, types.Array):\n if isinstance(val.base, dpctl_mem.Memory):\n self._unpack_device_array_argument(val, kernelargs)\n else:\n default_behavior = self.check_for_invalid_access_type(access_type)\n\n usm_buf = dpctl_mem.MemoryUSMShared(val.size * val.dtype.itemsize)\n usm_ndarr = np.ndarray(val.shape, buffer=usm_buf, dtype=val.dtype)\n\n if (default_behavior or\n self.valid_access_types[access_type] == _NUMBA_DPPL_READ_ONLY or\n self.valid_access_types[access_type] == _NUMBA_DPPL_READ_WRITE):\n np.copyto(usm_ndarr, val)\n\n device_arrs[-1] = (usm_buf, usm_ndarr, val)\n self._unpack_device_array_argument(usm_ndarr, kernelargs)\n\n elif ty == types.int64:\n cval = ctypes.c_long(val)\n kernelargs.append(cval)\n elif ty == types.uint64:\n cval = ctypes.c_long(val)\n kernelargs.append(cval)\n elif ty == types.int32:\n cval = ctypes.c_int(val)\n kernelargs.append(cval)\n elif ty == types.uint32:\n cval = ctypes.c_int(val)\n kernelargs.append(cval)\n elif ty == types.float64:\n cval = ctypes.c_double(val)\n kernelargs.append(cval)\n elif ty == types.float32:\n cval = ctypes.c_float(val)\n kernelargs.append(cval)\n elif ty == types.boolean:\n cval = ctypes.c_uint8(int(val))\n kernelargs.append(cval)\n elif ty == types.complex64:\n #kernelargs.append(ctypes.c_float(val.real))\n #kernelargs.append(ctypes.c_float(val.imag))\n raise NotImplementedError(ty, val)\n\n elif ty == types.complex128:\n #kernelargs.append(ctypes.c_double(val.real))\n #kernelargs.append(ctypes.c_double(val.imag))\n raise NotImplementedError(ty, val)\n\n else:\n raise NotImplementedError(ty, val)","def handle_input(self, data, feature_names, feature_types):\n data, feature_names, feature_types = self._maybe_cudf_dataframe(\n data, feature_names, feature_types)\n interfaces_str = _cudf_array_interfaces(data)\n handle = ctypes.c_void_p()\n _check_call(\n _LIB.XGDMatrixCreateFromArrayInterfaceColumns(\n interfaces_str,\n ctypes.c_float(self.missing),\n ctypes.c_int(self.nthread),\n ctypes.byref(handle)))\n return handle, feature_names, feature_types","def handle_arguments(self, args):\n debug(\"BloomGenerator.handle_arguments: got args -> \" + str(args))","def cpp_function(self):","def _create_args(self, func_args):\n self.llvm_ret_type = self._from_ctype(self.signature.ret_type)\n self.llvm_arg_types = \\\n [self._from_ctype(a) for a in self.signature.arg_ctypes]","def compile(self, args):\n if args not in self._compileinfos:\n cres = compile_with_dppl(self.py_func, None, args, debug=self.debug)\n func = cres.library.get_function(cres.fndesc.llvm_func_name)\n cres.target_context.mark_ocl_device(func)\n first_definition = not self._compileinfos\n self._compileinfos[args] = cres\n libs = [cres.library]\n\n if first_definition:\n # First definition\n cres.target_context.insert_user_function(self, cres.fndesc,\n libs)\n else:\n cres.target_context.add_user_function(self, cres.fndesc, libs)\n\n else:\n cres = self._compileinfos[args]\n\n return cres.signature","def call(self, inputs):\n raise NotImplementedError","def wrapper(self, *args, **kwargs):\n torch_args = [torch.tensor(arg, requires_grad=True, dtype=torch.float64) if type(arg) is ndarray else arg for arg in args]\n torch_kwargs = {key: torch.tensor(kwarg, requires_grad=True, dtype=torch.float64) if type(kwarg) is ndarray else kwarg for key, kwarg in kwargs.items()}\n result = function(self, *torch_args, **torch_kwargs)\n return result.cpu().detach().numpy()","def real_decorator(f):\n\n def inner(*args, **kwargs):\n \"\"\"returns a torch tensor for cpu or gpu when appropriate.\"\"\"\n\n s, r, d, p = f(*args, **kwargs)\n\n if torch.cuda.is_available():\n s = torch.from_numpy(s).cuda().type(dtype)\n\n else:\n s = torch.from_numpy(s).type(dtype)\n\n return s, r, d, p\n\n return inner","def varfunc(self, fields=[]):\n self.func_arguments = fields","def handle_input(self, data, feature_names, feature_types):\n data, feature_names, feature_types = self._maybe_cudf_dataframe(\n data, feature_names, feature_types)\n interfaces_str = _cudf_array_interfaces(data)\n handle = ctypes.c_void_p()\n _check_call(\n _LIB.XGDeviceQuantileDMatrixCreateFromArrayInterfaceColumns(\n interfaces_str,\n ctypes.c_float(self.missing), ctypes.c_int(self.nthread),\n ctypes.c_int(self.max_bin), ctypes.byref(handle)))\n return handle, feature_names, feature_types","def f_onearg(self, arg1) :\n pass","def dispatchMacroEnvFunction(self, tree, tree_parent):\n cpp_func_name = \"getMacroProperty\"\n py_func = tree.attr\n # extract type from function name\n py_type = py_func[len(cpp_func_name):]\n if py_type not in self._fgpu_types:\n self.RaiseError(tree, f\"'{py_type}' is not a valid FLAME GPU type\")\n # get cpp type\n t = self._fgpu_types[py_type]\n cpp_func_name += f\"<{t}\"\n # mess with the parent to extract (and remove arguments so they dont end up in the argument list)\n if not tree_parent.args :\n self.RaiseError(tree, f\" Macro environment function '{py_func}' is expected to have some arguments.\")\n # if more than one arg then the rest are bounds to translate\n if len(tree_parent.args) > 1:\n bounds = tree_parent.args[1:]\n # process bounds by appending to cpp function template arguments\n for i in bounds:\n if isinstance(i, ast.Num): # num required for python 3.7\n if not isinstance(i.n, int):\n self.RaiseError(tree, f\" Macro environment function argument '{i}' should be an integer value.\")\n cpp_func_name += f\", {i.n}\"\n else: # all Python > 3.7 \n if not isinstance(i, ast.Constant):\n self.RaiseError(tree, f\" Macro environment function argument '{i}' should be an constant value (or Num in Python <3.8).\")\n if not isinstance(i.value, int):\n self.RaiseError(tree, f\" Macro environment function argument '{i}' should be an integer value.\")\n cpp_func_name += f\", {i.value}\"\n # remove bounds from argument list (in place)\n del tree_parent.args[1:]\n cpp_func_name += \">\"\n self.write(cpp_func_name)","def tffunc(*argtypes):\n placeholders = list(map(tf.placeholder, argtypes))\n def wrap(f):\n out = f(*placeholders)\n def wrapper(*args, **kw):\n return out.eval(dict(zip(placeholders, args)), session=kw.get('session'))\n return wrapper\n return wrap","def call_top_interface_args_with_func_def(self, node: AnnCastCall):\n # call container is used to scope parameters\n call_con_name = call_container_name(node)\n\n # create argument and parameter variables\n # argument variables are inputs to the top interface\n # paramter variables are outputs of the top interface\n for i, n in enumerate(node.arguments):\n # argument name and scope str\n arg_name = call_argument_name(node, i)\n arg_con_scopestr = con_scope_to_str(node.func.con_scope)\n\n # parameter name and scopestr\n func_def = self.pipeline_state.func_def_node_from_id(node.func.id)\n param = func_def.func_args[i]\n assert(isinstance(param, AnnCastVar))\n param_name = param.val.name\n param_con_scopestr = con_scope_to_str(node.func.con_scope + [call_con_name])\n\n # argument and parameter share id, and start with initial version\n id = self.pipeline_state.next_collapsed_id()\n version = VAR_INIT_VERSION\n\n # build and store GrFN variables for argument and parameter\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\n # store arg_fullid\n node.arg_index_to_fullid[i] = arg_fullid\n # create From Source metadata for the GrFN var\n from_source = False\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\n\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\n # store param_fullid\n node.param_index_to_fullid[i] = param_fullid\n # create From Source metadata for the GrFN var\n add_metadata_from_name_node(param_grfn_var, param.val)\n\n # link argument and parameter through top interface\n node.top_interface_in[id] = arg_fullid\n node.top_interface_out[id] = param_fullid\n\n # DEBUG printing\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\n print(\"After create_call_args_and_params():\")\n print(f\"\\ttop_interface_in = {node.top_interface_in}\")\n print(f\"\\ttop_interface_out = {node.top_interface_out}\")","def deconvolution_fusion_para(x, y):\n input_memory_type = x.get(\"addr_type\") \\\n if \"addr_type\" in x else 0\n output_memory_type = y.get(\"addr_type\") \\\n if \"addr_type\" in y else 0\n valid_shape = x.get(\"valid_shape\") \\\n if \"valid_shape\" in x else ()\n slice_offset = x.get(\"slice_offset\") \\\n if \"slice_offset\" in x else ()\n output_offset = y.get(\"slice_offset\") \\\n if \"slice_offset\" in y else ()\n l1_fusion_type = x.get(\"L1_fusion_type\") \\\n if \"L1_fusion_type\" in x else -1\n fmap_l1_addr_flag = x.get(\"L1_addr_flag\", False)\n fmap_l1_valid_size = x.get(\"L1_valid_size\", 0)\n\n\n\n l1_fusion_enable_flag = get_L1_info(\"L1_fusion_enabled\")\n\n if input_memory_type not in (0, 1, 2):\n args_dict = {\n \"errCode\": \"E65008\",\n \"input_memory_type_range\": \"(0, 1, 2)\",\n \"input_memory_type\": str(input_memory_type)\n }\n raise RuntimeError(args_dict,\n err_man.get_error_message(args_dict))\n if output_memory_type not in (0, 1, 2):\n args_dict = {\n \"errCode\": \"E65009\",\n \"output_memory_type_range\": \"(0, 1, 2)\",\n \"output_memory_type\": str(output_memory_type)\n }\n raise RuntimeError(args_dict,\n err_man.get_error_message(args_dict))\n if valid_shape and not slice_offset:\n reason = \"valid shape exists, slice shape cannot be []\"\n args_dict = {\n \"errCode\": \"E60108\",\n \"reason\": reason\n }\n raise RuntimeError(args_dict,\n err_man.get_error_message(args_dict))\n if valid_shape and not output_offset:\n reason = \"valid shape exists, output offset cannot be []\"\n args_dict = {\n \"errCode\": \"E60108\",\n \"reason\": reason\n }\n raise RuntimeError(args_dict,\n err_man.get_error_message(args_dict))\n\n valid_shape = shape_to_list(valid_shape)\n slice_offset = shape_to_list(slice_offset)\n output_offset = shape_to_list(output_offset)\n\n if not l1_fusion_enable_flag:\n input_memory_type = 0\n output_memory_type = 0\n valid_shape = []\n slice_offset = []\n output_offset = []\n l1_fusion_type = -1\n fmap_l1_addr_flag = False\n fmap_l1_valid_size = 0\n\n fusion_para = {\"input_memory_type\": input_memory_type,\n \"output_memory_type\": output_memory_type,\n \"valid_shape\": valid_shape,\n \"slice_offset\": slice_offset,\n \"output_offset\": output_offset,\n \"l1_fusion_type\": l1_fusion_type,\n \"fmap_l1_addr_flag\": fmap_l1_addr_flag,\n \"fmap_l1_valid_size\": fmap_l1_valid_size}\n\n return fusion_para","def tffunc(*argtypes):\n placeholders = list(map(tf.placeholder, argtypes))\n\n def wrap(f):\n out = f(*placeholders)\n\n def wrapper(*args, **kw):\n return out.eval(dict(zip(placeholders, args)), session=kw.get('session'))\n\n return wrapper\n\n return wrap","def handle(args):\n if args.list_targets:\n handle_list_targets(args)\n else:\n if hasattr(args, \"flow_func\"):\n args.flow_func(args)\n else:\n print(\"Invalid command. Check 'mlonmcu flow --help' for the available subcommands!\")\n sys.exit(1)","def device(request):\n d = request.param()\n\n # enable GPU error checking\n if isinstance(d, hoomd.device.GPU):\n d.gpu_error_checking = True\n\n return d","def run_on_device(self, *args, **kwargs) -> DeviceOutputs:\n return DeviceOutputs(args=list(args), kwargs=kwargs)","def fn(name : str, *, input : 'NET', gnd : 'NET', output : 'NET'):\n return make_component(name, type, {\"in\": input, \"gnd\":gnd, \"out\":output}, [], {'voltage': voltage}, prefix=\"U\")","def process_function(engine: Engine, batch: Tuple[torch.Tensor, torch.Tensor, List[int]]) -> \\\n Tuple[float, torch.Tensor, torch.Tensor]:\n model.train()\n optimizer.zero_grad()\n x, y, lengths = batch\n x, y = x.to(model.device), y.to(model.device)\n y_pred: torch.Tensor = model(x, lengths)\n loss: torch.Tensor = loss_fn(y_pred, y)\n loss.backward()\n optimizer.step()\n return loss.item(), torch.max(y_pred, dim=1)[1], y","def setup(args):\n print(\"[INFO] args:\", json.dumps(args))\n\n token = args['_parameters'].get('token')\n device_type = args['_parameters'].get('device_type')\n\n if not token:\n print(\"[ERROR] Ubidots token not specified\")\n return {\"status\":\"error\"}\n\n elif not device_type and token:\n print(\"[INFO] device type not specified\")\n device_type = \"\"\n\n if device_type != \"\":\n device_type_data = set_device_type(device_type)\n try:\n res = create_device_type(device_type_data, token)\n print(res)\n if res.status_code == 409:\n print(\"[INFO] A device type with this name already exists.\")\n elif res.status_code == 201:\n print(\"[INFO] Device type created successfully.\")\n except Exception as e:\n print(\"[INFO] Setup function ran, but could not create a device type.\")\n print(e)\n else:\n print({\"[INFO] No device type created\"})\n\n return {\"status\":\"finished\"}","def convert_result_as_arg(self, node, ordered_functions):\n return ordered_functions # XXX - do nothing for now\n options = node.options\n fmt_func = node.fmtdict\n# if options.F_string_len_trim is False: # XXX what about vector?\n# return\n\n ast = node.ast\n result_typemap = ast.typemap\n result_name = None\n\n # Check if result needs to be an argument.\n attrs = ast.attrs\n meta = ast.metaattrs\n if meta[\"deref\"] == \"raw\":\n # No bufferify required for raw pointer result.\n pass\n elif result_typemap.sgroup in [\"char\", \"string\"]:\n result_name = fmt_func.F_string_result_as_arg\n# result_as_arg = fmt_func.F_string_result_as_arg\n# result_name = result_as_arg or fmt_func.C_string_result_as_arg\n# elif result_typemap.base == \"vector\":\n# has_vector_result = True\n# elif result_is_ptr:\n# if meta[\"deref\"] in [\"allocatable\", \"pointer\"]:\n# need_cdesc_result = True\n# elif attrs[\"dimension\"]:\n# need_cdesc_result = True\n\n if not result_name:\n return\n\n##########\n # Create a new C function and change arguments\n # and add attributes.\n C_new = node.clone()\n ordered_functions.append(C_new)\n self.append_function_index(C_new)\n\n# generated_suffix = \"buf\"\n C_new._generated = \"result_to_arg\"\n fmt_func = C_new.fmtdict\n# fmt_func.function_suffix = fmt_func.function_suffix + fmt_func.C_bufferify_suffix + \"XXX\"\n# fmt_func.function_suffix = fmt_func.function_suffix\n\n options = C_new.options\n C_new.wrap.assign(c=True, fortran=True)\n C_new._PTR_C_CXX_index = node._function_index\n##########\n\n # decl: const char * getCharPtr2()\n new_arg = C_new.ast.result_as_arg(result_name)\n new_arg.const = False # must be writeable\n# attrs = new_arg.attrs\n# new_arg.metaattrs[\"deref\"] = None\n # Special case for wrapf.py to override \"allocatable\"\n\n # Special case for wrapf.py to override \"allocatable\"\n node.ast.metaattrs[\"deref\"] = None\n new_arg.metaattrs[\"deref\"] = \"result\"\n new_arg.metaattrs[\"is_result\"] = True\n C_new.ast.metaattrs[\"intent\"] = \"subroutine\"\n C_new.ast.metaattrs[\"deref\"] = None\n\n node.wrap.fortran = False\n# node.wrap.c = False\n\n return\n F_new = self.result_as_arg(node, C_new)\n ordered_functions.append(F_new)\n self.append_function_index(F_new)","def f_twoargs(self, arg1, arg2) :\n pass","def _prepare_forward_inputs(\n device: torch.device,\n input_dtype: Optional[torch.dtype],\n *args: Any,\n **kwargs: Any,\n) -> Tuple[Any, Any]:\n # TODO: Do not use the side stream for tensor copies for now; investigate\n # the perf with/without it.\n # TODO: For mixed precision, move the inputs to the compute device and cast\n # to reduced-precision in a single `to()` call.\n args_tuple, kwargs_tuple = _to_kwargs(args, kwargs, device.index, False)\n args = args_tuple[0]\n kwargs = kwargs_tuple[0]\n if input_dtype is not None:\n args, kwargs = _cast_fp_inputs_to_dtype(input_dtype, *args, **kwargs)\n return args, kwargs","def compile_kernel(context, queue, source_code, function_name,\n compiler_flags=None):\n if cdouble(queue)(42).nbytes >= 8:\n type_definitions = \"\"\"\n #define cdouble double\n \"\"\"\n else:\n print('WARNING: no 64bit float support available for this device.')\n type_definitions = \"\"\"\n #define cdouble float\n \"\"\"\n # The definition of cfloat and cshort is fixed for now since I do\n # not know of any cases where these are not available. If this\n # happens to be the case, we can add a check as for double here.\n type_definitions += \"\"\"\n #define cfloat float\n #define cshort short\n \"\"\"\n flags = BUILD_OPTIONS[:]\n if compiler_flags is not None:\n flags.extend(compiler_flags)\n full_source = type_definitions + basic_code() + source_code\n program = ocl.Program(context, full_source).build(flags)\n return getattr(program, function_name)","def __call__(self, *args, **kwargs) -> None:\n # The device portion needs to run every step to produce a stable graph\n device_outputs = self.run_on_device(*args, **kwargs)\n assert isinstance(device_outputs, DeviceOutputs), (\n f\"Expected device outputs to be of type `DeviceOutputs`, \"\n f\"but got `{type(device_outputs)}`.\"\n )\n\n # Detach and clone device outputs to ensure we use the \"current\" value\n for idx, tensor in enumerate(device_outputs.args):\n if isinstance(tensor, torch.Tensor):\n device_outputs.args[idx] = tensor.detach().clone()\n for key, tensor in device_outputs.kwargs.items():\n if isinstance(tensor, torch.Tensor):\n device_outputs.kwargs[key] = tensor.detach().clone()\n\n if cm.use_cs():\n state = cbtorch.state()\n state.track_object(\n {\n \"cb_summary\": {\n self.name: [device_outputs.args, device_outputs.kwargs]\n }\n },\n force=self._is_appliance,\n )\n\n if self._is_appliance:\n\n def _on_activations_received():\n cpu_args = [\n state.get_activation_for_output(tensor)\n if isinstance(tensor, torch.Tensor)\n else tensor\n for tensor in device_outputs.args\n ]\n cpu_kwargs = {\n key: state.get_activation_for_output(tensor)\n if isinstance(tensor, torch.Tensor)\n else tensor\n for key, tensor in device_outputs.kwargs.items()\n }\n\n self._cached_cpu_activations.append(\n self.run_on_host(*cpu_args, **cpu_kwargs)\n )\n\n state.register_activation_callback(_on_activations_received)\n else:\n\n @cm.step_closure\n def _run_on_host_closure(\n device_args: List[Any], device_kwargs: Dict[str, Any],\n ):\n device_args = cm.to_cpu(device_args)\n device_kwargs = cm.to_cpu(device_kwargs)\n self._cached_cpu_activations.append(\n self.run_on_host(*device_args, **device_kwargs)\n )\n\n _run_on_host_closure(\n device_outputs.args, device_outputs.kwargs,\n )","def __init__(self, kern, f, f_scope):\n super().__init__(kern.input_dim,active_dims=kern.active_dims)\n self.kern = kern\n self._f = lambda x: tf.cast(f(x), gp.settings.float_type) #function to call on input\n self._f_scope = f_scope #learnable variables that f depends on","def load_c_functions(self):\n\n # Load shared object\n lib = ctypes.cdll.LoadLibrary(os.path.join(self.working_directory,\"models/doubly_constrained/flow_forward_models.so\"))\n lib2 = ctypes.cdll.LoadLibrary(os.path.join(self.working_directory,\"models/doubly_constrained/potential_function.so\"))\n\n # Load DSF procedure flow inference\n self.infer_flows_dsf_procedure = lib.infer_flows_dsf_procedure\n self.infer_flows_dsf_procedure.restype = ctypes.c_double\n self.infer_flows_dsf_procedure.argtypes = [ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ctypes.c_size_t,\n ctypes.c_size_t,\n ctypes.c_double,\n ctypes.c_size_t,\n ctypes.c_bool,\n ctypes.c_bool]\n\n\n # Load Newton Raphson procedure flow inference\n self.infer_flows_newton_raphson = lib.infer_flows_newton_raphson\n self.infer_flows_newton_raphson.restype = None #ctypes.c_double\n self.infer_flows_newton_raphson.argtypes = [ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ctypes.c_double,\n ctypes.c_size_t,\n ctypes.c_size_t,\n ctypes.c_size_t,\n ctypes.c_size_t]\n\n # Load Iterative proportional filtering procedure flow inference\n self.infer_flows_ipf_procedure = lib.infer_flows_ipf_procedure\n self.infer_flows_ipf_procedure.restype = ctypes.c_double\n self.infer_flows_ipf_procedure.argtypes = [ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ctypes.c_size_t,\n ctypes.c_size_t,\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ctypes.c_size_t,\n ctypes.c_double,\n ctypes.c_bool]\n\n # Load Iterative proportional filtering procedure flow inference\n self.infer_flows_ipf_procedure_singly = lib.infer_flows_ipf_procedure_singly\n self.infer_flows_ipf_procedure_singly.restype = ctypes.c_double\n self.infer_flows_ipf_procedure_singly.argtypes = [ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ctypes.c_size_t,\n ctypes.c_size_t,\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ctypes.c_size_t,\n ctypes.c_double,\n ctypes.c_bool]\n\n # Load potential function\n self.potential_stochastic = lib2.potential_stochastic\n self.potential_stochastic.restype = ctypes.c_double\n self.potential_stochastic.argtypes = [ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ndpointer(ctypes.c_double, flags=\"C_CONTIGUOUS\"),\n ctypes.c_size_t,\n ctypes.c_size_t]","def _fusion(self, expert_outputs):\n raise NotImplementedError","def generate_definition(self):\n apientry = \"\"\n if self.__name[:2] == \"gl\":\n apientry = \"DNLOAD_APIENTRY \"\n params = \"void\"\n if self.__parameters:\n params = \", \".join(self.__parameters)\n return \"%s (%s*%s)(%s)\" % (self.__returntype, apientry, self.__name, params)","def build(self, cres):\n _launch_threads()\n # Build wrapper for ufunc entry point\n ctx = cres.target_context\n library = cres.library\n signature = cres.signature\n llvm_func = library.get_function(cres.fndesc.llvm_func_name)\n wrapper, env = build_gufunc_wrapper(library, ctx, llvm_func,\n signature, self.sin, self.sout,\n fndesc=cres.fndesc,\n env=cres.environment)\n\n ptr = library.get_pointer_to_function(wrapper.name)\n\n # Get dtypes\n dtypenums = []\n for a in signature.args:\n if isinstance(a, types.Array):\n ty = a.dtype\n else:\n ty = a\n dtypenums.append(as_dtype(ty).num)\n\n return dtypenums, ptr, env","def numeric(*args):","def handle_input(data, device=None):\n if type(data) == tuple:\n x, y = data\n x = torch.tensor(x, dtype=torch.float)\n y = torch.tensor(y, dtype=torch.float)\n if not device is None:\n x = x.to(device)\n y = y.to(device)\n return x, y\n if type(data) == np.ndarray:\n x = torch.tensor(data, dtype=torch.float)\n if not device is None:\n x = x.to(device)\n return x\n else:\n return data","def separate_device_addr(*args, **kwargs):\n return _uhd_swig.separate_device_addr(*args, **kwargs)","def call_top_interface_args_with_no_func_def(self, node: AnnCastCall):\n # call container is used to scope parameters\n call_con_name = call_container_name(node)\n\n # create argument and parameter variables\n # argument variables are inputs to the top interface\n # paramter variables are outputs of the top interface\n for i, n in enumerate(node.arguments):\n # argument name and scope str\n arg_name = call_argument_name(node, i)\n arg_con_scopestr = con_scope_to_str(node.func.con_scope)\n\n # parameter name and scopestr\n param_name = call_param_name(node, i)\n param_con_scopestr = con_scope_to_str(node.func.con_scope + [call_con_name])\n \n # argument and parameter share id, and start with initial version\n id = self.pipeline_state.next_collapsed_id()\n version = VAR_INIT_VERSION\n\n # build and store GrFN variables for argument and parameter\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\n # store arg_fullid\n node.arg_index_to_fullid[i] = arg_fullid\n # create From Source metadata for the GrFN var\n from_source = False\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\n\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\n # store param_fullid\n node.param_index_to_fullid[i] = param_fullid\n # create From Source metadata for the GrFN var\n # when we don't have the function def, we create a paramter with a default name\n add_metadata_to_grfn_var(param_grfn_var, from_source_mdata)\n\n # link argument and parameter through top interface\n node.top_interface_in[id] = arg_fullid\n node.top_interface_out[id] = param_fullid\n\n # DEBUG printing\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\n print(\"After create_call_args_and_params():\")\n print(f\"\\ttop_interface_in = {node.top_interface_in}\")\n print(f\"\\ttop_interface_out = {node.top_interface_out}\")","def do_type_checking(self, node):\r\n\r\n if not isinstance(node.inputs[0].type, GpuArrayType):\r\n raise NotImplementedError()","def real_decorator(f):\n\n def inner(*args, **kwargs):\n \"\"\"returns a torch tensor for cpu or gpu when appropriate.\"\"\"\n\n s = f(*args, **kwargs)\n\n if torch.cuda.is_available():\n return torch.from_numpy(s).cuda().type(dtype)\n\n return torch.from_numpy(s).type(dtype)\n\n return inner","def process_kernel_arg(\n self, var, llvm_arg, arg_type, index, modified_arrays, sycl_queue_val\n ):\n if isinstance(arg_type, types.npytypes.Array):\n if llvm_arg is None:\n raise NotImplementedError(arg_type, var)\n\n storage = cgutils.alloca_once(self.builder, utils.LLVMTypes.int64_t)\n self.builder.store(self.context.get_constant(types.int64, 0), storage)\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\n self._form_kernel_arg_and_arg_ty(\n self.builder.bitcast(\n storage,\n utils.get_llvm_type(context=self.context, type=types.voidptr),\n ),\n ty,\n )\n\n storage = cgutils.alloca_once(self.builder, utils.LLVMTypes.int64_t)\n self.builder.store(self.context.get_constant(types.int64, 0), storage)\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\n self._form_kernel_arg_and_arg_ty(\n self.builder.bitcast(\n storage,\n utils.get_llvm_type(context=self.context, type=types.voidptr),\n ),\n ty,\n )\n\n # Handle array size\n array_size_member = self.builder.gep(\n llvm_arg,\n [\n self.context.get_constant(types.int32, 0),\n self.context.get_constant(types.int32, 2),\n ],\n )\n\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\n self._form_kernel_arg_and_arg_ty(\n self.builder.bitcast(\n array_size_member,\n utils.get_llvm_type(context=self.context, type=types.voidptr),\n ),\n ty,\n )\n\n # Handle itemsize\n item_size_member = self.builder.gep(\n llvm_arg,\n [\n self.context.get_constant(types.int32, 0),\n self.context.get_constant(types.int32, 3),\n ],\n )\n\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\n self._form_kernel_arg_and_arg_ty(\n self.builder.bitcast(\n item_size_member,\n utils.get_llvm_type(context=self.context, type=types.voidptr),\n ),\n ty,\n )\n\n # Calculate total buffer size\n total_size = cgutils.alloca_once(\n self.builder,\n utils.get_llvm_type(context=self.context, type=types.intp),\n size=utils.get_one(context=self.context),\n name=\"total_size\" + str(self.cur_arg),\n )\n self.builder.store(\n self.builder.sext(\n self.builder.mul(\n self.builder.load(array_size_member),\n self.builder.load(item_size_member),\n ),\n utils.get_llvm_type(context=self.context, type=types.intp),\n ),\n total_size,\n )\n\n # Handle data\n data_member = self.builder.gep(\n llvm_arg,\n [\n self.context.get_constant(types.int32, 0),\n self.context.get_constant(types.int32, 4),\n ],\n )\n\n # names are replaced using legalize names, we have to do the same\n # here for them to match.\n legal_names = legalize_names([var])\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.voidptr)\n\n if isinstance(arg_type, nus.UsmSharedArrayType):\n self._form_kernel_arg_and_arg_ty(\n self.builder.bitcast(\n self.builder.load(data_member),\n utils.get_llvm_type(context=self.context, type=types.voidptr),\n ),\n ty,\n )\n else:\n malloc_fn = DpctlCAPIFnBuilder.get_dpctl_malloc_shared(\n builder=self.builder, context=self.context\n )\n memcpy_fn = DpctlCAPIFnBuilder.get_dpctl_queue_memcpy(\n builder=self.builder, context=self.context\n )\n event_del_fn = DpctlCAPIFnBuilder.get_dpctl_event_delete(\n builder=self.builder, context=self.context\n )\n event_wait_fn = DpctlCAPIFnBuilder.get_dpctl_event_wait(\n builder=self.builder, context=self.context\n )\n\n # Not known to be USM so we need to copy to USM.\n buffer_name = \"buffer_ptr\" + str(self.cur_arg)\n # Create void * to hold new USM buffer.\n buffer_ptr = cgutils.alloca_once(\n self.builder,\n utils.get_llvm_type(context=self.context, type=types.voidptr),\n name=buffer_name,\n )\n # Setup the args to the USM allocator, size and SYCL queue.\n args = [\n self.builder.load(total_size),\n self.builder.load(sycl_queue_val),\n ]\n # Call USM shared allocator and store in buffer_ptr.\n self.builder.store(self.builder.call(malloc_fn, args), buffer_ptr)\n\n if legal_names[var] in modified_arrays:\n self.write_buffs.append((buffer_ptr, total_size, data_member))\n else:\n self.read_only_buffs.append((buffer_ptr, total_size, data_member))\n\n # We really need to detect when an array needs to be copied over\n if index < self.num_inputs:\n args = [\n self.builder.load(sycl_queue_val),\n self.builder.load(buffer_ptr),\n self.builder.bitcast(\n self.builder.load(data_member),\n utils.get_llvm_type(\n context=self.context, type=types.voidptr\n ),\n ),\n self.builder.load(total_size),\n ]\n event_ref = self.builder.call(memcpy_fn, args)\n self.builder.call(event_wait_fn, [event_ref])\n self.builder.call(event_del_fn, [event_ref])\n\n self._form_kernel_arg_and_arg_ty(self.builder.load(buffer_ptr), ty)\n\n # Handle shape\n shape_member = self.builder.gep(\n llvm_arg,\n [\n self.context.get_constant(types.int32, 0),\n self.context.get_constant(types.int32, 5),\n ],\n )\n\n for this_dim in range(arg_type.ndim):\n shape_entry = self.builder.gep(\n shape_member,\n [\n self.context.get_constant(types.int32, 0),\n self.context.get_constant(types.int32, this_dim),\n ],\n )\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\n self._form_kernel_arg_and_arg_ty(\n self.builder.bitcast(\n shape_entry,\n utils.get_llvm_type(context=self.context, type=types.voidptr),\n ),\n ty,\n )\n\n # Handle strides\n stride_member = self.builder.gep(\n llvm_arg,\n [\n self.context.get_constant(types.int32, 0),\n self.context.get_constant(types.int32, 6),\n ],\n )\n\n for this_stride in range(arg_type.ndim):\n stride_entry = self.builder.gep(\n stride_member,\n [\n self.context.get_constant(types.int32, 0),\n self.context.get_constant(types.int32, this_stride),\n ],\n )\n\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\n self._form_kernel_arg_and_arg_ty(\n self.builder.bitcast(\n stride_entry,\n utils.get_llvm_type(context=self.context, type=types.voidptr),\n ),\n ty,\n )\n\n else:\n ty = numba_type_to_dpctl_typenum(context=self.context, type=arg_type)\n self._form_kernel_arg_and_arg_ty(\n self.builder.bitcast(\n llvm_arg,\n utils.get_llvm_type(context=self.context, type=types.voidptr),\n ),\n ty,\n )","def exec_function(self, args):\n raise NotImplementedError()","def _device_implements(np_function):\n\n def decorator(func):\n _DEVICE_HANDLED_FUNCTIONS[np_function] = func\n return func\n\n return decorator","def serve_function(self):\n # Set name attribute of the input TensorSpec.\n input_signature = {\n name: tf.TensorSpec.from_spec(spec, name=name)\n for name, spec in self.features_inputter.input_signature().items()\n }\n\n @tf.function(input_signature=(input_signature,))\n def _run(features):\n features = self.features_inputter.make_features(features=features.copy())\n _, predictions = self(features)\n return predictions\n\n return _run","def gpuGridrec(tomo,angles,center,input_params):\n\n print('Starting GPU NUFFT recon')\n #allocate space for final answer \n af.set_device(input_params['gpu_device']) #Set the device number for gpu based code\n #Change tomopy format\n new_tomo=np.transpose(tomo,(1,2,0)) #slice, columns, angles\n im_size = new_tomo.shape[1]\n num_slice = new_tomo.shape[0]\n num_angles=new_tomo.shape[2]\n pad_size=np.int16(im_size*input_params['oversamp_factor'])\n# nufft_scaling = (np.pi/pad_size)**2\n #Initialize structures for NUFFT\n sino={}\n geom={}\n sino['Ns'] = pad_size#Sinogram size after padding\n sino['Ns_orig'] = im_size #size of original sinogram\n sino['center'] = center + (sino['Ns']/2 - sino['Ns_orig']/2) #for padded sinogram\n sino['angles'] = angles\n sino['filter'] = input_params['fbp_filter_param'] #Paramter to control strength of FBP filter normalized to [0,1]\n\n #Initialize NUFFT parameters\n nufft_params = init_nufft_params(sino,geom)\n rec_nufft = afnp.zeros((num_slice/2,sino['Ns_orig'],sino['Ns_orig']),dtype=afnp.complex64)\n Ax = afnp.zeros((sino['Ns'],num_angles),dtype=afnp.complex64)\n pad_idx = slice(sino['Ns']/2-sino['Ns_orig']/2,sino['Ns']/2+sino['Ns_orig']/2)\n rec_nufft_final=np.zeros((num_slice,sino['Ns_orig'],sino['Ns_orig']),dtype=np.float32)\n \n #Move all data to GPU\n slice_1=slice(0,num_slice,2)\n slice_2=slice(1,num_slice,2)\n gdata=afnp.array(new_tomo[slice_1]+1j*new_tomo[slice_2],dtype=afnp.complex64)\n x_recon = afnp.zeros((sino['Ns'],sino['Ns']),dtype=afnp.complex64)\n #loop over all slices\n for i in range(0,num_slice/2):\n Ax[pad_idx,:]=gdata[i]\n #filtered back-projection \n rec_nufft[i] = (back_project(Ax,nufft_params))[pad_idx,pad_idx]\n\n\n #Move to CPU\n #Rescale result to match tomopy\n rec_nufft=np.array(rec_nufft,dtype=np.complex64) #*nufft_scaling\n rec_nufft_final[slice_1]=np.array(rec_nufft.real,dtype=np.float32)\n rec_nufft_final[slice_2]=np.array(rec_nufft.imag,dtype=np.float32)\n return rec_nufft_final","def convert_to_user_call(*args):\n return _ida_hexrays.convert_to_user_call(*args)","def base_declaration(func, thnn_function, backends, backend_types, inplace=False):\n name, params = re.match(NAME_PARAM_REGEX, func['name']).groups()\n if inplace:\n name += '_'\n params = params.split(', ')\n arguments = [argument_to_declaration(a, func) for a in params]\n if not inplace:\n arguments += output_arguments(thnn_function)\n buffers = [argument_to_declaration('Tensor ' + buf)\n for buf in func.get('buffers', [])]\n\n return function_info(name, arguments, None, buffers, backends, inplace, backend_types)","def test_ddp_comm_hook_future_passing_gpu_gloo(self):\n process_group = self._get_process_group()\n\n # Get GPU model with simple_hook registered.\n gpu_model = self._gpu_model_with_ddp_comm_hook(process_group, self._simple_hook)\n\n # check whether the grads are equal to what simple_hook's then callback returns.\n # without the comm_hook, result would be 0.25 * torch.ones(2, 2).\n self._run_and_verify_hook(gpu_model, 8, 2 * torch.ones(2, 2))","def fusion_dispatcher(args=sys.argv[1:]):\n\n # If --clear-logs the log files are cleared\n if \"--clear-logs\" in args:\n clear_log_files(LOG_FILES)\n\n settings = {}\n settings.update(SETTINGS)\n if '--evaluate' in args:\n settings.update({\"default_output\": \"evaluation\"})\n\n command_args, _, api, session_file, _ = get_context(args, settings)\n\n # Selects the action to perform\n if a.has_value(command_args, \"fusion_models_\") or a.has_test(command_args):\n compute_output(api, command_args)\n u.log_message(\"_\" * 80 + \"\\n\", log_file=session_file)","def __call__(self, *inputs):\n raise NotImplementedError","def acfunct(arg):\n try:\n functions = [dynet.rectify, dynet.tanh]\n functions = { function.__name__ : function for function in functions}\n functions[\"None\"] = None\n return functions[str(arg)]\n except:\n raise argparse.ArgumentTypeError(\"String {} does not match required format\".format(arg,))","def dummy_fn(self, *args, **kwargs):","def set_device(in_arg): \n \n return torch.device(\"cuda\" if torch.cuda.is_available() and in_arg.gpu == 1 else \"cpu\")","def cuda(self, *args, **kwargs) -> 'BasePoints':\n original_type = type(self)\n return original_type(\n self.tensor.cuda(*args, **kwargs),\n points_dim=self.points_dim,\n attribute_dims=self.attribute_dims)","def _handleInput(self, paramInput):\n super()._handleInput(paramInput)\n dataFilename = paramInput.findFirst('dataFilename')\n if dataFilename != None:\n self.dataFilename = os.path.join(self.workingDir,dataFilename.value)\n else:\n self.raiseAnError(IOError,'<dataFilename> parameter needed for MultiDimensional Distributions!!!!')\n\n functionType = dataFilename.parameterValues['type']\n if functionType != None:\n self.functionType = functionType\n else:\n self.raiseAnError(IOError,'<functionType> parameter needed for MultiDimensional Distributions!!!!')\n\n self.initializeDistribution()","def get_float_type(*args):\n return _ida_hexrays.get_float_type(*args)","def deconv_compute_fusion_para(x, y):\n input_memory_type = x.op.attrs[\"addr_type\"].value \\\n if \"addr_type\" in x.op.attrs else 0\n valid_shape = x.op.attrs[\"valid_shape\"] \\\n if \"valid_shape\" in x.op.attrs else ()\n slice_offset = x.op.attrs[\"slice_offset\"] \\\n if \"slice_offset\" in x.op.attrs else ()\n output_offset = y.get(\"slice_offset\") \\\n if \"slice_offset\" in y else ()\n l1_fusion_type = x.op.attrs[\"L1_fusion_type\"].value \\\n if \"L1_fusion_type\" in x.op.attrs else -1\n fmap_l1_addr_flag = x.op.attrs[\"L1_addr_flag\"].value \\\n if \"L1_addr_flag\" in x.op.attrs else False\n fmap_l1_valid_size = x.op.attrs[\"L1_valid_size\"].value\\\n if \"L1_valid_size\" in x.op.attrs else 0\n\n l1_fusion_enable_flag = get_L1_info(\"L1_fusion_enabled\")\n\n if input_memory_type not in (0, 1, 2):\n args_dict = {\n \"errCode\": \"E65008\",\n \"input_memory_type_range\": \"(0, 1, 2)\",\n \"input_memory_type\": str(input_memory_type)\n }\n raise RuntimeError(args_dict,\n err_man.get_error_message(args_dict))\n\n if valid_shape and not slice_offset:\n reason = \"valid shape exists, slice shape cannot be []\"\n args_dict = {\n \"errCode\": \"E60108\",\n \"reason\": reason\n }\n raise RuntimeError(args_dict,\n err_man.get_error_message(args_dict))\n if valid_shape and not output_offset:\n reason = \"valid shape exists, output offset cannot be []\"\n args_dict = {\n \"errCode\": \"E60108\",\n \"reason\": reason\n }\n raise RuntimeError(args_dict,\n err_man.get_error_message(args_dict))\n\n valid_shape = shape_to_list(valid_shape)\n slice_offset = shape_to_list(slice_offset)\n output_offset = shape_to_list(output_offset)\n\n if not l1_fusion_enable_flag:\n input_memory_type = 0\n valid_shape = []\n slice_offset = []\n output_offset = []\n l1_fusion_type = -1\n fmap_l1_addr_flag = False\n fmap_l1_valid_size = 0\n\n fusion_para = {\"input_memory_type\": input_memory_type,\n \"output_memory_type\": \"fuse_flag\",\n \"valid_shape\": valid_shape,\n \"slice_offset\": slice_offset,\n \"output_offset\": output_offset,\n \"l1_fusion_type\": l1_fusion_type,\n \"fmap_l1_addr_flag\": fmap_l1_addr_flag,\n \"fmap_l1_valid_size\": fmap_l1_valid_size}\n\n return fusion_para","def EvaluateFunction(self, p_float=..., p_float=..., p_float=...):\n ...","def execute_objective(arguments):\n #later call define_tr_val from within this function for speedup.\n #score, pred_ = self.define_tr_val() #just get the train and test...\n \n\n cv_samples, parallel_arguments, parameters, windowsOS, id_ = arguments\n device = parallel_arguments[\"device\"]\n declaration_args = parallel_arguments[\"declaration_args\"]\n backprop_args = parallel_arguments[\"backprop_args\"]\n #del declaration_args['reservoir']\n\n cv_args = parallel_arguments[\"cv_args\"]\n log_score, rounds, tr_score_prop = cv_args[\"log_score\"], cv_args[\"rounds\"], cv_args[\"tr_score_prop\"]\n\n # if windowsOS == True:\n # #move specific arguments to the gpu.\n # cv_samples_ = []\n # for i, cv_sample in enumerate(cv_samples):\n # if cv_sample[\"tr_y\"].device != device:\n # #if cv_sample[\"tr_x\"]:\n # train_x, validate_x = cv_sample[\"tr_x\"].to(device), cv_sample[\"val_x\"].to(device)\n\n # train_y, validate_y = cv_sample[\"tr_y\"].to(device), cv_sample[\"val_y\"].to(device)\n # else:\n # #consider not sending the cv_sample[\"x\"] if it's a pure prediction.\n # train_x, train_y = cv_sample[\"tr_x\"], cv_sample[\"tr_y\"]\n # validate_x, validate_y = cv_sample[\"val_x\"], cv_sample[\"val_y\"]\n # cv_samples_.append((train_x, train_y, validate_x, validate_y))\n\n # #now move the input weights and the reservoir arguments to the gpu.\n # #deepcopy()\n # assert declaration_args[\"reservoir\"] != None\n # if declaration_args[\"reservoir\"] != None:\n # declaration_args[\"reservoir\"].in_weights = declaration_args[\"reservoir\"].in_weights.to(device)\n # declaration_args[\"reservoir\"].accept = declaration_args[\"reservoir\"].accept.to(device)\n # declaration_args[\"reservoir\"].reservoir_pre_weights = declaration_args[\"reservoir\"].reservoir_pre_weights.to(device)\n\n # RC = EchoStateNetwork(**declaration_args, **parameters, id_ = id_)\n \n RC = EchoStateNetwork(**declaration_args, **parameters, id_ = id_)\n cv_samples_ = []\n for i, cv_sample in enumerate(cv_samples):\n cv_samples_.append((cv_sample[\"tr_x\"], cv_sample[\"tr_y\"], cv_sample[\"val_x\"], cv_sample[\"val_y\"]))\n\n ODE_order = parallel_arguments[\"train_args\"][\"ODE_order\"]\n\n train_args = parallel_arguments[\"train_args\"]\n test_args = parallel_arguments[\"test_args\"]\n\n total_score = 0\n\n backprop_f = backprop_args[\"backprop_f\"]\n\n if type(train_args[\"init_conditions\"][0]) == list:\n multiple_ICs = True if len(train_args[\"init_conditions\"][0]) > 1 else False\n else:\n multiple_ICs = False\n\n for i, cv_sample in enumerate(cv_samples_):\n #print(i)\n train_x, train_y, validate_x, validate_y = cv_sample\n\n cv_sample_score = 0\n\n #divis = rounds *2\n if ODE_order:\n if multiple_ICs:\n # if train_score:\n # return {\"scores\" : scores, \n # \"weights\": gd_weights, \n # \"biases\" : gd_biases,\n # \"ys\" : ys,\n # \"ydots\" : ydots,\n # \"losses\" : Ls}\n dictt = RC.fit(X = train_x, y = train_y, train_score = True, **train_args)\n train_scores = dictt[\"scores\"]\n val_scores, pred_, id_ = RC.test(X=validate_x, y= validate_y, **test_args)\n\n \n for i, train_score in enumerate(train_scores):\n \n train_scores[i] = process_score(train_score, device = device)\n\n val_score = process_score(val_scores[i], device = device)# / divis\n\n round_score = combine_score(train_score, val_score, tr_score_prop, log_score) \n cv_sample_score += round_score \n total_score += cv_sample_score\n\n else:\n #train_score = RC.fit(X = train_x, y = train_y, train_score = True, **parallel_arguments[\"train_args\"])\n results = RC.fit(X = train_x, y = train_y, train_score = True, **train_args)\n train_scores = results[\"scores\"]\n train_score = train_scores[0]\n\n train_score = process_score(train_score, device = device)# / divis\n\n val_scores, pred_, id_ = RC.test(X=validate_x, y= validate_y, **test_args)\n val_score = process_score(val_scores[0], device = device)# / divis\n\n cv_sample_score = combine_score(train_score, val_score, tr_score_prop, log_score) \n\n \n else:\n _ = RC.fit(X = train_x, y = train_y,**parallel_arguments[\"train_args\"])\n #train_score = process_score(train_score)\n \n val_score, pred_, id_ = RC.test(X=validate_x, y= validate_y, **parallel_arguments[\"test_args\"])\n val_score = process_score(val_score)\n\n cv_sample_score = val_score #combine_score(train_score, val_score)\n\n #del train_x; del train_y;\n #del validate_x;\n\n if id_ != 0:\n del validate_y; del pred_;\n\n total_score += cv_sample_score\n #if device == torch.device('cuda'):\n # torch.cuda.empty_cache()\n\n #del RC;\n #score_mu = total_score/len(cv_samples)\n #del cv_samples;\n nn = len(cv_samples_) * len(train_scores)\n\n total_score = total_score / nn\n if id_ == 0:\n try:\n validate_y = validate_y.to(\"cpu\")\n except:\n pass\n return float(total_score), {\"pred\": pred_.to(\"cpu\"), \"val_y\" : validate_y}, id_\n else:\n return float(total_score), None, id_","def _handleInput(self, paramInput):\n pass","def prepare_arguments(self, ftyp, args):\n # Determine fixed and variable arguments:\n if ftyp.is_vararg:\n fixed_amount = len(ftyp.arguments)\n fixed_args = args[:fixed_amount]\n var_args = args[fixed_amount:]\n else:\n fixed_args = args\n var_args = []\n\n # Evaluate arguments:\n ir_arguments = []\n\n # If return value is complex, reserve room for it an pass pointer\n if ftyp.return_type.is_struct:\n size, alignment = self.data_layout(ftyp.return_type)\n rval_alloc = self.emit(ir.Alloc(\"rval_alloc\", size, alignment))\n rval_ptr = self.emit(ir.AddressOf(rval_alloc, \"rval_ptr\"))\n ir_arguments.append(rval_ptr)\n else:\n rval_alloc = None\n\n # Place other arguments:\n for argument in fixed_args:\n value = self.gen_expr(argument, rvalue=True)\n ir_arguments.append(value)\n\n # Handle variable arguments:\n if ftyp.is_vararg:\n vararg_ptr = self.gen_fill_varargs(var_args)\n ir_arguments.append(vararg_ptr)\n else:\n assert not var_args\n\n return ir_arguments, rval_alloc","def _graph_fn_parameterize(self, *parameters):\n raise NotImplementedError","def call_megam(args):\n ...","def egd(*args, **kwargs): # real signature unknown\n pass","def _body(step, *args):\n updated_embedding_vars = args[0:num_embedding_vars]\n updated_fc_vars = args[num_embedding_vars:num_embedding_vars +\n num_fc_vars]\n train_embeddings = self.embedding_fn(\n self.data.train_images,\n params=collections.OrderedDict(\n zip(embedding_vars_keys, updated_embedding_vars)),\n depth_multiplier=self.depth_multiplier,\n reuse=True)['embeddings']\n\n if self.proto_maml_fc_layer_on_support_set:\n # Set fc layer weights with prototypical equivalent values.\n prototypes = self.proto_maml_prototypes(train_embeddings)\n pmaml_fc_weights = self.proto_maml_fc_weights(\n prototypes, zero_pad_to_max_way=True)\n pmaml_fc_bias = self.proto_maml_fc_bias(\n prototypes, zero_pad_to_max_way=True)\n train_logits = tf.matmul(train_embeddings,\n pmaml_fc_weights) + pmaml_fc_bias\n else:\n updated_fc_weights, updated_fc_bias = updated_fc_vars\n train_logits = tf.matmul(train_embeddings,\n updated_fc_weights) + updated_fc_bias\n\n train_logits = train_logits[:, 0:self.way]\n loss = tf.losses.softmax_cross_entropy(one_hot_train_labels, train_logits)\n\n if self.debug_log:\n print_op = tf.print(['step: ', step, updated_fc_bias[0], 'loss:', loss])\n else:\n print_op = tf.no_op()\n\n embedding_grads = tf.gradients(loss, updated_embedding_vars)\n # Only computes fc grad when it's not created from prototypes.\n if not self.proto_maml_fc_layer_on_support_set:\n fc_grads = tf.gradients(loss, updated_fc_vars)\n\n if self.first_order:\n\n def _stop_grads(grads):\n return [tf.stop_gradient(dv) for dv in grads]\n\n embedding_grads = _stop_grads(embedding_grads)\n if not self.proto_maml_fc_layer_on_support_set:\n fc_grads = _stop_grads(fc_grads)\n\n # Apply gradients\n def _apply_grads(variables, grads):\n \"\"\"Applies gradients using SGD on a list of variables.\"\"\"\n v_new = []\n for (v, dv) in zip(variables, grads):\n if (not self.train_batch_norm and\n ('offset' in v.name or 'scale' in v.name)):\n v_new.append(v)\n else:\n v_new.append(v - self.alpha * dv)\n return v_new\n\n with tf.control_dependencies([print_op]):\n updated_embedding_vars = _apply_grads(updated_embedding_vars,\n embedding_grads)\n # Only apply fc grad when it's not created from prototypes.\n if not self.proto_maml_fc_layer_on_support_set:\n updated_fc_vars = _apply_grads(updated_fc_vars, fc_grads)\n step = step + 1\n return tuple([step] + list(updated_embedding_vars) +\n list(updated_fc_vars))","def itkMultipleValuedCostFunction_cast(*args):\n return _ITKCostFunctionsPython.itkMultipleValuedCostFunction_cast(*args)","def my_func(a, b):","def test_hostfromgpu_shape_i():\r\n pass\r\n\r\n m = mode_with_gpu.including('local_dot_to_dot22',\r\n 'local_dot22_to_dot22scalar','specialize')\r\n a = T.fmatrix('a')\r\n ca = theano.sandbox.cuda.var.CudaNdarrayType((False, False))()\r\n\r\n av = numpy.asarray(numpy.random.rand(5, 4), dtype='float32')\r\n cv = cuda.CudaNdarray(numpy.asarray(numpy.random.rand(5, 4),\r\n dtype='float32'))\r\n\r\n f = theano.function([a], cuda.basic_ops.gpu_from_host(a), mode=m)\r\n assert cuda.basic_ops.gpu_from_host in [x.op\r\n for x in f.maker.fgraph.toposort()]\r\n f = theano.function([a], cuda.basic_ops.gpu_from_host(a).shape, mode=m)\r\n topo = f.maker.fgraph.toposort()\r\n assert isinstance(topo[0].op, T.opt.Shape_i)\r\n assert isinstance(topo[1].op, T.opt.Shape_i)\r\n assert isinstance(topo[2].op, T.opt.MakeVector)\r\n assert tuple(f(av)) == (5, 4)\r\n\r\n\r\n\r\n f = theano.function([ca], cuda.basic_ops.host_from_gpu(ca), mode=m)\r\n assert cuda.basic_ops.host_from_gpu in [x.op\r\n for x in f.maker.fgraph.toposort()]\r\n f = theano.function([ca], cuda.basic_ops.host_from_gpu(ca).shape, mode=m)\r\n topo = f.maker.fgraph.toposort()\r\n assert isinstance(topo[0].op, T.opt.Shape_i)\r\n assert isinstance(topo[1].op, T.opt.Shape_i)\r\n assert isinstance(topo[2].op, T.opt.MakeVector)\r\n assert tuple(f(cv)) == (5, 4)","def _expr_kernel(self,\n arguments: Any,\n batch: RecordBatch) -> Any:\n pass"],"string":"[\n \"def _deviceVariableFunctionName(self, tree, permitted_prefixes, allow_lengths = True):\\n cpp_func_name = \\\"\\\"\\n py_func = tree.attr\\n # extract function name start\\n for prefix in permitted_prefixes:\\n if py_func.startswith(prefix):\\n cpp_func_name = prefix\\n py_func = py_func[len(prefix):]\\n break # dont allow the else\\n else:\\n return None\\n # check type and lengths\\n if allow_lengths:\\n #split to get type and Array Length (This could **potentially** be looked up from the model description but current syntax is consistent with swig bindings) \\n type_and_length = py_func.split(\\\"Array\\\")\\n if type_and_length[0] not in self._fgpu_types:\\n self.RaiseError(tree, f\\\"'{type_and_length[0]}' is not a valid FLAME GPU type\\\")\\n t = self._fgpu_types[type_and_length[0]]\\n # generate template args\\n if (len(type_and_length) == 1):\\n cpp_func_name += f\\\"<{t}>\\\"\\n elif (len(type_and_length) == 2):\\n cpp_func_name += f\\\"<{t}, {type_and_length[1]}>\\\"\\n else:\\n return None\\n else:\\n if py_func not in self._fgpu_types:\\n self.RaiseError(tree, f\\\"'{py_func}' is not a valid FLAME GPU type\\\")\\n t = self._fgpu_types[py_func]\\n cpp_func_name += f\\\"<{t}>\\\"\\n # return \\n return cpp_func_name\",\n \"def _FunctionDef(self, t):\\n self.write(\\\"\\\\n\\\")\\n # check decorators\\n if len(t.decorator_list) != 1 or not isinstance(t.decorator_list[0], ast.Attribute):\\n self.RaiseError(t, \\\"Function definitions require a single pyflamegpu decorator of either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'\\\") \\n # FLAMEGPU_AGENT_FUNCTION\\n if t.decorator_list[0].attr == 'agent_function' and t.decorator_list[0].value.id == 'pyflamegpu':\\n if getattr(t, \\\"returns\\\", False):\\n self.RaiseWarning(t, \\\"Function definition return type not supported on 'pyflamegpu.agent_function'\\\")\\n self.fill(f\\\"FLAMEGPU_AGENT_FUNCTION({t.name}, \\\")\\n self.dispatchFGPUFunctionArgs(t)\\n self.write(\\\")\\\")\\n # FLAMEGPU_DEVICE_FUNCTION\\n elif t.decorator_list[0].attr == 'device_function' and t.decorator_list[0].value.id == 'pyflamegpu':\\n self.fill(f\\\"FLAMEGPU_DEVICE_FUNCTION \\\")\\n if t.returns:\\n self.dispatchType(t.returns)\\n else:\\n self.write(\\\"void\\\")\\n self.write(f\\\" {t.name}(\\\")\\n self.dispatchFGPUDeviceFunctionArgs(t)\\n self.write(\\\")\\\")\\n # add to list of defined functions that can be called\\n self._device_functions.append(t.name)\\n # FLAMEGPU_DEVICE_FUNCTION\\n elif t.decorator_list[0].attr == 'agent_function_condition' and t.decorator_list[0].value.id == 'pyflamegpu':\\n # check for return annotation\\n if not hasattr(t, \\\"returns\\\"):\\n self.RaiseError(t, \\\"Agent function conditions must have a 'bool' return type specified as a return type annotation\\\")\\n # check for return annotation type\\n if not isinstance(t.returns, ast.Name):\\n self.RaiseError(t, \\\"Agent function conditions return type must be 'bool'\\\")\\n if t.returns.id is not 'bool':\\n self.RaiseError(t, \\\"Agent function conditions return type must be 'bool'\\\")\\n # check to ensure no arguments (discard any with a warning)\\n if t.args.args:\\n self.RaiseWarning(t, \\\"Agent function conditions does not support arguments. These will be discarded.\\\")\\n # write the agent function macro\\n self.fill(f\\\"FLAMEGPU_AGENT_FUNCTION_CONDITION({t.name})\\\")\\n else:\\n self.RaiseError(t, \\\"Function definition uses an unsupported decorator. Must use either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'\\\")\\n self.enter()\\n self.dispatch(t.body)\\n self.leave()\",\n \"def inner(*args, **kwargs):\\n\\n s = f(*args, **kwargs)\\n\\n if torch.cuda.is_available():\\n return torch.from_numpy(s).cuda().type(dtype)\\n\\n return torch.from_numpy(s).type(dtype)\",\n \"def inner(*args, **kwargs):\\n\\n s, r, d, p = f(*args, **kwargs)\\n\\n if torch.cuda.is_available():\\n s = torch.from_numpy(s).cuda().type(dtype)\\n\\n else:\\n s = torch.from_numpy(s).type(dtype)\\n\\n return s, r, d, p\",\n \"def set_arg_types( self ):\\n if self.mode == 'grad':\\n self.function = terms.dw_grad\\n use_method_with_name( self, self.get_fargs_grad, 'get_fargs' )\\n elif self.mode == 'div':\\n self.function = terms.dw_div\\n use_method_with_name( self, self.get_fargs_div, 'get_fargs' )\\n else:\\n self.function = self.d_eval\\n use_method_with_name( self, self.get_fargs_eval, 'get_fargs' )\\n self.use_caches = {'state_in_volume_qp' : [['parameter_s']],\\n 'div_vector' : [['parameter_v']]}\",\n \"def arg_to_CFI(self, node, ordered_functions):\\n options = node.options\\n fmt_func = node.fmtdict\\n\\n if options.wrap_fortran is False:\\n # The buffer function is intended to be called by Fortran.\\n # No Fortran, no need for buffer function.\\n return\\n\\n ast = node.ast\\n declarator = ast.declarator\\n result_typemap = ast.typemap\\n # shadow classes have not been added yet.\\n # Only care about string, vector here.\\n result_is_ptr = declarator.is_indirect()\\n if (\\n result_typemap\\n and result_typemap.base in [\\\"string\\\", \\\"vector\\\"]\\n and result_typemap.name != \\\"char\\\"\\n and not result_is_ptr\\n ):\\n node.wrap.c = False\\n # node.wrap.fortran = False\\n self.config.log.write(\\n \\\"Skipping {}, unable to create C wrapper \\\"\\n \\\"for function returning {} instance\\\"\\n \\\" (must return a pointer or reference).\\\"\\n \\\" Bufferify version will still be created.\\\\n\\\".format(\\n result_typemap.cxx_type, declarator.user_name\\n )\\n )\\n \\n cfi_args = {}\\n for arg in ast.declarator.params:\\n declarator = arg.declarator\\n name = declarator.user_name\\n attrs = declarator.attrs\\n meta = declarator.metaattrs\\n cfi_args[name] = False\\n arg_typemap = arg.typemap\\n if meta[\\\"api\\\"]:\\n # API explicitly set by user.\\n continue\\n elif meta[\\\"assumed-rank\\\"]:\\n cfi_args[name] = True\\n elif attrs[\\\"rank\\\"]:\\n cfi_args[name] = True\\n elif arg_typemap.sgroup == \\\"string\\\":\\n cfi_args[name] = True\\n elif arg_typemap.sgroup == \\\"char\\\":\\n if declarator.is_indirect():\\n cfi_args[name] = True\\n elif meta[\\\"deref\\\"] in [\\\"allocatable\\\", \\\"pointer\\\"]:\\n cfi_args[name] = True\\n has_cfi_arg = any(cfi_args.values())\\n\\n # Function result.\\n need_buf_result = None\\n\\n result_as_arg = \\\"\\\" # Only applies to string functions\\n # when the result is added as an argument to the Fortran api.\\n\\n # Check if result needs to be an argument.\\n declarator = ast.declarator\\n attrs = declarator.attrs\\n meta = declarator.metaattrs\\n if meta[\\\"deref\\\"] == \\\"raw\\\":\\n # No bufferify required for raw pointer result.\\n pass\\n elif result_typemap.sgroup == \\\"string\\\":\\n need_buf_result = \\\"cfi\\\"\\n result_as_arg = fmt_func.F_string_result_as_arg\\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\\n elif result_typemap.sgroup == \\\"char\\\" and result_is_ptr:\\n need_buf_result = \\\"cfi\\\"\\n result_as_arg = fmt_func.F_string_result_as_arg\\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\\n elif meta[\\\"deref\\\"] in [\\\"allocatable\\\", \\\"pointer\\\"]:\\n need_buf_result = \\\"cfi\\\"\\n\\n if not (need_buf_result or\\n has_cfi_arg):\\n return False\\n\\n options.wrap_fortran = False\\n\\n # Create a new C function and change arguments\\n # and add attributes.\\n C_new = node.clone()\\n ordered_functions.append(C_new)\\n self.append_function_index(C_new)\\n\\n generated_suffix = \\\"cfi\\\"\\n C_new._generated = \\\"arg_to_cfi\\\"\\n C_new.splicer_group = \\\"cfi\\\"\\n if need_buf_result:\\n C_new.ast.declarator.metaattrs[\\\"api\\\"] = need_buf_result\\n fmt_func = C_new.fmtdict\\n fmt_func.function_suffix = fmt_func.function_suffix + fmt_func.C_cfi_suffix\\n\\n C_new.wrap.assign(c=True)#, fortran=True)\\n C_new._PTR_C_CXX_index = node._function_index\\n\\n for arg in C_new.ast.declarator.params:\\n name = arg.declarator.user_name\\n if cfi_args[name]:\\n arg.declarator.metaattrs[\\\"api\\\"] = generated_suffix\\n\\n ast = C_new.ast\\n if True: # preserve to avoid changing indention for now.\\n f_attrs = node.ast.declarator.attrs # Fortran function attributes\\n f_meta = node.ast.declarator.metaattrs # Fortran function attributes\\n if result_as_arg:\\n # decl: const char * getCharPtr2() +len(30)\\n # +len implies copying into users buffer.\\n result_as_string = ast.result_as_arg(result_name)\\n result_as_string.const = False # must be writeable\\n attrs = result_as_string.declarator.attrs\\n # Special case for wrapf.py to override \\\"allocatable\\\"\\n f_meta[\\\"deref\\\"] = None\\n result_as_string.declarator.metaattrs[\\\"api\\\"] = \\\"cfi\\\"\\n result_as_string.declarator.metaattrs[\\\"deref\\\"] = \\\"result\\\"\\n result_as_string.declarator.metaattrs[\\\"is_result\\\"] = True\\n C_new.ast.declarator.metaattrs[\\\"api\\\"] = None\\n C_new.ast.declarator.metaattrs[\\\"intent\\\"] = \\\"subroutine\\\"\\n C_new.ast.declarator.metaattrs[\\\"deref\\\"] = None\\n\\n if result_as_arg:\\n F_new = self.result_as_arg(node, C_new)\\n ordered_functions.append(F_new)\\n self.append_function_index(F_new)\\n else:\\n if node._generated in [\\\"result_to_arg\\\", \\\"fortran_generic\\\", \\\"getter/setter\\\"]:\\n node.wrap.c = False\\n # Fortran function may call C subroutine if string/vector result\\n # Fortran function calls bufferify function.\\n node._PTR_F_C_index = C_new._function_index\\n return True\",\n \"def __init__(self, *args, **kwargs):\\n super(MadryEtAlMultiGPU, self).__init__(*args, **kwargs)\\n self.structural_kwargs += ['ngpu']\",\n \"def _handle_arg(obj, arg):\\n if isinstance(arg, PythonTensor):\\n if arg.has_init:\\n arg.init_data()\\n if not arg.const_arg:\\n return arg\\n elif isinstance(arg, (Tensor, CSRTensor, COOTensor)):\\n return arg\\n elif hasattr(arg, \\\"__ms_mutable__\\\") and getattr(arg, \\\"__ms_mutable__\\\"):\\n # mutable([]) will be eliminated by FuncGraphSpecializer, and empty list is not supported by backend.\\n if isinstance(arg, list) and not arg:\\n return None\\n return arg\\n elif context.get_context(\\\"grad_for_scalar\\\") and isinstance(arg, (int, float)):\\n return arg\\n elif hasattr(obj, \\\"enable_tuple_broaden\\\") and obj.enable_tuple_broaden and isinstance(arg, tuple) and \\\\\\n _check_all_tensor(arg):\\n return arg\\n return None\",\n \"def build_func_body(func_name, arg_dict, return_type):\\n body = \\\"\\\"\\n arg_list = \\\"\\\"\\n\\n # the following are pointers to scalar outputs\\n # Note: pBufferSize was renamed pBufferSizeInBytes in v6.5\\n scalar_ptr_outputs = ['nnzTotalDevHostPtr',\\n 'pBufferSize',\\n 'pBufferSizeInBytes',\\n 'resultDevHostPtr']\\n\\n is_creator = 'cusparseCreate' in func_name\\n is_getter = 'cusparseGet' in func_name\\n\\n if return_type == 'cusparseStatus_t' and not (is_creator or is_getter):\\n is_return = False\\n else:\\n is_return = True\\n\\n # else:\\n return_str = ''\\n for k, v in arg_dict.items():\\n\\n \\\"\\\"\\\"\\n set some flags based on the name/type of the argument\\n will use these flags to determine whether and how to call ffi.new or\\n ffi.cast on each variable\\n \\\"\\\"\\\"\\n is_ptr = '*' in v\\n is_cusparse_type = '_t' in v\\n is_cusparse_ptr = is_ptr and is_cusparse_type\\n is_output_scalar = k in scalar_ptr_outputs\\n if k in ['alpha', 'beta']:\\n is_scalar = True\\n else:\\n is_scalar = False\\n if is_getter:\\n is_gpu_array = False\\n else:\\n is_gpu_array = is_ptr and (not is_cusparse_ptr) and (not is_scalar)\\n if 'Complex' in v:\\n is_complex = True\\n else:\\n is_complex = False\\n\\n # convert variable to appropriate type for the FFI\\n if is_output_scalar:\\n # for scalar outputs make a new pointer\\n body += \\\"%s = ffi.cast('%s', %s)\\\\n\\\" % (k, v, k)\\n elif is_getter and is_ptr and (return_type == 'cusparseStatus_t'):\\n # any pointers in cusparseGet* are new outputs to be created\\n body += \\\"%s = ffi.new('%s')\\\\n\\\" % (k, v)\\n elif is_gpu_array:\\n # pass pointer to GPU array data (use either .ptr or .gpudata)\\n body += \\\"%s = ffi.cast('%s', %s.ptr)\\\\n\\\" % (k, v, k)\\n elif is_cusparse_ptr:\\n if is_creator:\\n # generate custom cusparse type\\n body += \\\"%s = ffi.new('%s')\\\\n\\\" % (k, v)\\n else:\\n # cast to the custom cusparse type\\n body += \\\"%s = ffi.cast('%s', %s)\\\\n\\\" % (k, v, k)\\n elif is_ptr and is_scalar:\\n # create new pointer, with value initialized to scalar\\n if is_complex:\\n # complex case is a bit tricky. requires ffi.buffer\\n body += \\\"%sffi = ffi.new('%s')\\\\n\\\" % (k, v)\\n if 'cusparseC' in func_name:\\n body += \\\"ffi.buffer(%sffi)[:] = \\\\\\n np.complex64(%s).tostring()\\\\n\\\" % (k, k)\\n elif 'cusparseZ' in func_name:\\n body += \\\"ffi.buffer(%sffi)[:] = \\\\\\n np.complex128(%s).tostring()\\\\n\\\" % (k, k)\\n else:\\n body += \\\"%s = ffi.new('%s', %s)\\\\n\\\" % (k, v, k)\\n elif is_ptr or v == 'cudaStream_t':\\n # case non-scalar pointer to appropriate type\\n body += \\\"%s = ffi.cast('%s', %s)\\\\n\\\" % (k, v, k)\\n else:\\n # don't need explicit cast for plain int, float, etc\\n pass\\n\\n # build the list of arguments to pass to the API\\n if is_ptr and is_scalar and is_complex:\\n # take into account modified argument name for complex scalars\\n arg_list += \\\"%sffi, \\\" % k\\n else:\\n arg_list += \\\"%s, \\\" % k\\n\\n # add the function call and optionally return the result\\n last_key = k\\n arg_list = arg_list[:-2] # remove trailing \\\", \\\"\\n if is_getter and return_type != 'cusparseStatus_t':\\n body += \\\"return ffi_lib.%s(%s)\\\\n\\\" % (func_name, arg_list)\\n else:\\n # check cusparseStatus_t state before returning\\n call_str = \\\"status = ffi_lib.%s(%s)\\\\n\\\" % (func_name, arg_list)\\n body += split_line(call_str, break_pattern=', ', nmax=76)\\n body += \\\"cusparseCheckStatus(status)\\\\n\\\"\\n if is_return:\\n # len(arg_dict) == 2) is to avoid return for cusparseGetLevelInfo\\n if is_creator or (is_getter and (len(arg_dict) == 2)):\\n body += \\\"return %s[0]\\\\n\\\" % last_key\\n else:\\n body += \\\"#TODO: return the appropriate result\\\"\\n body += '\\\\n\\\\n'\\n return reindent(body, numSpaces=4, lstrip=False)\",\n \"def get_helper_c_code_args(self):\\r\\n return {'c_prefix': 'PyGpuArray',\\r\\n 'strides_mul': 1\\r\\n }\",\n \"def apply(op_type, device, inputs, **kwargs):\\n cache = ExecutionCache.get_cache(op_type)\\n run_config = cache.get_config(device, **kwargs)\\n return FunctionLib._forward(inputs, run_config, **kwargs)\",\n \"def fn(*args, **kwargs):\\n pass\",\n \"def function(args):\\n pass\",\n \"def handle_args():\\n parser = argparse.ArgumentParser(description=\\\"Faster-RCNN Implementation\\\")\\n parser.add_argument(\\\"-handle-gpu\\\", action=\\\"store_true\\\", help=\\\"Tensorflow 2 GPU compatibility flag\\\")\\n args = parser.parse_args()\\n return args\",\n \"def parse_params(self, ngpu=1, **kwargs):\\n\\n return_status = super(MadryEtAlMultiGPU, self).parse_params(**kwargs)\\n self.ngpu = ngpu\\n\\n return return_status\",\n \"def arg_to_buffer(self, node, ordered_functions):\\n options = node.options\\n fmt_func = node.fmtdict\\n\\n if node.wrap.c is False:\\n# if options.wrap_c is False: # XXX cdesc.yaml GetScalar2\\n # The user does not require a C wrapper.\\n # This can be the case if the Fortran wrapper is doing all\\n # the work via splicer or fstatements.\\n return\\n\\n # If a C++ function returns a std::string instance,\\n # the default wrapper will not compile since the wrapper\\n # will be declared as char. It will also want to return the\\n # c_str of a stack variable. Warn and turn off the wrapper.\\n ast = node.ast\\n declarator = ast.declarator\\n result_typemap = ast.typemap\\n # shadow classes have not been added yet.\\n # Only care about string, vector here.\\n result_is_ptr = ast.declarator.is_indirect()\\n if (\\n result_typemap\\n and result_typemap.base in [\\\"string\\\", \\\"vector\\\"]\\n and result_typemap.name != \\\"char\\\"\\n and not result_is_ptr\\n ):\\n node.wrap.c = False\\n # node.wrap.fortran = False\\n self.config.log.write(\\n \\\"Skipping {}, unable to create C wrapper \\\"\\n \\\"for function returning {} instance\\\"\\n \\\" (must return a pointer or reference).\\\"\\n \\\" Bufferify version will still be created.\\\\n\\\".format(\\n result_typemap.cxx_type, declarator.user_name\\n )\\n )\\n\\n if node.wrap.fortran is False:\\n # The buffer function is intended to be called by Fortran.\\n # No Fortran, no need for buffer function.\\n return\\n if options.F_string_len_trim is False: # XXX what about vector?\\n return\\n\\n # Arguments.\\n # Is result or any argument a string or vector?\\n # If so, additional arguments will be passed down so\\n # create buffer version of function.\\n buf_args = {}\\n for arg in declarator.params:\\n has_buf_arg = None\\n arg_typemap = arg.typemap\\n declarator = arg.declarator\\n attrs = declarator.attrs\\n meta = declarator.metaattrs\\n if meta[\\\"api\\\"]:\\n # API explicitly set by user.\\n continue\\n elif attrs[\\\"cdesc\\\"]:\\n # User requested cdesc.\\n has_buf_arg = \\\"cdesc\\\"\\n elif arg_typemap.sgroup == \\\"string\\\":\\n if meta[\\\"deref\\\"] in [\\\"allocatable\\\", \\\"pointer\\\", \\\"copy\\\"]:\\n has_buf_arg = \\\"cdesc\\\"\\n # XXX - this is not tested\\n # XXX - tested with string **arg+intent(out)+dimension(ndim)\\n else:\\n has_buf_arg = \\\"buf\\\"\\n elif arg_typemap.sgroup == \\\"char\\\":\\n if arg.ftrim_char_in:\\n pass\\n elif declarator.is_indirect():\\n if meta[\\\"deref\\\"] in [\\\"allocatable\\\", \\\"pointer\\\"]:\\n has_buf_arg = \\\"cdesc\\\"\\n else:\\n has_buf_arg = \\\"buf\\\"\\n elif arg_typemap.sgroup == \\\"vector\\\":\\n if meta[\\\"intent\\\"] == \\\"in\\\":\\n # Pass SIZE.\\n has_buf_arg = \\\"buf\\\"\\n else:\\n has_buf_arg = \\\"cdesc\\\"\\n elif (arg_typemap.sgroup == \\\"native\\\" and\\n meta[\\\"intent\\\"] == \\\"out\\\" and\\n meta[\\\"deref\\\"] != \\\"raw\\\" and\\n declarator.get_indirect_stmt() in [\\\"**\\\", \\\"*&\\\"]):\\n # double **values +intent(out) +deref(pointer)\\n has_buf_arg = \\\"cdesc\\\"\\n #has_buf_arg = \\\"buf\\\" # XXX - for scalar?\\n buf_args[declarator.user_name] = has_buf_arg\\n # --- End loop over function parameters\\n has_buf_arg = any(buf_args.values())\\n\\n # Function result.\\n need_buf_result = None\\n\\n result_as_arg = \\\"\\\" # Only applies to string functions\\n # when the result is added as an argument to the Fortran api.\\n\\n # Check if result needs to be an argument.\\n attrs = ast.declarator.attrs\\n meta = ast.declarator.metaattrs\\n if meta[\\\"deref\\\"] == \\\"raw\\\":\\n # No bufferify required for raw pointer result.\\n pass\\n elif result_typemap.sgroup == \\\"string\\\":\\n if meta[\\\"deref\\\"] in [\\\"allocatable\\\", \\\"pointer\\\"]:\\n need_buf_result = \\\"cdesc\\\"\\n else:\\n need_buf_result = \\\"buf\\\"\\n result_as_arg = fmt_func.F_string_result_as_arg\\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\\n elif result_typemap.sgroup == \\\"char\\\" and result_is_ptr:\\n if meta[\\\"deref\\\"] in [\\\"allocatable\\\", \\\"pointer\\\"]:\\n # Result default to \\\"allocatable\\\".\\n need_buf_result = \\\"cdesc\\\"\\n else:\\n need_buf_result = \\\"buf\\\"\\n result_as_arg = fmt_func.F_string_result_as_arg\\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\\n elif result_typemap.base == \\\"vector\\\":\\n need_buf_result = \\\"cdesc\\\"\\n elif result_is_ptr:\\n if meta[\\\"deref\\\"] in [\\\"allocatable\\\", \\\"pointer\\\"]:\\n if meta[\\\"dimension\\\"]:\\n # int *get_array() +deref(pointer)+dimension(10)\\n need_buf_result = \\\"cdesc\\\"\\n\\n # Functions with these results need wrappers.\\n if not (need_buf_result or\\n has_buf_arg):\\n return\\n\\n # XXX node.wrap.fortran = False\\n # Preserve wrap.c.\\n # This keep a version which accepts char * arguments.\\n\\n # Create a new C function and change arguments\\n # and add attributes.\\n C_new = node.clone()\\n ordered_functions.append(C_new)\\n self.append_function_index(C_new)\\n\\n generated_suffix = \\\"buf\\\"\\n C_new._generated = \\\"arg_to_buffer\\\"\\n C_new.splicer_group = \\\"buf\\\"\\n if need_buf_result:\\n C_new.ast.declarator.metaattrs[\\\"api\\\"] = need_buf_result\\n \\n fmt_func = C_new.fmtdict\\n fmt_func.function_suffix = fmt_func.function_suffix + fmt_func.C_bufferify_suffix\\n\\n options = C_new.options\\n C_new.wrap.assign(c=node.options.wrap_c)\\n C_new._PTR_C_CXX_index = node._function_index\\n\\n for arg in C_new.ast.declarator.params:\\n declarator = arg.declarator\\n attrs = declarator.attrs\\n meta = declarator.metaattrs\\n if buf_args[declarator.user_name]:\\n meta[\\\"api\\\"] = buf_args[declarator.user_name]\\n if arg.ftrim_char_in:\\n continue\\n arg_typemap = arg.typemap\\n if arg_typemap.base == \\\"vector\\\":\\n # Do not wrap the orignal C function with vector argument.\\n # Meaningless to call without the size argument.\\n # TODO: add an option where char** length is determined by looking\\n # for trailing NULL pointer. { \\\"foo\\\", \\\"bar\\\", NULL };\\n node.wrap.c = False\\n node.wrap.lua = False # NotImplemented\\n\\n ast = C_new.ast\\n if True: # preserve to avoid changing indention for now.\\n # Add additional argument to hold result.\\n # This will allocate a new character variable to hold the\\n # results of the C++ function.\\n f_attrs = node.ast.declarator.attrs # Fortran function attributes\\n f_meta = node.ast.declarator.metaattrs # Fortran function attributes\\n\\n if result_as_arg:\\n # decl: const char * getCharPtr2() +len(30)\\n # +len implies copying into users buffer.\\n result_as_string = ast.result_as_arg(result_name)\\n result_as_string.const = False # must be writeable\\n attrs = result_as_string.declarator.attrs\\n # Special case for wrapf.py to override \\\"allocatable\\\"\\n f_meta[\\\"deref\\\"] = None\\n # We've added an argument to fill, use api=buf.\\n result_as_string.declarator.metaattrs[\\\"api\\\"] = \\\"buf\\\"\\n result_as_string.declarator.metaattrs[\\\"deref\\\"] = \\\"result\\\"\\n result_as_string.declarator.metaattrs[\\\"is_result\\\"] = True\\n C_new.ast.declarator.metaattrs[\\\"api\\\"] = None\\n C_new.ast.declarator.metaattrs[\\\"intent\\\"] = \\\"subroutine\\\"\\n C_new.ast.declarator.metaattrs[\\\"deref\\\"] = None\\n\\n if result_as_arg:\\n F_new = self.result_as_arg(node, C_new)\\n ordered_functions.append(F_new)\\n self.append_function_index(F_new)\\n else:\\n if node._generated in [\\\"result_to_arg\\\", \\\"fortran_generic\\\", \\\"getter/setter\\\"]:\\n node.wrap.c = False\\n \\n # Fortran function may call C subroutine if string/vector result\\n node._PTR_F_C_index = C_new._function_index\",\n \"def handle_args(args):\\n\\n if args.id and len(args.id) > MAX_ID_LENGTH:\\n raise ValueError\\n if args.payload and (len(args.payload) % 2 != 0 or len(args.payload) > MAX_PAYLOAD_LENGTH):\\n raise ValueError\\n\\n if args.id_bitmap:\\n if len(args.id_bitmap) > MAX_ID_LENGTH:\\n raise ValueError\\n for i in range(len(args.id_bitmap)):\\n args.id_bitmap[i] = string_to_bool(args.id_bitmap[i])\\n if args.payload_bitmap:\\n if len(args.payload_bitmap) > MAX_PAYLOAD_LENGTH:\\n raise ValueError\\n for i in range(len(args.payload_bitmap)):\\n args.payload_bitmap[i] = string_to_bool(args.payload_bitmap[i])\\n\\n if args.alg == \\\"random\\\":\\n __handle_random(args)\\n elif args.alg == \\\"linear\\\":\\n __handle_linear(args)\\n elif args.alg == \\\"replay\\\":\\n __handle_replay(args)\\n elif args.alg == \\\"ring_bf\\\":\\n __handle_ring_bf(args)\\n elif args.alg == \\\"mutate\\\":\\n __handle_mutate(args)\\n else:\\n raise ValueError\",\n \"def call_function(self):\\n try:\\n arg_list = self.argument_list()\\n function_dict = {}\\n info = []\\n for name_arg in arg_list:\\n type_arg = self.arguments_type[name_arg]\\n function_dict[name_arg] = utils.value_from_rpc(self.argument(name_arg)[1])\\n info.append('{0}({1}): {2}'.format(name_arg, type_arg, function_dict[name_arg]))\\n\\n log.info('Execute command \\\\'{0}\\\\' with arguments [{1}] from device \\\\'{2}\\\\''\\n .format(self.name(), '; '.join(info), self.device.id))\\n self.function(self.device, **function_dict)\\n\\n except Exception as err:\\n t = traceback.format_exc()\\n log.error('Command \\\\'{0}\\\\' raise exception: {1}'.format(self.name(), decode_string(t)))\",\n \"def __call__(self, *args, backend=\\\"auto\\\", device_id=-1, ranges=None, out=None):\\n\\n dtype = args[0].dtype.__str__().split(\\\".\\\")[1]\\n\\n nx, ny = get_sizes(self.aliases, *args)\\n nout, nred = (nx, ny) if self.axis == 1 else (ny, nx)\\n\\n if \\\"Arg\\\" in self.reduction_op:\\n # when using Arg type reductions,\\n # if nred is greater than 16 millions and dtype=float32, the result is not reliable\\n # because we encode indices as floats, so we raise an exception ;\\n # same with float16 type and nred>2048\\n if nred > 1.6e7 and dtype in (\\\"float32\\\", \\\"float\\\"):\\n raise ValueError(\\n \\\"size of input array is too large for Arg type reduction with single precision. Use double precision.\\\"\\n )\\n elif nred > 2048 and dtype in (\\\"float16\\\", \\\"half\\\"):\\n raise ValueError(\\n \\\"size of input array is too large for Arg type reduction with float16 dtype..\\\"\\n )\\n\\n out = GenredAutograd.apply(\\n self.formula,\\n self.aliases,\\n backend,\\n dtype,\\n device_id,\\n ranges,\\n self.optional_flags,\\n self.rec_multVar_highdim,\\n nx,\\n ny,\\n out,\\n *args\\n )\\n\\n return postprocess(out, \\\"torch\\\", self.reduction_op, nout, self.opt_arg, dtype)\",\n \"def handle_input(self, data, feature_names, feature_types):\\n interface = data.__cuda_array_interface__\\n if 'mask' in interface:\\n interface['mask'] = interface['mask'].__cuda_array_interface__\\n interface_str = bytes(json.dumps(interface, indent=2), 'utf-8')\\n\\n handle = ctypes.c_void_p()\\n _check_call(\\n _LIB.XGDMatrixCreateFromArrayInterface(\\n interface_str,\\n ctypes.c_float(self.missing),\\n ctypes.c_int(self.nthread),\\n ctypes.byref(handle)))\\n return handle, feature_names, feature_types\",\n \"def compile_function(self, function, arguments):\",\n \"def _set_function_parameters(self, p_args=None) -> bool:\\n if self.get_type() == self.C_UNIT_CONV_LENGTH:\\n self.units = {\\n 'fm' : 1000000000000000,\\n 'pm' : 1000000000000,\\n 'nm' : 1000000000,\\n 'um' : 1000000,\\n 'mm' : 1000,\\n 'cm' : 100,\\n 'm' : 1.0,\\n 'dam' : 0.1,\\n 'hm' : 0.01,\\n 'km' : 0.001,\\n 'Mm' : 0.000001,\\n 'Gm' : 0.000000001,\\n 'Tm' : 0.000000000001,\\n 'Pm' : 0.000000000000001,\\n 'inch' : 39.3701,\\n 'ft' : 3.28084,\\n 'yd' : 1.09361,\\n 'mi' : 0.000621371,\\n 'nautMi' : 1.0/1852.0,\\n 'lightYear' : 1.0/(9.4607304725808*(10**15))\\n }\\n \\n elif self.get_type() == self.C_UNIT_CONV_PRESSURE:\\n self.units = {\\n 'Pa' : 100000.0,\\n 'hPa' : 1000.0,\\n 'kPa' : 100.0,\\n 'MPa' : 0.1,\\n 'bar' : 1.0,\\n 'mbar' : 1000.0,\\n 'ubar' : 1000000.0,\\n 'kgcm2' : 1.01972,\\n 'atm' : 0.986923,\\n 'mmHg' : 750.062,\\n 'mmH2O' : 10197.162129779,\\n 'mH2O' : 10.197162129779,\\n 'ftH2O' : 33.455256555148,\\n 'inH2O' : 401.865,\\n 'inHg' : 29.53,\\n 'psi' : 14.5038\\n }\\n \\n elif self.get_type() == self.C_UNIT_CONV_CURRENT:\\n self.units = {\\n 'fA' : 1000000000000000,\\n 'pA' : 1000000000000,\\n 'nA' : 1000000000,\\n 'uA' : 1000000,\\n 'mA' : 1000,\\n 'cA' : 100,\\n 'A' : 1.0,\\n 'daA' : 0.1,\\n 'hA' : 0.01,\\n 'kA' : 0.001,\\n 'MA' : 0.000001,\\n 'GA' : 0.000000001,\\n 'TA' : 0.000000000001,\\n 'PA' : 0.000000000000001,\\n }\\n \\n elif self.get_type() == self.C_UNIT_CONV_FORCE:\\n self.units = {\\n 'fN' : 1000000000000000,\\n 'pN' : 1000000000000,\\n 'nN' : 1000000000,\\n 'uN' : 1000000,\\n 'mN' : 1000,\\n 'cN' : 100,\\n 'N' : 1.0,\\n 'daN' : 0.1,\\n 'hN' : 0.01,\\n 'kN' : 0.001,\\n 'MN' : 0.000001,\\n 'GN' : 0.000000001,\\n 'TN' : 0.000000000001,\\n 'PN' : 0.000000000000001,\\n 'shortTonF' : 1.124045e-4,\\n 'longTonF' : 1.003611e-4,\\n 'kipf' : 2.248089e-4,\\n 'lbf' : 2.248089431e-1,\\n 'ozf' : 3.5969430896,\\n 'pdf' : 7.2330138512,\\n 'gf' : 1.019716213e+2,\\n 'kgf' : 1.019716213e-1,\\n 'dyn' : 1e+5,\\n 'J/m' : 1.0,\\n 'J/cm' : 100.0\\n }\\n \\n elif self.get_type() == self.C_UNIT_CONV_POWER:\\n self.units = {\\n 'fW' : 1000000000000000*1e3,\\n 'pW' : 1000000000000*1e3,\\n 'nW' : 1000000000*1e3,\\n 'uW' : 1000000*1e3,\\n 'mW' : 1000*1e3,\\n 'cW' : 100*1e3,\\n 'W' : 1.0*1e3,\\n 'daW' : 0.1*1e3,\\n 'hW' : 0.01*1e3,\\n 'kW' : 0.001*1e3,\\n 'MW' : 0.000001*1e3,\\n 'GW' : 0.000000001*1e3,\\n 'TW' : 0.000000000001*1e3,\\n 'PW' : 0.000000000000001*1e3,\\n 'BTU/hr' : 3412.14,\\n 'BTU/min' : 56.869,\\n 'BTU/sec' : 0.94781666666,\\n 'cal/sec' : 238.85,\\n 'cal/min' : 238.85*60,\\n 'cal/hr' : 238.85*60*60,\\n 'erg/sec' : 10e9,\\n 'erg/min' : 10e9*60,\\n 'erg/hr' : 10e9*60*60,\\n 'ftlb/sec' : 737.56,\\n 'kCal/sec' : 0.24,\\n 'kCal/min' : 0.24*60,\\n 'kCal/hr' : 0.24*60*60,\\n 'VA' : 1e3,\\n 'metric_ton_ref' : 0.259,\\n 'US_ton_ref' : 0.2843451361,\\n 'J/sec' : 1000.0,\\n 'J/min' : 1000.0*60,\\n 'J/hr' : 1000.0*60*60,\\n 'kgf-m/sec' : 101.97162129779,\\n 'hp_mech' : 1.3410220888,\\n 'hp_ele' : 1.3404825737,\\n 'hp_metric' : 1.359621617304\\n }\\n \\n elif self.get_type() == self.C_UNIT_CONV_MASS:\\n self.units = {\\n 'fg' : 1000000000000000*1e3,\\n 'pg' : 1000000000000*1e3,\\n 'ng' : 1000000000*1e3,\\n 'ug' : 1000000*1e3,\\n 'mg' : 1000*1e3,\\n 'cg' : 100*1e3,\\n 'g' : 1.0*1e3,\\n 'dag' : 0.1*1e3,\\n 'hg' : 0.01*1e3,\\n 'kg' : 0.001*1e3,\\n 'Mg' : 0.000001*1e3,\\n 'Gg' : 0.000000001*1e3,\\n 'Tg' : 0.000000000001*1e3,\\n 'Pg' : 0.000000000000001*1e3,\\n 'metricTon' : 1.0/1000.0,\\n 'shortTon' : 1.0/907.185,\\n 'longTon' : 1.0/1016.047,\\n 'slug' : 1.0/14.5939029,\\n 'lb' : 2.2046226218,\\n 'oz' : 35.274,\\n 'grain' : 2.2046226218*7000.0\\n }\\n \\n elif self.get_type() == self.C_UNIT_CONV_TIME:\\n self.units = {\\n 'fs' : 1000000000000000,\\n 'ps' : 1000000000000,\\n 'ns' : 1000000000,\\n 'us' : 1000000,\\n 'ms' : 1000,\\n 'cs' : 100,\\n 's' : 1.0,\\n 'das' : 0.1,\\n 'hs' : 0.01,\\n 'ks' : 0.001,\\n 'Ms' : 0.000001,\\n 'Gs' : 0.000000001,\\n 'Ts' : 0.000000000001,\\n 'Ps' : 0.000000000000001,\\n 'min' : 1.0/60.0,\\n 'hr' : 1.0/60.0/60.0,\\n 'day' : 1.0/60.0/60.0/24.0\\n }\\n \\n elif self.get_type() == self.C_UNIT_CONV_TEMPERATURE:\\n self.units = {\\n 'K' : 'Kelvin',\\n 'R' : 'Rankine',\\n 'F' : 'Fahrenheit',\\n 'C' : 'Celcius',\\n }\\n \\n if self.units.get(self._unit_in) is not None and self.units.get(self._unit_out) is not None:\\n return True\\n else:\\n raise NotImplementedError('The input and/or output units do not exist!')\",\n \"def run(self, fn, args=(), kwargs=None, options=None): # pylint: disable=useless-super-delegation\\n return super(OneDeviceStrategy, self).run(fn, args, kwargs, options)\",\n \"def ubercam(*args, **kwargs)->AnyStr:\\n pass\",\n \"def __call__(self, *args, **kwargs):\\n dprint(2, \\\"FunctionMetadata::__call__\\\", self.func.__name__, args, kwargs, self.numba_args)\\n atypes = tuple([type(x) for x in args])\\n try_again = True\\n count = 0\\n if not self.numba_pfunc:\\n if len(self.numba_args) == 0 and not self.no_global_cache:\\n self.numba_pfunc = get_fm(FillerFunc(self.func), True)\\n self.numba_func = get_fm(FillerFunc(self.func), False)\\n else:\\n self.numba_pfunc = numba.njit(parallel=True, **self.numba_args)(self.func)\\n self.numba_func = numba.njit(**self.numba_args)(self.func)\\n\\n if gpu_present:\\n dprint(1, \\\"using gpu context\\\")\\n\\n with dpctl.device_context(\\\"level0:gpu\\\"):\\n while try_again and count < 2:\\n count += 1\\n try_again = False\\n if self.ngfunc.get(atypes, True):\\n try:\\n ret = self.numba_pfunc(*args, **kwargs)\\n self.ngfunc[atypes] = True\\n return ret\\n except numba.core.errors.TypingError as te:\\n tetxt = str(te)\\n tesplit = tetxt.splitlines()\\n for teline in tesplit:\\n if \\\"Untyped global name\\\" in teline and \\\"ramba.StencilMetadata\\\" in teline:\\n try_again = True\\n # Name of global that is of type ramba.StencilMetadata\\n tes = teline[21:].split()[0][:-2]\\n outer_globals = self.func.__globals__\\n outer_locals = {}\\n etes = eval(tes, outer_globals, outer_locals)\\n etes.compile() # Converts to a Numba StencilFunc\\n outer_globals[tes] = etes.sfunc # Rewrite the global to the Numba StencilFunc\\n self.numba_pfunc = numba.njit(parallel=True, **self.numba_args)(self.func)\\n self.numba_func = numba.njit(**self.numba_args)(self.func)\\n if not try_again:\\n self.ngfunc[atypes] = False\\n dprint(1, \\\"Numba GPU ParallelAccelerator attempt failed.\\\")\\n except:\\n self.ngfunc[atypes] = False\\n dprint(1, \\\"Numba GPU ParallelAccelerator attempt failed.\\\")\\n\\n while try_again and count < 2:\\n count += 1\\n try_again = False\\n if self.npfunc.get(atypes, True):\\n try:\\n ret = self.numba_pfunc(*args, **kwargs)\\n self.npfunc[atypes] = True\\n return ret\\n except numba.core.errors.TypingError as te:\\n tetxt = str(te)\\n tesplit = tetxt.splitlines()\\n for teline in tesplit:\\n if \\\"Untyped global name\\\" in teline and \\\"ramba.StencilMetadata\\\" in teline:\\n try_again = True\\n # Name of global that is of type ramba.StencilMetadata\\n tes = teline[21:].split()[0][:-2]\\n outer_globals = self.func.__globals__\\n outer_locals = {}\\n etes = eval(tes, outer_globals, outer_locals)\\n etes.compile() # Converts to a Numba StencilFunc\\n outer_globals[tes] = etes.sfunc # Rewrite the global to the Numba StencilFunc\\n self.numba_pfunc = numba.njit(parallel=True, **self.numba_args)(self.func)\\n self.numba_func = numba.njit(**self.numba_args)(self.func)\\n if not try_again:\\n self.npfunc[atypes] = False\\n dprint(1, \\\"Numba ParallelAccelerator attempt failed.\\\")\\n except:\\n self.npfunc[atypes] = False\\n dprint(1, \\\"Numba ParallelAccelerator attempt failed.\\\")\\n\\n if self.nfunc.get(atypes, True):\\n try:\\n ret = self.numba_func(*args, **kwargs)\\n self.nfunc[atypes] = True\\n dprint(3, \\\"Numba attempt succeeded.\\\")\\n return ret\\n except numba.core.errors.TypingError as te:\\n print(\\\"Ramba TypingError:\\\", te, type(te))\\n self.npfunc[atypes] = False\\n dprint(1, \\\"Numba attempt failed.\\\")\\n except:\\n self.nfunc[atypes] = False\\n dprint(1, \\\"Numba attempt failed.\\\")\\n raise\\n\\n return self.func(*args, **kwargs)\",\n \"def run(fn, *input_values, **kwds):\\n \\n ee = kwds.get('ee', shared_exec_engine)\\n input_types = [arg.type for arg in fn.args]\\n gv_inputs = [gv_from_python(x, t) \\n for (x,t) in \\n zip(input_values, input_types)]\\n \\n return run_with_generic_values(fn, gv_inputs, ee)\",\n \"def cudify(fn):\\n\\n @functools.wraps(fn)\\n def wrapper(*args, **kwargs):\\n result = fn(*args, **kwargs)\\n return cuda_if_gpu(result)\\n\\n return wrapper\",\n \"def fsig(arg_types: ArgTypes, name: Text, span: Span, ctx: DeduceCtx,\\n _: Optional[ParametricBindings]) -> ConcreteType:\\n checker = _Checker(arg_types, name, span).len(2).is_bits(0).is_array(1)\\n\\n arg0 = arg_types[0]\\n arg1 = arg_types[1]\\n assert isinstance(arg1, ArrayType), arg1\\n assert isinstance(arg1.size.value, int), arg1\\n return_type = arg1.element_type\\n checker.check_is_bits(return_type,\\n 'Want arg 1 element type to be bits; got {0}')\\n checker.check_is_len(arg1, arg0.size,\\n 'bit width {target} must match {t} array size {t.size}')\\n return FunctionType(arg_types, return_type)\",\n \"def feed_arg(self, ws, name, value, dtype):\\n ws.FeedTensor(name, numpy.array(value, dtype), self._arg_device)\",\n \"def handle_func_command(cls, command):\\n cmd, _, args, kwargs = command\\n\\n try: # will work if tensors are wrappers\\n\\n # Replace all TensorFlow tensor with their child attribute\\n # Note that we return also args_type which helps handling case 3 in the docstring\\n new_args, new_kwargs, new_type, args_type = hook_args.unwrap_args_from_function(\\n cmd, args, kwargs, return_args_type=True\\n )\\n # This handles case 3: it redirects the command to the appropriate class depending\\n # of the syft type of the arguments and returns\\n if args_type not in FrameworkTensor:\\n return args_type.handle_func_command(command)\\n\\n # build the new command\\n new_command = (cmd, None, new_args, new_kwargs)\\n # Send it to the appropriate class and get the response\\n response = new_type.handle_func_command(new_command)\\n # Put back the wrappers where needed\\n response = hook_args.hook_response(cmd, response, wrap_type=args_type)\\n except PureFrameworkTensorFoundError: # means that it's not a wrapper but a pure tensor\\n\\n # Check that the function has not been overwritten\\n try:\\n # Try to get recursively the attributes in cmd = \\\"<attr1>.<attr2>.<attr3>...\\\"\\n command = cls.rgetattr(cls, cmd)\\n return command(*args, **kwargs)\\n except AttributeError:\\n pass\\n\\n # TODO: clean this line\\n cmd_split = cmd.split(\\\".\\\")\\n cmd_path = cmd_split[:-1]\\n cmd_name = cmd_split[-1]\\n cmd = \\\"syft.local_worker.hook.\\\" + \\\".\\\".join(cmd_path) + \\\".native_\\\" + cmd_name\\n\\n # Run the native function with the new args\\n # Note the the cmd should already be checked upon reception by the worker\\n # in the execute_command function\\n if isinstance(args, tuple):\\n response = eval(cmd)(*args, **kwargs)\\n else:\\n response = eval(cmd)(args, **kwargs)\\n\\n return response\",\n \"def _Call(self, t):\\n # check calls but let attributes check in their own dispatcher\\n funcs = self._device_functions + self.pythonbuiltins + [self._input_message_var] # message_input variable is a valid function name as certain message types have arguments on iterator\\n if isinstance(t.func, ast.Name):\\n if (t.func.id not in funcs):\\n self.RaiseWarning(t, \\\"Function call is not a defined FLAME GPU device function or a supported python built in.\\\")\\n # dispatch even if warning raised\\n self.dispatch(t.func)\\n elif isinstance(t.func, ast.Lambda):\\n self.dispatch(t.func) # not supported\\n else:\\n # special handler for dispatching member function calls\\n # This would otherwise be an attribute\\n self.dispatchMemberFunction(t.func, t) \\n self.write(\\\"(\\\")\\n self._CallArguments(t)\\n self.write(\\\")\\\")\",\n \"def handle_input(self, data, feature_names, feature_types):\\n if not hasattr(data, '__cuda_array_interface__') and hasattr(\\n data, '__array__'):\\n import cupy # pylint: disable=import-error\\n data = cupy.array(data, copy=False)\\n\\n interface = data.__cuda_array_interface__\\n if 'mask' in interface:\\n interface['mask'] = interface['mask'].__cuda_array_interface__\\n interface_str = bytes(json.dumps(interface, indent=2), 'utf-8')\\n\\n handle = ctypes.c_void_p()\\n _check_call(\\n _LIB.XGDeviceQuantileDMatrixCreateFromArrayInterface(\\n interface_str,\\n ctypes.c_float(self.missing), ctypes.c_int(self.nthread),\\n ctypes.c_int(self.max_bin), ctypes.byref(handle)))\\n return handle, feature_names, feature_types\",\n \"def _unpack_argument(self, ty, val, sycl_queue, retr, kernelargs,\\n device_arrs, access_type):\\n\\n device_arrs.append(None)\\n\\n if isinstance(ty, types.Array):\\n if isinstance(val.base, dpctl_mem.Memory):\\n self._unpack_device_array_argument(val, kernelargs)\\n else:\\n default_behavior = self.check_for_invalid_access_type(access_type)\\n\\n usm_buf = dpctl_mem.MemoryUSMShared(val.size * val.dtype.itemsize)\\n usm_ndarr = np.ndarray(val.shape, buffer=usm_buf, dtype=val.dtype)\\n\\n if (default_behavior or\\n self.valid_access_types[access_type] == _NUMBA_DPPL_READ_ONLY or\\n self.valid_access_types[access_type] == _NUMBA_DPPL_READ_WRITE):\\n np.copyto(usm_ndarr, val)\\n\\n device_arrs[-1] = (usm_buf, usm_ndarr, val)\\n self._unpack_device_array_argument(usm_ndarr, kernelargs)\\n\\n elif ty == types.int64:\\n cval = ctypes.c_long(val)\\n kernelargs.append(cval)\\n elif ty == types.uint64:\\n cval = ctypes.c_long(val)\\n kernelargs.append(cval)\\n elif ty == types.int32:\\n cval = ctypes.c_int(val)\\n kernelargs.append(cval)\\n elif ty == types.uint32:\\n cval = ctypes.c_int(val)\\n kernelargs.append(cval)\\n elif ty == types.float64:\\n cval = ctypes.c_double(val)\\n kernelargs.append(cval)\\n elif ty == types.float32:\\n cval = ctypes.c_float(val)\\n kernelargs.append(cval)\\n elif ty == types.boolean:\\n cval = ctypes.c_uint8(int(val))\\n kernelargs.append(cval)\\n elif ty == types.complex64:\\n #kernelargs.append(ctypes.c_float(val.real))\\n #kernelargs.append(ctypes.c_float(val.imag))\\n raise NotImplementedError(ty, val)\\n\\n elif ty == types.complex128:\\n #kernelargs.append(ctypes.c_double(val.real))\\n #kernelargs.append(ctypes.c_double(val.imag))\\n raise NotImplementedError(ty, val)\\n\\n else:\\n raise NotImplementedError(ty, val)\",\n \"def handle_input(self, data, feature_names, feature_types):\\n data, feature_names, feature_types = self._maybe_cudf_dataframe(\\n data, feature_names, feature_types)\\n interfaces_str = _cudf_array_interfaces(data)\\n handle = ctypes.c_void_p()\\n _check_call(\\n _LIB.XGDMatrixCreateFromArrayInterfaceColumns(\\n interfaces_str,\\n ctypes.c_float(self.missing),\\n ctypes.c_int(self.nthread),\\n ctypes.byref(handle)))\\n return handle, feature_names, feature_types\",\n \"def handle_arguments(self, args):\\n debug(\\\"BloomGenerator.handle_arguments: got args -> \\\" + str(args))\",\n \"def cpp_function(self):\",\n \"def _create_args(self, func_args):\\n self.llvm_ret_type = self._from_ctype(self.signature.ret_type)\\n self.llvm_arg_types = \\\\\\n [self._from_ctype(a) for a in self.signature.arg_ctypes]\",\n \"def compile(self, args):\\n if args not in self._compileinfos:\\n cres = compile_with_dppl(self.py_func, None, args, debug=self.debug)\\n func = cres.library.get_function(cres.fndesc.llvm_func_name)\\n cres.target_context.mark_ocl_device(func)\\n first_definition = not self._compileinfos\\n self._compileinfos[args] = cres\\n libs = [cres.library]\\n\\n if first_definition:\\n # First definition\\n cres.target_context.insert_user_function(self, cres.fndesc,\\n libs)\\n else:\\n cres.target_context.add_user_function(self, cres.fndesc, libs)\\n\\n else:\\n cres = self._compileinfos[args]\\n\\n return cres.signature\",\n \"def call(self, inputs):\\n raise NotImplementedError\",\n \"def wrapper(self, *args, **kwargs):\\n torch_args = [torch.tensor(arg, requires_grad=True, dtype=torch.float64) if type(arg) is ndarray else arg for arg in args]\\n torch_kwargs = {key: torch.tensor(kwarg, requires_grad=True, dtype=torch.float64) if type(kwarg) is ndarray else kwarg for key, kwarg in kwargs.items()}\\n result = function(self, *torch_args, **torch_kwargs)\\n return result.cpu().detach().numpy()\",\n \"def real_decorator(f):\\n\\n def inner(*args, **kwargs):\\n \\\"\\\"\\\"returns a torch tensor for cpu or gpu when appropriate.\\\"\\\"\\\"\\n\\n s, r, d, p = f(*args, **kwargs)\\n\\n if torch.cuda.is_available():\\n s = torch.from_numpy(s).cuda().type(dtype)\\n\\n else:\\n s = torch.from_numpy(s).type(dtype)\\n\\n return s, r, d, p\\n\\n return inner\",\n \"def varfunc(self, fields=[]):\\n self.func_arguments = fields\",\n \"def handle_input(self, data, feature_names, feature_types):\\n data, feature_names, feature_types = self._maybe_cudf_dataframe(\\n data, feature_names, feature_types)\\n interfaces_str = _cudf_array_interfaces(data)\\n handle = ctypes.c_void_p()\\n _check_call(\\n _LIB.XGDeviceQuantileDMatrixCreateFromArrayInterfaceColumns(\\n interfaces_str,\\n ctypes.c_float(self.missing), ctypes.c_int(self.nthread),\\n ctypes.c_int(self.max_bin), ctypes.byref(handle)))\\n return handle, feature_names, feature_types\",\n \"def f_onearg(self, arg1) :\\n pass\",\n \"def dispatchMacroEnvFunction(self, tree, tree_parent):\\n cpp_func_name = \\\"getMacroProperty\\\"\\n py_func = tree.attr\\n # extract type from function name\\n py_type = py_func[len(cpp_func_name):]\\n if py_type not in self._fgpu_types:\\n self.RaiseError(tree, f\\\"'{py_type}' is not a valid FLAME GPU type\\\")\\n # get cpp type\\n t = self._fgpu_types[py_type]\\n cpp_func_name += f\\\"<{t}\\\"\\n # mess with the parent to extract (and remove arguments so they dont end up in the argument list)\\n if not tree_parent.args :\\n self.RaiseError(tree, f\\\" Macro environment function '{py_func}' is expected to have some arguments.\\\")\\n # if more than one arg then the rest are bounds to translate\\n if len(tree_parent.args) > 1:\\n bounds = tree_parent.args[1:]\\n # process bounds by appending to cpp function template arguments\\n for i in bounds:\\n if isinstance(i, ast.Num): # num required for python 3.7\\n if not isinstance(i.n, int):\\n self.RaiseError(tree, f\\\" Macro environment function argument '{i}' should be an integer value.\\\")\\n cpp_func_name += f\\\", {i.n}\\\"\\n else: # all Python > 3.7 \\n if not isinstance(i, ast.Constant):\\n self.RaiseError(tree, f\\\" Macro environment function argument '{i}' should be an constant value (or Num in Python <3.8).\\\")\\n if not isinstance(i.value, int):\\n self.RaiseError(tree, f\\\" Macro environment function argument '{i}' should be an integer value.\\\")\\n cpp_func_name += f\\\", {i.value}\\\"\\n # remove bounds from argument list (in place)\\n del tree_parent.args[1:]\\n cpp_func_name += \\\">\\\"\\n self.write(cpp_func_name)\",\n \"def tffunc(*argtypes):\\n placeholders = list(map(tf.placeholder, argtypes))\\n def wrap(f):\\n out = f(*placeholders)\\n def wrapper(*args, **kw):\\n return out.eval(dict(zip(placeholders, args)), session=kw.get('session'))\\n return wrapper\\n return wrap\",\n \"def call_top_interface_args_with_func_def(self, node: AnnCastCall):\\n # call container is used to scope parameters\\n call_con_name = call_container_name(node)\\n\\n # create argument and parameter variables\\n # argument variables are inputs to the top interface\\n # paramter variables are outputs of the top interface\\n for i, n in enumerate(node.arguments):\\n # argument name and scope str\\n arg_name = call_argument_name(node, i)\\n arg_con_scopestr = con_scope_to_str(node.func.con_scope)\\n\\n # parameter name and scopestr\\n func_def = self.pipeline_state.func_def_node_from_id(node.func.id)\\n param = func_def.func_args[i]\\n assert(isinstance(param, AnnCastVar))\\n param_name = param.val.name\\n param_con_scopestr = con_scope_to_str(node.func.con_scope + [call_con_name])\\n\\n # argument and parameter share id, and start with initial version\\n id = self.pipeline_state.next_collapsed_id()\\n version = VAR_INIT_VERSION\\n\\n # build and store GrFN variables for argument and parameter\\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\\n # store arg_fullid\\n node.arg_index_to_fullid[i] = arg_fullid\\n # create From Source metadata for the GrFN var\\n from_source = False\\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\\n\\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\\n # store param_fullid\\n node.param_index_to_fullid[i] = param_fullid\\n # create From Source metadata for the GrFN var\\n add_metadata_from_name_node(param_grfn_var, param.val)\\n\\n # link argument and parameter through top interface\\n node.top_interface_in[id] = arg_fullid\\n node.top_interface_out[id] = param_fullid\\n\\n # DEBUG printing\\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\\n print(\\\"After create_call_args_and_params():\\\")\\n print(f\\\"\\\\ttop_interface_in = {node.top_interface_in}\\\")\\n print(f\\\"\\\\ttop_interface_out = {node.top_interface_out}\\\")\",\n \"def deconvolution_fusion_para(x, y):\\n input_memory_type = x.get(\\\"addr_type\\\") \\\\\\n if \\\"addr_type\\\" in x else 0\\n output_memory_type = y.get(\\\"addr_type\\\") \\\\\\n if \\\"addr_type\\\" in y else 0\\n valid_shape = x.get(\\\"valid_shape\\\") \\\\\\n if \\\"valid_shape\\\" in x else ()\\n slice_offset = x.get(\\\"slice_offset\\\") \\\\\\n if \\\"slice_offset\\\" in x else ()\\n output_offset = y.get(\\\"slice_offset\\\") \\\\\\n if \\\"slice_offset\\\" in y else ()\\n l1_fusion_type = x.get(\\\"L1_fusion_type\\\") \\\\\\n if \\\"L1_fusion_type\\\" in x else -1\\n fmap_l1_addr_flag = x.get(\\\"L1_addr_flag\\\", False)\\n fmap_l1_valid_size = x.get(\\\"L1_valid_size\\\", 0)\\n\\n\\n\\n l1_fusion_enable_flag = get_L1_info(\\\"L1_fusion_enabled\\\")\\n\\n if input_memory_type not in (0, 1, 2):\\n args_dict = {\\n \\\"errCode\\\": \\\"E65008\\\",\\n \\\"input_memory_type_range\\\": \\\"(0, 1, 2)\\\",\\n \\\"input_memory_type\\\": str(input_memory_type)\\n }\\n raise RuntimeError(args_dict,\\n err_man.get_error_message(args_dict))\\n if output_memory_type not in (0, 1, 2):\\n args_dict = {\\n \\\"errCode\\\": \\\"E65009\\\",\\n \\\"output_memory_type_range\\\": \\\"(0, 1, 2)\\\",\\n \\\"output_memory_type\\\": str(output_memory_type)\\n }\\n raise RuntimeError(args_dict,\\n err_man.get_error_message(args_dict))\\n if valid_shape and not slice_offset:\\n reason = \\\"valid shape exists, slice shape cannot be []\\\"\\n args_dict = {\\n \\\"errCode\\\": \\\"E60108\\\",\\n \\\"reason\\\": reason\\n }\\n raise RuntimeError(args_dict,\\n err_man.get_error_message(args_dict))\\n if valid_shape and not output_offset:\\n reason = \\\"valid shape exists, output offset cannot be []\\\"\\n args_dict = {\\n \\\"errCode\\\": \\\"E60108\\\",\\n \\\"reason\\\": reason\\n }\\n raise RuntimeError(args_dict,\\n err_man.get_error_message(args_dict))\\n\\n valid_shape = shape_to_list(valid_shape)\\n slice_offset = shape_to_list(slice_offset)\\n output_offset = shape_to_list(output_offset)\\n\\n if not l1_fusion_enable_flag:\\n input_memory_type = 0\\n output_memory_type = 0\\n valid_shape = []\\n slice_offset = []\\n output_offset = []\\n l1_fusion_type = -1\\n fmap_l1_addr_flag = False\\n fmap_l1_valid_size = 0\\n\\n fusion_para = {\\\"input_memory_type\\\": input_memory_type,\\n \\\"output_memory_type\\\": output_memory_type,\\n \\\"valid_shape\\\": valid_shape,\\n \\\"slice_offset\\\": slice_offset,\\n \\\"output_offset\\\": output_offset,\\n \\\"l1_fusion_type\\\": l1_fusion_type,\\n \\\"fmap_l1_addr_flag\\\": fmap_l1_addr_flag,\\n \\\"fmap_l1_valid_size\\\": fmap_l1_valid_size}\\n\\n return fusion_para\",\n \"def tffunc(*argtypes):\\n placeholders = list(map(tf.placeholder, argtypes))\\n\\n def wrap(f):\\n out = f(*placeholders)\\n\\n def wrapper(*args, **kw):\\n return out.eval(dict(zip(placeholders, args)), session=kw.get('session'))\\n\\n return wrapper\\n\\n return wrap\",\n \"def handle(args):\\n if args.list_targets:\\n handle_list_targets(args)\\n else:\\n if hasattr(args, \\\"flow_func\\\"):\\n args.flow_func(args)\\n else:\\n print(\\\"Invalid command. Check 'mlonmcu flow --help' for the available subcommands!\\\")\\n sys.exit(1)\",\n \"def device(request):\\n d = request.param()\\n\\n # enable GPU error checking\\n if isinstance(d, hoomd.device.GPU):\\n d.gpu_error_checking = True\\n\\n return d\",\n \"def run_on_device(self, *args, **kwargs) -> DeviceOutputs:\\n return DeviceOutputs(args=list(args), kwargs=kwargs)\",\n \"def fn(name : str, *, input : 'NET', gnd : 'NET', output : 'NET'):\\n return make_component(name, type, {\\\"in\\\": input, \\\"gnd\\\":gnd, \\\"out\\\":output}, [], {'voltage': voltage}, prefix=\\\"U\\\")\",\n \"def process_function(engine: Engine, batch: Tuple[torch.Tensor, torch.Tensor, List[int]]) -> \\\\\\n Tuple[float, torch.Tensor, torch.Tensor]:\\n model.train()\\n optimizer.zero_grad()\\n x, y, lengths = batch\\n x, y = x.to(model.device), y.to(model.device)\\n y_pred: torch.Tensor = model(x, lengths)\\n loss: torch.Tensor = loss_fn(y_pred, y)\\n loss.backward()\\n optimizer.step()\\n return loss.item(), torch.max(y_pred, dim=1)[1], y\",\n \"def setup(args):\\n print(\\\"[INFO] args:\\\", json.dumps(args))\\n\\n token = args['_parameters'].get('token')\\n device_type = args['_parameters'].get('device_type')\\n\\n if not token:\\n print(\\\"[ERROR] Ubidots token not specified\\\")\\n return {\\\"status\\\":\\\"error\\\"}\\n\\n elif not device_type and token:\\n print(\\\"[INFO] device type not specified\\\")\\n device_type = \\\"\\\"\\n\\n if device_type != \\\"\\\":\\n device_type_data = set_device_type(device_type)\\n try:\\n res = create_device_type(device_type_data, token)\\n print(res)\\n if res.status_code == 409:\\n print(\\\"[INFO] A device type with this name already exists.\\\")\\n elif res.status_code == 201:\\n print(\\\"[INFO] Device type created successfully.\\\")\\n except Exception as e:\\n print(\\\"[INFO] Setup function ran, but could not create a device type.\\\")\\n print(e)\\n else:\\n print({\\\"[INFO] No device type created\\\"})\\n\\n return {\\\"status\\\":\\\"finished\\\"}\",\n \"def convert_result_as_arg(self, node, ordered_functions):\\n return ordered_functions # XXX - do nothing for now\\n options = node.options\\n fmt_func = node.fmtdict\\n# if options.F_string_len_trim is False: # XXX what about vector?\\n# return\\n\\n ast = node.ast\\n result_typemap = ast.typemap\\n result_name = None\\n\\n # Check if result needs to be an argument.\\n attrs = ast.attrs\\n meta = ast.metaattrs\\n if meta[\\\"deref\\\"] == \\\"raw\\\":\\n # No bufferify required for raw pointer result.\\n pass\\n elif result_typemap.sgroup in [\\\"char\\\", \\\"string\\\"]:\\n result_name = fmt_func.F_string_result_as_arg\\n# result_as_arg = fmt_func.F_string_result_as_arg\\n# result_name = result_as_arg or fmt_func.C_string_result_as_arg\\n# elif result_typemap.base == \\\"vector\\\":\\n# has_vector_result = True\\n# elif result_is_ptr:\\n# if meta[\\\"deref\\\"] in [\\\"allocatable\\\", \\\"pointer\\\"]:\\n# need_cdesc_result = True\\n# elif attrs[\\\"dimension\\\"]:\\n# need_cdesc_result = True\\n\\n if not result_name:\\n return\\n\\n##########\\n # Create a new C function and change arguments\\n # and add attributes.\\n C_new = node.clone()\\n ordered_functions.append(C_new)\\n self.append_function_index(C_new)\\n\\n# generated_suffix = \\\"buf\\\"\\n C_new._generated = \\\"result_to_arg\\\"\\n fmt_func = C_new.fmtdict\\n# fmt_func.function_suffix = fmt_func.function_suffix + fmt_func.C_bufferify_suffix + \\\"XXX\\\"\\n# fmt_func.function_suffix = fmt_func.function_suffix\\n\\n options = C_new.options\\n C_new.wrap.assign(c=True, fortran=True)\\n C_new._PTR_C_CXX_index = node._function_index\\n##########\\n\\n # decl: const char * getCharPtr2()\\n new_arg = C_new.ast.result_as_arg(result_name)\\n new_arg.const = False # must be writeable\\n# attrs = new_arg.attrs\\n# new_arg.metaattrs[\\\"deref\\\"] = None\\n # Special case for wrapf.py to override \\\"allocatable\\\"\\n\\n # Special case for wrapf.py to override \\\"allocatable\\\"\\n node.ast.metaattrs[\\\"deref\\\"] = None\\n new_arg.metaattrs[\\\"deref\\\"] = \\\"result\\\"\\n new_arg.metaattrs[\\\"is_result\\\"] = True\\n C_new.ast.metaattrs[\\\"intent\\\"] = \\\"subroutine\\\"\\n C_new.ast.metaattrs[\\\"deref\\\"] = None\\n\\n node.wrap.fortran = False\\n# node.wrap.c = False\\n\\n return\\n F_new = self.result_as_arg(node, C_new)\\n ordered_functions.append(F_new)\\n self.append_function_index(F_new)\",\n \"def f_twoargs(self, arg1, arg2) :\\n pass\",\n \"def _prepare_forward_inputs(\\n device: torch.device,\\n input_dtype: Optional[torch.dtype],\\n *args: Any,\\n **kwargs: Any,\\n) -> Tuple[Any, Any]:\\n # TODO: Do not use the side stream for tensor copies for now; investigate\\n # the perf with/without it.\\n # TODO: For mixed precision, move the inputs to the compute device and cast\\n # to reduced-precision in a single `to()` call.\\n args_tuple, kwargs_tuple = _to_kwargs(args, kwargs, device.index, False)\\n args = args_tuple[0]\\n kwargs = kwargs_tuple[0]\\n if input_dtype is not None:\\n args, kwargs = _cast_fp_inputs_to_dtype(input_dtype, *args, **kwargs)\\n return args, kwargs\",\n \"def compile_kernel(context, queue, source_code, function_name,\\n compiler_flags=None):\\n if cdouble(queue)(42).nbytes >= 8:\\n type_definitions = \\\"\\\"\\\"\\n #define cdouble double\\n \\\"\\\"\\\"\\n else:\\n print('WARNING: no 64bit float support available for this device.')\\n type_definitions = \\\"\\\"\\\"\\n #define cdouble float\\n \\\"\\\"\\\"\\n # The definition of cfloat and cshort is fixed for now since I do\\n # not know of any cases where these are not available. If this\\n # happens to be the case, we can add a check as for double here.\\n type_definitions += \\\"\\\"\\\"\\n #define cfloat float\\n #define cshort short\\n \\\"\\\"\\\"\\n flags = BUILD_OPTIONS[:]\\n if compiler_flags is not None:\\n flags.extend(compiler_flags)\\n full_source = type_definitions + basic_code() + source_code\\n program = ocl.Program(context, full_source).build(flags)\\n return getattr(program, function_name)\",\n \"def __call__(self, *args, **kwargs) -> None:\\n # The device portion needs to run every step to produce a stable graph\\n device_outputs = self.run_on_device(*args, **kwargs)\\n assert isinstance(device_outputs, DeviceOutputs), (\\n f\\\"Expected device outputs to be of type `DeviceOutputs`, \\\"\\n f\\\"but got `{type(device_outputs)}`.\\\"\\n )\\n\\n # Detach and clone device outputs to ensure we use the \\\"current\\\" value\\n for idx, tensor in enumerate(device_outputs.args):\\n if isinstance(tensor, torch.Tensor):\\n device_outputs.args[idx] = tensor.detach().clone()\\n for key, tensor in device_outputs.kwargs.items():\\n if isinstance(tensor, torch.Tensor):\\n device_outputs.kwargs[key] = tensor.detach().clone()\\n\\n if cm.use_cs():\\n state = cbtorch.state()\\n state.track_object(\\n {\\n \\\"cb_summary\\\": {\\n self.name: [device_outputs.args, device_outputs.kwargs]\\n }\\n },\\n force=self._is_appliance,\\n )\\n\\n if self._is_appliance:\\n\\n def _on_activations_received():\\n cpu_args = [\\n state.get_activation_for_output(tensor)\\n if isinstance(tensor, torch.Tensor)\\n else tensor\\n for tensor in device_outputs.args\\n ]\\n cpu_kwargs = {\\n key: state.get_activation_for_output(tensor)\\n if isinstance(tensor, torch.Tensor)\\n else tensor\\n for key, tensor in device_outputs.kwargs.items()\\n }\\n\\n self._cached_cpu_activations.append(\\n self.run_on_host(*cpu_args, **cpu_kwargs)\\n )\\n\\n state.register_activation_callback(_on_activations_received)\\n else:\\n\\n @cm.step_closure\\n def _run_on_host_closure(\\n device_args: List[Any], device_kwargs: Dict[str, Any],\\n ):\\n device_args = cm.to_cpu(device_args)\\n device_kwargs = cm.to_cpu(device_kwargs)\\n self._cached_cpu_activations.append(\\n self.run_on_host(*device_args, **device_kwargs)\\n )\\n\\n _run_on_host_closure(\\n device_outputs.args, device_outputs.kwargs,\\n )\",\n \"def __init__(self, kern, f, f_scope):\\n super().__init__(kern.input_dim,active_dims=kern.active_dims)\\n self.kern = kern\\n self._f = lambda x: tf.cast(f(x), gp.settings.float_type) #function to call on input\\n self._f_scope = f_scope #learnable variables that f depends on\",\n \"def load_c_functions(self):\\n\\n # Load shared object\\n lib = ctypes.cdll.LoadLibrary(os.path.join(self.working_directory,\\\"models/doubly_constrained/flow_forward_models.so\\\"))\\n lib2 = ctypes.cdll.LoadLibrary(os.path.join(self.working_directory,\\\"models/doubly_constrained/potential_function.so\\\"))\\n\\n # Load DSF procedure flow inference\\n self.infer_flows_dsf_procedure = lib.infer_flows_dsf_procedure\\n self.infer_flows_dsf_procedure.restype = ctypes.c_double\\n self.infer_flows_dsf_procedure.argtypes = [ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ctypes.c_size_t,\\n ctypes.c_size_t,\\n ctypes.c_double,\\n ctypes.c_size_t,\\n ctypes.c_bool,\\n ctypes.c_bool]\\n\\n\\n # Load Newton Raphson procedure flow inference\\n self.infer_flows_newton_raphson = lib.infer_flows_newton_raphson\\n self.infer_flows_newton_raphson.restype = None #ctypes.c_double\\n self.infer_flows_newton_raphson.argtypes = [ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ctypes.c_double,\\n ctypes.c_size_t,\\n ctypes.c_size_t,\\n ctypes.c_size_t,\\n ctypes.c_size_t]\\n\\n # Load Iterative proportional filtering procedure flow inference\\n self.infer_flows_ipf_procedure = lib.infer_flows_ipf_procedure\\n self.infer_flows_ipf_procedure.restype = ctypes.c_double\\n self.infer_flows_ipf_procedure.argtypes = [ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ctypes.c_size_t,\\n ctypes.c_size_t,\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ctypes.c_size_t,\\n ctypes.c_double,\\n ctypes.c_bool]\\n\\n # Load Iterative proportional filtering procedure flow inference\\n self.infer_flows_ipf_procedure_singly = lib.infer_flows_ipf_procedure_singly\\n self.infer_flows_ipf_procedure_singly.restype = ctypes.c_double\\n self.infer_flows_ipf_procedure_singly.argtypes = [ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ctypes.c_size_t,\\n ctypes.c_size_t,\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ctypes.c_size_t,\\n ctypes.c_double,\\n ctypes.c_bool]\\n\\n # Load potential function\\n self.potential_stochastic = lib2.potential_stochastic\\n self.potential_stochastic.restype = ctypes.c_double\\n self.potential_stochastic.argtypes = [ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ndpointer(ctypes.c_double, flags=\\\"C_CONTIGUOUS\\\"),\\n ctypes.c_size_t,\\n ctypes.c_size_t]\",\n \"def _fusion(self, expert_outputs):\\n raise NotImplementedError\",\n \"def generate_definition(self):\\n apientry = \\\"\\\"\\n if self.__name[:2] == \\\"gl\\\":\\n apientry = \\\"DNLOAD_APIENTRY \\\"\\n params = \\\"void\\\"\\n if self.__parameters:\\n params = \\\", \\\".join(self.__parameters)\\n return \\\"%s (%s*%s)(%s)\\\" % (self.__returntype, apientry, self.__name, params)\",\n \"def build(self, cres):\\n _launch_threads()\\n # Build wrapper for ufunc entry point\\n ctx = cres.target_context\\n library = cres.library\\n signature = cres.signature\\n llvm_func = library.get_function(cres.fndesc.llvm_func_name)\\n wrapper, env = build_gufunc_wrapper(library, ctx, llvm_func,\\n signature, self.sin, self.sout,\\n fndesc=cres.fndesc,\\n env=cres.environment)\\n\\n ptr = library.get_pointer_to_function(wrapper.name)\\n\\n # Get dtypes\\n dtypenums = []\\n for a in signature.args:\\n if isinstance(a, types.Array):\\n ty = a.dtype\\n else:\\n ty = a\\n dtypenums.append(as_dtype(ty).num)\\n\\n return dtypenums, ptr, env\",\n \"def numeric(*args):\",\n \"def handle_input(data, device=None):\\n if type(data) == tuple:\\n x, y = data\\n x = torch.tensor(x, dtype=torch.float)\\n y = torch.tensor(y, dtype=torch.float)\\n if not device is None:\\n x = x.to(device)\\n y = y.to(device)\\n return x, y\\n if type(data) == np.ndarray:\\n x = torch.tensor(data, dtype=torch.float)\\n if not device is None:\\n x = x.to(device)\\n return x\\n else:\\n return data\",\n \"def separate_device_addr(*args, **kwargs):\\n return _uhd_swig.separate_device_addr(*args, **kwargs)\",\n \"def call_top_interface_args_with_no_func_def(self, node: AnnCastCall):\\n # call container is used to scope parameters\\n call_con_name = call_container_name(node)\\n\\n # create argument and parameter variables\\n # argument variables are inputs to the top interface\\n # paramter variables are outputs of the top interface\\n for i, n in enumerate(node.arguments):\\n # argument name and scope str\\n arg_name = call_argument_name(node, i)\\n arg_con_scopestr = con_scope_to_str(node.func.con_scope)\\n\\n # parameter name and scopestr\\n param_name = call_param_name(node, i)\\n param_con_scopestr = con_scope_to_str(node.func.con_scope + [call_con_name])\\n \\n # argument and parameter share id, and start with initial version\\n id = self.pipeline_state.next_collapsed_id()\\n version = VAR_INIT_VERSION\\n\\n # build and store GrFN variables for argument and parameter\\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\\n # store arg_fullid\\n node.arg_index_to_fullid[i] = arg_fullid\\n # create From Source metadata for the GrFN var\\n from_source = False\\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\\n\\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\\n # store param_fullid\\n node.param_index_to_fullid[i] = param_fullid\\n # create From Source metadata for the GrFN var\\n # when we don't have the function def, we create a paramter with a default name\\n add_metadata_to_grfn_var(param_grfn_var, from_source_mdata)\\n\\n # link argument and parameter through top interface\\n node.top_interface_in[id] = arg_fullid\\n node.top_interface_out[id] = param_fullid\\n\\n # DEBUG printing\\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\\n print(\\\"After create_call_args_and_params():\\\")\\n print(f\\\"\\\\ttop_interface_in = {node.top_interface_in}\\\")\\n print(f\\\"\\\\ttop_interface_out = {node.top_interface_out}\\\")\",\n \"def do_type_checking(self, node):\\r\\n\\r\\n if not isinstance(node.inputs[0].type, GpuArrayType):\\r\\n raise NotImplementedError()\",\n \"def real_decorator(f):\\n\\n def inner(*args, **kwargs):\\n \\\"\\\"\\\"returns a torch tensor for cpu or gpu when appropriate.\\\"\\\"\\\"\\n\\n s = f(*args, **kwargs)\\n\\n if torch.cuda.is_available():\\n return torch.from_numpy(s).cuda().type(dtype)\\n\\n return torch.from_numpy(s).type(dtype)\\n\\n return inner\",\n \"def process_kernel_arg(\\n self, var, llvm_arg, arg_type, index, modified_arrays, sycl_queue_val\\n ):\\n if isinstance(arg_type, types.npytypes.Array):\\n if llvm_arg is None:\\n raise NotImplementedError(arg_type, var)\\n\\n storage = cgutils.alloca_once(self.builder, utils.LLVMTypes.int64_t)\\n self.builder.store(self.context.get_constant(types.int64, 0), storage)\\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\\n self._form_kernel_arg_and_arg_ty(\\n self.builder.bitcast(\\n storage,\\n utils.get_llvm_type(context=self.context, type=types.voidptr),\\n ),\\n ty,\\n )\\n\\n storage = cgutils.alloca_once(self.builder, utils.LLVMTypes.int64_t)\\n self.builder.store(self.context.get_constant(types.int64, 0), storage)\\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\\n self._form_kernel_arg_and_arg_ty(\\n self.builder.bitcast(\\n storage,\\n utils.get_llvm_type(context=self.context, type=types.voidptr),\\n ),\\n ty,\\n )\\n\\n # Handle array size\\n array_size_member = self.builder.gep(\\n llvm_arg,\\n [\\n self.context.get_constant(types.int32, 0),\\n self.context.get_constant(types.int32, 2),\\n ],\\n )\\n\\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\\n self._form_kernel_arg_and_arg_ty(\\n self.builder.bitcast(\\n array_size_member,\\n utils.get_llvm_type(context=self.context, type=types.voidptr),\\n ),\\n ty,\\n )\\n\\n # Handle itemsize\\n item_size_member = self.builder.gep(\\n llvm_arg,\\n [\\n self.context.get_constant(types.int32, 0),\\n self.context.get_constant(types.int32, 3),\\n ],\\n )\\n\\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\\n self._form_kernel_arg_and_arg_ty(\\n self.builder.bitcast(\\n item_size_member,\\n utils.get_llvm_type(context=self.context, type=types.voidptr),\\n ),\\n ty,\\n )\\n\\n # Calculate total buffer size\\n total_size = cgutils.alloca_once(\\n self.builder,\\n utils.get_llvm_type(context=self.context, type=types.intp),\\n size=utils.get_one(context=self.context),\\n name=\\\"total_size\\\" + str(self.cur_arg),\\n )\\n self.builder.store(\\n self.builder.sext(\\n self.builder.mul(\\n self.builder.load(array_size_member),\\n self.builder.load(item_size_member),\\n ),\\n utils.get_llvm_type(context=self.context, type=types.intp),\\n ),\\n total_size,\\n )\\n\\n # Handle data\\n data_member = self.builder.gep(\\n llvm_arg,\\n [\\n self.context.get_constant(types.int32, 0),\\n self.context.get_constant(types.int32, 4),\\n ],\\n )\\n\\n # names are replaced using legalize names, we have to do the same\\n # here for them to match.\\n legal_names = legalize_names([var])\\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.voidptr)\\n\\n if isinstance(arg_type, nus.UsmSharedArrayType):\\n self._form_kernel_arg_and_arg_ty(\\n self.builder.bitcast(\\n self.builder.load(data_member),\\n utils.get_llvm_type(context=self.context, type=types.voidptr),\\n ),\\n ty,\\n )\\n else:\\n malloc_fn = DpctlCAPIFnBuilder.get_dpctl_malloc_shared(\\n builder=self.builder, context=self.context\\n )\\n memcpy_fn = DpctlCAPIFnBuilder.get_dpctl_queue_memcpy(\\n builder=self.builder, context=self.context\\n )\\n event_del_fn = DpctlCAPIFnBuilder.get_dpctl_event_delete(\\n builder=self.builder, context=self.context\\n )\\n event_wait_fn = DpctlCAPIFnBuilder.get_dpctl_event_wait(\\n builder=self.builder, context=self.context\\n )\\n\\n # Not known to be USM so we need to copy to USM.\\n buffer_name = \\\"buffer_ptr\\\" + str(self.cur_arg)\\n # Create void * to hold new USM buffer.\\n buffer_ptr = cgutils.alloca_once(\\n self.builder,\\n utils.get_llvm_type(context=self.context, type=types.voidptr),\\n name=buffer_name,\\n )\\n # Setup the args to the USM allocator, size and SYCL queue.\\n args = [\\n self.builder.load(total_size),\\n self.builder.load(sycl_queue_val),\\n ]\\n # Call USM shared allocator and store in buffer_ptr.\\n self.builder.store(self.builder.call(malloc_fn, args), buffer_ptr)\\n\\n if legal_names[var] in modified_arrays:\\n self.write_buffs.append((buffer_ptr, total_size, data_member))\\n else:\\n self.read_only_buffs.append((buffer_ptr, total_size, data_member))\\n\\n # We really need to detect when an array needs to be copied over\\n if index < self.num_inputs:\\n args = [\\n self.builder.load(sycl_queue_val),\\n self.builder.load(buffer_ptr),\\n self.builder.bitcast(\\n self.builder.load(data_member),\\n utils.get_llvm_type(\\n context=self.context, type=types.voidptr\\n ),\\n ),\\n self.builder.load(total_size),\\n ]\\n event_ref = self.builder.call(memcpy_fn, args)\\n self.builder.call(event_wait_fn, [event_ref])\\n self.builder.call(event_del_fn, [event_ref])\\n\\n self._form_kernel_arg_and_arg_ty(self.builder.load(buffer_ptr), ty)\\n\\n # Handle shape\\n shape_member = self.builder.gep(\\n llvm_arg,\\n [\\n self.context.get_constant(types.int32, 0),\\n self.context.get_constant(types.int32, 5),\\n ],\\n )\\n\\n for this_dim in range(arg_type.ndim):\\n shape_entry = self.builder.gep(\\n shape_member,\\n [\\n self.context.get_constant(types.int32, 0),\\n self.context.get_constant(types.int32, this_dim),\\n ],\\n )\\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\\n self._form_kernel_arg_and_arg_ty(\\n self.builder.bitcast(\\n shape_entry,\\n utils.get_llvm_type(context=self.context, type=types.voidptr),\\n ),\\n ty,\\n )\\n\\n # Handle strides\\n stride_member = self.builder.gep(\\n llvm_arg,\\n [\\n self.context.get_constant(types.int32, 0),\\n self.context.get_constant(types.int32, 6),\\n ],\\n )\\n\\n for this_stride in range(arg_type.ndim):\\n stride_entry = self.builder.gep(\\n stride_member,\\n [\\n self.context.get_constant(types.int32, 0),\\n self.context.get_constant(types.int32, this_stride),\\n ],\\n )\\n\\n ty = numba_type_to_dpctl_typenum(context=self.context, type=types.int64)\\n self._form_kernel_arg_and_arg_ty(\\n self.builder.bitcast(\\n stride_entry,\\n utils.get_llvm_type(context=self.context, type=types.voidptr),\\n ),\\n ty,\\n )\\n\\n else:\\n ty = numba_type_to_dpctl_typenum(context=self.context, type=arg_type)\\n self._form_kernel_arg_and_arg_ty(\\n self.builder.bitcast(\\n llvm_arg,\\n utils.get_llvm_type(context=self.context, type=types.voidptr),\\n ),\\n ty,\\n )\",\n \"def exec_function(self, args):\\n raise NotImplementedError()\",\n \"def _device_implements(np_function):\\n\\n def decorator(func):\\n _DEVICE_HANDLED_FUNCTIONS[np_function] = func\\n return func\\n\\n return decorator\",\n \"def serve_function(self):\\n # Set name attribute of the input TensorSpec.\\n input_signature = {\\n name: tf.TensorSpec.from_spec(spec, name=name)\\n for name, spec in self.features_inputter.input_signature().items()\\n }\\n\\n @tf.function(input_signature=(input_signature,))\\n def _run(features):\\n features = self.features_inputter.make_features(features=features.copy())\\n _, predictions = self(features)\\n return predictions\\n\\n return _run\",\n \"def gpuGridrec(tomo,angles,center,input_params):\\n\\n print('Starting GPU NUFFT recon')\\n #allocate space for final answer \\n af.set_device(input_params['gpu_device']) #Set the device number for gpu based code\\n #Change tomopy format\\n new_tomo=np.transpose(tomo,(1,2,0)) #slice, columns, angles\\n im_size = new_tomo.shape[1]\\n num_slice = new_tomo.shape[0]\\n num_angles=new_tomo.shape[2]\\n pad_size=np.int16(im_size*input_params['oversamp_factor'])\\n# nufft_scaling = (np.pi/pad_size)**2\\n #Initialize structures for NUFFT\\n sino={}\\n geom={}\\n sino['Ns'] = pad_size#Sinogram size after padding\\n sino['Ns_orig'] = im_size #size of original sinogram\\n sino['center'] = center + (sino['Ns']/2 - sino['Ns_orig']/2) #for padded sinogram\\n sino['angles'] = angles\\n sino['filter'] = input_params['fbp_filter_param'] #Paramter to control strength of FBP filter normalized to [0,1]\\n\\n #Initialize NUFFT parameters\\n nufft_params = init_nufft_params(sino,geom)\\n rec_nufft = afnp.zeros((num_slice/2,sino['Ns_orig'],sino['Ns_orig']),dtype=afnp.complex64)\\n Ax = afnp.zeros((sino['Ns'],num_angles),dtype=afnp.complex64)\\n pad_idx = slice(sino['Ns']/2-sino['Ns_orig']/2,sino['Ns']/2+sino['Ns_orig']/2)\\n rec_nufft_final=np.zeros((num_slice,sino['Ns_orig'],sino['Ns_orig']),dtype=np.float32)\\n \\n #Move all data to GPU\\n slice_1=slice(0,num_slice,2)\\n slice_2=slice(1,num_slice,2)\\n gdata=afnp.array(new_tomo[slice_1]+1j*new_tomo[slice_2],dtype=afnp.complex64)\\n x_recon = afnp.zeros((sino['Ns'],sino['Ns']),dtype=afnp.complex64)\\n #loop over all slices\\n for i in range(0,num_slice/2):\\n Ax[pad_idx,:]=gdata[i]\\n #filtered back-projection \\n rec_nufft[i] = (back_project(Ax,nufft_params))[pad_idx,pad_idx]\\n\\n\\n #Move to CPU\\n #Rescale result to match tomopy\\n rec_nufft=np.array(rec_nufft,dtype=np.complex64) #*nufft_scaling\\n rec_nufft_final[slice_1]=np.array(rec_nufft.real,dtype=np.float32)\\n rec_nufft_final[slice_2]=np.array(rec_nufft.imag,dtype=np.float32)\\n return rec_nufft_final\",\n \"def convert_to_user_call(*args):\\n return _ida_hexrays.convert_to_user_call(*args)\",\n \"def base_declaration(func, thnn_function, backends, backend_types, inplace=False):\\n name, params = re.match(NAME_PARAM_REGEX, func['name']).groups()\\n if inplace:\\n name += '_'\\n params = params.split(', ')\\n arguments = [argument_to_declaration(a, func) for a in params]\\n if not inplace:\\n arguments += output_arguments(thnn_function)\\n buffers = [argument_to_declaration('Tensor ' + buf)\\n for buf in func.get('buffers', [])]\\n\\n return function_info(name, arguments, None, buffers, backends, inplace, backend_types)\",\n \"def test_ddp_comm_hook_future_passing_gpu_gloo(self):\\n process_group = self._get_process_group()\\n\\n # Get GPU model with simple_hook registered.\\n gpu_model = self._gpu_model_with_ddp_comm_hook(process_group, self._simple_hook)\\n\\n # check whether the grads are equal to what simple_hook's then callback returns.\\n # without the comm_hook, result would be 0.25 * torch.ones(2, 2).\\n self._run_and_verify_hook(gpu_model, 8, 2 * torch.ones(2, 2))\",\n \"def fusion_dispatcher(args=sys.argv[1:]):\\n\\n # If --clear-logs the log files are cleared\\n if \\\"--clear-logs\\\" in args:\\n clear_log_files(LOG_FILES)\\n\\n settings = {}\\n settings.update(SETTINGS)\\n if '--evaluate' in args:\\n settings.update({\\\"default_output\\\": \\\"evaluation\\\"})\\n\\n command_args, _, api, session_file, _ = get_context(args, settings)\\n\\n # Selects the action to perform\\n if a.has_value(command_args, \\\"fusion_models_\\\") or a.has_test(command_args):\\n compute_output(api, command_args)\\n u.log_message(\\\"_\\\" * 80 + \\\"\\\\n\\\", log_file=session_file)\",\n \"def __call__(self, *inputs):\\n raise NotImplementedError\",\n \"def acfunct(arg):\\n try:\\n functions = [dynet.rectify, dynet.tanh]\\n functions = { function.__name__ : function for function in functions}\\n functions[\\\"None\\\"] = None\\n return functions[str(arg)]\\n except:\\n raise argparse.ArgumentTypeError(\\\"String {} does not match required format\\\".format(arg,))\",\n \"def dummy_fn(self, *args, **kwargs):\",\n \"def set_device(in_arg): \\n \\n return torch.device(\\\"cuda\\\" if torch.cuda.is_available() and in_arg.gpu == 1 else \\\"cpu\\\")\",\n \"def cuda(self, *args, **kwargs) -> 'BasePoints':\\n original_type = type(self)\\n return original_type(\\n self.tensor.cuda(*args, **kwargs),\\n points_dim=self.points_dim,\\n attribute_dims=self.attribute_dims)\",\n \"def _handleInput(self, paramInput):\\n super()._handleInput(paramInput)\\n dataFilename = paramInput.findFirst('dataFilename')\\n if dataFilename != None:\\n self.dataFilename = os.path.join(self.workingDir,dataFilename.value)\\n else:\\n self.raiseAnError(IOError,'<dataFilename> parameter needed for MultiDimensional Distributions!!!!')\\n\\n functionType = dataFilename.parameterValues['type']\\n if functionType != None:\\n self.functionType = functionType\\n else:\\n self.raiseAnError(IOError,'<functionType> parameter needed for MultiDimensional Distributions!!!!')\\n\\n self.initializeDistribution()\",\n \"def get_float_type(*args):\\n return _ida_hexrays.get_float_type(*args)\",\n \"def deconv_compute_fusion_para(x, y):\\n input_memory_type = x.op.attrs[\\\"addr_type\\\"].value \\\\\\n if \\\"addr_type\\\" in x.op.attrs else 0\\n valid_shape = x.op.attrs[\\\"valid_shape\\\"] \\\\\\n if \\\"valid_shape\\\" in x.op.attrs else ()\\n slice_offset = x.op.attrs[\\\"slice_offset\\\"] \\\\\\n if \\\"slice_offset\\\" in x.op.attrs else ()\\n output_offset = y.get(\\\"slice_offset\\\") \\\\\\n if \\\"slice_offset\\\" in y else ()\\n l1_fusion_type = x.op.attrs[\\\"L1_fusion_type\\\"].value \\\\\\n if \\\"L1_fusion_type\\\" in x.op.attrs else -1\\n fmap_l1_addr_flag = x.op.attrs[\\\"L1_addr_flag\\\"].value \\\\\\n if \\\"L1_addr_flag\\\" in x.op.attrs else False\\n fmap_l1_valid_size = x.op.attrs[\\\"L1_valid_size\\\"].value\\\\\\n if \\\"L1_valid_size\\\" in x.op.attrs else 0\\n\\n l1_fusion_enable_flag = get_L1_info(\\\"L1_fusion_enabled\\\")\\n\\n if input_memory_type not in (0, 1, 2):\\n args_dict = {\\n \\\"errCode\\\": \\\"E65008\\\",\\n \\\"input_memory_type_range\\\": \\\"(0, 1, 2)\\\",\\n \\\"input_memory_type\\\": str(input_memory_type)\\n }\\n raise RuntimeError(args_dict,\\n err_man.get_error_message(args_dict))\\n\\n if valid_shape and not slice_offset:\\n reason = \\\"valid shape exists, slice shape cannot be []\\\"\\n args_dict = {\\n \\\"errCode\\\": \\\"E60108\\\",\\n \\\"reason\\\": reason\\n }\\n raise RuntimeError(args_dict,\\n err_man.get_error_message(args_dict))\\n if valid_shape and not output_offset:\\n reason = \\\"valid shape exists, output offset cannot be []\\\"\\n args_dict = {\\n \\\"errCode\\\": \\\"E60108\\\",\\n \\\"reason\\\": reason\\n }\\n raise RuntimeError(args_dict,\\n err_man.get_error_message(args_dict))\\n\\n valid_shape = shape_to_list(valid_shape)\\n slice_offset = shape_to_list(slice_offset)\\n output_offset = shape_to_list(output_offset)\\n\\n if not l1_fusion_enable_flag:\\n input_memory_type = 0\\n valid_shape = []\\n slice_offset = []\\n output_offset = []\\n l1_fusion_type = -1\\n fmap_l1_addr_flag = False\\n fmap_l1_valid_size = 0\\n\\n fusion_para = {\\\"input_memory_type\\\": input_memory_type,\\n \\\"output_memory_type\\\": \\\"fuse_flag\\\",\\n \\\"valid_shape\\\": valid_shape,\\n \\\"slice_offset\\\": slice_offset,\\n \\\"output_offset\\\": output_offset,\\n \\\"l1_fusion_type\\\": l1_fusion_type,\\n \\\"fmap_l1_addr_flag\\\": fmap_l1_addr_flag,\\n \\\"fmap_l1_valid_size\\\": fmap_l1_valid_size}\\n\\n return fusion_para\",\n \"def EvaluateFunction(self, p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def execute_objective(arguments):\\n #later call define_tr_val from within this function for speedup.\\n #score, pred_ = self.define_tr_val() #just get the train and test...\\n \\n\\n cv_samples, parallel_arguments, parameters, windowsOS, id_ = arguments\\n device = parallel_arguments[\\\"device\\\"]\\n declaration_args = parallel_arguments[\\\"declaration_args\\\"]\\n backprop_args = parallel_arguments[\\\"backprop_args\\\"]\\n #del declaration_args['reservoir']\\n\\n cv_args = parallel_arguments[\\\"cv_args\\\"]\\n log_score, rounds, tr_score_prop = cv_args[\\\"log_score\\\"], cv_args[\\\"rounds\\\"], cv_args[\\\"tr_score_prop\\\"]\\n\\n # if windowsOS == True:\\n # #move specific arguments to the gpu.\\n # cv_samples_ = []\\n # for i, cv_sample in enumerate(cv_samples):\\n # if cv_sample[\\\"tr_y\\\"].device != device:\\n # #if cv_sample[\\\"tr_x\\\"]:\\n # train_x, validate_x = cv_sample[\\\"tr_x\\\"].to(device), cv_sample[\\\"val_x\\\"].to(device)\\n\\n # train_y, validate_y = cv_sample[\\\"tr_y\\\"].to(device), cv_sample[\\\"val_y\\\"].to(device)\\n # else:\\n # #consider not sending the cv_sample[\\\"x\\\"] if it's a pure prediction.\\n # train_x, train_y = cv_sample[\\\"tr_x\\\"], cv_sample[\\\"tr_y\\\"]\\n # validate_x, validate_y = cv_sample[\\\"val_x\\\"], cv_sample[\\\"val_y\\\"]\\n # cv_samples_.append((train_x, train_y, validate_x, validate_y))\\n\\n # #now move the input weights and the reservoir arguments to the gpu.\\n # #deepcopy()\\n # assert declaration_args[\\\"reservoir\\\"] != None\\n # if declaration_args[\\\"reservoir\\\"] != None:\\n # declaration_args[\\\"reservoir\\\"].in_weights = declaration_args[\\\"reservoir\\\"].in_weights.to(device)\\n # declaration_args[\\\"reservoir\\\"].accept = declaration_args[\\\"reservoir\\\"].accept.to(device)\\n # declaration_args[\\\"reservoir\\\"].reservoir_pre_weights = declaration_args[\\\"reservoir\\\"].reservoir_pre_weights.to(device)\\n\\n # RC = EchoStateNetwork(**declaration_args, **parameters, id_ = id_)\\n \\n RC = EchoStateNetwork(**declaration_args, **parameters, id_ = id_)\\n cv_samples_ = []\\n for i, cv_sample in enumerate(cv_samples):\\n cv_samples_.append((cv_sample[\\\"tr_x\\\"], cv_sample[\\\"tr_y\\\"], cv_sample[\\\"val_x\\\"], cv_sample[\\\"val_y\\\"]))\\n\\n ODE_order = parallel_arguments[\\\"train_args\\\"][\\\"ODE_order\\\"]\\n\\n train_args = parallel_arguments[\\\"train_args\\\"]\\n test_args = parallel_arguments[\\\"test_args\\\"]\\n\\n total_score = 0\\n\\n backprop_f = backprop_args[\\\"backprop_f\\\"]\\n\\n if type(train_args[\\\"init_conditions\\\"][0]) == list:\\n multiple_ICs = True if len(train_args[\\\"init_conditions\\\"][0]) > 1 else False\\n else:\\n multiple_ICs = False\\n\\n for i, cv_sample in enumerate(cv_samples_):\\n #print(i)\\n train_x, train_y, validate_x, validate_y = cv_sample\\n\\n cv_sample_score = 0\\n\\n #divis = rounds *2\\n if ODE_order:\\n if multiple_ICs:\\n # if train_score:\\n # return {\\\"scores\\\" : scores, \\n # \\\"weights\\\": gd_weights, \\n # \\\"biases\\\" : gd_biases,\\n # \\\"ys\\\" : ys,\\n # \\\"ydots\\\" : ydots,\\n # \\\"losses\\\" : Ls}\\n dictt = RC.fit(X = train_x, y = train_y, train_score = True, **train_args)\\n train_scores = dictt[\\\"scores\\\"]\\n val_scores, pred_, id_ = RC.test(X=validate_x, y= validate_y, **test_args)\\n\\n \\n for i, train_score in enumerate(train_scores):\\n \\n train_scores[i] = process_score(train_score, device = device)\\n\\n val_score = process_score(val_scores[i], device = device)# / divis\\n\\n round_score = combine_score(train_score, val_score, tr_score_prop, log_score) \\n cv_sample_score += round_score \\n total_score += cv_sample_score\\n\\n else:\\n #train_score = RC.fit(X = train_x, y = train_y, train_score = True, **parallel_arguments[\\\"train_args\\\"])\\n results = RC.fit(X = train_x, y = train_y, train_score = True, **train_args)\\n train_scores = results[\\\"scores\\\"]\\n train_score = train_scores[0]\\n\\n train_score = process_score(train_score, device = device)# / divis\\n\\n val_scores, pred_, id_ = RC.test(X=validate_x, y= validate_y, **test_args)\\n val_score = process_score(val_scores[0], device = device)# / divis\\n\\n cv_sample_score = combine_score(train_score, val_score, tr_score_prop, log_score) \\n\\n \\n else:\\n _ = RC.fit(X = train_x, y = train_y,**parallel_arguments[\\\"train_args\\\"])\\n #train_score = process_score(train_score)\\n \\n val_score, pred_, id_ = RC.test(X=validate_x, y= validate_y, **parallel_arguments[\\\"test_args\\\"])\\n val_score = process_score(val_score)\\n\\n cv_sample_score = val_score #combine_score(train_score, val_score)\\n\\n #del train_x; del train_y;\\n #del validate_x;\\n\\n if id_ != 0:\\n del validate_y; del pred_;\\n\\n total_score += cv_sample_score\\n #if device == torch.device('cuda'):\\n # torch.cuda.empty_cache()\\n\\n #del RC;\\n #score_mu = total_score/len(cv_samples)\\n #del cv_samples;\\n nn = len(cv_samples_) * len(train_scores)\\n\\n total_score = total_score / nn\\n if id_ == 0:\\n try:\\n validate_y = validate_y.to(\\\"cpu\\\")\\n except:\\n pass\\n return float(total_score), {\\\"pred\\\": pred_.to(\\\"cpu\\\"), \\\"val_y\\\" : validate_y}, id_\\n else:\\n return float(total_score), None, id_\",\n \"def _handleInput(self, paramInput):\\n pass\",\n \"def prepare_arguments(self, ftyp, args):\\n # Determine fixed and variable arguments:\\n if ftyp.is_vararg:\\n fixed_amount = len(ftyp.arguments)\\n fixed_args = args[:fixed_amount]\\n var_args = args[fixed_amount:]\\n else:\\n fixed_args = args\\n var_args = []\\n\\n # Evaluate arguments:\\n ir_arguments = []\\n\\n # If return value is complex, reserve room for it an pass pointer\\n if ftyp.return_type.is_struct:\\n size, alignment = self.data_layout(ftyp.return_type)\\n rval_alloc = self.emit(ir.Alloc(\\\"rval_alloc\\\", size, alignment))\\n rval_ptr = self.emit(ir.AddressOf(rval_alloc, \\\"rval_ptr\\\"))\\n ir_arguments.append(rval_ptr)\\n else:\\n rval_alloc = None\\n\\n # Place other arguments:\\n for argument in fixed_args:\\n value = self.gen_expr(argument, rvalue=True)\\n ir_arguments.append(value)\\n\\n # Handle variable arguments:\\n if ftyp.is_vararg:\\n vararg_ptr = self.gen_fill_varargs(var_args)\\n ir_arguments.append(vararg_ptr)\\n else:\\n assert not var_args\\n\\n return ir_arguments, rval_alloc\",\n \"def _graph_fn_parameterize(self, *parameters):\\n raise NotImplementedError\",\n \"def call_megam(args):\\n ...\",\n \"def egd(*args, **kwargs): # real signature unknown\\n pass\",\n \"def _body(step, *args):\\n updated_embedding_vars = args[0:num_embedding_vars]\\n updated_fc_vars = args[num_embedding_vars:num_embedding_vars +\\n num_fc_vars]\\n train_embeddings = self.embedding_fn(\\n self.data.train_images,\\n params=collections.OrderedDict(\\n zip(embedding_vars_keys, updated_embedding_vars)),\\n depth_multiplier=self.depth_multiplier,\\n reuse=True)['embeddings']\\n\\n if self.proto_maml_fc_layer_on_support_set:\\n # Set fc layer weights with prototypical equivalent values.\\n prototypes = self.proto_maml_prototypes(train_embeddings)\\n pmaml_fc_weights = self.proto_maml_fc_weights(\\n prototypes, zero_pad_to_max_way=True)\\n pmaml_fc_bias = self.proto_maml_fc_bias(\\n prototypes, zero_pad_to_max_way=True)\\n train_logits = tf.matmul(train_embeddings,\\n pmaml_fc_weights) + pmaml_fc_bias\\n else:\\n updated_fc_weights, updated_fc_bias = updated_fc_vars\\n train_logits = tf.matmul(train_embeddings,\\n updated_fc_weights) + updated_fc_bias\\n\\n train_logits = train_logits[:, 0:self.way]\\n loss = tf.losses.softmax_cross_entropy(one_hot_train_labels, train_logits)\\n\\n if self.debug_log:\\n print_op = tf.print(['step: ', step, updated_fc_bias[0], 'loss:', loss])\\n else:\\n print_op = tf.no_op()\\n\\n embedding_grads = tf.gradients(loss, updated_embedding_vars)\\n # Only computes fc grad when it's not created from prototypes.\\n if not self.proto_maml_fc_layer_on_support_set:\\n fc_grads = tf.gradients(loss, updated_fc_vars)\\n\\n if self.first_order:\\n\\n def _stop_grads(grads):\\n return [tf.stop_gradient(dv) for dv in grads]\\n\\n embedding_grads = _stop_grads(embedding_grads)\\n if not self.proto_maml_fc_layer_on_support_set:\\n fc_grads = _stop_grads(fc_grads)\\n\\n # Apply gradients\\n def _apply_grads(variables, grads):\\n \\\"\\\"\\\"Applies gradients using SGD on a list of variables.\\\"\\\"\\\"\\n v_new = []\\n for (v, dv) in zip(variables, grads):\\n if (not self.train_batch_norm and\\n ('offset' in v.name or 'scale' in v.name)):\\n v_new.append(v)\\n else:\\n v_new.append(v - self.alpha * dv)\\n return v_new\\n\\n with tf.control_dependencies([print_op]):\\n updated_embedding_vars = _apply_grads(updated_embedding_vars,\\n embedding_grads)\\n # Only apply fc grad when it's not created from prototypes.\\n if not self.proto_maml_fc_layer_on_support_set:\\n updated_fc_vars = _apply_grads(updated_fc_vars, fc_grads)\\n step = step + 1\\n return tuple([step] + list(updated_embedding_vars) +\\n list(updated_fc_vars))\",\n \"def itkMultipleValuedCostFunction_cast(*args):\\n return _ITKCostFunctionsPython.itkMultipleValuedCostFunction_cast(*args)\",\n \"def my_func(a, b):\",\n \"def test_hostfromgpu_shape_i():\\r\\n pass\\r\\n\\r\\n m = mode_with_gpu.including('local_dot_to_dot22',\\r\\n 'local_dot22_to_dot22scalar','specialize')\\r\\n a = T.fmatrix('a')\\r\\n ca = theano.sandbox.cuda.var.CudaNdarrayType((False, False))()\\r\\n\\r\\n av = numpy.asarray(numpy.random.rand(5, 4), dtype='float32')\\r\\n cv = cuda.CudaNdarray(numpy.asarray(numpy.random.rand(5, 4),\\r\\n dtype='float32'))\\r\\n\\r\\n f = theano.function([a], cuda.basic_ops.gpu_from_host(a), mode=m)\\r\\n assert cuda.basic_ops.gpu_from_host in [x.op\\r\\n for x in f.maker.fgraph.toposort()]\\r\\n f = theano.function([a], cuda.basic_ops.gpu_from_host(a).shape, mode=m)\\r\\n topo = f.maker.fgraph.toposort()\\r\\n assert isinstance(topo[0].op, T.opt.Shape_i)\\r\\n assert isinstance(topo[1].op, T.opt.Shape_i)\\r\\n assert isinstance(topo[2].op, T.opt.MakeVector)\\r\\n assert tuple(f(av)) == (5, 4)\\r\\n\\r\\n\\r\\n\\r\\n f = theano.function([ca], cuda.basic_ops.host_from_gpu(ca), mode=m)\\r\\n assert cuda.basic_ops.host_from_gpu in [x.op\\r\\n for x in f.maker.fgraph.toposort()]\\r\\n f = theano.function([ca], cuda.basic_ops.host_from_gpu(ca).shape, mode=m)\\r\\n topo = f.maker.fgraph.toposort()\\r\\n assert isinstance(topo[0].op, T.opt.Shape_i)\\r\\n assert isinstance(topo[1].op, T.opt.Shape_i)\\r\\n assert isinstance(topo[2].op, T.opt.MakeVector)\\r\\n assert tuple(f(cv)) == (5, 4)\",\n \"def _expr_kernel(self,\\n arguments: Any,\\n batch: RecordBatch) -> Any:\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6057609","0.5804018","0.5786905","0.5687931","0.5632874","0.55926454","0.55586314","0.54775053","0.5402661","0.53772503","0.5370256","0.5299433","0.5298682","0.5296351","0.52958226","0.52583855","0.52490014","0.5243063","0.52260655","0.51923186","0.5190427","0.5190425","0.51797056","0.5172453","0.5171298","0.5169099","0.51653165","0.51564175","0.5156381","0.5146902","0.5115577","0.5113744","0.51035464","0.5090692","0.50711346","0.5070762","0.50677204","0.505885","0.5055975","0.50485396","0.503698","0.5018637","0.5018308","0.5014922","0.501074","0.49832258","0.49830115","0.49684775","0.49628752","0.49608532","0.49479693","0.49425635","0.4936847","0.49265522","0.4924723","0.4913925","0.49107727","0.4907401","0.49059618","0.48993146","0.48978978","0.4895881","0.4891916","0.48873103","0.48783702","0.48760775","0.4869619","0.48662078","0.48658037","0.48654956","0.48488912","0.48393142","0.48233616","0.48185223","0.48017147","0.47912484","0.47881866","0.47822395","0.47818673","0.47804177","0.4766009","0.47622463","0.47570786","0.47505066","0.47450814","0.4742163","0.47374246","0.4733884","0.47305715","0.47197968","0.4713746","0.4708918","0.4702102","0.4694929","0.46947572","0.46935782","0.46919346","0.4688214","0.46813348","0.4677486"],"string":"[\n \"0.6057609\",\n \"0.5804018\",\n \"0.5786905\",\n \"0.5687931\",\n \"0.5632874\",\n \"0.55926454\",\n \"0.55586314\",\n \"0.54775053\",\n \"0.5402661\",\n \"0.53772503\",\n \"0.5370256\",\n \"0.5299433\",\n \"0.5298682\",\n \"0.5296351\",\n \"0.52958226\",\n \"0.52583855\",\n \"0.52490014\",\n \"0.5243063\",\n \"0.52260655\",\n \"0.51923186\",\n \"0.5190427\",\n \"0.5190425\",\n \"0.51797056\",\n \"0.5172453\",\n \"0.5171298\",\n \"0.5169099\",\n \"0.51653165\",\n \"0.51564175\",\n \"0.5156381\",\n \"0.5146902\",\n \"0.5115577\",\n \"0.5113744\",\n \"0.51035464\",\n \"0.5090692\",\n \"0.50711346\",\n \"0.5070762\",\n \"0.50677204\",\n \"0.505885\",\n \"0.5055975\",\n \"0.50485396\",\n \"0.503698\",\n \"0.5018637\",\n \"0.5018308\",\n \"0.5014922\",\n \"0.501074\",\n \"0.49832258\",\n \"0.49830115\",\n \"0.49684775\",\n \"0.49628752\",\n \"0.49608532\",\n \"0.49479693\",\n \"0.49425635\",\n \"0.4936847\",\n \"0.49265522\",\n \"0.4924723\",\n \"0.4913925\",\n \"0.49107727\",\n \"0.4907401\",\n \"0.49059618\",\n \"0.48993146\",\n \"0.48978978\",\n \"0.4895881\",\n \"0.4891916\",\n \"0.48873103\",\n \"0.48783702\",\n \"0.48760775\",\n \"0.4869619\",\n \"0.48662078\",\n \"0.48658037\",\n \"0.48654956\",\n \"0.48488912\",\n \"0.48393142\",\n \"0.48233616\",\n \"0.48185223\",\n \"0.48017147\",\n \"0.47912484\",\n \"0.47881866\",\n \"0.47822395\",\n \"0.47818673\",\n \"0.47804177\",\n \"0.4766009\",\n \"0.47622463\",\n \"0.47570786\",\n \"0.47505066\",\n \"0.47450814\",\n \"0.4742163\",\n \"0.47374246\",\n \"0.4733884\",\n \"0.47305715\",\n \"0.47197968\",\n \"0.4713746\",\n \"0.4708918\",\n \"0.4702102\",\n \"0.4694929\",\n \"0.46947572\",\n \"0.46935782\",\n \"0.46919346\",\n \"0.4688214\",\n \"0.46813348\",\n \"0.4677486\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.77259445"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17784,"cells":{"query":{"kind":"string","value":"Message iterator call maybe a simple one (e.g. message_in(x, y, z)) or a call to a member (e.g. message_in.wrap()) Using this function avoid using the global call one which may accept member function calls to things that are not iterators."},"document":{"kind":"string","value":"def dispatchMessageIteratorCall(self, tree):\n # simple case not a member function just an iterator with arguments\n if isinstance(tree.func, ast.Name):\n self.write(f\"FLAMEGPU->{tree.func.id}\")\n if isinstance(tree.func, ast.Attribute) :\n if isinstance(tree.func.value, ast.Name):\n # check that the iterator is supported\n if not tree.func.attr in self.fgpu_input_msg_iter_funcs:\n self.RaiseError(tree, f\"Message input loop iterator '{tree.func.attr}' is not supported.\")\n self.write(f\"FLAMEGPU->{tree.func.value.id}.{tree.func.attr}\")\n else:\n self.RaiseError(tree, \"Message input loop iterator format incorrect.\")\n\n # handle function arguments \n self.write(\"(\")\n self._CallArguments(tree)\n self.write(\")\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def call(self, message: Message) -> None:\n self.fn(message)","def _call(self, x):\n return x.inner(x)","def test_dispatch_inbound(self):\n msg_helper = MessageHelper()\n worker_helper = WorkerHelper()\n broker = self.setup_broker(worker_helper)\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [])\n msg = msg_helper.make_inbound('message')\n yield worker_helper.dispatch_inbound(msg, 'fooconn')\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [msg])","def __iter__(self):\n yield from self.calls","def __iter__(self):\n return iter([self.format_message(record) for record in self._messages])","def iter_call(self, name, *args, **kwargs):\r\n return self.client.iter_call(self.name, name, *args, **kwargs)","def test_dispatch_inbound_no_connector(self):\n msg_helper = MessageHelper()\n worker_helper = WorkerHelper(connector_name='fooconn')\n broker = self.setup_broker(worker_helper)\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [])\n msg = msg_helper.make_inbound('message')\n yield worker_helper.dispatch_inbound(msg)\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [msg])","def test_dispatch_raw(self):\n msg_helper = MessageHelper()\n worker_helper = WorkerHelper()\n broker = self.setup_broker(worker_helper)\n self.assertEqual(broker.get_messages('vumi', 'fooconn.foo'), [])\n msg = msg_helper.make_inbound('message')\n yield worker_helper.dispatch_raw('fooconn.foo', msg)\n self.assertEqual(broker.get_messages('vumi', 'fooconn.foo'), [msg])","def __next__(self):\n return self.read_message()","def call(self, msg, cb=None):\r\n self._call(msg, cb or self._cb)","def callable(func, message):\n return func, message","def dispatchMessageLoop(self, tree):\n self.fill(\"for (const auto& \")\n self.dispatch(tree.target)\n self.write(\" : \")\n # if simple message iterator\n if isinstance(tree.iter, ast.Name):\n if not tree.iter.id == self._input_message_var:\n self.RaiseError(t, f\"Message input loop requires use of '{self._input_message_var}' as iterator.\")\n # write with prefix\n self.write(f\"FLAMEGPU->{self._input_message_var}\")\n # if it is a call then handle the different cases\n elif isinstance(tree.iter, ast.Call):\n self.dispatchMessageIteratorCall(tree.iter)\n #otherwise not supported\n else :\n self.RaiseError(tree, f\"Message input loop iterator in unsupported format\")\n self.write(\")\")\n self._message_iterator_var = tree.target.id\n self.enter()\n self.dispatch(tree.body)\n self.leave()\n self._message_iterator_var = None","def nextinline(self, irc, msg, args):\n channel = self.registryValue('checkOpsInChannel')\n if channel == '':\n self.log.error('checkOpsInChannel not set!')\n return\n if channel not in irc.state.channels:\n self.log.warn('not in %s' % channel)\n return\n if msg.nick not in irc.state.channels[channel].ops:\n self.log.warn('denying access to non-chanop user %r' % msg.nick)\n return\n if len(self._queue) > 0:\n nick, notice = self._queue.pop(0)\n response = \"Next in line is %s\" % nick\n if notice is not None:\n response += \" with notice: %s\" % notice\n self._dump_queue()\n irc.reply(response)\n else:\n irc.reply(\"There's nobody queued up right now.\")","def __call__(self, *args, **kwargs):\n return self.method(self.receiver, *args, **kwargs)","def _iter_call_meth(self, method, *args, **kwargs):\n for obj in self:\n if hasattr(obj, method):\n f = op.methodcaller(method, *args, **kwargs)\n f(obj)","def get_message(self, i):\n pass","def _dispatch_messages(self):\n while True:\n select_obj = (yield)\n if select_obj == self._message_queue.selobj:\n msg = self._message_queue.get_nowait()\n if msg is not None:\n msg_type = msg.get('type', None)\n if msg_type is not None:\n msg_handler = self._message_handlers.get(msg_type, None)\n if msg_handler is not None:\n msg_handler(msg['data'])","def __call__(self):\r\n return self.next()","def _call(self, msg, cb, *args):\r\n if not self._status:\r\n raise InterfaceDisabledError('A disabled interface should not be '\r\n 'called.')\r\n\r\n if not callable(cb):\r\n raise TypeError('Callback has to be callable.')\r\n\r\n uid = uuid4().hex\r\n deferred = Deferred()\r\n deferred.addCallback(cb, *args)\r\n self._responses[uid] = deferred\r\n\r\n self._conn.sendMessage(self._iTag, self._clsName, msg, uid)","def processMessage(self, *args, **kwargs):\r\n pass","def send_method(\n self, call: Callable[[Any, str], str]\n ) -> Callable[[Any, str], str]:\n assert self._send_raw_message_call is None\n self._send_raw_message_call = call\n return call","def __call__(self, *args, **kwargs):\n return self.call(*args, **kwargs)","def __call__(self, func, *args, **kwargs):\n\n @wraps(func) # To keep its own namespace\n def wrapper(*args, **kwargs):\n gener = self.__iter__()\n return func(gener, *args, **kwargs)\n return wrapper","def process(self, message: Message, **kwargs: Any) -> None:","def process(self, msg):\n raise NotImplemented","def _incoming_handler(self, context, message, fake_reply):\r\n return self._map[message.method](context, fake_reply, *message.args, **message.kwargs)","def imap_c(func):\n return functools.partial(imap, func)","def sender_iter(self):\n while 1:\n yield self.send_next()","def __call__(self, *args, **kw):\n return self.callable(*args, **kw)","def call_all(self, msg_tag, message):\n return self.hub.call_all(self.get_private_key(), msg_tag, message)","def _process_message(self, obj):\n pass","def message(message, name=None):\n def decorator(func):\n wf = update_wrapper(Msg(func, message), func)\n if name:\n wf.name = name\n return wf\n return decorator","def test_get_dispatched_inbound(self):\n msg_helper = MessageHelper()\n worker_helper = WorkerHelper()\n dispatched = worker_helper.get_dispatched_inbound('fooconn')\n self.assertEqual(dispatched, [])\n msg = msg_helper.make_inbound('message')\n self._add_to_dispatched(\n worker_helper.broker, 'fooconn.inbound', msg)\n dispatched = worker_helper.get_dispatched_inbound('fooconn')\n self.assertEqual(dispatched, [msg])","def __call__(self, *args, **kwargs):\n return self.call(*args, **kwargs)","def test_wrap_method(self):\n myreactor = FakeReactor()\n c = EventLoop(lambda: myreactor, lambda f, g: None)\n c.no_setup()\n calls = []\n\n class C(object):\n def func(self, a, b, c):\n calls.append((a, b, c))\n\n f = c.run_in_reactor(C().func)\n f(4, 5, c=6)\n self.assertEqual(calls, [(4, 5, 6)])","def _generator(self):\n\t\twhile(1):\n\t\t\ttry:\n\t\t\t\tm = self.messages.pop(0) # pop the first Flash2Message in the list\n\t\t\t\tyield m\n\t\t\texcept IndexError:\n\t\t\t\traise StopIteration","def call(self, *args, **kwargs):","def test_is_iterable(self):\r\n msg_list = messages.MessageList()\r\n\r\n # Adds 3 Message objects to the list.\r\n msg_list.push(messages.StringMessage(\"a\"))\r\n msg_list.push(messages.StringMessage(\"b\"))\r\n msg_list.push(messages.StringMessage(\"c\"))\r\n\r\n self.assertEqual([\"ab\", \"bb\", \"cb\"], [x.msg + \"b\" for x in msg_list])","def __next__(self):\n if self.iterator_idx >= self.num_conversations:\n print('You reached the end of the conversations.')\n self.reset() # return the iterator idx to 0\n return None\n\n conv = self.conversations[self.iterator_idx]\n self.iterator_idx += 1\n\n return conv","def __call__(self, *arg, **kwargs):\n return self._fun(*arg, **kwargs)","def __getattr__(self, name):\n try:\n super(MessageListWrapper, self).__getattr__(name)\n except AttributeError:\n return getattr(self.wrapped, name)","def send_async_ex_method(\n self, call: Callable[[Any, str, Message], Awaitable[str]]\n ) -> Callable[[Any, str, Message], Awaitable[str]]:\n assert self._send_async_raw_message_ex_call is None\n self._send_async_raw_message_ex_call = call\n return call","def MessageStream(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def MessageStream(self, request, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def process_messages(self):\n pass","def __call__ ( self , *x ) :\n return partial ( self.__index ,\n self.func ,\n x ,\n self.step ,\n self.order ,\n self.err )","def message_from_id(func):\n @functools.wraps(func)\n def inner(request, message_id, *args, **kwargs):\n # Fetch the message\n try:\n message = Message.objects.get(id=message_id)\n except Message.DoesNotExist:\n raise Http404(\"No such message\")\n # Run the inner\n return func(request, message=message, *args, **kwargs)\n return inner","def _f(message):\n return message","def __iter__(self):\n while True:\n m = self.recv(timeout=1.0)\n if m is not None:\n yield m\n logger.debug(\"done iterating over bus messages\")","def func(*args, **kwargs):\n return call(*args, **kwargs) # pylint: disable = E1102","async def on_call(message, client):\n pass","def dispatch_next(self):\r\n self._dispatch_amount += 1\r\n while self._dispatch_amount:\r\n try:\r\n # XXX: possible race condition shuffling the order of\r\n # dispatches in the next two lines.\r\n func, args, kwargs = next(self._original_iterable)\r\n self.dispatch(func, args, kwargs)\r\n self._dispatch_amount -= 1\r\n except ValueError:\r\n \"\"\" Race condition in accessing a generator, we skip,\r\n the dispatch will be done later.\r\n \"\"\"\r\n except StopIteration:\r\n self._iterating = False\r\n self._original_iterable = None\r\n return","def test_get_dispatched(self):\n msg_helper = MessageHelper()\n worker_helper = WorkerHelper()\n dispatched = worker_helper.get_dispatched(\n 'fooconn', 'inbound', TransportUserMessage)\n self.assertEqual(dispatched, [])\n msg = msg_helper.make_inbound('message')\n self._add_to_dispatched(\n worker_helper.broker, 'fooconn.inbound', msg)\n dispatched = worker_helper.get_dispatched(\n 'fooconn', 'inbound', TransportUserMessage)\n self.assertEqual(dispatched, [msg])","def __call__(self, *args, **kwargs):\n return self.func(*args, **kwargs)","def __call__(self, *args, **kwargs):\n return self.func(*args, **kwargs)","def __call__(self, *args, **kwargs):\n if len(args) == 0:\n args = tuple(arg.default() for arg in self[1:])\n return Call(self, *args, **kwargs)","def __iter__(self):\n return iter(())","def __call__(self, *args, **kwargs):\n return self._func(*args, **kwargs)","def callback(parsed_msg, msg_object):\n assert msg_object.stream_id == stream_id\n assert parsed_msg in msg","def _iterate_messages(self):\n if not self.targets:\n raise sex.SullyRuntimeError(\"No targets specified in session\")\n\n if not self.edges_from(self.root.id):\n raise sex.SullyRuntimeError(\"No requests specified in session\")\n\n self._reset_fuzz_state()\n\n for x in self._iterate_messages_recursive(this_node=self.root, path=[]):\n yield x","def Chat(self, request_iterator, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def _Call(self, t):\n # check calls but let attributes check in their own dispatcher\n funcs = self._device_functions + self.pythonbuiltins + [self._input_message_var] # message_input variable is a valid function name as certain message types have arguments on iterator\n if isinstance(t.func, ast.Name):\n if (t.func.id not in funcs):\n self.RaiseWarning(t, \"Function call is not a defined FLAME GPU device function or a supported python built in.\")\n # dispatch even if warning raised\n self.dispatch(t.func)\n elif isinstance(t.func, ast.Lambda):\n self.dispatch(t.func) # not supported\n else:\n # special handler for dispatching member function calls\n # This would otherwise be an attribute\n self.dispatchMemberFunction(t.func, t) \n self.write(\"(\")\n self._CallArguments(t)\n self.write(\")\")","def messages(self):\n return list(iter(self))","def call_all(self, private_key, msg_tag, message):\n return self._samp_hub.callAll(private_key, msg_tag, message)","def simple_send():\n i = None\n while True:\n i = yield i","def __call__(self, *args, **kwargs):\n return self.f(*args, **kwargs)","def next(self): # wait for 5 minutes after sending message\n if self.queue:\n messages = self.queue.get_messages(1,visibility_timeout=self.visibility_timeout)\n if messages:\n for m in messages:\n return m\n raise StopIteration","def __ne__(self, *args):\n return _ida_hexrays.udcall_map_iterator_t___ne__(self, *args)","def messageCollector():\n\ttempui = CatchallUI()\n\trealui = base.ui\n\ttry:\n\t\tbase.ui = tempui\n\t\tyield tempui\n\tfinally:\n\t\tbase.ui = realui","def dispatch_message(self, addr, message_dict, kind):\n try:\n yield from self.dispatcher.dispatch_message(addr, message_dict, kind)\n except Exception as e:\n self.logger.error(\n \"Failed to dispatch mochad message {}: {}\".format(\n message_dict, e))","def test_get_dispatched_inbound_no_connector(self):\n msg_helper = MessageHelper()\n worker_helper = WorkerHelper(connector_name='fooconn')\n dispatched = worker_helper.get_dispatched_inbound()\n self.assertEqual(dispatched, [])\n msg = msg_helper.make_inbound('message')\n self._add_to_dispatched(\n worker_helper.broker, 'fooconn.inbound', msg)\n dispatched = worker_helper.get_dispatched_inbound()\n self.assertEqual(dispatched, [msg])","def get_messages_from_queue(fx):\n\n for msg in queue.receive_messages():\n fx(msg)","def __call__(self, *args, **params):\n\t\treturn self.send(params)","def get_command_called(self, slack_message: str) -> _SingleCommand:\n for command in self:\n command_part, _ = command.split_message(slack_message)\n if command_part:\n return command","def __call__(self, *args, **kwargs):\n return self.method(*args, **kwargs)","def flag_messages(self, org, messages):\n pass","def _process_msg(cls, msg):\n raise NotImplementedError","def chek_iter_obj(func):\n\n def wrapper(self, lst):\n try:\n iter(lst)\n return func(self, lst)\n except TypeError:\n raise TypeError(f\"{lst} isn't iterable object\")\n\n return wrapper","def _handle_one_message(self):\n\n type, data = self.cxn.recv_message()\n\n if type.startswith(\"call\"):\n if len(data) != 3:\n message = (type, data)\n raise MessageError.invalid(message, \"incorrect number of args\")\n flags = {\n \"want_response\": type == \"call\",\n }\n call = Call(data[0], data[1], data[2], flags, self.client)\n self._handle_call(call)\n return False\n\n raise MessageError.bad_type(type)","def __iter__(self):\r\n return self._iterate()","def iter(space, w_collection_or_callable, w_sentinel=None):\n if w_sentinel is None:\n return space.iter(w_collection_or_callable)\n else:\n return iter_sentinel(space, w_collection_or_callable, w_sentinel)","def iter_py():\n s = \"Hello, World!\"\n it = iter(s)\n while True:\n try:\n print(next(it))\n except:\n break\n\n ## Output\n # H\n # e\n # l\n # l\n # o\n # ,\n #\n # W\n # o\n # r\n # l\n # d\n # !","def test_dispatch_raw_with_exchange(self):\n msg_helper = MessageHelper()\n worker_helper = WorkerHelper()\n broker = worker_helper.broker\n broker.exchange_declare('blah', 'direct', durable=True)\n self.assertEqual(broker.get_messages('blah', 'fooconn.foo'), [])\n msg = msg_helper.make_inbound('message')\n yield worker_helper.dispatch_raw('fooconn.foo', msg, exchange='blah')\n self.assertEqual(broker.get_messages('blah', 'fooconn.foo'), [msg])","def process_message(self, msg, src):","def process_messages(self, messages):\n\n return messages","def _invokeMember(obj, memberName, *args, **kwargs):\n return getattr(obj, memberName)(*args, **kwargs)","def _process_message_general(self, context, message):\r\n f = self._fake_next_op(context, message)\r\n\r\n if f is True:\r\n return True\r\n elif f is not False:\r\n return f\r\n elif message.method == \"__testing__\":\r\n self._general_testing(context, *message.args, **message.kwargs)\r\n return True\r\n else:\r\n return False","def test_wait_for_dispatched_inbound(self):\n msg_helper = MessageHelper()\n worker_helper = WorkerHelper()\n d = worker_helper.wait_for_dispatched_inbound(1, 'fooconn')\n self.assertNoResult(d)\n msg = msg_helper.make_inbound('message')\n yield self._add_to_dispatched(\n worker_helper.broker, 'fooconn.inbound', msg, kick=True)\n dispatched = success_result_of(d)\n self.assertEqual(dispatched, [msg])","def process(self, message: Message, **kwargs: Any) -> None:\n pass","def on_identity(self, messages):\n for message in messages:\n # get cache object linked to this request and stop timeout from occurring\n identifier = MissingMemberCache.message_to_identifier(message)\n cache = self._request_cache.pop(identifier, MissingMemberCache)\n if cache:\n for func, args in cache.callbacks:\n func(message, *args)","def Message(self, *args, **kwargs):\n pass","def __getattr__(self, item):\n return Caller(self, item)","def _handler(self, message):\n\n data = pickle.loads(message['data'])\n\n if not data[2]:\n # empty method call; bail out\n return\n\n # call the function and respond to the proxy object with return value\n uuid = data[0]\n proxy = data[1]\n func = getattr(self, data[2])\n result = (uuid, func(*data[3], **data[4]))\n self._redis.publish('proxy:%s' % proxy, pickle.dumps(result))","def test_filter_messages_non_message(self):\n pass","def test_make_dispatch_inbound_defaults(self):\n md_helper = MessageDispatchHelper(\n MessageHelper(), WorkerHelper('fooconn'))\n broker = self.setup_broker(md_helper)\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [])\n msg = yield md_helper.make_dispatch_inbound('inbound message')\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [msg])\n self.assert_message_fields(msg, {\n 'content': 'inbound message',\n 'from_addr': md_helper.msg_helper.mobile_addr,\n 'to_addr': md_helper.msg_helper.transport_addr,\n 'transport_type': md_helper.msg_helper.transport_type,\n 'transport_name': md_helper.msg_helper.transport_name,\n 'helper_metadata': {},\n 'transport_metadata': {},\n })","def HelloMsg(self, request_iterator, context):\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\n context.set_details('Method not implemented!')\n raise NotImplementedError('Method not implemented!')","def run(\n self, stub_class: Callable, call_func: str, streaming: bool = False, data: dict | None = None\n ) -> Generator:\n if data is None:\n data = {}\n with self.get_conn() as channel:\n stub = stub_class(channel)\n try:\n rpc_func = getattr(stub, call_func)\n response = rpc_func(**data)\n if not streaming:\n yield response\n else:\n yield from response\n except grpc.RpcError as ex:\n self.log.exception(\n \"Error occurred when calling the grpc service: %s, method: %s \\\n status code: %s, error details: %s\",\n stub.__class__.__name__,\n call_func,\n ex.code(),\n ex.details(),\n )\n raise ex","def __call__():","def __call__():","def __call__():"],"string":"[\n \"def call(self, message: Message) -> None:\\n self.fn(message)\",\n \"def _call(self, x):\\n return x.inner(x)\",\n \"def test_dispatch_inbound(self):\\n msg_helper = MessageHelper()\\n worker_helper = WorkerHelper()\\n broker = self.setup_broker(worker_helper)\\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [])\\n msg = msg_helper.make_inbound('message')\\n yield worker_helper.dispatch_inbound(msg, 'fooconn')\\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [msg])\",\n \"def __iter__(self):\\n yield from self.calls\",\n \"def __iter__(self):\\n return iter([self.format_message(record) for record in self._messages])\",\n \"def iter_call(self, name, *args, **kwargs):\\r\\n return self.client.iter_call(self.name, name, *args, **kwargs)\",\n \"def test_dispatch_inbound_no_connector(self):\\n msg_helper = MessageHelper()\\n worker_helper = WorkerHelper(connector_name='fooconn')\\n broker = self.setup_broker(worker_helper)\\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [])\\n msg = msg_helper.make_inbound('message')\\n yield worker_helper.dispatch_inbound(msg)\\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [msg])\",\n \"def test_dispatch_raw(self):\\n msg_helper = MessageHelper()\\n worker_helper = WorkerHelper()\\n broker = self.setup_broker(worker_helper)\\n self.assertEqual(broker.get_messages('vumi', 'fooconn.foo'), [])\\n msg = msg_helper.make_inbound('message')\\n yield worker_helper.dispatch_raw('fooconn.foo', msg)\\n self.assertEqual(broker.get_messages('vumi', 'fooconn.foo'), [msg])\",\n \"def __next__(self):\\n return self.read_message()\",\n \"def call(self, msg, cb=None):\\r\\n self._call(msg, cb or self._cb)\",\n \"def callable(func, message):\\n return func, message\",\n \"def dispatchMessageLoop(self, tree):\\n self.fill(\\\"for (const auto& \\\")\\n self.dispatch(tree.target)\\n self.write(\\\" : \\\")\\n # if simple message iterator\\n if isinstance(tree.iter, ast.Name):\\n if not tree.iter.id == self._input_message_var:\\n self.RaiseError(t, f\\\"Message input loop requires use of '{self._input_message_var}' as iterator.\\\")\\n # write with prefix\\n self.write(f\\\"FLAMEGPU->{self._input_message_var}\\\")\\n # if it is a call then handle the different cases\\n elif isinstance(tree.iter, ast.Call):\\n self.dispatchMessageIteratorCall(tree.iter)\\n #otherwise not supported\\n else :\\n self.RaiseError(tree, f\\\"Message input loop iterator in unsupported format\\\")\\n self.write(\\\")\\\")\\n self._message_iterator_var = tree.target.id\\n self.enter()\\n self.dispatch(tree.body)\\n self.leave()\\n self._message_iterator_var = None\",\n \"def nextinline(self, irc, msg, args):\\n channel = self.registryValue('checkOpsInChannel')\\n if channel == '':\\n self.log.error('checkOpsInChannel not set!')\\n return\\n if channel not in irc.state.channels:\\n self.log.warn('not in %s' % channel)\\n return\\n if msg.nick not in irc.state.channels[channel].ops:\\n self.log.warn('denying access to non-chanop user %r' % msg.nick)\\n return\\n if len(self._queue) > 0:\\n nick, notice = self._queue.pop(0)\\n response = \\\"Next in line is %s\\\" % nick\\n if notice is not None:\\n response += \\\" with notice: %s\\\" % notice\\n self._dump_queue()\\n irc.reply(response)\\n else:\\n irc.reply(\\\"There's nobody queued up right now.\\\")\",\n \"def __call__(self, *args, **kwargs):\\n return self.method(self.receiver, *args, **kwargs)\",\n \"def _iter_call_meth(self, method, *args, **kwargs):\\n for obj in self:\\n if hasattr(obj, method):\\n f = op.methodcaller(method, *args, **kwargs)\\n f(obj)\",\n \"def get_message(self, i):\\n pass\",\n \"def _dispatch_messages(self):\\n while True:\\n select_obj = (yield)\\n if select_obj == self._message_queue.selobj:\\n msg = self._message_queue.get_nowait()\\n if msg is not None:\\n msg_type = msg.get('type', None)\\n if msg_type is not None:\\n msg_handler = self._message_handlers.get(msg_type, None)\\n if msg_handler is not None:\\n msg_handler(msg['data'])\",\n \"def __call__(self):\\r\\n return self.next()\",\n \"def _call(self, msg, cb, *args):\\r\\n if not self._status:\\r\\n raise InterfaceDisabledError('A disabled interface should not be '\\r\\n 'called.')\\r\\n\\r\\n if not callable(cb):\\r\\n raise TypeError('Callback has to be callable.')\\r\\n\\r\\n uid = uuid4().hex\\r\\n deferred = Deferred()\\r\\n deferred.addCallback(cb, *args)\\r\\n self._responses[uid] = deferred\\r\\n\\r\\n self._conn.sendMessage(self._iTag, self._clsName, msg, uid)\",\n \"def processMessage(self, *args, **kwargs):\\r\\n pass\",\n \"def send_method(\\n self, call: Callable[[Any, str], str]\\n ) -> Callable[[Any, str], str]:\\n assert self._send_raw_message_call is None\\n self._send_raw_message_call = call\\n return call\",\n \"def __call__(self, *args, **kwargs):\\n return self.call(*args, **kwargs)\",\n \"def __call__(self, func, *args, **kwargs):\\n\\n @wraps(func) # To keep its own namespace\\n def wrapper(*args, **kwargs):\\n gener = self.__iter__()\\n return func(gener, *args, **kwargs)\\n return wrapper\",\n \"def process(self, message: Message, **kwargs: Any) -> None:\",\n \"def process(self, msg):\\n raise NotImplemented\",\n \"def _incoming_handler(self, context, message, fake_reply):\\r\\n return self._map[message.method](context, fake_reply, *message.args, **message.kwargs)\",\n \"def imap_c(func):\\n return functools.partial(imap, func)\",\n \"def sender_iter(self):\\n while 1:\\n yield self.send_next()\",\n \"def __call__(self, *args, **kw):\\n return self.callable(*args, **kw)\",\n \"def call_all(self, msg_tag, message):\\n return self.hub.call_all(self.get_private_key(), msg_tag, message)\",\n \"def _process_message(self, obj):\\n pass\",\n \"def message(message, name=None):\\n def decorator(func):\\n wf = update_wrapper(Msg(func, message), func)\\n if name:\\n wf.name = name\\n return wf\\n return decorator\",\n \"def test_get_dispatched_inbound(self):\\n msg_helper = MessageHelper()\\n worker_helper = WorkerHelper()\\n dispatched = worker_helper.get_dispatched_inbound('fooconn')\\n self.assertEqual(dispatched, [])\\n msg = msg_helper.make_inbound('message')\\n self._add_to_dispatched(\\n worker_helper.broker, 'fooconn.inbound', msg)\\n dispatched = worker_helper.get_dispatched_inbound('fooconn')\\n self.assertEqual(dispatched, [msg])\",\n \"def __call__(self, *args, **kwargs):\\n return self.call(*args, **kwargs)\",\n \"def test_wrap_method(self):\\n myreactor = FakeReactor()\\n c = EventLoop(lambda: myreactor, lambda f, g: None)\\n c.no_setup()\\n calls = []\\n\\n class C(object):\\n def func(self, a, b, c):\\n calls.append((a, b, c))\\n\\n f = c.run_in_reactor(C().func)\\n f(4, 5, c=6)\\n self.assertEqual(calls, [(4, 5, 6)])\",\n \"def _generator(self):\\n\\t\\twhile(1):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tm = self.messages.pop(0) # pop the first Flash2Message in the list\\n\\t\\t\\t\\tyield m\\n\\t\\t\\texcept IndexError:\\n\\t\\t\\t\\traise StopIteration\",\n \"def call(self, *args, **kwargs):\",\n \"def test_is_iterable(self):\\r\\n msg_list = messages.MessageList()\\r\\n\\r\\n # Adds 3 Message objects to the list.\\r\\n msg_list.push(messages.StringMessage(\\\"a\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"b\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"c\\\"))\\r\\n\\r\\n self.assertEqual([\\\"ab\\\", \\\"bb\\\", \\\"cb\\\"], [x.msg + \\\"b\\\" for x in msg_list])\",\n \"def __next__(self):\\n if self.iterator_idx >= self.num_conversations:\\n print('You reached the end of the conversations.')\\n self.reset() # return the iterator idx to 0\\n return None\\n\\n conv = self.conversations[self.iterator_idx]\\n self.iterator_idx += 1\\n\\n return conv\",\n \"def __call__(self, *arg, **kwargs):\\n return self._fun(*arg, **kwargs)\",\n \"def __getattr__(self, name):\\n try:\\n super(MessageListWrapper, self).__getattr__(name)\\n except AttributeError:\\n return getattr(self.wrapped, name)\",\n \"def send_async_ex_method(\\n self, call: Callable[[Any, str, Message], Awaitable[str]]\\n ) -> Callable[[Any, str, Message], Awaitable[str]]:\\n assert self._send_async_raw_message_ex_call is None\\n self._send_async_raw_message_ex_call = call\\n return call\",\n \"def MessageStream(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def MessageStream(self, request, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def process_messages(self):\\n pass\",\n \"def __call__ ( self , *x ) :\\n return partial ( self.__index ,\\n self.func ,\\n x ,\\n self.step ,\\n self.order ,\\n self.err )\",\n \"def message_from_id(func):\\n @functools.wraps(func)\\n def inner(request, message_id, *args, **kwargs):\\n # Fetch the message\\n try:\\n message = Message.objects.get(id=message_id)\\n except Message.DoesNotExist:\\n raise Http404(\\\"No such message\\\")\\n # Run the inner\\n return func(request, message=message, *args, **kwargs)\\n return inner\",\n \"def _f(message):\\n return message\",\n \"def __iter__(self):\\n while True:\\n m = self.recv(timeout=1.0)\\n if m is not None:\\n yield m\\n logger.debug(\\\"done iterating over bus messages\\\")\",\n \"def func(*args, **kwargs):\\n return call(*args, **kwargs) # pylint: disable = E1102\",\n \"async def on_call(message, client):\\n pass\",\n \"def dispatch_next(self):\\r\\n self._dispatch_amount += 1\\r\\n while self._dispatch_amount:\\r\\n try:\\r\\n # XXX: possible race condition shuffling the order of\\r\\n # dispatches in the next two lines.\\r\\n func, args, kwargs = next(self._original_iterable)\\r\\n self.dispatch(func, args, kwargs)\\r\\n self._dispatch_amount -= 1\\r\\n except ValueError:\\r\\n \\\"\\\"\\\" Race condition in accessing a generator, we skip,\\r\\n the dispatch will be done later.\\r\\n \\\"\\\"\\\"\\r\\n except StopIteration:\\r\\n self._iterating = False\\r\\n self._original_iterable = None\\r\\n return\",\n \"def test_get_dispatched(self):\\n msg_helper = MessageHelper()\\n worker_helper = WorkerHelper()\\n dispatched = worker_helper.get_dispatched(\\n 'fooconn', 'inbound', TransportUserMessage)\\n self.assertEqual(dispatched, [])\\n msg = msg_helper.make_inbound('message')\\n self._add_to_dispatched(\\n worker_helper.broker, 'fooconn.inbound', msg)\\n dispatched = worker_helper.get_dispatched(\\n 'fooconn', 'inbound', TransportUserMessage)\\n self.assertEqual(dispatched, [msg])\",\n \"def __call__(self, *args, **kwargs):\\n return self.func(*args, **kwargs)\",\n \"def __call__(self, *args, **kwargs):\\n return self.func(*args, **kwargs)\",\n \"def __call__(self, *args, **kwargs):\\n if len(args) == 0:\\n args = tuple(arg.default() for arg in self[1:])\\n return Call(self, *args, **kwargs)\",\n \"def __iter__(self):\\n return iter(())\",\n \"def __call__(self, *args, **kwargs):\\n return self._func(*args, **kwargs)\",\n \"def callback(parsed_msg, msg_object):\\n assert msg_object.stream_id == stream_id\\n assert parsed_msg in msg\",\n \"def _iterate_messages(self):\\n if not self.targets:\\n raise sex.SullyRuntimeError(\\\"No targets specified in session\\\")\\n\\n if not self.edges_from(self.root.id):\\n raise sex.SullyRuntimeError(\\\"No requests specified in session\\\")\\n\\n self._reset_fuzz_state()\\n\\n for x in self._iterate_messages_recursive(this_node=self.root, path=[]):\\n yield x\",\n \"def Chat(self, request_iterator, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def _Call(self, t):\\n # check calls but let attributes check in their own dispatcher\\n funcs = self._device_functions + self.pythonbuiltins + [self._input_message_var] # message_input variable is a valid function name as certain message types have arguments on iterator\\n if isinstance(t.func, ast.Name):\\n if (t.func.id not in funcs):\\n self.RaiseWarning(t, \\\"Function call is not a defined FLAME GPU device function or a supported python built in.\\\")\\n # dispatch even if warning raised\\n self.dispatch(t.func)\\n elif isinstance(t.func, ast.Lambda):\\n self.dispatch(t.func) # not supported\\n else:\\n # special handler for dispatching member function calls\\n # This would otherwise be an attribute\\n self.dispatchMemberFunction(t.func, t) \\n self.write(\\\"(\\\")\\n self._CallArguments(t)\\n self.write(\\\")\\\")\",\n \"def messages(self):\\n return list(iter(self))\",\n \"def call_all(self, private_key, msg_tag, message):\\n return self._samp_hub.callAll(private_key, msg_tag, message)\",\n \"def simple_send():\\n i = None\\n while True:\\n i = yield i\",\n \"def __call__(self, *args, **kwargs):\\n return self.f(*args, **kwargs)\",\n \"def next(self): # wait for 5 minutes after sending message\\n if self.queue:\\n messages = self.queue.get_messages(1,visibility_timeout=self.visibility_timeout)\\n if messages:\\n for m in messages:\\n return m\\n raise StopIteration\",\n \"def __ne__(self, *args):\\n return _ida_hexrays.udcall_map_iterator_t___ne__(self, *args)\",\n \"def messageCollector():\\n\\ttempui = CatchallUI()\\n\\trealui = base.ui\\n\\ttry:\\n\\t\\tbase.ui = tempui\\n\\t\\tyield tempui\\n\\tfinally:\\n\\t\\tbase.ui = realui\",\n \"def dispatch_message(self, addr, message_dict, kind):\\n try:\\n yield from self.dispatcher.dispatch_message(addr, message_dict, kind)\\n except Exception as e:\\n self.logger.error(\\n \\\"Failed to dispatch mochad message {}: {}\\\".format(\\n message_dict, e))\",\n \"def test_get_dispatched_inbound_no_connector(self):\\n msg_helper = MessageHelper()\\n worker_helper = WorkerHelper(connector_name='fooconn')\\n dispatched = worker_helper.get_dispatched_inbound()\\n self.assertEqual(dispatched, [])\\n msg = msg_helper.make_inbound('message')\\n self._add_to_dispatched(\\n worker_helper.broker, 'fooconn.inbound', msg)\\n dispatched = worker_helper.get_dispatched_inbound()\\n self.assertEqual(dispatched, [msg])\",\n \"def get_messages_from_queue(fx):\\n\\n for msg in queue.receive_messages():\\n fx(msg)\",\n \"def __call__(self, *args, **params):\\n\\t\\treturn self.send(params)\",\n \"def get_command_called(self, slack_message: str) -> _SingleCommand:\\n for command in self:\\n command_part, _ = command.split_message(slack_message)\\n if command_part:\\n return command\",\n \"def __call__(self, *args, **kwargs):\\n return self.method(*args, **kwargs)\",\n \"def flag_messages(self, org, messages):\\n pass\",\n \"def _process_msg(cls, msg):\\n raise NotImplementedError\",\n \"def chek_iter_obj(func):\\n\\n def wrapper(self, lst):\\n try:\\n iter(lst)\\n return func(self, lst)\\n except TypeError:\\n raise TypeError(f\\\"{lst} isn't iterable object\\\")\\n\\n return wrapper\",\n \"def _handle_one_message(self):\\n\\n type, data = self.cxn.recv_message()\\n\\n if type.startswith(\\\"call\\\"):\\n if len(data) != 3:\\n message = (type, data)\\n raise MessageError.invalid(message, \\\"incorrect number of args\\\")\\n flags = {\\n \\\"want_response\\\": type == \\\"call\\\",\\n }\\n call = Call(data[0], data[1], data[2], flags, self.client)\\n self._handle_call(call)\\n return False\\n\\n raise MessageError.bad_type(type)\",\n \"def __iter__(self):\\r\\n return self._iterate()\",\n \"def iter(space, w_collection_or_callable, w_sentinel=None):\\n if w_sentinel is None:\\n return space.iter(w_collection_or_callable)\\n else:\\n return iter_sentinel(space, w_collection_or_callable, w_sentinel)\",\n \"def iter_py():\\n s = \\\"Hello, World!\\\"\\n it = iter(s)\\n while True:\\n try:\\n print(next(it))\\n except:\\n break\\n\\n ## Output\\n # H\\n # e\\n # l\\n # l\\n # o\\n # ,\\n #\\n # W\\n # o\\n # r\\n # l\\n # d\\n # !\",\n \"def test_dispatch_raw_with_exchange(self):\\n msg_helper = MessageHelper()\\n worker_helper = WorkerHelper()\\n broker = worker_helper.broker\\n broker.exchange_declare('blah', 'direct', durable=True)\\n self.assertEqual(broker.get_messages('blah', 'fooconn.foo'), [])\\n msg = msg_helper.make_inbound('message')\\n yield worker_helper.dispatch_raw('fooconn.foo', msg, exchange='blah')\\n self.assertEqual(broker.get_messages('blah', 'fooconn.foo'), [msg])\",\n \"def process_message(self, msg, src):\",\n \"def process_messages(self, messages):\\n\\n return messages\",\n \"def _invokeMember(obj, memberName, *args, **kwargs):\\n return getattr(obj, memberName)(*args, **kwargs)\",\n \"def _process_message_general(self, context, message):\\r\\n f = self._fake_next_op(context, message)\\r\\n\\r\\n if f is True:\\r\\n return True\\r\\n elif f is not False:\\r\\n return f\\r\\n elif message.method == \\\"__testing__\\\":\\r\\n self._general_testing(context, *message.args, **message.kwargs)\\r\\n return True\\r\\n else:\\r\\n return False\",\n \"def test_wait_for_dispatched_inbound(self):\\n msg_helper = MessageHelper()\\n worker_helper = WorkerHelper()\\n d = worker_helper.wait_for_dispatched_inbound(1, 'fooconn')\\n self.assertNoResult(d)\\n msg = msg_helper.make_inbound('message')\\n yield self._add_to_dispatched(\\n worker_helper.broker, 'fooconn.inbound', msg, kick=True)\\n dispatched = success_result_of(d)\\n self.assertEqual(dispatched, [msg])\",\n \"def process(self, message: Message, **kwargs: Any) -> None:\\n pass\",\n \"def on_identity(self, messages):\\n for message in messages:\\n # get cache object linked to this request and stop timeout from occurring\\n identifier = MissingMemberCache.message_to_identifier(message)\\n cache = self._request_cache.pop(identifier, MissingMemberCache)\\n if cache:\\n for func, args in cache.callbacks:\\n func(message, *args)\",\n \"def Message(self, *args, **kwargs):\\n pass\",\n \"def __getattr__(self, item):\\n return Caller(self, item)\",\n \"def _handler(self, message):\\n\\n data = pickle.loads(message['data'])\\n\\n if not data[2]:\\n # empty method call; bail out\\n return\\n\\n # call the function and respond to the proxy object with return value\\n uuid = data[0]\\n proxy = data[1]\\n func = getattr(self, data[2])\\n result = (uuid, func(*data[3], **data[4]))\\n self._redis.publish('proxy:%s' % proxy, pickle.dumps(result))\",\n \"def test_filter_messages_non_message(self):\\n pass\",\n \"def test_make_dispatch_inbound_defaults(self):\\n md_helper = MessageDispatchHelper(\\n MessageHelper(), WorkerHelper('fooconn'))\\n broker = self.setup_broker(md_helper)\\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [])\\n msg = yield md_helper.make_dispatch_inbound('inbound message')\\n self.assertEqual(broker.get_messages('vumi', 'fooconn.inbound'), [msg])\\n self.assert_message_fields(msg, {\\n 'content': 'inbound message',\\n 'from_addr': md_helper.msg_helper.mobile_addr,\\n 'to_addr': md_helper.msg_helper.transport_addr,\\n 'transport_type': md_helper.msg_helper.transport_type,\\n 'transport_name': md_helper.msg_helper.transport_name,\\n 'helper_metadata': {},\\n 'transport_metadata': {},\\n })\",\n \"def HelloMsg(self, request_iterator, context):\\n context.set_code(grpc.StatusCode.UNIMPLEMENTED)\\n context.set_details('Method not implemented!')\\n raise NotImplementedError('Method not implemented!')\",\n \"def run(\\n self, stub_class: Callable, call_func: str, streaming: bool = False, data: dict | None = None\\n ) -> Generator:\\n if data is None:\\n data = {}\\n with self.get_conn() as channel:\\n stub = stub_class(channel)\\n try:\\n rpc_func = getattr(stub, call_func)\\n response = rpc_func(**data)\\n if not streaming:\\n yield response\\n else:\\n yield from response\\n except grpc.RpcError as ex:\\n self.log.exception(\\n \\\"Error occurred when calling the grpc service: %s, method: %s \\\\\\n status code: %s, error details: %s\\\",\\n stub.__class__.__name__,\\n call_func,\\n ex.code(),\\n ex.details(),\\n )\\n raise ex\",\n \"def __call__():\",\n \"def __call__():\",\n \"def __call__():\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5965189","0.5781531","0.5567268","0.5560412","0.5555918","0.5474647","0.53749025","0.5369496","0.5356947","0.532416","0.53201944","0.52734697","0.5214312","0.51924616","0.51724297","0.51710767","0.51597893","0.51278067","0.5124339","0.5106569","0.509719","0.5078223","0.5075271","0.505164","0.5017414","0.49991345","0.49987322","0.4972996","0.4969931","0.496608","0.49639013","0.49568772","0.49567086","0.49419624","0.4939769","0.49205247","0.49173808","0.49171788","0.4908964","0.49060288","0.49043965","0.49034128","0.49005038","0.49005038","0.4885155","0.48734158","0.48674044","0.48505142","0.4849121","0.4845576","0.48226434","0.48223624","0.4817161","0.48112217","0.48112217","0.4800593","0.47988477","0.47928894","0.47913694","0.47887373","0.47748426","0.47674018","0.47671604","0.47591376","0.47519925","0.47476935","0.47437236","0.47377223","0.47227985","0.47153768","0.47084504","0.4701428","0.47013515","0.46950755","0.4693776","0.4692672","0.46758676","0.4675623","0.46735364","0.46670508","0.4666518","0.46640024","0.46593413","0.46559632","0.4653415","0.46520412","0.46498516","0.46421173","0.46299684","0.46292147","0.4628147","0.46234164","0.46215686","0.46214533","0.4620552","0.46188006","0.4617153","0.4614668","0.4614668","0.4614668"],"string":"[\n \"0.5965189\",\n \"0.5781531\",\n \"0.5567268\",\n \"0.5560412\",\n \"0.5555918\",\n \"0.5474647\",\n \"0.53749025\",\n \"0.5369496\",\n \"0.5356947\",\n \"0.532416\",\n \"0.53201944\",\n \"0.52734697\",\n \"0.5214312\",\n \"0.51924616\",\n \"0.51724297\",\n \"0.51710767\",\n \"0.51597893\",\n \"0.51278067\",\n \"0.5124339\",\n \"0.5106569\",\n \"0.509719\",\n \"0.5078223\",\n \"0.5075271\",\n \"0.505164\",\n \"0.5017414\",\n \"0.49991345\",\n \"0.49987322\",\n \"0.4972996\",\n \"0.4969931\",\n \"0.496608\",\n \"0.49639013\",\n \"0.49568772\",\n \"0.49567086\",\n \"0.49419624\",\n \"0.4939769\",\n \"0.49205247\",\n \"0.49173808\",\n \"0.49171788\",\n \"0.4908964\",\n \"0.49060288\",\n \"0.49043965\",\n \"0.49034128\",\n \"0.49005038\",\n \"0.49005038\",\n \"0.4885155\",\n \"0.48734158\",\n \"0.48674044\",\n \"0.48505142\",\n \"0.4849121\",\n \"0.4845576\",\n \"0.48226434\",\n \"0.48223624\",\n \"0.4817161\",\n \"0.48112217\",\n \"0.48112217\",\n \"0.4800593\",\n \"0.47988477\",\n \"0.47928894\",\n \"0.47913694\",\n \"0.47887373\",\n \"0.47748426\",\n \"0.47674018\",\n \"0.47671604\",\n \"0.47591376\",\n \"0.47519925\",\n \"0.47476935\",\n \"0.47437236\",\n \"0.47377223\",\n \"0.47227985\",\n \"0.47153768\",\n \"0.47084504\",\n \"0.4701428\",\n \"0.47013515\",\n \"0.46950755\",\n \"0.4693776\",\n \"0.4692672\",\n \"0.46758676\",\n \"0.4675623\",\n \"0.46735364\",\n \"0.46670508\",\n \"0.4666518\",\n \"0.46640024\",\n \"0.46593413\",\n \"0.46559632\",\n \"0.4653415\",\n \"0.46520412\",\n \"0.46498516\",\n \"0.46421173\",\n \"0.46299684\",\n \"0.46292147\",\n \"0.4628147\",\n \"0.46234164\",\n \"0.46215686\",\n \"0.46214533\",\n \"0.4620552\",\n \"0.46188006\",\n \"0.4617153\",\n \"0.4614668\",\n \"0.4614668\",\n \"0.4614668\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.73008895"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17785,"cells":{"query":{"kind":"string","value":"This is a special case of a range based for loop in which iterator item returns a const referecne to the message. Any user specified message value can be used."},"document":{"kind":"string","value":"def dispatchMessageLoop(self, tree):\n self.fill(\"for (const auto& \")\n self.dispatch(tree.target)\n self.write(\" : \")\n # if simple message iterator\n if isinstance(tree.iter, ast.Name):\n if not tree.iter.id == self._input_message_var:\n self.RaiseError(t, f\"Message input loop requires use of '{self._input_message_var}' as iterator.\")\n # write with prefix\n self.write(f\"FLAMEGPU->{self._input_message_var}\")\n # if it is a call then handle the different cases\n elif isinstance(tree.iter, ast.Call):\n self.dispatchMessageIteratorCall(tree.iter)\n #otherwise not supported\n else :\n self.RaiseError(tree, f\"Message input loop iterator in unsupported format\")\n self.write(\")\")\n self._message_iterator_var = tree.target.id\n self.enter()\n self.dispatch(tree.body)\n self.leave()\n self._message_iterator_var = None"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def get_message(self, i):\n pass","def __iter__(self):\n return iter([self.format_message(record) for record in self._messages])","def _For(self, t):\n # if message loop then process differently\n if isinstance(t.iter, ast.Name):\n if t.iter.id == self._input_message_var:\n self.dispatchMessageLoop(t)\n else:\n self.RaiseError(t, \"Range based for loops only support message iteration using 'message_in' iterator\")\n # do not support for else\n elif t.orelse:\n self.RaiseError(t, \"For else not supported\")\n # allow calls but only to range function\n elif isinstance(t.iter, ast.Call):\n # simple function call e.g. message_in() or range()\n if isinstance(t.iter.func, ast.Name):\n # catch case of message_input with arguments (e.g. spatial messaging)\n if t.iter.func.id == self._input_message_var:\n self.dispatchMessageLoop(t)\n # otherwise permit only range based for loops\n elif t.iter.func.id == \"range\":\n # switch on different uses of range based on number of arguments\n if len(t.iter.args) == 1:\n self.fill(f\"for (int \")\n self.dispatch(t.target)\n self.write(\"=0;\")\n self.dispatch(t.target)\n self.write(\"<\")\n self.dispatch(t.iter.args[0])\n self.write(\";\")\n self.dispatch(t.target)\n self.write(\"++)\")\n elif len(t.iter.args) == 2:\n self.fill(f\"for (int \")\n self.dispatch(t.target)\n self.write(\"=\")\n self.dispatch(t.iter.args[0])\n self.write(\";\")\n self.dispatch(t.target)\n self.write(\"<\")\n self.dispatch(t.iter.args[1])\n self.write(\";\")\n self.dispatch(t.target)\n self.write(\"++)\")\n elif len(t.iter.args) == 3:\n self.fill(f\"for (int \")\n self.dispatch(t.target)\n self.write(\"=\")\n self.dispatch(t.iter.args[0])\n self.write(\";\")\n self.dispatch(t.target)\n self.write(\"<\")\n self.dispatch(t.iter.args[1])\n self.write(\";\")\n self.dispatch(t.target)\n self.write(\"+=\")\n self.dispatch(t.iter.args[2])\n self.write(\")\")\n else:\n self.RaiseError(t, \"Range based for loops requires use of 'range' function with arguments and not keywords\")\n self.enter()\n self.dispatch(t.body)\n self.leave()\n else:\n self.RaiseError(t, \"Range based for loops only support calls to the 'range' function\")\n # member function call can only be on message_in.func() type call.\n elif isinstance(t.iter.func, ast.Attribute):\n # must be an attribute (e.g. calling a member of message_in)\n if t.iter.func.value.id == self._input_message_var:\n self.dispatchMessageLoop(t)\n else:\n self.RaiseError(t, \"Range based for loops only support calling members of message input variable\")\n else:\n self.RaiseError(t, \"Range based for loops only support message iteration or use of 'range'\")\n else:\n self.RaiseError(t, \"Range based for loops only support message iteration or use of 'range'\")","def msg_ele(msg):\n for word in msg:\n if '=' in word:\n return\n yield word","def get_user_message(self, message, index):\n userMessage = ''\n for i in range(index, len(message)):\n userMessage += message[i] + ' '\n return userMessage","def list_messages(self):","def _generator(self):\n\t\twhile(1):\n\t\t\ttry:\n\t\t\t\tm = self.messages.pop(0) # pop the first Flash2Message in the list\n\t\t\t\tyield m\n\t\t\texcept IndexError:\n\t\t\t\traise StopIteration","def dispatchMessageIteratorCall(self, tree):\n # simple case not a member function just an iterator with arguments\n if isinstance(tree.func, ast.Name):\n self.write(f\"FLAMEGPU->{tree.func.id}\")\n if isinstance(tree.func, ast.Attribute) :\n if isinstance(tree.func.value, ast.Name):\n # check that the iterator is supported\n if not tree.func.attr in self.fgpu_input_msg_iter_funcs:\n self.RaiseError(tree, f\"Message input loop iterator '{tree.func.attr}' is not supported.\")\n self.write(f\"FLAMEGPU->{tree.func.value.id}.{tree.func.attr}\")\n else:\n self.RaiseError(tree, \"Message input loop iterator format incorrect.\")\n\n # handle function arguments \n self.write(\"(\")\n self._CallArguments(tree)\n self.write(\")\")","def sendMessage(self, message):\n for component in CraftChatMessage.fromString(message):\n self.block.sendMessage(component)","def progress_iterator(collection: Collection, message: str) -> Iterable:\n num_items = len(collection)\n last_percentage = -1\n for i, item in enumerate(collection):\n percentage = 100 * i // num_items\n if percentage > last_percentage:\n last_percentage = percentage\n print(f\"{message} {percentage}%\", end='\\r')\n yield item\n print(f\"{message} 100%\")","def _enumerate_message(self, name, message_class):\n key_value_pair = []\n assert(message_class is not None) \n assert(dir(message_class) is not None) \n \n # TODO: to review, hack\n for key in dir(message_class):\n if key.isupper():\n \n # set the class attributes to the Enum values\n key_value_pair.append((key, getattr(message_class, key)))\n \n return Enum(name, key_value_pair)","def test_is_iterable(self):\r\n msg_list = messages.MessageList()\r\n\r\n # Adds 3 Message objects to the list.\r\n msg_list.push(messages.StringMessage(\"a\"))\r\n msg_list.push(messages.StringMessage(\"b\"))\r\n msg_list.push(messages.StringMessage(\"c\"))\r\n\r\n self.assertEqual([\"ab\", \"bb\", \"cb\"], [x.msg + \"b\" for x in msg_list])","def counter():\n for value in range(5):\n yield \"<{}>\".format(value)","def _iterate_messages(self):\n if not self.targets:\n raise sex.SullyRuntimeError(\"No targets specified in session\")\n\n if not self.edges_from(self.root.id):\n raise sex.SullyRuntimeError(\"No requests specified in session\")\n\n self._reset_fuzz_state()\n\n for x in self._iterate_messages_recursive(this_node=self.root, path=[]):\n yield x","def next(self): # wait for 5 minutes after sending message\n if self.queue:\n messages = self.queue.get_messages(1,visibility_timeout=self.visibility_timeout)\n if messages:\n for m in messages:\n return m\n raise StopIteration","def get_user_messages(user_id):\n pass \n # user_message_list = []\n\n # for message in sent messages:","def test_message_list():","def messages(self):\n return list(iter(self))","def _iterate_messages_recursive(self, this_node, path):\n # step through every edge from the current node.\n for edge in self.edges_from(this_node.id):\n # keep track of the path as we walk through it\n # we keep track of edges as opposed to nodes because if there is more then one path through a set of\n # given nodes we don't want any ambiguity.\n path.append(edge)\n\n message_path = \"->\".join([self.nodes[e.dst].name for e in path])\n logging.debug('checking: {0}'.format(message_path))\n\n self.fuzz_node = self.nodes[path[-1].dst]\n self.total_mutant_index += 1\n yield (path,)\n\n for x in self._iterate_messages_recursive(self.fuzz_node, path):\n yield x\n\n # finished with the last node on the path, pop it off the path stack.\n if path:\n path.pop()","def test_generator_continuous():\n RANGE_MAX = 100\n prev_value = RANGE_MAX // 2\n for msg in it.islice(generate_msgs(0, RANGE_MAX), 0, 42):\n curr_value = Message.parse(msg).power\n assert curr_value - prev_value <= 1\n prev_value = curr_value","def sender_iter(self):\n while 1:\n yield self.send_next()","def reverse_send() -> Generator[int, int, str]:\n start = yield 0\n for index in range(start, -1, -1):\n yield index\n\n return \"done\"","def process(self, msg):\n raise NotImplemented","def _next_message(self):\n msg = yield self._read_message()\n message = self._slack_to_chat(msg)\n\n raise gen.Return(message)","def __message_content__(self) -> MessageContent:","def comsume_msg(self, msg_type):","def _generate_message(self, occurences, criteria=max):\n message = ''\n for occurence in occurences:\n message += chr(occurence.index(criteria(occurence)) + 97)\n return message","def iteration(self) -> global___Statement.Iteration:","def iteration(self) -> global___Statement.Iteration:","def fetch(self, messages, uid):\n return list(zip(range(1, len(self.msgObjs) + 1), self.msgObjs))","def process_messages(self, messages):\n\n return messages","def _f(message):\n return message","def list_message(self):\n\t\tmessage = \"\"\n\t\tlength = len(self.object_list)\n\t\tfor i in range(length - 1):\n\t\t\to = self.object_list[i]\n\t\t\tmessage += self.index_letter_string(i) + o.display_name(True) + \", \"\n\t\tlast_object = self.object_list[length - 1]\n\t\tmessage += self.index_letter_string(length - 1) + last_object.display_name(True) + \", (?)\"\n\t\treturn message","def next_message(self):\n while self.queue.consuming:\n yield self.queue.channel._consume_message()","def __next__(self):\n return self.read_message()","def show_message(messages):\n for message in messages:\n printed_message = f\"{message}\"\n print(printed_message)","def _message_delete_multiple_private_task_message_id_iterator(groups):\n message_group_new, message_group_old, message_group_old_own = groups\n for item in message_group_new:\n yield item[1]\n \n yield from message_group_old\n yield from message_group_old_own","def __iter__(self) -> Iterator[AlertInterface]:","def sendMessage_0(self, messages):\n for message in messages:\n self.sendMessage(message)","def parse_message(self, it, line):\n match = self.message_line_re.match(line)\n if match is None:\n return None, next(it)\n\n file_path = os.path.normpath(\n os.path.join(os.path.dirname(self.analyzer_result),\n match.group('path')))\n\n message = Message(\n file_path,\n int(match.group('line')),\n 0,\n match.group('message').strip(),\n match.group('checker').strip())\n\n try:\n return message, next(it)\n except StopIteration:\n return message, ''","def message_all(self, message):\n # We copy the _clients into a list to avoid dictionary changing\n # size during iteration.\n for character in self.players.values():\n character.message(message)","def next(self):\n return self.read_message()","def report_on_interval(\n iterable,\n message='processed {i} of {n} items',\n item_count=None,\n printer=print,\n get_deltas=compute_reporting_interval,\n report_at_end=False,\n extra_actions=[],\n extras_at_end=False,\n):\n if item_count is not None:\n n = item_count\n else:\n iterable = list(iterable)\n n = len(iterable)\n delta = get_deltas(n)\n for (i, item) in enumerate(iterable):\n yield item\n if (i + 1) % delta == 0:\n printer(message.format(i=i + 1, n=n))\n [a() for a in extra_actions]\n if n % delta != 0:\n if report_at_end:\n printer(message.format(i=n, n=n))\n if extras_at_end:\n [a() for a in extra_actions]","def __iter__(self):\n while True:\n m = self.recv(timeout=1.0)\n if m is not None:\n yield m\n logger.debug(\"done iterating over bus messages\")","async def g(self, ctx, *message):\n searchContent = \"\"\n text = message\n for i in range(0, len(text)):\n searchContent = searchContent + text[i]\n\n for j in search(searchContent, num=1, stop=1):\n await ctx.send(j)","async def ri(self, ctx: commands.Context, *, msg: str):\n\n def mapper(s: str):\n if s.startswith('<'):\n return s\n return '\\u200b'.join(self.characters.get(c.lower(), c) for c in s)\n\n strings = re.split(r\"(<\\S*>)\", msg)\n\n new_msg = ''.join(map(mapper, strings))\n\n await ctx.message.edit(content=new_msg)","def simple_send():\n i = None\n while True:\n i = yield i","def send_messages(messages: dict) -> str:\n\n approval = input(\"\\nDo you wish to send these messages? Type 'yes' or 'no': \")\n if approval.lower() != 'yes':\n return \"Messages not approved. Please run the program again.\"\n\n for number in messages:\n body = messages[number]\n from_number = TWILIO_NUM # Already has +1 on it.\n to_number = \"+1\" + number\n\n message = client.messages.create(body=body, from_=from_number, to=to_number)\n print(message.sid)\n\n return \"All messages sent!\"","def __iter__(self):\r\n yield from self.phrase","def test_message_int():\n result = True\n\n message = msg.Message()\n for i in range(num_it):\n message.appendInt(i)\n if message.length != msg.HEADER_SIZE + (i+1)*msg.intStruct.size:\n print(\"Size is \", message.length, \" but should be \", msg.HEADER_SIZE + (i+1)*msg.intStruct.size)\n print(\"Error : message.appendInt\")\n result = False\n\n message.resetCursor()\n for i in range(num_it):\n r = message.readInt()\n if r != i:\n print(r, \" vs \", i)\n print(\"Error : message.read/appendInt\")\n result = False\n\n return result","def test_format_of_generated_msgs():\n for msg in it.islice(generate_msgs(), 0, 5):\n message = Message.parse(msg) # checks the json keys have the right names\n assert type(message.timestamp) is datetime\n assert type(message.power) is int","def server_message_iterator(self) -> Iterator[ServerMessage]:\n while not self._is_closed():\n with self._cv:\n self._cv.wait_for(\n lambda: self._status\n in [Status.CLOSED, Status.SERVER_MESSAGE_AVAILABLE]\n )\n\n self._raise_if_closed()\n\n server_message = self._server_message # Read\n self._server_message = None # Reset\n\n # Transition before yielding as after the yield the execution of this\n # function is paused and will resume when next is called again.\n # Also release condition variable by exiting the context\n self._transition(Status.AWAITING_CLIENT_MESSAGE)\n\n if server_message is None:\n raise Exception(\"Server message can not be None\")\n\n yield server_message","def consolidate_messages(self, msg):","def __getitem__(self, i):\n j, old = i.stop if not isinstance(i, int) else i, self.__i\n while j is None or self.__i <= j:\n try:\n next(self)\n except StopIteration:\n break\n self.__i = old\n return self.__coerce(''.join(map(str, self.__queue[i])))","def iterate(self):","def image_generator(self, some_messages):\n offset = 0\n outer = 0\n inner = 0\n\n for a_message in some_messages:\n msg_id = a_message.gmail_id\n for att in a_message.attachments():\n if att.type in ATTACHMENT_MIMES:\n att_type = att.type.split(\"/\")[1]\n an_image = Image(a_message, att)\n\n # map each image id with a corresponding message id for later parsing\n if an_image.id in self.mapping:\n self.mapping[msg_id].append(a_message)\n else:\n self.mapping[msg_id] = [a_message]\n\n self.num_attachments = self.count_attachments(self.num_attachments)\n\n yield an_image","def iteritems(self):","def process_messages(imap, messages):\n for i in messages:\n # fetch the email message by ID\n res, msg = imap.fetch(str(i), \"(RFC822)\")\n for response in msg:\n if isinstance(response, tuple):\n # parse bytes email into a message object\n msg = email.message_from_bytes(response[1])\n #print(msg.keys())\n\n # decode the email subject\n subject = decode_header(msg[\"Subject\"])[0][0]\n if isinstance(subject, bytes):\n # if it's a bytes, decode to str\n subject = subject.decode()\n\n # decode email sender\n From, encoding = decode_header(msg.get(\"From\"))[0]\n if isinstance(From, bytes):\n From = From.decode(encoding)\n\n # decode email Date\n Date, encoding = decode_header(msg.get(\"Date\"))[0]\n if isinstance(From, bytes):\n Date = Date.decode(encoding)\n\n print(\"Subject: \", subject)\n print(\"From: \", From)\n print(\"Date: \", Date)\n\n print(\"=\"*100)","def test_new_iterate_last_checked(self):\n old_last_check = self.alice_inbox.last_checked\n # Emulate iterate\n messages = list(self.alice_inbox.new)\n self.assertEqual(self.alice_inbox.last_checked, old_last_check)","def get_messages(self, student):\n students = self.choose_students(student)\n return ((to_student, self.generate_messages(student, to_student)) for to_student in students)","def test_generator_method_name(self):\n for i in range(0, 4):\n yield 'try_odd', i","def iter_raw_lexemes(self):\n yield \"abcde\"","async def ripgupta(self, ctx, count, *, message):\n int(count)\n gupta = 468209010978455552\n channel = 617525238392946699\n mloop = 0\n int(mloop) \n while mloop > count:\n await channel.send(\"{} {}\".format(gupta.mention, message))\n int(mloop)\n mloop = mloop + 1","def item_iter(self, a):\n raise NotImplementedError","def handle_message(self, message):","async def send_interactive(\n self, messages: Iterable[str], box_lang: str = None, timeout: int = 15\n) -> List[discord.Message]:\n messages = tuple(messages)\n ret = []\n\n for idx, page in enumerate(messages, 1):\n if box_lang is None:\n msg = await self.send(page)\n else:\n msg = await self.send(box(page, lang=box_lang))\n ret.append(msg)\n n_remaining = len(messages) - idx\n if n_remaining > 0:\n if n_remaining == 1:\n plural = \"\"\n is_are = \"is\"\n else:\n plural = \"s\"\n is_are = \"are\"\n\n omega = SPECIAL_AUTHOR_CASES.get(self.author.id, OMEGA)\n query = await self.send(\n \"There {} still {} message{} remaining. \"\n f\"Type {random.choice(omega)} to continue.\"\n \"\".format(is_are, n_remaining, plural)\n )\n try:\n resp = await self.bot.wait_for(\n \"message\",\n check=MessagePredicate.lower_contained_in(FULL_MORE_LIST, self),\n timeout=timeout,\n )\n except asyncio.TimeoutError:\n with contextlib.suppress(discord.HTTPException):\n await query.delete()\n break\n else:\n try:\n await self.channel.delete_messages((query, resp))\n except (discord.HTTPException, AttributeError):\n # In case the bot can't delete other users' messages,\n # or is not a bot account\n # or channel is a DM\n with contextlib.suppress(discord.HTTPException):\n await query.delete()\n return ret","def iteritems(self, keyword):","def test_read_iterate_last_checked(self):\n old_last_check = self.alice_inbox.last_checked\n # Emulate iterate\n messages = list(self.alice_inbox.read)\n self.assertEqual(self.alice_inbox.last_checked, old_last_check)","def test_unread_iterate_last_checked(self):\n old_last_check = self.alice_inbox.last_checked\n # Emulate iterate\n messages = list(self.alice_inbox.unread)\n self.assertEqual(self.alice_inbox.last_checked, old_last_check)","def oneIteration(self):\n\t\traise NotImplementedError","def process_message(self, msg, src):","def __getitem__(self, i):\n try:\n return self.__getIthNode(i).getPayload()\n except IndexError:\n print(\"ERROR: Index value out of range.\")","def choose(message, items=[], timeout=0):\n warnings.warn(\"This Method moved uwstyle.choose()\", DeprecationWarning)\n return uwstyle.choose(message, items, timeout)","def item_iter(self, *a):\r\n raise NotImplementedError","def test_old_for_statement():\n r = convert_code(\n \"{foreach item=bar from=foo}content{/foreach}\")\n assert r == \"{% for bar in foo %}content{% endfor %}\"","def iter_log_messages(self, build_id=None, max_date=None,\n min_date=None, min_level=None):\n pass","def generate_messages(cls, parts):\n for message_parts in cls.generate_message_parts(parts):\n yield \".\".join(message_parts)","def next(self, default: Any = None) -> Any:\n while True:\n vtype, data = self._read_next_obj()\n if vtype is None or data is None:\n if default is None:\n raise StopIteration\n return default\n\n elif vtype == T_DESCRIPTOR_NAME:\n self._next_descr_name = data.decode(\"utf8\")\n self._next_descr = self._dpool.FindMessageTypeByName(self._next_descr_name)\n\n elif vtype == T_MESSAGE:\n if self.return_raw_object:\n return self._next_descr_name, self._next_descr, data\n else:\n return reflection.ParseMessage(self._next_descr, data)\n\n else:\n raise Exception(f\"Unknown message type {vtype}\")","def gen_ciphertext(message: str) -> str:\r\n key = 1\r\n for i in range(26):\r\n ciphertext = cipher(key, message)\r\n yield f\"Key #{key}: {ciphertext}\"\r\n key += 1","async def bother(self, ctx, user: discord.Member):\n for i in range(5):\n msg = await ctx.send(user.mention)\n await msg.delete()","def get_messages_by_index(message_indexes):\n if not isinstance(message_indexes, list):\n message_indexes = [message_indexes,]\n for idx in message_indexes: #make sure the data is clean\n if not isinstance(idx, int):\n raise Exception(\"All items in message_indexes are not integers\")\n \n cursor = db.cursor()\n cursor.execute('SELECT * FROM messages WHERE message_index IN (%s) ORDER BY message_index ASC'\n % (','.join([str(x) for x in message_indexes]),))\n messages = cursor.fetchall()\n cursor.close()\n return messages","def get_all_message(): \n return \"<br>\".join(messages)","def make_reply(msg):\n reply = ''\n if msg is not None:\n for i in range(len(messages)):\n if msg == message[i]:\n reply = m_responses[i]\n return reply","def repeated_iteration(self) -> global___Statement.Iteration.RepeatedIteration:","def item_iter(self, a):\r\n for i in a[0]:\r\n yield i","def result(self):\n return Result(self.messages[:])","def create_message_list(in_range_list: list, user_zip_code: str) -> list:\n message_list = []\n user_coords = ZIP_MAP_DICT[user_zip_code][0]\n for location in in_range_list:\n store_name = location['properties']['provider_brand_name']\n address = f\"{location['properties']['address']}, \" +\\\n f\"{location['properties']['city']}, \" +\\\n f\"{location['properties']['state']}, \" +\\\n f\"{location['properties']['postal_code']}\"\n loc_coord = location['geometry']['coordinates'][::-1]\n radius = str(earth_distance(loc_coord[0], loc_coord[1],\n user_coords[0], user_coords[1]))\n radius = radius[:radius.find('.')+2]\n url = location['properties']['url']\n message = f'{store_name}\\n{address}\\n' +\\\n f'Distance to store: {radius} miles\\n{url}\\n'\n message_list.append((radius, message))\n message_list.sort()\n return [message_tup[1] for message_tup in message_list]","def getLineMessageFromMessage(self, messages=[]):\n return [LineMessage(self, message) for message in messages]","def format(self, message):","def process_messages(self):\n pass","def sqs_messages(queue: str) -> Generator[Dict[str, Any], None, None]:\n\n while True:\n response = get_client(\"sqs\").receive_message(QueueUrl=queue)\n if \"Messages\" not in response:\n break\n msg = json.loads(response[\"Messages\"][0][\"Body\"])\n records = json.loads(msg[\"Message\"])\n retd = {}\n retd[\"key\"] = records[\"Records\"][0][\"s3\"][\"object\"][\"key\"]\n retd[\"bucket\"] = records[\"Records\"][0][\"s3\"][\"bucket\"][\"name\"]\n retd[\"ReceiptHandle\"] = response[\"Messages\"][0][\"ReceiptHandle\"]\n yield retd","def test_msgs():\n msgs = [\n \"Hi, how are you doing today? Do you want to get a beer? See you soon!\",\n \"If I can't let it go out of my mind?\",\n \"I would lie to you but never for you.\",\n \"Man I'm so bored. Let's do something fun. Call me.\",\n \"Hey babe, somebody told me you're not coming home tonight.\",\n \"What Katie did sometimes makes me wonder.\",\n \"All the time I lie awake questioning my decision.\",\n \"I am what I am. Love me.\",\n \"I love New York! Let's go there! \",\n \"So are you coming now? Let's party until midnight!\",\n \"Happy Valentine's Day!\",\n \"One good thing about music. When it hits you, you feel no pain.\",\n \"Where words fail, music speaks.\",\n \"Life without you is a mistake. Please take me back.\",\n \"I'm glad I have a friend like you.\"\n ]\n return msgs","def __getitem__(self, i):\n raise NotImplementedError(\"Not implmented!\")","def item_iter(self, a):\n for i in a[0]:\n yield i","def __getitem__(self, i):\n raise NotImplementedError","def __get_linked_message(message_reference_list):\n if not message_reference_list:\n return \"EMPTY\"\n else:\n return random.choice(\n [\"EMPTY\", random.choice(message_reference_list)])","def __getitem__(self,i):\n return self._items[i]","def fetch(self, messages, uid):\n if uid:\n messagesToFetch = self._uidMessageSetToSeqDict(messages)\n else:\n messagesToFetch = self._seqMessageSetToSeqDict(messages)\n for seq, filename in messagesToFetch.items():\n uid = self.getUID(seq)\n flags = self.metadata['flags'].get(uid, [])\n yield seq, MaildirMessage(filename,flags,uid)","def getMessage():\n return message","def __iter__(self):\n pass\n\n # TODO: range, based on iterator"],"string":"[\n \"def get_message(self, i):\\n pass\",\n \"def __iter__(self):\\n return iter([self.format_message(record) for record in self._messages])\",\n \"def _For(self, t):\\n # if message loop then process differently\\n if isinstance(t.iter, ast.Name):\\n if t.iter.id == self._input_message_var:\\n self.dispatchMessageLoop(t)\\n else:\\n self.RaiseError(t, \\\"Range based for loops only support message iteration using 'message_in' iterator\\\")\\n # do not support for else\\n elif t.orelse:\\n self.RaiseError(t, \\\"For else not supported\\\")\\n # allow calls but only to range function\\n elif isinstance(t.iter, ast.Call):\\n # simple function call e.g. message_in() or range()\\n if isinstance(t.iter.func, ast.Name):\\n # catch case of message_input with arguments (e.g. spatial messaging)\\n if t.iter.func.id == self._input_message_var:\\n self.dispatchMessageLoop(t)\\n # otherwise permit only range based for loops\\n elif t.iter.func.id == \\\"range\\\":\\n # switch on different uses of range based on number of arguments\\n if len(t.iter.args) == 1:\\n self.fill(f\\\"for (int \\\")\\n self.dispatch(t.target)\\n self.write(\\\"=0;\\\")\\n self.dispatch(t.target)\\n self.write(\\\"<\\\")\\n self.dispatch(t.iter.args[0])\\n self.write(\\\";\\\")\\n self.dispatch(t.target)\\n self.write(\\\"++)\\\")\\n elif len(t.iter.args) == 2:\\n self.fill(f\\\"for (int \\\")\\n self.dispatch(t.target)\\n self.write(\\\"=\\\")\\n self.dispatch(t.iter.args[0])\\n self.write(\\\";\\\")\\n self.dispatch(t.target)\\n self.write(\\\"<\\\")\\n self.dispatch(t.iter.args[1])\\n self.write(\\\";\\\")\\n self.dispatch(t.target)\\n self.write(\\\"++)\\\")\\n elif len(t.iter.args) == 3:\\n self.fill(f\\\"for (int \\\")\\n self.dispatch(t.target)\\n self.write(\\\"=\\\")\\n self.dispatch(t.iter.args[0])\\n self.write(\\\";\\\")\\n self.dispatch(t.target)\\n self.write(\\\"<\\\")\\n self.dispatch(t.iter.args[1])\\n self.write(\\\";\\\")\\n self.dispatch(t.target)\\n self.write(\\\"+=\\\")\\n self.dispatch(t.iter.args[2])\\n self.write(\\\")\\\")\\n else:\\n self.RaiseError(t, \\\"Range based for loops requires use of 'range' function with arguments and not keywords\\\")\\n self.enter()\\n self.dispatch(t.body)\\n self.leave()\\n else:\\n self.RaiseError(t, \\\"Range based for loops only support calls to the 'range' function\\\")\\n # member function call can only be on message_in.func() type call.\\n elif isinstance(t.iter.func, ast.Attribute):\\n # must be an attribute (e.g. calling a member of message_in)\\n if t.iter.func.value.id == self._input_message_var:\\n self.dispatchMessageLoop(t)\\n else:\\n self.RaiseError(t, \\\"Range based for loops only support calling members of message input variable\\\")\\n else:\\n self.RaiseError(t, \\\"Range based for loops only support message iteration or use of 'range'\\\")\\n else:\\n self.RaiseError(t, \\\"Range based for loops only support message iteration or use of 'range'\\\")\",\n \"def msg_ele(msg):\\n for word in msg:\\n if '=' in word:\\n return\\n yield word\",\n \"def get_user_message(self, message, index):\\n userMessage = ''\\n for i in range(index, len(message)):\\n userMessage += message[i] + ' '\\n return userMessage\",\n \"def list_messages(self):\",\n \"def _generator(self):\\n\\t\\twhile(1):\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tm = self.messages.pop(0) # pop the first Flash2Message in the list\\n\\t\\t\\t\\tyield m\\n\\t\\t\\texcept IndexError:\\n\\t\\t\\t\\traise StopIteration\",\n \"def dispatchMessageIteratorCall(self, tree):\\n # simple case not a member function just an iterator with arguments\\n if isinstance(tree.func, ast.Name):\\n self.write(f\\\"FLAMEGPU->{tree.func.id}\\\")\\n if isinstance(tree.func, ast.Attribute) :\\n if isinstance(tree.func.value, ast.Name):\\n # check that the iterator is supported\\n if not tree.func.attr in self.fgpu_input_msg_iter_funcs:\\n self.RaiseError(tree, f\\\"Message input loop iterator '{tree.func.attr}' is not supported.\\\")\\n self.write(f\\\"FLAMEGPU->{tree.func.value.id}.{tree.func.attr}\\\")\\n else:\\n self.RaiseError(tree, \\\"Message input loop iterator format incorrect.\\\")\\n\\n # handle function arguments \\n self.write(\\\"(\\\")\\n self._CallArguments(tree)\\n self.write(\\\")\\\")\",\n \"def sendMessage(self, message):\\n for component in CraftChatMessage.fromString(message):\\n self.block.sendMessage(component)\",\n \"def progress_iterator(collection: Collection, message: str) -> Iterable:\\n num_items = len(collection)\\n last_percentage = -1\\n for i, item in enumerate(collection):\\n percentage = 100 * i // num_items\\n if percentage > last_percentage:\\n last_percentage = percentage\\n print(f\\\"{message} {percentage}%\\\", end='\\\\r')\\n yield item\\n print(f\\\"{message} 100%\\\")\",\n \"def _enumerate_message(self, name, message_class):\\n key_value_pair = []\\n assert(message_class is not None) \\n assert(dir(message_class) is not None) \\n \\n # TODO: to review, hack\\n for key in dir(message_class):\\n if key.isupper():\\n \\n # set the class attributes to the Enum values\\n key_value_pair.append((key, getattr(message_class, key)))\\n \\n return Enum(name, key_value_pair)\",\n \"def test_is_iterable(self):\\r\\n msg_list = messages.MessageList()\\r\\n\\r\\n # Adds 3 Message objects to the list.\\r\\n msg_list.push(messages.StringMessage(\\\"a\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"b\\\"))\\r\\n msg_list.push(messages.StringMessage(\\\"c\\\"))\\r\\n\\r\\n self.assertEqual([\\\"ab\\\", \\\"bb\\\", \\\"cb\\\"], [x.msg + \\\"b\\\" for x in msg_list])\",\n \"def counter():\\n for value in range(5):\\n yield \\\"<{}>\\\".format(value)\",\n \"def _iterate_messages(self):\\n if not self.targets:\\n raise sex.SullyRuntimeError(\\\"No targets specified in session\\\")\\n\\n if not self.edges_from(self.root.id):\\n raise sex.SullyRuntimeError(\\\"No requests specified in session\\\")\\n\\n self._reset_fuzz_state()\\n\\n for x in self._iterate_messages_recursive(this_node=self.root, path=[]):\\n yield x\",\n \"def next(self): # wait for 5 minutes after sending message\\n if self.queue:\\n messages = self.queue.get_messages(1,visibility_timeout=self.visibility_timeout)\\n if messages:\\n for m in messages:\\n return m\\n raise StopIteration\",\n \"def get_user_messages(user_id):\\n pass \\n # user_message_list = []\\n\\n # for message in sent messages:\",\n \"def test_message_list():\",\n \"def messages(self):\\n return list(iter(self))\",\n \"def _iterate_messages_recursive(self, this_node, path):\\n # step through every edge from the current node.\\n for edge in self.edges_from(this_node.id):\\n # keep track of the path as we walk through it\\n # we keep track of edges as opposed to nodes because if there is more then one path through a set of\\n # given nodes we don't want any ambiguity.\\n path.append(edge)\\n\\n message_path = \\\"->\\\".join([self.nodes[e.dst].name for e in path])\\n logging.debug('checking: {0}'.format(message_path))\\n\\n self.fuzz_node = self.nodes[path[-1].dst]\\n self.total_mutant_index += 1\\n yield (path,)\\n\\n for x in self._iterate_messages_recursive(self.fuzz_node, path):\\n yield x\\n\\n # finished with the last node on the path, pop it off the path stack.\\n if path:\\n path.pop()\",\n \"def test_generator_continuous():\\n RANGE_MAX = 100\\n prev_value = RANGE_MAX // 2\\n for msg in it.islice(generate_msgs(0, RANGE_MAX), 0, 42):\\n curr_value = Message.parse(msg).power\\n assert curr_value - prev_value <= 1\\n prev_value = curr_value\",\n \"def sender_iter(self):\\n while 1:\\n yield self.send_next()\",\n \"def reverse_send() -> Generator[int, int, str]:\\n start = yield 0\\n for index in range(start, -1, -1):\\n yield index\\n\\n return \\\"done\\\"\",\n \"def process(self, msg):\\n raise NotImplemented\",\n \"def _next_message(self):\\n msg = yield self._read_message()\\n message = self._slack_to_chat(msg)\\n\\n raise gen.Return(message)\",\n \"def __message_content__(self) -> MessageContent:\",\n \"def comsume_msg(self, msg_type):\",\n \"def _generate_message(self, occurences, criteria=max):\\n message = ''\\n for occurence in occurences:\\n message += chr(occurence.index(criteria(occurence)) + 97)\\n return message\",\n \"def iteration(self) -> global___Statement.Iteration:\",\n \"def iteration(self) -> global___Statement.Iteration:\",\n \"def fetch(self, messages, uid):\\n return list(zip(range(1, len(self.msgObjs) + 1), self.msgObjs))\",\n \"def process_messages(self, messages):\\n\\n return messages\",\n \"def _f(message):\\n return message\",\n \"def list_message(self):\\n\\t\\tmessage = \\\"\\\"\\n\\t\\tlength = len(self.object_list)\\n\\t\\tfor i in range(length - 1):\\n\\t\\t\\to = self.object_list[i]\\n\\t\\t\\tmessage += self.index_letter_string(i) + o.display_name(True) + \\\", \\\"\\n\\t\\tlast_object = self.object_list[length - 1]\\n\\t\\tmessage += self.index_letter_string(length - 1) + last_object.display_name(True) + \\\", (?)\\\"\\n\\t\\treturn message\",\n \"def next_message(self):\\n while self.queue.consuming:\\n yield self.queue.channel._consume_message()\",\n \"def __next__(self):\\n return self.read_message()\",\n \"def show_message(messages):\\n for message in messages:\\n printed_message = f\\\"{message}\\\"\\n print(printed_message)\",\n \"def _message_delete_multiple_private_task_message_id_iterator(groups):\\n message_group_new, message_group_old, message_group_old_own = groups\\n for item in message_group_new:\\n yield item[1]\\n \\n yield from message_group_old\\n yield from message_group_old_own\",\n \"def __iter__(self) -> Iterator[AlertInterface]:\",\n \"def sendMessage_0(self, messages):\\n for message in messages:\\n self.sendMessage(message)\",\n \"def parse_message(self, it, line):\\n match = self.message_line_re.match(line)\\n if match is None:\\n return None, next(it)\\n\\n file_path = os.path.normpath(\\n os.path.join(os.path.dirname(self.analyzer_result),\\n match.group('path')))\\n\\n message = Message(\\n file_path,\\n int(match.group('line')),\\n 0,\\n match.group('message').strip(),\\n match.group('checker').strip())\\n\\n try:\\n return message, next(it)\\n except StopIteration:\\n return message, ''\",\n \"def message_all(self, message):\\n # We copy the _clients into a list to avoid dictionary changing\\n # size during iteration.\\n for character in self.players.values():\\n character.message(message)\",\n \"def next(self):\\n return self.read_message()\",\n \"def report_on_interval(\\n iterable,\\n message='processed {i} of {n} items',\\n item_count=None,\\n printer=print,\\n get_deltas=compute_reporting_interval,\\n report_at_end=False,\\n extra_actions=[],\\n extras_at_end=False,\\n):\\n if item_count is not None:\\n n = item_count\\n else:\\n iterable = list(iterable)\\n n = len(iterable)\\n delta = get_deltas(n)\\n for (i, item) in enumerate(iterable):\\n yield item\\n if (i + 1) % delta == 0:\\n printer(message.format(i=i + 1, n=n))\\n [a() for a in extra_actions]\\n if n % delta != 0:\\n if report_at_end:\\n printer(message.format(i=n, n=n))\\n if extras_at_end:\\n [a() for a in extra_actions]\",\n \"def __iter__(self):\\n while True:\\n m = self.recv(timeout=1.0)\\n if m is not None:\\n yield m\\n logger.debug(\\\"done iterating over bus messages\\\")\",\n \"async def g(self, ctx, *message):\\n searchContent = \\\"\\\"\\n text = message\\n for i in range(0, len(text)):\\n searchContent = searchContent + text[i]\\n\\n for j in search(searchContent, num=1, stop=1):\\n await ctx.send(j)\",\n \"async def ri(self, ctx: commands.Context, *, msg: str):\\n\\n def mapper(s: str):\\n if s.startswith('<'):\\n return s\\n return '\\\\u200b'.join(self.characters.get(c.lower(), c) for c in s)\\n\\n strings = re.split(r\\\"(<\\\\S*>)\\\", msg)\\n\\n new_msg = ''.join(map(mapper, strings))\\n\\n await ctx.message.edit(content=new_msg)\",\n \"def simple_send():\\n i = None\\n while True:\\n i = yield i\",\n \"def send_messages(messages: dict) -> str:\\n\\n approval = input(\\\"\\\\nDo you wish to send these messages? Type 'yes' or 'no': \\\")\\n if approval.lower() != 'yes':\\n return \\\"Messages not approved. Please run the program again.\\\"\\n\\n for number in messages:\\n body = messages[number]\\n from_number = TWILIO_NUM # Already has +1 on it.\\n to_number = \\\"+1\\\" + number\\n\\n message = client.messages.create(body=body, from_=from_number, to=to_number)\\n print(message.sid)\\n\\n return \\\"All messages sent!\\\"\",\n \"def __iter__(self):\\r\\n yield from self.phrase\",\n \"def test_message_int():\\n result = True\\n\\n message = msg.Message()\\n for i in range(num_it):\\n message.appendInt(i)\\n if message.length != msg.HEADER_SIZE + (i+1)*msg.intStruct.size:\\n print(\\\"Size is \\\", message.length, \\\" but should be \\\", msg.HEADER_SIZE + (i+1)*msg.intStruct.size)\\n print(\\\"Error : message.appendInt\\\")\\n result = False\\n\\n message.resetCursor()\\n for i in range(num_it):\\n r = message.readInt()\\n if r != i:\\n print(r, \\\" vs \\\", i)\\n print(\\\"Error : message.read/appendInt\\\")\\n result = False\\n\\n return result\",\n \"def test_format_of_generated_msgs():\\n for msg in it.islice(generate_msgs(), 0, 5):\\n message = Message.parse(msg) # checks the json keys have the right names\\n assert type(message.timestamp) is datetime\\n assert type(message.power) is int\",\n \"def server_message_iterator(self) -> Iterator[ServerMessage]:\\n while not self._is_closed():\\n with self._cv:\\n self._cv.wait_for(\\n lambda: self._status\\n in [Status.CLOSED, Status.SERVER_MESSAGE_AVAILABLE]\\n )\\n\\n self._raise_if_closed()\\n\\n server_message = self._server_message # Read\\n self._server_message = None # Reset\\n\\n # Transition before yielding as after the yield the execution of this\\n # function is paused and will resume when next is called again.\\n # Also release condition variable by exiting the context\\n self._transition(Status.AWAITING_CLIENT_MESSAGE)\\n\\n if server_message is None:\\n raise Exception(\\\"Server message can not be None\\\")\\n\\n yield server_message\",\n \"def consolidate_messages(self, msg):\",\n \"def __getitem__(self, i):\\n j, old = i.stop if not isinstance(i, int) else i, self.__i\\n while j is None or self.__i <= j:\\n try:\\n next(self)\\n except StopIteration:\\n break\\n self.__i = old\\n return self.__coerce(''.join(map(str, self.__queue[i])))\",\n \"def iterate(self):\",\n \"def image_generator(self, some_messages):\\n offset = 0\\n outer = 0\\n inner = 0\\n\\n for a_message in some_messages:\\n msg_id = a_message.gmail_id\\n for att in a_message.attachments():\\n if att.type in ATTACHMENT_MIMES:\\n att_type = att.type.split(\\\"/\\\")[1]\\n an_image = Image(a_message, att)\\n\\n # map each image id with a corresponding message id for later parsing\\n if an_image.id in self.mapping:\\n self.mapping[msg_id].append(a_message)\\n else:\\n self.mapping[msg_id] = [a_message]\\n\\n self.num_attachments = self.count_attachments(self.num_attachments)\\n\\n yield an_image\",\n \"def iteritems(self):\",\n \"def process_messages(imap, messages):\\n for i in messages:\\n # fetch the email message by ID\\n res, msg = imap.fetch(str(i), \\\"(RFC822)\\\")\\n for response in msg:\\n if isinstance(response, tuple):\\n # parse bytes email into a message object\\n msg = email.message_from_bytes(response[1])\\n #print(msg.keys())\\n\\n # decode the email subject\\n subject = decode_header(msg[\\\"Subject\\\"])[0][0]\\n if isinstance(subject, bytes):\\n # if it's a bytes, decode to str\\n subject = subject.decode()\\n\\n # decode email sender\\n From, encoding = decode_header(msg.get(\\\"From\\\"))[0]\\n if isinstance(From, bytes):\\n From = From.decode(encoding)\\n\\n # decode email Date\\n Date, encoding = decode_header(msg.get(\\\"Date\\\"))[0]\\n if isinstance(From, bytes):\\n Date = Date.decode(encoding)\\n\\n print(\\\"Subject: \\\", subject)\\n print(\\\"From: \\\", From)\\n print(\\\"Date: \\\", Date)\\n\\n print(\\\"=\\\"*100)\",\n \"def test_new_iterate_last_checked(self):\\n old_last_check = self.alice_inbox.last_checked\\n # Emulate iterate\\n messages = list(self.alice_inbox.new)\\n self.assertEqual(self.alice_inbox.last_checked, old_last_check)\",\n \"def get_messages(self, student):\\n students = self.choose_students(student)\\n return ((to_student, self.generate_messages(student, to_student)) for to_student in students)\",\n \"def test_generator_method_name(self):\\n for i in range(0, 4):\\n yield 'try_odd', i\",\n \"def iter_raw_lexemes(self):\\n yield \\\"abcde\\\"\",\n \"async def ripgupta(self, ctx, count, *, message):\\n int(count)\\n gupta = 468209010978455552\\n channel = 617525238392946699\\n mloop = 0\\n int(mloop) \\n while mloop > count:\\n await channel.send(\\\"{} {}\\\".format(gupta.mention, message))\\n int(mloop)\\n mloop = mloop + 1\",\n \"def item_iter(self, a):\\n raise NotImplementedError\",\n \"def handle_message(self, message):\",\n \"async def send_interactive(\\n self, messages: Iterable[str], box_lang: str = None, timeout: int = 15\\n) -> List[discord.Message]:\\n messages = tuple(messages)\\n ret = []\\n\\n for idx, page in enumerate(messages, 1):\\n if box_lang is None:\\n msg = await self.send(page)\\n else:\\n msg = await self.send(box(page, lang=box_lang))\\n ret.append(msg)\\n n_remaining = len(messages) - idx\\n if n_remaining > 0:\\n if n_remaining == 1:\\n plural = \\\"\\\"\\n is_are = \\\"is\\\"\\n else:\\n plural = \\\"s\\\"\\n is_are = \\\"are\\\"\\n\\n omega = SPECIAL_AUTHOR_CASES.get(self.author.id, OMEGA)\\n query = await self.send(\\n \\\"There {} still {} message{} remaining. \\\"\\n f\\\"Type {random.choice(omega)} to continue.\\\"\\n \\\"\\\".format(is_are, n_remaining, plural)\\n )\\n try:\\n resp = await self.bot.wait_for(\\n \\\"message\\\",\\n check=MessagePredicate.lower_contained_in(FULL_MORE_LIST, self),\\n timeout=timeout,\\n )\\n except asyncio.TimeoutError:\\n with contextlib.suppress(discord.HTTPException):\\n await query.delete()\\n break\\n else:\\n try:\\n await self.channel.delete_messages((query, resp))\\n except (discord.HTTPException, AttributeError):\\n # In case the bot can't delete other users' messages,\\n # or is not a bot account\\n # or channel is a DM\\n with contextlib.suppress(discord.HTTPException):\\n await query.delete()\\n return ret\",\n \"def iteritems(self, keyword):\",\n \"def test_read_iterate_last_checked(self):\\n old_last_check = self.alice_inbox.last_checked\\n # Emulate iterate\\n messages = list(self.alice_inbox.read)\\n self.assertEqual(self.alice_inbox.last_checked, old_last_check)\",\n \"def test_unread_iterate_last_checked(self):\\n old_last_check = self.alice_inbox.last_checked\\n # Emulate iterate\\n messages = list(self.alice_inbox.unread)\\n self.assertEqual(self.alice_inbox.last_checked, old_last_check)\",\n \"def oneIteration(self):\\n\\t\\traise NotImplementedError\",\n \"def process_message(self, msg, src):\",\n \"def __getitem__(self, i):\\n try:\\n return self.__getIthNode(i).getPayload()\\n except IndexError:\\n print(\\\"ERROR: Index value out of range.\\\")\",\n \"def choose(message, items=[], timeout=0):\\n warnings.warn(\\\"This Method moved uwstyle.choose()\\\", DeprecationWarning)\\n return uwstyle.choose(message, items, timeout)\",\n \"def item_iter(self, *a):\\r\\n raise NotImplementedError\",\n \"def test_old_for_statement():\\n r = convert_code(\\n \\\"{foreach item=bar from=foo}content{/foreach}\\\")\\n assert r == \\\"{% for bar in foo %}content{% endfor %}\\\"\",\n \"def iter_log_messages(self, build_id=None, max_date=None,\\n min_date=None, min_level=None):\\n pass\",\n \"def generate_messages(cls, parts):\\n for message_parts in cls.generate_message_parts(parts):\\n yield \\\".\\\".join(message_parts)\",\n \"def next(self, default: Any = None) -> Any:\\n while True:\\n vtype, data = self._read_next_obj()\\n if vtype is None or data is None:\\n if default is None:\\n raise StopIteration\\n return default\\n\\n elif vtype == T_DESCRIPTOR_NAME:\\n self._next_descr_name = data.decode(\\\"utf8\\\")\\n self._next_descr = self._dpool.FindMessageTypeByName(self._next_descr_name)\\n\\n elif vtype == T_MESSAGE:\\n if self.return_raw_object:\\n return self._next_descr_name, self._next_descr, data\\n else:\\n return reflection.ParseMessage(self._next_descr, data)\\n\\n else:\\n raise Exception(f\\\"Unknown message type {vtype}\\\")\",\n \"def gen_ciphertext(message: str) -> str:\\r\\n key = 1\\r\\n for i in range(26):\\r\\n ciphertext = cipher(key, message)\\r\\n yield f\\\"Key #{key}: {ciphertext}\\\"\\r\\n key += 1\",\n \"async def bother(self, ctx, user: discord.Member):\\n for i in range(5):\\n msg = await ctx.send(user.mention)\\n await msg.delete()\",\n \"def get_messages_by_index(message_indexes):\\n if not isinstance(message_indexes, list):\\n message_indexes = [message_indexes,]\\n for idx in message_indexes: #make sure the data is clean\\n if not isinstance(idx, int):\\n raise Exception(\\\"All items in message_indexes are not integers\\\")\\n \\n cursor = db.cursor()\\n cursor.execute('SELECT * FROM messages WHERE message_index IN (%s) ORDER BY message_index ASC'\\n % (','.join([str(x) for x in message_indexes]),))\\n messages = cursor.fetchall()\\n cursor.close()\\n return messages\",\n \"def get_all_message(): \\n return \\\"<br>\\\".join(messages)\",\n \"def make_reply(msg):\\n reply = ''\\n if msg is not None:\\n for i in range(len(messages)):\\n if msg == message[i]:\\n reply = m_responses[i]\\n return reply\",\n \"def repeated_iteration(self) -> global___Statement.Iteration.RepeatedIteration:\",\n \"def item_iter(self, a):\\r\\n for i in a[0]:\\r\\n yield i\",\n \"def result(self):\\n return Result(self.messages[:])\",\n \"def create_message_list(in_range_list: list, user_zip_code: str) -> list:\\n message_list = []\\n user_coords = ZIP_MAP_DICT[user_zip_code][0]\\n for location in in_range_list:\\n store_name = location['properties']['provider_brand_name']\\n address = f\\\"{location['properties']['address']}, \\\" +\\\\\\n f\\\"{location['properties']['city']}, \\\" +\\\\\\n f\\\"{location['properties']['state']}, \\\" +\\\\\\n f\\\"{location['properties']['postal_code']}\\\"\\n loc_coord = location['geometry']['coordinates'][::-1]\\n radius = str(earth_distance(loc_coord[0], loc_coord[1],\\n user_coords[0], user_coords[1]))\\n radius = radius[:radius.find('.')+2]\\n url = location['properties']['url']\\n message = f'{store_name}\\\\n{address}\\\\n' +\\\\\\n f'Distance to store: {radius} miles\\\\n{url}\\\\n'\\n message_list.append((radius, message))\\n message_list.sort()\\n return [message_tup[1] for message_tup in message_list]\",\n \"def getLineMessageFromMessage(self, messages=[]):\\n return [LineMessage(self, message) for message in messages]\",\n \"def format(self, message):\",\n \"def process_messages(self):\\n pass\",\n \"def sqs_messages(queue: str) -> Generator[Dict[str, Any], None, None]:\\n\\n while True:\\n response = get_client(\\\"sqs\\\").receive_message(QueueUrl=queue)\\n if \\\"Messages\\\" not in response:\\n break\\n msg = json.loads(response[\\\"Messages\\\"][0][\\\"Body\\\"])\\n records = json.loads(msg[\\\"Message\\\"])\\n retd = {}\\n retd[\\\"key\\\"] = records[\\\"Records\\\"][0][\\\"s3\\\"][\\\"object\\\"][\\\"key\\\"]\\n retd[\\\"bucket\\\"] = records[\\\"Records\\\"][0][\\\"s3\\\"][\\\"bucket\\\"][\\\"name\\\"]\\n retd[\\\"ReceiptHandle\\\"] = response[\\\"Messages\\\"][0][\\\"ReceiptHandle\\\"]\\n yield retd\",\n \"def test_msgs():\\n msgs = [\\n \\\"Hi, how are you doing today? Do you want to get a beer? See you soon!\\\",\\n \\\"If I can't let it go out of my mind?\\\",\\n \\\"I would lie to you but never for you.\\\",\\n \\\"Man I'm so bored. Let's do something fun. Call me.\\\",\\n \\\"Hey babe, somebody told me you're not coming home tonight.\\\",\\n \\\"What Katie did sometimes makes me wonder.\\\",\\n \\\"All the time I lie awake questioning my decision.\\\",\\n \\\"I am what I am. Love me.\\\",\\n \\\"I love New York! Let's go there! \\\",\\n \\\"So are you coming now? Let's party until midnight!\\\",\\n \\\"Happy Valentine's Day!\\\",\\n \\\"One good thing about music. When it hits you, you feel no pain.\\\",\\n \\\"Where words fail, music speaks.\\\",\\n \\\"Life without you is a mistake. Please take me back.\\\",\\n \\\"I'm glad I have a friend like you.\\\"\\n ]\\n return msgs\",\n \"def __getitem__(self, i):\\n raise NotImplementedError(\\\"Not implmented!\\\")\",\n \"def item_iter(self, a):\\n for i in a[0]:\\n yield i\",\n \"def __getitem__(self, i):\\n raise NotImplementedError\",\n \"def __get_linked_message(message_reference_list):\\n if not message_reference_list:\\n return \\\"EMPTY\\\"\\n else:\\n return random.choice(\\n [\\\"EMPTY\\\", random.choice(message_reference_list)])\",\n \"def __getitem__(self,i):\\n return self._items[i]\",\n \"def fetch(self, messages, uid):\\n if uid:\\n messagesToFetch = self._uidMessageSetToSeqDict(messages)\\n else:\\n messagesToFetch = self._seqMessageSetToSeqDict(messages)\\n for seq, filename in messagesToFetch.items():\\n uid = self.getUID(seq)\\n flags = self.metadata['flags'].get(uid, [])\\n yield seq, MaildirMessage(filename,flags,uid)\",\n \"def getMessage():\\n return message\",\n \"def __iter__(self):\\n pass\\n\\n # TODO: range, based on iterator\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6812082","0.6144076","0.59823215","0.57963836","0.5497247","0.5477851","0.5458338","0.5450091","0.5326207","0.5284618","0.52789956","0.51939684","0.5179175","0.51783484","0.51677006","0.5141689","0.510057","0.5071497","0.5068647","0.5062799","0.5049284","0.500638","0.49851647","0.49801075","0.49451336","0.49311978","0.49254078","0.49234802","0.49234802","0.49185285","0.4907507","0.49072903","0.4906843","0.4894759","0.4891625","0.48832148","0.48792067","0.48723838","0.48606014","0.4855965","0.48546585","0.48535204","0.48474836","0.48379382","0.48367524","0.48296988","0.482917","0.48246932","0.48218513","0.47709733","0.476211","0.474522","0.4744939","0.4722037","0.47189635","0.47180638","0.47068286","0.47032118","0.47006825","0.46984774","0.46921435","0.46845627","0.46821076","0.4677444","0.46720102","0.46708134","0.4657184","0.46516743","0.46508816","0.46494594","0.4648674","0.4646556","0.46441826","0.4637127","0.46197355","0.46154583","0.46102703","0.46062845","0.46012354","0.46007335","0.46005034","0.4591517","0.45778292","0.4576875","0.45725802","0.45719394","0.45707983","0.45662138","0.45639342","0.45626497","0.45591363","0.45516226","0.45449644","0.45449105","0.45422876","0.45297933","0.4528329","0.45243692","0.45237687","0.45218822"],"string":"[\n \"0.6812082\",\n \"0.6144076\",\n \"0.59823215\",\n \"0.57963836\",\n \"0.5497247\",\n \"0.5477851\",\n \"0.5458338\",\n \"0.5450091\",\n \"0.5326207\",\n \"0.5284618\",\n \"0.52789956\",\n \"0.51939684\",\n \"0.5179175\",\n \"0.51783484\",\n \"0.51677006\",\n \"0.5141689\",\n \"0.510057\",\n \"0.5071497\",\n \"0.5068647\",\n \"0.5062799\",\n \"0.5049284\",\n \"0.500638\",\n \"0.49851647\",\n \"0.49801075\",\n \"0.49451336\",\n \"0.49311978\",\n \"0.49254078\",\n \"0.49234802\",\n \"0.49234802\",\n \"0.49185285\",\n \"0.4907507\",\n \"0.49072903\",\n \"0.4906843\",\n \"0.4894759\",\n \"0.4891625\",\n \"0.48832148\",\n \"0.48792067\",\n \"0.48723838\",\n \"0.48606014\",\n \"0.4855965\",\n \"0.48546585\",\n \"0.48535204\",\n \"0.48474836\",\n \"0.48379382\",\n \"0.48367524\",\n \"0.48296988\",\n \"0.482917\",\n \"0.48246932\",\n \"0.48218513\",\n \"0.47709733\",\n \"0.476211\",\n \"0.474522\",\n \"0.4744939\",\n \"0.4722037\",\n \"0.47189635\",\n \"0.47180638\",\n \"0.47068286\",\n \"0.47032118\",\n \"0.47006825\",\n \"0.46984774\",\n \"0.46921435\",\n \"0.46845627\",\n \"0.46821076\",\n \"0.4677444\",\n \"0.46720102\",\n \"0.46708134\",\n \"0.4657184\",\n \"0.46516743\",\n \"0.46508816\",\n \"0.46494594\",\n \"0.4648674\",\n \"0.4646556\",\n \"0.46441826\",\n \"0.4637127\",\n \"0.46197355\",\n \"0.46154583\",\n \"0.46102703\",\n \"0.46062845\",\n \"0.46012354\",\n \"0.46007335\",\n \"0.46005034\",\n \"0.4591517\",\n \"0.45778292\",\n \"0.4576875\",\n \"0.45725802\",\n \"0.45719394\",\n \"0.45707983\",\n \"0.45662138\",\n \"0.45639342\",\n \"0.45626497\",\n \"0.45591363\",\n \"0.45516226\",\n \"0.45449644\",\n \"0.45449105\",\n \"0.45422876\",\n \"0.45297933\",\n \"0.4528329\",\n \"0.45243692\",\n \"0.45237687\",\n \"0.45218822\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5924833"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":17786,"cells":{"query":{"kind":"string","value":"A very limited set of function calls to members are supported so these are fully evaluated here. t_parent is the Call ast object required if the argument need to be modified (i.e. in the case of macro environment properties) Function calls permitted are; pyflamegpu.function a supported function call. e.g. pyflamegpu.getVariableFloat(). This will be translated into a typed Cpp call. message_input.function a call to the message input variable (the name of which is specified in the function definition) msg.function a call to the message input iterator objection variable (the name of which is specified in the message function loop) message_output.function a call to the message output variable (the name of which is specified in the function definition) pyflamegpu.environment.function the only nested attribute type. This will be translated into a typed Cpp call. math.function Any function calls from python `math` are translated to calls raw function calls. E.g. `math.sin()` becomes `sin()` numpy.type Any numpy types are translated to static casts"},"document":{"kind":"string","value":"def dispatchMemberFunction(self, t, t_parent):\n # it could be possible that the Call object has no value property e.g. a()()\n if not hasattr(t, \"value\"):\n self.RaiseError(t, f\"Function call is in an unsupported format.\")\n\n # Nested member functions (e.g. x.y.z())\n if isinstance(t.value, ast.Attribute):\n # store some information about the source of this function call in parent as this may be useful for validation in whatever has called this function\n t_parent.call_type = None\n # only nested attribute type is environment\n if not isinstance(t.value.value, ast.Name):\n self.RaiseError(t, \"Unknown or unsupported nested attribute\")\n # pyflamegpu.environment\n if t.value.value.id == \"pyflamegpu\" and t.value.attr == \"environment\":\n # check it is a supported environment function\n self.write(\"FLAMEGPU->environment.\")\n if t.attr in self.fgpu_env_funcs: \n # proceed\n self.write(t.attr)\n else: \n # simple getProperty type function\n if t.attr.startswith('getProperty') :\n # possible getter setter type function\n py_func = self._deviceVariableFunctionName(t, [\"getProperty\"])\n if not py_func:\n self.RaiseError(t, f\"Function '{t.attr}' is not a supported pyflamegpu.environment property function.\")\n # write the getProperty type function\n self.write(py_func)\n t_parent.call_type = \"Environment\"\n # need to catch case of getMacroProperty as arguments need to be translated into template parameters in cpp (and py_func can be ignored)\n elif t.attr.startswith(\"getMacroProperty\"):\n # possible getter setter type function (Note: getMacroProperty only supports a subset of types but type checking is not performed. This is best left to the compiler.)\n # no not permit lengths (e.g. Float4) as these will be passed as arguments\n py_func = self._deviceVariableFunctionName(t, [\"getMacroProperty\"], allow_lengths=False)\n if not py_func:\n self.RaiseError(t, f\"Function '{t.attr}' is not a supported pyflamegpu.environment macro property function.\")\n # handle case\n self.dispatchMacroEnvFunction(t, t_parent)\n t_parent.call_type = \"MacroEnvironment\"\n else:\n self.RaiseError(t, f\"Function '{t.attr}' does not exist in pyflamegpu.environment object\")\n \n # pyflamegpu.random\n elif t.value.value.id == \"pyflamegpu\" and t.value.attr == \"random\":\n # check it is a supported random function\n self.write(\"FLAMEGPU->random.\")\n if t.attr in self.fgpu_rand_funcs: \n # proceed\n self.write(t.attr)\n else: \n # possible getter setter type function\n py_func = self._deviceVariableFunctionName(t, [\"uniform\", \"normal\", \"logNormal\"], allow_lengths=False)\n if not py_func:\n self.RaiseError(t, f\"Function '{t.attr}' does not exist in pyflamegpu.random object\")\n # proceed\n self.write(py_func) \n t_parent.call_type = \"Random\"\n elif t.value.value.id == \"pyflamegpu\" and t.value.attr == \"agent_out\":\n # check it is a supported agent_out function\n self.write(\"FLAMEGPU->agent_out.\")\n if t.attr in self.fgpu_agent_out_msg_funcs: \n # proceed\n self.write(t.attr)\n else: \n # possible getter setter type function\n py_func = self._deviceVariableFunctionName(t, [\"setVariable\"])\n if not py_func:\n self.RaiseError(t, f\"Function '{t.attr}' does not exist in pyflamegpu.agent_out object\")\n # proceed\n self.write(py_func)\n t_parent.call_type = \"AgentOut\"\n else:\n self.RaiseError(t, f\"Unknown or unsupported nested attribute in {t.value.value.id}\")\n # Non nested member functions (e.g. x.y())\n elif isinstance(t.value, ast.Name):\n # pyflamegpu singleton\n if t.value.id == \"pyflamegpu\":\n # check for legit FGPU function calls \n self.write(\"FLAMEGPU->\")\n if t.attr in self.fgpu_funcs:\n # proceed\n self.write(t.attr)\n else:\n # possible getter setter type function\n py_func = self._deviceVariableFunctionName(t, [\"getVariable\", \"setVariable\"])\n if not py_func:\n self.RaiseError(t, f\"Function '{t.attr}' does not exist in pyflamegpu object\")\n # proceed\n self.write(py_func)\n\n # message_in function using whatever variable was named in function declaration (e.g radius)\n elif t.value.id == self._input_message_var:\n # only process functions on message_in that are not iterators\n if t.attr in self.fgpu_input_msg_funcs:\n self.write(f\"FLAMEGPU->{self._input_message_var}.\")\n self.write(t.attr) \n else:\n self.RaiseError(t, f\"Message input variable '{self._input_message_var}' does not have a supported function '{t.attr}'\") \n\n # message input iterator arg\n elif self._message_iterator_var and t.value.id == self._message_iterator_var:\n self.write(f\"{self._message_iterator_var}.\")\n # check for legit FGPU function calls and translate\n if t.attr in self.fgpu_input_msg_iter_var_funcs: \n # proceed\n self.write(t.attr)\n else:\n # possible getter setter type function\n py_func = self._deviceVariableFunctionName(t, [\"getVariable\"])\n if not py_func:\n self.RaiseError(t, f\"Function '{t.attr}' does not exist in '{self._message_iterator_var}' message input iterable object\")\n # proceed\n self.write(py_func)\n \n # message output arg\n elif t.value.id == self._output_message_var:\n # check for legit FGPU function calls and translate\n self.write(\"FLAMEGPU->message_out.\")\n if t.attr in self.fgpu_output_msg_funcs: \n # proceed\n self.write(t.attr)\n else:\n # possible getter setter type function\n py_func = self._deviceVariableFunctionName(t, [\"setVariable\"])\n if not py_func:\n self.RaiseError(t, f\"Function '{t.attr}' does not exist in '{self._output_message_var}' message output object\")\n # proceed\n self.write(py_func)\n \n \n \n # math functions (try them in raw function call format) or constants\n elif t.value.id == \"math\":\n self.write(t.attr)\n # numpy types\n elif t.value.id == \"numpy\" or t.value.id == \"np\":\n if t.attr in self.numpytypes:\n self.write(f\"static_cast<{self.numpytypes[t.attr]}>\")\n else: \n self.RaiseError(t, f\"Unsupported numpy type {t.attr}\")\n # allow any call on any locals (too many cases to enforce without type checking)\n elif t.value.id in self._locals:\n self.write(f\"{t.value.id}.{t.attr}\")\n else:\n self.RaiseError(t, f\"Global '{t.value.id}' identifier not supported\")\n # Call is a very nested situation which can occur only on macro environment properties. E.g. 'pyflamegpu.environment.getMacroPropertyInt('a').exchange(10)'\n elif isinstance(t.value, ast.Call):\n # handle the call by recursively calling this function to do the depth first execution of pyflamegpu.environment.getMacroPropertyInt('a')\n self.dispatchMemberFunction(t.value.func, t.value)\n # check that the handler was actually for macro environment \n if t.value.call_type != \"MacroEnvironment\" :\n self.RaiseError(t, f\"Function call {t.attr} is not supported\")\n # now append the outer call by making sure the thing been called is a valid macro env function\n if not t.attr in self.fgpu_env_macro_funcs:\n self.RaiseError(t, f\"Function {t.attr} is not a valid macro environment function\")\n # write inner call args\n self.write(\"(\")\n self._CallArguments(t.value)\n self.write(\")\")\n # write outer function (call args will be completed by _Call)\n self.write(f\".{t.attr}\")\n \n \n else:\n self.RaiseError(t, \"Unsupported function call syntax\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _Call(self, t):\n # check calls but let attributes check in their own dispatcher\n funcs = self._device_functions + self.pythonbuiltins + [self._input_message_var] # message_input variable is a valid function name as certain message types have arguments on iterator\n if isinstance(t.func, ast.Name):\n if (t.func.id not in funcs):\n self.RaiseWarning(t, \"Function call is not a defined FLAME GPU device function or a supported python built in.\")\n # dispatch even if warning raised\n self.dispatch(t.func)\n elif isinstance(t.func, ast.Lambda):\n self.dispatch(t.func) # not supported\n else:\n # special handler for dispatching member function calls\n # This would otherwise be an attribute\n self.dispatchMemberFunction(t.func, t) \n self.write(\"(\")\n self._CallArguments(t)\n self.write(\")\")","def call_module(\n self,\n node: torch.fx.Node,\n parent_onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,\n fx_name_to_onnxscript_value: Dict[\n str,\n Union[\n onnxscript_graph_building.TorchScriptTensor,\n Tuple[onnxscript_graph_building.TorchScriptTensor, ...],\n ],\n ],\n tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,\n root_fx_graph_module: torch.fx.GraphModule,\n onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,\n op_level_debug: bool,\n ) -> None:\n assert isinstance(\n node.target, str\n ), f\"node.target must be a str, not {type(node.target)} for node {node}.\"\n\n sub_module = root_fx_graph_module.get_submodule(node.target)\n\n assert isinstance(\n sub_module, torch.fx.GraphModule\n ), f\"sub_module must be a torch.fx.GraphModule, not {type(sub_module)} for node {node}.\"\n\n sub_onnxscript_graph = self.run(\n sub_module, onnxfunction_dispatcher, op_level_debug, parent_onnxscript_graph\n )\n\n onnx_args, _ = _wrap_fx_args_as_onnxscript_args(\n list(node.args), {}, fx_name_to_onnxscript_value, tracer\n )\n\n # TODO: We may want to consider other naming styles. The goal is to be stable and\n # unique such that it can be easily identified in case of kernel substitution.\n # Example for current style is combination of qualified module class name and\n # module attribute name: `torch_nn_modules_conv_Conv2d_conv1`.\n # Other naming styles such as qualified module class name made unique can also\n # be considered.\n unique_module_name = f\"{sub_module._get_name()}_{node.target}\"\n\n outputs: Union[ # type: ignore[no-redef]\n onnxscript_graph_building.TorchScriptTensor,\n Tuple[onnxscript_graph_building.TorchScriptTensor, ...],\n ] = parent_onnxscript_graph.add_module_call(\n unique_module_name, sub_onnxscript_graph, onnx_args\n )\n\n assert isinstance(\n outputs, (onnxscript_graph_building.TorchScriptTensor, tuple)\n ), f\"Unexpected outputs type {type(outputs)} for node {node}.\"\n\n _fill_tensor_shape_type(outputs, node.name, node.meta[\"val\"])\n fx_name_to_onnxscript_value[node.name] = outputs\n\n # Skip op_level_validation for call_module. Subgraph nodes are validated individually.","def func_call(self, t):\n func, params = t\n func_name = func.value\n func.value = \"({}({}))\".format(func_name, params)\n return func","def dispatchMacroEnvFunction(self, tree, tree_parent):\n cpp_func_name = \"getMacroProperty\"\n py_func = tree.attr\n # extract type from function name\n py_type = py_func[len(cpp_func_name):]\n if py_type not in self._fgpu_types:\n self.RaiseError(tree, f\"'{py_type}' is not a valid FLAME GPU type\")\n # get cpp type\n t = self._fgpu_types[py_type]\n cpp_func_name += f\"<{t}\"\n # mess with the parent to extract (and remove arguments so they dont end up in the argument list)\n if not tree_parent.args :\n self.RaiseError(tree, f\" Macro environment function '{py_func}' is expected to have some arguments.\")\n # if more than one arg then the rest are bounds to translate\n if len(tree_parent.args) > 1:\n bounds = tree_parent.args[1:]\n # process bounds by appending to cpp function template arguments\n for i in bounds:\n if isinstance(i, ast.Num): # num required for python 3.7\n if not isinstance(i.n, int):\n self.RaiseError(tree, f\" Macro environment function argument '{i}' should be an integer value.\")\n cpp_func_name += f\", {i.n}\"\n else: # all Python > 3.7 \n if not isinstance(i, ast.Constant):\n self.RaiseError(tree, f\" Macro environment function argument '{i}' should be an constant value (or Num in Python <3.8).\")\n if not isinstance(i.value, int):\n self.RaiseError(tree, f\" Macro environment function argument '{i}' should be an integer value.\")\n cpp_func_name += f\", {i.value}\"\n # remove bounds from argument list (in place)\n del tree_parent.args[1:]\n cpp_func_name += \">\"\n self.write(cpp_func_name)","def _(self, node: Call):\n\n args = []\n for n in node.arguments:\n args.append(self.visit(n))\n\n func_args = \" \".join(args)\n\n return f\"( call {node.func.name} {func_args} )\"","def visit_Call(self, node):\n assert hasattr(node, 'args')\n if node.args:\n assert isinstance(node.args[0], gast.Starred)\n # modify args\n if isinstance(node.args[0].value, gast.Name):\n node.args[0].value.id += '_new'\n\n assert hasattr(node, 'keywords')\n if node.keywords:\n assert isinstance(node.keywords[0], gast.keyword)\n self.generic_visit(node)\n return node","def call_statement(env, node):\n fun = env['f'][node.name]\n func_env = Environment(env).create(env['f'])\n args = fun['args'].interpret(env)\n call_args_interpretuated = node.args.interpret(env)\n args_counter = 0\n for arg in args:\n func_env['v'][arg] = call_args_interpretuated[args_counter].interpret(env)\n args_counter += 1\n fun['body'].interpret(func_env)\n return func_env['r']","def visit_Call(self, node: ast.Call) -> None:\n self._check_wrong_function_called(node)\n self._check_boolean_arguments(node)\n self._check_isinstance_call(node)\n\n if functions.given_function_called(node, {'super'}):\n self._check_super_context(node)\n self._check_super_arguments(node)\n\n self.generic_visit(node)","def _FunctionDef(self, t):\n self.write(\"\\n\")\n # check decorators\n if len(t.decorator_list) != 1 or not isinstance(t.decorator_list[0], ast.Attribute):\n self.RaiseError(t, \"Function definitions require a single pyflamegpu decorator of either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'\") \n # FLAMEGPU_AGENT_FUNCTION\n if t.decorator_list[0].attr == 'agent_function' and t.decorator_list[0].value.id == 'pyflamegpu':\n if getattr(t, \"returns\", False):\n self.RaiseWarning(t, \"Function definition return type not supported on 'pyflamegpu.agent_function'\")\n self.fill(f\"FLAMEGPU_AGENT_FUNCTION({t.name}, \")\n self.dispatchFGPUFunctionArgs(t)\n self.write(\")\")\n # FLAMEGPU_DEVICE_FUNCTION\n elif t.decorator_list[0].attr == 'device_function' and t.decorator_list[0].value.id == 'pyflamegpu':\n self.fill(f\"FLAMEGPU_DEVICE_FUNCTION \")\n if t.returns:\n self.dispatchType(t.returns)\n else:\n self.write(\"void\")\n self.write(f\" {t.name}(\")\n self.dispatchFGPUDeviceFunctionArgs(t)\n self.write(\")\")\n # add to list of defined functions that can be called\n self._device_functions.append(t.name)\n # FLAMEGPU_DEVICE_FUNCTION\n elif t.decorator_list[0].attr == 'agent_function_condition' and t.decorator_list[0].value.id == 'pyflamegpu':\n # check for return annotation\n if not hasattr(t, \"returns\"):\n self.RaiseError(t, \"Agent function conditions must have a 'bool' return type specified as a return type annotation\")\n # check for return annotation type\n if not isinstance(t.returns, ast.Name):\n self.RaiseError(t, \"Agent function conditions return type must be 'bool'\")\n if t.returns.id is not 'bool':\n self.RaiseError(t, \"Agent function conditions return type must be 'bool'\")\n # check to ensure no arguments (discard any with a warning)\n if t.args.args:\n self.RaiseWarning(t, \"Agent function conditions does not support arguments. These will be discarded.\")\n # write the agent function macro\n self.fill(f\"FLAMEGPU_AGENT_FUNCTION_CONDITION({t.name})\")\n else:\n self.RaiseError(t, \"Function definition uses an unsupported decorator. Must use either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'\")\n self.enter()\n self.dispatch(t.body)\n self.leave()","def dispatchFGPUDeviceFunctionArgs(self, tree):\n # reset the locals variable stack\n self._locals = [\"pyflamegpu\"]\n # input message\n first = True\n annotation = None\n for arg in tree.args.args:\n # ensure that there is a type annotation\n if not arg.annotation:\n self.RaiseError(arg, \"Device function argument requires type annotation\")\n # comma if not first\n if not first:\n self.write(\", \")\n self.dispatchType(arg.annotation)\n self.write(f\" {arg.arg}\")\n # add arg to local variable stack\n self._locals.append(arg.arg)\n first = False","def call_top_interface_args_with_func_def(self, node: AnnCastCall):\n # call container is used to scope parameters\n call_con_name = call_container_name(node)\n\n # create argument and parameter variables\n # argument variables are inputs to the top interface\n # paramter variables are outputs of the top interface\n for i, n in enumerate(node.arguments):\n # argument name and scope str\n arg_name = call_argument_name(node, i)\n arg_con_scopestr = con_scope_to_str(node.func.con_scope)\n\n # parameter name and scopestr\n func_def = self.pipeline_state.func_def_node_from_id(node.func.id)\n param = func_def.func_args[i]\n assert(isinstance(param, AnnCastVar))\n param_name = param.val.name\n param_con_scopestr = con_scope_to_str(node.func.con_scope + [call_con_name])\n\n # argument and parameter share id, and start with initial version\n id = self.pipeline_state.next_collapsed_id()\n version = VAR_INIT_VERSION\n\n # build and store GrFN variables for argument and parameter\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\n # store arg_fullid\n node.arg_index_to_fullid[i] = arg_fullid\n # create From Source metadata for the GrFN var\n from_source = False\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\n\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\n # store param_fullid\n node.param_index_to_fullid[i] = param_fullid\n # create From Source metadata for the GrFN var\n add_metadata_from_name_node(param_grfn_var, param.val)\n\n # link argument and parameter through top interface\n node.top_interface_in[id] = arg_fullid\n node.top_interface_out[id] = param_fullid\n\n # DEBUG printing\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\n print(\"After create_call_args_and_params():\")\n print(f\"\\ttop_interface_in = {node.top_interface_in}\")\n print(f\"\\ttop_interface_out = {node.top_interface_out}\")","def eval_function_call(func_call, motif_node_dict):\n print(\"\\x1b[6;30;42m\" + 'Evaluating ' + func_call.name.name + ' function...' + '\\x1b[0m')\n # CamFlow \"alloc_provenance\" take two arguments but only the first is needed for modeling.\n if func_call.name.name == 'alloc_provenance':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n return provenance.alloc_provenance(arg_names[0], None)\n # CamFlow \"task_cred_xxx\" take two arguments but no argument is needed for modeling.\n elif func_call.name.name == 'task_cred_xxx':\n return provenance.task_cred_xxx(None, None)\n # CamFlow \"branch_mmap\" take two arguments but no argument is needed for modeling.\n elif func_call.name.name == 'branch_mmap':\n return provenance.branch_mmap(None, None)\n # CamFlow \"uses_two\" function takes five arguments but only the first three are needed for modeling.\n elif func_call.name.name == 'uses_two':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second and third arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses_two must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses_two must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses_two must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses_two must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.uses_two(arg_names[0], val1, val2, None, None, motif_node_dict)\n # CamFlow \"informs\" function takes five arguments but only the first three are needed for modeling.\n elif func_call.name.name == 'informs':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second and third arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in informs must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in informs must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in informs must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in informs must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.informs(arg_names[0], val1, val2, None, None, motif_node_dict)\n # CamFlow \"record_terminate\" function takes two arguments.\n elif func_call.name.name == 'record_terminate':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second arguments must be converted to MotifNode object first.\n arg1 = arg_names[1]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_terminate must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_terminate must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_terminate(arg_names[0], val1, motif_node_dict)\n # CamFlow \"generates\" function takes six arguments but only the first four are needed for modeling.\n elif func_call.name.name == 'generates':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n arg3 = arg_names[3]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in generates must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in generates must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in generates must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in generates must have values in the dictionary.\\033[0m')\n exit(1)\n if arg3 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in generates must exist in the dictionary.\\033[0m')\n exit(1)\n val3 = getLastValueFromKey(motif_node_dict, arg3)\n if not val3:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in generates must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.generates(arg_names[0], val1, val2, val3, None, None, motif_node_dict)\n # CamFlow \"get_task_provenance\" takes no arguments.\n elif func_call.name.name == 'get_task_provenance':\n return provenance.get_task_provenance()\n # CamFlow \"get_cred_provenance\" takes no arguments.\n elif func_call.name.name == 'get_cred_provenance':\n return provenance.get_cred_provenance(motif_node_dict)\n # CamFlow \"uses\" takes six arguments but only the first four are needed for modeling.\n elif func_call.name.name == 'uses':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n arg3 = arg_names[3]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses must have values in the dictionary.\\033[0m')\n exit(1)\n if arg3 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in uses must exist in the dictionary.\\033[0m')\n exit(1)\n val3 = getLastValueFromKey(motif_node_dict, arg3)\n if not val3:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in uses must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.uses(arg_names[0], val1, val2, val3, None, None, motif_node_dict)\n # CamFlow \"refresh_inode_provenance\" takes two arguments but only the second one is needed for modeling.\n elif func_call.name.name == 'refresh_inode_provenance':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second argument must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in refresh_inode_provenance must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in refresh_inode_provenance must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.refresh_inode_provenance(None, val1, motif_node_dict)\n # CamFlow \"get_inode_provenance\" takes two arguments but only the second argument is needed for modeling.\n elif func_call.name.name == 'get_inode_provenance':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n if arg_names[1] == 'false':\n arg1 = False\n elif arg_names[1] == 'true':\n arg1 = True\n else:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_inode_provenance is unknown.\\033[0m')\n exit(1)\n return provenance.get_inode_provenance(None, arg1, motif_node_dict)\n # CamFlow \"get_dentry_provenance\" takes two arguments but only the second argument is needed for modeling. \n elif func_call.name.name == 'get_dentry_provenance':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n if arg_names[1] == 'false':\n arg1 = False\n elif arg_names[1] == 'true':\n arg1 = True\n else:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_dentry_provenance is unknown.\\033[0m')\n exit(1)\n return provenance.get_dentry_provenance(None, arg1, motif_node_dict)\n # CamFlow \"record_inode_name_from_dentry\" takes three arguments, but only the second and the third arguments are needed for modeling.\n elif func_call.name.name == 'record_inode_name_from_dentry':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second argument must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_inode_name_from_dentry must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_inode_name_from_dentry must have values in the dictionary.\\033[0m')\n exit(1)\n if arg_names[2] == 'false':\n arg2 = False\n elif arg_names[2] == 'true':\n arg2 = True\n else:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_inode_name_from_dentry is unknown.\\033[0m')\n exit(1)\n return provenance.record_inode_name_from_dentry(None, val1, arg2, motif_node_dict)\n # CamFlow \"record_node_name\" takes three arguments, but only the first and the third arguments are needed for modeling.\n elif func_call.name.name == 'record_node_name':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second argument must be converted to MotifNode objects first.\n arg0 = arg_names[0]\n if arg0 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_node_name must exist in the dictionary.\\033[0m')\n exit(1)\n val0 = getLastValueFromKey(motif_node_dict, arg0)\n if not val0:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_node_name must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_node_name(val0, None, arg_names[2], motif_node_dict)\n # CamFlow \"derives\" function takes five arguments but only the first three are needed for modeling.\n elif func_call.name.name == 'derives':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second and third arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in derives must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in derives must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in derives must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in derives must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.derives(arg_names[0], val1, val2, None, None, motif_node_dict)\n # CamFlow \"record_write_xattr\" function takes eight arguments but only the first four are needed for modeling.\n elif func_call.name.name == 'record_write_xattr':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n arg3 = arg_names[3]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_write_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_write_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_write_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_write_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n if arg3 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in record_write_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val3 = getLastValueFromKey(motif_node_dict, arg3)\n if not val3:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in record_write_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_write_xattr(arg_names[0], val1, val2, val3, None, None, None, None, motif_node_dict)\n # CamFlow \"record_read_xattr\" function takes four arguments but only the first three are needed for modeling.\n elif func_call.name.name == 'record_read_xattr':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n arg0 = arg_names[0]\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n if arg0 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_read_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val0 = getLastValueFromKey(motif_node_dict, arg0)\n if not val0:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_read_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_read_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_read_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_read_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_read_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_read_xattr(val0, val1, val2, None, motif_node_dict)\n # CamFlow \"get_file_provenance\" takes two arguments but only the second argument is needed for modeling. \n elif func_call.name.name == 'get_file_provenance':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n if arg_names[1] == 'false':\n arg1 = False\n elif arg_names[1] == 'true':\n arg1 = True\n else:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_file_provenance is unknown.\\033[0m')\n exit(1)\n return provenance.get_file_provenance(None, arg1, motif_node_dict)\n # CamFlow \"influences_kernel\" function takes four arguments but only the first three are needed for modeling.\n elif func_call.name.name == 'influences_kernel':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second and third arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in influences_kernel must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in influences_kernel must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in influences_kernel must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in influences_kernel must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.influences_kernel(arg_names[0], val1, val2, None, motif_node_dict)\n # CamFlow \"get_socket_inode_provenance\" takes one argument but it is not needed for modeling.\n elif func_call.name.name == 'get_socket_provenance':\n return provenance.get_socket_provenance(None, motif_node_dict)\n # CamFlow \"get_socket_inode_provenance\" takes one argument but it is not needed for modeling. \n elif func_call.name.name == 'get_socket_inode_provenance':\n return provenance.get_socket_inode_provenance(None, motif_node_dict)\n # CamFlow \"record_address\" takes three arguments but only the last argument is needed for modeling. \n elif func_call.name.name == 'record_address':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n arg2 = arg_names[2]\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_address must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_address must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_address(None, None, val2, motif_node_dict)\n # CamFlow \"get_sk_inode_provenance\" takes one argument but it is not needed for modeling. \n elif func_call.name.name == 'get_sk_inode_provenance':\n return provenance.get_sk_inode_provenance(None, motif_node_dict)\n # CamFlow \"get_sk_provenance\" takes one argument but it is not needed for modeling.\n elif func_call.name.name == 'get_sk_provenance':\n return provenance.get_sk_provenance(None, motif_node_dict)\n # CamFlow \"record_packet_content\" takes two arguments but only the second argument is needed for modeling. \n elif func_call.name.name == 'record_packet_content':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n arg1 = arg_names[1]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_packet_content must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_packet_content must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_packet_content(None, val1, motif_node_dict)\n # CamFlow \"record_args\" takes two arguments but only the first argument is needed for modeling.\n elif func_call.name.name == 'record_args':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n arg0 = arg_names[0]\n if arg0 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_args must exist in the dictionary.\\033[0m')\n exit(1)\n val0 = getLastValueFromKey(motif_node_dict, arg0)\n if not val0:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_args must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_args(val0, None, motif_node_dict)\n else:\n return None, None","def _partition_call_operator(self, inputs, attr):\n\n try:\n from tensorflow.python.framework import function_def_to_graph\n except ImportError as e:\n raise ImportError(f\"Unable to import tensorflow which is required {e}\")\n\n main_graph_proto = self._main_graph_proto\n outer_graph_def = main_graph_proto._graph\n\n node_func_name = attr.get(\"f\").name\n func = next(\n (f for f in outer_graph_def.library.function if f.signature.name == node_func_name),\n None,\n )\n if func:\n devices = set(node.device for node in func.node_def)\n if len(devices) > 1:\n raise Exception(\n \"Found inconsistent Device assignment in the \"\n \"Stateful Partitioned SubGraph. Rejecting \"\n \"the subgraph \"\n )\n # Convert function definition to graph\n func_input_shapes = func.attr[\"_input_shapes\"].list.shape\n subgraph, _ = function_def_to_graph.function_def_to_graph_def(func, func_input_shapes)\n\n # Computing subgraph's input shape dictionary\n subgraph_shape_dict, input_expr_dict = {}, {}\n for f_arg, input in zip(func.signature.input_arg, inputs):\n input_expr_dict[f_arg.name] = input\n subgraph_shape_dict[f_arg.name] = _infer_shape(input, main_graph_proto._mod)\n\n func_name = f\"func_{func.signature.name}\"\n try:\n global_func = main_graph_proto._mod[func_name]\n sub_func = global_func\n sub_params = main_graph_proto._params\n except ValueError:\n # Construct relay nodes from the subgraph\n g1 = SubGraphProto(main_graph_proto)\n sub_func, sub_params = g1.from_tensorflow(subgraph, shape=subgraph_shape_dict)\n main_graph_proto._params.update(sub_params)\n func_expr = _function.Function(sub_func.params, sub_func.body)\n global_func = tvm.relay.GlobalVar(func_name)\n main_graph_proto._mod[global_func] = func_expr\n main_graph_proto._mod = InferType()(main_graph_proto._mod)\n\n param_exprs = []\n for param_expr in sub_func.params:\n # sub_params is subset of sub_func.params\n param_name = param_expr.vid.name_hint\n if param_name in input_expr_dict.keys():\n param_exprs.append(input_expr_dict[param_name])\n elif param_name in sub_params.keys():\n param_exprs.append(param_expr)\n else:\n raise Exception(f\"Input parameter {param_name} not found\")\n\n sb = tvm.relay.scope_builder.ScopeBuilder()\n loop_ret = global_func(*param_exprs)\n sb.ret(loop_ret)\n ret = sb.get()\n else:\n raise Exception(f\"Function not found - {node_func_name}\")\n return ret","def call_top_interface_args_with_no_func_def(self, node: AnnCastCall):\n # call container is used to scope parameters\n call_con_name = call_container_name(node)\n\n # create argument and parameter variables\n # argument variables are inputs to the top interface\n # paramter variables are outputs of the top interface\n for i, n in enumerate(node.arguments):\n # argument name and scope str\n arg_name = call_argument_name(node, i)\n arg_con_scopestr = con_scope_to_str(node.func.con_scope)\n\n # parameter name and scopestr\n param_name = call_param_name(node, i)\n param_con_scopestr = con_scope_to_str(node.func.con_scope + [call_con_name])\n \n # argument and parameter share id, and start with initial version\n id = self.pipeline_state.next_collapsed_id()\n version = VAR_INIT_VERSION\n\n # build and store GrFN variables for argument and parameter\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\n # store arg_fullid\n node.arg_index_to_fullid[i] = arg_fullid\n # create From Source metadata for the GrFN var\n from_source = False\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\n\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\n # store param_fullid\n node.param_index_to_fullid[i] = param_fullid\n # create From Source metadata for the GrFN var\n # when we don't have the function def, we create a paramter with a default name\n add_metadata_to_grfn_var(param_grfn_var, from_source_mdata)\n\n # link argument and parameter through top interface\n node.top_interface_in[id] = arg_fullid\n node.top_interface_out[id] = param_fullid\n\n # DEBUG printing\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\n print(\"After create_call_args_and_params():\")\n print(f\"\\ttop_interface_in = {node.top_interface_in}\")\n print(f\"\\ttop_interface_out = {node.top_interface_out}\")","def visit_Call(self, node: ast.Call) -> None:\n self._check_wrong_function_called(node)\n self._check_boolean_arguments(node)\n self._check_super_call(node)\n self.generic_visit(node)","def get_method_calls(function_node, object_name):\n calls = {}\n for node in ast.walk(function_node):\n # These series of if's check that the node is an assignment node that\n # follows the pattern:\n # object_name.attribute(arg, otherarg, finalarg, keyword=keywordarg, keyword2=otherkeywordarg)\n if isinstance(node, ast.Expr):\n expression = node\n if isinstance(expression.value, ast.Call):\n call = expression.value\n if isinstance(call.func, ast.Attribute):\n attribute = call.func # this is a type not the name of the attribute\n if isinstance(attribute.value, ast.Name):\n name = attribute.value\n if name.id == object_name:\n attr = attribute.attr # attr is the actual atribute name the name of the method called\n raw_args = call.args\n args = []\n for arg in raw_args:\n args.append(convert_arg(arg))\n\n keyword_args = {}\n keywords = call.keywords\n for keyword in keywords:\n key = keyword.arg\n raw_value = keyword.value\n value = convert_arg(raw_value)\n keyword_args[key] = value\n\n call = MethodCall(object_name=object_name, method_name=attr, args=args, keywords=keyword_args)\n \n calls[attr] = call \n return calls","def eval_node(node, env):\n global genv\n global result\n node_type = node_name(node)\n\n if node_type == 'Expr':\n return eval_node(node.value, env)\n elif node_type == 'Assign':\n val = eval_node(node.value, env)\n\n while type(val) is tuple and len(val) == 2 and (type(val[1]) == GlobalEnv or type(val[1]) == LocalEnv):\n val = val[0]\n\n # extract the variable name, evaluate the RHS, then extend the environment.\n return 0, env.extend([node.targets[0].id], [val])\n elif node_type == 'BinOp':\n # get the left and right operands (we use only single operands) and the operator.\n # evaluate the operands and apply the operator. return the number, env.\n\n left = eval_node(node.left, env)[0]\n right = eval_node(node.right, env)[0]\n\n left = left[0] if type(left) is tuple else left\n right = right[0] if type(right) is tuple else right\n\n op = node_name(node.op)\n\n if op == \"Add\":\n return (left + right), env\n elif op == \"Sub\":\n return (left - right), env\n elif op == \"Mult\":\n return (left * right), env\n elif op == \"Div\":\n return (left / right), env\n elif op == \"Mod\":\n return (left % right), env\n return 0, env\n elif node_type == 'FunctionDef':\n # need the function id (name), args, and body. Extend the environment.\n # you can leave the args wrapped in the ast class and the body and unpack them\n # when the function is called.\n\n return 0, env.extend([node.name], [(node.args, node.body)])\n elif node_type == 'Call':\n # get any values passed in to the function from the Call object.\n # get the fxn name and look up its parameters, if any, and body from the env.\n # get lists for parameter names and values and extend a LocalEnv with those bindings.\n # evaluate the body in the local env, return the value, env.\n\n func = eval_node(node.func, env)[0]\n local_env = LocalEnv(None, env)\n\n args = func[0].args\n body = func[1]\n\n index = 0\n for val in node.args:\n local_env = local_env.extend([args[index].arg], [eval_node(val, local_env)[0]])\n index += 1\n\n for node in body:\n val = eval_node(node, local_env)\n\n if node_name(node) == \"Return\":\n output_val = val[0]\n local_env = val[1]\n return output_val, env\n elif node_type == 'Return':\n # evaluate the node, return the value, env.\n return eval_node(node.value, env)\n elif node_type == 'Name':\n # Name(identifier id)- lookup the value binding in the env\n # return the value, env\n return env.lookup(node.id), env\n # Num(object n) -- a number, return the number, env.\n elif node_type == 'Num':\n return node.n, env","def visit_Call(self, node: ast.Call) -> None:\n self._check_buggy_super_context(node)\n self.generic_visit(node)","def get_call(call_node):\n if not isinstance(call_node, ast.Call):\n # print(\"this node is \" + str(type(call_node)) + \" node, not call node\")\n return None\n\n elif isinstance(call_node.func, ast.Name):\n return call_node.func.id\n\n elif isinstance(call_node.func, ast.Attribute):\n if isinstance(call_node.func.value, ast.Name):\n return call_node.func.value.id + '.' + call_node.func.attr\n else:\n get_call(call_node.func.value)\n\n elif isinstance(call_node.func, ast.Call):\n get_call(call_node.func)","def get_node_target(submodules: Mapping[str, torch.nn.Module], node: pippy.fx.Node) -> str:\n\n assert node.op in CALLABLE_NODE_OPS, (\n \"Expect op types of \" + \", \".join(CALLABLE_NODE_OPS) + f\", but found {node.op}\"\n )\n\n if node.op == \"call_module\":\n assert isinstance(node.target, str)\n submod = submodules[node.target]\n submod_type = getattr(submod, \"_base_class_origin\", type(submod))\n return get_acc_ops_name(submod_type)\n elif node.op == \"call_function\":\n target: Any = node.target\n return (\n f\"acc_ops.{target.__name__}\"\n if target.__module__ is not None and \"acc_ops\" in target.__module__\n else _get_qualified_name(target)\n )\n else:\n assert isinstance(node.target, str)\n return node.target","def run_node(\n self,\n node,\n fx_graph_module: torch.fx.GraphModule,\n onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,\n op_level_debug: bool,\n onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,\n onnxscript_tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,\n fx_name_to_onnxscript_value: Dict[\n str,\n Union[\n onnxscript_graph_building.TorchScriptTensor,\n Tuple[onnxscript_graph_building.TorchScriptTensor, ...],\n ],\n ],\n ):\n # Record stack trace of node in diagnostic.\n node_stack_trace = node.stack_trace\n if node_stack_trace:\n diagnostic = self.diagnostic_context.inflight_diagnostic(\n rule=diagnostics.rules.fx_node_to_onnx\n )\n diagnostic.with_additional_message(\n f\"### PyTorch source information\\n```\\n{node_stack_trace}\\n```\"\n )\n location = _location_from_fx_stack_trace(node_stack_trace)\n if location is not None:\n diagnostic.with_location(location)\n\n if node.op == \"placeholder\":\n self.placeholder(node, onnxscript_graph, fx_name_to_onnxscript_value)\n elif node.op == \"get_attr\":\n self.get_attr(\n node,\n onnxscript_graph,\n fx_name_to_onnxscript_value,\n fx_graph_module,\n )\n elif node.op == \"call_function\":\n self.call_function(\n node,\n onnxscript_tracer,\n fx_name_to_onnxscript_value,\n onnxfunction_dispatcher,\n op_level_debug,\n fx_graph_module,\n )\n elif node.op == \"call_method\":\n self.call_method(node)\n elif node.op == \"call_module\":\n self.call_module(\n node,\n onnxscript_graph,\n fx_name_to_onnxscript_value,\n onnxscript_tracer,\n fx_graph_module,\n onnxfunction_dispatcher,\n op_level_debug,\n )\n elif node.op == \"output\":\n self.output(node, onnxscript_graph, fx_name_to_onnxscript_value)\n else:\n raise RuntimeError(f\"Found node type not defined in torch.fx: {node.op}\")","def __call__(self, *args):\n\n func_env = Environment(self.parent)\n self.define_args(func_env, *args)\n return evaluate(self.body, func_env)","def dispatchMessageIteratorCall(self, tree):\n # simple case not a member function just an iterator with arguments\n if isinstance(tree.func, ast.Name):\n self.write(f\"FLAMEGPU->{tree.func.id}\")\n if isinstance(tree.func, ast.Attribute) :\n if isinstance(tree.func.value, ast.Name):\n # check that the iterator is supported\n if not tree.func.attr in self.fgpu_input_msg_iter_funcs:\n self.RaiseError(tree, f\"Message input loop iterator '{tree.func.attr}' is not supported.\")\n self.write(f\"FLAMEGPU->{tree.func.value.id}.{tree.func.attr}\")\n else:\n self.RaiseError(tree, \"Message input loop iterator format incorrect.\")\n\n # handle function arguments \n self.write(\"(\")\n self._CallArguments(tree)\n self.write(\")\")","def visit_Call(self, node: ast.Call) -> None:\n self._check_open_call_context(node)\n self._check_type_compare(node)\n self._check_range_len(node)\n self.generic_visit(node)","def visit_Call(self, node: ast.Call) -> None:\n self._check_floating_nan(node)\n self.generic_visit(node)","def __call__(fun_name):","def transform_call(call):\n return {\n 'type': 'call',\n 'chain': [str(fn.name) for fn in call.names()],\n 'arguments': [str(arg) for arg in call.arguments()],\n 'body': transform_block(call.body())\n }","def _(self, node: FunctionDef):\n body_nodes = []\n for n in node.body:\n curr_piece = self.visit(n)\n if len(curr_piece) > 0:\n body_nodes.append(curr_piece)\n\n func_body = \" \".join(body_nodes)\n\n return f\"( {node.name} {func_body} )\"","def generic_function(self, node, ordered_functions):\n for generic in node.fortran_generic:\n new = node.clone()\n ordered_functions.append(new)\n self.append_function_index(new)\n new._generated = \"fortran_generic\"\n fmt = new.fmtdict\n # XXX append to existing suffix\n if generic.fmtdict:\n fmt.update(generic.fmtdict)\n fmt.function_suffix = fmt.function_suffix + generic.function_suffix\n new.fortran_generic = {}\n new.wrap.assign(fortran=True)\n new.ast.declarator.params = generic.decls\n\n # Try to call original C function if possible.\n # All arguments are native scalar.\n need_wrapper = False\n if new.ast.declarator.is_indirect():\n need_wrapper = True\n \n for arg in new.ast.declarator.params:\n if arg.declarator.is_indirect():\n need_wrapper = True\n break\n elif arg.typemap.sgroup == \"native\":\n pass\n else:\n need_wrapper = True\n break\n\n if need_wrapper:\n # The C wrapper is required to cast constants.\n # generic.yaml: GenericReal\n new.C_force_wrapper = True\n new.wrap.c = True\n new._PTR_C_CXX_index = node._function_index\n else:\n new._PTR_F_C_index = node._function_index\n \n # Do not process templated node, instead process\n # generated functions above.\n # node.wrap.c = False\n node.wrap.fortran = False","def visit_function(self, node):\n # ignore actual functions or method within a new style class\n if not node.is_method():\n return\n klass = node.parent.frame()\n for stmt in node.nodes_of_class(astroid.CallFunc):\n if node_frame_class(stmt) != node_frame_class(node):\n # Don't look down in other scopes.\n continue\n expr = stmt.func\n if not isinstance(expr, astroid.Getattr):\n continue\n call = expr.expr\n # skip the test if using super\n if isinstance(call, astroid.CallFunc) and \\\n isinstance(call.func, astroid.Name) and \\\n call.func.name == 'super':\n confidence = (INFERENCE if has_known_bases(klass)\n else INFERENCE_FAILURE)\n if not klass.newstyle:\n # super should not be used on an old style class\n self.add_message('super-on-old-class', node=node,\n confidence=confidence)\n else:\n # super first arg should be the class\n if not call.args and sys.version_info[0] == 3:\n # unless Python 3\n continue\n\n try:\n supcls = (call.args and next(call.args[0].infer())\n or None)\n except astroid.InferenceError:\n continue\n\n if supcls is None:\n self.add_message('missing-super-argument', node=call,\n confidence=confidence)\n continue\n\n if klass is not supcls:\n name = None\n # if supcls is not YES, then supcls was infered\n # and use its name. Otherwise, try to look\n # for call.args[0].name\n if supcls is not astroid.YES:\n name = supcls.name\n else:\n if hasattr(call.args[0], 'name'):\n name = call.args[0].name\n if name is not None:\n self.add_message('bad-super-call',\n node=call,\n args=(name, ),\n confidence=confidence)","def get_call_value(call_node):\n s = get_name_value(call_node.func)\n if isinstance(call_node.func.ctx, ast.Load):\n # convert ast args to literals\n args = [convert_arg(a) for a in call_node.args]\n # suround literal strings with a set of quotes for easy placing into\n # a string\n args = ['\"' + a + '\"' if isinstance(a, str) else a for a in args]\n # join all the args into a set of parens\n s += \"(\" + \",\".join(args) + \")\"\n return s","def __call__(self, params: Mapping[str, Any],\n batch: Mapping[str, jnp.ndarray]) -> PyTreeDef:\n ...","def convert(value):\n if isinstance(value, (Function, NodeBase)):\n return value\n\n if callable(value):\n return _convert_tvm_func(value)\n\n return _convert_to_node(value)","def visit_FunctionCallNode(self, node: FunctionCallNode, symbol_table: SymbolTable,\n superclass: bool = False) -> None:\n if symbol_table:\n if superclass:\n res = symbol_table['super'][node.func_name]\n else:\n res = symbol_table[node.func_name]\n if isinstance(res, tuple):\n class_node = symbol_table[node.func_name][0]\n\n new_symbol_table = SymbolTable()\n new_symbol_table[\"inst\"] = SymbolTable()\n inst = new_symbol_table[\"inst\"]\n initialization = symbol_table[node.func_name][0].special_methods['init']\n node.params = [self.visit(param, symbol_table) for param in node.params]\n inst[\"params\"] = {}\n for param, name_of_param in zip(node.params, initialization.params[1:]):\n inst[\"params\"][name_of_param.value] = param\n\n if class_node.inherit:\n inheriting_class = symbol_table[class_node.inherit][0]\n super_cls = {'super': {}}\n for key_spec, super_spec in zip_longest(class_node.special_methods.keys(),\n inheriting_class.special_methods.keys()):\n inst[key_spec] = class_node.special_methods.get(key_spec)\n if super_spec:\n super_cls['super'][super_spec] = inheriting_class.special_methods.get(super_spec)\n for key, key_super in zip_longest(class_node.methods.keys(),\n inheriting_class.methods.keys()):\n inst[key] = class_node.methods.get(key)\n if key_super:\n super_cls['super'][key_super] = inheriting_class.methods.get(key_super)\n inst.symbols.update(super_cls)\n else:\n inst.symbols.update(class_node.methods)\n\n new_obj = ObjectNode(name=class_node.name, methods=class_node.methods,\n special_methods=class_node.special_methods,\n attributes=class_node.attributes, symbol_table=new_symbol_table,\n global_attrs=class_node.class_attrs)\n new_item = (new_obj, new_obj.class_attrs)\n\n for line in initialization.body:\n if line:\n self.visit(line, new_obj.local_symbol_table)\n\n if \"params\" in inst.symbols.keys():\n del inst[\"params\"]\n return new_item\n\n else:\n node.params = [self.visit(param, symbol_table) for param in node.params]\n if 'inst' in [param.value for param in (\n symbol_table['super'][node.func_name] if superclass else symbol_table[node.func_name]).params]:\n node.params = [symbol_table] + node.params\n try:\n if superclass:\n func = symbol_table['super'][node.func_name]\n else:\n func = symbol_table[node.func_name]\n except KeyError:\n func = self.symbol_table[node.func_name]\n\n if 'inst' in func.local_symbol_table.symbols.keys() and not isinstance(func.local_symbol_table[\"inst\"],\n SymbolTable):\n func.local_symbol_table[\"inst\"] = symbol_table\n func.local_symbol_table[\"inst\"][\"params\"] = {}\n for param, name_of_param in zip(node.params, func.params):\n if 'inst' in func.local_symbol_table.symbols.keys():\n func.local_symbol_table[\"inst\"][\"params\"][name_of_param.value] = param\n if name_of_param.value in func.local_symbol_table.symbols.keys() and name_of_param.value != 'inst':\n del func.local_symbol_table[name_of_param.value]\n else:\n func.local_symbol_table[name_of_param.value] = param\n for line in func.body:\n if line:\n try:\n result = self.visit(line, func.local_symbol_table)\n except KeyError:\n result = self.visit(line, symbol_table)\n if isinstance(line, ReturnNode):\n res = result\n if isinstance(line.expression, AccessNode) and \\\n isinstance(line.expression.item_to_access, FunctionCallNode):\n if 'inst' in func.local_symbol_table.symbols.keys():\n del func.local_symbol_table[\"inst\"][\"params\"]\n return res\n elif isinstance(result, ReturnNode):\n res = self.visit(result, func.local_symbol_table)\n if 'inst' in func.local_symbol_table.symbols.keys():\n del func.local_symbol_table[\"inst\"][\"params\"]\n return res\n if 'inst' in func.local_symbol_table.symbols.keys():\n del func.local_symbol_table[\"inst\"][\"params\"]","def visit(self, token, parent):\r\n\r\n\r\n for i, child in enumerate(token.children):\r\n\r\n if child.type == Token.T_GROUPING and child.value == \"{}\":\r\n if (token.type == Token.T_MODULE) or \\\r\n (token.type == Token.T_ASYNC_FUNCTION) or \\\r\n (token.type == Token.T_ANONYMOUS_FUNCTION) or \\\r\n (token.type == Token.T_ANONYMOUS_GENERATOR) or \\\r\n (token.type == Token.T_ASYNC_GENERATOR) or \\\r\n (token.type == Token.T_ASYNC_ANONYMOUS_FUNCTION) or \\\r\n (token.type == Token.T_ASYNC_ANONYMOUS_GENERATOR) or \\\r\n (token.type == Token.T_METHOD) or \\\r\n (token.type == Token.T_CLASS) or \\\r\n (token.type == Token.T_BLOCK) or \\\r\n (token.type == Token.T_FINALLY) or \\\r\n (token.type == Token.T_LAMBDA) or \\\r\n (token.type == Token.T_GROUPING and token.value == \"{}\"):\r\n # next test this is not an object\r\n # objects:\r\n # {} {a} {a:b} {...a} {a,b} {a:b,c:d}\r\n # not objects:\r\n # {1} {a.b} {f()}\r\n ref = self._isObject(child)\r\n if ref is None:\r\n child.type = Token.T_OBJECT\r\n else:\r\n child.type = Token.T_BLOCK\r\n else:\r\n ref = self._isObject(child)\r\n if ref is not None:\r\n # in some cases convert the invalid object\r\n # into a block label.\r\n if token.type == Token.T_BINARY and token.value == \":\" and len(token.children) == 2:\r\n\r\n lhs, rhs = token.children\r\n\r\n if parent.type == Token.T_OBJECT:\r\n parent.type = Token.T_BLOCK\r\n\r\n if parent.type == Token.T_BLOCK or parent.type == Token.T_MODULE:\r\n if rhs.type == Token.T_GROUPING:\r\n rhs.type = Token.T_BLOCK\r\n\r\n index = parent.children.index(token)\r\n parent.children.insert(index+1, rhs)\r\n\r\n token.type = Token.T_BLOCK_LABEL\r\n token.value = lhs.value\r\n token.children = []\r\n\r\n continue\r\n\r\n print(\"\\n%s:\"%ref)\r\n print(\"parent\", parent.type, parent.value)\r\n print(\"token\", token.type, token.value)\r\n print(\"parent\", parent.toString(3))\r\n raise ref\r\n child.type = Token.T_OBJECT\r\n\r\n elif child.type == Token.T_CASE or child.type == Token.T_DEFAULT:\r\n j = i + 1\r\n while j < len(token.children):\r\n tmp = token.children[j]\r\n if tmp.type == Token.T_CASE or tmp.type == Token.T_DEFAULT:\r\n break\r\n child.children.append(token.children.pop(j))","def __init__(self, function: ast3.FunctionDef, module: \"Module\") -> None:\n\n # easy data\n self._function = function\n self.name = function.name\n self.line = function.lineno\n self.column = function.col_offset\n self.body = function.body\n self.module = module\n self.decorators = function.decorator_list\n\n # time to parse arguments\n self._args = function.args.args\n self.args = [Arg(x) for x in self._args]\n self.functions = [\n Function(x, self.module)\n for x in self.body\n if isinstance(x, ast3.FunctionDef)\n ]\n self.classes = [\n Class(x, self.module) for x in self.body if isinstance(x, ast3.ClassDef)\n ]\n self.untyped = [\n x for x in self.args if not x.typed and x not in DEFAULT_ARG_IGNORE\n ]\n self.doc = None\n self.returns = None\n self.return_typed = False\n self.missing_args: Set[str] = set()\n self.unexpected_args: Set[str] = set()\n arg_names = set(x.name for x in self.args if x.name not in DEFAULT_ARG_IGNORE)\n self.missing_args = arg_names\n if isinstance(self.body[0], ast3.Expr):\n # this is most likely a doc string\n self.doc = Doc(self.body[0], Doc.Type.FUNCTION)\n doc_arg_names = set(x for x, y in self.doc.args.items())\n self.missing_args = arg_names - doc_arg_names\n self.unexpected_args = doc_arg_names - arg_names\n if function.returns:\n self.return_typed = True\n self.returns = parse_elt(function.returns) # type: ignore\n\n # complexity checks\n self._radon = cc_visit_ast(self._function)[0]\n self.complexity = self._radon.complexity\n self.is_method = self._radon.is_method\n self._halstead = h_visit_ast(self._function)","def eval_function_body(function_body, motif_node_dict):\n # The body of FuncDef is a Compound, which is a placeholder for a block surrounded by {}\n # The following goes through the declarations and statements in the function body\n tree = None\n for item in function_body.block_items:\n if type(item).__name__ == 'FuncCall': # Case 1: provenance-graph-related function call\n motif_node, tree_node = eval_function_call(item, motif_node_dict)\n if tree_node != None:\n tree = provenance.create_group_node(tree, tree_node)\n elif type(item).__name__ == 'Assignment': # Case 2: rc = provenance-graph-related function call\n tree_node = eval_assignment(item, motif_node_dict)\n if tree_node != None:\n tree = provenance.create_group_node(tree, tree_node)\n elif type(item).__name__ == 'Decl': # Case 3: declaration with initialization\n tree_node = eval_declaration(item, motif_node_dict)\n if tree_node != None:\n tree = provenance.create_group_node(tree, tree_node)\n elif type(item).__name__ == 'If': # Case 4: if/else\n tree_node = eval_if_else(item, motif_node_dict)\n if tree_node != None:\n tree = provenance.create_group_node(tree, tree_node)\n elif type(item).__name__ == 'Return': # Case 5: return with function call\n tree_node = eval_return(item, motif_node_dict)\n if tree_node != None:\n tree = provenance.create_group_node(tree, tree_node)\n return tree","def __call__(self, node):\n if node.inputs:\n if node.inputs[0].is_constant(Primitive):\n prim = node.inputs[0].value\n visit = getattr(self, f'_visit_{prim}', None)\n if visit is None:\n return node.inputs[1:]\n return visit(node)\n else:\n return node.inputs\n return []","def visit_FuncCall(self, node): #pylint: disable=invalid-name\n # TODO: should we always ignore functions called in the arguments\n # of a function? Maybe its best to for OpenSSL purposes...\n self.call_order[self.current_function].append(node)","def expand_callable(self, call_expr):\n call_expr.func = ast.Attribute(value=call_expr.func, attr='__call__')","def __call__(self, function: FuncSpeechArg):\n self._add_attr(function)\n return function","def analyse_label_function_calls(node):\n global SUBGRAPHS\n regex = r\"(?:\\|_\\w+\\\\ \\=\\\\ |\\|)\\w+\\\\\\s\\(\\w*\\)\"\n label = node.get_attributes()['label']\n for match in re.finditer(regex, label, re.MULTILINE):\n function_call = re.search(r'\\w+\\\\\\s\\(\\w*\\)', match.group()).group()\n function_name = re.search(r'\\w+', function_call)\n if function_name is not None:\n if function_name.group() in SUBGRAPHS:\n return (function_name.group(), function_call, match.start())\n return (None, None, None)","def run_op_node(input_data, op_fun, *args):\n runtime = get_runtime()\n comp_args = []\n op_fun_args = []\n comp_inputs = []\n for idx, data in enumerate(input_data):\n if np.isscalar(data):\n op_fun_args.append(ng.constant(data, _get_numpy_dtype(data)))\n else:\n node = ng.parameter(data.shape, name=ascii_uppercase[idx], dtype=data.dtype)\n op_fun_args.append(node)\n comp_args.append(node)\n comp_inputs.append(data)\n op_fun_args.extend(args)\n node = op_fun(*op_fun_args)\n computation = runtime.computation(node, *comp_args)\n return computation(*comp_inputs)","def _tf1_ ( self , *args ) :\n #\n if not hasattr ( self , '_wo1' ) : self._wo1 = _WO1_ ( self )\n if not self._wo1 : self._wo1 = _WO1_ ( self )\n ## \n _wo = self._wo1 \n fun = ROOT.TF1 ( funID() , _wo , *args )\n fun.SetNpx ( 500 ) \n #\n return fun","def convert(ast):\n\n if ast and ast.type == \"Function\":\n # Activity function conversion\n if (\n ast.name != \"molecularActivity\"\n and ast.name in belspec[\"namespaces\"][\"Activity\"][\"list\"]\n ):\n print(\"name\", ast.name, \"type\", ast.type)\n ast = convert_activity(ast)\n return ast # Otherwise - this will trigger on the BEL2 molecularActivity\n\n # translocation conversion\n elif ast.name in [\"tloc\", \"translocation\"]:\n ast = convert_tloc(ast)\n\n fus_flag = False\n for idx, arg in enumerate(ast.args):\n if arg.__class__.__name__ == \"Function\":\n\n # Fix substitution -> variation()\n if arg.name in [\"sub\", \"substitution\"]:\n ast.args[idx] = convert_sub(arg)\n\n elif arg.name in [\"trunc\", \"truncation\"]:\n ast.args[idx] = convert_trunc(arg)\n\n elif arg.name in [\"pmod\", \"proteinModification\"]:\n ast.args[idx] = convert_pmod(arg)\n\n elif arg.name in [\"fus\", \"fusion\"]:\n fus_flag = True\n\n # Recursively process Functions\n ast.args[idx] = convert(ast.args[idx])\n\n if fus_flag:\n ast = convert_fus(ast)\n\n return ast","def test_superApply(self):\n one = t.Action(\"1\")\n x = t.Action(\"x\")\n a = t.Apply(\"super\", \"main\", [one, x])\n self.assertEqual(writePython(a),\n dd(\"\"\"\n _G_python_1, lastError = 1, None\n self.considerError(lastError, None)\n _G_python_2, lastError = eval('x', self.globals, _locals), None\n self.considerError(lastError, None)\n _G_apply_3, lastError = self.superApply(\"main\", _G_python_1, _G_python_2)\n self.considerError(lastError, None)\n _G_apply_3\n \"\"\"))","def __call__(self, node_A):\r\n new_node = Op.__call__(self)\r\n new_node.inputs = [node_A]\r\n new_node.name = \"Sqrt(%s)\" % (node_A.name)\r\n return new_node","def visit_Call(self, node: ast.Call) -> None:\n self._check_unnecessary_literals(node)\n self.generic_visit(node)","def external_call( d, output, stack, context ):\n\targuments = d[\"args\"].split(\" \")\n\tstack_collect = stack[-len(arguments):]\n\tfor arg, desired in zip(stack_collect, arguments):\n\t\tresult = type_system.type_reduce( arg, desired, output, stack, context )\n\t\tif result != \"success\":\n\t\t\treturn \"pass\"\n\toutput.append(\" call %s\\n\" % d[\"name\"])\n\tfor i in arguments:\n\t\tstack.pop()\n\tretvals = d[\"ret\"].split(\" \")\n\tfor val in retvals:\n\t\tstack.append( type_system.Datum( [[val]], [None] ) )\n\treturn \"success\"","def test_RestrictingNodeTransformer__visit_Call__1():\n result = compile_restricted_exec('a = max([1, 2, 3])')\n assert result.errors == ()\n loc = {}\n exec(result.code, {}, loc)\n assert loc['a'] == 3\n assert result.used_names == {'max': True}","def on_apply(self, node):\n if node.inputs[0].is_constant(Primitive):\n fn = node.inputs[0].value\n conv = MAP.get(fn)\n if conv is not None:\n return conv(self, *node.inputs[1:])\n return relay.Call(self.ref(node.inputs[0]),\n [self.ref(i) for i in node.inputs[1:]])","def grfn_2_2_call_top_interface_args(self, node: AnnCastCall):\n # call container is used to scope parameters\n call_con_name = call_container_name(node)\n\n # create argument and parameter variables\n # argument variables are inputs to the top interface\n # paramter variables are outputs of the top interface\n # if we are generating GrFN 2.2, we would like the parameter to lie in the \n # copied function def container, we do this by aliasing versions during GrfnVarCreation pass\n for i, n in enumerate(node.arguments):\n # argument name and scope str\n arg_name = call_argument_name(node, i)\n arg_con_scopestr = con_scope_to_str(node.func.con_scope)\n\n # parameter name and scopestr\n param = node.func_def_copy.func_args[i]\n assert(isinstance(param, AnnCastVar))\n param_name = param.val.name\n param_con_scopestr = con_scope_to_str(node.func.con_scope + [call_con_name])\n \n # argument and parameter share id, and start with initial version\n id = self.pipeline_state.next_collapsed_id()\n version = VAR_INIT_VERSION\n\n # build and store GrFN variables for argument and parameter\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\n # store arg_fullid\n node.arg_index_to_fullid[i] = arg_fullid\n # create From Source metadata for the GrFN var\n from_source = False\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\n\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\n # store param_fullid\n node.param_index_to_fullid[i] = param_fullid\n add_metadata_from_name_node(param_grfn_var, param.val)\n\n # link argument and parameter through top interface\n node.top_interface_in[id] = arg_fullid\n node.top_interface_out[id] = param_fullid\n\n\n # DEBUG printing\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\n print(\"After create_call_args_and_params():\")\n print(f\"\\ttop_interface_in = {node.top_interface_in}\")\n print(f\"\\ttop_interface_out = {node.top_interface_out}\")","def compile_function(self, function, arguments):","def visit_papi_operations(json_metadata: JsonObject,\n call_fn: OperationMappingCallFunction,\n initial_accumulator: Accumulator) -> Accumulator:\n\n accumulator = initial_accumulator\n\n def examine_calls(calls: JsonObject, path_so_far: List[AnyStr]) -> None:\n for call_name in calls:\n attempts = calls[call_name]\n for attempt in attempts:\n operation_id = attempt.get('jobId')\n sub_workflow_metadata = attempt.get('subWorkflowMetadata')\n path = build_call_path(call_name, path_so_far, attempt)\n if operation_id:\n call_fn(accumulator, operation_id, path, attempt)\n if sub_workflow_metadata:\n examine_calls(sub_workflow_metadata.get('calls', {}), path)\n\n def build_call_path(call_name: str, path_so_far: List[AnyStr], attempt: dict) -> List[AnyStr]:\n call_path = path_so_far.copy()\n\n # Remove confusing duplication in subworkflow call names.\n # A parent workflow would name a subworkflow call \"parent_wf.sub_wf\".\n # The subworkflow would name its calls \"sub_wf.sub_call\".\n # If those call components were simply joined the result would be\n # \"parent_wf.sub_wf.sub_wf.sub_call\". This logic removes the duplication of \"sub_wf\",\n # resulting in \"parent_wf.sub_wf.sub_call\".\n deduplicated_call_name = call_name\n if len(path_so_far) > 0:\n this_call_components = call_name.split('.')\n if len(this_call_components) > 1 and path_so_far[-1].endswith('.' + this_call_components[0]):\n deduplicated_call_name = '.'.join(this_call_components[1:])\n\n call_path.append(deduplicated_call_name)\n shard_index = attempt.get('shardIndex', -1)\n if shard_index != -1:\n call_path.append(f\"shard_{shard_index:04d}\")\n\n return call_path\n\n examine_calls(calls=json_metadata.get('calls', {}), path_so_far=[])\n\n return accumulator","def get_func_ast(obj : types.FunctionType):\n return get_ast(obj).body[0]","def __init__(\n self,\n name,\n input_ports=[],\n output_ports=[],\n *,\n parameters=[],\n attributes={},\n body_builder=None,\n loc=None,\n ip=None,\n ):\n # Copy the mutable default arguments. 'Cause python.\n input_ports = list(input_ports)\n output_ports = list(output_ports)\n parameters = list(parameters)\n attributes = dict(attributes)\n\n operands = []\n results = []\n attributes[\"sym_name\"] = StringAttr.get(str(name))\n\n input_types = []\n input_names = []\n input_locs = []\n unknownLoc = Location.unknown().attr\n for (i, (port_name, port_type)) in enumerate(input_ports):\n input_types.append(port_type)\n input_names.append(StringAttr.get(str(port_name)))\n input_locs.append(unknownLoc)\n attributes[\"argNames\"] = ArrayAttr.get(input_names)\n attributes[\"argLocs\"] = ArrayAttr.get(input_locs)\n\n output_types = []\n output_names = []\n output_locs = []\n for (i, (port_name, port_type)) in enumerate(output_ports):\n output_types.append(port_type)\n output_names.append(StringAttr.get(str(port_name)))\n output_locs.append(unknownLoc)\n attributes[\"resultNames\"] = ArrayAttr.get(output_names)\n attributes[\"resultLocs\"] = ArrayAttr.get(output_locs)\n\n if len(parameters) > 0 or \"parameters\" not in attributes:\n attributes[\"parameters\"] = ArrayAttr.get(parameters)\n\n attributes[\"function_type\"] = TypeAttr.get(\n FunctionType.get(inputs=input_types, results=output_types))\n\n super().__init__(\n self.build_generic(attributes=attributes,\n results=results,\n operands=operands,\n loc=loc,\n ip=ip))\n\n if body_builder:\n entry_block = self.add_entry_block()\n\n with InsertionPoint(entry_block):\n with support.BackedgeBuilder():\n outputs = body_builder(self)\n _create_output_op(name, output_ports, entry_block, outputs)","def transfo(self):\n\n idx = -1\n for elem in self.data.body:\n idx += 1\n\n if isinstance(elem, nodes.Decl) and self._name in self.here().modules:\n if not self.checkExists(elem, self._dict.values()):\n self._diagnostic.notify(Severity.ERROR,\n \"unknown function {} in module {}\".format(elem._name, self._name),\n LocationInfo.from_stream(self._stream))\n raise self._diagnostic\n self._oldFuncs.append(deepcopy(elem))\n elem._name = self.getMangling(elem, -1)\n\n\n elif (isinstance(elem, atMember) and not isinstance(elem, atVirtual)) or (\n hasattr(elem, '_ctype') and isinstance(elem._ctype, nodes.FuncType) and len(\n elem._ctype._params) > 0 and elem._ctype._params[\n 0]._ctype._identifier == self._name and isinstance(elem._ctype._params[0]._ctype._decltype,\n nodes.PointerType)):\n if hasattr(elem, '_stmt') and not self.checkExists(elem._stmt, self._dict[1].values()):\n self._diagnostic.notify(Severity.ERROR,\n \"unknown function {} in class {}\".format(elem._stmt._name, self._name),\n LocationInfo.from_stream(self._stream))\n raise self._diagnostic\n elif hasattr(elem, '_name') and not self.checkExists(elem, self._dict[1].values()):\n self._diagnostic.notify(Severity.ERROR,\n \"unknown function {} in class {}\".format(elem._name, self._name),\n LocationInfo.from_stream(self._stream))\n raise self._diagnostic\n if isinstance(elem, atMember):\n ctype = nodes.PrimaryType(self._name)\n ctype._decltype = nodes.PointerType()\n arg = nodes.Decl('self', ctype)\n if hasattr(elem._stmt._ctype, '_params'):\n elem._stmt._ctype._params.insert(0, arg)\n else:\n elem._stmt._ctype._params = [arg]\n if elem._stmt._name == 'init':\n elem._stmt.body.body.insert(0, deepcopy(self._template[2].body.body[0]))\n elem._stmt.body.body[0].expr.params[1].params[1].params[0].value += self._name\n self._oldFuncs.append(deepcopy(elem._stmt))\n elem._stmt._name = self.getMangling(elem._stmt, 1)\n self.data.body[idx] = elem._stmt\n else:\n self.data.body[idx] = elem\n\n\n elif isinstance(elem, atVirtual):\n if not self.checkExists(elem._stmt, self._dict[2].values()):\n self._diagnostic.notify(Severity.ERROR,\n \"unknown function {} in class {}\".format(elem._stmt._name, self._name),\n LocationInfo.from_stream(self._stream))\n raise self._diagnostic\n ctype = nodes.PrimaryType(self._name)\n ctype._decltype = nodes.PointerType()\n arg = nodes.Decl('self', ctype)\n elem._stmt._ctype._params.insert(0, arg)\n self._oldFuncs.append(deepcopy(elem._stmt))\n elem._stmt._name = self.getMangling(elem._stmt, 2)\n self.data.body[idx] = elem._stmt\n\n\n elif self._name in self.here().classes and (\n hasattr(elem, '_ctype') and isinstance(elem._ctype, nodes.FuncType)):\n if not self.checkExists(elem, self._dict[0].values()):\n self._diagnostic.notify(Severity.ERROR,\n \"unknown function {} in class {}\".format(elem._name, self._name),\n LocationInfo.from_stream(self._stream))\n raise self._diagnostic\n self._oldFuncs.append(deepcopy(elem))\n elem._name = self.getMangling(elem, 0)\n\n\n else:\n self._diagnostic.notify(Severity.ERROR,\n \"%s invalid definiton or declaration in class/module statement\" % str(\n elem._name), LocationInfo.from_stream(self._stream))\n raise self._diagnostic\n return","def parent_function(root_node: ast.AST, node: ast.AST):\n for parent in parent_list(root_node, node):\n if isinstance(parent, ast.FunctionDef):\n return parent\n raise KeyError","def get_calls_in_function( ea ):\r\n\tcallist = []\r\n\tflowgraph = create_flowgraph_from( ea )\r\n\tfor x in flowgraph.nodes.items():\r\n\t\tname = x[0]\r\n\t\tblock = get_basic_block( string.atol( name, 16 ))\r\n\t\tfor instruction in block:\r\n\t\t\tif instruction[ 1 ] == \"call\":\r\n\t\t\t\tcallist.append( instruction )\r\n\treturn callist","def recursive_run_function(tree, right, level, func, *args, **kwargs):\n # logging.debug(\"Top of function\")\n # logging.debug(\"tree=%s\", tree)\n if level == \"mappings\":\n do_func_for = [dict, list]\n elif level == \"atomic\":\n do_func_for = [str, int, float]\n if six.PY2:\n do_func_for.append(unicode)\n elif level == \"always\":\n do_func_for = [str, dict, list, int, float, bool]\n elif level == \"keys\":\n do_func_for = []\n else:\n do_func_for = []\n\n # Python 2/3 error in YAML parser, bad workaround:\n if six.PY2:\n if isinstance(right, unicode):\n logging.warning(\"Unicode type detected, converting to a regular string!\")\n right = right.encode(\"utf-8\")\n assert isinstance(right, str)\n logging.warning(right)\n\n logging.debug(\"Type right: %s\", type(right))\n logging.debug(\"Do func for: %s\", do_func_for)\n\n if level is \"keys\" and isinstance(right, dict):\n keys = list(right)\n for key in keys:\n old_value = right[key]\n returned_key = func(tree + [key], key, *args, **kwargs)\n del right[key]\n right.update({returned_key: old_value})\n\n # logger.debug(\"right is a %s!\", type(right))\n if type(right) in do_func_for:\n if isinstance(right, dict):\n keys = list(right)\n for key in keys:\n value = right[key]\n logging.debug(\"Deleting key %s\", key)\n logging.debug(\n \"Start func %s with %s, %s sent from us\",\n func.__name__,\n tree + [key],\n value,\n \"type_of_sender=dict\",\n )\n returned_dict = func(tree + [key], value, *args, **kwargs)\n del right[key]\n # logger.debug(\"Back out of func %s\", func.__name__)\n # logger.debug(\"Got as returned_dict: %s\", returned_dict)\n right.update(returned_dict)\n # elif isinstance(right, list):\n # for index, item in enumerate(right):\n # del right[0]\n # right.append(func(tree + [None], item, *args, **kwargs))\n else:\n right = func(tree + [None], right, *args, **kwargs)\n\n # logger.debug(\"finished with do_func_for\")\n\n if isinstance(right, list):\n for index, item in enumerate(right):\n new_item = recursive_run_function(\n tree + [None], item, level, func, *args, **kwargs\n )\n right[index] = new_item\n elif isinstance(right, dict):\n keys = list(right)\n for key in keys:\n value = right[key]\n right[key] = recursive_run_function(\n tree + [key], value, level, func, *args, **kwargs\n )\n return right","def op_impl(self):\n # timing is stored by node, we compute timing by Op on demand\n rval = {}\n for fgraph, node in self.apply_callcount:\n if self.apply_cimpl[node]:\n rval[node.op] = \"C \"\n else:\n rval[node.op] = \"Py\"\n return rval","def __call__(self, node_A, node_B, trans_A=False, trans_B=False):\r\n new_node = Op.__call__(self)\r\n new_node.matmul_attr_trans_A = trans_A\r\n new_node.matmul_attr_trans_B = trans_B\r\n new_node.inputs = [node_A, node_B]\r\n new_node.name = \"MatMul(%s,%s,%s,%s)\" % (node_A.name, node_B.name, str(trans_A), str(trans_B))\r\n return new_node","def __call__(self, node_A, node_B, trans_A=False, trans_B=False):\n new_node = Op.__call__(self)\n new_node.matmul_attr_trans_A = trans_A\n new_node.matmul_attr_trans_B = trans_B\n new_node.inputs = [node_A, node_B]\n new_node.name = \"MatMul(%s,%s,%s,%s)\" % (node_A.name, node_B.name, str(trans_A), str(trans_B))\n return new_node","def rotating_frame_transformation_propagator_new(U, t: float, H):\n\n U_RF = (1j*H*t).expm()\n if U.type=='super':\n U_RF=qtp.to_super(U_RF)\n\n U_prime = U_RF * U \n \"\"\" U_RF only on one side because that's the operator that\n satisfies the Schroedinger equation in the interaction picture.\n \"\"\"\n\n return U_prime","def convert_activity(ast):\n\n if len(ast.args) > 1:\n logger.error(f\"Activity should not have more than 1 argument {ast.to_string()}\")\n\n p_arg = ast.args[0] # protein argument\n print(\"p_arg\", p_arg)\n ma_arg = Function(\"ma\", version=version)\n ma_arg.add_argument(StrArg(ast.name, ma_arg))\n p_arg.change_parent_fn(ma_arg)\n ast = Function(\"activity\", version=version)\n p_arg.change_parent_fn(ast)\n ast.add_argument(p_arg)\n ast.add_argument(ma_arg)\n\n return ast","def _update_call(self):\n for entry in self._entry_nodes:\n self._update_call_visit(entry.get_func_first_node(), {})","def run(\n self,\n fx_graph_module: torch.fx.GraphModule,\n onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,\n op_level_debug: bool,\n parent_onnxscript_graph: Optional[\n onnxscript_graph_building.TorchScriptGraph\n ] = None,\n ) -> onnxscript_graph_building.TorchScriptGraph:\n onnxscript_graph = onnxscript_graph_building.TorchScriptGraph(\n parent_onnxscript_graph\n )\n onnxscript_tracer = onnxscript_graph_building.TorchScriptTracingEvaluator(\n onnxscript_graph\n )\n # In the following loop, a TorchScript graph is created to\n # represent the input FX graph with ONNX symbols (e.g., onnx::add).\n # To connect the values to nodes in the TorchScript graph, we maintian\n # fx_name_to_onnxscript_value. Basically, we want to translate\n # fx_tensor_x (type: torch.fx.Node) -> fx_node_1 -> fx_tensor_y (type: torch.fx.Node)\n # to\n # fx_name_to_onnxscript_value[fx_tensor_x.name] -> onnx_node_1 -> fx_name_to_onnxscript_value[fx_tensor_y.name]\n fx_name_to_onnxscript_value: Dict[\n str,\n Union[\n onnxscript_graph_building.TorchScriptTensor,\n Tuple[onnxscript_graph_building.TorchScriptTensor, ...],\n ],\n ] = {}\n\n # TODO: Fix FakeTensorMode limitation asap\n # We want to pass list of ints and floats to TorchScript graph correctly\n # in _export_fx_to_ts, so we must disable FakeTensorMode. Otherwise, graph may\n # receive FakeTensor and results runtime error. In addition, TorchScript-based\n # ONNX exporter used in _ts_graph_to_onnx_model_in_protobuf is not compatible\n # with FakeTensorMode.\n with torch.utils._mode_utils.no_dispatch():\n # node_fixed_shape is only used on op_level_debug purpose.\n for node in fx_graph_module.graph.nodes:\n self.run_node(\n node,\n fx_graph_module,\n onnxfunction_dispatcher,\n op_level_debug,\n onnxscript_graph,\n onnxscript_tracer,\n fx_name_to_onnxscript_value,\n )\n\n return onnxscript_graph","def doTrans(self):\n\n if self.defineType() == False:\n self._diagnostic.notify(Severity.ERROR, \"type '%s' has never been defined\" % self._name,\n LocationInfo.from_stream(self._stream))\n raise self._diagnostic\n\n self.transfo()\n\n if self._name in self.here().classes:\n self.declVtable()\n\n for elem in self.data.body:\n if hasattr(elem, '_ctype') and not isinstance(elem._ctype, nodes.FuncType):\n self._diagnostic.notify(Severity.ERROR, \"%s is not a function\" % elem._name,\n LocationInfo.from_stream(self._stream))\n raise self._diagnostic\n self.here().body.insert(self._idx + 1, elem)\n\n if self._name in self.here().classes:\n self.addBuiltins()\n\n self._dict = self._dict if self._name in self.here().modules else self._dict[0]\n\n for key, value in self._dict.items():\n if not isinstance(value._ctype, nodes.FuncType):\n cpy = deepcopy(value)\n cpy._name = key\n self.here().body.insert(self._idx + 1, cpy)","def FunctionCall(self):\n id = self.currtok[0]\n self.currtok = next(self.tg)\n if self.currtok[1].name == \"LPAREN\":\n self.currtok = next(self.tg)\n params = list()\n\n while self.currtok[1].name in {\"BOOL\", \"INTLIT\", \"IDENT\", \"REAL\"}:\n param = self.Expression()\n if self.currtok[1].name != \"RPAREN\":\n if self.currtok[1].name == \"COMMA\":\n self.currtok = next(self.tg)\n else:\n raise SLUCSyntaxError(\"ERROR: Missing comma on line {0}\".format(str(self.currtok[2] - 1)))\n params.append(param)\n\n if self.currtok[1].name == \"RPAREN\":\n self.currtok = next(self.tg)\n return FuncIDExpr(id, params)\n\n raise SLUCSyntaxError(\"ERROR: Missing right paren on line {0}\".format(str(self.currtok[2] - 1)))\n raise SLUCSyntaxError(\"ERROR: Missing left paren on line {0}\".format(str(self.currtok[2] - 1)))","def __call__(value):","def solve_user_calls(sv, tree=Special, user_set=set([])):\r\n # traverse program\r\n if tree==Special: \r\n user_set=set([x for x in sv.Object_list if sv.Object[x].isuserfunc]) # create list of user-defined function names\r\n for nam in list(sv.Object_list): # explore only currently defined objects (more to come)\r\n for pair in sv.Object[nam].clauses: # solve user function calls in clauses\r\n make_real_clauses(sv, pair, user_set)\r\n return \r\n\r\n # analyze a tree\r\n if not tree: return\r\n user_call=tree_join(tree) # calculate a name for the tree\r\n if not(user_call in sv.Object and (sv.Object[user_call].issolved or sv.Object[user_call].isuserdef)): \r\n if user_call in sv.Object: # get or create object\r\n nod=sv.Object[user_call]\r\n else:\r\n nod=add_object(sv, user_call) # create object\r\n nod.isnew=True # allow expression processing\r\n \r\n # get virtual args and name of user function\r\n if tree[0] in user_set and tree[1]: \r\n fun=tree[0]\r\n local_arg_list, local_function_name, accessories=get_virtual_arg_list(sv, fun) \r\n \r\n # get and verify real arguments\r\n real_arg_list = get_real_arg_list(tree, local_arg_list, accessories)\r\n \r\n # create accessory variables\r\n real_arg_list, real_arg_names = make_accessory(sv, fun, user_call, accessories, real_arg_list)\r\n \r\n # substitute real clauses\r\n make_user_clauses(sv, user_set, local_function_name, local_arg_list, accessories, real_arg_list, real_arg_names)\r\n\r\n if tree[1] or tree[2]: # process tree branches\r\n if tree[0]==Comma:\r\n for t in tree[1]:\r\n solve_user_calls(sv, t, user_set) # recurse to analyze each list element\r\n else:\r\n solve_user_calls(sv, tree[1], user_set) # recurse to analyze each branch \r\n solve_user_calls(sv, tree[2], user_set)","def __call__(self, *args):\n # pylint: disable=import-outside-toplevel\n if all(isinstance(x, RelayExpr) for x in args):\n from tvm import relay\n\n return relay.Call(self, args)\n elif all(isinstance(x, (Number, PrimExpr)) for x in args):\n return tvm.tir.call_tir(self, *args)\n\n arg_types = [type(x) for x in args]\n raise RuntimeError(f\"Do not know how to handle GlobalVar.__call__ for types {arg_types}\")","def transform_ast(node: NodeNG, ty=None, file=None):\n if isinstance(node, FunctionDef):\n if node.returns: # Return type annotation is there.\n body = [(n, node.returns) if isinstance(n, Return) else n\n for n in node.body]\n else:\n # We just stick to the body without any return (types).\n body = node.body\n return NonTerminal(\"FunctionDef\", [transform_ast(node.args)] +\n [transform_ast(b) for b in body])\n if isinstance(node, Arguments):\n return NonTerminal(\n \"Arguments\",\n [\n parse_terminal_annotated(arg, ann)\n for (arg, ann) in zip(node.args, node.annotations)\n ],\n )\n if isinstance(node, BinOp):\n return NonTerminal(\"BinOp:\" + node.op, [transform_ast(node.left)] +\n [transform_ast(node.right)])\n if isinstance(node, UnaryOp):\n return NonTerminal(\n \"UnaryOp:\" + node.op,\n [transform_ast(child) for child in node.get_children()])\n if isinstance(node, BoolOp):\n return NonTerminal(\n \"BoolOp:\" + node.op,\n [transform_ast(child) for child in node.get_children()])\n if (isinstance(node, tuple) and len(node) == 2\n and isinstance(node[0], Return) and isinstance(node[1], Name)\n ): # The case where we already know the type of the return variable.\n if isinstance(node[0].value, Name):\n # We only annotate a return type if\n # its direct child is already a terminal.\n return NonTerminal(\"Return\",\n [Terminal(node[0].value.name, node[1].name)])\n return NonTerminal(\"Return\", [transform_ast(node[0].value)])\n if isinstance(node, Assign):\n # if isinstance(\n # node.value,\n # Call): # If the right side is a call, we don't infer the type!\n # return NonTerminal(\"Assign\", [\n # transform_ast(target, \"METHOD_CALL\") for target in node.targets\n # ] + [transform_ast(node.value)])\n\n value_ty = get_type(node.value)\n return NonTerminal(\n \"Assign\",\n [transform_ast(target, value_ty)\n for target in node.targets] + [transform_ast(node.value)])\n if isinstance(node, AnnAssign):\n return NonTerminal(\n \"Assign\",\n [parse_terminal_annotated(node.target, node.annotation)] +\n [transform_ast(node.value)],\n )\n if isinstance(node, Const):\n return Terminal(\"const\",\n node.inferred()[0].pytype().replace(\"builtins.\", \"\"))\n if isinstance(node, Attribute):\n return NonTerminal(\n \"Attribute\",\n [\n transform_ast(node.expr),\n Terminal(node.attrname, get_type(node.expr), True)\n ],\n )\n if isinstance(node, Import):\n return NonTerminal(\n \"Import\",\n [\n Terminal(\"var\", module) if alias is None else Terminal(\n \"var\", module) for module, alias in node.names\n ],\n )\n if isinstance(node, ImportFrom):\n import_module = node.modname\n return NonTerminal(\n \"ImportFrom\",\n [\n Terminal(\"var\", import_module + \".\" + module) if alias is None\n else Terminal(\"var\", import_module + \".\" + module)\n for module, alias in node.names\n ],\n )\n if isinstance(node, AssignName):\n return parse_terminal(node, ty)\n if isinstance(node, Name):\n return parse_terminal(node, ty)\n else:\n if file:\n return NonTerminal(\n type(node).__name__,\n [transform_ast(c) for c in node.get_children()], file)\n return NonTerminal(\n type(node).__name__,\n [transform_ast(c) for c in node.get_children()])","def _deviceVariableFunctionName(self, tree, permitted_prefixes, allow_lengths = True):\n cpp_func_name = \"\"\n py_func = tree.attr\n # extract function name start\n for prefix in permitted_prefixes:\n if py_func.startswith(prefix):\n cpp_func_name = prefix\n py_func = py_func[len(prefix):]\n break # dont allow the else\n else:\n return None\n # check type and lengths\n if allow_lengths:\n #split to get type and Array Length (This could **potentially** be looked up from the model description but current syntax is consistent with swig bindings) \n type_and_length = py_func.split(\"Array\")\n if type_and_length[0] not in self._fgpu_types:\n self.RaiseError(tree, f\"'{type_and_length[0]}' is not a valid FLAME GPU type\")\n t = self._fgpu_types[type_and_length[0]]\n # generate template args\n if (len(type_and_length) == 1):\n cpp_func_name += f\"<{t}>\"\n elif (len(type_and_length) == 2):\n cpp_func_name += f\"<{t}, {type_and_length[1]}>\"\n else:\n return None\n else:\n if py_func not in self._fgpu_types:\n self.RaiseError(tree, f\"'{py_func}' is not a valid FLAME GPU type\")\n t = self._fgpu_types[py_func]\n cpp_func_name += f\"<{t}>\"\n # return \n return cpp_func_name","def __init__(self, callable_, time=1):\n Function.__init__(self) # callable_ could go here\n self.time = time\n self.callable = callable_","def test_callable_attributes(self) -> None:\n\n class TestClass:\n def __init__(self, name, child=None) -> None:\n self.child = child\n self.bar = name\n\n def foo(self):\n return self.bar + \"foo\"\n\n def getself(self):\n return self\n\n t1 = TestClass('t1', TestClass('t1child'))\n t2 = TestClass('t2', TestClass('t2child'))\n t3 = TestClass('t3')\n\n nl = NodeList([t1, t2, t3])\n assert nl.foo() == ['t1foo', 't2foo', 't3foo'], nl.foo()\n assert nl.bar == ['t1', 't2', 't3'], nl.bar\n assert nl.getself().bar == ['t1', 't2', 't3'], nl.getself().bar\n assert nl[0:2].child.foo() == ['t1childfoo', 't2childfoo'], \\\n nl[0:2].child.foo()\n assert nl[0:2].child.bar == ['t1child', 't2child'], \\\n nl[0:2].child.bar","def __compile_subroutine_call(self):\r\n num_of_expressions = 0\r\n if self.__tokenizer.peek() == \"(\":\r\n if self.__tokenizer.identifier() in self.__method_list: # if it's a method call\r\n num_of_expressions += 1\r\n self.__vmwriter.write_push(\"pointer\", 0) # push this as argument 0\r\n name = self.__class_name + \".\" + self.__tokenizer.identifier()\r\n else:\r\n in_method = False\r\n if self.__subroutine_symbols.contains(self.__tokenizer.identifier()):\r\n name = self.__subroutine_symbols.type_of(self.__tokenizer.identifier())\r\n kind = self.__subroutine_symbols.kind_of(self.__tokenizer.identifier())\r\n index = self.__subroutine_symbols.index_of(self.__tokenizer.identifier())\r\n in_method = True\r\n elif self.__class_symbols.contains(self.__tokenizer.identifier()):\r\n name = self.__class_symbols.type_of(self.__tokenizer.identifier())\r\n kind = self.__class_symbols.kind_of(self.__tokenizer.identifier())\r\n index = self.__class_symbols.index_of(self.__tokenizer.identifier())\r\n in_method = True\r\n else:\r\n name = self.__tokenizer.identifier()\r\n kind = None # we'll never use this\r\n index = 0 # we'll never use this\r\n self.__advance(n=2)\r\n if in_method: # if it's a method call\r\n num_of_expressions += 1\r\n self.__vmwriter.write_push(kind, index)\r\n name += \".\" + self.__tokenizer.identifier()\r\n self.__advance(n=2)\r\n num_of_expressions += self.compile_expression_list()\r\n self.__vmwriter.write_call(name, num_of_expressions)\r\n self.__advance()","def _get_current_T(self, parent, child):\n # Find the most recent time when the transformation was computed between the two frames.\n latest_time = self._trans_lst.getLatestCommonTime(parent, child)\n\n # Look up the most transform -- returns translation and rotation separately.\n trans, rot = self._trans_lst.lookupTransform(parent, child, latest_time)\n\n # Reconstruct into two matrices, using tf library functions.\n t, R = tf.transformations.translation_matrix(trans), tf.transformations.quaternion_matrix(rot)\n\n # Return the homogeneous 4x4 transformation matrix.\n return t.dot(R)","def process_call(\n caller: Address,\n target: Address,\n data: bytes,\n value: U256,\n gas: U256,\n depth: Uint,\n env: Environment,\n) -> Tuple[U256, List[Log]]:\n evm = Evm(\n pc=Uint(0),\n stack=[],\n memory=bytearray(),\n code=env.state[target].code,\n gas_left=gas,\n current=target,\n caller=caller,\n data=data,\n value=value,\n depth=depth,\n env=env,\n refund_counter=Uint(0),\n )\n\n logs: List[Log] = []\n\n if evm.value != 0:\n evm.env.state[evm.caller].balance -= evm.value\n evm.env.state[evm.current].balance += evm.value\n\n while evm.pc < len(evm.code):\n op = evm.code[evm.pc]\n op_implementation[op](evm)\n evm.pc += 1\n\n gas_used = gas - evm.gas_left\n refund = min(gas_used // 2, evm.refund_counter)\n\n return evm.gas_left + refund, logs","def node_data(self, node_data):\n self.node_data_ = node_data\n self.label = node_data.label\n self.node_type = node_data.node_type\n self.arity = node_data.arity\n self.min_depth = node_data.min_depth\n self.child_type = node_data.child_type\n self.numpy_func = node_data.numpy_func\n self.tensorflow_func = node_data.tensorflow_func","def visit_Call(self, node):\n val, _ = self.constraint_from_call(node)\n return val","def arg_to_CFI(self, node, ordered_functions):\n options = node.options\n fmt_func = node.fmtdict\n\n if options.wrap_fortran is False:\n # The buffer function is intended to be called by Fortran.\n # No Fortran, no need for buffer function.\n return\n\n ast = node.ast\n declarator = ast.declarator\n result_typemap = ast.typemap\n # shadow classes have not been added yet.\n # Only care about string, vector here.\n result_is_ptr = declarator.is_indirect()\n if (\n result_typemap\n and result_typemap.base in [\"string\", \"vector\"]\n and result_typemap.name != \"char\"\n and not result_is_ptr\n ):\n node.wrap.c = False\n # node.wrap.fortran = False\n self.config.log.write(\n \"Skipping {}, unable to create C wrapper \"\n \"for function returning {} instance\"\n \" (must return a pointer or reference).\"\n \" Bufferify version will still be created.\\n\".format(\n result_typemap.cxx_type, declarator.user_name\n )\n )\n \n cfi_args = {}\n for arg in ast.declarator.params:\n declarator = arg.declarator\n name = declarator.user_name\n attrs = declarator.attrs\n meta = declarator.metaattrs\n cfi_args[name] = False\n arg_typemap = arg.typemap\n if meta[\"api\"]:\n # API explicitly set by user.\n continue\n elif meta[\"assumed-rank\"]:\n cfi_args[name] = True\n elif attrs[\"rank\"]:\n cfi_args[name] = True\n elif arg_typemap.sgroup == \"string\":\n cfi_args[name] = True\n elif arg_typemap.sgroup == \"char\":\n if declarator.is_indirect():\n cfi_args[name] = True\n elif meta[\"deref\"] in [\"allocatable\", \"pointer\"]:\n cfi_args[name] = True\n has_cfi_arg = any(cfi_args.values())\n\n # Function result.\n need_buf_result = None\n\n result_as_arg = \"\" # Only applies to string functions\n # when the result is added as an argument to the Fortran api.\n\n # Check if result needs to be an argument.\n declarator = ast.declarator\n attrs = declarator.attrs\n meta = declarator.metaattrs\n if meta[\"deref\"] == \"raw\":\n # No bufferify required for raw pointer result.\n pass\n elif result_typemap.sgroup == \"string\":\n need_buf_result = \"cfi\"\n result_as_arg = fmt_func.F_string_result_as_arg\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\n elif result_typemap.sgroup == \"char\" and result_is_ptr:\n need_buf_result = \"cfi\"\n result_as_arg = fmt_func.F_string_result_as_arg\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\n elif meta[\"deref\"] in [\"allocatable\", \"pointer\"]:\n need_buf_result = \"cfi\"\n\n if not (need_buf_result or\n has_cfi_arg):\n return False\n\n options.wrap_fortran = False\n\n # Create a new C function and change arguments\n # and add attributes.\n C_new = node.clone()\n ordered_functions.append(C_new)\n self.append_function_index(C_new)\n\n generated_suffix = \"cfi\"\n C_new._generated = \"arg_to_cfi\"\n C_new.splicer_group = \"cfi\"\n if need_buf_result:\n C_new.ast.declarator.metaattrs[\"api\"] = need_buf_result\n fmt_func = C_new.fmtdict\n fmt_func.function_suffix = fmt_func.function_suffix + fmt_func.C_cfi_suffix\n\n C_new.wrap.assign(c=True)#, fortran=True)\n C_new._PTR_C_CXX_index = node._function_index\n\n for arg in C_new.ast.declarator.params:\n name = arg.declarator.user_name\n if cfi_args[name]:\n arg.declarator.metaattrs[\"api\"] = generated_suffix\n\n ast = C_new.ast\n if True: # preserve to avoid changing indention for now.\n f_attrs = node.ast.declarator.attrs # Fortran function attributes\n f_meta = node.ast.declarator.metaattrs # Fortran function attributes\n if result_as_arg:\n # decl: const char * getCharPtr2() +len(30)\n # +len implies copying into users buffer.\n result_as_string = ast.result_as_arg(result_name)\n result_as_string.const = False # must be writeable\n attrs = result_as_string.declarator.attrs\n # Special case for wrapf.py to override \"allocatable\"\n f_meta[\"deref\"] = None\n result_as_string.declarator.metaattrs[\"api\"] = \"cfi\"\n result_as_string.declarator.metaattrs[\"deref\"] = \"result\"\n result_as_string.declarator.metaattrs[\"is_result\"] = True\n C_new.ast.declarator.metaattrs[\"api\"] = None\n C_new.ast.declarator.metaattrs[\"intent\"] = \"subroutine\"\n C_new.ast.declarator.metaattrs[\"deref\"] = None\n\n if result_as_arg:\n F_new = self.result_as_arg(node, C_new)\n ordered_functions.append(F_new)\n self.append_function_index(F_new)\n else:\n if node._generated in [\"result_to_arg\", \"fortran_generic\", \"getter/setter\"]:\n node.wrap.c = False\n # Fortran function may call C subroutine if string/vector result\n # Fortran function calls bufferify function.\n node._PTR_F_C_index = C_new._function_index\n return True","def contractor(self) -> typing.Callable[[Vec, Vec, Term], Expr]:\n pass","def node(func, name=None):\n return NamedFunc(func, name)","def describe_func(obj, parent_class, module_name):\n\n try:\n name = obj.__name__\n except AttributeError:\n # Funny comtypes...\n return\n\n if name.startswith('_') and '__init__' not in name:\n return\n\n name = parent_class.name + '.' + name\n\n docs = getdoc(obj)\n comments = getcomments(obj)\n\n if isfunction(obj):\n # in Py3 unbound methods have same type as functions.\n if isinstance(parent_class, Class):\n method = object_types.METHOD\n else:\n method = object_types.FUNCTION\n elif ismethod(obj):\n method = object_types.METHOD\n elif ismethoddescriptor(obj):\n method = object_types.METHOD_DESCRIPTOR\n\n if isinstance(obj, types.MethodType):\n method = object_types.INSTANCE_METHOD\n\n try:\n source_code = getsource(obj)\n except (IOError, TypeError):\n source_code = ''\n\n klass = Method(name, method)\n klass.docs = docs\n\n klass_module = getmodule(obj)\n if klass_module and klass_module.__name__ != module_name:\n klass.is_redundant = True\n\n if source_code:\n inspect_source(klass, obj, source_code)\n klass.number_lines = '%d' % len(source_code.split('\\n'))\n\n if isinstance(obj, staticmethod):\n klass.kind = method = object_types.STATIC_METHOD\n\n if is_classmethod(obj):\n klass.kind = method = object_types.CLASS_METHOD\n\n try:\n code = None\n if method in [object_types.METHOD, object_types.METHOD_DESCRIPTOR, object_types.INSTANCE_METHOD]:\n if isPython3():\n code = obj.__func__.__code__\n else:\n code = obj.im_func.func_code\n elif method == object_types.STATIC_METHOD:\n if isPython3():\n code = obj.__func__.__code__\n else:\n code = obj.im_func.func_code\n else:\n if isPython3():\n code = obj.__code__\n else:\n code = obj.func_code\n except AttributeError:\n code = None\n\n if code is not None:\n klass.firstlineno = '%d' % code.co_firstlineno\n\n parent_class.Add(klass)","def fortran_functions(self) -> str:\n result = ''\n for member in self.members:\n result += member.fortran_function()\n return result","def op_impl(self):\r\n # timing is stored by node, we compute timing by Op on demand\r\n rval = {}\r\n for node in self.apply_callcount:\r\n if self.apply_cimpl[node]:\r\n rval[node.op] = 'C '\r\n else:\r\n rval[node.op] = 'Py'\r\n return rval","def node(_func=None, *, name=None):\n\n def decorator(function):\n newfunc = SimpleFunction(function, name=name)\n return newfunc\n\n if not _func:\n return decorator\n return decorator(_func)","def c_code(self, node, name, (x, y), (z, ), sub):\r\n # raise NotImplementedError(\"Unlike Python, C's modulo returns negative\r\n # modulo on negative dividend (to implement)\")\r\n t = node.inputs[0].type.upcast(*[i.type for i in node.inputs[1:]])\r\n if (str(t) in imap(str, discrete_types) or\r\n t in ['uint8', 'int8', 'uint16', 'int16'] or\r\n t in ['uint32', 'int32', 'uint64', 'int64'] or\r\n t in discrete_types):\r\n # The above or's should not be needed anymore. However, for now we\r\n # keep them out of safety, and verify they are useless with an\r\n # assert.\r\n assert str(t) in imap(str, discrete_types)\r\n x_mod_y = \"THEANO_MACRO_MOD(%(x)s, %(y)s)\" % locals()\r\n x_mod_ymm = \"THEANO_MACRO_MOD(-%(x)s, -%(y)s)\" % locals()\r\n x_mod_ypm = \"THEANO_MACRO_MOD(%(x)s, -%(y)s)\" % locals()\r\n x_mod_ymp = \"THEANO_MACRO_MOD(-%(x)s, %(y)s)\" % locals()\r\n elif (str(t) in imap(str, float_types) or\r\n t in ['float32', 'float64'] or\r\n t in float_types):\r\n # The above or's should not be needed anymore. However, for now we\r\n # keep them out of safety, and verify they are useless with an\r\n # assert.\r\n assert str(t) in imap(str, float_types)\r\n x_mod_y = \"fmod(%(x)s,%(y)s)\" % locals()\r\n x_mod_ymm = \"fmod(-%(x)s,-%(y)s)\" % locals()\r\n x_mod_ypm = \"fmod(%(x)s,-%(y)s)\" % locals()\r\n x_mod_ymp = \"fmod(-%(x)s,%(y)s)\" % locals()\r\n elif str(t) in imap(str, complex_types):\r\n raise self.complex_error\r\n else:\r\n raise NotImplementedError('type not supported', t)\r\n\r\n return dedent(\"\"\"\r\n if (%(x)s < 0){\r\n if (%(y)s < 0){\r\n %(z)s = -(%(x_mod_ymm)s);\r\n }else{\r\n %(z)s = - %(x_mod_ymp)s + (%(x_mod_ymp)s != 0 ? %(y)s : 0);\r\n }\r\n }else if (%(y)s < 0){\r\n %(z)s = (%(x_mod_ypm)s) + (%(x_mod_ypm)s != 0 ? %(y)s : 0);\r\n }else{\r\n %(z)s = %(x_mod_y)s;\r\n }\r\n \"\"\") % locals()","def func_def_top_interface_args(self, node: AnnCastFunctionDef):\n # function container is used to scope parameters\n param_con_scopestr = con_scope_to_str(node.con_scope)\n # enclosing container is used to scope arguments\n enclosing_con_scope = node.con_scope[:-1]\n arg_con_scopestr = con_scope_to_str(enclosing_con_scope)\n\n # create argument and parameter variables\n # argument variables are inputs to the top interface\n # paramter variables are outputs of the top interface\n for i, param in enumerate(node.func_args):\n # argument name and scope str\n arg_name = func_def_argument_name(node, i)\n\n # parameter name and scopestr\n assert(isinstance(param, AnnCastVar))\n param_name = param.val.name\n\n # argument and parameter share id, and start with initial version\n id = param.val.id\n version = VAR_INIT_VERSION\n\n # build and store GrFN variables for argument and parameter\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\n # store arg_fullid\n node.arg_index_to_fullid[i] = arg_fullid\n # create From Source metadata for the GrFN var\n from_source = False\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\n\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\n # store param_fullid\n node.param_index_to_fullid[i] = param_fullid\n # store metadata in paramter GrFN Var\n add_metadata_from_name_node(param_grfn_var, param.val)\n\n # link argument and parameter through top interface\n node.top_interface_in[id] = arg_fullid\n node.top_interface_out[id] = param_fullid\n\n # DEBUG printing\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\n print(f\"For FunctionDef {node.name.name}\")\n print(\"After func_def_top_iface_args():\")\n print(f\"\\ttop_interface_in = {node.top_interface_in}\")\n print(f\"\\ttop_interface_out = {node.top_interface_out}\")","def transform_npu_function(self, _, func: relay.Function) -> relay.Function:\n\n tir_mod, const_dict = _lower_to_tir(func, self.scheduler)\n\n for param in const_dict.keys():\n const_dict[param] = tvm.nd.array(const_dict[param])\n\n compiler_name = \"ethos-u\"\n primfunc = tir_mod[\"main\"]\n primfunc = primfunc.with_attr(\"global_symbol\", func.attrs[\"global_symbol\"])\n primfunc = primfunc.with_attr(\"ethos-u.constants\", const_dict)\n primfunc = primfunc.with_attr(\"target\", tvm.target.Target(compiler_name))\n return primfunc","def generic_visit(self, node, parent_map):\n node_map = []\n parent_map.append((node, node_map))\n \n for field, value in ast.iter_fields(node):\n if isinstance(value, list):\n for item in value:\n if isinstance(item, ast.AST):\n self.visit(item, node_map)\n elif isinstance(value, ast.AST):\n self.visit(value, node_map)","def _Attribute(self,t):\n # Only a limited set of globals supported\n func_dict = None\n \n # pyflamegpu singleton\n if isinstance(t.value, ast.Name):\n if t.value.id == \"pyflamegpu\":\n if t.attr in self.fgpu_attrs:\n # proceed\n self.write(\"flamegpu::\")\n self.write(t.attr)\n else:\n self.RaiseError(t, f\"Attribute '{t.attr}' does not exist in pyflamegpu object\")\n # math functions (try them in raw function call format) or constants\n elif t.value.id == \"math\":\n if t.attr in self.mathconsts:\n self.write(self.mathconsts[t.attr])\n else:\n self.RaiseError(t, f\"Unsupported math constant '{t.attr}'\")\n # numpy types\n elif t.value.id == \"numpy\" or t.value.id == \"np\":\n # not sure how a numpy attribute would be used without function call or type hint but translate anyway \n if t.attr in self.numpytypes:\n self.write(self.numpytypes[t.attr])\n else: \n self.RaiseError(t, f\"Unsupported numpy type {t.attr}\")\n else:\n self.RaiseError(t, f\"Global '{t.value.id}' identifiers not supported\")\n else:\n self.RaiseError(t, \"Unsupported attribute\")","def __call__(self):\r\n new_node = Op.__call__(self)\r\n return new_node","def _core(fn=None, **flags):\n\n # need set the attr and access on c++\n def deco(fn):\n fn._func_graph_flags = {\n 'core': True,\n **flags,\n }\n return fn\n\n if fn is not None:\n ret = deco(fn)\n else:\n ret = deco\n return ret","def transform(self, node):\n return self.get_transform_func(node)(node)","def _format_call_vertical(value: ast3.Call, context: types.Context) -> typing.Text:\n\t# If possible, prefer to put the first arg on the\n\t# same line as the function name. This only works\n\t# if we have no explicitly-named args because\n\t# otherwise we have one arg not line up badly.\n\ttry:\n\t\tif value.args and not value.keywords:\n\t\t\treturn _format_call_vertical_same_line(value, context)\n\texcept errors.NotPossible:\n\t\tpass\n\targs = [\n\t\t_format_value(arg, context) for arg in value.args\n\t]\n\tmax_kwarg_key_len = max(len(k.arg) for k in value.keywords) if value.keywords else 0\n\tkwargs = [\n\t\t_format_keyword(k, context, pad_key=max_kwarg_key_len) for k in sorted(value.keywords, key=lambda keyword: keyword.arg)\n\t]\n\tall_args = args + kwargs\n\t# Add \",\" to the end of all but the last argument\n\tfor i, arg in enumerate(all_args[:-1]):\n\t\tall_args[i] = arg + \",\"\n\t# Add indentation to all but the first argument\n\t# for i, arg in enumerate(all_args[1:]):\n\t\t# all_args[i] = context.tab + arg\n\t# Switch from considering 'args' to considering 'lines'\n\t# because we may have args that have already introduced\n\t# their own newlines\n\targ_lines = \"\\n\".join(all_args).split(\"\\n\")\n\tfunc = _format_value(value.func, context)\n\targuments = \"\\n\".join(context.add_indent(arg_lines))\n\treturn \"{func}(\\n{arguments})\".format(\n\t\targuments=arguments,\n\t\tfunc=_format_value(value.func, context)\n\t)","def EvaluateFunction(self, p_float=..., p_float=..., p_float=...):\n ...","def transform_child(child):\n # Is it a field name or a call?\n if isinstance(child, Call):\n return transform_call(child)\n else:\n return str(child.name)","def parse(cls, data):\r\n try:\r\n # Parse the function here\r\n result = cls.FuncDefn.parseString(data)\r\n result_list = result.asList()\r\n args = result_list[3:result_list.index(')')]\r\n # Return single line or multi-line function body\r\n fn_body = re.sub(r'[^\\{]+\\{', '', data, count=1)\r\n parts = fn_body.strip().split('\\n')\r\n fn_body = '\\n'.join(parts[0:-1])\r\n return cls.GroovyFunction(result[1], args, fn_body, data)\r\n except Exception, ex:\r\n return {}"],"string":"[\n \"def _Call(self, t):\\n # check calls but let attributes check in their own dispatcher\\n funcs = self._device_functions + self.pythonbuiltins + [self._input_message_var] # message_input variable is a valid function name as certain message types have arguments on iterator\\n if isinstance(t.func, ast.Name):\\n if (t.func.id not in funcs):\\n self.RaiseWarning(t, \\\"Function call is not a defined FLAME GPU device function or a supported python built in.\\\")\\n # dispatch even if warning raised\\n self.dispatch(t.func)\\n elif isinstance(t.func, ast.Lambda):\\n self.dispatch(t.func) # not supported\\n else:\\n # special handler for dispatching member function calls\\n # This would otherwise be an attribute\\n self.dispatchMemberFunction(t.func, t) \\n self.write(\\\"(\\\")\\n self._CallArguments(t)\\n self.write(\\\")\\\")\",\n \"def call_module(\\n self,\\n node: torch.fx.Node,\\n parent_onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,\\n fx_name_to_onnxscript_value: Dict[\\n str,\\n Union[\\n onnxscript_graph_building.TorchScriptTensor,\\n Tuple[onnxscript_graph_building.TorchScriptTensor, ...],\\n ],\\n ],\\n tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,\\n root_fx_graph_module: torch.fx.GraphModule,\\n onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,\\n op_level_debug: bool,\\n ) -> None:\\n assert isinstance(\\n node.target, str\\n ), f\\\"node.target must be a str, not {type(node.target)} for node {node}.\\\"\\n\\n sub_module = root_fx_graph_module.get_submodule(node.target)\\n\\n assert isinstance(\\n sub_module, torch.fx.GraphModule\\n ), f\\\"sub_module must be a torch.fx.GraphModule, not {type(sub_module)} for node {node}.\\\"\\n\\n sub_onnxscript_graph = self.run(\\n sub_module, onnxfunction_dispatcher, op_level_debug, parent_onnxscript_graph\\n )\\n\\n onnx_args, _ = _wrap_fx_args_as_onnxscript_args(\\n list(node.args), {}, fx_name_to_onnxscript_value, tracer\\n )\\n\\n # TODO: We may want to consider other naming styles. The goal is to be stable and\\n # unique such that it can be easily identified in case of kernel substitution.\\n # Example for current style is combination of qualified module class name and\\n # module attribute name: `torch_nn_modules_conv_Conv2d_conv1`.\\n # Other naming styles such as qualified module class name made unique can also\\n # be considered.\\n unique_module_name = f\\\"{sub_module._get_name()}_{node.target}\\\"\\n\\n outputs: Union[ # type: ignore[no-redef]\\n onnxscript_graph_building.TorchScriptTensor,\\n Tuple[onnxscript_graph_building.TorchScriptTensor, ...],\\n ] = parent_onnxscript_graph.add_module_call(\\n unique_module_name, sub_onnxscript_graph, onnx_args\\n )\\n\\n assert isinstance(\\n outputs, (onnxscript_graph_building.TorchScriptTensor, tuple)\\n ), f\\\"Unexpected outputs type {type(outputs)} for node {node}.\\\"\\n\\n _fill_tensor_shape_type(outputs, node.name, node.meta[\\\"val\\\"])\\n fx_name_to_onnxscript_value[node.name] = outputs\\n\\n # Skip op_level_validation for call_module. Subgraph nodes are validated individually.\",\n \"def func_call(self, t):\\n func, params = t\\n func_name = func.value\\n func.value = \\\"({}({}))\\\".format(func_name, params)\\n return func\",\n \"def dispatchMacroEnvFunction(self, tree, tree_parent):\\n cpp_func_name = \\\"getMacroProperty\\\"\\n py_func = tree.attr\\n # extract type from function name\\n py_type = py_func[len(cpp_func_name):]\\n if py_type not in self._fgpu_types:\\n self.RaiseError(tree, f\\\"'{py_type}' is not a valid FLAME GPU type\\\")\\n # get cpp type\\n t = self._fgpu_types[py_type]\\n cpp_func_name += f\\\"<{t}\\\"\\n # mess with the parent to extract (and remove arguments so they dont end up in the argument list)\\n if not tree_parent.args :\\n self.RaiseError(tree, f\\\" Macro environment function '{py_func}' is expected to have some arguments.\\\")\\n # if more than one arg then the rest are bounds to translate\\n if len(tree_parent.args) > 1:\\n bounds = tree_parent.args[1:]\\n # process bounds by appending to cpp function template arguments\\n for i in bounds:\\n if isinstance(i, ast.Num): # num required for python 3.7\\n if not isinstance(i.n, int):\\n self.RaiseError(tree, f\\\" Macro environment function argument '{i}' should be an integer value.\\\")\\n cpp_func_name += f\\\", {i.n}\\\"\\n else: # all Python > 3.7 \\n if not isinstance(i, ast.Constant):\\n self.RaiseError(tree, f\\\" Macro environment function argument '{i}' should be an constant value (or Num in Python <3.8).\\\")\\n if not isinstance(i.value, int):\\n self.RaiseError(tree, f\\\" Macro environment function argument '{i}' should be an integer value.\\\")\\n cpp_func_name += f\\\", {i.value}\\\"\\n # remove bounds from argument list (in place)\\n del tree_parent.args[1:]\\n cpp_func_name += \\\">\\\"\\n self.write(cpp_func_name)\",\n \"def _(self, node: Call):\\n\\n args = []\\n for n in node.arguments:\\n args.append(self.visit(n))\\n\\n func_args = \\\" \\\".join(args)\\n\\n return f\\\"( call {node.func.name} {func_args} )\\\"\",\n \"def visit_Call(self, node):\\n assert hasattr(node, 'args')\\n if node.args:\\n assert isinstance(node.args[0], gast.Starred)\\n # modify args\\n if isinstance(node.args[0].value, gast.Name):\\n node.args[0].value.id += '_new'\\n\\n assert hasattr(node, 'keywords')\\n if node.keywords:\\n assert isinstance(node.keywords[0], gast.keyword)\\n self.generic_visit(node)\\n return node\",\n \"def call_statement(env, node):\\n fun = env['f'][node.name]\\n func_env = Environment(env).create(env['f'])\\n args = fun['args'].interpret(env)\\n call_args_interpretuated = node.args.interpret(env)\\n args_counter = 0\\n for arg in args:\\n func_env['v'][arg] = call_args_interpretuated[args_counter].interpret(env)\\n args_counter += 1\\n fun['body'].interpret(func_env)\\n return func_env['r']\",\n \"def visit_Call(self, node: ast.Call) -> None:\\n self._check_wrong_function_called(node)\\n self._check_boolean_arguments(node)\\n self._check_isinstance_call(node)\\n\\n if functions.given_function_called(node, {'super'}):\\n self._check_super_context(node)\\n self._check_super_arguments(node)\\n\\n self.generic_visit(node)\",\n \"def _FunctionDef(self, t):\\n self.write(\\\"\\\\n\\\")\\n # check decorators\\n if len(t.decorator_list) != 1 or not isinstance(t.decorator_list[0], ast.Attribute):\\n self.RaiseError(t, \\\"Function definitions require a single pyflamegpu decorator of either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'\\\") \\n # FLAMEGPU_AGENT_FUNCTION\\n if t.decorator_list[0].attr == 'agent_function' and t.decorator_list[0].value.id == 'pyflamegpu':\\n if getattr(t, \\\"returns\\\", False):\\n self.RaiseWarning(t, \\\"Function definition return type not supported on 'pyflamegpu.agent_function'\\\")\\n self.fill(f\\\"FLAMEGPU_AGENT_FUNCTION({t.name}, \\\")\\n self.dispatchFGPUFunctionArgs(t)\\n self.write(\\\")\\\")\\n # FLAMEGPU_DEVICE_FUNCTION\\n elif t.decorator_list[0].attr == 'device_function' and t.decorator_list[0].value.id == 'pyflamegpu':\\n self.fill(f\\\"FLAMEGPU_DEVICE_FUNCTION \\\")\\n if t.returns:\\n self.dispatchType(t.returns)\\n else:\\n self.write(\\\"void\\\")\\n self.write(f\\\" {t.name}(\\\")\\n self.dispatchFGPUDeviceFunctionArgs(t)\\n self.write(\\\")\\\")\\n # add to list of defined functions that can be called\\n self._device_functions.append(t.name)\\n # FLAMEGPU_DEVICE_FUNCTION\\n elif t.decorator_list[0].attr == 'agent_function_condition' and t.decorator_list[0].value.id == 'pyflamegpu':\\n # check for return annotation\\n if not hasattr(t, \\\"returns\\\"):\\n self.RaiseError(t, \\\"Agent function conditions must have a 'bool' return type specified as a return type annotation\\\")\\n # check for return annotation type\\n if not isinstance(t.returns, ast.Name):\\n self.RaiseError(t, \\\"Agent function conditions return type must be 'bool'\\\")\\n if t.returns.id is not 'bool':\\n self.RaiseError(t, \\\"Agent function conditions return type must be 'bool'\\\")\\n # check to ensure no arguments (discard any with a warning)\\n if t.args.args:\\n self.RaiseWarning(t, \\\"Agent function conditions does not support arguments. These will be discarded.\\\")\\n # write the agent function macro\\n self.fill(f\\\"FLAMEGPU_AGENT_FUNCTION_CONDITION({t.name})\\\")\\n else:\\n self.RaiseError(t, \\\"Function definition uses an unsupported decorator. Must use either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'\\\")\\n self.enter()\\n self.dispatch(t.body)\\n self.leave()\",\n \"def dispatchFGPUDeviceFunctionArgs(self, tree):\\n # reset the locals variable stack\\n self._locals = [\\\"pyflamegpu\\\"]\\n # input message\\n first = True\\n annotation = None\\n for arg in tree.args.args:\\n # ensure that there is a type annotation\\n if not arg.annotation:\\n self.RaiseError(arg, \\\"Device function argument requires type annotation\\\")\\n # comma if not first\\n if not first:\\n self.write(\\\", \\\")\\n self.dispatchType(arg.annotation)\\n self.write(f\\\" {arg.arg}\\\")\\n # add arg to local variable stack\\n self._locals.append(arg.arg)\\n first = False\",\n \"def call_top_interface_args_with_func_def(self, node: AnnCastCall):\\n # call container is used to scope parameters\\n call_con_name = call_container_name(node)\\n\\n # create argument and parameter variables\\n # argument variables are inputs to the top interface\\n # paramter variables are outputs of the top interface\\n for i, n in enumerate(node.arguments):\\n # argument name and scope str\\n arg_name = call_argument_name(node, i)\\n arg_con_scopestr = con_scope_to_str(node.func.con_scope)\\n\\n # parameter name and scopestr\\n func_def = self.pipeline_state.func_def_node_from_id(node.func.id)\\n param = func_def.func_args[i]\\n assert(isinstance(param, AnnCastVar))\\n param_name = param.val.name\\n param_con_scopestr = con_scope_to_str(node.func.con_scope + [call_con_name])\\n\\n # argument and parameter share id, and start with initial version\\n id = self.pipeline_state.next_collapsed_id()\\n version = VAR_INIT_VERSION\\n\\n # build and store GrFN variables for argument and parameter\\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\\n # store arg_fullid\\n node.arg_index_to_fullid[i] = arg_fullid\\n # create From Source metadata for the GrFN var\\n from_source = False\\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\\n\\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\\n # store param_fullid\\n node.param_index_to_fullid[i] = param_fullid\\n # create From Source metadata for the GrFN var\\n add_metadata_from_name_node(param_grfn_var, param.val)\\n\\n # link argument and parameter through top interface\\n node.top_interface_in[id] = arg_fullid\\n node.top_interface_out[id] = param_fullid\\n\\n # DEBUG printing\\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\\n print(\\\"After create_call_args_and_params():\\\")\\n print(f\\\"\\\\ttop_interface_in = {node.top_interface_in}\\\")\\n print(f\\\"\\\\ttop_interface_out = {node.top_interface_out}\\\")\",\n \"def eval_function_call(func_call, motif_node_dict):\\n print(\\\"\\\\x1b[6;30;42m\\\" + 'Evaluating ' + func_call.name.name + ' function...' + '\\\\x1b[0m')\\n # CamFlow \\\"alloc_provenance\\\" take two arguments but only the first is needed for modeling.\\n if func_call.name.name == 'alloc_provenance':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n return provenance.alloc_provenance(arg_names[0], None)\\n # CamFlow \\\"task_cred_xxx\\\" take two arguments but no argument is needed for modeling.\\n elif func_call.name.name == 'task_cred_xxx':\\n return provenance.task_cred_xxx(None, None)\\n # CamFlow \\\"branch_mmap\\\" take two arguments but no argument is needed for modeling.\\n elif func_call.name.name == 'branch_mmap':\\n return provenance.branch_mmap(None, None)\\n # CamFlow \\\"uses_two\\\" function takes five arguments but only the first three are needed for modeling.\\n elif func_call.name.name == 'uses_two':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second and third arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses_two must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses_two must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses_two must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses_two must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.uses_two(arg_names[0], val1, val2, None, None, motif_node_dict)\\n # CamFlow \\\"informs\\\" function takes five arguments but only the first three are needed for modeling.\\n elif func_call.name.name == 'informs':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second and third arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in informs must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in informs must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in informs must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in informs must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.informs(arg_names[0], val1, val2, None, None, motif_node_dict)\\n # CamFlow \\\"record_terminate\\\" function takes two arguments.\\n elif func_call.name.name == 'record_terminate':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second arguments must be converted to MotifNode object first.\\n arg1 = arg_names[1]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_terminate must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_terminate must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_terminate(arg_names[0], val1, motif_node_dict)\\n # CamFlow \\\"generates\\\" function takes six arguments but only the first four are needed for modeling.\\n elif func_call.name.name == 'generates':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n arg3 = arg_names[3]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in generates must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in generates must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in generates must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in generates must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg3 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in generates must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val3 = getLastValueFromKey(motif_node_dict, arg3)\\n if not val3:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in generates must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.generates(arg_names[0], val1, val2, val3, None, None, motif_node_dict)\\n # CamFlow \\\"get_task_provenance\\\" takes no arguments.\\n elif func_call.name.name == 'get_task_provenance':\\n return provenance.get_task_provenance()\\n # CamFlow \\\"get_cred_provenance\\\" takes no arguments.\\n elif func_call.name.name == 'get_cred_provenance':\\n return provenance.get_cred_provenance(motif_node_dict)\\n # CamFlow \\\"uses\\\" takes six arguments but only the first four are needed for modeling.\\n elif func_call.name.name == 'uses':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n arg3 = arg_names[3]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg3 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in uses must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val3 = getLastValueFromKey(motif_node_dict, arg3)\\n if not val3:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in uses must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.uses(arg_names[0], val1, val2, val3, None, None, motif_node_dict)\\n # CamFlow \\\"refresh_inode_provenance\\\" takes two arguments but only the second one is needed for modeling.\\n elif func_call.name.name == 'refresh_inode_provenance':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second argument must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in refresh_inode_provenance must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in refresh_inode_provenance must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.refresh_inode_provenance(None, val1, motif_node_dict)\\n # CamFlow \\\"get_inode_provenance\\\" takes two arguments but only the second argument is needed for modeling.\\n elif func_call.name.name == 'get_inode_provenance':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n if arg_names[1] == 'false':\\n arg1 = False\\n elif arg_names[1] == 'true':\\n arg1 = True\\n else:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_inode_provenance is unknown.\\\\033[0m')\\n exit(1)\\n return provenance.get_inode_provenance(None, arg1, motif_node_dict)\\n # CamFlow \\\"get_dentry_provenance\\\" takes two arguments but only the second argument is needed for modeling. \\n elif func_call.name.name == 'get_dentry_provenance':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n if arg_names[1] == 'false':\\n arg1 = False\\n elif arg_names[1] == 'true':\\n arg1 = True\\n else:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_dentry_provenance is unknown.\\\\033[0m')\\n exit(1)\\n return provenance.get_dentry_provenance(None, arg1, motif_node_dict)\\n # CamFlow \\\"record_inode_name_from_dentry\\\" takes three arguments, but only the second and the third arguments are needed for modeling.\\n elif func_call.name.name == 'record_inode_name_from_dentry':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second argument must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_inode_name_from_dentry must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_inode_name_from_dentry must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg_names[2] == 'false':\\n arg2 = False\\n elif arg_names[2] == 'true':\\n arg2 = True\\n else:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_inode_name_from_dentry is unknown.\\\\033[0m')\\n exit(1)\\n return provenance.record_inode_name_from_dentry(None, val1, arg2, motif_node_dict)\\n # CamFlow \\\"record_node_name\\\" takes three arguments, but only the first and the third arguments are needed for modeling.\\n elif func_call.name.name == 'record_node_name':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second argument must be converted to MotifNode objects first.\\n arg0 = arg_names[0]\\n if arg0 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_node_name must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val0 = getLastValueFromKey(motif_node_dict, arg0)\\n if not val0:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_node_name must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_node_name(val0, None, arg_names[2], motif_node_dict)\\n # CamFlow \\\"derives\\\" function takes five arguments but only the first three are needed for modeling.\\n elif func_call.name.name == 'derives':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second and third arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in derives must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in derives must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in derives must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in derives must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.derives(arg_names[0], val1, val2, None, None, motif_node_dict)\\n # CamFlow \\\"record_write_xattr\\\" function takes eight arguments but only the first four are needed for modeling.\\n elif func_call.name.name == 'record_write_xattr':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n arg3 = arg_names[3]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_write_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_write_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_write_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_write_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg3 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in record_write_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val3 = getLastValueFromKey(motif_node_dict, arg3)\\n if not val3:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in record_write_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_write_xattr(arg_names[0], val1, val2, val3, None, None, None, None, motif_node_dict)\\n # CamFlow \\\"record_read_xattr\\\" function takes four arguments but only the first three are needed for modeling.\\n elif func_call.name.name == 'record_read_xattr':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n arg0 = arg_names[0]\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n if arg0 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_read_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val0 = getLastValueFromKey(motif_node_dict, arg0)\\n if not val0:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_read_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_read_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_read_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_read_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_read_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_read_xattr(val0, val1, val2, None, motif_node_dict)\\n # CamFlow \\\"get_file_provenance\\\" takes two arguments but only the second argument is needed for modeling. \\n elif func_call.name.name == 'get_file_provenance':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n if arg_names[1] == 'false':\\n arg1 = False\\n elif arg_names[1] == 'true':\\n arg1 = True\\n else:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_file_provenance is unknown.\\\\033[0m')\\n exit(1)\\n return provenance.get_file_provenance(None, arg1, motif_node_dict)\\n # CamFlow \\\"influences_kernel\\\" function takes four arguments but only the first three are needed for modeling.\\n elif func_call.name.name == 'influences_kernel':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second and third arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in influences_kernel must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in influences_kernel must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in influences_kernel must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in influences_kernel must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.influences_kernel(arg_names[0], val1, val2, None, motif_node_dict)\\n # CamFlow \\\"get_socket_inode_provenance\\\" takes one argument but it is not needed for modeling.\\n elif func_call.name.name == 'get_socket_provenance':\\n return provenance.get_socket_provenance(None, motif_node_dict)\\n # CamFlow \\\"get_socket_inode_provenance\\\" takes one argument but it is not needed for modeling. \\n elif func_call.name.name == 'get_socket_inode_provenance':\\n return provenance.get_socket_inode_provenance(None, motif_node_dict)\\n # CamFlow \\\"record_address\\\" takes three arguments but only the last argument is needed for modeling. \\n elif func_call.name.name == 'record_address':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n arg2 = arg_names[2]\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_address must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_address must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_address(None, None, val2, motif_node_dict)\\n # CamFlow \\\"get_sk_inode_provenance\\\" takes one argument but it is not needed for modeling. \\n elif func_call.name.name == 'get_sk_inode_provenance':\\n return provenance.get_sk_inode_provenance(None, motif_node_dict)\\n # CamFlow \\\"get_sk_provenance\\\" takes one argument but it is not needed for modeling.\\n elif func_call.name.name == 'get_sk_provenance':\\n return provenance.get_sk_provenance(None, motif_node_dict)\\n # CamFlow \\\"record_packet_content\\\" takes two arguments but only the second argument is needed for modeling. \\n elif func_call.name.name == 'record_packet_content':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n arg1 = arg_names[1]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_packet_content must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_packet_content must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_packet_content(None, val1, motif_node_dict)\\n # CamFlow \\\"record_args\\\" takes two arguments but only the first argument is needed for modeling.\\n elif func_call.name.name == 'record_args':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n arg0 = arg_names[0]\\n if arg0 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_args must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val0 = getLastValueFromKey(motif_node_dict, arg0)\\n if not val0:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_args must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_args(val0, None, motif_node_dict)\\n else:\\n return None, None\",\n \"def _partition_call_operator(self, inputs, attr):\\n\\n try:\\n from tensorflow.python.framework import function_def_to_graph\\n except ImportError as e:\\n raise ImportError(f\\\"Unable to import tensorflow which is required {e}\\\")\\n\\n main_graph_proto = self._main_graph_proto\\n outer_graph_def = main_graph_proto._graph\\n\\n node_func_name = attr.get(\\\"f\\\").name\\n func = next(\\n (f for f in outer_graph_def.library.function if f.signature.name == node_func_name),\\n None,\\n )\\n if func:\\n devices = set(node.device for node in func.node_def)\\n if len(devices) > 1:\\n raise Exception(\\n \\\"Found inconsistent Device assignment in the \\\"\\n \\\"Stateful Partitioned SubGraph. Rejecting \\\"\\n \\\"the subgraph \\\"\\n )\\n # Convert function definition to graph\\n func_input_shapes = func.attr[\\\"_input_shapes\\\"].list.shape\\n subgraph, _ = function_def_to_graph.function_def_to_graph_def(func, func_input_shapes)\\n\\n # Computing subgraph's input shape dictionary\\n subgraph_shape_dict, input_expr_dict = {}, {}\\n for f_arg, input in zip(func.signature.input_arg, inputs):\\n input_expr_dict[f_arg.name] = input\\n subgraph_shape_dict[f_arg.name] = _infer_shape(input, main_graph_proto._mod)\\n\\n func_name = f\\\"func_{func.signature.name}\\\"\\n try:\\n global_func = main_graph_proto._mod[func_name]\\n sub_func = global_func\\n sub_params = main_graph_proto._params\\n except ValueError:\\n # Construct relay nodes from the subgraph\\n g1 = SubGraphProto(main_graph_proto)\\n sub_func, sub_params = g1.from_tensorflow(subgraph, shape=subgraph_shape_dict)\\n main_graph_proto._params.update(sub_params)\\n func_expr = _function.Function(sub_func.params, sub_func.body)\\n global_func = tvm.relay.GlobalVar(func_name)\\n main_graph_proto._mod[global_func] = func_expr\\n main_graph_proto._mod = InferType()(main_graph_proto._mod)\\n\\n param_exprs = []\\n for param_expr in sub_func.params:\\n # sub_params is subset of sub_func.params\\n param_name = param_expr.vid.name_hint\\n if param_name in input_expr_dict.keys():\\n param_exprs.append(input_expr_dict[param_name])\\n elif param_name in sub_params.keys():\\n param_exprs.append(param_expr)\\n else:\\n raise Exception(f\\\"Input parameter {param_name} not found\\\")\\n\\n sb = tvm.relay.scope_builder.ScopeBuilder()\\n loop_ret = global_func(*param_exprs)\\n sb.ret(loop_ret)\\n ret = sb.get()\\n else:\\n raise Exception(f\\\"Function not found - {node_func_name}\\\")\\n return ret\",\n \"def call_top_interface_args_with_no_func_def(self, node: AnnCastCall):\\n # call container is used to scope parameters\\n call_con_name = call_container_name(node)\\n\\n # create argument and parameter variables\\n # argument variables are inputs to the top interface\\n # paramter variables are outputs of the top interface\\n for i, n in enumerate(node.arguments):\\n # argument name and scope str\\n arg_name = call_argument_name(node, i)\\n arg_con_scopestr = con_scope_to_str(node.func.con_scope)\\n\\n # parameter name and scopestr\\n param_name = call_param_name(node, i)\\n param_con_scopestr = con_scope_to_str(node.func.con_scope + [call_con_name])\\n \\n # argument and parameter share id, and start with initial version\\n id = self.pipeline_state.next_collapsed_id()\\n version = VAR_INIT_VERSION\\n\\n # build and store GrFN variables for argument and parameter\\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\\n # store arg_fullid\\n node.arg_index_to_fullid[i] = arg_fullid\\n # create From Source metadata for the GrFN var\\n from_source = False\\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\\n\\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\\n # store param_fullid\\n node.param_index_to_fullid[i] = param_fullid\\n # create From Source metadata for the GrFN var\\n # when we don't have the function def, we create a paramter with a default name\\n add_metadata_to_grfn_var(param_grfn_var, from_source_mdata)\\n\\n # link argument and parameter through top interface\\n node.top_interface_in[id] = arg_fullid\\n node.top_interface_out[id] = param_fullid\\n\\n # DEBUG printing\\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\\n print(\\\"After create_call_args_and_params():\\\")\\n print(f\\\"\\\\ttop_interface_in = {node.top_interface_in}\\\")\\n print(f\\\"\\\\ttop_interface_out = {node.top_interface_out}\\\")\",\n \"def visit_Call(self, node: ast.Call) -> None:\\n self._check_wrong_function_called(node)\\n self._check_boolean_arguments(node)\\n self._check_super_call(node)\\n self.generic_visit(node)\",\n \"def get_method_calls(function_node, object_name):\\n calls = {}\\n for node in ast.walk(function_node):\\n # These series of if's check that the node is an assignment node that\\n # follows the pattern:\\n # object_name.attribute(arg, otherarg, finalarg, keyword=keywordarg, keyword2=otherkeywordarg)\\n if isinstance(node, ast.Expr):\\n expression = node\\n if isinstance(expression.value, ast.Call):\\n call = expression.value\\n if isinstance(call.func, ast.Attribute):\\n attribute = call.func # this is a type not the name of the attribute\\n if isinstance(attribute.value, ast.Name):\\n name = attribute.value\\n if name.id == object_name:\\n attr = attribute.attr # attr is the actual atribute name the name of the method called\\n raw_args = call.args\\n args = []\\n for arg in raw_args:\\n args.append(convert_arg(arg))\\n\\n keyword_args = {}\\n keywords = call.keywords\\n for keyword in keywords:\\n key = keyword.arg\\n raw_value = keyword.value\\n value = convert_arg(raw_value)\\n keyword_args[key] = value\\n\\n call = MethodCall(object_name=object_name, method_name=attr, args=args, keywords=keyword_args)\\n \\n calls[attr] = call \\n return calls\",\n \"def eval_node(node, env):\\n global genv\\n global result\\n node_type = node_name(node)\\n\\n if node_type == 'Expr':\\n return eval_node(node.value, env)\\n elif node_type == 'Assign':\\n val = eval_node(node.value, env)\\n\\n while type(val) is tuple and len(val) == 2 and (type(val[1]) == GlobalEnv or type(val[1]) == LocalEnv):\\n val = val[0]\\n\\n # extract the variable name, evaluate the RHS, then extend the environment.\\n return 0, env.extend([node.targets[0].id], [val])\\n elif node_type == 'BinOp':\\n # get the left and right operands (we use only single operands) and the operator.\\n # evaluate the operands and apply the operator. return the number, env.\\n\\n left = eval_node(node.left, env)[0]\\n right = eval_node(node.right, env)[0]\\n\\n left = left[0] if type(left) is tuple else left\\n right = right[0] if type(right) is tuple else right\\n\\n op = node_name(node.op)\\n\\n if op == \\\"Add\\\":\\n return (left + right), env\\n elif op == \\\"Sub\\\":\\n return (left - right), env\\n elif op == \\\"Mult\\\":\\n return (left * right), env\\n elif op == \\\"Div\\\":\\n return (left / right), env\\n elif op == \\\"Mod\\\":\\n return (left % right), env\\n return 0, env\\n elif node_type == 'FunctionDef':\\n # need the function id (name), args, and body. Extend the environment.\\n # you can leave the args wrapped in the ast class and the body and unpack them\\n # when the function is called.\\n\\n return 0, env.extend([node.name], [(node.args, node.body)])\\n elif node_type == 'Call':\\n # get any values passed in to the function from the Call object.\\n # get the fxn name and look up its parameters, if any, and body from the env.\\n # get lists for parameter names and values and extend a LocalEnv with those bindings.\\n # evaluate the body in the local env, return the value, env.\\n\\n func = eval_node(node.func, env)[0]\\n local_env = LocalEnv(None, env)\\n\\n args = func[0].args\\n body = func[1]\\n\\n index = 0\\n for val in node.args:\\n local_env = local_env.extend([args[index].arg], [eval_node(val, local_env)[0]])\\n index += 1\\n\\n for node in body:\\n val = eval_node(node, local_env)\\n\\n if node_name(node) == \\\"Return\\\":\\n output_val = val[0]\\n local_env = val[1]\\n return output_val, env\\n elif node_type == 'Return':\\n # evaluate the node, return the value, env.\\n return eval_node(node.value, env)\\n elif node_type == 'Name':\\n # Name(identifier id)- lookup the value binding in the env\\n # return the value, env\\n return env.lookup(node.id), env\\n # Num(object n) -- a number, return the number, env.\\n elif node_type == 'Num':\\n return node.n, env\",\n \"def visit_Call(self, node: ast.Call) -> None:\\n self._check_buggy_super_context(node)\\n self.generic_visit(node)\",\n \"def get_call(call_node):\\n if not isinstance(call_node, ast.Call):\\n # print(\\\"this node is \\\" + str(type(call_node)) + \\\" node, not call node\\\")\\n return None\\n\\n elif isinstance(call_node.func, ast.Name):\\n return call_node.func.id\\n\\n elif isinstance(call_node.func, ast.Attribute):\\n if isinstance(call_node.func.value, ast.Name):\\n return call_node.func.value.id + '.' + call_node.func.attr\\n else:\\n get_call(call_node.func.value)\\n\\n elif isinstance(call_node.func, ast.Call):\\n get_call(call_node.func)\",\n \"def get_node_target(submodules: Mapping[str, torch.nn.Module], node: pippy.fx.Node) -> str:\\n\\n assert node.op in CALLABLE_NODE_OPS, (\\n \\\"Expect op types of \\\" + \\\", \\\".join(CALLABLE_NODE_OPS) + f\\\", but found {node.op}\\\"\\n )\\n\\n if node.op == \\\"call_module\\\":\\n assert isinstance(node.target, str)\\n submod = submodules[node.target]\\n submod_type = getattr(submod, \\\"_base_class_origin\\\", type(submod))\\n return get_acc_ops_name(submod_type)\\n elif node.op == \\\"call_function\\\":\\n target: Any = node.target\\n return (\\n f\\\"acc_ops.{target.__name__}\\\"\\n if target.__module__ is not None and \\\"acc_ops\\\" in target.__module__\\n else _get_qualified_name(target)\\n )\\n else:\\n assert isinstance(node.target, str)\\n return node.target\",\n \"def run_node(\\n self,\\n node,\\n fx_graph_module: torch.fx.GraphModule,\\n onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,\\n op_level_debug: bool,\\n onnxscript_graph: onnxscript_graph_building.TorchScriptGraph,\\n onnxscript_tracer: onnxscript_graph_building.TorchScriptTracingEvaluator,\\n fx_name_to_onnxscript_value: Dict[\\n str,\\n Union[\\n onnxscript_graph_building.TorchScriptTensor,\\n Tuple[onnxscript_graph_building.TorchScriptTensor, ...],\\n ],\\n ],\\n ):\\n # Record stack trace of node in diagnostic.\\n node_stack_trace = node.stack_trace\\n if node_stack_trace:\\n diagnostic = self.diagnostic_context.inflight_diagnostic(\\n rule=diagnostics.rules.fx_node_to_onnx\\n )\\n diagnostic.with_additional_message(\\n f\\\"### PyTorch source information\\\\n```\\\\n{node_stack_trace}\\\\n```\\\"\\n )\\n location = _location_from_fx_stack_trace(node_stack_trace)\\n if location is not None:\\n diagnostic.with_location(location)\\n\\n if node.op == \\\"placeholder\\\":\\n self.placeholder(node, onnxscript_graph, fx_name_to_onnxscript_value)\\n elif node.op == \\\"get_attr\\\":\\n self.get_attr(\\n node,\\n onnxscript_graph,\\n fx_name_to_onnxscript_value,\\n fx_graph_module,\\n )\\n elif node.op == \\\"call_function\\\":\\n self.call_function(\\n node,\\n onnxscript_tracer,\\n fx_name_to_onnxscript_value,\\n onnxfunction_dispatcher,\\n op_level_debug,\\n fx_graph_module,\\n )\\n elif node.op == \\\"call_method\\\":\\n self.call_method(node)\\n elif node.op == \\\"call_module\\\":\\n self.call_module(\\n node,\\n onnxscript_graph,\\n fx_name_to_onnxscript_value,\\n onnxscript_tracer,\\n fx_graph_module,\\n onnxfunction_dispatcher,\\n op_level_debug,\\n )\\n elif node.op == \\\"output\\\":\\n self.output(node, onnxscript_graph, fx_name_to_onnxscript_value)\\n else:\\n raise RuntimeError(f\\\"Found node type not defined in torch.fx: {node.op}\\\")\",\n \"def __call__(self, *args):\\n\\n func_env = Environment(self.parent)\\n self.define_args(func_env, *args)\\n return evaluate(self.body, func_env)\",\n \"def dispatchMessageIteratorCall(self, tree):\\n # simple case not a member function just an iterator with arguments\\n if isinstance(tree.func, ast.Name):\\n self.write(f\\\"FLAMEGPU->{tree.func.id}\\\")\\n if isinstance(tree.func, ast.Attribute) :\\n if isinstance(tree.func.value, ast.Name):\\n # check that the iterator is supported\\n if not tree.func.attr in self.fgpu_input_msg_iter_funcs:\\n self.RaiseError(tree, f\\\"Message input loop iterator '{tree.func.attr}' is not supported.\\\")\\n self.write(f\\\"FLAMEGPU->{tree.func.value.id}.{tree.func.attr}\\\")\\n else:\\n self.RaiseError(tree, \\\"Message input loop iterator format incorrect.\\\")\\n\\n # handle function arguments \\n self.write(\\\"(\\\")\\n self._CallArguments(tree)\\n self.write(\\\")\\\")\",\n \"def visit_Call(self, node: ast.Call) -> None:\\n self._check_open_call_context(node)\\n self._check_type_compare(node)\\n self._check_range_len(node)\\n self.generic_visit(node)\",\n \"def visit_Call(self, node: ast.Call) -> None:\\n self._check_floating_nan(node)\\n self.generic_visit(node)\",\n \"def __call__(fun_name):\",\n \"def transform_call(call):\\n return {\\n 'type': 'call',\\n 'chain': [str(fn.name) for fn in call.names()],\\n 'arguments': [str(arg) for arg in call.arguments()],\\n 'body': transform_block(call.body())\\n }\",\n \"def _(self, node: FunctionDef):\\n body_nodes = []\\n for n in node.body:\\n curr_piece = self.visit(n)\\n if len(curr_piece) > 0:\\n body_nodes.append(curr_piece)\\n\\n func_body = \\\" \\\".join(body_nodes)\\n\\n return f\\\"( {node.name} {func_body} )\\\"\",\n \"def generic_function(self, node, ordered_functions):\\n for generic in node.fortran_generic:\\n new = node.clone()\\n ordered_functions.append(new)\\n self.append_function_index(new)\\n new._generated = \\\"fortran_generic\\\"\\n fmt = new.fmtdict\\n # XXX append to existing suffix\\n if generic.fmtdict:\\n fmt.update(generic.fmtdict)\\n fmt.function_suffix = fmt.function_suffix + generic.function_suffix\\n new.fortran_generic = {}\\n new.wrap.assign(fortran=True)\\n new.ast.declarator.params = generic.decls\\n\\n # Try to call original C function if possible.\\n # All arguments are native scalar.\\n need_wrapper = False\\n if new.ast.declarator.is_indirect():\\n need_wrapper = True\\n \\n for arg in new.ast.declarator.params:\\n if arg.declarator.is_indirect():\\n need_wrapper = True\\n break\\n elif arg.typemap.sgroup == \\\"native\\\":\\n pass\\n else:\\n need_wrapper = True\\n break\\n\\n if need_wrapper:\\n # The C wrapper is required to cast constants.\\n # generic.yaml: GenericReal\\n new.C_force_wrapper = True\\n new.wrap.c = True\\n new._PTR_C_CXX_index = node._function_index\\n else:\\n new._PTR_F_C_index = node._function_index\\n \\n # Do not process templated node, instead process\\n # generated functions above.\\n # node.wrap.c = False\\n node.wrap.fortran = False\",\n \"def visit_function(self, node):\\n # ignore actual functions or method within a new style class\\n if not node.is_method():\\n return\\n klass = node.parent.frame()\\n for stmt in node.nodes_of_class(astroid.CallFunc):\\n if node_frame_class(stmt) != node_frame_class(node):\\n # Don't look down in other scopes.\\n continue\\n expr = stmt.func\\n if not isinstance(expr, astroid.Getattr):\\n continue\\n call = expr.expr\\n # skip the test if using super\\n if isinstance(call, astroid.CallFunc) and \\\\\\n isinstance(call.func, astroid.Name) and \\\\\\n call.func.name == 'super':\\n confidence = (INFERENCE if has_known_bases(klass)\\n else INFERENCE_FAILURE)\\n if not klass.newstyle:\\n # super should not be used on an old style class\\n self.add_message('super-on-old-class', node=node,\\n confidence=confidence)\\n else:\\n # super first arg should be the class\\n if not call.args and sys.version_info[0] == 3:\\n # unless Python 3\\n continue\\n\\n try:\\n supcls = (call.args and next(call.args[0].infer())\\n or None)\\n except astroid.InferenceError:\\n continue\\n\\n if supcls is None:\\n self.add_message('missing-super-argument', node=call,\\n confidence=confidence)\\n continue\\n\\n if klass is not supcls:\\n name = None\\n # if supcls is not YES, then supcls was infered\\n # and use its name. Otherwise, try to look\\n # for call.args[0].name\\n if supcls is not astroid.YES:\\n name = supcls.name\\n else:\\n if hasattr(call.args[0], 'name'):\\n name = call.args[0].name\\n if name is not None:\\n self.add_message('bad-super-call',\\n node=call,\\n args=(name, ),\\n confidence=confidence)\",\n \"def get_call_value(call_node):\\n s = get_name_value(call_node.func)\\n if isinstance(call_node.func.ctx, ast.Load):\\n # convert ast args to literals\\n args = [convert_arg(a) for a in call_node.args]\\n # suround literal strings with a set of quotes for easy placing into\\n # a string\\n args = ['\\\"' + a + '\\\"' if isinstance(a, str) else a for a in args]\\n # join all the args into a set of parens\\n s += \\\"(\\\" + \\\",\\\".join(args) + \\\")\\\"\\n return s\",\n \"def __call__(self, params: Mapping[str, Any],\\n batch: Mapping[str, jnp.ndarray]) -> PyTreeDef:\\n ...\",\n \"def convert(value):\\n if isinstance(value, (Function, NodeBase)):\\n return value\\n\\n if callable(value):\\n return _convert_tvm_func(value)\\n\\n return _convert_to_node(value)\",\n \"def visit_FunctionCallNode(self, node: FunctionCallNode, symbol_table: SymbolTable,\\n superclass: bool = False) -> None:\\n if symbol_table:\\n if superclass:\\n res = symbol_table['super'][node.func_name]\\n else:\\n res = symbol_table[node.func_name]\\n if isinstance(res, tuple):\\n class_node = symbol_table[node.func_name][0]\\n\\n new_symbol_table = SymbolTable()\\n new_symbol_table[\\\"inst\\\"] = SymbolTable()\\n inst = new_symbol_table[\\\"inst\\\"]\\n initialization = symbol_table[node.func_name][0].special_methods['init']\\n node.params = [self.visit(param, symbol_table) for param in node.params]\\n inst[\\\"params\\\"] = {}\\n for param, name_of_param in zip(node.params, initialization.params[1:]):\\n inst[\\\"params\\\"][name_of_param.value] = param\\n\\n if class_node.inherit:\\n inheriting_class = symbol_table[class_node.inherit][0]\\n super_cls = {'super': {}}\\n for key_spec, super_spec in zip_longest(class_node.special_methods.keys(),\\n inheriting_class.special_methods.keys()):\\n inst[key_spec] = class_node.special_methods.get(key_spec)\\n if super_spec:\\n super_cls['super'][super_spec] = inheriting_class.special_methods.get(super_spec)\\n for key, key_super in zip_longest(class_node.methods.keys(),\\n inheriting_class.methods.keys()):\\n inst[key] = class_node.methods.get(key)\\n if key_super:\\n super_cls['super'][key_super] = inheriting_class.methods.get(key_super)\\n inst.symbols.update(super_cls)\\n else:\\n inst.symbols.update(class_node.methods)\\n\\n new_obj = ObjectNode(name=class_node.name, methods=class_node.methods,\\n special_methods=class_node.special_methods,\\n attributes=class_node.attributes, symbol_table=new_symbol_table,\\n global_attrs=class_node.class_attrs)\\n new_item = (new_obj, new_obj.class_attrs)\\n\\n for line in initialization.body:\\n if line:\\n self.visit(line, new_obj.local_symbol_table)\\n\\n if \\\"params\\\" in inst.symbols.keys():\\n del inst[\\\"params\\\"]\\n return new_item\\n\\n else:\\n node.params = [self.visit(param, symbol_table) for param in node.params]\\n if 'inst' in [param.value for param in (\\n symbol_table['super'][node.func_name] if superclass else symbol_table[node.func_name]).params]:\\n node.params = [symbol_table] + node.params\\n try:\\n if superclass:\\n func = symbol_table['super'][node.func_name]\\n else:\\n func = symbol_table[node.func_name]\\n except KeyError:\\n func = self.symbol_table[node.func_name]\\n\\n if 'inst' in func.local_symbol_table.symbols.keys() and not isinstance(func.local_symbol_table[\\\"inst\\\"],\\n SymbolTable):\\n func.local_symbol_table[\\\"inst\\\"] = symbol_table\\n func.local_symbol_table[\\\"inst\\\"][\\\"params\\\"] = {}\\n for param, name_of_param in zip(node.params, func.params):\\n if 'inst' in func.local_symbol_table.symbols.keys():\\n func.local_symbol_table[\\\"inst\\\"][\\\"params\\\"][name_of_param.value] = param\\n if name_of_param.value in func.local_symbol_table.symbols.keys() and name_of_param.value != 'inst':\\n del func.local_symbol_table[name_of_param.value]\\n else:\\n func.local_symbol_table[name_of_param.value] = param\\n for line in func.body:\\n if line:\\n try:\\n result = self.visit(line, func.local_symbol_table)\\n except KeyError:\\n result = self.visit(line, symbol_table)\\n if isinstance(line, ReturnNode):\\n res = result\\n if isinstance(line.expression, AccessNode) and \\\\\\n isinstance(line.expression.item_to_access, FunctionCallNode):\\n if 'inst' in func.local_symbol_table.symbols.keys():\\n del func.local_symbol_table[\\\"inst\\\"][\\\"params\\\"]\\n return res\\n elif isinstance(result, ReturnNode):\\n res = self.visit(result, func.local_symbol_table)\\n if 'inst' in func.local_symbol_table.symbols.keys():\\n del func.local_symbol_table[\\\"inst\\\"][\\\"params\\\"]\\n return res\\n if 'inst' in func.local_symbol_table.symbols.keys():\\n del func.local_symbol_table[\\\"inst\\\"][\\\"params\\\"]\",\n \"def visit(self, token, parent):\\r\\n\\r\\n\\r\\n for i, child in enumerate(token.children):\\r\\n\\r\\n if child.type == Token.T_GROUPING and child.value == \\\"{}\\\":\\r\\n if (token.type == Token.T_MODULE) or \\\\\\r\\n (token.type == Token.T_ASYNC_FUNCTION) or \\\\\\r\\n (token.type == Token.T_ANONYMOUS_FUNCTION) or \\\\\\r\\n (token.type == Token.T_ANONYMOUS_GENERATOR) or \\\\\\r\\n (token.type == Token.T_ASYNC_GENERATOR) or \\\\\\r\\n (token.type == Token.T_ASYNC_ANONYMOUS_FUNCTION) or \\\\\\r\\n (token.type == Token.T_ASYNC_ANONYMOUS_GENERATOR) or \\\\\\r\\n (token.type == Token.T_METHOD) or \\\\\\r\\n (token.type == Token.T_CLASS) or \\\\\\r\\n (token.type == Token.T_BLOCK) or \\\\\\r\\n (token.type == Token.T_FINALLY) or \\\\\\r\\n (token.type == Token.T_LAMBDA) or \\\\\\r\\n (token.type == Token.T_GROUPING and token.value == \\\"{}\\\"):\\r\\n # next test this is not an object\\r\\n # objects:\\r\\n # {} {a} {a:b} {...a} {a,b} {a:b,c:d}\\r\\n # not objects:\\r\\n # {1} {a.b} {f()}\\r\\n ref = self._isObject(child)\\r\\n if ref is None:\\r\\n child.type = Token.T_OBJECT\\r\\n else:\\r\\n child.type = Token.T_BLOCK\\r\\n else:\\r\\n ref = self._isObject(child)\\r\\n if ref is not None:\\r\\n # in some cases convert the invalid object\\r\\n # into a block label.\\r\\n if token.type == Token.T_BINARY and token.value == \\\":\\\" and len(token.children) == 2:\\r\\n\\r\\n lhs, rhs = token.children\\r\\n\\r\\n if parent.type == Token.T_OBJECT:\\r\\n parent.type = Token.T_BLOCK\\r\\n\\r\\n if parent.type == Token.T_BLOCK or parent.type == Token.T_MODULE:\\r\\n if rhs.type == Token.T_GROUPING:\\r\\n rhs.type = Token.T_BLOCK\\r\\n\\r\\n index = parent.children.index(token)\\r\\n parent.children.insert(index+1, rhs)\\r\\n\\r\\n token.type = Token.T_BLOCK_LABEL\\r\\n token.value = lhs.value\\r\\n token.children = []\\r\\n\\r\\n continue\\r\\n\\r\\n print(\\\"\\\\n%s:\\\"%ref)\\r\\n print(\\\"parent\\\", parent.type, parent.value)\\r\\n print(\\\"token\\\", token.type, token.value)\\r\\n print(\\\"parent\\\", parent.toString(3))\\r\\n raise ref\\r\\n child.type = Token.T_OBJECT\\r\\n\\r\\n elif child.type == Token.T_CASE or child.type == Token.T_DEFAULT:\\r\\n j = i + 1\\r\\n while j < len(token.children):\\r\\n tmp = token.children[j]\\r\\n if tmp.type == Token.T_CASE or tmp.type == Token.T_DEFAULT:\\r\\n break\\r\\n child.children.append(token.children.pop(j))\",\n \"def __init__(self, function: ast3.FunctionDef, module: \\\"Module\\\") -> None:\\n\\n # easy data\\n self._function = function\\n self.name = function.name\\n self.line = function.lineno\\n self.column = function.col_offset\\n self.body = function.body\\n self.module = module\\n self.decorators = function.decorator_list\\n\\n # time to parse arguments\\n self._args = function.args.args\\n self.args = [Arg(x) for x in self._args]\\n self.functions = [\\n Function(x, self.module)\\n for x in self.body\\n if isinstance(x, ast3.FunctionDef)\\n ]\\n self.classes = [\\n Class(x, self.module) for x in self.body if isinstance(x, ast3.ClassDef)\\n ]\\n self.untyped = [\\n x for x in self.args if not x.typed and x not in DEFAULT_ARG_IGNORE\\n ]\\n self.doc = None\\n self.returns = None\\n self.return_typed = False\\n self.missing_args: Set[str] = set()\\n self.unexpected_args: Set[str] = set()\\n arg_names = set(x.name for x in self.args if x.name not in DEFAULT_ARG_IGNORE)\\n self.missing_args = arg_names\\n if isinstance(self.body[0], ast3.Expr):\\n # this is most likely a doc string\\n self.doc = Doc(self.body[0], Doc.Type.FUNCTION)\\n doc_arg_names = set(x for x, y in self.doc.args.items())\\n self.missing_args = arg_names - doc_arg_names\\n self.unexpected_args = doc_arg_names - arg_names\\n if function.returns:\\n self.return_typed = True\\n self.returns = parse_elt(function.returns) # type: ignore\\n\\n # complexity checks\\n self._radon = cc_visit_ast(self._function)[0]\\n self.complexity = self._radon.complexity\\n self.is_method = self._radon.is_method\\n self._halstead = h_visit_ast(self._function)\",\n \"def eval_function_body(function_body, motif_node_dict):\\n # The body of FuncDef is a Compound, which is a placeholder for a block surrounded by {}\\n # The following goes through the declarations and statements in the function body\\n tree = None\\n for item in function_body.block_items:\\n if type(item).__name__ == 'FuncCall': # Case 1: provenance-graph-related function call\\n motif_node, tree_node = eval_function_call(item, motif_node_dict)\\n if tree_node != None:\\n tree = provenance.create_group_node(tree, tree_node)\\n elif type(item).__name__ == 'Assignment': # Case 2: rc = provenance-graph-related function call\\n tree_node = eval_assignment(item, motif_node_dict)\\n if tree_node != None:\\n tree = provenance.create_group_node(tree, tree_node)\\n elif type(item).__name__ == 'Decl': # Case 3: declaration with initialization\\n tree_node = eval_declaration(item, motif_node_dict)\\n if tree_node != None:\\n tree = provenance.create_group_node(tree, tree_node)\\n elif type(item).__name__ == 'If': # Case 4: if/else\\n tree_node = eval_if_else(item, motif_node_dict)\\n if tree_node != None:\\n tree = provenance.create_group_node(tree, tree_node)\\n elif type(item).__name__ == 'Return': # Case 5: return with function call\\n tree_node = eval_return(item, motif_node_dict)\\n if tree_node != None:\\n tree = provenance.create_group_node(tree, tree_node)\\n return tree\",\n \"def __call__(self, node):\\n if node.inputs:\\n if node.inputs[0].is_constant(Primitive):\\n prim = node.inputs[0].value\\n visit = getattr(self, f'_visit_{prim}', None)\\n if visit is None:\\n return node.inputs[1:]\\n return visit(node)\\n else:\\n return node.inputs\\n return []\",\n \"def visit_FuncCall(self, node): #pylint: disable=invalid-name\\n # TODO: should we always ignore functions called in the arguments\\n # of a function? Maybe its best to for OpenSSL purposes...\\n self.call_order[self.current_function].append(node)\",\n \"def expand_callable(self, call_expr):\\n call_expr.func = ast.Attribute(value=call_expr.func, attr='__call__')\",\n \"def __call__(self, function: FuncSpeechArg):\\n self._add_attr(function)\\n return function\",\n \"def analyse_label_function_calls(node):\\n global SUBGRAPHS\\n regex = r\\\"(?:\\\\|_\\\\w+\\\\\\\\ \\\\=\\\\\\\\ |\\\\|)\\\\w+\\\\\\\\\\\\s\\\\(\\\\w*\\\\)\\\"\\n label = node.get_attributes()['label']\\n for match in re.finditer(regex, label, re.MULTILINE):\\n function_call = re.search(r'\\\\w+\\\\\\\\\\\\s\\\\(\\\\w*\\\\)', match.group()).group()\\n function_name = re.search(r'\\\\w+', function_call)\\n if function_name is not None:\\n if function_name.group() in SUBGRAPHS:\\n return (function_name.group(), function_call, match.start())\\n return (None, None, None)\",\n \"def run_op_node(input_data, op_fun, *args):\\n runtime = get_runtime()\\n comp_args = []\\n op_fun_args = []\\n comp_inputs = []\\n for idx, data in enumerate(input_data):\\n if np.isscalar(data):\\n op_fun_args.append(ng.constant(data, _get_numpy_dtype(data)))\\n else:\\n node = ng.parameter(data.shape, name=ascii_uppercase[idx], dtype=data.dtype)\\n op_fun_args.append(node)\\n comp_args.append(node)\\n comp_inputs.append(data)\\n op_fun_args.extend(args)\\n node = op_fun(*op_fun_args)\\n computation = runtime.computation(node, *comp_args)\\n return computation(*comp_inputs)\",\n \"def _tf1_ ( self , *args ) :\\n #\\n if not hasattr ( self , '_wo1' ) : self._wo1 = _WO1_ ( self )\\n if not self._wo1 : self._wo1 = _WO1_ ( self )\\n ## \\n _wo = self._wo1 \\n fun = ROOT.TF1 ( funID() , _wo , *args )\\n fun.SetNpx ( 500 ) \\n #\\n return fun\",\n \"def convert(ast):\\n\\n if ast and ast.type == \\\"Function\\\":\\n # Activity function conversion\\n if (\\n ast.name != \\\"molecularActivity\\\"\\n and ast.name in belspec[\\\"namespaces\\\"][\\\"Activity\\\"][\\\"list\\\"]\\n ):\\n print(\\\"name\\\", ast.name, \\\"type\\\", ast.type)\\n ast = convert_activity(ast)\\n return ast # Otherwise - this will trigger on the BEL2 molecularActivity\\n\\n # translocation conversion\\n elif ast.name in [\\\"tloc\\\", \\\"translocation\\\"]:\\n ast = convert_tloc(ast)\\n\\n fus_flag = False\\n for idx, arg in enumerate(ast.args):\\n if arg.__class__.__name__ == \\\"Function\\\":\\n\\n # Fix substitution -> variation()\\n if arg.name in [\\\"sub\\\", \\\"substitution\\\"]:\\n ast.args[idx] = convert_sub(arg)\\n\\n elif arg.name in [\\\"trunc\\\", \\\"truncation\\\"]:\\n ast.args[idx] = convert_trunc(arg)\\n\\n elif arg.name in [\\\"pmod\\\", \\\"proteinModification\\\"]:\\n ast.args[idx] = convert_pmod(arg)\\n\\n elif arg.name in [\\\"fus\\\", \\\"fusion\\\"]:\\n fus_flag = True\\n\\n # Recursively process Functions\\n ast.args[idx] = convert(ast.args[idx])\\n\\n if fus_flag:\\n ast = convert_fus(ast)\\n\\n return ast\",\n \"def test_superApply(self):\\n one = t.Action(\\\"1\\\")\\n x = t.Action(\\\"x\\\")\\n a = t.Apply(\\\"super\\\", \\\"main\\\", [one, x])\\n self.assertEqual(writePython(a),\\n dd(\\\"\\\"\\\"\\n _G_python_1, lastError = 1, None\\n self.considerError(lastError, None)\\n _G_python_2, lastError = eval('x', self.globals, _locals), None\\n self.considerError(lastError, None)\\n _G_apply_3, lastError = self.superApply(\\\"main\\\", _G_python_1, _G_python_2)\\n self.considerError(lastError, None)\\n _G_apply_3\\n \\\"\\\"\\\"))\",\n \"def __call__(self, node_A):\\r\\n new_node = Op.__call__(self)\\r\\n new_node.inputs = [node_A]\\r\\n new_node.name = \\\"Sqrt(%s)\\\" % (node_A.name)\\r\\n return new_node\",\n \"def visit_Call(self, node: ast.Call) -> None:\\n self._check_unnecessary_literals(node)\\n self.generic_visit(node)\",\n \"def external_call( d, output, stack, context ):\\n\\targuments = d[\\\"args\\\"].split(\\\" \\\")\\n\\tstack_collect = stack[-len(arguments):]\\n\\tfor arg, desired in zip(stack_collect, arguments):\\n\\t\\tresult = type_system.type_reduce( arg, desired, output, stack, context )\\n\\t\\tif result != \\\"success\\\":\\n\\t\\t\\treturn \\\"pass\\\"\\n\\toutput.append(\\\" call %s\\\\n\\\" % d[\\\"name\\\"])\\n\\tfor i in arguments:\\n\\t\\tstack.pop()\\n\\tretvals = d[\\\"ret\\\"].split(\\\" \\\")\\n\\tfor val in retvals:\\n\\t\\tstack.append( type_system.Datum( [[val]], [None] ) )\\n\\treturn \\\"success\\\"\",\n \"def test_RestrictingNodeTransformer__visit_Call__1():\\n result = compile_restricted_exec('a = max([1, 2, 3])')\\n assert result.errors == ()\\n loc = {}\\n exec(result.code, {}, loc)\\n assert loc['a'] == 3\\n assert result.used_names == {'max': True}\",\n \"def on_apply(self, node):\\n if node.inputs[0].is_constant(Primitive):\\n fn = node.inputs[0].value\\n conv = MAP.get(fn)\\n if conv is not None:\\n return conv(self, *node.inputs[1:])\\n return relay.Call(self.ref(node.inputs[0]),\\n [self.ref(i) for i in node.inputs[1:]])\",\n \"def grfn_2_2_call_top_interface_args(self, node: AnnCastCall):\\n # call container is used to scope parameters\\n call_con_name = call_container_name(node)\\n\\n # create argument and parameter variables\\n # argument variables are inputs to the top interface\\n # paramter variables are outputs of the top interface\\n # if we are generating GrFN 2.2, we would like the parameter to lie in the \\n # copied function def container, we do this by aliasing versions during GrfnVarCreation pass\\n for i, n in enumerate(node.arguments):\\n # argument name and scope str\\n arg_name = call_argument_name(node, i)\\n arg_con_scopestr = con_scope_to_str(node.func.con_scope)\\n\\n # parameter name and scopestr\\n param = node.func_def_copy.func_args[i]\\n assert(isinstance(param, AnnCastVar))\\n param_name = param.val.name\\n param_con_scopestr = con_scope_to_str(node.func.con_scope + [call_con_name])\\n \\n # argument and parameter share id, and start with initial version\\n id = self.pipeline_state.next_collapsed_id()\\n version = VAR_INIT_VERSION\\n\\n # build and store GrFN variables for argument and parameter\\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\\n # store arg_fullid\\n node.arg_index_to_fullid[i] = arg_fullid\\n # create From Source metadata for the GrFN var\\n from_source = False\\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\\n\\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\\n # store param_fullid\\n node.param_index_to_fullid[i] = param_fullid\\n add_metadata_from_name_node(param_grfn_var, param.val)\\n\\n # link argument and parameter through top interface\\n node.top_interface_in[id] = arg_fullid\\n node.top_interface_out[id] = param_fullid\\n\\n\\n # DEBUG printing\\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\\n print(\\\"After create_call_args_and_params():\\\")\\n print(f\\\"\\\\ttop_interface_in = {node.top_interface_in}\\\")\\n print(f\\\"\\\\ttop_interface_out = {node.top_interface_out}\\\")\",\n \"def compile_function(self, function, arguments):\",\n \"def visit_papi_operations(json_metadata: JsonObject,\\n call_fn: OperationMappingCallFunction,\\n initial_accumulator: Accumulator) -> Accumulator:\\n\\n accumulator = initial_accumulator\\n\\n def examine_calls(calls: JsonObject, path_so_far: List[AnyStr]) -> None:\\n for call_name in calls:\\n attempts = calls[call_name]\\n for attempt in attempts:\\n operation_id = attempt.get('jobId')\\n sub_workflow_metadata = attempt.get('subWorkflowMetadata')\\n path = build_call_path(call_name, path_so_far, attempt)\\n if operation_id:\\n call_fn(accumulator, operation_id, path, attempt)\\n if sub_workflow_metadata:\\n examine_calls(sub_workflow_metadata.get('calls', {}), path)\\n\\n def build_call_path(call_name: str, path_so_far: List[AnyStr], attempt: dict) -> List[AnyStr]:\\n call_path = path_so_far.copy()\\n\\n # Remove confusing duplication in subworkflow call names.\\n # A parent workflow would name a subworkflow call \\\"parent_wf.sub_wf\\\".\\n # The subworkflow would name its calls \\\"sub_wf.sub_call\\\".\\n # If those call components were simply joined the result would be\\n # \\\"parent_wf.sub_wf.sub_wf.sub_call\\\". This logic removes the duplication of \\\"sub_wf\\\",\\n # resulting in \\\"parent_wf.sub_wf.sub_call\\\".\\n deduplicated_call_name = call_name\\n if len(path_so_far) > 0:\\n this_call_components = call_name.split('.')\\n if len(this_call_components) > 1 and path_so_far[-1].endswith('.' + this_call_components[0]):\\n deduplicated_call_name = '.'.join(this_call_components[1:])\\n\\n call_path.append(deduplicated_call_name)\\n shard_index = attempt.get('shardIndex', -1)\\n if shard_index != -1:\\n call_path.append(f\\\"shard_{shard_index:04d}\\\")\\n\\n return call_path\\n\\n examine_calls(calls=json_metadata.get('calls', {}), path_so_far=[])\\n\\n return accumulator\",\n \"def get_func_ast(obj : types.FunctionType):\\n return get_ast(obj).body[0]\",\n \"def __init__(\\n self,\\n name,\\n input_ports=[],\\n output_ports=[],\\n *,\\n parameters=[],\\n attributes={},\\n body_builder=None,\\n loc=None,\\n ip=None,\\n ):\\n # Copy the mutable default arguments. 'Cause python.\\n input_ports = list(input_ports)\\n output_ports = list(output_ports)\\n parameters = list(parameters)\\n attributes = dict(attributes)\\n\\n operands = []\\n results = []\\n attributes[\\\"sym_name\\\"] = StringAttr.get(str(name))\\n\\n input_types = []\\n input_names = []\\n input_locs = []\\n unknownLoc = Location.unknown().attr\\n for (i, (port_name, port_type)) in enumerate(input_ports):\\n input_types.append(port_type)\\n input_names.append(StringAttr.get(str(port_name)))\\n input_locs.append(unknownLoc)\\n attributes[\\\"argNames\\\"] = ArrayAttr.get(input_names)\\n attributes[\\\"argLocs\\\"] = ArrayAttr.get(input_locs)\\n\\n output_types = []\\n output_names = []\\n output_locs = []\\n for (i, (port_name, port_type)) in enumerate(output_ports):\\n output_types.append(port_type)\\n output_names.append(StringAttr.get(str(port_name)))\\n output_locs.append(unknownLoc)\\n attributes[\\\"resultNames\\\"] = ArrayAttr.get(output_names)\\n attributes[\\\"resultLocs\\\"] = ArrayAttr.get(output_locs)\\n\\n if len(parameters) > 0 or \\\"parameters\\\" not in attributes:\\n attributes[\\\"parameters\\\"] = ArrayAttr.get(parameters)\\n\\n attributes[\\\"function_type\\\"] = TypeAttr.get(\\n FunctionType.get(inputs=input_types, results=output_types))\\n\\n super().__init__(\\n self.build_generic(attributes=attributes,\\n results=results,\\n operands=operands,\\n loc=loc,\\n ip=ip))\\n\\n if body_builder:\\n entry_block = self.add_entry_block()\\n\\n with InsertionPoint(entry_block):\\n with support.BackedgeBuilder():\\n outputs = body_builder(self)\\n _create_output_op(name, output_ports, entry_block, outputs)\",\n \"def transfo(self):\\n\\n idx = -1\\n for elem in self.data.body:\\n idx += 1\\n\\n if isinstance(elem, nodes.Decl) and self._name in self.here().modules:\\n if not self.checkExists(elem, self._dict.values()):\\n self._diagnostic.notify(Severity.ERROR,\\n \\\"unknown function {} in module {}\\\".format(elem._name, self._name),\\n LocationInfo.from_stream(self._stream))\\n raise self._diagnostic\\n self._oldFuncs.append(deepcopy(elem))\\n elem._name = self.getMangling(elem, -1)\\n\\n\\n elif (isinstance(elem, atMember) and not isinstance(elem, atVirtual)) or (\\n hasattr(elem, '_ctype') and isinstance(elem._ctype, nodes.FuncType) and len(\\n elem._ctype._params) > 0 and elem._ctype._params[\\n 0]._ctype._identifier == self._name and isinstance(elem._ctype._params[0]._ctype._decltype,\\n nodes.PointerType)):\\n if hasattr(elem, '_stmt') and not self.checkExists(elem._stmt, self._dict[1].values()):\\n self._diagnostic.notify(Severity.ERROR,\\n \\\"unknown function {} in class {}\\\".format(elem._stmt._name, self._name),\\n LocationInfo.from_stream(self._stream))\\n raise self._diagnostic\\n elif hasattr(elem, '_name') and not self.checkExists(elem, self._dict[1].values()):\\n self._diagnostic.notify(Severity.ERROR,\\n \\\"unknown function {} in class {}\\\".format(elem._name, self._name),\\n LocationInfo.from_stream(self._stream))\\n raise self._diagnostic\\n if isinstance(elem, atMember):\\n ctype = nodes.PrimaryType(self._name)\\n ctype._decltype = nodes.PointerType()\\n arg = nodes.Decl('self', ctype)\\n if hasattr(elem._stmt._ctype, '_params'):\\n elem._stmt._ctype._params.insert(0, arg)\\n else:\\n elem._stmt._ctype._params = [arg]\\n if elem._stmt._name == 'init':\\n elem._stmt.body.body.insert(0, deepcopy(self._template[2].body.body[0]))\\n elem._stmt.body.body[0].expr.params[1].params[1].params[0].value += self._name\\n self._oldFuncs.append(deepcopy(elem._stmt))\\n elem._stmt._name = self.getMangling(elem._stmt, 1)\\n self.data.body[idx] = elem._stmt\\n else:\\n self.data.body[idx] = elem\\n\\n\\n elif isinstance(elem, atVirtual):\\n if not self.checkExists(elem._stmt, self._dict[2].values()):\\n self._diagnostic.notify(Severity.ERROR,\\n \\\"unknown function {} in class {}\\\".format(elem._stmt._name, self._name),\\n LocationInfo.from_stream(self._stream))\\n raise self._diagnostic\\n ctype = nodes.PrimaryType(self._name)\\n ctype._decltype = nodes.PointerType()\\n arg = nodes.Decl('self', ctype)\\n elem._stmt._ctype._params.insert(0, arg)\\n self._oldFuncs.append(deepcopy(elem._stmt))\\n elem._stmt._name = self.getMangling(elem._stmt, 2)\\n self.data.body[idx] = elem._stmt\\n\\n\\n elif self._name in self.here().classes and (\\n hasattr(elem, '_ctype') and isinstance(elem._ctype, nodes.FuncType)):\\n if not self.checkExists(elem, self._dict[0].values()):\\n self._diagnostic.notify(Severity.ERROR,\\n \\\"unknown function {} in class {}\\\".format(elem._name, self._name),\\n LocationInfo.from_stream(self._stream))\\n raise self._diagnostic\\n self._oldFuncs.append(deepcopy(elem))\\n elem._name = self.getMangling(elem, 0)\\n\\n\\n else:\\n self._diagnostic.notify(Severity.ERROR,\\n \\\"%s invalid definiton or declaration in class/module statement\\\" % str(\\n elem._name), LocationInfo.from_stream(self._stream))\\n raise self._diagnostic\\n return\",\n \"def parent_function(root_node: ast.AST, node: ast.AST):\\n for parent in parent_list(root_node, node):\\n if isinstance(parent, ast.FunctionDef):\\n return parent\\n raise KeyError\",\n \"def get_calls_in_function( ea ):\\r\\n\\tcallist = []\\r\\n\\tflowgraph = create_flowgraph_from( ea )\\r\\n\\tfor x in flowgraph.nodes.items():\\r\\n\\t\\tname = x[0]\\r\\n\\t\\tblock = get_basic_block( string.atol( name, 16 ))\\r\\n\\t\\tfor instruction in block:\\r\\n\\t\\t\\tif instruction[ 1 ] == \\\"call\\\":\\r\\n\\t\\t\\t\\tcallist.append( instruction )\\r\\n\\treturn callist\",\n \"def recursive_run_function(tree, right, level, func, *args, **kwargs):\\n # logging.debug(\\\"Top of function\\\")\\n # logging.debug(\\\"tree=%s\\\", tree)\\n if level == \\\"mappings\\\":\\n do_func_for = [dict, list]\\n elif level == \\\"atomic\\\":\\n do_func_for = [str, int, float]\\n if six.PY2:\\n do_func_for.append(unicode)\\n elif level == \\\"always\\\":\\n do_func_for = [str, dict, list, int, float, bool]\\n elif level == \\\"keys\\\":\\n do_func_for = []\\n else:\\n do_func_for = []\\n\\n # Python 2/3 error in YAML parser, bad workaround:\\n if six.PY2:\\n if isinstance(right, unicode):\\n logging.warning(\\\"Unicode type detected, converting to a regular string!\\\")\\n right = right.encode(\\\"utf-8\\\")\\n assert isinstance(right, str)\\n logging.warning(right)\\n\\n logging.debug(\\\"Type right: %s\\\", type(right))\\n logging.debug(\\\"Do func for: %s\\\", do_func_for)\\n\\n if level is \\\"keys\\\" and isinstance(right, dict):\\n keys = list(right)\\n for key in keys:\\n old_value = right[key]\\n returned_key = func(tree + [key], key, *args, **kwargs)\\n del right[key]\\n right.update({returned_key: old_value})\\n\\n # logger.debug(\\\"right is a %s!\\\", type(right))\\n if type(right) in do_func_for:\\n if isinstance(right, dict):\\n keys = list(right)\\n for key in keys:\\n value = right[key]\\n logging.debug(\\\"Deleting key %s\\\", key)\\n logging.debug(\\n \\\"Start func %s with %s, %s sent from us\\\",\\n func.__name__,\\n tree + [key],\\n value,\\n \\\"type_of_sender=dict\\\",\\n )\\n returned_dict = func(tree + [key], value, *args, **kwargs)\\n del right[key]\\n # logger.debug(\\\"Back out of func %s\\\", func.__name__)\\n # logger.debug(\\\"Got as returned_dict: %s\\\", returned_dict)\\n right.update(returned_dict)\\n # elif isinstance(right, list):\\n # for index, item in enumerate(right):\\n # del right[0]\\n # right.append(func(tree + [None], item, *args, **kwargs))\\n else:\\n right = func(tree + [None], right, *args, **kwargs)\\n\\n # logger.debug(\\\"finished with do_func_for\\\")\\n\\n if isinstance(right, list):\\n for index, item in enumerate(right):\\n new_item = recursive_run_function(\\n tree + [None], item, level, func, *args, **kwargs\\n )\\n right[index] = new_item\\n elif isinstance(right, dict):\\n keys = list(right)\\n for key in keys:\\n value = right[key]\\n right[key] = recursive_run_function(\\n tree + [key], value, level, func, *args, **kwargs\\n )\\n return right\",\n \"def op_impl(self):\\n # timing is stored by node, we compute timing by Op on demand\\n rval = {}\\n for fgraph, node in self.apply_callcount:\\n if self.apply_cimpl[node]:\\n rval[node.op] = \\\"C \\\"\\n else:\\n rval[node.op] = \\\"Py\\\"\\n return rval\",\n \"def __call__(self, node_A, node_B, trans_A=False, trans_B=False):\\r\\n new_node = Op.__call__(self)\\r\\n new_node.matmul_attr_trans_A = trans_A\\r\\n new_node.matmul_attr_trans_B = trans_B\\r\\n new_node.inputs = [node_A, node_B]\\r\\n new_node.name = \\\"MatMul(%s,%s,%s,%s)\\\" % (node_A.name, node_B.name, str(trans_A), str(trans_B))\\r\\n return new_node\",\n \"def __call__(self, node_A, node_B, trans_A=False, trans_B=False):\\n new_node = Op.__call__(self)\\n new_node.matmul_attr_trans_A = trans_A\\n new_node.matmul_attr_trans_B = trans_B\\n new_node.inputs = [node_A, node_B]\\n new_node.name = \\\"MatMul(%s,%s,%s,%s)\\\" % (node_A.name, node_B.name, str(trans_A), str(trans_B))\\n return new_node\",\n \"def rotating_frame_transformation_propagator_new(U, t: float, H):\\n\\n U_RF = (1j*H*t).expm()\\n if U.type=='super':\\n U_RF=qtp.to_super(U_RF)\\n\\n U_prime = U_RF * U \\n \\\"\\\"\\\" U_RF only on one side because that's the operator that\\n satisfies the Schroedinger equation in the interaction picture.\\n \\\"\\\"\\\"\\n\\n return U_prime\",\n \"def convert_activity(ast):\\n\\n if len(ast.args) > 1:\\n logger.error(f\\\"Activity should not have more than 1 argument {ast.to_string()}\\\")\\n\\n p_arg = ast.args[0] # protein argument\\n print(\\\"p_arg\\\", p_arg)\\n ma_arg = Function(\\\"ma\\\", version=version)\\n ma_arg.add_argument(StrArg(ast.name, ma_arg))\\n p_arg.change_parent_fn(ma_arg)\\n ast = Function(\\\"activity\\\", version=version)\\n p_arg.change_parent_fn(ast)\\n ast.add_argument(p_arg)\\n ast.add_argument(ma_arg)\\n\\n return ast\",\n \"def _update_call(self):\\n for entry in self._entry_nodes:\\n self._update_call_visit(entry.get_func_first_node(), {})\",\n \"def run(\\n self,\\n fx_graph_module: torch.fx.GraphModule,\\n onnxfunction_dispatcher: onnxfunction_dispatcher.OnnxFunctionDispatcher,\\n op_level_debug: bool,\\n parent_onnxscript_graph: Optional[\\n onnxscript_graph_building.TorchScriptGraph\\n ] = None,\\n ) -> onnxscript_graph_building.TorchScriptGraph:\\n onnxscript_graph = onnxscript_graph_building.TorchScriptGraph(\\n parent_onnxscript_graph\\n )\\n onnxscript_tracer = onnxscript_graph_building.TorchScriptTracingEvaluator(\\n onnxscript_graph\\n )\\n # In the following loop, a TorchScript graph is created to\\n # represent the input FX graph with ONNX symbols (e.g., onnx::add).\\n # To connect the values to nodes in the TorchScript graph, we maintian\\n # fx_name_to_onnxscript_value. Basically, we want to translate\\n # fx_tensor_x (type: torch.fx.Node) -> fx_node_1 -> fx_tensor_y (type: torch.fx.Node)\\n # to\\n # fx_name_to_onnxscript_value[fx_tensor_x.name] -> onnx_node_1 -> fx_name_to_onnxscript_value[fx_tensor_y.name]\\n fx_name_to_onnxscript_value: Dict[\\n str,\\n Union[\\n onnxscript_graph_building.TorchScriptTensor,\\n Tuple[onnxscript_graph_building.TorchScriptTensor, ...],\\n ],\\n ] = {}\\n\\n # TODO: Fix FakeTensorMode limitation asap\\n # We want to pass list of ints and floats to TorchScript graph correctly\\n # in _export_fx_to_ts, so we must disable FakeTensorMode. Otherwise, graph may\\n # receive FakeTensor and results runtime error. In addition, TorchScript-based\\n # ONNX exporter used in _ts_graph_to_onnx_model_in_protobuf is not compatible\\n # with FakeTensorMode.\\n with torch.utils._mode_utils.no_dispatch():\\n # node_fixed_shape is only used on op_level_debug purpose.\\n for node in fx_graph_module.graph.nodes:\\n self.run_node(\\n node,\\n fx_graph_module,\\n onnxfunction_dispatcher,\\n op_level_debug,\\n onnxscript_graph,\\n onnxscript_tracer,\\n fx_name_to_onnxscript_value,\\n )\\n\\n return onnxscript_graph\",\n \"def doTrans(self):\\n\\n if self.defineType() == False:\\n self._diagnostic.notify(Severity.ERROR, \\\"type '%s' has never been defined\\\" % self._name,\\n LocationInfo.from_stream(self._stream))\\n raise self._diagnostic\\n\\n self.transfo()\\n\\n if self._name in self.here().classes:\\n self.declVtable()\\n\\n for elem in self.data.body:\\n if hasattr(elem, '_ctype') and not isinstance(elem._ctype, nodes.FuncType):\\n self._diagnostic.notify(Severity.ERROR, \\\"%s is not a function\\\" % elem._name,\\n LocationInfo.from_stream(self._stream))\\n raise self._diagnostic\\n self.here().body.insert(self._idx + 1, elem)\\n\\n if self._name in self.here().classes:\\n self.addBuiltins()\\n\\n self._dict = self._dict if self._name in self.here().modules else self._dict[0]\\n\\n for key, value in self._dict.items():\\n if not isinstance(value._ctype, nodes.FuncType):\\n cpy = deepcopy(value)\\n cpy._name = key\\n self.here().body.insert(self._idx + 1, cpy)\",\n \"def FunctionCall(self):\\n id = self.currtok[0]\\n self.currtok = next(self.tg)\\n if self.currtok[1].name == \\\"LPAREN\\\":\\n self.currtok = next(self.tg)\\n params = list()\\n\\n while self.currtok[1].name in {\\\"BOOL\\\", \\\"INTLIT\\\", \\\"IDENT\\\", \\\"REAL\\\"}:\\n param = self.Expression()\\n if self.currtok[1].name != \\\"RPAREN\\\":\\n if self.currtok[1].name == \\\"COMMA\\\":\\n self.currtok = next(self.tg)\\n else:\\n raise SLUCSyntaxError(\\\"ERROR: Missing comma on line {0}\\\".format(str(self.currtok[2] - 1)))\\n params.append(param)\\n\\n if self.currtok[1].name == \\\"RPAREN\\\":\\n self.currtok = next(self.tg)\\n return FuncIDExpr(id, params)\\n\\n raise SLUCSyntaxError(\\\"ERROR: Missing right paren on line {0}\\\".format(str(self.currtok[2] - 1)))\\n raise SLUCSyntaxError(\\\"ERROR: Missing left paren on line {0}\\\".format(str(self.currtok[2] - 1)))\",\n \"def __call__(value):\",\n \"def solve_user_calls(sv, tree=Special, user_set=set([])):\\r\\n # traverse program\\r\\n if tree==Special: \\r\\n user_set=set([x for x in sv.Object_list if sv.Object[x].isuserfunc]) # create list of user-defined function names\\r\\n for nam in list(sv.Object_list): # explore only currently defined objects (more to come)\\r\\n for pair in sv.Object[nam].clauses: # solve user function calls in clauses\\r\\n make_real_clauses(sv, pair, user_set)\\r\\n return \\r\\n\\r\\n # analyze a tree\\r\\n if not tree: return\\r\\n user_call=tree_join(tree) # calculate a name for the tree\\r\\n if not(user_call in sv.Object and (sv.Object[user_call].issolved or sv.Object[user_call].isuserdef)): \\r\\n if user_call in sv.Object: # get or create object\\r\\n nod=sv.Object[user_call]\\r\\n else:\\r\\n nod=add_object(sv, user_call) # create object\\r\\n nod.isnew=True # allow expression processing\\r\\n \\r\\n # get virtual args and name of user function\\r\\n if tree[0] in user_set and tree[1]: \\r\\n fun=tree[0]\\r\\n local_arg_list, local_function_name, accessories=get_virtual_arg_list(sv, fun) \\r\\n \\r\\n # get and verify real arguments\\r\\n real_arg_list = get_real_arg_list(tree, local_arg_list, accessories)\\r\\n \\r\\n # create accessory variables\\r\\n real_arg_list, real_arg_names = make_accessory(sv, fun, user_call, accessories, real_arg_list)\\r\\n \\r\\n # substitute real clauses\\r\\n make_user_clauses(sv, user_set, local_function_name, local_arg_list, accessories, real_arg_list, real_arg_names)\\r\\n\\r\\n if tree[1] or tree[2]: # process tree branches\\r\\n if tree[0]==Comma:\\r\\n for t in tree[1]:\\r\\n solve_user_calls(sv, t, user_set) # recurse to analyze each list element\\r\\n else:\\r\\n solve_user_calls(sv, tree[1], user_set) # recurse to analyze each branch \\r\\n solve_user_calls(sv, tree[2], user_set)\",\n \"def __call__(self, *args):\\n # pylint: disable=import-outside-toplevel\\n if all(isinstance(x, RelayExpr) for x in args):\\n from tvm import relay\\n\\n return relay.Call(self, args)\\n elif all(isinstance(x, (Number, PrimExpr)) for x in args):\\n return tvm.tir.call_tir(self, *args)\\n\\n arg_types = [type(x) for x in args]\\n raise RuntimeError(f\\\"Do not know how to handle GlobalVar.__call__ for types {arg_types}\\\")\",\n \"def transform_ast(node: NodeNG, ty=None, file=None):\\n if isinstance(node, FunctionDef):\\n if node.returns: # Return type annotation is there.\\n body = [(n, node.returns) if isinstance(n, Return) else n\\n for n in node.body]\\n else:\\n # We just stick to the body without any return (types).\\n body = node.body\\n return NonTerminal(\\\"FunctionDef\\\", [transform_ast(node.args)] +\\n [transform_ast(b) for b in body])\\n if isinstance(node, Arguments):\\n return NonTerminal(\\n \\\"Arguments\\\",\\n [\\n parse_terminal_annotated(arg, ann)\\n for (arg, ann) in zip(node.args, node.annotations)\\n ],\\n )\\n if isinstance(node, BinOp):\\n return NonTerminal(\\\"BinOp:\\\" + node.op, [transform_ast(node.left)] +\\n [transform_ast(node.right)])\\n if isinstance(node, UnaryOp):\\n return NonTerminal(\\n \\\"UnaryOp:\\\" + node.op,\\n [transform_ast(child) for child in node.get_children()])\\n if isinstance(node, BoolOp):\\n return NonTerminal(\\n \\\"BoolOp:\\\" + node.op,\\n [transform_ast(child) for child in node.get_children()])\\n if (isinstance(node, tuple) and len(node) == 2\\n and isinstance(node[0], Return) and isinstance(node[1], Name)\\n ): # The case where we already know the type of the return variable.\\n if isinstance(node[0].value, Name):\\n # We only annotate a return type if\\n # its direct child is already a terminal.\\n return NonTerminal(\\\"Return\\\",\\n [Terminal(node[0].value.name, node[1].name)])\\n return NonTerminal(\\\"Return\\\", [transform_ast(node[0].value)])\\n if isinstance(node, Assign):\\n # if isinstance(\\n # node.value,\\n # Call): # If the right side is a call, we don't infer the type!\\n # return NonTerminal(\\\"Assign\\\", [\\n # transform_ast(target, \\\"METHOD_CALL\\\") for target in node.targets\\n # ] + [transform_ast(node.value)])\\n\\n value_ty = get_type(node.value)\\n return NonTerminal(\\n \\\"Assign\\\",\\n [transform_ast(target, value_ty)\\n for target in node.targets] + [transform_ast(node.value)])\\n if isinstance(node, AnnAssign):\\n return NonTerminal(\\n \\\"Assign\\\",\\n [parse_terminal_annotated(node.target, node.annotation)] +\\n [transform_ast(node.value)],\\n )\\n if isinstance(node, Const):\\n return Terminal(\\\"const\\\",\\n node.inferred()[0].pytype().replace(\\\"builtins.\\\", \\\"\\\"))\\n if isinstance(node, Attribute):\\n return NonTerminal(\\n \\\"Attribute\\\",\\n [\\n transform_ast(node.expr),\\n Terminal(node.attrname, get_type(node.expr), True)\\n ],\\n )\\n if isinstance(node, Import):\\n return NonTerminal(\\n \\\"Import\\\",\\n [\\n Terminal(\\\"var\\\", module) if alias is None else Terminal(\\n \\\"var\\\", module) for module, alias in node.names\\n ],\\n )\\n if isinstance(node, ImportFrom):\\n import_module = node.modname\\n return NonTerminal(\\n \\\"ImportFrom\\\",\\n [\\n Terminal(\\\"var\\\", import_module + \\\".\\\" + module) if alias is None\\n else Terminal(\\\"var\\\", import_module + \\\".\\\" + module)\\n for module, alias in node.names\\n ],\\n )\\n if isinstance(node, AssignName):\\n return parse_terminal(node, ty)\\n if isinstance(node, Name):\\n return parse_terminal(node, ty)\\n else:\\n if file:\\n return NonTerminal(\\n type(node).__name__,\\n [transform_ast(c) for c in node.get_children()], file)\\n return NonTerminal(\\n type(node).__name__,\\n [transform_ast(c) for c in node.get_children()])\",\n \"def _deviceVariableFunctionName(self, tree, permitted_prefixes, allow_lengths = True):\\n cpp_func_name = \\\"\\\"\\n py_func = tree.attr\\n # extract function name start\\n for prefix in permitted_prefixes:\\n if py_func.startswith(prefix):\\n cpp_func_name = prefix\\n py_func = py_func[len(prefix):]\\n break # dont allow the else\\n else:\\n return None\\n # check type and lengths\\n if allow_lengths:\\n #split to get type and Array Length (This could **potentially** be looked up from the model description but current syntax is consistent with swig bindings) \\n type_and_length = py_func.split(\\\"Array\\\")\\n if type_and_length[0] not in self._fgpu_types:\\n self.RaiseError(tree, f\\\"'{type_and_length[0]}' is not a valid FLAME GPU type\\\")\\n t = self._fgpu_types[type_and_length[0]]\\n # generate template args\\n if (len(type_and_length) == 1):\\n cpp_func_name += f\\\"<{t}>\\\"\\n elif (len(type_and_length) == 2):\\n cpp_func_name += f\\\"<{t}, {type_and_length[1]}>\\\"\\n else:\\n return None\\n else:\\n if py_func not in self._fgpu_types:\\n self.RaiseError(tree, f\\\"'{py_func}' is not a valid FLAME GPU type\\\")\\n t = self._fgpu_types[py_func]\\n cpp_func_name += f\\\"<{t}>\\\"\\n # return \\n return cpp_func_name\",\n \"def __init__(self, callable_, time=1):\\n Function.__init__(self) # callable_ could go here\\n self.time = time\\n self.callable = callable_\",\n \"def test_callable_attributes(self) -> None:\\n\\n class TestClass:\\n def __init__(self, name, child=None) -> None:\\n self.child = child\\n self.bar = name\\n\\n def foo(self):\\n return self.bar + \\\"foo\\\"\\n\\n def getself(self):\\n return self\\n\\n t1 = TestClass('t1', TestClass('t1child'))\\n t2 = TestClass('t2', TestClass('t2child'))\\n t3 = TestClass('t3')\\n\\n nl = NodeList([t1, t2, t3])\\n assert nl.foo() == ['t1foo', 't2foo', 't3foo'], nl.foo()\\n assert nl.bar == ['t1', 't2', 't3'], nl.bar\\n assert nl.getself().bar == ['t1', 't2', 't3'], nl.getself().bar\\n assert nl[0:2].child.foo() == ['t1childfoo', 't2childfoo'], \\\\\\n nl[0:2].child.foo()\\n assert nl[0:2].child.bar == ['t1child', 't2child'], \\\\\\n nl[0:2].child.bar\",\n \"def __compile_subroutine_call(self):\\r\\n num_of_expressions = 0\\r\\n if self.__tokenizer.peek() == \\\"(\\\":\\r\\n if self.__tokenizer.identifier() in self.__method_list: # if it's a method call\\r\\n num_of_expressions += 1\\r\\n self.__vmwriter.write_push(\\\"pointer\\\", 0) # push this as argument 0\\r\\n name = self.__class_name + \\\".\\\" + self.__tokenizer.identifier()\\r\\n else:\\r\\n in_method = False\\r\\n if self.__subroutine_symbols.contains(self.__tokenizer.identifier()):\\r\\n name = self.__subroutine_symbols.type_of(self.__tokenizer.identifier())\\r\\n kind = self.__subroutine_symbols.kind_of(self.__tokenizer.identifier())\\r\\n index = self.__subroutine_symbols.index_of(self.__tokenizer.identifier())\\r\\n in_method = True\\r\\n elif self.__class_symbols.contains(self.__tokenizer.identifier()):\\r\\n name = self.__class_symbols.type_of(self.__tokenizer.identifier())\\r\\n kind = self.__class_symbols.kind_of(self.__tokenizer.identifier())\\r\\n index = self.__class_symbols.index_of(self.__tokenizer.identifier())\\r\\n in_method = True\\r\\n else:\\r\\n name = self.__tokenizer.identifier()\\r\\n kind = None # we'll never use this\\r\\n index = 0 # we'll never use this\\r\\n self.__advance(n=2)\\r\\n if in_method: # if it's a method call\\r\\n num_of_expressions += 1\\r\\n self.__vmwriter.write_push(kind, index)\\r\\n name += \\\".\\\" + self.__tokenizer.identifier()\\r\\n self.__advance(n=2)\\r\\n num_of_expressions += self.compile_expression_list()\\r\\n self.__vmwriter.write_call(name, num_of_expressions)\\r\\n self.__advance()\",\n \"def _get_current_T(self, parent, child):\\n # Find the most recent time when the transformation was computed between the two frames.\\n latest_time = self._trans_lst.getLatestCommonTime(parent, child)\\n\\n # Look up the most transform -- returns translation and rotation separately.\\n trans, rot = self._trans_lst.lookupTransform(parent, child, latest_time)\\n\\n # Reconstruct into two matrices, using tf library functions.\\n t, R = tf.transformations.translation_matrix(trans), tf.transformations.quaternion_matrix(rot)\\n\\n # Return the homogeneous 4x4 transformation matrix.\\n return t.dot(R)\",\n \"def process_call(\\n caller: Address,\\n target: Address,\\n data: bytes,\\n value: U256,\\n gas: U256,\\n depth: Uint,\\n env: Environment,\\n) -> Tuple[U256, List[Log]]:\\n evm = Evm(\\n pc=Uint(0),\\n stack=[],\\n memory=bytearray(),\\n code=env.state[target].code,\\n gas_left=gas,\\n current=target,\\n caller=caller,\\n data=data,\\n value=value,\\n depth=depth,\\n env=env,\\n refund_counter=Uint(0),\\n )\\n\\n logs: List[Log] = []\\n\\n if evm.value != 0:\\n evm.env.state[evm.caller].balance -= evm.value\\n evm.env.state[evm.current].balance += evm.value\\n\\n while evm.pc < len(evm.code):\\n op = evm.code[evm.pc]\\n op_implementation[op](evm)\\n evm.pc += 1\\n\\n gas_used = gas - evm.gas_left\\n refund = min(gas_used // 2, evm.refund_counter)\\n\\n return evm.gas_left + refund, logs\",\n \"def node_data(self, node_data):\\n self.node_data_ = node_data\\n self.label = node_data.label\\n self.node_type = node_data.node_type\\n self.arity = node_data.arity\\n self.min_depth = node_data.min_depth\\n self.child_type = node_data.child_type\\n self.numpy_func = node_data.numpy_func\\n self.tensorflow_func = node_data.tensorflow_func\",\n \"def visit_Call(self, node):\\n val, _ = self.constraint_from_call(node)\\n return val\",\n \"def arg_to_CFI(self, node, ordered_functions):\\n options = node.options\\n fmt_func = node.fmtdict\\n\\n if options.wrap_fortran is False:\\n # The buffer function is intended to be called by Fortran.\\n # No Fortran, no need for buffer function.\\n return\\n\\n ast = node.ast\\n declarator = ast.declarator\\n result_typemap = ast.typemap\\n # shadow classes have not been added yet.\\n # Only care about string, vector here.\\n result_is_ptr = declarator.is_indirect()\\n if (\\n result_typemap\\n and result_typemap.base in [\\\"string\\\", \\\"vector\\\"]\\n and result_typemap.name != \\\"char\\\"\\n and not result_is_ptr\\n ):\\n node.wrap.c = False\\n # node.wrap.fortran = False\\n self.config.log.write(\\n \\\"Skipping {}, unable to create C wrapper \\\"\\n \\\"for function returning {} instance\\\"\\n \\\" (must return a pointer or reference).\\\"\\n \\\" Bufferify version will still be created.\\\\n\\\".format(\\n result_typemap.cxx_type, declarator.user_name\\n )\\n )\\n \\n cfi_args = {}\\n for arg in ast.declarator.params:\\n declarator = arg.declarator\\n name = declarator.user_name\\n attrs = declarator.attrs\\n meta = declarator.metaattrs\\n cfi_args[name] = False\\n arg_typemap = arg.typemap\\n if meta[\\\"api\\\"]:\\n # API explicitly set by user.\\n continue\\n elif meta[\\\"assumed-rank\\\"]:\\n cfi_args[name] = True\\n elif attrs[\\\"rank\\\"]:\\n cfi_args[name] = True\\n elif arg_typemap.sgroup == \\\"string\\\":\\n cfi_args[name] = True\\n elif arg_typemap.sgroup == \\\"char\\\":\\n if declarator.is_indirect():\\n cfi_args[name] = True\\n elif meta[\\\"deref\\\"] in [\\\"allocatable\\\", \\\"pointer\\\"]:\\n cfi_args[name] = True\\n has_cfi_arg = any(cfi_args.values())\\n\\n # Function result.\\n need_buf_result = None\\n\\n result_as_arg = \\\"\\\" # Only applies to string functions\\n # when the result is added as an argument to the Fortran api.\\n\\n # Check if result needs to be an argument.\\n declarator = ast.declarator\\n attrs = declarator.attrs\\n meta = declarator.metaattrs\\n if meta[\\\"deref\\\"] == \\\"raw\\\":\\n # No bufferify required for raw pointer result.\\n pass\\n elif result_typemap.sgroup == \\\"string\\\":\\n need_buf_result = \\\"cfi\\\"\\n result_as_arg = fmt_func.F_string_result_as_arg\\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\\n elif result_typemap.sgroup == \\\"char\\\" and result_is_ptr:\\n need_buf_result = \\\"cfi\\\"\\n result_as_arg = fmt_func.F_string_result_as_arg\\n result_name = result_as_arg or fmt_func.C_string_result_as_arg\\n elif meta[\\\"deref\\\"] in [\\\"allocatable\\\", \\\"pointer\\\"]:\\n need_buf_result = \\\"cfi\\\"\\n\\n if not (need_buf_result or\\n has_cfi_arg):\\n return False\\n\\n options.wrap_fortran = False\\n\\n # Create a new C function and change arguments\\n # and add attributes.\\n C_new = node.clone()\\n ordered_functions.append(C_new)\\n self.append_function_index(C_new)\\n\\n generated_suffix = \\\"cfi\\\"\\n C_new._generated = \\\"arg_to_cfi\\\"\\n C_new.splicer_group = \\\"cfi\\\"\\n if need_buf_result:\\n C_new.ast.declarator.metaattrs[\\\"api\\\"] = need_buf_result\\n fmt_func = C_new.fmtdict\\n fmt_func.function_suffix = fmt_func.function_suffix + fmt_func.C_cfi_suffix\\n\\n C_new.wrap.assign(c=True)#, fortran=True)\\n C_new._PTR_C_CXX_index = node._function_index\\n\\n for arg in C_new.ast.declarator.params:\\n name = arg.declarator.user_name\\n if cfi_args[name]:\\n arg.declarator.metaattrs[\\\"api\\\"] = generated_suffix\\n\\n ast = C_new.ast\\n if True: # preserve to avoid changing indention for now.\\n f_attrs = node.ast.declarator.attrs # Fortran function attributes\\n f_meta = node.ast.declarator.metaattrs # Fortran function attributes\\n if result_as_arg:\\n # decl: const char * getCharPtr2() +len(30)\\n # +len implies copying into users buffer.\\n result_as_string = ast.result_as_arg(result_name)\\n result_as_string.const = False # must be writeable\\n attrs = result_as_string.declarator.attrs\\n # Special case for wrapf.py to override \\\"allocatable\\\"\\n f_meta[\\\"deref\\\"] = None\\n result_as_string.declarator.metaattrs[\\\"api\\\"] = \\\"cfi\\\"\\n result_as_string.declarator.metaattrs[\\\"deref\\\"] = \\\"result\\\"\\n result_as_string.declarator.metaattrs[\\\"is_result\\\"] = True\\n C_new.ast.declarator.metaattrs[\\\"api\\\"] = None\\n C_new.ast.declarator.metaattrs[\\\"intent\\\"] = \\\"subroutine\\\"\\n C_new.ast.declarator.metaattrs[\\\"deref\\\"] = None\\n\\n if result_as_arg:\\n F_new = self.result_as_arg(node, C_new)\\n ordered_functions.append(F_new)\\n self.append_function_index(F_new)\\n else:\\n if node._generated in [\\\"result_to_arg\\\", \\\"fortran_generic\\\", \\\"getter/setter\\\"]:\\n node.wrap.c = False\\n # Fortran function may call C subroutine if string/vector result\\n # Fortran function calls bufferify function.\\n node._PTR_F_C_index = C_new._function_index\\n return True\",\n \"def contractor(self) -> typing.Callable[[Vec, Vec, Term], Expr]:\\n pass\",\n \"def node(func, name=None):\\n return NamedFunc(func, name)\",\n \"def describe_func(obj, parent_class, module_name):\\n\\n try:\\n name = obj.__name__\\n except AttributeError:\\n # Funny comtypes...\\n return\\n\\n if name.startswith('_') and '__init__' not in name:\\n return\\n\\n name = parent_class.name + '.' + name\\n\\n docs = getdoc(obj)\\n comments = getcomments(obj)\\n\\n if isfunction(obj):\\n # in Py3 unbound methods have same type as functions.\\n if isinstance(parent_class, Class):\\n method = object_types.METHOD\\n else:\\n method = object_types.FUNCTION\\n elif ismethod(obj):\\n method = object_types.METHOD\\n elif ismethoddescriptor(obj):\\n method = object_types.METHOD_DESCRIPTOR\\n\\n if isinstance(obj, types.MethodType):\\n method = object_types.INSTANCE_METHOD\\n\\n try:\\n source_code = getsource(obj)\\n except (IOError, TypeError):\\n source_code = ''\\n\\n klass = Method(name, method)\\n klass.docs = docs\\n\\n klass_module = getmodule(obj)\\n if klass_module and klass_module.__name__ != module_name:\\n klass.is_redundant = True\\n\\n if source_code:\\n inspect_source(klass, obj, source_code)\\n klass.number_lines = '%d' % len(source_code.split('\\\\n'))\\n\\n if isinstance(obj, staticmethod):\\n klass.kind = method = object_types.STATIC_METHOD\\n\\n if is_classmethod(obj):\\n klass.kind = method = object_types.CLASS_METHOD\\n\\n try:\\n code = None\\n if method in [object_types.METHOD, object_types.METHOD_DESCRIPTOR, object_types.INSTANCE_METHOD]:\\n if isPython3():\\n code = obj.__func__.__code__\\n else:\\n code = obj.im_func.func_code\\n elif method == object_types.STATIC_METHOD:\\n if isPython3():\\n code = obj.__func__.__code__\\n else:\\n code = obj.im_func.func_code\\n else:\\n if isPython3():\\n code = obj.__code__\\n else:\\n code = obj.func_code\\n except AttributeError:\\n code = None\\n\\n if code is not None:\\n klass.firstlineno = '%d' % code.co_firstlineno\\n\\n parent_class.Add(klass)\",\n \"def fortran_functions(self) -> str:\\n result = ''\\n for member in self.members:\\n result += member.fortran_function()\\n return result\",\n \"def op_impl(self):\\r\\n # timing is stored by node, we compute timing by Op on demand\\r\\n rval = {}\\r\\n for node in self.apply_callcount:\\r\\n if self.apply_cimpl[node]:\\r\\n rval[node.op] = 'C '\\r\\n else:\\r\\n rval[node.op] = 'Py'\\r\\n return rval\",\n \"def node(_func=None, *, name=None):\\n\\n def decorator(function):\\n newfunc = SimpleFunction(function, name=name)\\n return newfunc\\n\\n if not _func:\\n return decorator\\n return decorator(_func)\",\n \"def c_code(self, node, name, (x, y), (z, ), sub):\\r\\n # raise NotImplementedError(\\\"Unlike Python, C's modulo returns negative\\r\\n # modulo on negative dividend (to implement)\\\")\\r\\n t = node.inputs[0].type.upcast(*[i.type for i in node.inputs[1:]])\\r\\n if (str(t) in imap(str, discrete_types) or\\r\\n t in ['uint8', 'int8', 'uint16', 'int16'] or\\r\\n t in ['uint32', 'int32', 'uint64', 'int64'] or\\r\\n t in discrete_types):\\r\\n # The above or's should not be needed anymore. However, for now we\\r\\n # keep them out of safety, and verify they are useless with an\\r\\n # assert.\\r\\n assert str(t) in imap(str, discrete_types)\\r\\n x_mod_y = \\\"THEANO_MACRO_MOD(%(x)s, %(y)s)\\\" % locals()\\r\\n x_mod_ymm = \\\"THEANO_MACRO_MOD(-%(x)s, -%(y)s)\\\" % locals()\\r\\n x_mod_ypm = \\\"THEANO_MACRO_MOD(%(x)s, -%(y)s)\\\" % locals()\\r\\n x_mod_ymp = \\\"THEANO_MACRO_MOD(-%(x)s, %(y)s)\\\" % locals()\\r\\n elif (str(t) in imap(str, float_types) or\\r\\n t in ['float32', 'float64'] or\\r\\n t in float_types):\\r\\n # The above or's should not be needed anymore. However, for now we\\r\\n # keep them out of safety, and verify they are useless with an\\r\\n # assert.\\r\\n assert str(t) in imap(str, float_types)\\r\\n x_mod_y = \\\"fmod(%(x)s,%(y)s)\\\" % locals()\\r\\n x_mod_ymm = \\\"fmod(-%(x)s,-%(y)s)\\\" % locals()\\r\\n x_mod_ypm = \\\"fmod(%(x)s,-%(y)s)\\\" % locals()\\r\\n x_mod_ymp = \\\"fmod(-%(x)s,%(y)s)\\\" % locals()\\r\\n elif str(t) in imap(str, complex_types):\\r\\n raise self.complex_error\\r\\n else:\\r\\n raise NotImplementedError('type not supported', t)\\r\\n\\r\\n return dedent(\\\"\\\"\\\"\\r\\n if (%(x)s < 0){\\r\\n if (%(y)s < 0){\\r\\n %(z)s = -(%(x_mod_ymm)s);\\r\\n }else{\\r\\n %(z)s = - %(x_mod_ymp)s + (%(x_mod_ymp)s != 0 ? %(y)s : 0);\\r\\n }\\r\\n }else if (%(y)s < 0){\\r\\n %(z)s = (%(x_mod_ypm)s) + (%(x_mod_ypm)s != 0 ? %(y)s : 0);\\r\\n }else{\\r\\n %(z)s = %(x_mod_y)s;\\r\\n }\\r\\n \\\"\\\"\\\") % locals()\",\n \"def func_def_top_interface_args(self, node: AnnCastFunctionDef):\\n # function container is used to scope parameters\\n param_con_scopestr = con_scope_to_str(node.con_scope)\\n # enclosing container is used to scope arguments\\n enclosing_con_scope = node.con_scope[:-1]\\n arg_con_scopestr = con_scope_to_str(enclosing_con_scope)\\n\\n # create argument and parameter variables\\n # argument variables are inputs to the top interface\\n # paramter variables are outputs of the top interface\\n for i, param in enumerate(node.func_args):\\n # argument name and scope str\\n arg_name = func_def_argument_name(node, i)\\n\\n # parameter name and scopestr\\n assert(isinstance(param, AnnCastVar))\\n param_name = param.val.name\\n\\n # argument and parameter share id, and start with initial version\\n id = param.val.id\\n version = VAR_INIT_VERSION\\n\\n # build and store GrFN variables for argument and parameter\\n arg_grfn_var = create_grfn_var(arg_name, id, version, arg_con_scopestr)\\n arg_fullid = build_fullid(arg_name, id, version, arg_con_scopestr)\\n self.pipeline_state.store_grfn_var(arg_fullid, arg_grfn_var)\\n # store arg_fullid\\n node.arg_index_to_fullid[i] = arg_fullid\\n # create From Source metadata for the GrFN var\\n from_source = False\\n from_source_mdata = generate_from_source_metadata(from_source, VariableCreationReason.FUNC_ARG)\\n add_metadata_to_grfn_var(arg_grfn_var, from_source_mdata)\\n\\n param_grfn_var = create_grfn_var(param_name, id, version, param_con_scopestr)\\n param_fullid = build_fullid(param_name, id, version, param_con_scopestr)\\n self.pipeline_state.store_grfn_var(param_fullid, param_grfn_var)\\n # store param_fullid\\n node.param_index_to_fullid[i] = param_fullid\\n # store metadata in paramter GrFN Var\\n add_metadata_from_name_node(param_grfn_var, param.val)\\n\\n # link argument and parameter through top interface\\n node.top_interface_in[id] = arg_fullid\\n node.top_interface_out[id] = param_fullid\\n\\n # DEBUG printing\\n if self.pipeline_state.PRINT_DEBUGGING_INFO:\\n print(f\\\"For FunctionDef {node.name.name}\\\")\\n print(\\\"After func_def_top_iface_args():\\\")\\n print(f\\\"\\\\ttop_interface_in = {node.top_interface_in}\\\")\\n print(f\\\"\\\\ttop_interface_out = {node.top_interface_out}\\\")\",\n \"def transform_npu_function(self, _, func: relay.Function) -> relay.Function:\\n\\n tir_mod, const_dict = _lower_to_tir(func, self.scheduler)\\n\\n for param in const_dict.keys():\\n const_dict[param] = tvm.nd.array(const_dict[param])\\n\\n compiler_name = \\\"ethos-u\\\"\\n primfunc = tir_mod[\\\"main\\\"]\\n primfunc = primfunc.with_attr(\\\"global_symbol\\\", func.attrs[\\\"global_symbol\\\"])\\n primfunc = primfunc.with_attr(\\\"ethos-u.constants\\\", const_dict)\\n primfunc = primfunc.with_attr(\\\"target\\\", tvm.target.Target(compiler_name))\\n return primfunc\",\n \"def generic_visit(self, node, parent_map):\\n node_map = []\\n parent_map.append((node, node_map))\\n \\n for field, value in ast.iter_fields(node):\\n if isinstance(value, list):\\n for item in value:\\n if isinstance(item, ast.AST):\\n self.visit(item, node_map)\\n elif isinstance(value, ast.AST):\\n self.visit(value, node_map)\",\n \"def _Attribute(self,t):\\n # Only a limited set of globals supported\\n func_dict = None\\n \\n # pyflamegpu singleton\\n if isinstance(t.value, ast.Name):\\n if t.value.id == \\\"pyflamegpu\\\":\\n if t.attr in self.fgpu_attrs:\\n # proceed\\n self.write(\\\"flamegpu::\\\")\\n self.write(t.attr)\\n else:\\n self.RaiseError(t, f\\\"Attribute '{t.attr}' does not exist in pyflamegpu object\\\")\\n # math functions (try them in raw function call format) or constants\\n elif t.value.id == \\\"math\\\":\\n if t.attr in self.mathconsts:\\n self.write(self.mathconsts[t.attr])\\n else:\\n self.RaiseError(t, f\\\"Unsupported math constant '{t.attr}'\\\")\\n # numpy types\\n elif t.value.id == \\\"numpy\\\" or t.value.id == \\\"np\\\":\\n # not sure how a numpy attribute would be used without function call or type hint but translate anyway \\n if t.attr in self.numpytypes:\\n self.write(self.numpytypes[t.attr])\\n else: \\n self.RaiseError(t, f\\\"Unsupported numpy type {t.attr}\\\")\\n else:\\n self.RaiseError(t, f\\\"Global '{t.value.id}' identifiers not supported\\\")\\n else:\\n self.RaiseError(t, \\\"Unsupported attribute\\\")\",\n \"def __call__(self):\\r\\n new_node = Op.__call__(self)\\r\\n return new_node\",\n \"def _core(fn=None, **flags):\\n\\n # need set the attr and access on c++\\n def deco(fn):\\n fn._func_graph_flags = {\\n 'core': True,\\n **flags,\\n }\\n return fn\\n\\n if fn is not None:\\n ret = deco(fn)\\n else:\\n ret = deco\\n return ret\",\n \"def transform(self, node):\\n return self.get_transform_func(node)(node)\",\n \"def _format_call_vertical(value: ast3.Call, context: types.Context) -> typing.Text:\\n\\t# If possible, prefer to put the first arg on the\\n\\t# same line as the function name. This only works\\n\\t# if we have no explicitly-named args because\\n\\t# otherwise we have one arg not line up badly.\\n\\ttry:\\n\\t\\tif value.args and not value.keywords:\\n\\t\\t\\treturn _format_call_vertical_same_line(value, context)\\n\\texcept errors.NotPossible:\\n\\t\\tpass\\n\\targs = [\\n\\t\\t_format_value(arg, context) for arg in value.args\\n\\t]\\n\\tmax_kwarg_key_len = max(len(k.arg) for k in value.keywords) if value.keywords else 0\\n\\tkwargs = [\\n\\t\\t_format_keyword(k, context, pad_key=max_kwarg_key_len) for k in sorted(value.keywords, key=lambda keyword: keyword.arg)\\n\\t]\\n\\tall_args = args + kwargs\\n\\t# Add \\\",\\\" to the end of all but the last argument\\n\\tfor i, arg in enumerate(all_args[:-1]):\\n\\t\\tall_args[i] = arg + \\\",\\\"\\n\\t# Add indentation to all but the first argument\\n\\t# for i, arg in enumerate(all_args[1:]):\\n\\t\\t# all_args[i] = context.tab + arg\\n\\t# Switch from considering 'args' to considering 'lines'\\n\\t# because we may have args that have already introduced\\n\\t# their own newlines\\n\\targ_lines = \\\"\\\\n\\\".join(all_args).split(\\\"\\\\n\\\")\\n\\tfunc = _format_value(value.func, context)\\n\\targuments = \\\"\\\\n\\\".join(context.add_indent(arg_lines))\\n\\treturn \\\"{func}(\\\\n{arguments})\\\".format(\\n\\t\\targuments=arguments,\\n\\t\\tfunc=_format_value(value.func, context)\\n\\t)\",\n \"def EvaluateFunction(self, p_float=..., p_float=..., p_float=...):\\n ...\",\n \"def transform_child(child):\\n # Is it a field name or a call?\\n if isinstance(child, Call):\\n return transform_call(child)\\n else:\\n return str(child.name)\",\n \"def parse(cls, data):\\r\\n try:\\r\\n # Parse the function here\\r\\n result = cls.FuncDefn.parseString(data)\\r\\n result_list = result.asList()\\r\\n args = result_list[3:result_list.index(')')]\\r\\n # Return single line or multi-line function body\\r\\n fn_body = re.sub(r'[^\\\\{]+\\\\{', '', data, count=1)\\r\\n parts = fn_body.strip().split('\\\\n')\\r\\n fn_body = '\\\\n'.join(parts[0:-1])\\r\\n return cls.GroovyFunction(result[1], args, fn_body, data)\\r\\n except Exception, ex:\\r\\n return {}\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6362595","0.58942914","0.57930183","0.5696235","0.56648827","0.5555311","0.5387862","0.52408487","0.5235522","0.52275836","0.51776576","0.5120119","0.51014704","0.509886","0.506699","0.50667155","0.5059268","0.50211924","0.49914703","0.4978251","0.49437723","0.4930713","0.4911009","0.49019656","0.48932612","0.48795524","0.4822934","0.4794427","0.47923595","0.47719607","0.4747839","0.47405252","0.4726047","0.46930307","0.4676279","0.46699587","0.46682099","0.4654785","0.46510816","0.46454918","0.46327636","0.46304846","0.46287373","0.46150988","0.46048176","0.45985094","0.45911625","0.4587623","0.45859426","0.45426056","0.45362353","0.45338383","0.45327517","0.45253104","0.4524044","0.45201913","0.45193502","0.45143554","0.45114267","0.44885945","0.44744763","0.44736746","0.4467362","0.44661236","0.44636765","0.44564345","0.44504458","0.4447486","0.44427347","0.4438308","0.44308752","0.4422109","0.4411446","0.44104257","0.44046533","0.44032064","0.4401585","0.44005764","0.44001552","0.43989503","0.43975604","0.43851244","0.43771","0.43745652","0.43738487","0.4354308","0.43528244","0.43528238","0.43525714","0.4351056","0.43429026","0.43344665","0.43287772","0.43271074","0.43264917","0.43172768","0.43145093","0.43103027","0.43091914","0.43074718"],"string":"[\n \"0.6362595\",\n \"0.58942914\",\n \"0.57930183\",\n \"0.5696235\",\n \"0.56648827\",\n \"0.5555311\",\n \"0.5387862\",\n \"0.52408487\",\n \"0.5235522\",\n \"0.52275836\",\n \"0.51776576\",\n \"0.5120119\",\n \"0.51014704\",\n \"0.509886\",\n \"0.506699\",\n \"0.50667155\",\n \"0.5059268\",\n \"0.50211924\",\n \"0.49914703\",\n \"0.4978251\",\n \"0.49437723\",\n \"0.4930713\",\n \"0.4911009\",\n \"0.49019656\",\n \"0.48932612\",\n \"0.48795524\",\n \"0.4822934\",\n \"0.4794427\",\n \"0.47923595\",\n \"0.47719607\",\n \"0.4747839\",\n \"0.47405252\",\n \"0.4726047\",\n \"0.46930307\",\n \"0.4676279\",\n \"0.46699587\",\n \"0.46682099\",\n \"0.4654785\",\n \"0.46510816\",\n \"0.46454918\",\n \"0.46327636\",\n \"0.46304846\",\n \"0.46287373\",\n \"0.46150988\",\n \"0.46048176\",\n \"0.45985094\",\n \"0.45911625\",\n \"0.4587623\",\n \"0.45859426\",\n \"0.45426056\",\n \"0.45362353\",\n \"0.45338383\",\n \"0.45327517\",\n \"0.45253104\",\n \"0.4524044\",\n \"0.45201913\",\n \"0.45193502\",\n \"0.45143554\",\n \"0.45114267\",\n \"0.44885945\",\n \"0.44744763\",\n \"0.44736746\",\n \"0.4467362\",\n \"0.44661236\",\n \"0.44636765\",\n \"0.44564345\",\n \"0.44504458\",\n \"0.4447486\",\n \"0.44427347\",\n \"0.4438308\",\n \"0.44308752\",\n \"0.4422109\",\n \"0.4411446\",\n \"0.44104257\",\n \"0.44046533\",\n \"0.44032064\",\n \"0.4401585\",\n \"0.44005764\",\n \"0.44001552\",\n \"0.43989503\",\n \"0.43975604\",\n \"0.43851244\",\n \"0.43771\",\n \"0.43745652\",\n \"0.43738487\",\n \"0.4354308\",\n \"0.43528244\",\n \"0.43528238\",\n \"0.43525714\",\n \"0.4351056\",\n \"0.43429026\",\n \"0.43344665\",\n \"0.43287772\",\n \"0.43271074\",\n \"0.43264917\",\n \"0.43172768\",\n \"0.43145093\",\n \"0.43103027\",\n \"0.43091914\",\n \"0.43074718\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.790886"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17787,"cells":{"query":{"kind":"string","value":"Same as a standard python expression but ends with semicolon"},"document":{"kind":"string","value":"def _Expr(self, tree):\n # Catch odd case of multi line strings and doc strings which are Expr with a Constant string type value\n if isinstance(tree.value, ast.Constant):\n if isinstance(tree.value.value, str):\n return\n # catch special case of Python 3.7 Where doc string is a Str and not a Constant\n elif isinstance(tree.value, ast.Str):\n return \n # otherwise treat like a normal expression\n self.fill()\n self.dispatch(tree.value)\n self.write(\";\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def expr():\n def p(tok):\n # commas can appear in quantified variables\n return not(tok.value in [';','.'])\n return reparse('expr').process(c.balanced_condition(p))","def compile(expression):","def evaluate(compiled_expression):","def result(self) -> global___Expression:","def expression(self):\n assert not self._handle_used\n self._expression_used = True\n return self._expression","def replaceTerminator(self, op):\n if not (op in (',', ';')):\n raise RuntimeError(\"invalid replacement terminator for GlslBlockStatement: '%s'\" % (op))\n self.__terminator = op","def _build_and(self) -> str:\n return dedent(\n \"\"\"\n @SP\n M=M-1\n A=M\n D=M\n @SP\n M=M-1\n A=M\n M=M&D\n @SP\n M=M+1\n \"\"\"\n )","def Statement(self):\n t = self.token\n if t.stmt_begin:\n self._advance()\n return t.std()\n ex = self.expression(0)\n self._advance([\"NEWLINE\", \"END\", \"DEDENT\"])\n return ex","def expression( ):#DOUBLE CHECK THIS\n\t\n\ttok = tokens.peek( )\n\tif debug: print(\"Expression: \", tok)\n\tleft = andExpr( ) #does the left side of the grammar \n\ttok = tokens.peek( )\n\twhile tok == \"or\": #checks to see if there is the token or and will preform what is inside the curly bracket since it is a series \n\t\ttokens.next()\n\t\tright = andExpr( )\n\t\tleft = BinaryExpr(tok, left, right) # MIGHT HAVE TO CHANGE THIS TO STRING CAUSE ITS \"or\"\n\t\ttok = tokens.peek( )\n\treturn left","def _build_or(self) -> str:\n return dedent(\n \"\"\"\n @SP\n M=M-1\n A=M\n D=M\n @SP\n M=M-1\n A=M\n M=M|D\n @SP\n M=M+1\n \"\"\"\n )","def test_01():\n text = \"a = 2 + 3 * (4 + 5)\"\n\n c = _ast.parse(text)\n print(_ast.dump(c))","def plan_step_to_expr(atom: clingo.Symbol) -> str:\n # The predicate and its arguments are double-quoted. Simply extract them\n matches = re.findall(r'\\\"(.+?)\\\"', str(atom))\n predicate = matches[0]\n args = f'({\",\".join(matches[1:])})' if matches[1:] else ''\n return predicate + args","def compile_return(self) -> None:\n self._consume('return')\n if self._get_current_token() != ';':\n self.compile_expression()\n else:\n self.writer.write_push('CONST', 0)\n self.writer.write_return()\n self._consume(';')","def evaluateValue(compiled_expression):","def unquote():\n def _unquote(quoted):\n return quoted.subexpression\n yield (\"(λ &[any] . any)\", _unquote)","def expression(self) -> Expression:\n ...","def quote(self, expr):\n return \"'\" + self.escape(str(expr)) + \"'\"","def _quote_slashes(match):\r\n matched = match.group(0)\r\n # We have to escape ';', because that is our\r\n # escape sequence identifier (otherwise, the escaping)\r\n # couldn't distinguish between us adding ';_' to the string\r\n # and ';_' appearing naturally in the string\r\n if matched == ';':\r\n return ';;'\r\n elif matched == '/':\r\n return ';_'\r\n else:\r\n return matched","def compile_return(self):\r\n self.tokenizer.advance()\r\n if self.tokenizer.token_type() == JackTokenizer.SYMBOL_T and self.tokenizer.symbol() == \";\":\r\n self.code_writer.write_push(\"constant\", 0)\r\n else:\r\n self.compile_expression()\r\n self.code_writer.write_return()","def new_split_iter(expr):\r\n expr = expr + \";\" # append new symbol to mark end of data, for simplicity\r\n pos = 0; #begin at first character position in the list\r\n while expr[pos] != \";\": \t #repeat until the end of the input is found.\r\n \r\n if expr[pos].isdigit():\r\n expr1 = \"\"\r\n while expr[pos].isdigit():\r\n expr1 += expr[pos]\r\n pos += 1\r\n yield expr1\r\n\r\n elif expr[pos].isalpha():\r\n expr1 = \"\"\r\n while expr[pos].isalnum():\r\n expr1 += expr[pos]\r\n pos += 1\r\n yield expr1\r\n \r\n elif expr[pos]==' ':\r\n pos += 1 \r\n\r\n elif expr[pos] == '=' or '>' or '<' or '!':\r\n if expr[pos+1] == '=':\r\n expr1 = expr[pos] + expr[pos+1]\r\n yield expr1\r\n pos +=2 \r\n else: \r\n yield expr[pos]\r\n pos +=1\r\n \r\n else:\r\n if expr[pos] != ' ':\r\n yield expr[pos]\r\n pos +=1\r\n \r\n yield \";\"","def semicolon_to_newline(text):\n # Convert semicolons to newlines.\n # Return converted text.\n return '\\n'.join(x for x in text.split(';'))","def parse(self):\n return self.expr()","def literal_symbol(literal):\n if literal.op == '~':\n return literal.args[0]\n else:\n return literal","def semicolon(f: Callable) -> Callable:\n\n @functools.wraps(f)\n def wrapper(*args, **kwargs):\n return f(*args, **kwargs) + \";\"\n\n return wrapper","def SimpleEval(source):\n itertokens = generate_tokens(StringIO(source).readline)\n next = (token[1] for token in itertokens if token[0] is not NL).next\n res = atom(next, next())\n if next():\n raise SyntaxError('bogus data after expression')\n return res","def test_dotexpr_lhs():\n a = Var(ArrayLiteral([1]).find(lambda v: v == 1))\n b = Var(Let(lambda b=[1, 2]: b).find(lambda v: v == 1))\n c = Var(String(\"hello\").concat(String(\" world\")))\n ignore(b)\n ignore(c)\n return a","def _despace(statement):\n return re.sub(r' +', ' ', statement)","def gen_expression_statement(self, statement):\n self.gen_expr(statement.expression, rvalue=True)\n # TODO: issue a warning when expression result is non void?","def evaluateMacro(compiled_expression):","def value(self) -> global___Expression:","def value(self) -> global___Expression:","def expr(self):\n return self._express","def literal():\n return Literal()","def expression(self, rbp=0):\n t = self.token\n self.token = next(self.token_gen)\n left = t.nud()\n while rbp < self.token.lbp:\n t = self.token\n self.token = next(self.token_gen)\n left = t.led(left)\n return left","def test_function_statement_at_operator():\n r = convert_code(\"{@foo arg1=bar arg2=3}\")\n assert r == \"{{ {'arg1': bar, 'arg2': 3}|foo }}\"","def expression(self, expr):\n self.set(expression=expr)","def bracket(expr):\n\n if isinstance(expr, utils.LazyString):\n return utils.LazyString(\"(%s)\", expr)\n elif utils.is_integer_type(expr) or expr.isdigit() or is_simple_name(expr):\n return str(expr)\n return \"(\" + expr + \")\"","def evaluateText(compiled_expression):","def compile_return(self):\r\n self.__advance()\r\n if self.__tokenizer.token_type() == TYPES_DIC[\"SYMBOL\"] and self.__tokenizer.symbol() == \\\r\n \";\":\r\n self.__vmwriter.write_push(\"constant\", 0)\r\n else:\r\n self.compile_expression()\r\n self.__vmwriter.write_return()\r\n self.__advance()","def evaluateBoolean(compiled_expression):","def doctest_DKBCCCsvStatementParser():","def embeded_triple_quotes():\n pass","def stringbuilderexpr(self) :\n\t\ttry :\n\t\t\treturn self._stringbuilderexpr\n\t\texcept Exception as e:\n\t\t\traise e","def expr(self):\n\n starts = []\n ends = []\n for p in self.delim:\n start, end = p.split()\n starts.append(f\"{re.escape(start)}\")\n ends.append(f\"{re.escape(end)}\")\n\n expr = [rf\"({'|'.join(starts)})\",\n # rf\"(\\s*?)\",\n rf\"(?P<head>{self.key})\",\n r\"\\s+\",\n rf\"(?P<body>.*?)\",\n r\"\\s*\",\n rf\"({'|'.join(ends)})\"]\n\n return re.compile(''.join(expr), re.M | re.S)","def newline_to_semicolon(text):\n # Convert newlines to semicolons.\n # Return converted text.\n return ';'.join(x for x in text.split('\\n'))","def expand(self, expression):\n if not expression:\n return b\"\"\n if expression[0] in self._operators:\n operator, expression = expression[:1], expression[1:]\n if operator == b\"+\":\n return self._expand(expression, reserved)\n elif operator == b\"#\":\n return self._expand(expression, reserved, prefix=b\"#\")\n elif operator == b\".\":\n return self._expand(expression, prefix=b\".\", separator=b\".\")\n elif operator == b\"/\":\n return self._expand(expression, prefix=b\"/\", separator=b\"/\")\n elif operator == b\";\":\n return self._expand(expression, prefix=b\";\", separator=b\";\",\n with_keys=True, trim_empty_equals=True)\n elif operator == b\"?\":\n return self._expand(expression, prefix=b\"?\", separator=b\"&\",\n with_keys=True)\n elif operator == b\"&\":\n return self._expand(expression, prefix=b\"&\", separator=b\"&\",\n with_keys=True)\n else:\n return self._expand(expression)","def multi_statement() -> None:\n pass; print(\"hello\")","def gen_char_literal(self, expr):\n return self.emit_const(expr.value, expr.typ)","def _print_dot(_self, expr):\r\n return r'{((%s) \\cdot (%s))}' % (expr.args[0], expr.args[1])","def compile_let(self) -> None:\n self._consume('let')\n name = self._get_current_token()\n kind = convert_kind(self.table.kind_of(name))\n index = self.table.index_of(name)\n\n self._consume(TokenTypes.IDENTIFIER)\n if self._get_current_token() == '[':\n self._consume('[')\n self.compile_expression()\n self._consume(']')\n\n self.writer.write_push(kind, index)\n self.writer.write_arithmetic('ADD')\n self.writer.write_pop('TEMP', 0)\n\n self._consume('=')\n self.compile_expression()\n self.writer.write_push('TEMP', 0)\n self.writer.write_pop('POINTER', 1)\n self.writer.write_pop('THAT', 0)\n\n else:\n self._consume('=')\n self.compile_expression()\n self.writer.write_pop(kind, index)\n\n self._consume(';')","def sep(s):\n return ' '+s if s else s","def _unquote_slashes(match):\r\n matched = match.group(0)\r\n if matched == ';;':\r\n return ';'\r\n elif matched == ';_':\r\n return '/'\r\n else:\r\n return matched","def condense_semicolons(css):\n log.debug(\"Condensing all unnecessary multiple adjacent semicolons.\")\n return re.sub(r\";;+\", \";\", css)","def test_AppendMatch( self ):\n\t\tsource = BasicMethodSource()\n\t\tresult = self.parse( \"\"\"\n\t\t\tx := c*\n\t\t\tc := 'c'\n\t\t\"\"\", 'x', 'ccc', source)\n\t\tassert result == (1,[\n\t\t\t'c','c','c',\n\t\t],3), \"\"\"Result was %s\"\"\"%( result, )","def compile_expression(self):\n\t\t\n\t\tself.outfile.write('<expression>\\n')\n\t\tself.compile_term()\n\t\tself.outfile.write('</expression>\\n')","def eval(self) -> typing.Any:\n return self.expr()","def cython_literal(self, lit):\n if isinstance(lit, Number):\n cy_lit = str(lit).replace('-', 'Neg').replace('+', 'Pos')\\\n .replace('.', 'point')\n elif isinstance(lit, basestring):\n cy_lit = repr(lit)\n return cy_lit","def jsquote(prim) :\n\tif isinstance(prim, URIRef) :\n\t\treturn 'u' + str(prim)\n\telif isinstance(prim, float) :\n\t\treturn 'f' + str(prim)\n\telif isinstance(prim, int) :\n\t\treturn 'i' + str(prim)\n\telif isinstance(prim, basestring) :\n\t\treturn 's' + prim.replace('\"', '\\\\\"').replace(\"'\", \"\\\\'\").replace(\"\\n\", \"\\\\n\")\n\telse :\n\t\treturn None","def addExpr( ):\n\n\ttok = tokens.peek( )\n\tif debug: print (\"addExpr: \", tok)\n\tleft = term( )\n\ttok = tokens.peek( )\n\twhile tok == \"+\" or tok == \"-\":\n\t\ttokens.next()\n\t\tright = term( )\n\t\tleft = BinaryExpr( tok, left, right )\n\t\ttok = tokens.peek( )\n\treturn left","def _var():\n return var() | c.paren(var() + opt_colon_sort)","def _canon_cg(expr):\n return expr.replace(CG, _canon_cg_core)","def stuff_G(self, row_start, row_end, col_start, col_end, expr, row_stride = None):\n yield \"\"","def test_expression(x, y, z):\n return x * y + y / z","def condition(self) -> global___Expression:","def condition(self) -> global___Expression:","def from_statement(cls, statement):\r\n return cls('\\n'.join(textwrap.dedent(statement).splitlines()[1:]))","def test_expr(self):\n self.common_test_expr(True)","def brackets(expr):\n expr_latex = sp.latex(expr)\n if '+' in expr_latex or '-' in expr_latex:\n return \"(\" + expr_latex + \")\"\n else:\n return expr_latex","def _expr_code(self, expr):\n if \"|\" in expr:\n pipes = expr.split(\"|\")\n code = self._expr_code(pipes[0])\n for func in pipes[1:]:\n self._variable(func, self.all_vars)\n code = f\"c_{func}({code})\"\n elif \".\" in expr:\n dots = expr.split(\".\")\n code = self._expr_code(dots[0])\n args = \", \".join(repr(d) for d in dots[1:])\n code = f\"do_dots({code}, {args})\"\n else:\n self._variable(expr, self.all_vars)\n code = \"c_%s\" % expr\n return code","def translate(expr):\n return from_python(ast.parse(expr))","def test_function_statement3():\n r = convert_code(\n \"{foo arg1=bar[1]|modifier arg2=foo.bar.foo arg3=foo.bar[3]|modifier:array[0]:\\\"hello $foo \\\" arg4=foo.bar.awesome[3]|modifier2:7:'hello':\\\"hello\\\":\\\"`$apple.banana`\\\"}\")\n assert r == \"{{ {\\'arg1\\': bar[1]|modifier, \\'arg2\\': foo.bar.foo, \\'arg3\\': foo.bar[3]|modifier(array[0], \\\"hello ${foo} \\\"), \\'arg4\\': foo.bar.awesome[3]|modifier2(7, \\'hello\\', \\\"hello\\\", \\\"${apple.banana}\\\")}|foo }}\"","def expr(s):\n if isinstance(s, Expr): return s\n if isnumber(s): return Expr(s)\n ## Replace the alternative spellings of operators with canonical spellings\n s = s.replace('==>', '>>').replace('<==', '<<')\n s = s.replace('<=>', '%').replace('=/=', '^')\n ## Replace a symbol or number, such as 'P' with 'Expr(\"P\")'\n s = re.sub(r'([a-zA-Z0-9_.]+)', r'Expr(\"\\1\")', s)\n ## Now eval the string. (A security hole; do not use with an adversary.)\n return eval(s, {'Expr':Expr})","def _is_semicolon(self, symbol):\n if symbol.type == self.scanner.SEMICOLON:\n return True\n else:\n return False","def evaluator(operator: str, value1: str, value2: str) -> str:\n\n evaluation_function: str = value1 + operator + value2\n #Because all three are strings, the + operator simply appends them together to be simplified. \n\n result: str = str(simplify(evaluation_function))\n return result","def test_if_and_filter_statement():\n r = convert_code(\n \"{if foo and awesome.string|banana:\\\"foo\\\\\\\" $a\\\"}\\nbar\\n{/if}\")\n assert r == \"{% if foo and awesome.string|banana(\\\"foo\\\\\\\" ${a}\\\") %}\\nbar\\n{% endif %}\"","def parseValue(expr):\n\n\ttry:\n\t\treturn eval(expr)\n\texcept:\n\t\treturn eval(re.sub(\"\\s+\", \",\", expr))\n\telse:\n\t\treturn expr","def test_lambda_support_no_parameters_complex_expression(self):\n code = \"\"\"\n () -> {\n if (true) return 21;\n else\n {\n int result = 21;\n return result / 2;\n }\n };\"\"\"\n self.assert_contains_lambda_expression_in_m(\n parse.parse(setup_java_class(code)))","def test_expr(self):\n x = t.Action(\"returnStuff()\")\n self.assertEqual(writePython(x),\n dd(\"\"\"\n _G_python_1, lastError = eval('returnStuff()', self.globals, _locals), None\n self.considerError(lastError, None)\n _G_python_1\n \"\"\"))","def _orderby_expression(self):\n return ''","def exeval(expression): \n if len(expression) <= 3: #Assuming no spaces (\" \") between each value given in the expression\n if expression[0] == \"+\":\n return float(expression[1]) + float(expression[2])\n elif expression[0] == \"-\":\n return float(expression[1]) - float(expression[2])\n else:\n if expression[0] == \"+\":\n return float(expression[1]) + exeval(expression[2:])\n elif expression[0] == \"-\":\n return float(expression[1]) - exeval(expression[2:])","def eval_statement(self, line):\n if line[0] in self.env:\n self.env[line[0]](line[1::])\n elif line[1] == \"=\":\n self.assign_to_env(line)\n else:\n print(\"ERROR: Undefined function {}\".format(line[0]))\n quit()","def check_for_extra_semicolon(sql_str):\r\n try:\r\n if len(sql_str.split(';')) > 2:\r\n raise sqlErr(\"Extra Semi-Colon Detected!\")\r\n except Exception as e:\r\n raise e","def __format_condition_expression(self,expression):\n from re import sub\n return sub (r'(^|[^A-Z])([A-Z])($|[^A-Z])',r'\\1{\\2}\\3',expression)","def ReturnStmt(self):\n self.currtok = next(self.tg)\n express = self.Expression()\n if self.currtok[1].name == \"SEMI\":\n self.currtok = next(self.tg)\n return returnStmt(express)\n raise SLUCSyntaxError(\"ERROR: Missing Semicolon on line {0}\".format(str(self.currtok[2] - 1)))","def sql(self):\n return ';\\n'.join([x.sql() for x in self._statements]) + ';'","def write_expression(self, expression):\n self.try_print_indent()\n self.buffer.write_scope(\"__e = \")\n self.buffer.write_line(expression)\n self.buffer.scope_line(\"if __e != None and __e != 'None':\")\n self.buffer.scope += 1\n self.buffer.scope_line(\"__io.write(unicode(__e))\")\n # We rely on a hack in maybePrintNewline to determine\n # that the last token was an expression and to output the \\n at scope+1\n self.buffer.scope -= 1","def test_function_statement2():\n r = convert_code(\n \"{foo arg1=bar[1] arg2=foo.bar.foo arg3=foo.bar[3] arg4=foo.bar.awesome[3] }\")\n assert r == \"{{ {'arg1': bar[1], 'arg2': foo.bar.foo, 'arg3': foo.bar[3], 'arg4': foo.bar.awesome[3]}|foo }}\"","def new_split_iter( expr ):\n\t#takes out spaces in case there are spaces\n\texpr = expr.replace(\" \", \"\")\n\texpr += \";\"\n\tpos = 0\n\tnum = \"\" #initialized as str to make use of string addition\n\n\twhile expr[pos] != \";\":\n\n\t\tif expr[pos].isdigit() == False:\n\t\t\t#the only non-digits in the expression should be + - * / ( ) so they are yielded as they are\n\t\t\tyield (expr[pos])\n\t\t\tpos += 1\n\n\t\telse:\n\t\t\t#use string addition to add the digits of the number together, then yield that number as an int. reset the number string after\n\t\t\twhile expr[pos].isdigit():\n\t\t\t\t\n\t\t\t\tnum += expr[pos]\n\t\t\t\tpos += 1\n\t\t\n\t\t\tyield (int(num))\n\t\t\tnum = \"\"\n\n\tyield \";\"","def text_of_quotation(exp):\n return cadr(exp)","def evaluate(expr):\n def isdigit(ch):\n try:\n int(ch)\n return True\n except ValueError:\n return False\n\n def evaluate_helper(expr, index):\n ch = expr[index]\n if ch == '(':\n # complex\n index += 1 # move past (\n\n # get the left operand\n left, index = evaluate_helper(expr, index)\n opr = expr[index]\n index += 1 # move past the operator\n\n # get the right operand\n right, index = evaluate_helper(expr, index)\n index += 1 # to move past closing paranthesis\n if opr == '+':\n return left + right, index\n elif opr == '*':\n return left * right, index\n\n \n else:\n if isdigit(ch):\n value = 0\n while isdigit(ch):\n value = value * 10 + int(ch)\n index += 1\n if index < len(expr):\n ch = expr[index]\n else:\n break\n return value, index\n\n \n\n return evaluate_helper(expr, 0)[0]","def adp(lhs,rhs):\n test=lambda s: s[0]=='`'\n assert test(lhs)==True,'error: lhs should be non-terminal'\n lhs=so.getSymbol(lhs[1:],terminal=False,autocreate=True)\n rhs=[so.getSymbol(s[1:],False,True) if test(s) else so.getSymbol(s,True,True) for s in rhs]\n return addProduction(lhs,rhs)","def test_9_call_expression(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction foo(a:real;b:boolean):real; begin return 1; end\n\t\tprocedure main(); var x:real; begin x:=foo(1,false); {OK} end\n\t\tprocedure f(); var y:integer; y:real; begin end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,409))","def as_expression(self):\n data = [(key,self.__dict__[key]) for key in self.FIELDS]\n items = []\n for (key,value) in data:\n if isinstance(value,types.UnicodeType):\n value = value.encode('utf-8')\n if isinstance(value,types.StringTypes):\n value = trim_value(value)\n if isinstance(value,types.StringType):\n items.append(\"'%s': %s\" % (key,quote(value)))\n else:\n items.append(\"'%s': %s\" % (key,str(value)))\n r = '{\\n%s\\n}' % ',\\n'.join(items)\n return r","def test_function2(a, b):\n x = a + b\n y = a * b\n return x, y, x<y, x>y # < to ensure HTML is properly escaped","def test_calculate_multiplication_and_bracket_at_the_end(self):\n result = self.calcuate.calcuate('2x(1+3)')\n expected_result = \"8\"\n self.assertEqual(expected_result, result)","def ev(expr):\n return eval(expr,user_ns())","def make_op1(op, expr):\n\n if (op == None) or (expr == None):\n return None\n\n if op == 'NOT':\n op = '!'\n if is_assembler('beebasm') and (op == '!'):\n if isinstance(expr, utils.LazyString):\n return utils.LazyString(\"NOT(%s)\", expr)\n return 'NOT(' + expr + ')'\n if isinstance(expr, utils.LazyString):\n return utils.LazyString(\"%s%s\", op, bracket(expr))\n return op + bracket(expr)","def __init__(self, expr):\n super(Combine, self).__init__(_silent_pattern(expr))","def op(self) -> Literal[\"==\"] | Literal[\"<=\"] | Literal[\">=\"]:\n ...","def shunting_yard(expression):\n # remove leading =\n if expression.startswith('='):\n expression = expression[1:]\n\n p = ExcelParser();\n p.parse(expression)\n #print(p.prettyprint())\n\n # insert tokens for '(' and ')', to make things clearer below\n tokens = []\n for t in p.tokens.items:\n if t.ttype == ept.TOK_TYPE_FUNCTION and t.tsubtype ==ept.TOK_SUBTYPE_START:\n t.tsubtype = \"\"\n tokens.append(t)\n tokens.append(f_token('(', 'arglist', 'start'))\n elif t.ttype == ept.TOK_TYPE_FUNCTION and t.tsubtype ==ept.TOK_SUBTYPE_STOP:\n tokens.append(f_token(')', 'arglist', 'stop'))\n elif t.ttype == ept.TOK_TYPE_SUBEXPR and t.tsubtype == ept.TOK_SUBTYPE_START:\n t.tvalue = '('\n tokens.append(t)\n elif t.ttype == ept.TOK_TYPE_SUBEXPR and t.tsubtype == ept.TOK_SUBTYPE_STOP:\n t.tvalue = ')'\n tokens.append(t)\n else:\n tokens.append(t)\n\n # print \"tokens: \", \"|\".join([x.tvalue for x in tokens])\n\n # http://office.microsoft.com/en-us/excel-help/calculation-operators-and-precedence-HP010078886.aspx\n operators = {}\n operators[':'] = Operator(':', 8, 'left')\n operators[''] = Operator(' ', 8, 'left')\n operators[','] = Operator(',', 8, 'left')\n operators['u-'] = Operator('u-', 7, 'left') # unary negation\n operators['%'] = Operator('%', 6, 'left')\n operators['^'] = Operator('^', 5, 'left')\n operators['*'] = Operator('*', 4, 'left')\n operators['/'] = Operator('/', 4, 'left')\n operators['+'] = Operator('+', 3, 'left')\n operators['-'] = Operator('-', 3, 'left')\n operators['&'] = Operator('&', 2, 'left')\n operators['='] = Operator('=', 1, 'left')\n operators['<'] = Operator('<', 1, 'left')\n operators['>'] = Operator('>', 1, 'left')\n operators['<='] = Operator('<=', 1, 'left')\n operators['>='] = Operator('>=', 1, 'left')\n operators['<>'] = Operator('<>', 1, 'left')\n\n output = collections.deque()\n stack = []\n were_values = []\n arg_count = []\n\n for t in tokens:\n if t.ttype == ept.TOK_TYPE_OPERAND:\n\n output.append(create_node(t))\n if were_values:\n were_values.pop()\n were_values.append(True)\n\n elif t.ttype == ept.TOK_TYPE_FUNCTION:\n\n stack.append(t)\n arg_count.append(0)\n if were_values:\n were_values.pop()\n were_values.append(True)\n were_values.append(False)\n\n elif t.ttype == ept.TOK_TYPE_ARGUMENT:\n\n while stack and (stack[-1].tsubtype != ept.TOK_SUBTYPE_START):\n output.append(create_node(stack.pop()))\n\n if were_values.pop(): arg_count[-1] += 1\n were_values.append(False)\n\n if not len(stack):\n raise Exception(\"Mismatched or misplaced parentheses\")\n\n elif t.ttype in [ept.TOK_TYPE_OP_IN,ept.TOK_TYPE_OP_PRE,ept.TOK_TYPE_OP_POST]:\n\n if t.ttype == ept.TOK_TYPE_OP_PRE and t.tvalue == \"-\":\n o1 = operators['u-']\n else:\n o1 = operators[t.tvalue]\n\n while stack and stack[-1].ttype in [ept.TOK_TYPE_OP_IN,ept.TOK_TYPE_OP_PRE,ept.TOK_TYPE_OP_POST]:\n\n if stack[-1].ttype == ept.TOK_TYPE_OP_PRE and stack[-1].tvalue == \"-\":\n o2 = operators['u-']\n else:\n o2 = operators[stack[-1].tvalue]\n\n if ((o1.associativity == \"left\" and o1.precedence <= o2.precedence)\n or\n (o1.associativity == \"right\" and o1.precedence < o2.precedence)):\n\n output.append(create_node(stack.pop()))\n else:\n break\n\n stack.append(t)\n\n elif t.tsubtype == ept.TOK_SUBTYPE_START:\n stack.append(t)\n\n elif t.tsubtype == ept.TOK_SUBTYPE_STOP:\n\n while stack and stack[-1].tsubtype != ept.TOK_SUBTYPE_START:\n output.append(create_node(stack.pop()))\n\n if not stack:\n raise Exception(\"Mismatched or misplaced parentheses\")\n\n stack.pop()\n\n if stack and stack[-1].ttype == ept.TOK_TYPE_FUNCTION:\n f = create_node(stack.pop())\n a = arg_count.pop()\n w = were_values.pop()\n if w: a += 1\n f.num_args = a\n # print f, \"has \",a,\" args\"\n output.append(f)\n\n while stack:\n if stack[-1].tsubtype == ept.TOK_SUBTYPE_START or stack[-1].tsubtype == ept.TOK_SUBTYPE_STOP:\n raise Exception(\"Mismatched or misplaced parentheses\")\n\n output.append(create_node(stack.pop()))\n\n # print \"Stack is: \", \"|\".join(stack)\n # print \"Ouput is: \", \"|\".join([x.tvalue for x in output])\n\n # convert to list\n result = [x for x in output]\n return result"],"string":"[\n \"def expr():\\n def p(tok):\\n # commas can appear in quantified variables\\n return not(tok.value in [';','.'])\\n return reparse('expr').process(c.balanced_condition(p))\",\n \"def compile(expression):\",\n \"def evaluate(compiled_expression):\",\n \"def result(self) -> global___Expression:\",\n \"def expression(self):\\n assert not self._handle_used\\n self._expression_used = True\\n return self._expression\",\n \"def replaceTerminator(self, op):\\n if not (op in (',', ';')):\\n raise RuntimeError(\\\"invalid replacement terminator for GlslBlockStatement: '%s'\\\" % (op))\\n self.__terminator = op\",\n \"def _build_and(self) -> str:\\n return dedent(\\n \\\"\\\"\\\"\\n @SP\\n M=M-1\\n A=M\\n D=M\\n @SP\\n M=M-1\\n A=M\\n M=M&D\\n @SP\\n M=M+1\\n \\\"\\\"\\\"\\n )\",\n \"def Statement(self):\\n t = self.token\\n if t.stmt_begin:\\n self._advance()\\n return t.std()\\n ex = self.expression(0)\\n self._advance([\\\"NEWLINE\\\", \\\"END\\\", \\\"DEDENT\\\"])\\n return ex\",\n \"def expression( ):#DOUBLE CHECK THIS\\n\\t\\n\\ttok = tokens.peek( )\\n\\tif debug: print(\\\"Expression: \\\", tok)\\n\\tleft = andExpr( ) #does the left side of the grammar \\n\\ttok = tokens.peek( )\\n\\twhile tok == \\\"or\\\": #checks to see if there is the token or and will preform what is inside the curly bracket since it is a series \\n\\t\\ttokens.next()\\n\\t\\tright = andExpr( )\\n\\t\\tleft = BinaryExpr(tok, left, right) # MIGHT HAVE TO CHANGE THIS TO STRING CAUSE ITS \\\"or\\\"\\n\\t\\ttok = tokens.peek( )\\n\\treturn left\",\n \"def _build_or(self) -> str:\\n return dedent(\\n \\\"\\\"\\\"\\n @SP\\n M=M-1\\n A=M\\n D=M\\n @SP\\n M=M-1\\n A=M\\n M=M|D\\n @SP\\n M=M+1\\n \\\"\\\"\\\"\\n )\",\n \"def test_01():\\n text = \\\"a = 2 + 3 * (4 + 5)\\\"\\n\\n c = _ast.parse(text)\\n print(_ast.dump(c))\",\n \"def plan_step_to_expr(atom: clingo.Symbol) -> str:\\n # The predicate and its arguments are double-quoted. Simply extract them\\n matches = re.findall(r'\\\\\\\"(.+?)\\\\\\\"', str(atom))\\n predicate = matches[0]\\n args = f'({\\\",\\\".join(matches[1:])})' if matches[1:] else ''\\n return predicate + args\",\n \"def compile_return(self) -> None:\\n self._consume('return')\\n if self._get_current_token() != ';':\\n self.compile_expression()\\n else:\\n self.writer.write_push('CONST', 0)\\n self.writer.write_return()\\n self._consume(';')\",\n \"def evaluateValue(compiled_expression):\",\n \"def unquote():\\n def _unquote(quoted):\\n return quoted.subexpression\\n yield (\\\"(λ &[any] . any)\\\", _unquote)\",\n \"def expression(self) -> Expression:\\n ...\",\n \"def quote(self, expr):\\n return \\\"'\\\" + self.escape(str(expr)) + \\\"'\\\"\",\n \"def _quote_slashes(match):\\r\\n matched = match.group(0)\\r\\n # We have to escape ';', because that is our\\r\\n # escape sequence identifier (otherwise, the escaping)\\r\\n # couldn't distinguish between us adding ';_' to the string\\r\\n # and ';_' appearing naturally in the string\\r\\n if matched == ';':\\r\\n return ';;'\\r\\n elif matched == '/':\\r\\n return ';_'\\r\\n else:\\r\\n return matched\",\n \"def compile_return(self):\\r\\n self.tokenizer.advance()\\r\\n if self.tokenizer.token_type() == JackTokenizer.SYMBOL_T and self.tokenizer.symbol() == \\\";\\\":\\r\\n self.code_writer.write_push(\\\"constant\\\", 0)\\r\\n else:\\r\\n self.compile_expression()\\r\\n self.code_writer.write_return()\",\n \"def new_split_iter(expr):\\r\\n expr = expr + \\\";\\\" # append new symbol to mark end of data, for simplicity\\r\\n pos = 0; #begin at first character position in the list\\r\\n while expr[pos] != \\\";\\\": \\t #repeat until the end of the input is found.\\r\\n \\r\\n if expr[pos].isdigit():\\r\\n expr1 = \\\"\\\"\\r\\n while expr[pos].isdigit():\\r\\n expr1 += expr[pos]\\r\\n pos += 1\\r\\n yield expr1\\r\\n\\r\\n elif expr[pos].isalpha():\\r\\n expr1 = \\\"\\\"\\r\\n while expr[pos].isalnum():\\r\\n expr1 += expr[pos]\\r\\n pos += 1\\r\\n yield expr1\\r\\n \\r\\n elif expr[pos]==' ':\\r\\n pos += 1 \\r\\n\\r\\n elif expr[pos] == '=' or '>' or '<' or '!':\\r\\n if expr[pos+1] == '=':\\r\\n expr1 = expr[pos] + expr[pos+1]\\r\\n yield expr1\\r\\n pos +=2 \\r\\n else: \\r\\n yield expr[pos]\\r\\n pos +=1\\r\\n \\r\\n else:\\r\\n if expr[pos] != ' ':\\r\\n yield expr[pos]\\r\\n pos +=1\\r\\n \\r\\n yield \\\";\\\"\",\n \"def semicolon_to_newline(text):\\n # Convert semicolons to newlines.\\n # Return converted text.\\n return '\\\\n'.join(x for x in text.split(';'))\",\n \"def parse(self):\\n return self.expr()\",\n \"def literal_symbol(literal):\\n if literal.op == '~':\\n return literal.args[0]\\n else:\\n return literal\",\n \"def semicolon(f: Callable) -> Callable:\\n\\n @functools.wraps(f)\\n def wrapper(*args, **kwargs):\\n return f(*args, **kwargs) + \\\";\\\"\\n\\n return wrapper\",\n \"def SimpleEval(source):\\n itertokens = generate_tokens(StringIO(source).readline)\\n next = (token[1] for token in itertokens if token[0] is not NL).next\\n res = atom(next, next())\\n if next():\\n raise SyntaxError('bogus data after expression')\\n return res\",\n \"def test_dotexpr_lhs():\\n a = Var(ArrayLiteral([1]).find(lambda v: v == 1))\\n b = Var(Let(lambda b=[1, 2]: b).find(lambda v: v == 1))\\n c = Var(String(\\\"hello\\\").concat(String(\\\" world\\\")))\\n ignore(b)\\n ignore(c)\\n return a\",\n \"def _despace(statement):\\n return re.sub(r' +', ' ', statement)\",\n \"def gen_expression_statement(self, statement):\\n self.gen_expr(statement.expression, rvalue=True)\\n # TODO: issue a warning when expression result is non void?\",\n \"def evaluateMacro(compiled_expression):\",\n \"def value(self) -> global___Expression:\",\n \"def value(self) -> global___Expression:\",\n \"def expr(self):\\n return self._express\",\n \"def literal():\\n return Literal()\",\n \"def expression(self, rbp=0):\\n t = self.token\\n self.token = next(self.token_gen)\\n left = t.nud()\\n while rbp < self.token.lbp:\\n t = self.token\\n self.token = next(self.token_gen)\\n left = t.led(left)\\n return left\",\n \"def test_function_statement_at_operator():\\n r = convert_code(\\\"{@foo arg1=bar arg2=3}\\\")\\n assert r == \\\"{{ {'arg1': bar, 'arg2': 3}|foo }}\\\"\",\n \"def expression(self, expr):\\n self.set(expression=expr)\",\n \"def bracket(expr):\\n\\n if isinstance(expr, utils.LazyString):\\n return utils.LazyString(\\\"(%s)\\\", expr)\\n elif utils.is_integer_type(expr) or expr.isdigit() or is_simple_name(expr):\\n return str(expr)\\n return \\\"(\\\" + expr + \\\")\\\"\",\n \"def evaluateText(compiled_expression):\",\n \"def compile_return(self):\\r\\n self.__advance()\\r\\n if self.__tokenizer.token_type() == TYPES_DIC[\\\"SYMBOL\\\"] and self.__tokenizer.symbol() == \\\\\\r\\n \\\";\\\":\\r\\n self.__vmwriter.write_push(\\\"constant\\\", 0)\\r\\n else:\\r\\n self.compile_expression()\\r\\n self.__vmwriter.write_return()\\r\\n self.__advance()\",\n \"def evaluateBoolean(compiled_expression):\",\n \"def doctest_DKBCCCsvStatementParser():\",\n \"def embeded_triple_quotes():\\n pass\",\n \"def stringbuilderexpr(self) :\\n\\t\\ttry :\\n\\t\\t\\treturn self._stringbuilderexpr\\n\\t\\texcept Exception as e:\\n\\t\\t\\traise e\",\n \"def expr(self):\\n\\n starts = []\\n ends = []\\n for p in self.delim:\\n start, end = p.split()\\n starts.append(f\\\"{re.escape(start)}\\\")\\n ends.append(f\\\"{re.escape(end)}\\\")\\n\\n expr = [rf\\\"({'|'.join(starts)})\\\",\\n # rf\\\"(\\\\s*?)\\\",\\n rf\\\"(?P<head>{self.key})\\\",\\n r\\\"\\\\s+\\\",\\n rf\\\"(?P<body>.*?)\\\",\\n r\\\"\\\\s*\\\",\\n rf\\\"({'|'.join(ends)})\\\"]\\n\\n return re.compile(''.join(expr), re.M | re.S)\",\n \"def newline_to_semicolon(text):\\n # Convert newlines to semicolons.\\n # Return converted text.\\n return ';'.join(x for x in text.split('\\\\n'))\",\n \"def expand(self, expression):\\n if not expression:\\n return b\\\"\\\"\\n if expression[0] in self._operators:\\n operator, expression = expression[:1], expression[1:]\\n if operator == b\\\"+\\\":\\n return self._expand(expression, reserved)\\n elif operator == b\\\"#\\\":\\n return self._expand(expression, reserved, prefix=b\\\"#\\\")\\n elif operator == b\\\".\\\":\\n return self._expand(expression, prefix=b\\\".\\\", separator=b\\\".\\\")\\n elif operator == b\\\"/\\\":\\n return self._expand(expression, prefix=b\\\"/\\\", separator=b\\\"/\\\")\\n elif operator == b\\\";\\\":\\n return self._expand(expression, prefix=b\\\";\\\", separator=b\\\";\\\",\\n with_keys=True, trim_empty_equals=True)\\n elif operator == b\\\"?\\\":\\n return self._expand(expression, prefix=b\\\"?\\\", separator=b\\\"&\\\",\\n with_keys=True)\\n elif operator == b\\\"&\\\":\\n return self._expand(expression, prefix=b\\\"&\\\", separator=b\\\"&\\\",\\n with_keys=True)\\n else:\\n return self._expand(expression)\",\n \"def multi_statement() -> None:\\n pass; print(\\\"hello\\\")\",\n \"def gen_char_literal(self, expr):\\n return self.emit_const(expr.value, expr.typ)\",\n \"def _print_dot(_self, expr):\\r\\n return r'{((%s) \\\\cdot (%s))}' % (expr.args[0], expr.args[1])\",\n \"def compile_let(self) -> None:\\n self._consume('let')\\n name = self._get_current_token()\\n kind = convert_kind(self.table.kind_of(name))\\n index = self.table.index_of(name)\\n\\n self._consume(TokenTypes.IDENTIFIER)\\n if self._get_current_token() == '[':\\n self._consume('[')\\n self.compile_expression()\\n self._consume(']')\\n\\n self.writer.write_push(kind, index)\\n self.writer.write_arithmetic('ADD')\\n self.writer.write_pop('TEMP', 0)\\n\\n self._consume('=')\\n self.compile_expression()\\n self.writer.write_push('TEMP', 0)\\n self.writer.write_pop('POINTER', 1)\\n self.writer.write_pop('THAT', 0)\\n\\n else:\\n self._consume('=')\\n self.compile_expression()\\n self.writer.write_pop(kind, index)\\n\\n self._consume(';')\",\n \"def sep(s):\\n return ' '+s if s else s\",\n \"def _unquote_slashes(match):\\r\\n matched = match.group(0)\\r\\n if matched == ';;':\\r\\n return ';'\\r\\n elif matched == ';_':\\r\\n return '/'\\r\\n else:\\r\\n return matched\",\n \"def condense_semicolons(css):\\n log.debug(\\\"Condensing all unnecessary multiple adjacent semicolons.\\\")\\n return re.sub(r\\\";;+\\\", \\\";\\\", css)\",\n \"def test_AppendMatch( self ):\\n\\t\\tsource = BasicMethodSource()\\n\\t\\tresult = self.parse( \\\"\\\"\\\"\\n\\t\\t\\tx := c*\\n\\t\\t\\tc := 'c'\\n\\t\\t\\\"\\\"\\\", 'x', 'ccc', source)\\n\\t\\tassert result == (1,[\\n\\t\\t\\t'c','c','c',\\n\\t\\t],3), \\\"\\\"\\\"Result was %s\\\"\\\"\\\"%( result, )\",\n \"def compile_expression(self):\\n\\t\\t\\n\\t\\tself.outfile.write('<expression>\\\\n')\\n\\t\\tself.compile_term()\\n\\t\\tself.outfile.write('</expression>\\\\n')\",\n \"def eval(self) -> typing.Any:\\n return self.expr()\",\n \"def cython_literal(self, lit):\\n if isinstance(lit, Number):\\n cy_lit = str(lit).replace('-', 'Neg').replace('+', 'Pos')\\\\\\n .replace('.', 'point')\\n elif isinstance(lit, basestring):\\n cy_lit = repr(lit)\\n return cy_lit\",\n \"def jsquote(prim) :\\n\\tif isinstance(prim, URIRef) :\\n\\t\\treturn 'u' + str(prim)\\n\\telif isinstance(prim, float) :\\n\\t\\treturn 'f' + str(prim)\\n\\telif isinstance(prim, int) :\\n\\t\\treturn 'i' + str(prim)\\n\\telif isinstance(prim, basestring) :\\n\\t\\treturn 's' + prim.replace('\\\"', '\\\\\\\\\\\"').replace(\\\"'\\\", \\\"\\\\\\\\'\\\").replace(\\\"\\\\n\\\", \\\"\\\\\\\\n\\\")\\n\\telse :\\n\\t\\treturn None\",\n \"def addExpr( ):\\n\\n\\ttok = tokens.peek( )\\n\\tif debug: print (\\\"addExpr: \\\", tok)\\n\\tleft = term( )\\n\\ttok = tokens.peek( )\\n\\twhile tok == \\\"+\\\" or tok == \\\"-\\\":\\n\\t\\ttokens.next()\\n\\t\\tright = term( )\\n\\t\\tleft = BinaryExpr( tok, left, right )\\n\\t\\ttok = tokens.peek( )\\n\\treturn left\",\n \"def _var():\\n return var() | c.paren(var() + opt_colon_sort)\",\n \"def _canon_cg(expr):\\n return expr.replace(CG, _canon_cg_core)\",\n \"def stuff_G(self, row_start, row_end, col_start, col_end, expr, row_stride = None):\\n yield \\\"\\\"\",\n \"def test_expression(x, y, z):\\n return x * y + y / z\",\n \"def condition(self) -> global___Expression:\",\n \"def condition(self) -> global___Expression:\",\n \"def from_statement(cls, statement):\\r\\n return cls('\\\\n'.join(textwrap.dedent(statement).splitlines()[1:]))\",\n \"def test_expr(self):\\n self.common_test_expr(True)\",\n \"def brackets(expr):\\n expr_latex = sp.latex(expr)\\n if '+' in expr_latex or '-' in expr_latex:\\n return \\\"(\\\" + expr_latex + \\\")\\\"\\n else:\\n return expr_latex\",\n \"def _expr_code(self, expr):\\n if \\\"|\\\" in expr:\\n pipes = expr.split(\\\"|\\\")\\n code = self._expr_code(pipes[0])\\n for func in pipes[1:]:\\n self._variable(func, self.all_vars)\\n code = f\\\"c_{func}({code})\\\"\\n elif \\\".\\\" in expr:\\n dots = expr.split(\\\".\\\")\\n code = self._expr_code(dots[0])\\n args = \\\", \\\".join(repr(d) for d in dots[1:])\\n code = f\\\"do_dots({code}, {args})\\\"\\n else:\\n self._variable(expr, self.all_vars)\\n code = \\\"c_%s\\\" % expr\\n return code\",\n \"def translate(expr):\\n return from_python(ast.parse(expr))\",\n \"def test_function_statement3():\\n r = convert_code(\\n \\\"{foo arg1=bar[1]|modifier arg2=foo.bar.foo arg3=foo.bar[3]|modifier:array[0]:\\\\\\\"hello $foo \\\\\\\" arg4=foo.bar.awesome[3]|modifier2:7:'hello':\\\\\\\"hello\\\\\\\":\\\\\\\"`$apple.banana`\\\\\\\"}\\\")\\n assert r == \\\"{{ {\\\\'arg1\\\\': bar[1]|modifier, \\\\'arg2\\\\': foo.bar.foo, \\\\'arg3\\\\': foo.bar[3]|modifier(array[0], \\\\\\\"hello ${foo} \\\\\\\"), \\\\'arg4\\\\': foo.bar.awesome[3]|modifier2(7, \\\\'hello\\\\', \\\\\\\"hello\\\\\\\", \\\\\\\"${apple.banana}\\\\\\\")}|foo }}\\\"\",\n \"def expr(s):\\n if isinstance(s, Expr): return s\\n if isnumber(s): return Expr(s)\\n ## Replace the alternative spellings of operators with canonical spellings\\n s = s.replace('==>', '>>').replace('<==', '<<')\\n s = s.replace('<=>', '%').replace('=/=', '^')\\n ## Replace a symbol or number, such as 'P' with 'Expr(\\\"P\\\")'\\n s = re.sub(r'([a-zA-Z0-9_.]+)', r'Expr(\\\"\\\\1\\\")', s)\\n ## Now eval the string. (A security hole; do not use with an adversary.)\\n return eval(s, {'Expr':Expr})\",\n \"def _is_semicolon(self, symbol):\\n if symbol.type == self.scanner.SEMICOLON:\\n return True\\n else:\\n return False\",\n \"def evaluator(operator: str, value1: str, value2: str) -> str:\\n\\n evaluation_function: str = value1 + operator + value2\\n #Because all three are strings, the + operator simply appends them together to be simplified. \\n\\n result: str = str(simplify(evaluation_function))\\n return result\",\n \"def test_if_and_filter_statement():\\n r = convert_code(\\n \\\"{if foo and awesome.string|banana:\\\\\\\"foo\\\\\\\\\\\\\\\" $a\\\\\\\"}\\\\nbar\\\\n{/if}\\\")\\n assert r == \\\"{% if foo and awesome.string|banana(\\\\\\\"foo\\\\\\\\\\\\\\\" ${a}\\\\\\\") %}\\\\nbar\\\\n{% endif %}\\\"\",\n \"def parseValue(expr):\\n\\n\\ttry:\\n\\t\\treturn eval(expr)\\n\\texcept:\\n\\t\\treturn eval(re.sub(\\\"\\\\s+\\\", \\\",\\\", expr))\\n\\telse:\\n\\t\\treturn expr\",\n \"def test_lambda_support_no_parameters_complex_expression(self):\\n code = \\\"\\\"\\\"\\n () -> {\\n if (true) return 21;\\n else\\n {\\n int result = 21;\\n return result / 2;\\n }\\n };\\\"\\\"\\\"\\n self.assert_contains_lambda_expression_in_m(\\n parse.parse(setup_java_class(code)))\",\n \"def test_expr(self):\\n x = t.Action(\\\"returnStuff()\\\")\\n self.assertEqual(writePython(x),\\n dd(\\\"\\\"\\\"\\n _G_python_1, lastError = eval('returnStuff()', self.globals, _locals), None\\n self.considerError(lastError, None)\\n _G_python_1\\n \\\"\\\"\\\"))\",\n \"def _orderby_expression(self):\\n return ''\",\n \"def exeval(expression): \\n if len(expression) <= 3: #Assuming no spaces (\\\" \\\") between each value given in the expression\\n if expression[0] == \\\"+\\\":\\n return float(expression[1]) + float(expression[2])\\n elif expression[0] == \\\"-\\\":\\n return float(expression[1]) - float(expression[2])\\n else:\\n if expression[0] == \\\"+\\\":\\n return float(expression[1]) + exeval(expression[2:])\\n elif expression[0] == \\\"-\\\":\\n return float(expression[1]) - exeval(expression[2:])\",\n \"def eval_statement(self, line):\\n if line[0] in self.env:\\n self.env[line[0]](line[1::])\\n elif line[1] == \\\"=\\\":\\n self.assign_to_env(line)\\n else:\\n print(\\\"ERROR: Undefined function {}\\\".format(line[0]))\\n quit()\",\n \"def check_for_extra_semicolon(sql_str):\\r\\n try:\\r\\n if len(sql_str.split(';')) > 2:\\r\\n raise sqlErr(\\\"Extra Semi-Colon Detected!\\\")\\r\\n except Exception as e:\\r\\n raise e\",\n \"def __format_condition_expression(self,expression):\\n from re import sub\\n return sub (r'(^|[^A-Z])([A-Z])($|[^A-Z])',r'\\\\1{\\\\2}\\\\3',expression)\",\n \"def ReturnStmt(self):\\n self.currtok = next(self.tg)\\n express = self.Expression()\\n if self.currtok[1].name == \\\"SEMI\\\":\\n self.currtok = next(self.tg)\\n return returnStmt(express)\\n raise SLUCSyntaxError(\\\"ERROR: Missing Semicolon on line {0}\\\".format(str(self.currtok[2] - 1)))\",\n \"def sql(self):\\n return ';\\\\n'.join([x.sql() for x in self._statements]) + ';'\",\n \"def write_expression(self, expression):\\n self.try_print_indent()\\n self.buffer.write_scope(\\\"__e = \\\")\\n self.buffer.write_line(expression)\\n self.buffer.scope_line(\\\"if __e != None and __e != 'None':\\\")\\n self.buffer.scope += 1\\n self.buffer.scope_line(\\\"__io.write(unicode(__e))\\\")\\n # We rely on a hack in maybePrintNewline to determine\\n # that the last token was an expression and to output the \\\\n at scope+1\\n self.buffer.scope -= 1\",\n \"def test_function_statement2():\\n r = convert_code(\\n \\\"{foo arg1=bar[1] arg2=foo.bar.foo arg3=foo.bar[3] arg4=foo.bar.awesome[3] }\\\")\\n assert r == \\\"{{ {'arg1': bar[1], 'arg2': foo.bar.foo, 'arg3': foo.bar[3], 'arg4': foo.bar.awesome[3]}|foo }}\\\"\",\n \"def new_split_iter( expr ):\\n\\t#takes out spaces in case there are spaces\\n\\texpr = expr.replace(\\\" \\\", \\\"\\\")\\n\\texpr += \\\";\\\"\\n\\tpos = 0\\n\\tnum = \\\"\\\" #initialized as str to make use of string addition\\n\\n\\twhile expr[pos] != \\\";\\\":\\n\\n\\t\\tif expr[pos].isdigit() == False:\\n\\t\\t\\t#the only non-digits in the expression should be + - * / ( ) so they are yielded as they are\\n\\t\\t\\tyield (expr[pos])\\n\\t\\t\\tpos += 1\\n\\n\\t\\telse:\\n\\t\\t\\t#use string addition to add the digits of the number together, then yield that number as an int. reset the number string after\\n\\t\\t\\twhile expr[pos].isdigit():\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tnum += expr[pos]\\n\\t\\t\\t\\tpos += 1\\n\\t\\t\\n\\t\\t\\tyield (int(num))\\n\\t\\t\\tnum = \\\"\\\"\\n\\n\\tyield \\\";\\\"\",\n \"def text_of_quotation(exp):\\n return cadr(exp)\",\n \"def evaluate(expr):\\n def isdigit(ch):\\n try:\\n int(ch)\\n return True\\n except ValueError:\\n return False\\n\\n def evaluate_helper(expr, index):\\n ch = expr[index]\\n if ch == '(':\\n # complex\\n index += 1 # move past (\\n\\n # get the left operand\\n left, index = evaluate_helper(expr, index)\\n opr = expr[index]\\n index += 1 # move past the operator\\n\\n # get the right operand\\n right, index = evaluate_helper(expr, index)\\n index += 1 # to move past closing paranthesis\\n if opr == '+':\\n return left + right, index\\n elif opr == '*':\\n return left * right, index\\n\\n \\n else:\\n if isdigit(ch):\\n value = 0\\n while isdigit(ch):\\n value = value * 10 + int(ch)\\n index += 1\\n if index < len(expr):\\n ch = expr[index]\\n else:\\n break\\n return value, index\\n\\n \\n\\n return evaluate_helper(expr, 0)[0]\",\n \"def adp(lhs,rhs):\\n test=lambda s: s[0]=='`'\\n assert test(lhs)==True,'error: lhs should be non-terminal'\\n lhs=so.getSymbol(lhs[1:],terminal=False,autocreate=True)\\n rhs=[so.getSymbol(s[1:],False,True) if test(s) else so.getSymbol(s,True,True) for s in rhs]\\n return addProduction(lhs,rhs)\",\n \"def test_9_call_expression(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction foo(a:real;b:boolean):real; begin return 1; end\\n\\t\\tprocedure main(); var x:real; begin x:=foo(1,false); {OK} end\\n\\t\\tprocedure f(); var y:integer; y:real; begin end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,409))\",\n \"def as_expression(self):\\n data = [(key,self.__dict__[key]) for key in self.FIELDS]\\n items = []\\n for (key,value) in data:\\n if isinstance(value,types.UnicodeType):\\n value = value.encode('utf-8')\\n if isinstance(value,types.StringTypes):\\n value = trim_value(value)\\n if isinstance(value,types.StringType):\\n items.append(\\\"'%s': %s\\\" % (key,quote(value)))\\n else:\\n items.append(\\\"'%s': %s\\\" % (key,str(value)))\\n r = '{\\\\n%s\\\\n}' % ',\\\\n'.join(items)\\n return r\",\n \"def test_function2(a, b):\\n x = a + b\\n y = a * b\\n return x, y, x<y, x>y # < to ensure HTML is properly escaped\",\n \"def test_calculate_multiplication_and_bracket_at_the_end(self):\\n result = self.calcuate.calcuate('2x(1+3)')\\n expected_result = \\\"8\\\"\\n self.assertEqual(expected_result, result)\",\n \"def ev(expr):\\n return eval(expr,user_ns())\",\n \"def make_op1(op, expr):\\n\\n if (op == None) or (expr == None):\\n return None\\n\\n if op == 'NOT':\\n op = '!'\\n if is_assembler('beebasm') and (op == '!'):\\n if isinstance(expr, utils.LazyString):\\n return utils.LazyString(\\\"NOT(%s)\\\", expr)\\n return 'NOT(' + expr + ')'\\n if isinstance(expr, utils.LazyString):\\n return utils.LazyString(\\\"%s%s\\\", op, bracket(expr))\\n return op + bracket(expr)\",\n \"def __init__(self, expr):\\n super(Combine, self).__init__(_silent_pattern(expr))\",\n \"def op(self) -> Literal[\\\"==\\\"] | Literal[\\\"<=\\\"] | Literal[\\\">=\\\"]:\\n ...\",\n \"def shunting_yard(expression):\\n # remove leading =\\n if expression.startswith('='):\\n expression = expression[1:]\\n\\n p = ExcelParser();\\n p.parse(expression)\\n #print(p.prettyprint())\\n\\n # insert tokens for '(' and ')', to make things clearer below\\n tokens = []\\n for t in p.tokens.items:\\n if t.ttype == ept.TOK_TYPE_FUNCTION and t.tsubtype ==ept.TOK_SUBTYPE_START:\\n t.tsubtype = \\\"\\\"\\n tokens.append(t)\\n tokens.append(f_token('(', 'arglist', 'start'))\\n elif t.ttype == ept.TOK_TYPE_FUNCTION and t.tsubtype ==ept.TOK_SUBTYPE_STOP:\\n tokens.append(f_token(')', 'arglist', 'stop'))\\n elif t.ttype == ept.TOK_TYPE_SUBEXPR and t.tsubtype == ept.TOK_SUBTYPE_START:\\n t.tvalue = '('\\n tokens.append(t)\\n elif t.ttype == ept.TOK_TYPE_SUBEXPR and t.tsubtype == ept.TOK_SUBTYPE_STOP:\\n t.tvalue = ')'\\n tokens.append(t)\\n else:\\n tokens.append(t)\\n\\n # print \\\"tokens: \\\", \\\"|\\\".join([x.tvalue for x in tokens])\\n\\n # http://office.microsoft.com/en-us/excel-help/calculation-operators-and-precedence-HP010078886.aspx\\n operators = {}\\n operators[':'] = Operator(':', 8, 'left')\\n operators[''] = Operator(' ', 8, 'left')\\n operators[','] = Operator(',', 8, 'left')\\n operators['u-'] = Operator('u-', 7, 'left') # unary negation\\n operators['%'] = Operator('%', 6, 'left')\\n operators['^'] = Operator('^', 5, 'left')\\n operators['*'] = Operator('*', 4, 'left')\\n operators['/'] = Operator('/', 4, 'left')\\n operators['+'] = Operator('+', 3, 'left')\\n operators['-'] = Operator('-', 3, 'left')\\n operators['&'] = Operator('&', 2, 'left')\\n operators['='] = Operator('=', 1, 'left')\\n operators['<'] = Operator('<', 1, 'left')\\n operators['>'] = Operator('>', 1, 'left')\\n operators['<='] = Operator('<=', 1, 'left')\\n operators['>='] = Operator('>=', 1, 'left')\\n operators['<>'] = Operator('<>', 1, 'left')\\n\\n output = collections.deque()\\n stack = []\\n were_values = []\\n arg_count = []\\n\\n for t in tokens:\\n if t.ttype == ept.TOK_TYPE_OPERAND:\\n\\n output.append(create_node(t))\\n if were_values:\\n were_values.pop()\\n were_values.append(True)\\n\\n elif t.ttype == ept.TOK_TYPE_FUNCTION:\\n\\n stack.append(t)\\n arg_count.append(0)\\n if were_values:\\n were_values.pop()\\n were_values.append(True)\\n were_values.append(False)\\n\\n elif t.ttype == ept.TOK_TYPE_ARGUMENT:\\n\\n while stack and (stack[-1].tsubtype != ept.TOK_SUBTYPE_START):\\n output.append(create_node(stack.pop()))\\n\\n if were_values.pop(): arg_count[-1] += 1\\n were_values.append(False)\\n\\n if not len(stack):\\n raise Exception(\\\"Mismatched or misplaced parentheses\\\")\\n\\n elif t.ttype in [ept.TOK_TYPE_OP_IN,ept.TOK_TYPE_OP_PRE,ept.TOK_TYPE_OP_POST]:\\n\\n if t.ttype == ept.TOK_TYPE_OP_PRE and t.tvalue == \\\"-\\\":\\n o1 = operators['u-']\\n else:\\n o1 = operators[t.tvalue]\\n\\n while stack and stack[-1].ttype in [ept.TOK_TYPE_OP_IN,ept.TOK_TYPE_OP_PRE,ept.TOK_TYPE_OP_POST]:\\n\\n if stack[-1].ttype == ept.TOK_TYPE_OP_PRE and stack[-1].tvalue == \\\"-\\\":\\n o2 = operators['u-']\\n else:\\n o2 = operators[stack[-1].tvalue]\\n\\n if ((o1.associativity == \\\"left\\\" and o1.precedence <= o2.precedence)\\n or\\n (o1.associativity == \\\"right\\\" and o1.precedence < o2.precedence)):\\n\\n output.append(create_node(stack.pop()))\\n else:\\n break\\n\\n stack.append(t)\\n\\n elif t.tsubtype == ept.TOK_SUBTYPE_START:\\n stack.append(t)\\n\\n elif t.tsubtype == ept.TOK_SUBTYPE_STOP:\\n\\n while stack and stack[-1].tsubtype != ept.TOK_SUBTYPE_START:\\n output.append(create_node(stack.pop()))\\n\\n if not stack:\\n raise Exception(\\\"Mismatched or misplaced parentheses\\\")\\n\\n stack.pop()\\n\\n if stack and stack[-1].ttype == ept.TOK_TYPE_FUNCTION:\\n f = create_node(stack.pop())\\n a = arg_count.pop()\\n w = were_values.pop()\\n if w: a += 1\\n f.num_args = a\\n # print f, \\\"has \\\",a,\\\" args\\\"\\n output.append(f)\\n\\n while stack:\\n if stack[-1].tsubtype == ept.TOK_SUBTYPE_START or stack[-1].tsubtype == ept.TOK_SUBTYPE_STOP:\\n raise Exception(\\\"Mismatched or misplaced parentheses\\\")\\n\\n output.append(create_node(stack.pop()))\\n\\n # print \\\"Stack is: \\\", \\\"|\\\".join(stack)\\n # print \\\"Ouput is: \\\", \\\"|\\\".join([x.tvalue for x in output])\\n\\n # convert to list\\n result = [x for x in output]\\n return result\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65578294","0.5910947","0.5799009","0.5718435","0.562569","0.5539511","0.5498102","0.5459333","0.5452541","0.54354775","0.54278094","0.5413188","0.5406052","0.54030687","0.53684604","0.52868867","0.52864975","0.5281365","0.5280854","0.5279634","0.52743554","0.52593493","0.52494794","0.5247849","0.5238047","0.5216183","0.52131486","0.5206447","0.5180402","0.51766133","0.51766133","0.51657414","0.509779","0.50818855","0.50770473","0.5073058","0.5065201","0.50544226","0.50364196","0.5026201","0.5020235","0.5016158","0.50003755","0.49982363","0.4991881","0.49918476","0.4990405","0.49883458","0.49846792","0.49785534","0.49667636","0.49627864","0.49620068","0.4952758","0.49507385","0.49463075","0.4942365","0.49339268","0.49192396","0.49146926","0.4913653","0.49136135","0.49085256","0.48917836","0.48917836","0.4878839","0.48751926","0.48696133","0.4867327","0.4859391","0.4857631","0.48572454","0.48528135","0.48511562","0.48463064","0.48452014","0.48434058","0.48417196","0.4832159","0.48312724","0.48279878","0.4827228","0.48194644","0.4818191","0.4816802","0.4811006","0.48080945","0.48070905","0.4805131","0.48048186","0.4799204","0.47927356","0.47838423","0.47812104","0.47729865","0.47708595","0.4770084","0.47698298","0.47635925","0.47617075"],"string":"[\n \"0.65578294\",\n \"0.5910947\",\n \"0.5799009\",\n \"0.5718435\",\n \"0.562569\",\n \"0.5539511\",\n \"0.5498102\",\n \"0.5459333\",\n \"0.5452541\",\n \"0.54354775\",\n \"0.54278094\",\n \"0.5413188\",\n \"0.5406052\",\n \"0.54030687\",\n \"0.53684604\",\n \"0.52868867\",\n \"0.52864975\",\n \"0.5281365\",\n \"0.5280854\",\n \"0.5279634\",\n \"0.52743554\",\n \"0.52593493\",\n \"0.52494794\",\n \"0.5247849\",\n \"0.5238047\",\n \"0.5216183\",\n \"0.52131486\",\n \"0.5206447\",\n \"0.5180402\",\n \"0.51766133\",\n \"0.51766133\",\n \"0.51657414\",\n \"0.509779\",\n \"0.50818855\",\n \"0.50770473\",\n \"0.5073058\",\n \"0.5065201\",\n \"0.50544226\",\n \"0.50364196\",\n \"0.5026201\",\n \"0.5020235\",\n \"0.5016158\",\n \"0.50003755\",\n \"0.49982363\",\n \"0.4991881\",\n \"0.49918476\",\n \"0.4990405\",\n \"0.49883458\",\n \"0.49846792\",\n \"0.49785534\",\n \"0.49667636\",\n \"0.49627864\",\n \"0.49620068\",\n \"0.4952758\",\n \"0.49507385\",\n \"0.49463075\",\n \"0.4942365\",\n \"0.49339268\",\n \"0.49192396\",\n \"0.49146926\",\n \"0.4913653\",\n \"0.49136135\",\n \"0.49085256\",\n \"0.48917836\",\n \"0.48917836\",\n \"0.4878839\",\n \"0.48751926\",\n \"0.48696133\",\n \"0.4867327\",\n \"0.4859391\",\n \"0.4857631\",\n \"0.48572454\",\n \"0.48528135\",\n \"0.48511562\",\n \"0.48463064\",\n \"0.48452014\",\n \"0.48434058\",\n \"0.48417196\",\n \"0.4832159\",\n \"0.48312724\",\n \"0.48279878\",\n \"0.4827228\",\n \"0.48194644\",\n \"0.4818191\",\n \"0.4816802\",\n \"0.4811006\",\n \"0.48080945\",\n \"0.48070905\",\n \"0.4805131\",\n \"0.48048186\",\n \"0.4799204\",\n \"0.47927356\",\n \"0.47838423\",\n \"0.47812104\",\n \"0.47729865\",\n \"0.47708595\",\n \"0.4770084\",\n \"0.47698298\",\n \"0.47635925\",\n \"0.47617075\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.53314483"},"document_rank":{"kind":"string","value":"15"}}},{"rowIdx":17788,"cells":{"query":{"kind":"string","value":"No such concept in C++. Standard assignment can be used in any location."},"document":{"kind":"string","value":"def _NamedExpr(self, tree):\n self.write(\"(\")\n self.dispatch(tree.target)\n self.write(\" = \")\n self.dispatch(tree.value)\n self.write(\")\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_50_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 2]of real;\n\t\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,450))","def test_47_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 3]of real;\n\t\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,447))","def test_46_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tprocedure main(); var x:array[1 .. 3]of real; begin x[1]:=1;\n\t\twith y:integer;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,446))","def test_49_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 2] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 3]of real;\n\t\tbegin f()[1]:=x[1]:=1; end\"\"\"\n\t\texpect = \"Type Mismatch In Statement: Return(Some(Id(a)))\"\n\t\tself.assertTrue(TestChecker.test(input,expect,449))","def test_48_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 2] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 3]of real;\n\t\tbegin f()[1]:=x[1]:=1; end\"\"\"\n\t\texpect = \"Type Mismatch In Statement: Return(Some(Id(a)))\"\n\t\tself.assertTrue(TestChecker.test(input,expect,448))","def visit_assign(self: Parser, node: doc.Assign) -> None:\n if len(node.targets) != 1:\n self.report_error(node, \"Consequential assignments like 'a = b = c' are not supported.\")\n lhs = node.targets[0]\n\n if isinstance(node.value, doc.Subscript):\n check_slices = []\n if isinstance(node.value.slice, doc.Slice):\n check_slices = [node.value.slice]\n elif isinstance(node.value.slice, doc.Tuple):\n for p in node.value.slice.elts:\n if isinstance(p, doc.Slice):\n check_slices.append(p)\n for s in check_slices:\n if not s.step and s.upper and s.lower:\n s.step = doc.Constant(\n 1,\n None,\n 1,\n 1,\n s.upper.lineno,\n s.upper.end_col_offset + 1,\n s.upper.lineno,\n s.upper.end_col_offset + 2,\n )\n\n rhs = self.eval_expr(node.value)\n if isinstance(lhs, doc.Subscript):\n if isinstance(lhs.slice, doc.Tuple):\n indices = []\n for index in lhs.slice.elts:\n indices.append(self.eval_expr(index))\n else:\n indices = self.eval_expr(lhs.slice)\n T.buffer_store(self.eval_expr(lhs.value), rhs, indices)\n else:\n self.eval_assign(target=lhs, source=rhs, bind_value=bind_assign_value)","def smart_assign(variable, value, assign_fn=tf.assign,\n force_cast=False, force_sync=True):\n if force_cast and variable.dtype != value.dtype:\n value = tf.cast(value, dtype=variable.dtype)\n\n if not (tf.distribute.has_strategy() and tf.distribute.get_replica_context()):\n return assign_fn(variable, value)\n\n def merge_fn(distribution, variable, value):\n strategy = tf.distribute.get_strategy()\n if isinstance(strategy, tf.distribute.MirroredStrategy) and force_sync:\n value = strategy.reduce(tf.distribute.ReduceOp.MEAN, value)\n return distribution.extended.update(variable, assign_fn, args=(value,))\n\n return tf.distribute.get_replica_context().merge_call(\n merge_fn, args=(variable, value))","def assign(self, V, py):\n V.value = py","def assign(self, V, py):\n V.value = py","def assign(self, V, py):\n V.value = py","def assign(self, V, py):\n V.value = py","def __init__(self, concept_ref='', value=''):\n self.concept_ref = concept_ref\n self.value = value","def test_45_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tprocedure main(); var x:real; begin x:=1;\n\t\twith y:integer;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,445))","def test_remove_assignment_rule(self):\n pass","def assign(self, dst, req, src):\n if req == 'null':\n return\n if req in ('write', 'inplace'):\n dst[:] = src\n elif req == 'add':\n dst[:] += src","def __setitem__(self, idx, value):\n if not isinstance(value, nodes.Node):\n raise NotImplementedError(\"setitem with non-blaze rhs\")\n result = self.getitem(idx, context='set')\n result = Assign('assign', [result, value])\n result.eval()","def assign(obj, path, val, missing=None):\n return glom(obj, Assign(path, val, missing=missing))","def _compat_assign_gast_4(targets, value, type_comment):\n return gast.Assign(targets=targets, value=value)","def eval_assignment(exp, env):\n set_variable_value(assignment_variable(exp), m_eval(assignment_value(exp), env), env)\n return quote(\"ok\")","def _assign_op(dest, op, arg, val, path, scope):\n if op == '[':\n dest[arg] = val\n elif op == '.':\n setattr(dest, arg, val)\n elif op == 'P':\n _assign = scope[TargetRegistry].get_handler('assign', dest)\n try:\n _assign(dest, arg, val)\n except Exception as e:\n raise PathAssignError(e, path, arg)\n else: # pragma: no cover\n raise ValueError('unsupported T operation for assignment')","def test_obj_action_for_assignments():\n grammar = r\"\"\"\n S: a=\"foo\" b?=\"bar\" c=C+;\n C: val=\"baz\";\n \"\"\"\n\n g = Grammar.from_string(grammar)\n p = Parser(g)\n\n result = p.parse(\"foo bar baz baz baz\")\n\n assert isinstance(result, g.classes['S'])\n assert isinstance(result.c[0], g.classes['C'])\n\n assert result.a == \"foo\"\n assert result.b is True\n assert len(result.c) == 3\n assert all((c.val == \"baz\" for c in result.c))","def _AugAssign(self, t):\n if not isinstance(t.target, ast.Name):\n self.RaiseError(t, \"Augmented assignment to complex expressions not supported\")\n # check if target exists in locals\n if t.target.id not in self._locals :\n self.RaiseError(t, \"Augmented assignment not permitted on variables not already assigned previously\")\n self.fill()\n self.dispatch(t.target)\n self.write(\" \"+self.binop[t.op.__class__.__name__]+\"= \")\n self.dispatch(t.value)\n self.write(\";\")","def convert_assign(g, op, block):\n\n out = g.get_node(op.input(\"X\")[0])\n g.add_node(op.output(\"Out\")[0], out)","def _compat_assign_gast_5(targets, value, type_comment):\n return gast.Assign(targets=targets, value=value, type_comment=type_comment)","def test_001_lambda_assign(self):\r\n text = \"\"\"\r\n const f = (d,k,v) => d[k] = v\r\n \"\"\"\r\n tokens = Lexer().lex(text)\r\n ast = Parser().parse(tokens)\r\n expected = TOKEN('T_MODULE', '',\r\n TOKEN('T_VAR', 'const',\r\n TOKEN('T_ASSIGN', '=',\r\n TOKEN('T_TEXT', 'f'),\r\n TOKEN('T_LAMBDA', '=>',\r\n TOKEN('T_TEXT', 'Anonymous'),\r\n TOKEN('T_ARGLIST', '()',\r\n TOKEN('T_TEXT', 'd'),\r\n TOKEN('T_TEXT', 'k'),\r\n TOKEN('T_TEXT', 'v')\r\n ),\r\n TOKEN('T_ASSIGN', '=',\r\n TOKEN('T_SUBSCR', '',\r\n TOKEN('T_TEXT', 'd'),\r\n TOKEN('T_TEXT', 'k'))\r\n ),\r\n TOKEN('T_TEXT', 'v')\r\n )\r\n )\r\n )\r\n )\r\n\r\n self.assertFalse(parsecmp(expected, ast, False))","def irgen_assign(stmt, builder, table):\n lvalue = irgen_lvalue(stmt.exprs[0], builder, table)\n expr = irgen_expr(stmt.exprs[1], builder, table)\n builder.store(expr, lvalue)","def create_Assign(left_hand_side, right_hand_side):\n right_hand_side.ctx = ast.Load()\n left_hand_side.ctx = ast.Store()\n return ast.Assign(targets=[left_hand_side], value=right_hand_side)","def set_value(self, var_name, new_value, tf_session):\n\n if(var_name in self.assign_operator):\n\n tf_session.run(\n self.assign_operator[var_name], {\n self.l_param_input[var_name]: new_value})\n else:\n print(\"Thou shall only assign learning parameters!\")","def visit_aug_assign(self: Parser, node: doc.AugAssign) -> None:\n lhs_pos = (\n node.target.lineno,\n node.target.col_offset,\n node.target.end_lineno,\n node.target.end_col_offset,\n )\n rhs_pos = (\n node.value.lineno,\n node.value.col_offset,\n node.value.end_lineno,\n node.value.end_col_offset,\n )\n node.target.ctx = doc.Load(*lhs_pos)\n with self.var_table.with_frame():\n lhs_name = \"__tvm_tmp_value_aug_assign_lhs\"\n rhs_name = \"__tvm_tmp_value_aug_assign_rhs\"\n lhs_expr = self.eval_expr(node.target)\n rhs_expr = self.eval_expr(node.value)\n self.var_table.add(lhs_name, lhs_expr)\n self.var_table.add(rhs_name, rhs_expr)\n op = doc.BinOp(\n doc.Name(lhs_name, doc.Load(*lhs_pos), *lhs_pos),\n node.op,\n doc.Name(rhs_name, doc.Load(*rhs_pos), *rhs_pos),\n *lhs_pos,\n )\n rhs = self.eval_expr(op)\n lhs = node.target\n lhs.ctx = doc.Store(*lhs_pos)\n if isinstance(lhs, doc.Subscript):\n if isinstance(lhs.slice, doc.Tuple):\n indices = []\n for index in lhs.slice.elts:\n indices.append(self.eval_expr(index))\n else:\n indices = [self.eval_expr(lhs.slice)]\n T.buffer_store(self.eval_expr(lhs.value), rhs, indices)\n else:\n self.eval_assign(target=lhs, source=rhs, bind_value=bind_assign_value)","def visit_ann_assign(self: Parser, node: doc.AnnAssign) -> None:\n lhs = node.target\n rhs = self.eval_expr(node.value)\n ann_var = self.visit_tvm_annotation(node.annotation)\n if not isinstance(ann_var, Var):\n self.report_error(node.annotation, \"Annotation should be Var\")\n self.eval_assign(target=lhs, source=ann_var, bind_value=bind_assign_value)\n frame = T.LetStmt(rhs, var=ann_var)\n frame.add_callback(partial(frame.__exit__, None, None, None))\n frame.__enter__()","def test_variable_assign_boxed_access(self):\n self.trace('y = x', env={'x': 1})\n\n events = self.variable_events\n self.assertEqual(len(events), 2)\n self.assertIsInstance(events[0], TraceAccess)\n self.assertIsInstance(events[1], TraceAssign)\n self.assertEqual(events[1].value_event, events[0])","def variability(sv):\r\n unchanging(sv) # remove change clause for constants \r\n make_volatiles(sv) # create list of volatile objects and make their clause Always\r","def on_assign(self):","def assign(self, value):\n self.value = value","def visit_Assign(self, node):\n self.generic_visit(node)\n\n if node.col_offset == 0:\n mnode = ast.parse(\"\")\n mnode.body = [node]\n mnode = ast.fix_missing_locations(mnode)\n code = compile(mnode, \"<ast>\", \"exec\")\n try:\n exec(code, self.globals_)\n except Exception:\n pass\n self.globals_.pop(\"__builtins__\", None)\n self.globals_.pop(\"builtins\", None)","def fset(self, value):\n message = \"Overriding a constant value is an illegal operation: {0} = {1}.\".format(\n name.__name__,\n value)\n raise TypeError(message)","def assign_variable(self, name, value):\n return self.set_variable(name, value)","def variable(self, val):","def test_augassign_recursion():\n # infinitely recurses in python\n code = \"\"\"\n def rec():\n a = 0\n a += rec()\n return a\n rec()\n \"\"\"\n cls_node = extract_node(code)\n assert next(cls_node.infer()) is util.Uninferable","def setLocal(name, value):","def visit_AugAssign(self, node):\n target = node.target\n\n rhs_target = copy.deepcopy(target)\n rhs_target.ctx = ast.Load()\n ast.fix_missing_locations(rhs_target)\n\n bin_op = ast.BinOp(rhs_target, node.op, node.value)\n assignment = ast.Assign([target], bin_op)\n assignment.inplace_op = node.op\n return self.visit(assignment)","def visit_Assign(self, node):\n self.generic_visit(node)\n is_multiple = len(node.targets) > 1\n is_compound = any(map(is_sequence_node, node.targets))\n is_simple = not is_compound\n if is_simple and is_multiple:\n return self.visit_simple_assign(node)\n elif is_compound and (is_multiple or is_sequence_node(node.value)):\n return self.visit_compound_assign(node)\n return node","def __setattr__(self, name, value): # real signature unknown; restored from __doc__\n pass","def __setattr__(self, name, value): # real signature unknown; restored from __doc__\n pass","def assign_state(self, state):\n raise NotImplementedError()","def single_assignment_handler(target, value, assign_stmts, node, id_str):\n #print(\"single_assignment_handler\")\n\n temp_stmts = core_language.create_Assign(target, value)\n if hasattr(node, 'lineno'):\n temp_stmts.lineno = node.lineno\n temp_stmts.col_offset = node.col_offset\n\n assign_stmts.append(temp_stmts)\n return False","def setValue(self, value):\r\n\r\n if type(value) == NoneType: # INPUT = None. Valid (to set to None the object)\r\n self.setNone()\r\n return\r\n elif type(value) == StringType:\t\t\t\t\t# INPUT = String. Valid (constraint name)\r\n if not self.constName:\r\n self.constName = ATOM3String(value)\t\t\t# constraint name\r\n else: self.constName.setValue(value)\r\n return\r\n elif type(value) == TupleType and len(value) == 5: # Tuple Type of size 5\r\n name, language, kind, actions, code = value\r\n else:\r\n raise ATOM3BadAssignmentValue, \"ATOM3Constraint: Bad type in setValue()\"\r\n\r\n if not self.constName:\r\n self.constName = ATOM3String(name)\t\t\t\t# constraint name\r\n else: self.constName.setValue(name)\r\n\r\n if type(language) == TupleType:\t\t\t\t\t# May be is a tuple ([languages...], selected)\r\n self.language.setValue(language)\t\t\t\t# the pass it to self.language\r\n elif type(language) == IntType: \t# otherwise it is the selected language\r\n self.language.setValue((None, language))\t\t\r\n else:\r\n raise ATOM3BadAssignmentValue, \"ATOM3Constraint: Bad type in setValue() (2nd argument in tuple)\"\r\n\r\n if kind:\t\t\t\t\t\t\t\t# if it is present is a tuple ([types...], selectedType)\r\n if type(kind) == TupleType and len(kind) == 2:\r\n if not self.kind: self.kind = ATOM3Enum(kind[0], kind[1])\r\n else:\r\n try:\r\n self.kind.setValue((None,kind[1])) # set kind of constraint if specified\r\n except ATOM3BadAssignmentValue:\r\n raise ATOM3BadAssignmentValue, \"ATOM3Constraint: Bad type in setValue(), \"+ str(kind)\r\n elif type(kind) != NoneType:\r\n raise ATOM3BadAssignmentValue, \"ATOM3Constraint: Bad type in setValue() (3rd argument in tuple)\"\r\n\t\r\n if actions:\r\n if type(actions) == TupleType and len(actions) == 2:\r\n if not self.actions: self.actions = ATOM3MSEnum(actions[0], actions[1], 0, ATOM3MSEnum.LISTBOX)\r\n else: self.actions.setValue((None,actions[1]))\t\t# set action where it must be evaluated, if specified\r\n else:\r\n raise ATOM3BadAssignmentValue, \"ATOM3Constraint: Bad type in setValue() (4th argument in tuple)\"\r\n\r\n if type(code) == StringType or type(code) == NoneType:\r\n self.constCode = code\r\n if self.constCode and self.constCodeWidget:\r\n self.constCodeWidget.insert(1.0, self.constCode)\r\n else:\r\n raise ATOM3BadAssignmentValue, \"ATOM3Constraint: Bad type in setValue() (5th argument in tuple)\"","def let(**kw):\n raise NotImplementedError(\"`let` can only be used in rules, not code!\")","def visit_Assign(self, node):\n self.generic_visit(node)\n target = get_single_target(node)\n if isinstance(target, ast.Subscript):\n fun = to_attribute(self.operator, 'setitem')\n args = [target.value, self.index_to_expr(target.slice), node.value]\n return ast.Expr(to_call(fun, args))\n return node","def assign_value(Xj, Xk, csp, assignment):\r\n parent_assignment = assignment[Xj]\r\n for val in csp.curr_domains[Xk]:\r\n if csp.constraints(Xj, parent_assignment, Xk, val):\r\n return val\r\n\r\n # No consistent assignment available\r\n return None","def rhs(self):\n if not self.is_assign():\n raise AssertionError('Not an assignment')\n return self.initializer","def eval_assignment(assignment, motif_node_dict):\n if type(assignment.rvalue).__name__ == 'FuncCall':\n motif_node, tree_node = eval_function_call(assignment.rvalue, motif_node_dict)\n # consider \"var = XXX;\" and \"*var = XXX\" and \"&var = XXX\" situations\n if (type(assignment.lvalue).__name__ == 'ID' and assignment.lvalue.name in motif_node_dict) or (type(assignment.lvalue).__name__ == 'UnaryOp' and assignment.lvalue.expr.name in motif_node_dict):\n if not motif_node:\n print('\\33[101m' + '[error][eval_assignment]: ' + assignment.lvalue.name + ' is in the dictionary. MotifNode should not be None.\\033[0m')\n exit(1)\n else:\n motif_node_dict[assignment.lvalue.name].append(motif_node)\n return tree_node\n # In a case where a provenance node was declared but then assigned or reassigned. For example:\n # struct provenance *tprov;\n # ...\n # tprov = t->provenance;\n # tprov must then be in the motif_node_dict.\n elif type(assignment.lvalue).__name__ == 'ID' and assignment.lvalue.name in motif_node_dict:\n # we can only infer its type from the name of the variable\n motif_node = provenance.create_motif_node(assignment.lvalue.name)\n motif_node_dict[assignment.lvalue.name].append(motif_node)\n return None\n elif type(assignment.lvalue).__name__ == 'UnaryOp' and type(assignment.lvalue.expr).__name__ == 'ID' and assignment.lvalue.expr.name in motif_node_dict:\n # similar case as the previous one, except that we have: *tprov = ...\n # we can only infer its type from the name of the variable\n motif_node = provenance.create_motif_node(assignment.lvalue.expr.name)\n motif_node_dict[assignment.lvalue.expr.name].append(motif_node)\n return None\n else:\n #######################################################\n # We will consider other conditions if we ever see them\n # POSSIBLE CODE HERE.\n #######################################################\n return None","def setattr_ref(self, interp, attr, w_newvalue, contextclass):\n interp.warn('Attempt to modify property of non-object')\n return interp.space.w_Null","def test_statement_initialized_by_assignment():\n shap = Statement(shape_id=\"@photo\", prop_id=\"dcterms:creator\", value_type=\"URI\")\n shap2 = Statement()\n shap2.shape_id = \"@photo\"\n shap2.prop_id = \"dcterms:creator\"\n shap2.value_type = \"URI\"\n assert shap == shap2","def testDoubleReference(self): \n my_var=Var()\n list_var=Var()\n my_list=PList([my_var,2,3])\n Compound(\"=\",list_var,my_list).post_goal()\n self.assertNotEqual(resume(),False)\n Compound(\"=\",list_var.value()[0],3).post_goal()\n self.assertNotEqual(resume(),False)\n self.assertEqual(my_var.value(),3)","def visit_compound_assign(self, node):\n # Determine number of values (arity) of compound assignment.\n nvalues = { len(target.elts) for target in node.targets \n if is_sequence_node(target) }\n if len(nvalues) > 1:\n # A multiple, compound assignment with different arities, e.g.,\n # `x,y = a,b,c = ...` is not a syntax error in Python, though it\n # probably should be because it's guaranteed to cause a runtime\n # error. Raise the error here, since we cannot proceed.\n raise SyntaxError(\"Multiple assignment with different arities\")\n nvalues = nvalues.pop()\n\n # Assign temporary variables.\n temps = [ gensym() for i in range(nvalues) ]\n stmts = []\n if is_sequence_node(node.value) and len(node.value.elts) == nvalues:\n # Special case: RHS is sequence literal of correct length.\n for i in range(nvalues):\n temp_target = to_name(temps[i], ast.Store())\n stmts.append(ast.Assign([temp_target], node.value.elts[i]))\n else:\n # General case.\n temp_target = to_tuple(\n (to_name(temp, ast.Store()) for temp in temps), ast.Store())\n stmts.append(ast.Assign([temp_target], node.value))\n\n # Rewrite assignments as sequence of assignments.\n for target in reversed(node.targets):\n if is_sequence_node(target):\n stmts.extend(ast.Assign([target.elts[i]], to_name(temps[i]))\n for i in range(nvalues))\n else:\n temp_tuple = to_tuple(to_name(temp) for temp in temps)\n stmts.append(ast.Assign([target], temp_tuple))\n \n return stmts","def do_assign(parser, token):\n bits = token.contents.split()\n if len(bits) != 3:\n raise template.TemplateSyntaxError(\"'%s' tag takes two arguments\" % bits[0])\n value = parser.compile_filter(bits[2])\n return AssignNode(bits[1], value)","def do_assign(parser, token):\n bits = token.contents.split()\n if len(bits) != 3:\n raise template.TemplateSyntaxError(\"'%s' tag takes two arguments\" % bits[0])\n value = parser.compile_filter(bits[2])\n return AssignNode(bits[1], value)","def visit_AugAssign(self, node):\n # FIXME: Gensym the LHS to avoid two evaluations.\n self.generic_visit(node)\n rhs = to_call(self.op_to_function(node.op),\n [set_ctx(node.target), node.value])\n return ast.Assign([node.target], rhs)","def assign(self, other):\n\n assert isinstance(other, VarList)\n assert len(self) == len(other)\n ops = []\n for (my_var, other_var) in zip(self.vars_, other.vars_):\n ops.append(my_var.assign(other_var))\n return tf.group(*ops, name=\"assign_\"+self.name)","def convert_assign_value(g, op, block):\n\n keys = [\"bool_values\", \"fp32_values\", \"int32_values\", \"int64_values\"]\n dtypes = [\"bool\", \"float32\", \"int32\", \"int64\"]\n for i, key in enumerate(keys):\n dtype = dtypes[i]\n value = np.array(op.attr(key)).astype(dtype)\n if value is not None and value.size >= 1:\n break\n shape = op.attr(\"shape\")\n value = value.reshape(shape)\n out = _op.const(value, dtype=dtype)\n g.add_node(op.output(\"Out\")[0], out)","def test_compiler_assignment(patch, compiler, lines, tree):\n patch.many(Objects, ['names', 'entity'])\n tree.assignment_fragment.service = None\n tree.assignment_fragment.mutation = None\n compiler.assignment(tree, '1')\n Objects.names.assert_called_with(tree.path)\n fragment = tree.assignment_fragment\n entity = get_entity(fragment.expression)\n Objects.entity.assert_called_with(entity)\n kwargs = {'name': Objects.names(), 'args': [Objects.entity()],\n 'parent': '1'}\n lines.append.assert_called_with('set', tree.line(), **kwargs)","def visit_simple_assign(self, node):\n temp = gensym()\n temp_target = to_name(temp, ast.Store())\n stmts = [ ast.Assign([temp_target], node.value) ]\n stmts += [ ast.Assign([target], to_name(temp))\n for target in node.targets ]\n return stmts","def __set__(self, obj, value):\r\n pass","def assign(self, var, value):\n\t\tself._root = self._insert(self._root, var, value)","def assign_to_device(device, ps_device=None):\n ps_ops = ['Variable', 'VariableV2', 'AutoReloadVariable', 'MutableHashTable',\n 'MutableHashTableOfTensors', 'MutableDenseHashTable']\n\n def _assign(op):\n node_def = op if isinstance(op, tf.NodeDef) else op.node_def\n if node_def.op in ps_ops:\n return \"/\" + ps_device\n else:\n return device\n\n return _assign","def visit_Assign(self, node: Assign) -> None:\n\n node_type = type(node.right).__name__\n if isinstance(node.right, String):\n self._create_instruct(node_type)\n self.visit(node.left)\n instruct = self.visit(node.right)\n c_str = self.builder.alloca(instruct.type)\n self.builder.store(instruct, c_str)\n self.builder.ret_void()\n else:\n self._create_instruct(node_type)\n self.visit(node.left)\n instruct = self.visit(node.right)\n self.builder.ret(instruct)\n\n self.GLOBAL_MEMORY[node.left.value] = instruct","def _analyse_stmt_Assign(self, statement: ast.Assign, *, next: CFNode) -> CFNode:\n return self._ast_node(statement, next=next, error=self._raise)","def _Assign(self, t):\n if len(t.targets) > 1:\n self.RaiseError(t, \"Assignment to multiple targets not supported\")\n if not isinstance(t.targets[0], ast.Name):\n self.RaiseError(t, \"Assignment to complex expressions not supported\")\n self.fill()\n # check if target exists in locals\n if t.targets[0].id not in self._locals :\n self.write(\"auto \")\n self._locals.append(t.targets[0].id)\n self.dispatch(t.targets[0])\n self.write(\" = \")\n self.dispatch(t.value)\n self.write(\";\")","def Assignment(self):\n id = self.primary()\n if self.currtok[1].name == \"DECLERATION\":\n self.currtok = next(self.tg)\n if self.functions.get(self.currtok[0]) is not None:\n\n express = self.FunctionCall()\n return assignmentStmt(id, express)\n else:\n express = self.Expression()\n\n if self.currtok[1].name == \"SEMI\":\n self.currtok = next(self.tg)\n return assignmentStmt(id, express)\n raise SLUCSyntaxError(\"ERROR: Missing Semicolon on line {0}\".format(str(self.currtok[2] - 1)))\n raise SLUCSyntaxError(\"ERROR: Missing assignment on line {0}\".format(str(self.currtok[2] - 1)))","def test_statement_bad_field_initialized_by_assignment():\n shap = Statement()\n shap.foobar = \"@photo\"\n shap.prop_id = \"dcterms:creator\"\n shap.value_type = \"URI\"\n with pytest.raises(TypeError):\n assert shap == Statement(\n foobar=\"@photo\", prop_id=\"dcterms:creator\", value_type=\"URI\"\n )","def bind_assign_value(self: Parser, node: doc.expr, var_name: str, value: Any) -> Any:\n if isinstance(value, T.meta_var):\n return value.value\n elif isinstance(value, (list, tuple)):\n for i, v in enumerate(value):\n bind_assign_value(self, node, f\"{var_name}_{i}\", v)\n return value\n elif isinstance(value, Frame):\n value.add_callback(partial(value.__exit__, None, None, None))\n res = value.__enter__()\n IRBuilder.name(var_name, res)\n return res\n elif isinstance(value, (Buffer, IterVar)) or (\n isinstance(value, Var) and not self.var_table.exist(value)\n ):\n IRBuilder.name(var_name, value)\n return value\n else:\n value = tvm.runtime.convert(value)\n frame = T.LetStmt(value)\n var = frame.var\n IRBuilder.name(var_name, var)\n frame.add_callback(partial(frame.__exit__, None, None, None))\n frame.__enter__()\n return var","def syntax_var_assign():\n a = 'Hello'\n print(f'{a} is stored at {hex(id(a))}')\n a = \"World\"\n print(f'{a} is stored at {hex(id(a))}')\n\n ## Output\n # Hello is stored at 0x10d251340\n # World is stored at 0x10d251378\n\n ## Notes\n # id()\n # Return the “identity” of an object. This is an integer (or long integer) which is guaranteed\n # to be unique and constant for this object during its lifetime.","def _assignValue(value):\n if value == \"\":\n return None\n else:\n return value","def _analyse_stmt_AugAssign(\n self, statement: ast.AugAssign, *, next: CFNode\n ) -> CFNode:\n return self._ast_node(statement, next=next, error=self._raise)","def __setattr__ (self, attr, value):\n self.set_value (attr, value)","def test_assign_to_thread_write(self):\n\n class Node:\n my_metric = Metric(Int64)\n node = Node()\n\n my_metric = get_metric_object(node, 'my_metric')\n\n t = threading.Thread(target=my_metric.assign_to_current_thread, args=(node,))\n t.start()\n t.join()\n\n with self.assertRaises(IcypawException):\n node.my_metric.x = 75","def test_variable_assign(self):\n self.trace('x = 1')\n\n events = self.variable_events\n self.assertEqual(len(events), 1)\n event = events[0]\n self.assertIsInstance(event, TraceAssign)\n self.assertEqual(event.name, 'x')\n self.assertEqual(event.value, 1)","def visit_Assign(self, node):\n self.generic_visit(node)\n target = get_single_target(node)\n if isinstance(target, ast.Attribute):\n args = [ target.value, ast.Str(target.attr), node.value ]\n return ast.Expr(to_call(to_name('setattr'), args))\n return node","def assign_actual(segments_path, training_path):\n pass","def assign(self, *args):\n return _ida_hexrays.cif_t_assign(self, *args)","def assign_lr(self, session, lr):\n session.run(tf.assign(self.lr,lr))","def __setitem__(self, attribute_name, value):\n pass # pragma: no cover","def is_assign(self):\n return self.var.initializer is not None","def __setitem__(self, i, v):\n raise TypeError(\"'Factorization' object does not support item assignment\")","def declare_struct_variable(self, v):\n scope = self.sscope\n scope.set(v.args[0])\n self.resolve_to(v, RESOLUTION_NAKED)","def fix_for_python_gcc_declaration_distinction(self, con_scopestr, id, var_name):\n version = self.con_scope_to_highest_var_vers[con_scopestr][id]\n # this function should only be called in cases where we need to implement a dummy assignment\n # which creates a version 1 variable\n assert(version == VAR_INIT_VERSION)\n # increment the version, and create an GrFN variable for the incremented version\n self.incr_version_in_con_scope(con_scopestr, id, var_name)\n new_version = self.con_scope_to_highest_var_vers[con_scopestr][id]\n new_fullid = build_fullid(var_name, id, new_version, con_scopestr)\n grfn_var = self.pipeline_state.get_grfn_var(new_fullid)\n from_source_mdata = generate_from_source_metadata(False, VariableCreationReason.DUMMY_ASSIGN)\n add_metadata_to_grfn_var(grfn_var, from_source_mdata)\n\n # create a literal GrFN assignment for this dummy assignment\n assign_metadata = create_lambda_node_metadata(source_refs=[])\n literal_node = create_grfn_literal_node(assign_metadata)\n lambda_expr = \"lambda: None\"\n literal_node.func_str = lambda_expr\n literal_node.function = load_lambda_function(literal_node.func_str)\n dummy_assignment = GrfnAssignment(literal_node, LambdaType.LITERAL, lambda_expr=lambda_expr)\n dummy_assignment.outputs[new_fullid] = grfn_var.uid\n\n # add dummy assignment to function def node\n assert(self.pipeline_state.is_con_scopestr_func_def(con_scopestr))\n func_def_node = self.pipeline_state.func_def_node_from_scopestr(con_scopestr)\n\n func_def_node.dummy_grfn_assignments.append(dummy_assignment)","def assign(self, *args):\n return _ida_hexrays.cloop_t_assign(self, *args)","def test_variable_compound_assign_boxed_return(self):\n env = self.trace('foo, bar = objects.create_foo_and_bar()')\n\n events = self.variable_events\n event = next(evt for evt in events if isinstance(evt, TraceAssign))\n self.assertEqual(event.name, ('foo','bar'))\n self.assertEqual(event.value, (env['foo'],env['bar']))\n self.assertIsInstance(event.value_event, TraceReturn)\n self.assertEqual(event.value_event.function, objects.create_foo_and_bar)\n self.assertTrue(event.value_event.multiple_values)","def assign_term(urs):\n\n if isinstance(urs, six.string_types):\n return MAPPING.get(urs, UNKNOWN)\n return assign_term(urs.get_rna_type())","def testAssignment():\n\n conf = naiveConf.NaiveConf(exampleConfFname)\n conf.x1 = conf.x\n conf['x2'] = conf.x\n conf.x3 = conf['x']\n conf['x4'] = conf['x']\n\n assert conf.x == conf['x']\n assert conf.x1 == conf['x1']\n assert conf.x2 == conf['x2']\n assert conf.x3 == conf['x3']\n assert conf.x4 == conf['x4']\n assert conf.x1 == conf.x\n assert conf['x1'] == conf['x']\n assert conf.x2 == conf.x\n assert conf.x3 == conf.x\n assert conf.x4 == conf.x\n\n del conf.x4\n with pytest.raises(KeyError):\n print conf.x4","def assign_variable(executor, variable, value):\n variable = variable.replace(\" \", \"\")\n # TODO Should move parsing of this to ParsedStatementLet.\n # TODO Need to handle N-dimensional array element assignment.\n i = variable.find(\"(\")\n if i != -1:\n # Array reference\n j = variable.find(\")\", i+1)\n if j == -1:\n raise BasicSyntaxError(F\"Missing ) in in array assignment to {variable}\")\n if i+1 == j:\n raise BasicSyntaxError(F\"Missing array subscript in assignment to {variable}\")\n\n subscripts = variable[i+1:j].split(\",\")\n variable = variable[:i]\n is_valid_identifier(variable)\n subscripts = [int(eval_expression(executor._symbols, subscript)) - 1 for subscript in subscripts]\n executor.put_symbol_element(variable, value, subscripts)\n else:\n is_valid_identifier(variable)\n executor.put_symbol(variable, value, symbol_type=SymbolType.VARIABLE, arg=None)","def testMutable(self):\n alist = [1,2,3]\n aref = alist\n alist[1] = \"stasi\"\n self.assertEqual([1,\"stasi\",3],aref)","def literal():\n return Literal()","def set_assignment(self, var, value):\n self.variable_to_value[var] = value","def __setitem__(name, other):","def _analyse_stmt_AnnAssign(\n self, statement: ast.AnnAssign, *, next: CFNode\n ) -> CFNode:\n return self._ast_node(statement, next=next, error=self._raise)","def visit_VarAssignNode(self, node: VarAssignNode, symbol_table: SymbolTable) -> None:\n if isinstance(node.name, AccessNode) and isinstance(node.name.item_to_access, NumberNode):\n var = self.visit(node.name.accessor, symbol_table)\n var.vals[int(node.name.item_to_access.tok.value)] = self.visit(node.value, symbol_table)\n if isinstance(var, List):\n var.value = [item[idx].value for idx, item in enumerate(var.vals.values())]\n else:\n return f'Strings are immutable'\n else:\n assignment = self.visit(node.value, symbol_table)\n\n symbol_table[node.name] = assignment","def test_bug_652575():\n assert _do_test_raw(\"var x = 'capability.policy.';\").failed()","def __setattr__(self, name: str, value: Any) -> None:\n super().__setattr__(name, value)","def __setattr__(*args, **kwargs): # real signature unknown\n pass"],"string":"[\n \"def test_50_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 3] of real;\\n\\t\\tvar a: array[1 .. 3] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 2]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,450))\",\n \"def test_47_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 3] of real;\\n\\t\\tvar a: array[1 .. 3] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,447))\",\n \"def test_46_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real; begin x[1]:=1;\\n\\t\\twith y:integer;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,446))\",\n \"def test_49_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 2] of real;\\n\\t\\tvar a: array[1 .. 3] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Type Mismatch In Statement: Return(Some(Id(a)))\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,449))\",\n \"def test_48_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 3] of real;\\n\\t\\tvar a: array[1 .. 2] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Type Mismatch In Statement: Return(Some(Id(a)))\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,448))\",\n \"def visit_assign(self: Parser, node: doc.Assign) -> None:\\n if len(node.targets) != 1:\\n self.report_error(node, \\\"Consequential assignments like 'a = b = c' are not supported.\\\")\\n lhs = node.targets[0]\\n\\n if isinstance(node.value, doc.Subscript):\\n check_slices = []\\n if isinstance(node.value.slice, doc.Slice):\\n check_slices = [node.value.slice]\\n elif isinstance(node.value.slice, doc.Tuple):\\n for p in node.value.slice.elts:\\n if isinstance(p, doc.Slice):\\n check_slices.append(p)\\n for s in check_slices:\\n if not s.step and s.upper and s.lower:\\n s.step = doc.Constant(\\n 1,\\n None,\\n 1,\\n 1,\\n s.upper.lineno,\\n s.upper.end_col_offset + 1,\\n s.upper.lineno,\\n s.upper.end_col_offset + 2,\\n )\\n\\n rhs = self.eval_expr(node.value)\\n if isinstance(lhs, doc.Subscript):\\n if isinstance(lhs.slice, doc.Tuple):\\n indices = []\\n for index in lhs.slice.elts:\\n indices.append(self.eval_expr(index))\\n else:\\n indices = self.eval_expr(lhs.slice)\\n T.buffer_store(self.eval_expr(lhs.value), rhs, indices)\\n else:\\n self.eval_assign(target=lhs, source=rhs, bind_value=bind_assign_value)\",\n \"def smart_assign(variable, value, assign_fn=tf.assign,\\n force_cast=False, force_sync=True):\\n if force_cast and variable.dtype != value.dtype:\\n value = tf.cast(value, dtype=variable.dtype)\\n\\n if not (tf.distribute.has_strategy() and tf.distribute.get_replica_context()):\\n return assign_fn(variable, value)\\n\\n def merge_fn(distribution, variable, value):\\n strategy = tf.distribute.get_strategy()\\n if isinstance(strategy, tf.distribute.MirroredStrategy) and force_sync:\\n value = strategy.reduce(tf.distribute.ReduceOp.MEAN, value)\\n return distribution.extended.update(variable, assign_fn, args=(value,))\\n\\n return tf.distribute.get_replica_context().merge_call(\\n merge_fn, args=(variable, value))\",\n \"def assign(self, V, py):\\n V.value = py\",\n \"def assign(self, V, py):\\n V.value = py\",\n \"def assign(self, V, py):\\n V.value = py\",\n \"def assign(self, V, py):\\n V.value = py\",\n \"def __init__(self, concept_ref='', value=''):\\n self.concept_ref = concept_ref\\n self.value = value\",\n \"def test_45_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tprocedure main(); var x:real; begin x:=1;\\n\\t\\twith y:integer;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,445))\",\n \"def test_remove_assignment_rule(self):\\n pass\",\n \"def assign(self, dst, req, src):\\n if req == 'null':\\n return\\n if req in ('write', 'inplace'):\\n dst[:] = src\\n elif req == 'add':\\n dst[:] += src\",\n \"def __setitem__(self, idx, value):\\n if not isinstance(value, nodes.Node):\\n raise NotImplementedError(\\\"setitem with non-blaze rhs\\\")\\n result = self.getitem(idx, context='set')\\n result = Assign('assign', [result, value])\\n result.eval()\",\n \"def assign(obj, path, val, missing=None):\\n return glom(obj, Assign(path, val, missing=missing))\",\n \"def _compat_assign_gast_4(targets, value, type_comment):\\n return gast.Assign(targets=targets, value=value)\",\n \"def eval_assignment(exp, env):\\n set_variable_value(assignment_variable(exp), m_eval(assignment_value(exp), env), env)\\n return quote(\\\"ok\\\")\",\n \"def _assign_op(dest, op, arg, val, path, scope):\\n if op == '[':\\n dest[arg] = val\\n elif op == '.':\\n setattr(dest, arg, val)\\n elif op == 'P':\\n _assign = scope[TargetRegistry].get_handler('assign', dest)\\n try:\\n _assign(dest, arg, val)\\n except Exception as e:\\n raise PathAssignError(e, path, arg)\\n else: # pragma: no cover\\n raise ValueError('unsupported T operation for assignment')\",\n \"def test_obj_action_for_assignments():\\n grammar = r\\\"\\\"\\\"\\n S: a=\\\"foo\\\" b?=\\\"bar\\\" c=C+;\\n C: val=\\\"baz\\\";\\n \\\"\\\"\\\"\\n\\n g = Grammar.from_string(grammar)\\n p = Parser(g)\\n\\n result = p.parse(\\\"foo bar baz baz baz\\\")\\n\\n assert isinstance(result, g.classes['S'])\\n assert isinstance(result.c[0], g.classes['C'])\\n\\n assert result.a == \\\"foo\\\"\\n assert result.b is True\\n assert len(result.c) == 3\\n assert all((c.val == \\\"baz\\\" for c in result.c))\",\n \"def _AugAssign(self, t):\\n if not isinstance(t.target, ast.Name):\\n self.RaiseError(t, \\\"Augmented assignment to complex expressions not supported\\\")\\n # check if target exists in locals\\n if t.target.id not in self._locals :\\n self.RaiseError(t, \\\"Augmented assignment not permitted on variables not already assigned previously\\\")\\n self.fill()\\n self.dispatch(t.target)\\n self.write(\\\" \\\"+self.binop[t.op.__class__.__name__]+\\\"= \\\")\\n self.dispatch(t.value)\\n self.write(\\\";\\\")\",\n \"def convert_assign(g, op, block):\\n\\n out = g.get_node(op.input(\\\"X\\\")[0])\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def _compat_assign_gast_5(targets, value, type_comment):\\n return gast.Assign(targets=targets, value=value, type_comment=type_comment)\",\n \"def test_001_lambda_assign(self):\\r\\n text = \\\"\\\"\\\"\\r\\n const f = (d,k,v) => d[k] = v\\r\\n \\\"\\\"\\\"\\r\\n tokens = Lexer().lex(text)\\r\\n ast = Parser().parse(tokens)\\r\\n expected = TOKEN('T_MODULE', '',\\r\\n TOKEN('T_VAR', 'const',\\r\\n TOKEN('T_ASSIGN', '=',\\r\\n TOKEN('T_TEXT', 'f'),\\r\\n TOKEN('T_LAMBDA', '=>',\\r\\n TOKEN('T_TEXT', 'Anonymous'),\\r\\n TOKEN('T_ARGLIST', '()',\\r\\n TOKEN('T_TEXT', 'd'),\\r\\n TOKEN('T_TEXT', 'k'),\\r\\n TOKEN('T_TEXT', 'v')\\r\\n ),\\r\\n TOKEN('T_ASSIGN', '=',\\r\\n TOKEN('T_SUBSCR', '',\\r\\n TOKEN('T_TEXT', 'd'),\\r\\n TOKEN('T_TEXT', 'k'))\\r\\n ),\\r\\n TOKEN('T_TEXT', 'v')\\r\\n )\\r\\n )\\r\\n )\\r\\n )\\r\\n\\r\\n self.assertFalse(parsecmp(expected, ast, False))\",\n \"def irgen_assign(stmt, builder, table):\\n lvalue = irgen_lvalue(stmt.exprs[0], builder, table)\\n expr = irgen_expr(stmt.exprs[1], builder, table)\\n builder.store(expr, lvalue)\",\n \"def create_Assign(left_hand_side, right_hand_side):\\n right_hand_side.ctx = ast.Load()\\n left_hand_side.ctx = ast.Store()\\n return ast.Assign(targets=[left_hand_side], value=right_hand_side)\",\n \"def set_value(self, var_name, new_value, tf_session):\\n\\n if(var_name in self.assign_operator):\\n\\n tf_session.run(\\n self.assign_operator[var_name], {\\n self.l_param_input[var_name]: new_value})\\n else:\\n print(\\\"Thou shall only assign learning parameters!\\\")\",\n \"def visit_aug_assign(self: Parser, node: doc.AugAssign) -> None:\\n lhs_pos = (\\n node.target.lineno,\\n node.target.col_offset,\\n node.target.end_lineno,\\n node.target.end_col_offset,\\n )\\n rhs_pos = (\\n node.value.lineno,\\n node.value.col_offset,\\n node.value.end_lineno,\\n node.value.end_col_offset,\\n )\\n node.target.ctx = doc.Load(*lhs_pos)\\n with self.var_table.with_frame():\\n lhs_name = \\\"__tvm_tmp_value_aug_assign_lhs\\\"\\n rhs_name = \\\"__tvm_tmp_value_aug_assign_rhs\\\"\\n lhs_expr = self.eval_expr(node.target)\\n rhs_expr = self.eval_expr(node.value)\\n self.var_table.add(lhs_name, lhs_expr)\\n self.var_table.add(rhs_name, rhs_expr)\\n op = doc.BinOp(\\n doc.Name(lhs_name, doc.Load(*lhs_pos), *lhs_pos),\\n node.op,\\n doc.Name(rhs_name, doc.Load(*rhs_pos), *rhs_pos),\\n *lhs_pos,\\n )\\n rhs = self.eval_expr(op)\\n lhs = node.target\\n lhs.ctx = doc.Store(*lhs_pos)\\n if isinstance(lhs, doc.Subscript):\\n if isinstance(lhs.slice, doc.Tuple):\\n indices = []\\n for index in lhs.slice.elts:\\n indices.append(self.eval_expr(index))\\n else:\\n indices = [self.eval_expr(lhs.slice)]\\n T.buffer_store(self.eval_expr(lhs.value), rhs, indices)\\n else:\\n self.eval_assign(target=lhs, source=rhs, bind_value=bind_assign_value)\",\n \"def visit_ann_assign(self: Parser, node: doc.AnnAssign) -> None:\\n lhs = node.target\\n rhs = self.eval_expr(node.value)\\n ann_var = self.visit_tvm_annotation(node.annotation)\\n if not isinstance(ann_var, Var):\\n self.report_error(node.annotation, \\\"Annotation should be Var\\\")\\n self.eval_assign(target=lhs, source=ann_var, bind_value=bind_assign_value)\\n frame = T.LetStmt(rhs, var=ann_var)\\n frame.add_callback(partial(frame.__exit__, None, None, None))\\n frame.__enter__()\",\n \"def test_variable_assign_boxed_access(self):\\n self.trace('y = x', env={'x': 1})\\n\\n events = self.variable_events\\n self.assertEqual(len(events), 2)\\n self.assertIsInstance(events[0], TraceAccess)\\n self.assertIsInstance(events[1], TraceAssign)\\n self.assertEqual(events[1].value_event, events[0])\",\n \"def variability(sv):\\r\\n unchanging(sv) # remove change clause for constants \\r\\n make_volatiles(sv) # create list of volatile objects and make their clause Always\\r\",\n \"def on_assign(self):\",\n \"def assign(self, value):\\n self.value = value\",\n \"def visit_Assign(self, node):\\n self.generic_visit(node)\\n\\n if node.col_offset == 0:\\n mnode = ast.parse(\\\"\\\")\\n mnode.body = [node]\\n mnode = ast.fix_missing_locations(mnode)\\n code = compile(mnode, \\\"<ast>\\\", \\\"exec\\\")\\n try:\\n exec(code, self.globals_)\\n except Exception:\\n pass\\n self.globals_.pop(\\\"__builtins__\\\", None)\\n self.globals_.pop(\\\"builtins\\\", None)\",\n \"def fset(self, value):\\n message = \\\"Overriding a constant value is an illegal operation: {0} = {1}.\\\".format(\\n name.__name__,\\n value)\\n raise TypeError(message)\",\n \"def assign_variable(self, name, value):\\n return self.set_variable(name, value)\",\n \"def variable(self, val):\",\n \"def test_augassign_recursion():\\n # infinitely recurses in python\\n code = \\\"\\\"\\\"\\n def rec():\\n a = 0\\n a += rec()\\n return a\\n rec()\\n \\\"\\\"\\\"\\n cls_node = extract_node(code)\\n assert next(cls_node.infer()) is util.Uninferable\",\n \"def setLocal(name, value):\",\n \"def visit_AugAssign(self, node):\\n target = node.target\\n\\n rhs_target = copy.deepcopy(target)\\n rhs_target.ctx = ast.Load()\\n ast.fix_missing_locations(rhs_target)\\n\\n bin_op = ast.BinOp(rhs_target, node.op, node.value)\\n assignment = ast.Assign([target], bin_op)\\n assignment.inplace_op = node.op\\n return self.visit(assignment)\",\n \"def visit_Assign(self, node):\\n self.generic_visit(node)\\n is_multiple = len(node.targets) > 1\\n is_compound = any(map(is_sequence_node, node.targets))\\n is_simple = not is_compound\\n if is_simple and is_multiple:\\n return self.visit_simple_assign(node)\\n elif is_compound and (is_multiple or is_sequence_node(node.value)):\\n return self.visit_compound_assign(node)\\n return node\",\n \"def __setattr__(self, name, value): # real signature unknown; restored from __doc__\\n pass\",\n \"def __setattr__(self, name, value): # real signature unknown; restored from __doc__\\n pass\",\n \"def assign_state(self, state):\\n raise NotImplementedError()\",\n \"def single_assignment_handler(target, value, assign_stmts, node, id_str):\\n #print(\\\"single_assignment_handler\\\")\\n\\n temp_stmts = core_language.create_Assign(target, value)\\n if hasattr(node, 'lineno'):\\n temp_stmts.lineno = node.lineno\\n temp_stmts.col_offset = node.col_offset\\n\\n assign_stmts.append(temp_stmts)\\n return False\",\n \"def setValue(self, value):\\r\\n\\r\\n if type(value) == NoneType: # INPUT = None. Valid (to set to None the object)\\r\\n self.setNone()\\r\\n return\\r\\n elif type(value) == StringType:\\t\\t\\t\\t\\t# INPUT = String. Valid (constraint name)\\r\\n if not self.constName:\\r\\n self.constName = ATOM3String(value)\\t\\t\\t# constraint name\\r\\n else: self.constName.setValue(value)\\r\\n return\\r\\n elif type(value) == TupleType and len(value) == 5: # Tuple Type of size 5\\r\\n name, language, kind, actions, code = value\\r\\n else:\\r\\n raise ATOM3BadAssignmentValue, \\\"ATOM3Constraint: Bad type in setValue()\\\"\\r\\n\\r\\n if not self.constName:\\r\\n self.constName = ATOM3String(name)\\t\\t\\t\\t# constraint name\\r\\n else: self.constName.setValue(name)\\r\\n\\r\\n if type(language) == TupleType:\\t\\t\\t\\t\\t# May be is a tuple ([languages...], selected)\\r\\n self.language.setValue(language)\\t\\t\\t\\t# the pass it to self.language\\r\\n elif type(language) == IntType: \\t# otherwise it is the selected language\\r\\n self.language.setValue((None, language))\\t\\t\\r\\n else:\\r\\n raise ATOM3BadAssignmentValue, \\\"ATOM3Constraint: Bad type in setValue() (2nd argument in tuple)\\\"\\r\\n\\r\\n if kind:\\t\\t\\t\\t\\t\\t\\t\\t# if it is present is a tuple ([types...], selectedType)\\r\\n if type(kind) == TupleType and len(kind) == 2:\\r\\n if not self.kind: self.kind = ATOM3Enum(kind[0], kind[1])\\r\\n else:\\r\\n try:\\r\\n self.kind.setValue((None,kind[1])) # set kind of constraint if specified\\r\\n except ATOM3BadAssignmentValue:\\r\\n raise ATOM3BadAssignmentValue, \\\"ATOM3Constraint: Bad type in setValue(), \\\"+ str(kind)\\r\\n elif type(kind) != NoneType:\\r\\n raise ATOM3BadAssignmentValue, \\\"ATOM3Constraint: Bad type in setValue() (3rd argument in tuple)\\\"\\r\\n\\t\\r\\n if actions:\\r\\n if type(actions) == TupleType and len(actions) == 2:\\r\\n if not self.actions: self.actions = ATOM3MSEnum(actions[0], actions[1], 0, ATOM3MSEnum.LISTBOX)\\r\\n else: self.actions.setValue((None,actions[1]))\\t\\t# set action where it must be evaluated, if specified\\r\\n else:\\r\\n raise ATOM3BadAssignmentValue, \\\"ATOM3Constraint: Bad type in setValue() (4th argument in tuple)\\\"\\r\\n\\r\\n if type(code) == StringType or type(code) == NoneType:\\r\\n self.constCode = code\\r\\n if self.constCode and self.constCodeWidget:\\r\\n self.constCodeWidget.insert(1.0, self.constCode)\\r\\n else:\\r\\n raise ATOM3BadAssignmentValue, \\\"ATOM3Constraint: Bad type in setValue() (5th argument in tuple)\\\"\",\n \"def let(**kw):\\n raise NotImplementedError(\\\"`let` can only be used in rules, not code!\\\")\",\n \"def visit_Assign(self, node):\\n self.generic_visit(node)\\n target = get_single_target(node)\\n if isinstance(target, ast.Subscript):\\n fun = to_attribute(self.operator, 'setitem')\\n args = [target.value, self.index_to_expr(target.slice), node.value]\\n return ast.Expr(to_call(fun, args))\\n return node\",\n \"def assign_value(Xj, Xk, csp, assignment):\\r\\n parent_assignment = assignment[Xj]\\r\\n for val in csp.curr_domains[Xk]:\\r\\n if csp.constraints(Xj, parent_assignment, Xk, val):\\r\\n return val\\r\\n\\r\\n # No consistent assignment available\\r\\n return None\",\n \"def rhs(self):\\n if not self.is_assign():\\n raise AssertionError('Not an assignment')\\n return self.initializer\",\n \"def eval_assignment(assignment, motif_node_dict):\\n if type(assignment.rvalue).__name__ == 'FuncCall':\\n motif_node, tree_node = eval_function_call(assignment.rvalue, motif_node_dict)\\n # consider \\\"var = XXX;\\\" and \\\"*var = XXX\\\" and \\\"&var = XXX\\\" situations\\n if (type(assignment.lvalue).__name__ == 'ID' and assignment.lvalue.name in motif_node_dict) or (type(assignment.lvalue).__name__ == 'UnaryOp' and assignment.lvalue.expr.name in motif_node_dict):\\n if not motif_node:\\n print('\\\\33[101m' + '[error][eval_assignment]: ' + assignment.lvalue.name + ' is in the dictionary. MotifNode should not be None.\\\\033[0m')\\n exit(1)\\n else:\\n motif_node_dict[assignment.lvalue.name].append(motif_node)\\n return tree_node\\n # In a case where a provenance node was declared but then assigned or reassigned. For example:\\n # struct provenance *tprov;\\n # ...\\n # tprov = t->provenance;\\n # tprov must then be in the motif_node_dict.\\n elif type(assignment.lvalue).__name__ == 'ID' and assignment.lvalue.name in motif_node_dict:\\n # we can only infer its type from the name of the variable\\n motif_node = provenance.create_motif_node(assignment.lvalue.name)\\n motif_node_dict[assignment.lvalue.name].append(motif_node)\\n return None\\n elif type(assignment.lvalue).__name__ == 'UnaryOp' and type(assignment.lvalue.expr).__name__ == 'ID' and assignment.lvalue.expr.name in motif_node_dict:\\n # similar case as the previous one, except that we have: *tprov = ...\\n # we can only infer its type from the name of the variable\\n motif_node = provenance.create_motif_node(assignment.lvalue.expr.name)\\n motif_node_dict[assignment.lvalue.expr.name].append(motif_node)\\n return None\\n else:\\n #######################################################\\n # We will consider other conditions if we ever see them\\n # POSSIBLE CODE HERE.\\n #######################################################\\n return None\",\n \"def setattr_ref(self, interp, attr, w_newvalue, contextclass):\\n interp.warn('Attempt to modify property of non-object')\\n return interp.space.w_Null\",\n \"def test_statement_initialized_by_assignment():\\n shap = Statement(shape_id=\\\"@photo\\\", prop_id=\\\"dcterms:creator\\\", value_type=\\\"URI\\\")\\n shap2 = Statement()\\n shap2.shape_id = \\\"@photo\\\"\\n shap2.prop_id = \\\"dcterms:creator\\\"\\n shap2.value_type = \\\"URI\\\"\\n assert shap == shap2\",\n \"def testDoubleReference(self): \\n my_var=Var()\\n list_var=Var()\\n my_list=PList([my_var,2,3])\\n Compound(\\\"=\\\",list_var,my_list).post_goal()\\n self.assertNotEqual(resume(),False)\\n Compound(\\\"=\\\",list_var.value()[0],3).post_goal()\\n self.assertNotEqual(resume(),False)\\n self.assertEqual(my_var.value(),3)\",\n \"def visit_compound_assign(self, node):\\n # Determine number of values (arity) of compound assignment.\\n nvalues = { len(target.elts) for target in node.targets \\n if is_sequence_node(target) }\\n if len(nvalues) > 1:\\n # A multiple, compound assignment with different arities, e.g.,\\n # `x,y = a,b,c = ...` is not a syntax error in Python, though it\\n # probably should be because it's guaranteed to cause a runtime\\n # error. Raise the error here, since we cannot proceed.\\n raise SyntaxError(\\\"Multiple assignment with different arities\\\")\\n nvalues = nvalues.pop()\\n\\n # Assign temporary variables.\\n temps = [ gensym() for i in range(nvalues) ]\\n stmts = []\\n if is_sequence_node(node.value) and len(node.value.elts) == nvalues:\\n # Special case: RHS is sequence literal of correct length.\\n for i in range(nvalues):\\n temp_target = to_name(temps[i], ast.Store())\\n stmts.append(ast.Assign([temp_target], node.value.elts[i]))\\n else:\\n # General case.\\n temp_target = to_tuple(\\n (to_name(temp, ast.Store()) for temp in temps), ast.Store())\\n stmts.append(ast.Assign([temp_target], node.value))\\n\\n # Rewrite assignments as sequence of assignments.\\n for target in reversed(node.targets):\\n if is_sequence_node(target):\\n stmts.extend(ast.Assign([target.elts[i]], to_name(temps[i]))\\n for i in range(nvalues))\\n else:\\n temp_tuple = to_tuple(to_name(temp) for temp in temps)\\n stmts.append(ast.Assign([target], temp_tuple))\\n \\n return stmts\",\n \"def do_assign(parser, token):\\n bits = token.contents.split()\\n if len(bits) != 3:\\n raise template.TemplateSyntaxError(\\\"'%s' tag takes two arguments\\\" % bits[0])\\n value = parser.compile_filter(bits[2])\\n return AssignNode(bits[1], value)\",\n \"def do_assign(parser, token):\\n bits = token.contents.split()\\n if len(bits) != 3:\\n raise template.TemplateSyntaxError(\\\"'%s' tag takes two arguments\\\" % bits[0])\\n value = parser.compile_filter(bits[2])\\n return AssignNode(bits[1], value)\",\n \"def visit_AugAssign(self, node):\\n # FIXME: Gensym the LHS to avoid two evaluations.\\n self.generic_visit(node)\\n rhs = to_call(self.op_to_function(node.op),\\n [set_ctx(node.target), node.value])\\n return ast.Assign([node.target], rhs)\",\n \"def assign(self, other):\\n\\n assert isinstance(other, VarList)\\n assert len(self) == len(other)\\n ops = []\\n for (my_var, other_var) in zip(self.vars_, other.vars_):\\n ops.append(my_var.assign(other_var))\\n return tf.group(*ops, name=\\\"assign_\\\"+self.name)\",\n \"def convert_assign_value(g, op, block):\\n\\n keys = [\\\"bool_values\\\", \\\"fp32_values\\\", \\\"int32_values\\\", \\\"int64_values\\\"]\\n dtypes = [\\\"bool\\\", \\\"float32\\\", \\\"int32\\\", \\\"int64\\\"]\\n for i, key in enumerate(keys):\\n dtype = dtypes[i]\\n value = np.array(op.attr(key)).astype(dtype)\\n if value is not None and value.size >= 1:\\n break\\n shape = op.attr(\\\"shape\\\")\\n value = value.reshape(shape)\\n out = _op.const(value, dtype=dtype)\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def test_compiler_assignment(patch, compiler, lines, tree):\\n patch.many(Objects, ['names', 'entity'])\\n tree.assignment_fragment.service = None\\n tree.assignment_fragment.mutation = None\\n compiler.assignment(tree, '1')\\n Objects.names.assert_called_with(tree.path)\\n fragment = tree.assignment_fragment\\n entity = get_entity(fragment.expression)\\n Objects.entity.assert_called_with(entity)\\n kwargs = {'name': Objects.names(), 'args': [Objects.entity()],\\n 'parent': '1'}\\n lines.append.assert_called_with('set', tree.line(), **kwargs)\",\n \"def visit_simple_assign(self, node):\\n temp = gensym()\\n temp_target = to_name(temp, ast.Store())\\n stmts = [ ast.Assign([temp_target], node.value) ]\\n stmts += [ ast.Assign([target], to_name(temp))\\n for target in node.targets ]\\n return stmts\",\n \"def __set__(self, obj, value):\\r\\n pass\",\n \"def assign(self, var, value):\\n\\t\\tself._root = self._insert(self._root, var, value)\",\n \"def assign_to_device(device, ps_device=None):\\n ps_ops = ['Variable', 'VariableV2', 'AutoReloadVariable', 'MutableHashTable',\\n 'MutableHashTableOfTensors', 'MutableDenseHashTable']\\n\\n def _assign(op):\\n node_def = op if isinstance(op, tf.NodeDef) else op.node_def\\n if node_def.op in ps_ops:\\n return \\\"/\\\" + ps_device\\n else:\\n return device\\n\\n return _assign\",\n \"def visit_Assign(self, node: Assign) -> None:\\n\\n node_type = type(node.right).__name__\\n if isinstance(node.right, String):\\n self._create_instruct(node_type)\\n self.visit(node.left)\\n instruct = self.visit(node.right)\\n c_str = self.builder.alloca(instruct.type)\\n self.builder.store(instruct, c_str)\\n self.builder.ret_void()\\n else:\\n self._create_instruct(node_type)\\n self.visit(node.left)\\n instruct = self.visit(node.right)\\n self.builder.ret(instruct)\\n\\n self.GLOBAL_MEMORY[node.left.value] = instruct\",\n \"def _analyse_stmt_Assign(self, statement: ast.Assign, *, next: CFNode) -> CFNode:\\n return self._ast_node(statement, next=next, error=self._raise)\",\n \"def _Assign(self, t):\\n if len(t.targets) > 1:\\n self.RaiseError(t, \\\"Assignment to multiple targets not supported\\\")\\n if not isinstance(t.targets[0], ast.Name):\\n self.RaiseError(t, \\\"Assignment to complex expressions not supported\\\")\\n self.fill()\\n # check if target exists in locals\\n if t.targets[0].id not in self._locals :\\n self.write(\\\"auto \\\")\\n self._locals.append(t.targets[0].id)\\n self.dispatch(t.targets[0])\\n self.write(\\\" = \\\")\\n self.dispatch(t.value)\\n self.write(\\\";\\\")\",\n \"def Assignment(self):\\n id = self.primary()\\n if self.currtok[1].name == \\\"DECLERATION\\\":\\n self.currtok = next(self.tg)\\n if self.functions.get(self.currtok[0]) is not None:\\n\\n express = self.FunctionCall()\\n return assignmentStmt(id, express)\\n else:\\n express = self.Expression()\\n\\n if self.currtok[1].name == \\\"SEMI\\\":\\n self.currtok = next(self.tg)\\n return assignmentStmt(id, express)\\n raise SLUCSyntaxError(\\\"ERROR: Missing Semicolon on line {0}\\\".format(str(self.currtok[2] - 1)))\\n raise SLUCSyntaxError(\\\"ERROR: Missing assignment on line {0}\\\".format(str(self.currtok[2] - 1)))\",\n \"def test_statement_bad_field_initialized_by_assignment():\\n shap = Statement()\\n shap.foobar = \\\"@photo\\\"\\n shap.prop_id = \\\"dcterms:creator\\\"\\n shap.value_type = \\\"URI\\\"\\n with pytest.raises(TypeError):\\n assert shap == Statement(\\n foobar=\\\"@photo\\\", prop_id=\\\"dcterms:creator\\\", value_type=\\\"URI\\\"\\n )\",\n \"def bind_assign_value(self: Parser, node: doc.expr, var_name: str, value: Any) -> Any:\\n if isinstance(value, T.meta_var):\\n return value.value\\n elif isinstance(value, (list, tuple)):\\n for i, v in enumerate(value):\\n bind_assign_value(self, node, f\\\"{var_name}_{i}\\\", v)\\n return value\\n elif isinstance(value, Frame):\\n value.add_callback(partial(value.__exit__, None, None, None))\\n res = value.__enter__()\\n IRBuilder.name(var_name, res)\\n return res\\n elif isinstance(value, (Buffer, IterVar)) or (\\n isinstance(value, Var) and not self.var_table.exist(value)\\n ):\\n IRBuilder.name(var_name, value)\\n return value\\n else:\\n value = tvm.runtime.convert(value)\\n frame = T.LetStmt(value)\\n var = frame.var\\n IRBuilder.name(var_name, var)\\n frame.add_callback(partial(frame.__exit__, None, None, None))\\n frame.__enter__()\\n return var\",\n \"def syntax_var_assign():\\n a = 'Hello'\\n print(f'{a} is stored at {hex(id(a))}')\\n a = \\\"World\\\"\\n print(f'{a} is stored at {hex(id(a))}')\\n\\n ## Output\\n # Hello is stored at 0x10d251340\\n # World is stored at 0x10d251378\\n\\n ## Notes\\n # id()\\n # Return the “identity” of an object. This is an integer (or long integer) which is guaranteed\\n # to be unique and constant for this object during its lifetime.\",\n \"def _assignValue(value):\\n if value == \\\"\\\":\\n return None\\n else:\\n return value\",\n \"def _analyse_stmt_AugAssign(\\n self, statement: ast.AugAssign, *, next: CFNode\\n ) -> CFNode:\\n return self._ast_node(statement, next=next, error=self._raise)\",\n \"def __setattr__ (self, attr, value):\\n self.set_value (attr, value)\",\n \"def test_assign_to_thread_write(self):\\n\\n class Node:\\n my_metric = Metric(Int64)\\n node = Node()\\n\\n my_metric = get_metric_object(node, 'my_metric')\\n\\n t = threading.Thread(target=my_metric.assign_to_current_thread, args=(node,))\\n t.start()\\n t.join()\\n\\n with self.assertRaises(IcypawException):\\n node.my_metric.x = 75\",\n \"def test_variable_assign(self):\\n self.trace('x = 1')\\n\\n events = self.variable_events\\n self.assertEqual(len(events), 1)\\n event = events[0]\\n self.assertIsInstance(event, TraceAssign)\\n self.assertEqual(event.name, 'x')\\n self.assertEqual(event.value, 1)\",\n \"def visit_Assign(self, node):\\n self.generic_visit(node)\\n target = get_single_target(node)\\n if isinstance(target, ast.Attribute):\\n args = [ target.value, ast.Str(target.attr), node.value ]\\n return ast.Expr(to_call(to_name('setattr'), args))\\n return node\",\n \"def assign_actual(segments_path, training_path):\\n pass\",\n \"def assign(self, *args):\\n return _ida_hexrays.cif_t_assign(self, *args)\",\n \"def assign_lr(self, session, lr):\\n session.run(tf.assign(self.lr,lr))\",\n \"def __setitem__(self, attribute_name, value):\\n pass # pragma: no cover\",\n \"def is_assign(self):\\n return self.var.initializer is not None\",\n \"def __setitem__(self, i, v):\\n raise TypeError(\\\"'Factorization' object does not support item assignment\\\")\",\n \"def declare_struct_variable(self, v):\\n scope = self.sscope\\n scope.set(v.args[0])\\n self.resolve_to(v, RESOLUTION_NAKED)\",\n \"def fix_for_python_gcc_declaration_distinction(self, con_scopestr, id, var_name):\\n version = self.con_scope_to_highest_var_vers[con_scopestr][id]\\n # this function should only be called in cases where we need to implement a dummy assignment\\n # which creates a version 1 variable\\n assert(version == VAR_INIT_VERSION)\\n # increment the version, and create an GrFN variable for the incremented version\\n self.incr_version_in_con_scope(con_scopestr, id, var_name)\\n new_version = self.con_scope_to_highest_var_vers[con_scopestr][id]\\n new_fullid = build_fullid(var_name, id, new_version, con_scopestr)\\n grfn_var = self.pipeline_state.get_grfn_var(new_fullid)\\n from_source_mdata = generate_from_source_metadata(False, VariableCreationReason.DUMMY_ASSIGN)\\n add_metadata_to_grfn_var(grfn_var, from_source_mdata)\\n\\n # create a literal GrFN assignment for this dummy assignment\\n assign_metadata = create_lambda_node_metadata(source_refs=[])\\n literal_node = create_grfn_literal_node(assign_metadata)\\n lambda_expr = \\\"lambda: None\\\"\\n literal_node.func_str = lambda_expr\\n literal_node.function = load_lambda_function(literal_node.func_str)\\n dummy_assignment = GrfnAssignment(literal_node, LambdaType.LITERAL, lambda_expr=lambda_expr)\\n dummy_assignment.outputs[new_fullid] = grfn_var.uid\\n\\n # add dummy assignment to function def node\\n assert(self.pipeline_state.is_con_scopestr_func_def(con_scopestr))\\n func_def_node = self.pipeline_state.func_def_node_from_scopestr(con_scopestr)\\n\\n func_def_node.dummy_grfn_assignments.append(dummy_assignment)\",\n \"def assign(self, *args):\\n return _ida_hexrays.cloop_t_assign(self, *args)\",\n \"def test_variable_compound_assign_boxed_return(self):\\n env = self.trace('foo, bar = objects.create_foo_and_bar()')\\n\\n events = self.variable_events\\n event = next(evt for evt in events if isinstance(evt, TraceAssign))\\n self.assertEqual(event.name, ('foo','bar'))\\n self.assertEqual(event.value, (env['foo'],env['bar']))\\n self.assertIsInstance(event.value_event, TraceReturn)\\n self.assertEqual(event.value_event.function, objects.create_foo_and_bar)\\n self.assertTrue(event.value_event.multiple_values)\",\n \"def assign_term(urs):\\n\\n if isinstance(urs, six.string_types):\\n return MAPPING.get(urs, UNKNOWN)\\n return assign_term(urs.get_rna_type())\",\n \"def testAssignment():\\n\\n conf = naiveConf.NaiveConf(exampleConfFname)\\n conf.x1 = conf.x\\n conf['x2'] = conf.x\\n conf.x3 = conf['x']\\n conf['x4'] = conf['x']\\n\\n assert conf.x == conf['x']\\n assert conf.x1 == conf['x1']\\n assert conf.x2 == conf['x2']\\n assert conf.x3 == conf['x3']\\n assert conf.x4 == conf['x4']\\n assert conf.x1 == conf.x\\n assert conf['x1'] == conf['x']\\n assert conf.x2 == conf.x\\n assert conf.x3 == conf.x\\n assert conf.x4 == conf.x\\n\\n del conf.x4\\n with pytest.raises(KeyError):\\n print conf.x4\",\n \"def assign_variable(executor, variable, value):\\n variable = variable.replace(\\\" \\\", \\\"\\\")\\n # TODO Should move parsing of this to ParsedStatementLet.\\n # TODO Need to handle N-dimensional array element assignment.\\n i = variable.find(\\\"(\\\")\\n if i != -1:\\n # Array reference\\n j = variable.find(\\\")\\\", i+1)\\n if j == -1:\\n raise BasicSyntaxError(F\\\"Missing ) in in array assignment to {variable}\\\")\\n if i+1 == j:\\n raise BasicSyntaxError(F\\\"Missing array subscript in assignment to {variable}\\\")\\n\\n subscripts = variable[i+1:j].split(\\\",\\\")\\n variable = variable[:i]\\n is_valid_identifier(variable)\\n subscripts = [int(eval_expression(executor._symbols, subscript)) - 1 for subscript in subscripts]\\n executor.put_symbol_element(variable, value, subscripts)\\n else:\\n is_valid_identifier(variable)\\n executor.put_symbol(variable, value, symbol_type=SymbolType.VARIABLE, arg=None)\",\n \"def testMutable(self):\\n alist = [1,2,3]\\n aref = alist\\n alist[1] = \\\"stasi\\\"\\n self.assertEqual([1,\\\"stasi\\\",3],aref)\",\n \"def literal():\\n return Literal()\",\n \"def set_assignment(self, var, value):\\n self.variable_to_value[var] = value\",\n \"def __setitem__(name, other):\",\n \"def _analyse_stmt_AnnAssign(\\n self, statement: ast.AnnAssign, *, next: CFNode\\n ) -> CFNode:\\n return self._ast_node(statement, next=next, error=self._raise)\",\n \"def visit_VarAssignNode(self, node: VarAssignNode, symbol_table: SymbolTable) -> None:\\n if isinstance(node.name, AccessNode) and isinstance(node.name.item_to_access, NumberNode):\\n var = self.visit(node.name.accessor, symbol_table)\\n var.vals[int(node.name.item_to_access.tok.value)] = self.visit(node.value, symbol_table)\\n if isinstance(var, List):\\n var.value = [item[idx].value for idx, item in enumerate(var.vals.values())]\\n else:\\n return f'Strings are immutable'\\n else:\\n assignment = self.visit(node.value, symbol_table)\\n\\n symbol_table[node.name] = assignment\",\n \"def test_bug_652575():\\n assert _do_test_raw(\\\"var x = 'capability.policy.';\\\").failed()\",\n \"def __setattr__(self, name: str, value: Any) -> None:\\n super().__setattr__(name, value)\",\n \"def __setattr__(*args, **kwargs): # real signature unknown\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5709383","0.56389916","0.56078136","0.5524775","0.55129427","0.5444868","0.54026616","0.53342205","0.53342205","0.53342205","0.53342205","0.5263527","0.52464676","0.52318645","0.5230673","0.52164626","0.5199737","0.51738065","0.5153252","0.51271296","0.51219124","0.51030666","0.507905","0.50764763","0.50654286","0.49830276","0.49707133","0.49554223","0.4953322","0.49508137","0.48872158","0.48792404","0.4873506","0.48631743","0.48539245","0.48351976","0.4833976","0.48305583","0.48204136","0.4820093","0.48181444","0.48084375","0.47968838","0.47968838","0.47700065","0.47698632","0.47659314","0.47646892","0.47636512","0.47626498","0.47429645","0.47400263","0.4730839","0.47303647","0.47148383","0.4713435","0.47082487","0.47082487","0.47056153","0.46853852","0.46804333","0.46782482","0.46731153","0.4665816","0.46369877","0.46338275","0.462707","0.46238196","0.4623658","0.46175855","0.46086636","0.46026027","0.45933613","0.4592327","0.4586251","0.45712042","0.45682824","0.4567767","0.4563799","0.45600158","0.45591787","0.45528296","0.45373887","0.45317858","0.4530944","0.45303863","0.45190227","0.4508658","0.45066756","0.4505807","0.4503434","0.4501727","0.44970027","0.44901037","0.4474164","0.4473275","0.44721714","0.44708744","0.4467265","0.44644773","0.44630545"],"string":"[\n \"0.5709383\",\n \"0.56389916\",\n \"0.56078136\",\n \"0.5524775\",\n \"0.55129427\",\n \"0.5444868\",\n \"0.54026616\",\n \"0.53342205\",\n \"0.53342205\",\n \"0.53342205\",\n \"0.53342205\",\n \"0.5263527\",\n \"0.52464676\",\n \"0.52318645\",\n \"0.5230673\",\n \"0.52164626\",\n \"0.5199737\",\n \"0.51738065\",\n \"0.5153252\",\n \"0.51271296\",\n \"0.51219124\",\n \"0.51030666\",\n \"0.507905\",\n \"0.50764763\",\n \"0.50654286\",\n \"0.49830276\",\n \"0.49707133\",\n \"0.49554223\",\n \"0.4953322\",\n \"0.49508137\",\n \"0.48872158\",\n \"0.48792404\",\n \"0.4873506\",\n \"0.48631743\",\n \"0.48539245\",\n \"0.48351976\",\n \"0.4833976\",\n \"0.48305583\",\n \"0.48204136\",\n \"0.4820093\",\n \"0.48181444\",\n \"0.48084375\",\n \"0.47968838\",\n \"0.47968838\",\n \"0.47700065\",\n \"0.47698632\",\n \"0.47659314\",\n \"0.47646892\",\n \"0.47636512\",\n \"0.47626498\",\n \"0.47429645\",\n \"0.47400263\",\n \"0.4730839\",\n \"0.47303647\",\n \"0.47148383\",\n \"0.4713435\",\n \"0.47082487\",\n \"0.47082487\",\n \"0.47056153\",\n \"0.46853852\",\n \"0.46804333\",\n \"0.46782482\",\n \"0.46731153\",\n \"0.4665816\",\n \"0.46369877\",\n \"0.46338275\",\n \"0.462707\",\n \"0.46238196\",\n \"0.4623658\",\n \"0.46175855\",\n \"0.46086636\",\n \"0.46026027\",\n \"0.45933613\",\n \"0.4592327\",\n \"0.4586251\",\n \"0.45712042\",\n \"0.45682824\",\n \"0.4567767\",\n \"0.4563799\",\n \"0.45600158\",\n \"0.45591787\",\n \"0.45528296\",\n \"0.45373887\",\n \"0.45317858\",\n \"0.4530944\",\n \"0.45303863\",\n \"0.45190227\",\n \"0.4508658\",\n \"0.45066756\",\n \"0.4505807\",\n \"0.4503434\",\n \"0.4501727\",\n \"0.44970027\",\n \"0.44901037\",\n \"0.4474164\",\n \"0.4473275\",\n \"0.44721714\",\n \"0.44708744\",\n \"0.4467265\",\n \"0.44644773\",\n \"0.44630545\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17789,"cells":{"query":{"kind":"string","value":"Assignment will use the auto type to define a variable at first use else will perform standard assignment."},"document":{"kind":"string","value":"def _Assign(self, t):\n if len(t.targets) > 1:\n self.RaiseError(t, \"Assignment to multiple targets not supported\")\n if not isinstance(t.targets[0], ast.Name):\n self.RaiseError(t, \"Assignment to complex expressions not supported\")\n self.fill()\n # check if target exists in locals\n if t.targets[0].id not in self._locals :\n self.write(\"auto \")\n self._locals.append(t.targets[0].id)\n self.dispatch(t.targets[0])\n self.write(\" = \")\n self.dispatch(t.value)\n self.write(\";\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_47_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 3]of real;\n\t\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,447))","def syntax_var_assign():\n a = 'Hello'\n print(f'{a} is stored at {hex(id(a))}')\n a = \"World\"\n print(f'{a} is stored at {hex(id(a))}')\n\n ## Output\n # Hello is stored at 0x10d251340\n # World is stored at 0x10d251378\n\n ## Notes\n # id()\n # Return the “identity” of an object. This is an integer (or long integer) which is guaranteed\n # to be unique and constant for this object during its lifetime.","def test_50_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 2]of real;\n\t\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,450))","def test_46_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tprocedure main(); var x:array[1 .. 3]of real; begin x[1]:=1;\n\t\twith y:integer;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,446))","def _compat_assign_gast_4(targets, value, type_comment):\n return gast.Assign(targets=targets, value=value)","def test_49_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 2] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 3]of real;\n\t\tbegin f()[1]:=x[1]:=1; end\"\"\"\n\t\texpect = \"Type Mismatch In Statement: Return(Some(Id(a)))\"\n\t\tself.assertTrue(TestChecker.test(input,expect,449))","def assign_variable(self, name, value):\n return self.set_variable(name, value)","def test_48_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 2] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 3]of real;\n\t\tbegin f()[1]:=x[1]:=1; end\"\"\"\n\t\texpect = \"Type Mismatch In Statement: Return(Some(Id(a)))\"\n\t\tself.assertTrue(TestChecker.test(input,expect,448))","def test_45_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tprocedure main(); var x:real; begin x:=1;\n\t\twith y:integer;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,445))","def _compat_assign_gast_5(targets, value, type_comment):\n return gast.Assign(targets=targets, value=value, type_comment=type_comment)","def visit_Assign(self, node):\n var_name = node.left.value\n self.VARIABLES[var_name] = self.visit(node.right)","def set_assignment(self, var, value):\n self.variable_to_value[var] = value","def assign(self, V, py):\n V.value = py","def assign(self, V, py):\n V.value = py","def assign(self, V, py):\n V.value = py","def assign(self, V, py):\n V.value = py","def visit_Assign(self, node):\n self.generic_visit(node)\n\n if node.col_offset == 0:\n mnode = ast.parse(\"\")\n mnode.body = [node]\n mnode = ast.fix_missing_locations(mnode)\n code = compile(mnode, \"<ast>\", \"exec\")\n try:\n exec(code, self.globals_)\n except Exception:\n pass\n self.globals_.pop(\"__builtins__\", None)\n self.globals_.pop(\"builtins\", None)","def smart_assign(variable, value, assign_fn=tf.assign,\n force_cast=False, force_sync=True):\n if force_cast and variable.dtype != value.dtype:\n value = tf.cast(value, dtype=variable.dtype)\n\n if not (tf.distribute.has_strategy() and tf.distribute.get_replica_context()):\n return assign_fn(variable, value)\n\n def merge_fn(distribution, variable, value):\n strategy = tf.distribute.get_strategy()\n if isinstance(strategy, tf.distribute.MirroredStrategy) and force_sync:\n value = strategy.reduce(tf.distribute.ReduceOp.MEAN, value)\n return distribution.extended.update(variable, assign_fn, args=(value,))\n\n return tf.distribute.get_replica_context().merge_call(\n merge_fn, args=(variable, value))","def _assignValue(value):\n if value == \"\":\n return None\n else:\n return value","def visit_Assign(self, node: Assign) -> None:\n\n node_type = type(node.right).__name__\n if isinstance(node.right, String):\n self._create_instruct(node_type)\n self.visit(node.left)\n instruct = self.visit(node.right)\n c_str = self.builder.alloca(instruct.type)\n self.builder.store(instruct, c_str)\n self.builder.ret_void()\n else:\n self._create_instruct(node_type)\n self.visit(node.left)\n instruct = self.visit(node.right)\n self.builder.ret(instruct)\n\n self.GLOBAL_MEMORY[node.left.value] = instruct","def visit_Assign(self, node):\n self.generic_visit(node)\n is_multiple = len(node.targets) > 1\n is_compound = any(map(is_sequence_node, node.targets))\n is_simple = not is_compound\n if is_simple and is_multiple:\n return self.visit_simple_assign(node)\n elif is_compound and (is_multiple or is_sequence_node(node.value)):\n return self.visit_compound_assign(node)\n return node","def visit_Assign(self, node):\n var_name = node.left.token.value\n self.GLOBAL_SCOPE[var_name] = self.visit(node.right)","def create_Assign(left_hand_side, right_hand_side):\n right_hand_side.ctx = ast.Load()\n left_hand_side.ctx = ast.Store()\n return ast.Assign(targets=[left_hand_side], value=right_hand_side)","def visit_VarAssignNode(self, node: VarAssignNode, symbol_table: SymbolTable) -> None:\n if isinstance(node.name, AccessNode) and isinstance(node.name.item_to_access, NumberNode):\n var = self.visit(node.name.accessor, symbol_table)\n var.vals[int(node.name.item_to_access.tok.value)] = self.visit(node.value, symbol_table)\n if isinstance(var, List):\n var.value = [item[idx].value for idx, item in enumerate(var.vals.values())]\n else:\n return f'Strings are immutable'\n else:\n assignment = self.visit(node.value, symbol_table)\n\n symbol_table[node.name] = assignment","def visit_assign(self: Parser, node: doc.Assign) -> None:\n if len(node.targets) != 1:\n self.report_error(node, \"Consequential assignments like 'a = b = c' are not supported.\")\n lhs = node.targets[0]\n\n if isinstance(node.value, doc.Subscript):\n check_slices = []\n if isinstance(node.value.slice, doc.Slice):\n check_slices = [node.value.slice]\n elif isinstance(node.value.slice, doc.Tuple):\n for p in node.value.slice.elts:\n if isinstance(p, doc.Slice):\n check_slices.append(p)\n for s in check_slices:\n if not s.step and s.upper and s.lower:\n s.step = doc.Constant(\n 1,\n None,\n 1,\n 1,\n s.upper.lineno,\n s.upper.end_col_offset + 1,\n s.upper.lineno,\n s.upper.end_col_offset + 2,\n )\n\n rhs = self.eval_expr(node.value)\n if isinstance(lhs, doc.Subscript):\n if isinstance(lhs.slice, doc.Tuple):\n indices = []\n for index in lhs.slice.elts:\n indices.append(self.eval_expr(index))\n else:\n indices = self.eval_expr(lhs.slice)\n T.buffer_store(self.eval_expr(lhs.value), rhs, indices)\n else:\n self.eval_assign(target=lhs, source=rhs, bind_value=bind_assign_value)","def visit_Assign(self, node):\n self.generic_visit(node)\n target = get_single_target(node)\n if isinstance(target, ast.Attribute):\n args = [ target.value, ast.Str(target.attr), node.value ]\n return ast.Expr(to_call(to_name('setattr'), args))\n return node","def irgen_assign(stmt, builder, table):\n lvalue = irgen_lvalue(stmt.exprs[0], builder, table)\n expr = irgen_expr(stmt.exprs[1], builder, table)\n builder.store(expr, lvalue)","def assign(self, value):\n self.value = value","def bind_assign_value(self: Parser, node: doc.expr, var_name: str, value: Any) -> Any:\n if isinstance(value, T.meta_var):\n return value.value\n elif isinstance(value, (list, tuple)):\n for i, v in enumerate(value):\n bind_assign_value(self, node, f\"{var_name}_{i}\", v)\n return value\n elif isinstance(value, Frame):\n value.add_callback(partial(value.__exit__, None, None, None))\n res = value.__enter__()\n IRBuilder.name(var_name, res)\n return res\n elif isinstance(value, (Buffer, IterVar)) or (\n isinstance(value, Var) and not self.var_table.exist(value)\n ):\n IRBuilder.name(var_name, value)\n return value\n else:\n value = tvm.runtime.convert(value)\n frame = T.LetStmt(value)\n var = frame.var\n IRBuilder.name(var_name, var)\n frame.add_callback(partial(frame.__exit__, None, None, None))\n frame.__enter__()\n return var","def assign(self, var, value):\n\t\tself._root = self._insert(self._root, var, value)","def visit_simple_assign(self, node):\n temp = gensym()\n temp_target = to_name(temp, ast.Store())\n stmts = [ ast.Assign([temp_target], node.value) ]\n stmts += [ ast.Assign([target], to_name(temp))\n for target in node.targets ]\n return stmts","def assign_variable(self, v: syntax.Construct, is_nested: bool):\n scope = self.get_scope(v)\n if scope is None:\n # this means that we assign to a mxs scope variable\n if is_nested or v.args[0][0:2] == \"::\" or v.args[0] in self.ALWAYS_RESOLVE_RT:\n v.resolution = RESOLUTION_RT\n else:\n self.declare_variable(v)\n return\n # there is no impact on scopes if the variable already exists\n # but we should set the resolution of the current variable\n self.resolve_to_scope(v, scope)","def assign_variable(executor, variable, value):\n variable = variable.replace(\" \", \"\")\n # TODO Should move parsing of this to ParsedStatementLet.\n # TODO Need to handle N-dimensional array element assignment.\n i = variable.find(\"(\")\n if i != -1:\n # Array reference\n j = variable.find(\")\", i+1)\n if j == -1:\n raise BasicSyntaxError(F\"Missing ) in in array assignment to {variable}\")\n if i+1 == j:\n raise BasicSyntaxError(F\"Missing array subscript in assignment to {variable}\")\n\n subscripts = variable[i+1:j].split(\",\")\n variable = variable[:i]\n is_valid_identifier(variable)\n subscripts = [int(eval_expression(executor._symbols, subscript)) - 1 for subscript in subscripts]\n executor.put_symbol_element(variable, value, subscripts)\n else:\n is_valid_identifier(variable)\n executor.put_symbol(variable, value, symbol_type=SymbolType.VARIABLE, arg=None)","def set_value(self, target, value):\n\n\t\t# Type checking.\n\t\tvar_type = self.variables[target[0]]\n\t\tif var_type == 'acq_marker':\n\t\t\tif len(target) == 1:\n\t\t\t\traise TypeError('Must assign dictionary to acq_marker')\n\t\t\telse:\n\t\t\t\tif target[1] == 'marker_num':\n\t\t\t\t\tif not isinstance(value, int):\n\t\t\t\t\t\traise TypeError('Must assign int to acq_marker num')\n\t\t\t\telif target[1] == 'output':\n\t\t\t\t\tif not isinstance(value, basestring):\n\t\t\t\t\t\traise TypeError('Must assign string to acq_marker num')\n\t\telif var_type == 'delay':\n\t\t\ttry:\n\t\t\t\tvalue.assert_dimensions('s')\n\t\t\texcept (AttributeError, IncompatibleDimensions):\n\t\t\t\traise TypeError('Must assign time quantity to delay')\n\t\telif var_type == 'int':\n\t\t\tif not isinstance(value, int):\n\t\t\t\traise TypeError('Must assign integer to int')\n\t\telif var_type == 'pulse':\n\t\t\tif len(target) == 1:\n\t\t\t\traise TypeError('Must assign dictionary to pulse')\n\t\t\telse:\n\t\t\t\tif target[1] == 'amplitude':\n\t\t\t\t\ttry:\n\t\t\t\t\t\tvalue.assert_dimensions('V')\n\t\t\t\t\texcept (AttributeError, IncompatibleDimensions):\n\t\t\t\t\t\traise TypeError('Must assign voltage quantity to pulse amplitude')\n\t\t\t\telif target[1] == 'length':\n\t\t\t\t\ttry:\n\t\t\t\t\t\tvalue.assert_dimensions('s')\n\t\t\t\t\texcept (AttributeError, IncompatibleDimensions):\n\t\t\t\t\t\traise TypeError('Must assign time quantity to pulse length')\n\t\t\t\telif target[1] == 'shape':\n\t\t\t\t\tif not isinstance(value, basestring):\n\t\t\t\t\t\traise TypeError('Must assign string to pulse shape')\n\t\telse:\n\t\t\traise TypeError('Cannot assign to variable of type \"{0}\"'.format(var_type))\n\n\t\tif target in self.values and self.values[target] is not None:\n\t\t\traise TypeError('Re-assignment of {0}'.format(target))\n\t\telse:\n\t\t\tself.values[target] = value","def to_assignment_type(value: Union[AssignmentType, int, str]) -> AssignmentType:\n return AssignmentType(int(value))","def single_assignment_handler(target, value, assign_stmts, node, id_str):\n #print(\"single_assignment_handler\")\n\n temp_stmts = core_language.create_Assign(target, value)\n if hasattr(node, 'lineno'):\n temp_stmts.lineno = node.lineno\n temp_stmts.col_offset = node.col_offset\n\n assign_stmts.append(temp_stmts)\n return False","def parseAssign( ): # parse rountine for the assign and uses the assign class to print out the appropriate string\n\n\ttok = tokens.peek( )\n\tif debug: print( \"assign: \", tok )\n\tif re.match( Lexer.identifier, tok ):\n\t\tident = VarRef( tok )\n\telse: \n\t\terror( \"Invalid identifier\" )\n\ttok = tokens.next( )\n\tequals = match( \"=\" )\n\ttok = tokens.peek( )\n\texpr = expression( )\n\tmatch( \";\" )\n\tequals = VarRef( equals )\n\tstatement = assign( equals, ident, expr )\n\treturn statement","def _analyse_stmt_Assign(self, statement: ast.Assign, *, next: CFNode) -> CFNode:\n return self._ast_node(statement, next=next, error=self._raise)","def declare_variable(self, v):\n scope = self.get_default_declaration_scope()\n scope.set(v.args[0])\n self.resolve_to(v, RESOLUTION_NAKED)","def auto(self, auto):\n self._auto = auto","def convert_assign(g, op, block):\n\n out = g.get_node(op.input(\"X\")[0])\n g.add_node(op.output(\"Out\")[0], out)","def visit_ann_assign(self: Parser, node: doc.AnnAssign) -> None:\n lhs = node.target\n rhs = self.eval_expr(node.value)\n ann_var = self.visit_tvm_annotation(node.annotation)\n if not isinstance(ann_var, Var):\n self.report_error(node.annotation, \"Annotation should be Var\")\n self.eval_assign(target=lhs, source=ann_var, bind_value=bind_assign_value)\n frame = T.LetStmt(rhs, var=ann_var)\n frame.add_callback(partial(frame.__exit__, None, None, None))\n frame.__enter__()","def _(self, node: Assignment):\n\n # This check allows us to ignore the initialization nodes\n # in the CAST 'i.e. x0 = -1'\n if node.source_refs == None:\n if type(node.left) == Var:\n if type(node.right) == Number and node.right.number == -1:\n return \"\"\n\n left = self.visit(node.left)\n right = self.visit(node.right)\n\n to_ret = f\"( assign {left} {right} )\"\n return to_ret","def visit_assignname( # pylint: disable=too-many-branches\n self, node: nodes.AssignName\n ) -> None:\n frame = node.frame(future=True)\n assign_type = node.assign_type()\n\n # Check names defined in comprehensions\n if isinstance(assign_type, nodes.Comprehension):\n self._check_name(\"inlinevar\", node.name, node)\n\n # Check names defined in module scope\n elif isinstance(frame, nodes.Module):\n # Check names defined in Assign nodes\n if isinstance(assign_type, nodes.Assign):\n inferred_assign_type = utils.safe_infer(assign_type.value)\n\n # Check TypeVar's and TypeAliases assigned alone or in tuple assignment\n if isinstance(node.parent, nodes.Assign):\n if self._assigns_typevar(assign_type.value):\n self._check_name(\"typevar\", assign_type.targets[0].name, node)\n return\n if self._assigns_typealias(assign_type.value):\n self._check_name(\"typealias\", assign_type.targets[0].name, node)\n return\n\n if (\n isinstance(node.parent, nodes.Tuple)\n and isinstance(assign_type.value, nodes.Tuple)\n # protect against unbalanced tuple unpacking\n and node.parent.elts.index(node) < len(assign_type.value.elts)\n ):\n assigner = assign_type.value.elts[node.parent.elts.index(node)]\n if self._assigns_typevar(assigner):\n self._check_name(\n \"typevar\",\n assign_type.targets[0]\n .elts[node.parent.elts.index(node)]\n .name,\n node,\n )\n return\n if self._assigns_typealias(assigner):\n self._check_name(\n \"typealias\",\n assign_type.targets[0]\n .elts[node.parent.elts.index(node)]\n .name,\n node,\n )\n return\n\n # Check classes (TypeVar's are classes so they need to be excluded first)\n elif isinstance(inferred_assign_type, nodes.ClassDef):\n self._check_name(\"class\", node.name, node)\n\n # Don't emit if the name redefines an import in an ImportError except handler.\n elif not _redefines_import(node) and isinstance(\n inferred_assign_type, nodes.Const\n ):\n self._check_name(\"const\", node.name, node)\n else:\n self._check_name(\n \"variable\", node.name, node, disallowed_check_only=True\n )\n\n # Check names defined in AnnAssign nodes\n elif isinstance(assign_type, nodes.AnnAssign):\n if utils.is_assign_name_annotated_with(node, \"Final\"):\n self._check_name(\"const\", node.name, node)\n elif self._assigns_typealias(assign_type.annotation):\n self._check_name(\"typealias\", node.name, node)\n\n # Check names defined in function scopes\n elif isinstance(frame, nodes.FunctionDef):\n # global introduced variable aren't in the function locals\n if node.name in frame and node.name not in frame.argnames():\n if not _redefines_import(node):\n self._check_name(\"variable\", node.name, node)\n\n # Check names defined in class scopes\n elif isinstance(frame, nodes.ClassDef):\n if not list(frame.local_attr_ancestors(node.name)):\n for ancestor in frame.ancestors():\n if utils.is_enum(ancestor) or utils.is_assign_name_annotated_with(\n node, \"Final\"\n ):\n self._check_name(\"class_const\", node.name, node)\n break\n else:\n self._check_name(\"class_attribute\", node.name, node)","def _AugAssign(self, t):\n if not isinstance(t.target, ast.Name):\n self.RaiseError(t, \"Augmented assignment to complex expressions not supported\")\n # check if target exists in locals\n if t.target.id not in self._locals :\n self.RaiseError(t, \"Augmented assignment not permitted on variables not already assigned previously\")\n self.fill()\n self.dispatch(t.target)\n self.write(\" \"+self.binop[t.op.__class__.__name__]+\"= \")\n self.dispatch(t.value)\n self.write(\";\")","def assign(obj, path, val, missing=None):\n return glom(obj, Assign(path, val, missing=missing))","def visit_Assign(self, node):\n self.generic_visit(node)\n target = get_single_target(node)\n if isinstance(target, ast.Subscript):\n fun = to_attribute(self.operator, 'setitem')\n args = [target.value, self.index_to_expr(target.slice), node.value]\n return ast.Expr(to_call(fun, args))\n return node","def eval_assignment(exp, env):\n set_variable_value(assignment_variable(exp), m_eval(assignment_value(exp), env), env)\n return quote(\"ok\")","def visit_AugAssign(self, node):\n target = node.target\n\n rhs_target = copy.deepcopy(target)\n rhs_target.ctx = ast.Load()\n ast.fix_missing_locations(rhs_target)\n\n bin_op = ast.BinOp(rhs_target, node.op, node.value)\n assignment = ast.Assign([target], bin_op)\n assignment.inplace_op = node.op\n return self.visit(assignment)","def eval_assignment(assignment, motif_node_dict):\n if type(assignment.rvalue).__name__ == 'FuncCall':\n motif_node, tree_node = eval_function_call(assignment.rvalue, motif_node_dict)\n # consider \"var = XXX;\" and \"*var = XXX\" and \"&var = XXX\" situations\n if (type(assignment.lvalue).__name__ == 'ID' and assignment.lvalue.name in motif_node_dict) or (type(assignment.lvalue).__name__ == 'UnaryOp' and assignment.lvalue.expr.name in motif_node_dict):\n if not motif_node:\n print('\\33[101m' + '[error][eval_assignment]: ' + assignment.lvalue.name + ' is in the dictionary. MotifNode should not be None.\\033[0m')\n exit(1)\n else:\n motif_node_dict[assignment.lvalue.name].append(motif_node)\n return tree_node\n # In a case where a provenance node was declared but then assigned or reassigned. For example:\n # struct provenance *tprov;\n # ...\n # tprov = t->provenance;\n # tprov must then be in the motif_node_dict.\n elif type(assignment.lvalue).__name__ == 'ID' and assignment.lvalue.name in motif_node_dict:\n # we can only infer its type from the name of the variable\n motif_node = provenance.create_motif_node(assignment.lvalue.name)\n motif_node_dict[assignment.lvalue.name].append(motif_node)\n return None\n elif type(assignment.lvalue).__name__ == 'UnaryOp' and type(assignment.lvalue.expr).__name__ == 'ID' and assignment.lvalue.expr.name in motif_node_dict:\n # similar case as the previous one, except that we have: *tprov = ...\n # we can only infer its type from the name of the variable\n motif_node = provenance.create_motif_node(assignment.lvalue.expr.name)\n motif_node_dict[assignment.lvalue.expr.name].append(motif_node)\n return None\n else:\n #######################################################\n # We will consider other conditions if we ever see them\n # POSSIBLE CODE HERE.\n #######################################################\n return None","def test_statement_initialized_by_assignment():\n shap = Statement(shape_id=\"@photo\", prop_id=\"dcterms:creator\", value_type=\"URI\")\n shap2 = Statement()\n shap2.shape_id = \"@photo\"\n shap2.prop_id = \"dcterms:creator\"\n shap2.value_type = \"URI\"\n assert shap == shap2","def do_assignment(val):\n if isinstance(val, collections.Iterable):\n if len(val) == 2 and all([isinstance(i, (int, float)) for i in val]):\n val1, val2 = val\n else:\n raise TypeError(\"do_assignment:: \"\n \"Expecting an iterable with 2 numeric elements\")\n elif isinstance(val, (int, float)):\n val1 = val2 = val\n else:\n raise TypeError(\"do_assignemnt:: \"\n \"Expecting value to be a number or an iterable\")\n return (val1, val2)","def is_assign(self):\n return self.var.initializer is not None","def variable(self, val):","def Assignment(self):\n id = self.primary()\n if self.currtok[1].name == \"DECLERATION\":\n self.currtok = next(self.tg)\n if self.functions.get(self.currtok[0]) is not None:\n\n express = self.FunctionCall()\n return assignmentStmt(id, express)\n else:\n express = self.Expression()\n\n if self.currtok[1].name == \"SEMI\":\n self.currtok = next(self.tg)\n return assignmentStmt(id, express)\n raise SLUCSyntaxError(\"ERROR: Missing Semicolon on line {0}\".format(str(self.currtok[2] - 1)))\n raise SLUCSyntaxError(\"ERROR: Missing assignment on line {0}\".format(str(self.currtok[2] - 1)))","def test_variable_assign(self):\n self.trace('x = 1')\n\n events = self.variable_events\n self.assertEqual(len(events), 1)\n event = events[0]\n self.assertIsInstance(event, TraceAssign)\n self.assertEqual(event.name, 'x')\n self.assertEqual(event.value, 1)","def visit_AugAssign(self, node):\n # FIXME: Gensym the LHS to avoid two evaluations.\n self.generic_visit(node)\n rhs = to_call(self.op_to_function(node.op),\n [set_ctx(node.target), node.value])\n return ast.Assign([node.target], rhs)","def do_assign(parser, token):\n bits = token.contents.split()\n if len(bits) != 3:\n raise template.TemplateSyntaxError(\"'%s' tag takes two arguments\" % bits[0])\n value = parser.compile_filter(bits[2])\n return AssignNode(bits[1], value)","def do_assign(parser, token):\n bits = token.contents.split()\n if len(bits) != 3:\n raise template.TemplateSyntaxError(\"'%s' tag takes two arguments\" % bits[0])\n value = parser.compile_filter(bits[2])\n return AssignNode(bits[1], value)","def declare_variable(\n self,\n identifier: str,\n typ: types.Type,\n *,\n is_transient: t.Optional[bool] = None,\n initial_value: t.Optional[expressions.ValueOrExpression] = None,\n comment: t.Optional[str] = None,\n ) -> None:\n value = None\n if initial_value is not None:\n value = expressions.ensure_expr(initial_value)\n self.add_declaration(\n VariableDeclaration(\n identifier,\n typ,\n is_transient=is_transient,\n initial_value=value,\n comment=comment,\n )\n )","def __assign(self, root, item, args=None, value=None, default=False):\n item = util.unscalar(item)\n args = util.unscalar_list(args)\n atroot = root is self\n if root is None or item is None:\n return None\n elif self.PRIVATE and re.search(self.PRIVATE, item):\n return None\n elif isinstance(root, dict) or atroot:\n if not (default and root.get(item)):\n root[item] = value\n return value\n elif isinstance(root, (list, tuple)) and re.match(r\"-?\\d+$\", str(item)):\n item = int(item)\n if not (default and 0 <= item < len(root) and root[item]):\n root[item] = value\n return value\n elif util.is_object(root):\n if not (default and getattr(root, item)()):\n args.append(value)\n return getattr(root, item)(*args)\n else:\n raise Error(\"don't know how to assign to %s.%s\" % (root, item))\n\n return None","def assign(self, *args):\n return _libsbml.string_assign(self, *args)","def _assigns_typevar(node: nodes.NodeNG | None) -> bool:\n if isinstance(node, astroid.Call):\n inferred = utils.safe_infer(node.func)\n if (\n isinstance(inferred, astroid.ClassDef)\n and inferred.qname() in TYPE_VAR_QNAME\n ):\n return True\n return False","def visit_compound_assign(self, node):\n # Determine number of values (arity) of compound assignment.\n nvalues = { len(target.elts) for target in node.targets \n if is_sequence_node(target) }\n if len(nvalues) > 1:\n # A multiple, compound assignment with different arities, e.g.,\n # `x,y = a,b,c = ...` is not a syntax error in Python, though it\n # probably should be because it's guaranteed to cause a runtime\n # error. Raise the error here, since we cannot proceed.\n raise SyntaxError(\"Multiple assignment with different arities\")\n nvalues = nvalues.pop()\n\n # Assign temporary variables.\n temps = [ gensym() for i in range(nvalues) ]\n stmts = []\n if is_sequence_node(node.value) and len(node.value.elts) == nvalues:\n # Special case: RHS is sequence literal of correct length.\n for i in range(nvalues):\n temp_target = to_name(temps[i], ast.Store())\n stmts.append(ast.Assign([temp_target], node.value.elts[i]))\n else:\n # General case.\n temp_target = to_tuple(\n (to_name(temp, ast.Store()) for temp in temps), ast.Store())\n stmts.append(ast.Assign([temp_target], node.value))\n\n # Rewrite assignments as sequence of assignments.\n for target in reversed(node.targets):\n if is_sequence_node(target):\n stmts.extend(ast.Assign([target.elts[i]], to_name(temps[i]))\n for i in range(nvalues))\n else:\n temp_tuple = to_tuple(to_name(temp) for temp in temps)\n stmts.append(ast.Assign([target], temp_tuple))\n \n return stmts","def set_variable(self, name, value):\n if self._scalamagic and (not name.startswith(\"_i\")):\n self.scala_interpreter.bind(name, value)\n else:\n self.log.debug('Not setting variable %s', name)","def declare_struct_variable(self, v):\n scope = self.sscope\n scope.set(v.args[0])\n self.resolve_to(v, RESOLUTION_NAKED)","def visit_aug_assign(self: Parser, node: doc.AugAssign) -> None:\n lhs_pos = (\n node.target.lineno,\n node.target.col_offset,\n node.target.end_lineno,\n node.target.end_col_offset,\n )\n rhs_pos = (\n node.value.lineno,\n node.value.col_offset,\n node.value.end_lineno,\n node.value.end_col_offset,\n )\n node.target.ctx = doc.Load(*lhs_pos)\n with self.var_table.with_frame():\n lhs_name = \"__tvm_tmp_value_aug_assign_lhs\"\n rhs_name = \"__tvm_tmp_value_aug_assign_rhs\"\n lhs_expr = self.eval_expr(node.target)\n rhs_expr = self.eval_expr(node.value)\n self.var_table.add(lhs_name, lhs_expr)\n self.var_table.add(rhs_name, rhs_expr)\n op = doc.BinOp(\n doc.Name(lhs_name, doc.Load(*lhs_pos), *lhs_pos),\n node.op,\n doc.Name(rhs_name, doc.Load(*rhs_pos), *rhs_pos),\n *lhs_pos,\n )\n rhs = self.eval_expr(op)\n lhs = node.target\n lhs.ctx = doc.Store(*lhs_pos)\n if isinstance(lhs, doc.Subscript):\n if isinstance(lhs.slice, doc.Tuple):\n indices = []\n for index in lhs.slice.elts:\n indices.append(self.eval_expr(index))\n else:\n indices = [self.eval_expr(lhs.slice)]\n T.buffer_store(self.eval_expr(lhs.value), rhs, indices)\n else:\n self.eval_assign(target=lhs, source=rhs, bind_value=bind_assign_value)","def auto(self) -> None:\n self.value = None","def visit_Assign(self, node):\n assign_stmts = []\n value = node.value\n reversed_targets = node.targets\n reversed_targets.reverse()\n assign_stmts.append(stypy_functions.create_blank_line())\n if len(reversed_targets) > 1:\n assign_stmts.append(\n stypy_functions.create_src_comment(\n \"Multiple assignment of {0} elements.\".format(len(reversed_targets))))\n else:\n if hasattr(node, 'lineno'):\n assign_stmts.append(stypy_functions.create_src_comment(\n \"Assigning a {1} to a {0} (line {2}):\".format(type(reversed_targets[0]).__name__,\n type(value).__name__, node.lineno)))\n else:\n assign_stmts.append(stypy_functions.create_src_comment(\n \"Assigning a {1} to a {0}:\".format(type(reversed_targets[0]).__name__,\n type(value).__name__)))\n for assign_num in xrange(len(reversed_targets)):\n target = reversed_targets[assign_num]\n # Function guard is true? execute handler\n for handler_func_guard_tuple in self.__assignment_handlers:\n if handler_func_guard_tuple[0](target, value):\n id_str, handler_func = handler_func_guard_tuple[1]\n self.performed_transformations |= handler_func(target, value, assign_stmts, node, id_str)\n assign_stmts = stypy_functions.flatten_lists(assign_stmts)\n value = target\n break\n\n if len(assign_stmts) > 0:\n return assign_stmts\n return node","def get_assignment(self, var):\n return self.variable_to_value.get(var)","def let(self, var, val):\n\n self.d['__vstemp'] = val\n if var.endswith('+'):\n rvar = var.rstrip('+')\n # .. obj = eval(rvar,self.d)\n exec(\"%s.append(__vstemp)\" % rvar, self.d)\n else:\n exec(var + \" = __vstemp\", self.d)\n del self.d['__vstemp']","def _assign_op(dest, op, arg, val, path, scope):\n if op == '[':\n dest[arg] = val\n elif op == '.':\n setattr(dest, arg, val)\n elif op == 'P':\n _assign = scope[TargetRegistry].get_handler('assign', dest)\n try:\n _assign(dest, arg, val)\n except Exception as e:\n raise PathAssignError(e, path, arg)\n else: # pragma: no cover\n raise ValueError('unsupported T operation for assignment')","def _fset(self, value):\n # type: (...) -> None\n rtype = type_\n if isinstance(type_, TypeVar):\n type_map = dict(\n zip(self.__parameters__, self.__orig_class__.__args__)\n )\n rtype = type_map[type_]\n vars(self)[private_attr] = cast(rtype, value)","def define_var(self, var, value):\n self.binding[var] = value","def f_setvar(self, name, expr):\r\n self.locals_ptr[name] = self.eval(expr, self.locals_ptr)\r\n return \"\"","def assign_variable(variable_name):\n # check variable name, call function 5 to check type\n if not in_alphabet(variable_name):\n print(f\"Syntax Error.\")\n return\n # prompt user to enter a value\n value = input(f\"Enter a value for {variable_name}: \")\n # check value type, call function 6 to check type\n if not is_digit(value):\n print(f\"Syntax Error.\")\n return\n # update dictionary\n lookUpTable[variable_name] = int(value)","def on_assign(self):","def assign(self, *args):\n return _ida_hexrays.cexpr_t_assign(self, *args)","def _analyse_stmt_AnnAssign(\n self, statement: ast.AnnAssign, *, next: CFNode\n ) -> CFNode:\n return self._ast_node(statement, next=next, error=self._raise)","def _fset(self, value):\n # type: (...) -> None\n rtype = type_\n if isinstance(type_, TypeVar):\n type_map = dict(\n zip(self.__parameters__, self.__orig_class__.__args__)\n )\n rtype = type_map[type_]\n if not is_instance(value, rtype):\n raise TypeError(\n \"Cannot assign type of {} to attribute of type {}.\".format(\n _get_type_name(type(value)), _get_type_name(rtype)\n )\n )\n vars(self)[private_attr] = value","def set_value(v):\r\n try:\r\n type (eval(v))\r\n except NameError:\r\n return \"\"\r\n return eval(v)","def set_value(v):\r\n try:\r\n type (eval(v))\r\n except NameError:\r\n return \"\"\r\n return eval(v)","def visit_any_assign(self, node: types.AnyAssign) -> None:\n self._check_slots(node)\n self.generic_visit(node)","def test_tuple_assign(self):\r\n def local_test(x,y):\r\n m1=Module()\r\n m1.l=(x(), y())\r\n\r\n # create a Method that makes the second list element a shared Member\r\n m1.g=Method([], m1.l[0])\r\n m1.f=Method([], m1.l[1])\r\n m = m1.make()\r\n\r\n #assign 4 and 5 to the two variables' containers in m\r\n m.l = (4, 5)\r\n assert 5 == m.f()\r\n assert 4 == m.g()\r\n\r\n local_test(lambda:T.dscalar(),lambda:T.dscalar())","def assign(self, *args):\n return _ida_hexrays.cloop_t_assign(self, *args)","def assign(self, dst, req, src):\n if req == 'null':\n return\n if req in ('write', 'inplace'):\n dst[:] = src\n elif req == 'add':\n dst[:] += src","def assign(self, *args):\n return _ida_hexrays.cnumber_t_assign(self, *args)","def assign(self, *args):\n return _ida_hexrays.cif_t_assign(self, *args)","def test_001_lambda_assign(self):\r\n text = \"\"\"\r\n const f = (d,k,v) => d[k] = v\r\n \"\"\"\r\n tokens = Lexer().lex(text)\r\n ast = Parser().parse(tokens)\r\n expected = TOKEN('T_MODULE', '',\r\n TOKEN('T_VAR', 'const',\r\n TOKEN('T_ASSIGN', '=',\r\n TOKEN('T_TEXT', 'f'),\r\n TOKEN('T_LAMBDA', '=>',\r\n TOKEN('T_TEXT', 'Anonymous'),\r\n TOKEN('T_ARGLIST', '()',\r\n TOKEN('T_TEXT', 'd'),\r\n TOKEN('T_TEXT', 'k'),\r\n TOKEN('T_TEXT', 'v')\r\n ),\r\n TOKEN('T_ASSIGN', '=',\r\n TOKEN('T_SUBSCR', '',\r\n TOKEN('T_TEXT', 'd'),\r\n TOKEN('T_TEXT', 'k'))\r\n ),\r\n TOKEN('T_TEXT', 'v')\r\n )\r\n )\r\n )\r\n )\r\n\r\n self.assertFalse(parsecmp(expected, ast, False))","def test_swap_assignment():\n x,y = 5,10\n yield (x,y)\n x,y = y,x # no ref-counting here\n yield (x,y)","def __init__(self, name, value):\n super(VariableDeclarationNode, self).__init__()\n self._name = name\n self._value = value\n self._type = None","def _analyse_stmt_AugAssign(\n self, statement: ast.AugAssign, *, next: CFNode\n ) -> CFNode:\n return self._ast_node(statement, next=next, error=self._raise)","def setVariable(self, *args):\n return _libsbml.EventAssignment_setVariable(self, *args)","def assign(self, value):\n generator = (bits for bits in self.data if bits.is_null)\n if isinstance(value, int):\n series_of_bits = next(generator, _bits_sentinel)\n series_of_bits.assign(value)\n elif isinstance(value, typing.Iterable):\n for value, bits in zip(value, generator):\n bits.assign(value)\n return self","def multiple_value_call_assignment_handler(target, value, assign_stmts, node, id_str):\n #print(\"multiple_value_call_assignment_handler\")\n target_stmts, value_var = stypy_functions.create_temp_Assign(value, node.lineno, node.col_offset,\n \"{0}_assignment\".format(id_str))\n assign_stmts.append(target_stmts)\n\n #value_var_to_load = copy.deepcopy(value_var)\n value_var_to_load = ast.Name()\n value_var_to_load.col_offset = value_var.col_offset\n value_var_to_load.lineno = value_var.lineno\n value_var_to_load.id = value_var.id\n value_var_to_load.ctx = ast.Load()\n\n for i in xrange(len(target.elts)):\n # Assign values to each element.\n # getitem_att = core_language.create_attribute(value_var_to_load, '__getitem__', context=ast.Load(),\n # line=node.lineno,\n # column=node.col_offset)\n # item_call = functions.create_call(getitem_att, [core_language.create_num(i, node.lineno, node.col_offset)])\n # temp_stmts, temp_value = stypy_functions.create_temp_Assign(item_call, node.lineno, node.col_offset,\n # \"{0}_assignment\".format(id_str))\n stypy_interface = core_language.create_Name('stypy_interface')\n get_tuple_call = core_language.create_attribute(stypy_interface, 'stypy_get_value_from_tuple', context=ast.Load(),\n line=node.lineno,\n column=node.col_offset)\n\n item_call = functions.create_call(get_tuple_call, [value_var_to_load,\n core_language.create_num(len(target.elts), node.lineno, node.col_offset),\n core_language.create_num(i, node.lineno, node.col_offset)])\n temp_stmts, temp_value = stypy_functions.create_temp_Assign(item_call, node.lineno, node.col_offset,\n \"{0}_assignment\".format(id_str))\n if hasattr(node, 'lineno'):\n temp_stmts.lineno = node.lineno\n temp_stmts.col_offset = node.col_offset\n\n assign_stmts.append(temp_stmts)\n\n temp_stmts = core_language.create_Assign(target.elts[i], temp_value)\n if hasattr(node, 'lineno'):\n temp_stmts.lineno = node.lineno\n temp_stmts.col_offset = node.col_offset\n\n assign_stmts.append(temp_stmts)\n\n return True","def fix_for_python_gcc_declaration_distinction(self, con_scopestr, id, var_name):\n version = self.con_scope_to_highest_var_vers[con_scopestr][id]\n # this function should only be called in cases where we need to implement a dummy assignment\n # which creates a version 1 variable\n assert(version == VAR_INIT_VERSION)\n # increment the version, and create an GrFN variable for the incremented version\n self.incr_version_in_con_scope(con_scopestr, id, var_name)\n new_version = self.con_scope_to_highest_var_vers[con_scopestr][id]\n new_fullid = build_fullid(var_name, id, new_version, con_scopestr)\n grfn_var = self.pipeline_state.get_grfn_var(new_fullid)\n from_source_mdata = generate_from_source_metadata(False, VariableCreationReason.DUMMY_ASSIGN)\n add_metadata_to_grfn_var(grfn_var, from_source_mdata)\n\n # create a literal GrFN assignment for this dummy assignment\n assign_metadata = create_lambda_node_metadata(source_refs=[])\n literal_node = create_grfn_literal_node(assign_metadata)\n lambda_expr = \"lambda: None\"\n literal_node.func_str = lambda_expr\n literal_node.function = load_lambda_function(literal_node.func_str)\n dummy_assignment = GrfnAssignment(literal_node, LambdaType.LITERAL, lambda_expr=lambda_expr)\n dummy_assignment.outputs[new_fullid] = grfn_var.uid\n\n # add dummy assignment to function def node\n assert(self.pipeline_state.is_con_scopestr_func_def(con_scopestr))\n func_def_node = self.pipeline_state.func_def_node_from_scopestr(con_scopestr)\n\n func_def_node.dummy_grfn_assignments.append(dummy_assignment)","def _cast(self, value): # type: (Any) -> Any\n if self._strict:\n if isinstance(value, type(self._default)):\n return value\n else:\n self._setter_error('is not of type (%s)' % type(self._default), value)\n return self._default\n else:\n try:\n return type(self._default)(value)\n except (TypeError, ValueError):\n self._setter_error('could not be cast to (%s)'\n % type(self._default).__name__, value)\n return self._default","def declare_local_variable(self, v):\n scope = self.get_method_or_function_or_global_scope()\n scope.set(v.args[0])\n self.resolve_to(v, RESOLUTION_NAKED)","def _declare_auto_variable_mapping(self):\n if self.name not in self.nlp.variable_mappings:\n self.nlp.variable_mappings[self.name] = BiMapping(\n range(len(self.name_elements)), range(len(self.name_elements))\n )","def rhs(self):\n if not self.is_assign():\n raise AssertionError('Not an assignment')\n return self.initializer"],"string":"[\n \"def test_47_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 3] of real;\\n\\t\\tvar a: array[1 .. 3] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,447))\",\n \"def syntax_var_assign():\\n a = 'Hello'\\n print(f'{a} is stored at {hex(id(a))}')\\n a = \\\"World\\\"\\n print(f'{a} is stored at {hex(id(a))}')\\n\\n ## Output\\n # Hello is stored at 0x10d251340\\n # World is stored at 0x10d251378\\n\\n ## Notes\\n # id()\\n # Return the “identity” of an object. This is an integer (or long integer) which is guaranteed\\n # to be unique and constant for this object during its lifetime.\",\n \"def test_50_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 3] of real;\\n\\t\\tvar a: array[1 .. 3] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 2]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,450))\",\n \"def test_46_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real; begin x[1]:=1;\\n\\t\\twith y:integer;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,446))\",\n \"def _compat_assign_gast_4(targets, value, type_comment):\\n return gast.Assign(targets=targets, value=value)\",\n \"def test_49_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 2] of real;\\n\\t\\tvar a: array[1 .. 3] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Type Mismatch In Statement: Return(Some(Id(a)))\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,449))\",\n \"def assign_variable(self, name, value):\\n return self.set_variable(name, value)\",\n \"def test_48_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 3] of real;\\n\\t\\tvar a: array[1 .. 2] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Type Mismatch In Statement: Return(Some(Id(a)))\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,448))\",\n \"def test_45_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tprocedure main(); var x:real; begin x:=1;\\n\\t\\twith y:integer;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,445))\",\n \"def _compat_assign_gast_5(targets, value, type_comment):\\n return gast.Assign(targets=targets, value=value, type_comment=type_comment)\",\n \"def visit_Assign(self, node):\\n var_name = node.left.value\\n self.VARIABLES[var_name] = self.visit(node.right)\",\n \"def set_assignment(self, var, value):\\n self.variable_to_value[var] = value\",\n \"def assign(self, V, py):\\n V.value = py\",\n \"def assign(self, V, py):\\n V.value = py\",\n \"def assign(self, V, py):\\n V.value = py\",\n \"def assign(self, V, py):\\n V.value = py\",\n \"def visit_Assign(self, node):\\n self.generic_visit(node)\\n\\n if node.col_offset == 0:\\n mnode = ast.parse(\\\"\\\")\\n mnode.body = [node]\\n mnode = ast.fix_missing_locations(mnode)\\n code = compile(mnode, \\\"<ast>\\\", \\\"exec\\\")\\n try:\\n exec(code, self.globals_)\\n except Exception:\\n pass\\n self.globals_.pop(\\\"__builtins__\\\", None)\\n self.globals_.pop(\\\"builtins\\\", None)\",\n \"def smart_assign(variable, value, assign_fn=tf.assign,\\n force_cast=False, force_sync=True):\\n if force_cast and variable.dtype != value.dtype:\\n value = tf.cast(value, dtype=variable.dtype)\\n\\n if not (tf.distribute.has_strategy() and tf.distribute.get_replica_context()):\\n return assign_fn(variable, value)\\n\\n def merge_fn(distribution, variable, value):\\n strategy = tf.distribute.get_strategy()\\n if isinstance(strategy, tf.distribute.MirroredStrategy) and force_sync:\\n value = strategy.reduce(tf.distribute.ReduceOp.MEAN, value)\\n return distribution.extended.update(variable, assign_fn, args=(value,))\\n\\n return tf.distribute.get_replica_context().merge_call(\\n merge_fn, args=(variable, value))\",\n \"def _assignValue(value):\\n if value == \\\"\\\":\\n return None\\n else:\\n return value\",\n \"def visit_Assign(self, node: Assign) -> None:\\n\\n node_type = type(node.right).__name__\\n if isinstance(node.right, String):\\n self._create_instruct(node_type)\\n self.visit(node.left)\\n instruct = self.visit(node.right)\\n c_str = self.builder.alloca(instruct.type)\\n self.builder.store(instruct, c_str)\\n self.builder.ret_void()\\n else:\\n self._create_instruct(node_type)\\n self.visit(node.left)\\n instruct = self.visit(node.right)\\n self.builder.ret(instruct)\\n\\n self.GLOBAL_MEMORY[node.left.value] = instruct\",\n \"def visit_Assign(self, node):\\n self.generic_visit(node)\\n is_multiple = len(node.targets) > 1\\n is_compound = any(map(is_sequence_node, node.targets))\\n is_simple = not is_compound\\n if is_simple and is_multiple:\\n return self.visit_simple_assign(node)\\n elif is_compound and (is_multiple or is_sequence_node(node.value)):\\n return self.visit_compound_assign(node)\\n return node\",\n \"def visit_Assign(self, node):\\n var_name = node.left.token.value\\n self.GLOBAL_SCOPE[var_name] = self.visit(node.right)\",\n \"def create_Assign(left_hand_side, right_hand_side):\\n right_hand_side.ctx = ast.Load()\\n left_hand_side.ctx = ast.Store()\\n return ast.Assign(targets=[left_hand_side], value=right_hand_side)\",\n \"def visit_VarAssignNode(self, node: VarAssignNode, symbol_table: SymbolTable) -> None:\\n if isinstance(node.name, AccessNode) and isinstance(node.name.item_to_access, NumberNode):\\n var = self.visit(node.name.accessor, symbol_table)\\n var.vals[int(node.name.item_to_access.tok.value)] = self.visit(node.value, symbol_table)\\n if isinstance(var, List):\\n var.value = [item[idx].value for idx, item in enumerate(var.vals.values())]\\n else:\\n return f'Strings are immutable'\\n else:\\n assignment = self.visit(node.value, symbol_table)\\n\\n symbol_table[node.name] = assignment\",\n \"def visit_assign(self: Parser, node: doc.Assign) -> None:\\n if len(node.targets) != 1:\\n self.report_error(node, \\\"Consequential assignments like 'a = b = c' are not supported.\\\")\\n lhs = node.targets[0]\\n\\n if isinstance(node.value, doc.Subscript):\\n check_slices = []\\n if isinstance(node.value.slice, doc.Slice):\\n check_slices = [node.value.slice]\\n elif isinstance(node.value.slice, doc.Tuple):\\n for p in node.value.slice.elts:\\n if isinstance(p, doc.Slice):\\n check_slices.append(p)\\n for s in check_slices:\\n if not s.step and s.upper and s.lower:\\n s.step = doc.Constant(\\n 1,\\n None,\\n 1,\\n 1,\\n s.upper.lineno,\\n s.upper.end_col_offset + 1,\\n s.upper.lineno,\\n s.upper.end_col_offset + 2,\\n )\\n\\n rhs = self.eval_expr(node.value)\\n if isinstance(lhs, doc.Subscript):\\n if isinstance(lhs.slice, doc.Tuple):\\n indices = []\\n for index in lhs.slice.elts:\\n indices.append(self.eval_expr(index))\\n else:\\n indices = self.eval_expr(lhs.slice)\\n T.buffer_store(self.eval_expr(lhs.value), rhs, indices)\\n else:\\n self.eval_assign(target=lhs, source=rhs, bind_value=bind_assign_value)\",\n \"def visit_Assign(self, node):\\n self.generic_visit(node)\\n target = get_single_target(node)\\n if isinstance(target, ast.Attribute):\\n args = [ target.value, ast.Str(target.attr), node.value ]\\n return ast.Expr(to_call(to_name('setattr'), args))\\n return node\",\n \"def irgen_assign(stmt, builder, table):\\n lvalue = irgen_lvalue(stmt.exprs[0], builder, table)\\n expr = irgen_expr(stmt.exprs[1], builder, table)\\n builder.store(expr, lvalue)\",\n \"def assign(self, value):\\n self.value = value\",\n \"def bind_assign_value(self: Parser, node: doc.expr, var_name: str, value: Any) -> Any:\\n if isinstance(value, T.meta_var):\\n return value.value\\n elif isinstance(value, (list, tuple)):\\n for i, v in enumerate(value):\\n bind_assign_value(self, node, f\\\"{var_name}_{i}\\\", v)\\n return value\\n elif isinstance(value, Frame):\\n value.add_callback(partial(value.__exit__, None, None, None))\\n res = value.__enter__()\\n IRBuilder.name(var_name, res)\\n return res\\n elif isinstance(value, (Buffer, IterVar)) or (\\n isinstance(value, Var) and not self.var_table.exist(value)\\n ):\\n IRBuilder.name(var_name, value)\\n return value\\n else:\\n value = tvm.runtime.convert(value)\\n frame = T.LetStmt(value)\\n var = frame.var\\n IRBuilder.name(var_name, var)\\n frame.add_callback(partial(frame.__exit__, None, None, None))\\n frame.__enter__()\\n return var\",\n \"def assign(self, var, value):\\n\\t\\tself._root = self._insert(self._root, var, value)\",\n \"def visit_simple_assign(self, node):\\n temp = gensym()\\n temp_target = to_name(temp, ast.Store())\\n stmts = [ ast.Assign([temp_target], node.value) ]\\n stmts += [ ast.Assign([target], to_name(temp))\\n for target in node.targets ]\\n return stmts\",\n \"def assign_variable(self, v: syntax.Construct, is_nested: bool):\\n scope = self.get_scope(v)\\n if scope is None:\\n # this means that we assign to a mxs scope variable\\n if is_nested or v.args[0][0:2] == \\\"::\\\" or v.args[0] in self.ALWAYS_RESOLVE_RT:\\n v.resolution = RESOLUTION_RT\\n else:\\n self.declare_variable(v)\\n return\\n # there is no impact on scopes if the variable already exists\\n # but we should set the resolution of the current variable\\n self.resolve_to_scope(v, scope)\",\n \"def assign_variable(executor, variable, value):\\n variable = variable.replace(\\\" \\\", \\\"\\\")\\n # TODO Should move parsing of this to ParsedStatementLet.\\n # TODO Need to handle N-dimensional array element assignment.\\n i = variable.find(\\\"(\\\")\\n if i != -1:\\n # Array reference\\n j = variable.find(\\\")\\\", i+1)\\n if j == -1:\\n raise BasicSyntaxError(F\\\"Missing ) in in array assignment to {variable}\\\")\\n if i+1 == j:\\n raise BasicSyntaxError(F\\\"Missing array subscript in assignment to {variable}\\\")\\n\\n subscripts = variable[i+1:j].split(\\\",\\\")\\n variable = variable[:i]\\n is_valid_identifier(variable)\\n subscripts = [int(eval_expression(executor._symbols, subscript)) - 1 for subscript in subscripts]\\n executor.put_symbol_element(variable, value, subscripts)\\n else:\\n is_valid_identifier(variable)\\n executor.put_symbol(variable, value, symbol_type=SymbolType.VARIABLE, arg=None)\",\n \"def set_value(self, target, value):\\n\\n\\t\\t# Type checking.\\n\\t\\tvar_type = self.variables[target[0]]\\n\\t\\tif var_type == 'acq_marker':\\n\\t\\t\\tif len(target) == 1:\\n\\t\\t\\t\\traise TypeError('Must assign dictionary to acq_marker')\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif target[1] == 'marker_num':\\n\\t\\t\\t\\t\\tif not isinstance(value, int):\\n\\t\\t\\t\\t\\t\\traise TypeError('Must assign int to acq_marker num')\\n\\t\\t\\t\\telif target[1] == 'output':\\n\\t\\t\\t\\t\\tif not isinstance(value, basestring):\\n\\t\\t\\t\\t\\t\\traise TypeError('Must assign string to acq_marker num')\\n\\t\\telif var_type == 'delay':\\n\\t\\t\\ttry:\\n\\t\\t\\t\\tvalue.assert_dimensions('s')\\n\\t\\t\\texcept (AttributeError, IncompatibleDimensions):\\n\\t\\t\\t\\traise TypeError('Must assign time quantity to delay')\\n\\t\\telif var_type == 'int':\\n\\t\\t\\tif not isinstance(value, int):\\n\\t\\t\\t\\traise TypeError('Must assign integer to int')\\n\\t\\telif var_type == 'pulse':\\n\\t\\t\\tif len(target) == 1:\\n\\t\\t\\t\\traise TypeError('Must assign dictionary to pulse')\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif target[1] == 'amplitude':\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tvalue.assert_dimensions('V')\\n\\t\\t\\t\\t\\texcept (AttributeError, IncompatibleDimensions):\\n\\t\\t\\t\\t\\t\\traise TypeError('Must assign voltage quantity to pulse amplitude')\\n\\t\\t\\t\\telif target[1] == 'length':\\n\\t\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\t\\tvalue.assert_dimensions('s')\\n\\t\\t\\t\\t\\texcept (AttributeError, IncompatibleDimensions):\\n\\t\\t\\t\\t\\t\\traise TypeError('Must assign time quantity to pulse length')\\n\\t\\t\\t\\telif target[1] == 'shape':\\n\\t\\t\\t\\t\\tif not isinstance(value, basestring):\\n\\t\\t\\t\\t\\t\\traise TypeError('Must assign string to pulse shape')\\n\\t\\telse:\\n\\t\\t\\traise TypeError('Cannot assign to variable of type \\\"{0}\\\"'.format(var_type))\\n\\n\\t\\tif target in self.values and self.values[target] is not None:\\n\\t\\t\\traise TypeError('Re-assignment of {0}'.format(target))\\n\\t\\telse:\\n\\t\\t\\tself.values[target] = value\",\n \"def to_assignment_type(value: Union[AssignmentType, int, str]) -> AssignmentType:\\n return AssignmentType(int(value))\",\n \"def single_assignment_handler(target, value, assign_stmts, node, id_str):\\n #print(\\\"single_assignment_handler\\\")\\n\\n temp_stmts = core_language.create_Assign(target, value)\\n if hasattr(node, 'lineno'):\\n temp_stmts.lineno = node.lineno\\n temp_stmts.col_offset = node.col_offset\\n\\n assign_stmts.append(temp_stmts)\\n return False\",\n \"def parseAssign( ): # parse rountine for the assign and uses the assign class to print out the appropriate string\\n\\n\\ttok = tokens.peek( )\\n\\tif debug: print( \\\"assign: \\\", tok )\\n\\tif re.match( Lexer.identifier, tok ):\\n\\t\\tident = VarRef( tok )\\n\\telse: \\n\\t\\terror( \\\"Invalid identifier\\\" )\\n\\ttok = tokens.next( )\\n\\tequals = match( \\\"=\\\" )\\n\\ttok = tokens.peek( )\\n\\texpr = expression( )\\n\\tmatch( \\\";\\\" )\\n\\tequals = VarRef( equals )\\n\\tstatement = assign( equals, ident, expr )\\n\\treturn statement\",\n \"def _analyse_stmt_Assign(self, statement: ast.Assign, *, next: CFNode) -> CFNode:\\n return self._ast_node(statement, next=next, error=self._raise)\",\n \"def declare_variable(self, v):\\n scope = self.get_default_declaration_scope()\\n scope.set(v.args[0])\\n self.resolve_to(v, RESOLUTION_NAKED)\",\n \"def auto(self, auto):\\n self._auto = auto\",\n \"def convert_assign(g, op, block):\\n\\n out = g.get_node(op.input(\\\"X\\\")[0])\\n g.add_node(op.output(\\\"Out\\\")[0], out)\",\n \"def visit_ann_assign(self: Parser, node: doc.AnnAssign) -> None:\\n lhs = node.target\\n rhs = self.eval_expr(node.value)\\n ann_var = self.visit_tvm_annotation(node.annotation)\\n if not isinstance(ann_var, Var):\\n self.report_error(node.annotation, \\\"Annotation should be Var\\\")\\n self.eval_assign(target=lhs, source=ann_var, bind_value=bind_assign_value)\\n frame = T.LetStmt(rhs, var=ann_var)\\n frame.add_callback(partial(frame.__exit__, None, None, None))\\n frame.__enter__()\",\n \"def _(self, node: Assignment):\\n\\n # This check allows us to ignore the initialization nodes\\n # in the CAST 'i.e. x0 = -1'\\n if node.source_refs == None:\\n if type(node.left) == Var:\\n if type(node.right) == Number and node.right.number == -1:\\n return \\\"\\\"\\n\\n left = self.visit(node.left)\\n right = self.visit(node.right)\\n\\n to_ret = f\\\"( assign {left} {right} )\\\"\\n return to_ret\",\n \"def visit_assignname( # pylint: disable=too-many-branches\\n self, node: nodes.AssignName\\n ) -> None:\\n frame = node.frame(future=True)\\n assign_type = node.assign_type()\\n\\n # Check names defined in comprehensions\\n if isinstance(assign_type, nodes.Comprehension):\\n self._check_name(\\\"inlinevar\\\", node.name, node)\\n\\n # Check names defined in module scope\\n elif isinstance(frame, nodes.Module):\\n # Check names defined in Assign nodes\\n if isinstance(assign_type, nodes.Assign):\\n inferred_assign_type = utils.safe_infer(assign_type.value)\\n\\n # Check TypeVar's and TypeAliases assigned alone or in tuple assignment\\n if isinstance(node.parent, nodes.Assign):\\n if self._assigns_typevar(assign_type.value):\\n self._check_name(\\\"typevar\\\", assign_type.targets[0].name, node)\\n return\\n if self._assigns_typealias(assign_type.value):\\n self._check_name(\\\"typealias\\\", assign_type.targets[0].name, node)\\n return\\n\\n if (\\n isinstance(node.parent, nodes.Tuple)\\n and isinstance(assign_type.value, nodes.Tuple)\\n # protect against unbalanced tuple unpacking\\n and node.parent.elts.index(node) < len(assign_type.value.elts)\\n ):\\n assigner = assign_type.value.elts[node.parent.elts.index(node)]\\n if self._assigns_typevar(assigner):\\n self._check_name(\\n \\\"typevar\\\",\\n assign_type.targets[0]\\n .elts[node.parent.elts.index(node)]\\n .name,\\n node,\\n )\\n return\\n if self._assigns_typealias(assigner):\\n self._check_name(\\n \\\"typealias\\\",\\n assign_type.targets[0]\\n .elts[node.parent.elts.index(node)]\\n .name,\\n node,\\n )\\n return\\n\\n # Check classes (TypeVar's are classes so they need to be excluded first)\\n elif isinstance(inferred_assign_type, nodes.ClassDef):\\n self._check_name(\\\"class\\\", node.name, node)\\n\\n # Don't emit if the name redefines an import in an ImportError except handler.\\n elif not _redefines_import(node) and isinstance(\\n inferred_assign_type, nodes.Const\\n ):\\n self._check_name(\\\"const\\\", node.name, node)\\n else:\\n self._check_name(\\n \\\"variable\\\", node.name, node, disallowed_check_only=True\\n )\\n\\n # Check names defined in AnnAssign nodes\\n elif isinstance(assign_type, nodes.AnnAssign):\\n if utils.is_assign_name_annotated_with(node, \\\"Final\\\"):\\n self._check_name(\\\"const\\\", node.name, node)\\n elif self._assigns_typealias(assign_type.annotation):\\n self._check_name(\\\"typealias\\\", node.name, node)\\n\\n # Check names defined in function scopes\\n elif isinstance(frame, nodes.FunctionDef):\\n # global introduced variable aren't in the function locals\\n if node.name in frame and node.name not in frame.argnames():\\n if not _redefines_import(node):\\n self._check_name(\\\"variable\\\", node.name, node)\\n\\n # Check names defined in class scopes\\n elif isinstance(frame, nodes.ClassDef):\\n if not list(frame.local_attr_ancestors(node.name)):\\n for ancestor in frame.ancestors():\\n if utils.is_enum(ancestor) or utils.is_assign_name_annotated_with(\\n node, \\\"Final\\\"\\n ):\\n self._check_name(\\\"class_const\\\", node.name, node)\\n break\\n else:\\n self._check_name(\\\"class_attribute\\\", node.name, node)\",\n \"def _AugAssign(self, t):\\n if not isinstance(t.target, ast.Name):\\n self.RaiseError(t, \\\"Augmented assignment to complex expressions not supported\\\")\\n # check if target exists in locals\\n if t.target.id not in self._locals :\\n self.RaiseError(t, \\\"Augmented assignment not permitted on variables not already assigned previously\\\")\\n self.fill()\\n self.dispatch(t.target)\\n self.write(\\\" \\\"+self.binop[t.op.__class__.__name__]+\\\"= \\\")\\n self.dispatch(t.value)\\n self.write(\\\";\\\")\",\n \"def assign(obj, path, val, missing=None):\\n return glom(obj, Assign(path, val, missing=missing))\",\n \"def visit_Assign(self, node):\\n self.generic_visit(node)\\n target = get_single_target(node)\\n if isinstance(target, ast.Subscript):\\n fun = to_attribute(self.operator, 'setitem')\\n args = [target.value, self.index_to_expr(target.slice), node.value]\\n return ast.Expr(to_call(fun, args))\\n return node\",\n \"def eval_assignment(exp, env):\\n set_variable_value(assignment_variable(exp), m_eval(assignment_value(exp), env), env)\\n return quote(\\\"ok\\\")\",\n \"def visit_AugAssign(self, node):\\n target = node.target\\n\\n rhs_target = copy.deepcopy(target)\\n rhs_target.ctx = ast.Load()\\n ast.fix_missing_locations(rhs_target)\\n\\n bin_op = ast.BinOp(rhs_target, node.op, node.value)\\n assignment = ast.Assign([target], bin_op)\\n assignment.inplace_op = node.op\\n return self.visit(assignment)\",\n \"def eval_assignment(assignment, motif_node_dict):\\n if type(assignment.rvalue).__name__ == 'FuncCall':\\n motif_node, tree_node = eval_function_call(assignment.rvalue, motif_node_dict)\\n # consider \\\"var = XXX;\\\" and \\\"*var = XXX\\\" and \\\"&var = XXX\\\" situations\\n if (type(assignment.lvalue).__name__ == 'ID' and assignment.lvalue.name in motif_node_dict) or (type(assignment.lvalue).__name__ == 'UnaryOp' and assignment.lvalue.expr.name in motif_node_dict):\\n if not motif_node:\\n print('\\\\33[101m' + '[error][eval_assignment]: ' + assignment.lvalue.name + ' is in the dictionary. MotifNode should not be None.\\\\033[0m')\\n exit(1)\\n else:\\n motif_node_dict[assignment.lvalue.name].append(motif_node)\\n return tree_node\\n # In a case where a provenance node was declared but then assigned or reassigned. For example:\\n # struct provenance *tprov;\\n # ...\\n # tprov = t->provenance;\\n # tprov must then be in the motif_node_dict.\\n elif type(assignment.lvalue).__name__ == 'ID' and assignment.lvalue.name in motif_node_dict:\\n # we can only infer its type from the name of the variable\\n motif_node = provenance.create_motif_node(assignment.lvalue.name)\\n motif_node_dict[assignment.lvalue.name].append(motif_node)\\n return None\\n elif type(assignment.lvalue).__name__ == 'UnaryOp' and type(assignment.lvalue.expr).__name__ == 'ID' and assignment.lvalue.expr.name in motif_node_dict:\\n # similar case as the previous one, except that we have: *tprov = ...\\n # we can only infer its type from the name of the variable\\n motif_node = provenance.create_motif_node(assignment.lvalue.expr.name)\\n motif_node_dict[assignment.lvalue.expr.name].append(motif_node)\\n return None\\n else:\\n #######################################################\\n # We will consider other conditions if we ever see them\\n # POSSIBLE CODE HERE.\\n #######################################################\\n return None\",\n \"def test_statement_initialized_by_assignment():\\n shap = Statement(shape_id=\\\"@photo\\\", prop_id=\\\"dcterms:creator\\\", value_type=\\\"URI\\\")\\n shap2 = Statement()\\n shap2.shape_id = \\\"@photo\\\"\\n shap2.prop_id = \\\"dcterms:creator\\\"\\n shap2.value_type = \\\"URI\\\"\\n assert shap == shap2\",\n \"def do_assignment(val):\\n if isinstance(val, collections.Iterable):\\n if len(val) == 2 and all([isinstance(i, (int, float)) for i in val]):\\n val1, val2 = val\\n else:\\n raise TypeError(\\\"do_assignment:: \\\"\\n \\\"Expecting an iterable with 2 numeric elements\\\")\\n elif isinstance(val, (int, float)):\\n val1 = val2 = val\\n else:\\n raise TypeError(\\\"do_assignemnt:: \\\"\\n \\\"Expecting value to be a number or an iterable\\\")\\n return (val1, val2)\",\n \"def is_assign(self):\\n return self.var.initializer is not None\",\n \"def variable(self, val):\",\n \"def Assignment(self):\\n id = self.primary()\\n if self.currtok[1].name == \\\"DECLERATION\\\":\\n self.currtok = next(self.tg)\\n if self.functions.get(self.currtok[0]) is not None:\\n\\n express = self.FunctionCall()\\n return assignmentStmt(id, express)\\n else:\\n express = self.Expression()\\n\\n if self.currtok[1].name == \\\"SEMI\\\":\\n self.currtok = next(self.tg)\\n return assignmentStmt(id, express)\\n raise SLUCSyntaxError(\\\"ERROR: Missing Semicolon on line {0}\\\".format(str(self.currtok[2] - 1)))\\n raise SLUCSyntaxError(\\\"ERROR: Missing assignment on line {0}\\\".format(str(self.currtok[2] - 1)))\",\n \"def test_variable_assign(self):\\n self.trace('x = 1')\\n\\n events = self.variable_events\\n self.assertEqual(len(events), 1)\\n event = events[0]\\n self.assertIsInstance(event, TraceAssign)\\n self.assertEqual(event.name, 'x')\\n self.assertEqual(event.value, 1)\",\n \"def visit_AugAssign(self, node):\\n # FIXME: Gensym the LHS to avoid two evaluations.\\n self.generic_visit(node)\\n rhs = to_call(self.op_to_function(node.op),\\n [set_ctx(node.target), node.value])\\n return ast.Assign([node.target], rhs)\",\n \"def do_assign(parser, token):\\n bits = token.contents.split()\\n if len(bits) != 3:\\n raise template.TemplateSyntaxError(\\\"'%s' tag takes two arguments\\\" % bits[0])\\n value = parser.compile_filter(bits[2])\\n return AssignNode(bits[1], value)\",\n \"def do_assign(parser, token):\\n bits = token.contents.split()\\n if len(bits) != 3:\\n raise template.TemplateSyntaxError(\\\"'%s' tag takes two arguments\\\" % bits[0])\\n value = parser.compile_filter(bits[2])\\n return AssignNode(bits[1], value)\",\n \"def declare_variable(\\n self,\\n identifier: str,\\n typ: types.Type,\\n *,\\n is_transient: t.Optional[bool] = None,\\n initial_value: t.Optional[expressions.ValueOrExpression] = None,\\n comment: t.Optional[str] = None,\\n ) -> None:\\n value = None\\n if initial_value is not None:\\n value = expressions.ensure_expr(initial_value)\\n self.add_declaration(\\n VariableDeclaration(\\n identifier,\\n typ,\\n is_transient=is_transient,\\n initial_value=value,\\n comment=comment,\\n )\\n )\",\n \"def __assign(self, root, item, args=None, value=None, default=False):\\n item = util.unscalar(item)\\n args = util.unscalar_list(args)\\n atroot = root is self\\n if root is None or item is None:\\n return None\\n elif self.PRIVATE and re.search(self.PRIVATE, item):\\n return None\\n elif isinstance(root, dict) or atroot:\\n if not (default and root.get(item)):\\n root[item] = value\\n return value\\n elif isinstance(root, (list, tuple)) and re.match(r\\\"-?\\\\d+$\\\", str(item)):\\n item = int(item)\\n if not (default and 0 <= item < len(root) and root[item]):\\n root[item] = value\\n return value\\n elif util.is_object(root):\\n if not (default and getattr(root, item)()):\\n args.append(value)\\n return getattr(root, item)(*args)\\n else:\\n raise Error(\\\"don't know how to assign to %s.%s\\\" % (root, item))\\n\\n return None\",\n \"def assign(self, *args):\\n return _libsbml.string_assign(self, *args)\",\n \"def _assigns_typevar(node: nodes.NodeNG | None) -> bool:\\n if isinstance(node, astroid.Call):\\n inferred = utils.safe_infer(node.func)\\n if (\\n isinstance(inferred, astroid.ClassDef)\\n and inferred.qname() in TYPE_VAR_QNAME\\n ):\\n return True\\n return False\",\n \"def visit_compound_assign(self, node):\\n # Determine number of values (arity) of compound assignment.\\n nvalues = { len(target.elts) for target in node.targets \\n if is_sequence_node(target) }\\n if len(nvalues) > 1:\\n # A multiple, compound assignment with different arities, e.g.,\\n # `x,y = a,b,c = ...` is not a syntax error in Python, though it\\n # probably should be because it's guaranteed to cause a runtime\\n # error. Raise the error here, since we cannot proceed.\\n raise SyntaxError(\\\"Multiple assignment with different arities\\\")\\n nvalues = nvalues.pop()\\n\\n # Assign temporary variables.\\n temps = [ gensym() for i in range(nvalues) ]\\n stmts = []\\n if is_sequence_node(node.value) and len(node.value.elts) == nvalues:\\n # Special case: RHS is sequence literal of correct length.\\n for i in range(nvalues):\\n temp_target = to_name(temps[i], ast.Store())\\n stmts.append(ast.Assign([temp_target], node.value.elts[i]))\\n else:\\n # General case.\\n temp_target = to_tuple(\\n (to_name(temp, ast.Store()) for temp in temps), ast.Store())\\n stmts.append(ast.Assign([temp_target], node.value))\\n\\n # Rewrite assignments as sequence of assignments.\\n for target in reversed(node.targets):\\n if is_sequence_node(target):\\n stmts.extend(ast.Assign([target.elts[i]], to_name(temps[i]))\\n for i in range(nvalues))\\n else:\\n temp_tuple = to_tuple(to_name(temp) for temp in temps)\\n stmts.append(ast.Assign([target], temp_tuple))\\n \\n return stmts\",\n \"def set_variable(self, name, value):\\n if self._scalamagic and (not name.startswith(\\\"_i\\\")):\\n self.scala_interpreter.bind(name, value)\\n else:\\n self.log.debug('Not setting variable %s', name)\",\n \"def declare_struct_variable(self, v):\\n scope = self.sscope\\n scope.set(v.args[0])\\n self.resolve_to(v, RESOLUTION_NAKED)\",\n \"def visit_aug_assign(self: Parser, node: doc.AugAssign) -> None:\\n lhs_pos = (\\n node.target.lineno,\\n node.target.col_offset,\\n node.target.end_lineno,\\n node.target.end_col_offset,\\n )\\n rhs_pos = (\\n node.value.lineno,\\n node.value.col_offset,\\n node.value.end_lineno,\\n node.value.end_col_offset,\\n )\\n node.target.ctx = doc.Load(*lhs_pos)\\n with self.var_table.with_frame():\\n lhs_name = \\\"__tvm_tmp_value_aug_assign_lhs\\\"\\n rhs_name = \\\"__tvm_tmp_value_aug_assign_rhs\\\"\\n lhs_expr = self.eval_expr(node.target)\\n rhs_expr = self.eval_expr(node.value)\\n self.var_table.add(lhs_name, lhs_expr)\\n self.var_table.add(rhs_name, rhs_expr)\\n op = doc.BinOp(\\n doc.Name(lhs_name, doc.Load(*lhs_pos), *lhs_pos),\\n node.op,\\n doc.Name(rhs_name, doc.Load(*rhs_pos), *rhs_pos),\\n *lhs_pos,\\n )\\n rhs = self.eval_expr(op)\\n lhs = node.target\\n lhs.ctx = doc.Store(*lhs_pos)\\n if isinstance(lhs, doc.Subscript):\\n if isinstance(lhs.slice, doc.Tuple):\\n indices = []\\n for index in lhs.slice.elts:\\n indices.append(self.eval_expr(index))\\n else:\\n indices = [self.eval_expr(lhs.slice)]\\n T.buffer_store(self.eval_expr(lhs.value), rhs, indices)\\n else:\\n self.eval_assign(target=lhs, source=rhs, bind_value=bind_assign_value)\",\n \"def auto(self) -> None:\\n self.value = None\",\n \"def visit_Assign(self, node):\\n assign_stmts = []\\n value = node.value\\n reversed_targets = node.targets\\n reversed_targets.reverse()\\n assign_stmts.append(stypy_functions.create_blank_line())\\n if len(reversed_targets) > 1:\\n assign_stmts.append(\\n stypy_functions.create_src_comment(\\n \\\"Multiple assignment of {0} elements.\\\".format(len(reversed_targets))))\\n else:\\n if hasattr(node, 'lineno'):\\n assign_stmts.append(stypy_functions.create_src_comment(\\n \\\"Assigning a {1} to a {0} (line {2}):\\\".format(type(reversed_targets[0]).__name__,\\n type(value).__name__, node.lineno)))\\n else:\\n assign_stmts.append(stypy_functions.create_src_comment(\\n \\\"Assigning a {1} to a {0}:\\\".format(type(reversed_targets[0]).__name__,\\n type(value).__name__)))\\n for assign_num in xrange(len(reversed_targets)):\\n target = reversed_targets[assign_num]\\n # Function guard is true? execute handler\\n for handler_func_guard_tuple in self.__assignment_handlers:\\n if handler_func_guard_tuple[0](target, value):\\n id_str, handler_func = handler_func_guard_tuple[1]\\n self.performed_transformations |= handler_func(target, value, assign_stmts, node, id_str)\\n assign_stmts = stypy_functions.flatten_lists(assign_stmts)\\n value = target\\n break\\n\\n if len(assign_stmts) > 0:\\n return assign_stmts\\n return node\",\n \"def get_assignment(self, var):\\n return self.variable_to_value.get(var)\",\n \"def let(self, var, val):\\n\\n self.d['__vstemp'] = val\\n if var.endswith('+'):\\n rvar = var.rstrip('+')\\n # .. obj = eval(rvar,self.d)\\n exec(\\\"%s.append(__vstemp)\\\" % rvar, self.d)\\n else:\\n exec(var + \\\" = __vstemp\\\", self.d)\\n del self.d['__vstemp']\",\n \"def _assign_op(dest, op, arg, val, path, scope):\\n if op == '[':\\n dest[arg] = val\\n elif op == '.':\\n setattr(dest, arg, val)\\n elif op == 'P':\\n _assign = scope[TargetRegistry].get_handler('assign', dest)\\n try:\\n _assign(dest, arg, val)\\n except Exception as e:\\n raise PathAssignError(e, path, arg)\\n else: # pragma: no cover\\n raise ValueError('unsupported T operation for assignment')\",\n \"def _fset(self, value):\\n # type: (...) -> None\\n rtype = type_\\n if isinstance(type_, TypeVar):\\n type_map = dict(\\n zip(self.__parameters__, self.__orig_class__.__args__)\\n )\\n rtype = type_map[type_]\\n vars(self)[private_attr] = cast(rtype, value)\",\n \"def define_var(self, var, value):\\n self.binding[var] = value\",\n \"def f_setvar(self, name, expr):\\r\\n self.locals_ptr[name] = self.eval(expr, self.locals_ptr)\\r\\n return \\\"\\\"\",\n \"def assign_variable(variable_name):\\n # check variable name, call function 5 to check type\\n if not in_alphabet(variable_name):\\n print(f\\\"Syntax Error.\\\")\\n return\\n # prompt user to enter a value\\n value = input(f\\\"Enter a value for {variable_name}: \\\")\\n # check value type, call function 6 to check type\\n if not is_digit(value):\\n print(f\\\"Syntax Error.\\\")\\n return\\n # update dictionary\\n lookUpTable[variable_name] = int(value)\",\n \"def on_assign(self):\",\n \"def assign(self, *args):\\n return _ida_hexrays.cexpr_t_assign(self, *args)\",\n \"def _analyse_stmt_AnnAssign(\\n self, statement: ast.AnnAssign, *, next: CFNode\\n ) -> CFNode:\\n return self._ast_node(statement, next=next, error=self._raise)\",\n \"def _fset(self, value):\\n # type: (...) -> None\\n rtype = type_\\n if isinstance(type_, TypeVar):\\n type_map = dict(\\n zip(self.__parameters__, self.__orig_class__.__args__)\\n )\\n rtype = type_map[type_]\\n if not is_instance(value, rtype):\\n raise TypeError(\\n \\\"Cannot assign type of {} to attribute of type {}.\\\".format(\\n _get_type_name(type(value)), _get_type_name(rtype)\\n )\\n )\\n vars(self)[private_attr] = value\",\n \"def set_value(v):\\r\\n try:\\r\\n type (eval(v))\\r\\n except NameError:\\r\\n return \\\"\\\"\\r\\n return eval(v)\",\n \"def set_value(v):\\r\\n try:\\r\\n type (eval(v))\\r\\n except NameError:\\r\\n return \\\"\\\"\\r\\n return eval(v)\",\n \"def visit_any_assign(self, node: types.AnyAssign) -> None:\\n self._check_slots(node)\\n self.generic_visit(node)\",\n \"def test_tuple_assign(self):\\r\\n def local_test(x,y):\\r\\n m1=Module()\\r\\n m1.l=(x(), y())\\r\\n\\r\\n # create a Method that makes the second list element a shared Member\\r\\n m1.g=Method([], m1.l[0])\\r\\n m1.f=Method([], m1.l[1])\\r\\n m = m1.make()\\r\\n\\r\\n #assign 4 and 5 to the two variables' containers in m\\r\\n m.l = (4, 5)\\r\\n assert 5 == m.f()\\r\\n assert 4 == m.g()\\r\\n\\r\\n local_test(lambda:T.dscalar(),lambda:T.dscalar())\",\n \"def assign(self, *args):\\n return _ida_hexrays.cloop_t_assign(self, *args)\",\n \"def assign(self, dst, req, src):\\n if req == 'null':\\n return\\n if req in ('write', 'inplace'):\\n dst[:] = src\\n elif req == 'add':\\n dst[:] += src\",\n \"def assign(self, *args):\\n return _ida_hexrays.cnumber_t_assign(self, *args)\",\n \"def assign(self, *args):\\n return _ida_hexrays.cif_t_assign(self, *args)\",\n \"def test_001_lambda_assign(self):\\r\\n text = \\\"\\\"\\\"\\r\\n const f = (d,k,v) => d[k] = v\\r\\n \\\"\\\"\\\"\\r\\n tokens = Lexer().lex(text)\\r\\n ast = Parser().parse(tokens)\\r\\n expected = TOKEN('T_MODULE', '',\\r\\n TOKEN('T_VAR', 'const',\\r\\n TOKEN('T_ASSIGN', '=',\\r\\n TOKEN('T_TEXT', 'f'),\\r\\n TOKEN('T_LAMBDA', '=>',\\r\\n TOKEN('T_TEXT', 'Anonymous'),\\r\\n TOKEN('T_ARGLIST', '()',\\r\\n TOKEN('T_TEXT', 'd'),\\r\\n TOKEN('T_TEXT', 'k'),\\r\\n TOKEN('T_TEXT', 'v')\\r\\n ),\\r\\n TOKEN('T_ASSIGN', '=',\\r\\n TOKEN('T_SUBSCR', '',\\r\\n TOKEN('T_TEXT', 'd'),\\r\\n TOKEN('T_TEXT', 'k'))\\r\\n ),\\r\\n TOKEN('T_TEXT', 'v')\\r\\n )\\r\\n )\\r\\n )\\r\\n )\\r\\n\\r\\n self.assertFalse(parsecmp(expected, ast, False))\",\n \"def test_swap_assignment():\\n x,y = 5,10\\n yield (x,y)\\n x,y = y,x # no ref-counting here\\n yield (x,y)\",\n \"def __init__(self, name, value):\\n super(VariableDeclarationNode, self).__init__()\\n self._name = name\\n self._value = value\\n self._type = None\",\n \"def _analyse_stmt_AugAssign(\\n self, statement: ast.AugAssign, *, next: CFNode\\n ) -> CFNode:\\n return self._ast_node(statement, next=next, error=self._raise)\",\n \"def setVariable(self, *args):\\n return _libsbml.EventAssignment_setVariable(self, *args)\",\n \"def assign(self, value):\\n generator = (bits for bits in self.data if bits.is_null)\\n if isinstance(value, int):\\n series_of_bits = next(generator, _bits_sentinel)\\n series_of_bits.assign(value)\\n elif isinstance(value, typing.Iterable):\\n for value, bits in zip(value, generator):\\n bits.assign(value)\\n return self\",\n \"def multiple_value_call_assignment_handler(target, value, assign_stmts, node, id_str):\\n #print(\\\"multiple_value_call_assignment_handler\\\")\\n target_stmts, value_var = stypy_functions.create_temp_Assign(value, node.lineno, node.col_offset,\\n \\\"{0}_assignment\\\".format(id_str))\\n assign_stmts.append(target_stmts)\\n\\n #value_var_to_load = copy.deepcopy(value_var)\\n value_var_to_load = ast.Name()\\n value_var_to_load.col_offset = value_var.col_offset\\n value_var_to_load.lineno = value_var.lineno\\n value_var_to_load.id = value_var.id\\n value_var_to_load.ctx = ast.Load()\\n\\n for i in xrange(len(target.elts)):\\n # Assign values to each element.\\n # getitem_att = core_language.create_attribute(value_var_to_load, '__getitem__', context=ast.Load(),\\n # line=node.lineno,\\n # column=node.col_offset)\\n # item_call = functions.create_call(getitem_att, [core_language.create_num(i, node.lineno, node.col_offset)])\\n # temp_stmts, temp_value = stypy_functions.create_temp_Assign(item_call, node.lineno, node.col_offset,\\n # \\\"{0}_assignment\\\".format(id_str))\\n stypy_interface = core_language.create_Name('stypy_interface')\\n get_tuple_call = core_language.create_attribute(stypy_interface, 'stypy_get_value_from_tuple', context=ast.Load(),\\n line=node.lineno,\\n column=node.col_offset)\\n\\n item_call = functions.create_call(get_tuple_call, [value_var_to_load,\\n core_language.create_num(len(target.elts), node.lineno, node.col_offset),\\n core_language.create_num(i, node.lineno, node.col_offset)])\\n temp_stmts, temp_value = stypy_functions.create_temp_Assign(item_call, node.lineno, node.col_offset,\\n \\\"{0}_assignment\\\".format(id_str))\\n if hasattr(node, 'lineno'):\\n temp_stmts.lineno = node.lineno\\n temp_stmts.col_offset = node.col_offset\\n\\n assign_stmts.append(temp_stmts)\\n\\n temp_stmts = core_language.create_Assign(target.elts[i], temp_value)\\n if hasattr(node, 'lineno'):\\n temp_stmts.lineno = node.lineno\\n temp_stmts.col_offset = node.col_offset\\n\\n assign_stmts.append(temp_stmts)\\n\\n return True\",\n \"def fix_for_python_gcc_declaration_distinction(self, con_scopestr, id, var_name):\\n version = self.con_scope_to_highest_var_vers[con_scopestr][id]\\n # this function should only be called in cases where we need to implement a dummy assignment\\n # which creates a version 1 variable\\n assert(version == VAR_INIT_VERSION)\\n # increment the version, and create an GrFN variable for the incremented version\\n self.incr_version_in_con_scope(con_scopestr, id, var_name)\\n new_version = self.con_scope_to_highest_var_vers[con_scopestr][id]\\n new_fullid = build_fullid(var_name, id, new_version, con_scopestr)\\n grfn_var = self.pipeline_state.get_grfn_var(new_fullid)\\n from_source_mdata = generate_from_source_metadata(False, VariableCreationReason.DUMMY_ASSIGN)\\n add_metadata_to_grfn_var(grfn_var, from_source_mdata)\\n\\n # create a literal GrFN assignment for this dummy assignment\\n assign_metadata = create_lambda_node_metadata(source_refs=[])\\n literal_node = create_grfn_literal_node(assign_metadata)\\n lambda_expr = \\\"lambda: None\\\"\\n literal_node.func_str = lambda_expr\\n literal_node.function = load_lambda_function(literal_node.func_str)\\n dummy_assignment = GrfnAssignment(literal_node, LambdaType.LITERAL, lambda_expr=lambda_expr)\\n dummy_assignment.outputs[new_fullid] = grfn_var.uid\\n\\n # add dummy assignment to function def node\\n assert(self.pipeline_state.is_con_scopestr_func_def(con_scopestr))\\n func_def_node = self.pipeline_state.func_def_node_from_scopestr(con_scopestr)\\n\\n func_def_node.dummy_grfn_assignments.append(dummy_assignment)\",\n \"def _cast(self, value): # type: (Any) -> Any\\n if self._strict:\\n if isinstance(value, type(self._default)):\\n return value\\n else:\\n self._setter_error('is not of type (%s)' % type(self._default), value)\\n return self._default\\n else:\\n try:\\n return type(self._default)(value)\\n except (TypeError, ValueError):\\n self._setter_error('could not be cast to (%s)'\\n % type(self._default).__name__, value)\\n return self._default\",\n \"def declare_local_variable(self, v):\\n scope = self.get_method_or_function_or_global_scope()\\n scope.set(v.args[0])\\n self.resolve_to(v, RESOLUTION_NAKED)\",\n \"def _declare_auto_variable_mapping(self):\\n if self.name not in self.nlp.variable_mappings:\\n self.nlp.variable_mappings[self.name] = BiMapping(\\n range(len(self.name_elements)), range(len(self.name_elements))\\n )\",\n \"def rhs(self):\\n if not self.is_assign():\\n raise AssertionError('Not an assignment')\\n return self.initializer\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.65518016","0.63790643","0.6358774","0.6354869","0.61757886","0.61371475","0.6126114","0.6119964","0.60451835","0.5970678","0.5915198","0.58865404","0.5870152","0.5870152","0.5870152","0.5870152","0.5810308","0.5782519","0.57597","0.57563984","0.5729333","0.57085484","0.5693402","0.5683927","0.56602216","0.56470394","0.56452966","0.56315416","0.56312704","0.5624975","0.5599336","0.55934715","0.5573985","0.55625033","0.54943335","0.54856026","0.54748636","0.5453712","0.5414569","0.5414559","0.53961504","0.5365024","0.53527427","0.53412396","0.53131706","0.52545404","0.52265376","0.52041787","0.5173004","0.5171849","0.5150298","0.51493007","0.51348895","0.51331353","0.51317996","0.51064163","0.50725746","0.506332","0.506332","0.5061907","0.50301576","0.50283706","0.5012338","0.50051606","0.49990466","0.49911457","0.49901634","0.49804363","0.49782094","0.4977048","0.49402308","0.49400878","0.4936388","0.49351338","0.49214733","0.49164528","0.48796296","0.4879365","0.48717922","0.48682237","0.48602334","0.48602334","0.48564136","0.48527485","0.4852018","0.48448074","0.48408434","0.48221827","0.48174986","0.4813566","0.48083398","0.48029223","0.47740015","0.47668168","0.47563997","0.47504324","0.4744057","0.47384897","0.47341833","0.47338903"],"string":"[\n \"0.65518016\",\n \"0.63790643\",\n \"0.6358774\",\n \"0.6354869\",\n \"0.61757886\",\n \"0.61371475\",\n \"0.6126114\",\n \"0.6119964\",\n \"0.60451835\",\n \"0.5970678\",\n \"0.5915198\",\n \"0.58865404\",\n \"0.5870152\",\n \"0.5870152\",\n \"0.5870152\",\n \"0.5870152\",\n \"0.5810308\",\n \"0.5782519\",\n \"0.57597\",\n \"0.57563984\",\n \"0.5729333\",\n \"0.57085484\",\n \"0.5693402\",\n \"0.5683927\",\n \"0.56602216\",\n \"0.56470394\",\n \"0.56452966\",\n \"0.56315416\",\n \"0.56312704\",\n \"0.5624975\",\n \"0.5599336\",\n \"0.55934715\",\n \"0.5573985\",\n \"0.55625033\",\n \"0.54943335\",\n \"0.54856026\",\n \"0.54748636\",\n \"0.5453712\",\n \"0.5414569\",\n \"0.5414559\",\n \"0.53961504\",\n \"0.5365024\",\n \"0.53527427\",\n \"0.53412396\",\n \"0.53131706\",\n \"0.52545404\",\n \"0.52265376\",\n \"0.52041787\",\n \"0.5173004\",\n \"0.5171849\",\n \"0.5150298\",\n \"0.51493007\",\n \"0.51348895\",\n \"0.51331353\",\n \"0.51317996\",\n \"0.51064163\",\n \"0.50725746\",\n \"0.506332\",\n \"0.506332\",\n \"0.5061907\",\n \"0.50301576\",\n \"0.50283706\",\n \"0.5012338\",\n \"0.50051606\",\n \"0.49990466\",\n \"0.49911457\",\n \"0.49901634\",\n \"0.49804363\",\n \"0.49782094\",\n \"0.4977048\",\n \"0.49402308\",\n \"0.49400878\",\n \"0.4936388\",\n \"0.49351338\",\n \"0.49214733\",\n \"0.49164528\",\n \"0.48796296\",\n \"0.4879365\",\n \"0.48717922\",\n \"0.48682237\",\n \"0.48602334\",\n \"0.48602334\",\n \"0.48564136\",\n \"0.48527485\",\n \"0.4852018\",\n \"0.48448074\",\n \"0.48408434\",\n \"0.48221827\",\n \"0.48174986\",\n \"0.4813566\",\n \"0.48083398\",\n \"0.48029223\",\n \"0.47740015\",\n \"0.47668168\",\n \"0.47563997\",\n \"0.47504324\",\n \"0.4744057\",\n \"0.47384897\",\n \"0.47341833\",\n \"0.47338903\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6059261"},"document_rank":{"kind":"string","value":"8"}}},{"rowIdx":17790,"cells":{"query":{"kind":"string","value":"Similar to assignment in terms of restrictions. E.g. Allow only single named variable assignments. Also requires the named variable to already exist in scope."},"document":{"kind":"string","value":"def _AugAssign(self, t):\n if not isinstance(t.target, ast.Name):\n self.RaiseError(t, \"Augmented assignment to complex expressions not supported\")\n # check if target exists in locals\n if t.target.id not in self._locals :\n self.RaiseError(t, \"Augmented assignment not permitted on variables not already assigned previously\")\n self.fill()\n self.dispatch(t.target)\n self.write(\" \"+self.binop[t.op.__class__.__name__]+\"= \")\n self.dispatch(t.value)\n self.write(\";\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_46_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tprocedure main(); var x:array[1 .. 3]of real; begin x[1]:=1;\n\t\twith y:integer;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,446))","def _variable(self, name, vars_set):\n if not re.match(r\"[_a-zA-Z][_a-zA-Z0-9]*$\", name):\n self._syntax_error(\"Not a valid name\", name)\n vars_set.add(name)","def test_50_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 2]of real;\n\t\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,450))","def test_47_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 3]of real;\n\t\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,447))","def test_45_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tprocedure main(); var x:real; begin x:=1;\n\t\twith y:integer;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,445))","def _check_variable_definition(variable_name, variable_attrs):\n\n # Variable name must be type str\n if type(variable_name) != str:\n raise TypeError(\"Invalid variable name: \"+str(variable_name)+\" (must be string)\")\n\n # todo - add more tests to check validity of variable definition","def visit_assignname( # pylint: disable=too-many-branches\n self, node: nodes.AssignName\n ) -> None:\n frame = node.frame(future=True)\n assign_type = node.assign_type()\n\n # Check names defined in comprehensions\n if isinstance(assign_type, nodes.Comprehension):\n self._check_name(\"inlinevar\", node.name, node)\n\n # Check names defined in module scope\n elif isinstance(frame, nodes.Module):\n # Check names defined in Assign nodes\n if isinstance(assign_type, nodes.Assign):\n inferred_assign_type = utils.safe_infer(assign_type.value)\n\n # Check TypeVar's and TypeAliases assigned alone or in tuple assignment\n if isinstance(node.parent, nodes.Assign):\n if self._assigns_typevar(assign_type.value):\n self._check_name(\"typevar\", assign_type.targets[0].name, node)\n return\n if self._assigns_typealias(assign_type.value):\n self._check_name(\"typealias\", assign_type.targets[0].name, node)\n return\n\n if (\n isinstance(node.parent, nodes.Tuple)\n and isinstance(assign_type.value, nodes.Tuple)\n # protect against unbalanced tuple unpacking\n and node.parent.elts.index(node) < len(assign_type.value.elts)\n ):\n assigner = assign_type.value.elts[node.parent.elts.index(node)]\n if self._assigns_typevar(assigner):\n self._check_name(\n \"typevar\",\n assign_type.targets[0]\n .elts[node.parent.elts.index(node)]\n .name,\n node,\n )\n return\n if self._assigns_typealias(assigner):\n self._check_name(\n \"typealias\",\n assign_type.targets[0]\n .elts[node.parent.elts.index(node)]\n .name,\n node,\n )\n return\n\n # Check classes (TypeVar's are classes so they need to be excluded first)\n elif isinstance(inferred_assign_type, nodes.ClassDef):\n self._check_name(\"class\", node.name, node)\n\n # Don't emit if the name redefines an import in an ImportError except handler.\n elif not _redefines_import(node) and isinstance(\n inferred_assign_type, nodes.Const\n ):\n self._check_name(\"const\", node.name, node)\n else:\n self._check_name(\n \"variable\", node.name, node, disallowed_check_only=True\n )\n\n # Check names defined in AnnAssign nodes\n elif isinstance(assign_type, nodes.AnnAssign):\n if utils.is_assign_name_annotated_with(node, \"Final\"):\n self._check_name(\"const\", node.name, node)\n elif self._assigns_typealias(assign_type.annotation):\n self._check_name(\"typealias\", node.name, node)\n\n # Check names defined in function scopes\n elif isinstance(frame, nodes.FunctionDef):\n # global introduced variable aren't in the function locals\n if node.name in frame and node.name not in frame.argnames():\n if not _redefines_import(node):\n self._check_name(\"variable\", node.name, node)\n\n # Check names defined in class scopes\n elif isinstance(frame, nodes.ClassDef):\n if not list(frame.local_attr_ancestors(node.name)):\n for ancestor in frame.ancestors():\n if utils.is_enum(ancestor) or utils.is_assign_name_annotated_with(\n node, \"Final\"\n ):\n self._check_name(\"class_const\", node.name, node)\n break\n else:\n self._check_name(\"class_attribute\", node.name, node)","def var_name ( self , name ) :\n if name in self.__var_names and not NameDuplicates.allowed() :\n self.warning ( 'The variable name \"%s\" is already defined!' % name )\n \n self.__var_names.add ( name )\n self.__local_names.add ( name )\n return name","def test_3_redeclared(self):\n\t\tinput = \"\"\"var i:integer;\n\t\tprocedure main(); begin end\n\t\tprocedure p(i:boolean); begin with y:real;y:real; do begin end end\n\t\t\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,403))","def _check_typevar(self, name: str, node: nodes.AssignName) -> None:\n if isinstance(node.parent, nodes.Assign):\n keywords = node.assign_type().value.keywords\n args = node.assign_type().value.args\n elif isinstance(node.parent, nodes.Tuple):\n keywords = (\n node.assign_type().value.elts[node.parent.elts.index(node)].keywords\n )\n args = node.assign_type().value.elts[node.parent.elts.index(node)].args\n\n variance = TypeVarVariance.invariant\n name_arg = None\n for kw in keywords:\n if variance == TypeVarVariance.double_variant:\n pass\n elif kw.arg == \"covariant\" and kw.value.value:\n variance = (\n TypeVarVariance.covariant\n if variance != TypeVarVariance.contravariant\n else TypeVarVariance.double_variant\n )\n elif kw.arg == \"contravariant\" and kw.value.value:\n variance = (\n TypeVarVariance.contravariant\n if variance != TypeVarVariance.covariant\n else TypeVarVariance.double_variant\n )\n\n if kw.arg == \"name\" and isinstance(kw.value, nodes.Const):\n name_arg = kw.value.value\n\n if name_arg is None and args and isinstance(args[0], nodes.Const):\n name_arg = args[0].value\n\n if variance == TypeVarVariance.double_variant:\n self.add_message(\n \"typevar-double-variance\",\n node=node,\n confidence=interfaces.INFERENCE,\n )\n self.add_message(\n \"typevar-name-incorrect-variance\",\n node=node,\n args=(\"\",),\n confidence=interfaces.INFERENCE,\n )\n elif variance == TypeVarVariance.covariant and not name.endswith(\"_co\"):\n suggest_name = f\"{re.sub('_contra$', '', name)}_co\"\n self.add_message(\n \"typevar-name-incorrect-variance\",\n node=node,\n args=(f'. \"{name}\" is covariant, use \"{suggest_name}\" instead'),\n confidence=interfaces.INFERENCE,\n )\n elif variance == TypeVarVariance.contravariant and not name.endswith(\"_contra\"):\n suggest_name = f\"{re.sub('_co$', '', name)}_contra\"\n self.add_message(\n \"typevar-name-incorrect-variance\",\n node=node,\n args=(f'. \"{name}\" is contravariant, use \"{suggest_name}\" instead'),\n confidence=interfaces.INFERENCE,\n )\n elif variance == TypeVarVariance.invariant and (\n name.endswith(\"_co\") or name.endswith(\"_contra\")\n ):\n suggest_name = re.sub(\"_contra$|_co$\", \"\", name)\n self.add_message(\n \"typevar-name-incorrect-variance\",\n node=node,\n args=(f'. \"{name}\" is invariant, use \"{suggest_name}\" instead'),\n confidence=interfaces.INFERENCE,\n )\n\n if name_arg is not None and name_arg != name:\n self.add_message(\n \"typevar-name-mismatch\",\n node=node,\n args=(name_arg, name),\n confidence=interfaces.INFERENCE,\n )","def _is_valid_varname(self, name):\n if name in RESERVED or re.match(r'^str([0-9]+|L)$', name): return False\n return True if VALID_NAME_RE.match(name) else False","def _is_valid_varname(self, name):\n if name in RESERVED or re.match(r'^str[0-9]+$', name): return False\n return True if VALID_NAME_RE.match(name) else False","def is_assign_to_name(statement):\n return isinstance(statement, ast.Assign) and \\\n len(statement.targets) == 1 and \\\n isinstance(statement.targets[0], ast.Name)","def check_var_attrs(self, cls, node):\n ast = node.ast\n declarator = ast.declarator\n attrs = declarator.attrs\n for attr in attrs:\n if attr[0] == \"_\": # internal attribute\n continue\n # XXX - deref on class/struct members\n if attr not in [\"name\", \"readonly\", \"dimension\", \"deref\"]:\n raise RuntimeError(\n \"Illegal attribute '{}' for variable '{}' at line {}\".format(\n attr, node.name, node.linenumber\n ) + \"\\nonly 'name', 'readonly', 'dimension' and 'deref' are allowed on variables\"\n )\n\n is_ptr = declarator.is_indirect()\n if attrs[\"dimension\"] and not is_ptr:\n raise RuntimeError(\n \"dimension attribute can only be \"\n \"used on pointer and references\"\n )\n\n self.parse_attrs(node, ast)","def _check_function_unused_vars(self, scope, args, enclosing_statement=None):\n all_def_nodes = set(\n chain.from_iterable(scope.name_to_all_definition_nodes.values())\n )\n all_used_def_nodes = set(\n chain.from_iterable(scope.usage_to_definition_nodes.values())\n )\n arg_nodes = set(args)\n all_unused_nodes = all_def_nodes - all_used_def_nodes\n for unused in all_unused_nodes:\n # Ignore names not defined through a Name node (e.g., some function arguments)\n if not isinstance(unused, ast.Name):\n continue\n # Ignore names that are meant to be ignored\n if unused.id.startswith(\"_\"):\n continue\n # Ignore arguments\n if unused in arg_nodes:\n continue\n # Ignore names involved in global and similar declarations\n if unused.id in scope.accessed_from_special_nodes:\n continue\n replacement = None\n if self._name_node_to_statement is not None:\n # Ignore some names defined in unpacking assignments. This should behave as follows:\n # a, b = c() # error only if a and b are both unused\n # a, b = yield c.asynq() # same\n # a, b = yield (func1.asynq(), func2.asynq()) # error if either a or b is unused\n # [None for i in range(3)] # error\n # [a for a, b in pairs] # no error\n # [None for a, b in pairs] # error\n statement = self._name_node_to_statement[unused]\n if isinstance(statement, ast.Assign):\n # it's an assignment\n if not (\n isinstance(statement.value, ast.Yield)\n and isinstance(statement.value.value, ast.Tuple)\n ):\n # but not an assignment originating from yielding a tuple (which is probably an\n # async yield)\n\n # We need to loop over the targets to handle code like \"a, b = c = func()\". If\n # the target containing our unused variable is a tuple and some of its members\n # are not unused, ignore it.\n partly_used_target = False\n for target in statement.targets:\n if (\n isinstance(target, (ast.List, ast.Tuple))\n and _contains_node(target.elts, unused)\n and not _all_names_unused(target.elts, all_unused_nodes)\n ):\n partly_used_target = True\n break\n if partly_used_target:\n continue\n if len(statement.targets) == 1 and not isinstance(\n statement.targets[0], (ast.List, ast.Tuple)\n ):\n replacement = self.remove_node(unused, statement)\n elif isinstance(statement, ast.comprehension):\n if isinstance(statement.target, ast.Tuple):\n if not _all_names_unused(\n statement.target.elts, all_unused_nodes\n ):\n continue\n else:\n replacement = self.replace_node(\n unused,\n ast.Name(id=\"_\", ctx=ast.Store()),\n enclosing_statement,\n )\n elif hasattr(ast, \"AnnAssign\") and isinstance(statement, ast.AnnAssign):\n # (Python 3.6+ only) ignore assignments in AnnAssign nodes, which don't actually\n # bind the name\n continue\n self._show_error_if_checking(\n unused,\n \"Variable {} is not read after being written to\".format(unused.id),\n error_code=ErrorCode.unused_variable,\n replacement=replacement,\n )","def test_var_names(var_name):\n assert isinstance(var_name, str)\n if standard_names.is_valid_name(var_name):\n standard_names.StandardName(var_name)\n else:\n warnings.warn(\"not a valid standard name: {name}\".format(name=var_name))","def check_variable_line(self, line):\n self.E_str = \"check_variable_line\"\n line, any_vars = self.find_vars_in_str(line)\n words = [i for i in line.split('=') if i]\n words = self.fix_words(words)\n\n if len(words) < 2:\n self.print_error(\"The syntax for declaring variables is: \"\n + \"'<name> = <value>'\")","def select_unassigned_variable(csp:list,assignment:set,method=0) -> variable:\n if(method not in range(3)):\n return \"method out of bounds\"\n \n if(method == 0):\n y = rdint(0,len(csp)-1) #rdint is inclusive, hence the -1\n var = csp[y]\n while(var in assignment):\n y = rdint(0,len(csp)-1) #rdint is inclusive, hence the -1\n var = csp[y]\n return var\n \n elif(method == 1):\n #1:minimum-remaining value\n least_domain = math.inf\n low_var = None\n for var in csp:\n if(var not in assignment):\n dm_size = var.domain_size()\n if(dm_size == 0):\n return False\n if(dm_size < least_domain):\n least_domain = dm_size\n low_var = var\n return low_var\n \n elif(method == 2):\n #2:minimum-remaining value together with degree\n #the degree of the node works as a tie breaker, otherwise it works\n #just like minimum remaining value\n least_domain = math.inf\n low_var = None\n for var in csp:\n if(var not in assignment):\n dm_size = var.domain_size()\n if(dm_size == 0):\n return False\n if(dm_size < least_domain):\n least_domain = dm_size\n low_var = var\n elif(dm_size == least_domain and var.constraint_size() > low_var.constraint_size()):\n least_domain = dm_size\n low_var = var\n return low_var","def __setitem__(self, name, value):\n if name in self._variables_locked:\n raise Error(\"Attempted to set a variable which is already locked: {0}\".format(name))\n\n if isinstance(value, Delete):\n # Delete the variable\n self._variables.pop(name, None)\n self._variables_default.discard(name)\n elif isinstance(value, Default):\n # Only set if not already set or if the set value was another default\n if name not in self._variables or name in self._variables_default:\n self[name] = value._value\n # direct set below clears default flag, so add it again\n self._variables_default.add(name)\n elif isinstance(value, NoChange):\n pass\n elif isinstance(value, Description):\n self._var_desc[name] = value._desc\n self[name] = value._value\n elif isinstance(value, Locked):\n self[name] = value._value # Set before setting the locked flag\n self._variables_locked.add(name)\n else:\n self._variables[name] = value\n # direct set clears the default flag\n self._variables_default.discard(name)","def has_assignment_for(self, var):\n return self.variable_to_value.get(var) != None","def declare_variable(var, bound_variables):\n varname = var.name\n vartype = var.vartype\n\n # check if it is bound and has already been seen\n if bound_variables is not None and varname in bound_variables:\n yvar = bound_variables[varname].yices_term\n var.bound = True\n return yvar\n\n # check if it has already been seen\n yvar = Terms.get_by_name(varname)\n if yvar is not None:\n #now we need to see if it is free or bound\n tag = Terms.constructor(yvar)\n if tag == Constructor.VARIABLE:\n var.bound = True\n bound_variables[varname] = var\n return yvar\n\n type_term = vartype.yices_term\n type_name = vartype.name\n\n var_term = None\n\n if bound_variables is not None:\n # we need to make a yices variable not an uninterpreted term\n var_term = Terms.new_variable(type_term, varname)\n if var_term is None:\n sys.stderr.write(f'declare_variable: Term.new_variable failed {Yices.error_string()}\\n')\n return None\n bound_variables[varname] = var\n var.bound = True\n else:\n var_term = Terms.new_uninterpreted_term(type_term, varname)\n if var_term is None:\n sys.stderr.write(f'declare_variable: Term.new_uninterpreted_term failed {Yices.error_string()}\\n')\n return None\n\n YicesSignature.types_to_variables[type_name].add(var)\n\n return var_term","def assign_variable(self, name, value):\n return self.set_variable(name, value)","def keep_var(self, varnames):\n varnames = self._find_vars(varnames, empty_ok=False)\n vars_to_drop = set(self._varlist) - set(varnames)\n if len(vars_to_drop) > 0:\n self.drop_var(vars_to_drop)","def declare_assigned_locals_as_nonlocal(topconstruct: syntax.Construct):\n def declare_modifiable(item):\n def create_declaration(decl_construct, vname):\n return syntax.Construct(\n decl_construct,\n vname.args[0])\n\n construct = item.construct\n prop = construct.args[0]\n propname = prop.args[0]\n scopeset = construct.scopeset\n # if the variable resolves to the global or local scope we need\n # to locally declare it as global or locally declare it as\n # non local\n scope = scopeset.get_scope(propname)\n if scope.stype == SCOPE_GLOBAL:\n return create_declaration(syntax.PY_GLOBAL, propname)\n if scope.stype in [SCOPE_FUNCTION, SCOPE_METHOD]:\n return create_declaration(syntax.PY_NONLOCAL, propname)\n\n to_be_added = filter(\n lambda x: x is not None,\n map(declare_modifiable,\n query([is_layering([syntax.ASSIGNMENT])], TreeItem(topconstruct))))\n\n return to_be_added","def verify_naming(self, reserved):\n for w in reserved:\n if w in self.decisions:\n raise ParseError('Duplicate variable/block name \"{}\"'.format(w))","def bind_assign_value(self: Parser, node: doc.expr, var_name: str, value: Any) -> Any:\n if isinstance(value, T.meta_var):\n return value.value\n elif isinstance(value, (list, tuple)):\n for i, v in enumerate(value):\n bind_assign_value(self, node, f\"{var_name}_{i}\", v)\n return value\n elif isinstance(value, Frame):\n value.add_callback(partial(value.__exit__, None, None, None))\n res = value.__enter__()\n IRBuilder.name(var_name, res)\n return res\n elif isinstance(value, (Buffer, IterVar)) or (\n isinstance(value, Var) and not self.var_table.exist(value)\n ):\n IRBuilder.name(var_name, value)\n return value\n else:\n value = tvm.runtime.convert(value)\n frame = T.LetStmt(value)\n var = frame.var\n IRBuilder.name(var_name, var)\n frame.add_callback(partial(frame.__exit__, None, None, None))\n frame.__enter__()\n return var","def test_49_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 2] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 3]of real;\n\t\tbegin f()[1]:=x[1]:=1; end\"\"\"\n\t\texpect = \"Type Mismatch In Statement: Return(Some(Id(a)))\"\n\t\tself.assertTrue(TestChecker.test(input,expect,449))","def Variable(name):\n placeholder_node = placeholder_op()\n placeholder_node.name = name\n return placeholder_node","def _check_variables(datasets, necessary_short_names):\n dataset_name = datasets[0]['dataset']\n necessary_short_names = set(necessary_short_names)\n short_names = set(group_metadata(datasets, 'short_name').keys())\n if short_names != necessary_short_names:\n raise ValueError(\n f\"Expected variables {necessary_short_names} for dataset \"\n f\"'{dataset_name}', got {short_names}\")","def holds(self,assignment):\n return self.condition(*tuple(assignment[v] for v in self.scope))","def var():\n def _var(quoted_name):\n name = quoted_name.subexpression.name\n if (value := get_name(name)) is not None:\n return value\n else:\n raise TypeError(f\"Binding {name} not found\")\n yield (\"(λ &[name] . any)\", _var)","def select_unassigned_variable(csp):\n smallest = -1\n largest = 0\n multiple = False\n returned = None\n\n for unass in csp.variables:\n if not unass.is_assigned():\n if len(unass.domain) < smallest or smallest == -1:\n smallest = len(unass.domain)\n multiple = False\n returned = unass\n if len(unass.domain) == smallest:\n multiple = True\n\n if multiple == False:\n return returned\n else:\n for unass in csp.variables:\n if not unass.is_assigned():\n if len(unass.domain) == smallest:\n if len(csp.constraints[unass]) > largest:\n largest = len(csp.constraints[unass])\n returned = unass\n return returned\n\n\n\n\n\n # TODO implement this\n pass","def any_holds(self, domains, const, env, other_vars, ind=0, assigned_vars=[]):\r\n # All the variables in the scope of the constraints has been assigned.\r\n if ind == len(other_vars): \r\n return const.holds(env)\r\n else:\r\n var = other_vars[ind]\r\n for val in domains[var]:\r\n env[var] = val\r\n if is_all_unique(env, [var] + assigned_vars) and self.any_holds(domains, const, env, other_vars, ind + 1, [var] + assigned_vars):\r\n return True\r\n return False","def test_single_var(self):\r\n x, y, z = tensor.vectors('x', 'y', 'z')\r\n self.check([\r\n (x, x, (({}, True), )),\r\n (x, y, (({}, False), ({y: x}, True), )),\r\n (x, tensor.neg(x), (({}, False), )),\r\n (x, tensor.neg(y), (({}, False), )),\r\n ])","def has_uniquely_named_variables(formula: Formula) -> bool:\r\n forbidden_variables = set(formula.free_variables())\r\n def has_uniquely_named_variables_helper(formula: Formula) -> bool:\r\n if is_unary(formula.root):\r\n return has_uniquely_named_variables_helper(formula.first)\r\n elif is_binary(formula.root):\r\n return has_uniquely_named_variables_helper(formula.first) and \\\r\n has_uniquely_named_variables_helper(formula.second)\r\n elif is_quantifier(formula.root):\r\n if formula.variable in forbidden_variables:\r\n return False\r\n forbidden_variables.add(formula.variable)\r\n return has_uniquely_named_variables_helper(formula.predicate)\r\n else:\r\n assert is_relation(formula.root) or is_equality(formula.root)\r\n return True\r\n\r\n return has_uniquely_named_variables_helper(formula)","def check_variable_copy_condition(\n nlp, phase_idx: int, use_from_phase_idx: int, name: str, decision_variable_attribute: str\n ):\n return (\n use_from_phase_idx is not None\n and use_from_phase_idx < phase_idx\n and name in getattr(nlp[use_from_phase_idx], decision_variable_attribute)\n )","def test_48_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 2] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 3]of real;\n\t\tbegin f()[1]:=x[1]:=1; end\"\"\"\n\t\texpect = \"Type Mismatch In Statement: Return(Some(Id(a)))\"\n\t\tself.assertTrue(TestChecker.test(input,expect,448))","def var(self, name):\n raise NotImplementedError","def _check_var_conflicts(s, variables):\n # check conflicts with variable names\n vars_redefined = {x for x in s if x in variables}\n if vars_redefined:\n raise Exception('Variables redefined: {v}'.format(v=vars_redefined))\n # check conflicts with values of arbitrary finite data types\n for var, domain in variables.items():\n # not arbitrary finite type ?\n if not isinstance(domain, list):\n continue\n # var has arbitrary finite type\n conflicting_values = {x for x in s if x in domain}\n if conflicting_values:\n raise Exception(\n 'Values redefined: {v}'.format(v=conflicting_values))","def _var_check(self):\n missing = set()\n for v in self.variables:\n if getattr(self, v) is None:\n missing.add(v)\n self.missing = missing","def test_10_global_scope(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tprocedure main(); var x:real; begin x:=foo(1,false); {OK} end\n\t\tfunction foo(a:real;b:boolean):real; begin return 1e2; end\n\t\tprocedure f(); var y:integer; y:real; begin end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,410))","def visit_assign(self: Parser, node: doc.Assign) -> None:\n if len(node.targets) != 1:\n self.report_error(node, \"Consequential assignments like 'a = b = c' are not supported.\")\n lhs = node.targets[0]\n\n if isinstance(node.value, doc.Subscript):\n check_slices = []\n if isinstance(node.value.slice, doc.Slice):\n check_slices = [node.value.slice]\n elif isinstance(node.value.slice, doc.Tuple):\n for p in node.value.slice.elts:\n if isinstance(p, doc.Slice):\n check_slices.append(p)\n for s in check_slices:\n if not s.step and s.upper and s.lower:\n s.step = doc.Constant(\n 1,\n None,\n 1,\n 1,\n s.upper.lineno,\n s.upper.end_col_offset + 1,\n s.upper.lineno,\n s.upper.end_col_offset + 2,\n )\n\n rhs = self.eval_expr(node.value)\n if isinstance(lhs, doc.Subscript):\n if isinstance(lhs.slice, doc.Tuple):\n indices = []\n for index in lhs.slice.elts:\n indices.append(self.eval_expr(index))\n else:\n indices = self.eval_expr(lhs.slice)\n T.buffer_store(self.eval_expr(lhs.value), rhs, indices)\n else:\n self.eval_assign(target=lhs, source=rhs, bind_value=bind_assign_value)","def visit_Assign(self, node):\n var_name = node.left.value\n self.VARIABLES[var_name] = self.visit(node.right)","def legal_variable_name(name):\n var_name = name.replace(' ', '_')\n for char in r'''[]{}\\/^%$#@!+-<>?|'\"=~`,.;:''':\n if char in var_name:\n var_name = var_name.replace(char, '')\n for char in var_name:\n if ord(char) > 127: # remove non-ascii characters\n var_name = var_name.replace(char, '')\n return var_name","def is_variable(arg: Expr) -> bool:\n return str(arg)[0].islower()","def test_bind(self):\n x = t.Exactly(\"x\")\n b = t.Bind(\"var\", x)\n self.assertEqual(writePython(b),\n dd(\"\"\"\n _G_exactly_1, lastError = self.exactly('x')\n self.considerError(lastError, None)\n _locals['var'] = _G_exactly_1\n _locals['var']\n \"\"\"))","def consistent(self, assignment):\n # print(\"Entered consistent Function\")\n # print(\"assignment\")\n # print(assignment)\n\n overlaps = self.crossword.overlaps\n value_set = set()\n for variable in assignment: \n #checking overlaps with neighbors\n neighbors = self.crossword.neighbors(variable)\n for neighbor in neighbors:\n overlap = overlaps[(variable, neighbor)]\n if (neighbor in assignment):\n # print(\"var 1 overlap letter\")\n # print(assignment[variable][overlap[0]])\n # print(\"var 2 overlap letter\")\n # print(assignment[neighbor][overlap[1]])\n if (assignment[variable][overlap[0]] is not assignment[neighbor][overlap[1]]):\n return False\n \n # print(\"neighbors\")\n # print(neighbors)\n\n #checking that the assignment is the correct length for the variable\n if (variable.length != len(assignment[variable])):\n return False\n\n #the set to check for distinct variables later\n value_set.add(assignment[variable])\n\n #Checking that all variables are distinct\n #these should be the same length unless two or more variables share an value\n if( len(value_set) is not len(assignment)): \n return False\n \n return True\n\n # raise NotImplementedError","def unbound(name):","def check_vars(self):\n\n # The pipe name.\n if not has_pipe(self.pipe_name):\n raise RelaxNoPipeError(self.pipe_name)","def f_setvar(self, name, expr):\r\n self.locals_ptr[name] = self.eval(expr, self.locals_ptr)\r\n return \"\"","def assignment_complete(self, assignment):\n # for each variable in the crossword\n for variable in self.crossword.variables:\n # if the variable is not assigned a value\n if variable not in assignment:\n # the crossword is not complete\n return False\n return True","def _var(self,\n name,\n shape,\n collections=None,\n initializer=None,\n trainable=True,\n device='/cpu:0',\n dtype=tf.float32):\n collections = set(collections)\n collections.add(GKeys.GLOBAL_VARIABLES)\n var = tf.contrib.framework.variable(\n name=name,\n shape=shape,\n collections=list(collections),\n initializer=initializer,\n trainable=trainable,\n device=device,\n dtype=dtype\n )\n if GKeys.TRAIN_OP not in collections:\n tf.contrib.framework.add_model_variable(var)\n return var","def select_unassigned_variable(self, assignment):\n # Simply just pick the next value that has more than one value\n # in the variable list\n for key, value in assignment.iteritems():\n if len(value) > 1:\n return key, value","def __select_unassigned_variable(self, unassigned_vars):\n mrv = self.__minimum_remaining_values(unassigned_vars)\n copy_unassigned_vars = list(unassigned_vars)\n copy_unassigned_vars.remove(mrv)\n return mrv, copy_unassigned_vars","def select_unassigned_variable(self, assignment):\n var_list= []\n #add unassigned variabled to a list along with the number of words left in its domain\n for var in self.domains:\n if var not in assignment:\n var_list.append((var, len(self.domains[var])))\n #sort this list by the number of words left in its domain\n var_list.sort(key= lambda x:x[1])\n\n #list for variables that are tied for least words left in domain\n equal_vars= [list(var_list[0])]\n for i in range(len(var_list)):\n #adds variables with same number of words left in domain\n if var_list[0][1] == var_list[i][1] and var_list[i] != var_list[0]:\n equal_vars.append(list(var_list[i]))\n\n \n #change the encoded information for words left in domain to the number of neighbors the variable had (highest degree)\n for i in range(len(equal_vars)):\n equal_vars[i][1]= len(self.crossword.neighbors(equal_vars[i][0]))\n\n #sort the list by the highest degree\n equal_vars.sort(key= lambda x:x[1])\n \n #return var with highest degree\n return equal_vars[0][0]","def declare_variable(self, v):\n scope = self.get_default_declaration_scope()\n scope.set(v.args[0])\n self.resolve_to(v, RESOLUTION_NAKED)","def validVarConstructLength(self,varlen):\r\n if len(varlen)!=2:\r\n print 'variable must specify name and type'\r\n return False\r\n else:\r\n return True","def single_assignment_handler(target, value, assign_stmts, node, id_str):\n #print(\"single_assignment_handler\")\n\n temp_stmts = core_language.create_Assign(target, value)\n if hasattr(node, 'lineno'):\n temp_stmts.lineno = node.lineno\n temp_stmts.col_offset = node.col_offset\n\n assign_stmts.append(temp_stmts)\n return False","def validVarConstructName(self,varname):\r\n if (len(varname[0])>32):\r\n return False, ''\r\n if not(varname[0][0].isalpha()):\r\n return False, '' \r\n for ch in varname[0][1:]:\r\n if not(ch.isalpha() or ch.isdigit() or ch=='_'):\r\n return False, ''\r\n \r\n return True, varname","def require_complete(self):\n if len(self._lazy_var_names) > 0:\n for name in self._lazy_var_names:\n raise InvalidVariableException('Variable ' + name + ' is used but not defined')\n for port in self._ports:\n if not port.is_bound_to_input():\n raise InvalidVariableException('Variable ' + port.get_name() + ' must be bound to its input')\n if not port.is_bound_to_output():\n raise InvalidVariableException('Variable ' + port.get_name() + ' must be bound to its output')","def putvarname(self,j_,name_): # 3\n res = self.__obj.putvarname(j_,name_)\n if res != 0:\n result,msg = self.__getlasterror(res)\n raise Error(rescode(res),msg)","def assign_variable(variable_name):\n # check variable name, call function 5 to check type\n if not in_alphabet(variable_name):\n print(f\"Syntax Error.\")\n return\n # prompt user to enter a value\n value = input(f\"Enter a value for {variable_name}: \")\n # check value type, call function 6 to check type\n if not is_digit(value):\n print(f\"Syntax Error.\")\n return\n # update dictionary\n lookUpTable[variable_name] = int(value)","def add_var(self, name, comp):\n if name in self._var_names:\n raise InvalidVariableException('Variable ' + name + ' is already defined')\n elif name in self._lazy_var_names:\n action = name + ' <- ' + comp\n self._lazy_var_names.remove(name)\n self._var_names.add(name)\n self.add_action(action)\n else:\n action = name + ' <- ' + comp\n self._var_names.add(name)\n self.add_action(action)","def is_variable(item):\n return len(item) > 0 and item[0].upper() == item[0]","def _is_var_declaration(fortress, filename, start):\n v = \"var \"\n return fortress.source_code[filename][start : start + len(v)] == v","def addVariable(self, name, value, save = False):\r\n setattr(self, name, value)\r\n if save and name not in self.variables:\r\n self.variables.append(name)","def __getattribute__(self, name):\n if name in ('_special_names', '__dict__'):\n return super().__getattribute__(name)\n if hasattr(self, '_special_names'):\n if name in self._special_names:\n raise AttributeError(\n f\"{name} is a reserved variable name and it cannot be read\")\n return super().__getattribute__(name)","def assign_variable(executor, variable, value):\n variable = variable.replace(\" \", \"\")\n # TODO Should move parsing of this to ParsedStatementLet.\n # TODO Need to handle N-dimensional array element assignment.\n i = variable.find(\"(\")\n if i != -1:\n # Array reference\n j = variable.find(\")\", i+1)\n if j == -1:\n raise BasicSyntaxError(F\"Missing ) in in array assignment to {variable}\")\n if i+1 == j:\n raise BasicSyntaxError(F\"Missing array subscript in assignment to {variable}\")\n\n subscripts = variable[i+1:j].split(\",\")\n variable = variable[:i]\n is_valid_identifier(variable)\n subscripts = [int(eval_expression(executor._symbols, subscript)) - 1 for subscript in subscripts]\n executor.put_symbol_element(variable, value, subscripts)\n else:\n is_valid_identifier(variable)\n executor.put_symbol(variable, value, symbol_type=SymbolType.VARIABLE, arg=None)","def make_variable(self, name = None):\r\n return self.Variable(self, name = name)","def test_ordered_variable_names_is_valid(question, question_path):\n instance = question[\"instance\"]\n ordered_variable_names = instance.get(\"orderedVariableNames\", [])\n set_of_variable_names = frozenset(instance.get(\"variables\", {}).keys())\n\n if not ordered_variable_names:\n if len(set_of_variable_names) <= 1:\n assert question_path not in NO_ORDERED_VARIABLE_NAMES_QUESTIONS\n return\n assert question_path in NO_ORDERED_VARIABLE_NAMES_QUESTIONS\n pytest.skip(\"Whitelisted question with no orderedVariableNames\")\n\n assert question_path not in NO_ORDERED_VARIABLE_NAMES_QUESTIONS\n set_of_ordered_variable_names = frozenset(ordered_variable_names)\n # ordered variable names should not contain duplicates\n assert len(set_of_ordered_variable_names) == len(ordered_variable_names)\n assert set_of_ordered_variable_names == set_of_variable_names","def allow_unresolved_variable_tokens(self):\n return self._allow_unresolved_variable_tokens","def var(*args, **kwargs):\n return Variable(*args, **kwargs)","def checkValName(self):\n valLength = len(self.val)\n if valLength == 0:\n try:\n valsLength = len(self.val)\n if valsLength == 0:\n self.val = self.vals\n except Exception:\n print \"No value set\"","def visit_Assign(self, node):\n var_name = node.left.token.value\n self.GLOBAL_SCOPE[var_name] = self.visit(node.right)","def __validateTemplateVariables(self, vars):\n for requiredVarName in self.varNames():\n if requiredVarName not in vars:\n raise VariableNotFoundError(\n 'Could not find a value for the variable {0}'.format(\n requiredVarName\n )\n )","def assert_valid(self):\n if \"_is_valid\" in self.__dict__:\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\n if \"_is_valid\" in self.__dict__:\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\n if \"_is_valid\" in self.__dict__:\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\n if \"_is_valid\" in self.__dict__:\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\n if \"_is_valid\" in self.__dict__:\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\n if \"_is_valid\" in self.__dict__:\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\n if \"_is_valid\" in self.__dict__:\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\n if \"_is_valid\" in self.__dict__:\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\n if \"_is_valid\" in self.__dict__:\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def make_variable(self, name=None):\r\n return self.Variable(self, name=name)","def make_variable(self, name=None):\r\n return self.Variable(self, name=name)","def sanitize_python_var_name(var_name, replacement_text=\"\"):\n\n var_name = var_name.strip()\n sanitize_var_name = __RE_INVALID_VAR_NAME.sub(\n replacement_text, var_name)\n\n # delete invalid char(s) in the beginning of the variable name\n is_delete_head = any([\n dataproperty.is_empty_string(replacement_text),\n __RE_INVALID_VAR_NAME_HEAD.search(replacement_text) is not None,\n ])\n\n if is_delete_head:\n sanitize_var_name = __RE_INVALID_VAR_NAME_HEAD.sub(\n \"\", sanitize_var_name)\n else:\n match = __RE_INVALID_VAR_NAME_HEAD.search(sanitize_var_name)\n if match is not None:\n sanitize_var_name = (\n match.end() * replacement_text +\n __RE_INVALID_VAR_NAME_HEAD.sub(\"\", sanitize_var_name)\n )\n\n validate_python_var_name(sanitize_var_name)\n\n return sanitize_var_name","def test_wrong_variable_name(\n assert_errors,\n assert_error_text,\n parse_ast_tree,\n naming_template,\n default_options,\n mode,\n wrong_name,\n):\n tree = parse_ast_tree(mode(naming_template.format(wrong_name)))\n\n visitor = WrongNameVisitor(default_options, tree=tree)\n visitor.run()\n\n assert_errors(visitor, [WrongVariableNameViolation])\n assert_error_text(visitor, wrong_name)","def _check_variable(string, varname):\r\n prefix = '[(, ]' # Any character with the exception of '(, '\r\n suffix = '[), ]' # Any character with the exception of '), '\r\n pattern = prefix + varname + suffix\r\n re.match(pattern, string)","def is_assignment(*args):\n return _ida_hexrays.is_assignment(*args)","def validName(varname):\r\n if (len(varname[0])>32):\r\n return False\r\n if not(varname[0][0].isalpha()):\r\n return False \r\n for ch in varname[0][1:]:\r\n if not(ch.isalpha() or ch.isdigit() or ch=='_'):\r\n return False\r\n \r\n return True","def is_assign(self):\n return self.var.initializer is not None","def set_value(self, var_name, new_value, tf_session):\n\n if(var_name in self.assign_operator):\n\n tf_session.run(\n self.assign_operator[var_name], {\n self.l_param_input[var_name]: new_value})\n else:\n print(\"Thou shall only assign learning parameters!\")","def assert_valid(self):\r\n if \"_is_valid\" in self.__dict__:\r\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\r\n if \"_is_valid\" in self.__dict__:\r\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\r\n if \"_is_valid\" in self.__dict__:\r\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\r\n if \"_is_valid\" in self.__dict__:\r\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\r\n if \"_is_valid\" in self.__dict__:\r\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\r\n if \"_is_valid\" in self.__dict__:\r\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\r\n if \"_is_valid\" in self.__dict__:\r\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\"","def assert_valid(self):\r\n if \"_is_valid\" in self.__dict__:\r\n assert self.__dict__[\"_is_valid\"](), \"Variable has already been deleted\""],"string":"[\n \"def test_46_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real; begin x[1]:=1;\\n\\t\\twith y:integer;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,446))\",\n \"def _variable(self, name, vars_set):\\n if not re.match(r\\\"[_a-zA-Z][_a-zA-Z0-9]*$\\\", name):\\n self._syntax_error(\\\"Not a valid name\\\", name)\\n vars_set.add(name)\",\n \"def test_50_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 3] of real;\\n\\t\\tvar a: array[1 .. 3] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 2]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,450))\",\n \"def test_47_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 3] of real;\\n\\t\\tvar a: array[1 .. 3] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,447))\",\n \"def test_45_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tprocedure main(); var x:real; begin x:=1;\\n\\t\\twith y:integer;y:real; do begin end end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,445))\",\n \"def _check_variable_definition(variable_name, variable_attrs):\\n\\n # Variable name must be type str\\n if type(variable_name) != str:\\n raise TypeError(\\\"Invalid variable name: \\\"+str(variable_name)+\\\" (must be string)\\\")\\n\\n # todo - add more tests to check validity of variable definition\",\n \"def visit_assignname( # pylint: disable=too-many-branches\\n self, node: nodes.AssignName\\n ) -> None:\\n frame = node.frame(future=True)\\n assign_type = node.assign_type()\\n\\n # Check names defined in comprehensions\\n if isinstance(assign_type, nodes.Comprehension):\\n self._check_name(\\\"inlinevar\\\", node.name, node)\\n\\n # Check names defined in module scope\\n elif isinstance(frame, nodes.Module):\\n # Check names defined in Assign nodes\\n if isinstance(assign_type, nodes.Assign):\\n inferred_assign_type = utils.safe_infer(assign_type.value)\\n\\n # Check TypeVar's and TypeAliases assigned alone or in tuple assignment\\n if isinstance(node.parent, nodes.Assign):\\n if self._assigns_typevar(assign_type.value):\\n self._check_name(\\\"typevar\\\", assign_type.targets[0].name, node)\\n return\\n if self._assigns_typealias(assign_type.value):\\n self._check_name(\\\"typealias\\\", assign_type.targets[0].name, node)\\n return\\n\\n if (\\n isinstance(node.parent, nodes.Tuple)\\n and isinstance(assign_type.value, nodes.Tuple)\\n # protect against unbalanced tuple unpacking\\n and node.parent.elts.index(node) < len(assign_type.value.elts)\\n ):\\n assigner = assign_type.value.elts[node.parent.elts.index(node)]\\n if self._assigns_typevar(assigner):\\n self._check_name(\\n \\\"typevar\\\",\\n assign_type.targets[0]\\n .elts[node.parent.elts.index(node)]\\n .name,\\n node,\\n )\\n return\\n if self._assigns_typealias(assigner):\\n self._check_name(\\n \\\"typealias\\\",\\n assign_type.targets[0]\\n .elts[node.parent.elts.index(node)]\\n .name,\\n node,\\n )\\n return\\n\\n # Check classes (TypeVar's are classes so they need to be excluded first)\\n elif isinstance(inferred_assign_type, nodes.ClassDef):\\n self._check_name(\\\"class\\\", node.name, node)\\n\\n # Don't emit if the name redefines an import in an ImportError except handler.\\n elif not _redefines_import(node) and isinstance(\\n inferred_assign_type, nodes.Const\\n ):\\n self._check_name(\\\"const\\\", node.name, node)\\n else:\\n self._check_name(\\n \\\"variable\\\", node.name, node, disallowed_check_only=True\\n )\\n\\n # Check names defined in AnnAssign nodes\\n elif isinstance(assign_type, nodes.AnnAssign):\\n if utils.is_assign_name_annotated_with(node, \\\"Final\\\"):\\n self._check_name(\\\"const\\\", node.name, node)\\n elif self._assigns_typealias(assign_type.annotation):\\n self._check_name(\\\"typealias\\\", node.name, node)\\n\\n # Check names defined in function scopes\\n elif isinstance(frame, nodes.FunctionDef):\\n # global introduced variable aren't in the function locals\\n if node.name in frame and node.name not in frame.argnames():\\n if not _redefines_import(node):\\n self._check_name(\\\"variable\\\", node.name, node)\\n\\n # Check names defined in class scopes\\n elif isinstance(frame, nodes.ClassDef):\\n if not list(frame.local_attr_ancestors(node.name)):\\n for ancestor in frame.ancestors():\\n if utils.is_enum(ancestor) or utils.is_assign_name_annotated_with(\\n node, \\\"Final\\\"\\n ):\\n self._check_name(\\\"class_const\\\", node.name, node)\\n break\\n else:\\n self._check_name(\\\"class_attribute\\\", node.name, node)\",\n \"def var_name ( self , name ) :\\n if name in self.__var_names and not NameDuplicates.allowed() :\\n self.warning ( 'The variable name \\\"%s\\\" is already defined!' % name )\\n \\n self.__var_names.add ( name )\\n self.__local_names.add ( name )\\n return name\",\n \"def test_3_redeclared(self):\\n\\t\\tinput = \\\"\\\"\\\"var i:integer;\\n\\t\\tprocedure main(); begin end\\n\\t\\tprocedure p(i:boolean); begin with y:real;y:real; do begin end end\\n\\t\\t\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,403))\",\n \"def _check_typevar(self, name: str, node: nodes.AssignName) -> None:\\n if isinstance(node.parent, nodes.Assign):\\n keywords = node.assign_type().value.keywords\\n args = node.assign_type().value.args\\n elif isinstance(node.parent, nodes.Tuple):\\n keywords = (\\n node.assign_type().value.elts[node.parent.elts.index(node)].keywords\\n )\\n args = node.assign_type().value.elts[node.parent.elts.index(node)].args\\n\\n variance = TypeVarVariance.invariant\\n name_arg = None\\n for kw in keywords:\\n if variance == TypeVarVariance.double_variant:\\n pass\\n elif kw.arg == \\\"covariant\\\" and kw.value.value:\\n variance = (\\n TypeVarVariance.covariant\\n if variance != TypeVarVariance.contravariant\\n else TypeVarVariance.double_variant\\n )\\n elif kw.arg == \\\"contravariant\\\" and kw.value.value:\\n variance = (\\n TypeVarVariance.contravariant\\n if variance != TypeVarVariance.covariant\\n else TypeVarVariance.double_variant\\n )\\n\\n if kw.arg == \\\"name\\\" and isinstance(kw.value, nodes.Const):\\n name_arg = kw.value.value\\n\\n if name_arg is None and args and isinstance(args[0], nodes.Const):\\n name_arg = args[0].value\\n\\n if variance == TypeVarVariance.double_variant:\\n self.add_message(\\n \\\"typevar-double-variance\\\",\\n node=node,\\n confidence=interfaces.INFERENCE,\\n )\\n self.add_message(\\n \\\"typevar-name-incorrect-variance\\\",\\n node=node,\\n args=(\\\"\\\",),\\n confidence=interfaces.INFERENCE,\\n )\\n elif variance == TypeVarVariance.covariant and not name.endswith(\\\"_co\\\"):\\n suggest_name = f\\\"{re.sub('_contra$', '', name)}_co\\\"\\n self.add_message(\\n \\\"typevar-name-incorrect-variance\\\",\\n node=node,\\n args=(f'. \\\"{name}\\\" is covariant, use \\\"{suggest_name}\\\" instead'),\\n confidence=interfaces.INFERENCE,\\n )\\n elif variance == TypeVarVariance.contravariant and not name.endswith(\\\"_contra\\\"):\\n suggest_name = f\\\"{re.sub('_co$', '', name)}_contra\\\"\\n self.add_message(\\n \\\"typevar-name-incorrect-variance\\\",\\n node=node,\\n args=(f'. \\\"{name}\\\" is contravariant, use \\\"{suggest_name}\\\" instead'),\\n confidence=interfaces.INFERENCE,\\n )\\n elif variance == TypeVarVariance.invariant and (\\n name.endswith(\\\"_co\\\") or name.endswith(\\\"_contra\\\")\\n ):\\n suggest_name = re.sub(\\\"_contra$|_co$\\\", \\\"\\\", name)\\n self.add_message(\\n \\\"typevar-name-incorrect-variance\\\",\\n node=node,\\n args=(f'. \\\"{name}\\\" is invariant, use \\\"{suggest_name}\\\" instead'),\\n confidence=interfaces.INFERENCE,\\n )\\n\\n if name_arg is not None and name_arg != name:\\n self.add_message(\\n \\\"typevar-name-mismatch\\\",\\n node=node,\\n args=(name_arg, name),\\n confidence=interfaces.INFERENCE,\\n )\",\n \"def _is_valid_varname(self, name):\\n if name in RESERVED or re.match(r'^str([0-9]+|L)$', name): return False\\n return True if VALID_NAME_RE.match(name) else False\",\n \"def _is_valid_varname(self, name):\\n if name in RESERVED or re.match(r'^str[0-9]+$', name): return False\\n return True if VALID_NAME_RE.match(name) else False\",\n \"def is_assign_to_name(statement):\\n return isinstance(statement, ast.Assign) and \\\\\\n len(statement.targets) == 1 and \\\\\\n isinstance(statement.targets[0], ast.Name)\",\n \"def check_var_attrs(self, cls, node):\\n ast = node.ast\\n declarator = ast.declarator\\n attrs = declarator.attrs\\n for attr in attrs:\\n if attr[0] == \\\"_\\\": # internal attribute\\n continue\\n # XXX - deref on class/struct members\\n if attr not in [\\\"name\\\", \\\"readonly\\\", \\\"dimension\\\", \\\"deref\\\"]:\\n raise RuntimeError(\\n \\\"Illegal attribute '{}' for variable '{}' at line {}\\\".format(\\n attr, node.name, node.linenumber\\n ) + \\\"\\\\nonly 'name', 'readonly', 'dimension' and 'deref' are allowed on variables\\\"\\n )\\n\\n is_ptr = declarator.is_indirect()\\n if attrs[\\\"dimension\\\"] and not is_ptr:\\n raise RuntimeError(\\n \\\"dimension attribute can only be \\\"\\n \\\"used on pointer and references\\\"\\n )\\n\\n self.parse_attrs(node, ast)\",\n \"def _check_function_unused_vars(self, scope, args, enclosing_statement=None):\\n all_def_nodes = set(\\n chain.from_iterable(scope.name_to_all_definition_nodes.values())\\n )\\n all_used_def_nodes = set(\\n chain.from_iterable(scope.usage_to_definition_nodes.values())\\n )\\n arg_nodes = set(args)\\n all_unused_nodes = all_def_nodes - all_used_def_nodes\\n for unused in all_unused_nodes:\\n # Ignore names not defined through a Name node (e.g., some function arguments)\\n if not isinstance(unused, ast.Name):\\n continue\\n # Ignore names that are meant to be ignored\\n if unused.id.startswith(\\\"_\\\"):\\n continue\\n # Ignore arguments\\n if unused in arg_nodes:\\n continue\\n # Ignore names involved in global and similar declarations\\n if unused.id in scope.accessed_from_special_nodes:\\n continue\\n replacement = None\\n if self._name_node_to_statement is not None:\\n # Ignore some names defined in unpacking assignments. This should behave as follows:\\n # a, b = c() # error only if a and b are both unused\\n # a, b = yield c.asynq() # same\\n # a, b = yield (func1.asynq(), func2.asynq()) # error if either a or b is unused\\n # [None for i in range(3)] # error\\n # [a for a, b in pairs] # no error\\n # [None for a, b in pairs] # error\\n statement = self._name_node_to_statement[unused]\\n if isinstance(statement, ast.Assign):\\n # it's an assignment\\n if not (\\n isinstance(statement.value, ast.Yield)\\n and isinstance(statement.value.value, ast.Tuple)\\n ):\\n # but not an assignment originating from yielding a tuple (which is probably an\\n # async yield)\\n\\n # We need to loop over the targets to handle code like \\\"a, b = c = func()\\\". If\\n # the target containing our unused variable is a tuple and some of its members\\n # are not unused, ignore it.\\n partly_used_target = False\\n for target in statement.targets:\\n if (\\n isinstance(target, (ast.List, ast.Tuple))\\n and _contains_node(target.elts, unused)\\n and not _all_names_unused(target.elts, all_unused_nodes)\\n ):\\n partly_used_target = True\\n break\\n if partly_used_target:\\n continue\\n if len(statement.targets) == 1 and not isinstance(\\n statement.targets[0], (ast.List, ast.Tuple)\\n ):\\n replacement = self.remove_node(unused, statement)\\n elif isinstance(statement, ast.comprehension):\\n if isinstance(statement.target, ast.Tuple):\\n if not _all_names_unused(\\n statement.target.elts, all_unused_nodes\\n ):\\n continue\\n else:\\n replacement = self.replace_node(\\n unused,\\n ast.Name(id=\\\"_\\\", ctx=ast.Store()),\\n enclosing_statement,\\n )\\n elif hasattr(ast, \\\"AnnAssign\\\") and isinstance(statement, ast.AnnAssign):\\n # (Python 3.6+ only) ignore assignments in AnnAssign nodes, which don't actually\\n # bind the name\\n continue\\n self._show_error_if_checking(\\n unused,\\n \\\"Variable {} is not read after being written to\\\".format(unused.id),\\n error_code=ErrorCode.unused_variable,\\n replacement=replacement,\\n )\",\n \"def test_var_names(var_name):\\n assert isinstance(var_name, str)\\n if standard_names.is_valid_name(var_name):\\n standard_names.StandardName(var_name)\\n else:\\n warnings.warn(\\\"not a valid standard name: {name}\\\".format(name=var_name))\",\n \"def check_variable_line(self, line):\\n self.E_str = \\\"check_variable_line\\\"\\n line, any_vars = self.find_vars_in_str(line)\\n words = [i for i in line.split('=') if i]\\n words = self.fix_words(words)\\n\\n if len(words) < 2:\\n self.print_error(\\\"The syntax for declaring variables is: \\\"\\n + \\\"'<name> = <value>'\\\")\",\n \"def select_unassigned_variable(csp:list,assignment:set,method=0) -> variable:\\n if(method not in range(3)):\\n return \\\"method out of bounds\\\"\\n \\n if(method == 0):\\n y = rdint(0,len(csp)-1) #rdint is inclusive, hence the -1\\n var = csp[y]\\n while(var in assignment):\\n y = rdint(0,len(csp)-1) #rdint is inclusive, hence the -1\\n var = csp[y]\\n return var\\n \\n elif(method == 1):\\n #1:minimum-remaining value\\n least_domain = math.inf\\n low_var = None\\n for var in csp:\\n if(var not in assignment):\\n dm_size = var.domain_size()\\n if(dm_size == 0):\\n return False\\n if(dm_size < least_domain):\\n least_domain = dm_size\\n low_var = var\\n return low_var\\n \\n elif(method == 2):\\n #2:minimum-remaining value together with degree\\n #the degree of the node works as a tie breaker, otherwise it works\\n #just like minimum remaining value\\n least_domain = math.inf\\n low_var = None\\n for var in csp:\\n if(var not in assignment):\\n dm_size = var.domain_size()\\n if(dm_size == 0):\\n return False\\n if(dm_size < least_domain):\\n least_domain = dm_size\\n low_var = var\\n elif(dm_size == least_domain and var.constraint_size() > low_var.constraint_size()):\\n least_domain = dm_size\\n low_var = var\\n return low_var\",\n \"def __setitem__(self, name, value):\\n if name in self._variables_locked:\\n raise Error(\\\"Attempted to set a variable which is already locked: {0}\\\".format(name))\\n\\n if isinstance(value, Delete):\\n # Delete the variable\\n self._variables.pop(name, None)\\n self._variables_default.discard(name)\\n elif isinstance(value, Default):\\n # Only set if not already set or if the set value was another default\\n if name not in self._variables or name in self._variables_default:\\n self[name] = value._value\\n # direct set below clears default flag, so add it again\\n self._variables_default.add(name)\\n elif isinstance(value, NoChange):\\n pass\\n elif isinstance(value, Description):\\n self._var_desc[name] = value._desc\\n self[name] = value._value\\n elif isinstance(value, Locked):\\n self[name] = value._value # Set before setting the locked flag\\n self._variables_locked.add(name)\\n else:\\n self._variables[name] = value\\n # direct set clears the default flag\\n self._variables_default.discard(name)\",\n \"def has_assignment_for(self, var):\\n return self.variable_to_value.get(var) != None\",\n \"def declare_variable(var, bound_variables):\\n varname = var.name\\n vartype = var.vartype\\n\\n # check if it is bound and has already been seen\\n if bound_variables is not None and varname in bound_variables:\\n yvar = bound_variables[varname].yices_term\\n var.bound = True\\n return yvar\\n\\n # check if it has already been seen\\n yvar = Terms.get_by_name(varname)\\n if yvar is not None:\\n #now we need to see if it is free or bound\\n tag = Terms.constructor(yvar)\\n if tag == Constructor.VARIABLE:\\n var.bound = True\\n bound_variables[varname] = var\\n return yvar\\n\\n type_term = vartype.yices_term\\n type_name = vartype.name\\n\\n var_term = None\\n\\n if bound_variables is not None:\\n # we need to make a yices variable not an uninterpreted term\\n var_term = Terms.new_variable(type_term, varname)\\n if var_term is None:\\n sys.stderr.write(f'declare_variable: Term.new_variable failed {Yices.error_string()}\\\\n')\\n return None\\n bound_variables[varname] = var\\n var.bound = True\\n else:\\n var_term = Terms.new_uninterpreted_term(type_term, varname)\\n if var_term is None:\\n sys.stderr.write(f'declare_variable: Term.new_uninterpreted_term failed {Yices.error_string()}\\\\n')\\n return None\\n\\n YicesSignature.types_to_variables[type_name].add(var)\\n\\n return var_term\",\n \"def assign_variable(self, name, value):\\n return self.set_variable(name, value)\",\n \"def keep_var(self, varnames):\\n varnames = self._find_vars(varnames, empty_ok=False)\\n vars_to_drop = set(self._varlist) - set(varnames)\\n if len(vars_to_drop) > 0:\\n self.drop_var(vars_to_drop)\",\n \"def declare_assigned_locals_as_nonlocal(topconstruct: syntax.Construct):\\n def declare_modifiable(item):\\n def create_declaration(decl_construct, vname):\\n return syntax.Construct(\\n decl_construct,\\n vname.args[0])\\n\\n construct = item.construct\\n prop = construct.args[0]\\n propname = prop.args[0]\\n scopeset = construct.scopeset\\n # if the variable resolves to the global or local scope we need\\n # to locally declare it as global or locally declare it as\\n # non local\\n scope = scopeset.get_scope(propname)\\n if scope.stype == SCOPE_GLOBAL:\\n return create_declaration(syntax.PY_GLOBAL, propname)\\n if scope.stype in [SCOPE_FUNCTION, SCOPE_METHOD]:\\n return create_declaration(syntax.PY_NONLOCAL, propname)\\n\\n to_be_added = filter(\\n lambda x: x is not None,\\n map(declare_modifiable,\\n query([is_layering([syntax.ASSIGNMENT])], TreeItem(topconstruct))))\\n\\n return to_be_added\",\n \"def verify_naming(self, reserved):\\n for w in reserved:\\n if w in self.decisions:\\n raise ParseError('Duplicate variable/block name \\\"{}\\\"'.format(w))\",\n \"def bind_assign_value(self: Parser, node: doc.expr, var_name: str, value: Any) -> Any:\\n if isinstance(value, T.meta_var):\\n return value.value\\n elif isinstance(value, (list, tuple)):\\n for i, v in enumerate(value):\\n bind_assign_value(self, node, f\\\"{var_name}_{i}\\\", v)\\n return value\\n elif isinstance(value, Frame):\\n value.add_callback(partial(value.__exit__, None, None, None))\\n res = value.__enter__()\\n IRBuilder.name(var_name, res)\\n return res\\n elif isinstance(value, (Buffer, IterVar)) or (\\n isinstance(value, Var) and not self.var_table.exist(value)\\n ):\\n IRBuilder.name(var_name, value)\\n return value\\n else:\\n value = tvm.runtime.convert(value)\\n frame = T.LetStmt(value)\\n var = frame.var\\n IRBuilder.name(var_name, var)\\n frame.add_callback(partial(frame.__exit__, None, None, None))\\n frame.__enter__()\\n return var\",\n \"def test_49_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 2] of real;\\n\\t\\tvar a: array[1 .. 3] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Type Mismatch In Statement: Return(Some(Id(a)))\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,449))\",\n \"def Variable(name):\\n placeholder_node = placeholder_op()\\n placeholder_node.name = name\\n return placeholder_node\",\n \"def _check_variables(datasets, necessary_short_names):\\n dataset_name = datasets[0]['dataset']\\n necessary_short_names = set(necessary_short_names)\\n short_names = set(group_metadata(datasets, 'short_name').keys())\\n if short_names != necessary_short_names:\\n raise ValueError(\\n f\\\"Expected variables {necessary_short_names} for dataset \\\"\\n f\\\"'{dataset_name}', got {short_names}\\\")\",\n \"def holds(self,assignment):\\n return self.condition(*tuple(assignment[v] for v in self.scope))\",\n \"def var():\\n def _var(quoted_name):\\n name = quoted_name.subexpression.name\\n if (value := get_name(name)) is not None:\\n return value\\n else:\\n raise TypeError(f\\\"Binding {name} not found\\\")\\n yield (\\\"(λ &[name] . any)\\\", _var)\",\n \"def select_unassigned_variable(csp):\\n smallest = -1\\n largest = 0\\n multiple = False\\n returned = None\\n\\n for unass in csp.variables:\\n if not unass.is_assigned():\\n if len(unass.domain) < smallest or smallest == -1:\\n smallest = len(unass.domain)\\n multiple = False\\n returned = unass\\n if len(unass.domain) == smallest:\\n multiple = True\\n\\n if multiple == False:\\n return returned\\n else:\\n for unass in csp.variables:\\n if not unass.is_assigned():\\n if len(unass.domain) == smallest:\\n if len(csp.constraints[unass]) > largest:\\n largest = len(csp.constraints[unass])\\n returned = unass\\n return returned\\n\\n\\n\\n\\n\\n # TODO implement this\\n pass\",\n \"def any_holds(self, domains, const, env, other_vars, ind=0, assigned_vars=[]):\\r\\n # All the variables in the scope of the constraints has been assigned.\\r\\n if ind == len(other_vars): \\r\\n return const.holds(env)\\r\\n else:\\r\\n var = other_vars[ind]\\r\\n for val in domains[var]:\\r\\n env[var] = val\\r\\n if is_all_unique(env, [var] + assigned_vars) and self.any_holds(domains, const, env, other_vars, ind + 1, [var] + assigned_vars):\\r\\n return True\\r\\n return False\",\n \"def test_single_var(self):\\r\\n x, y, z = tensor.vectors('x', 'y', 'z')\\r\\n self.check([\\r\\n (x, x, (({}, True), )),\\r\\n (x, y, (({}, False), ({y: x}, True), )),\\r\\n (x, tensor.neg(x), (({}, False), )),\\r\\n (x, tensor.neg(y), (({}, False), )),\\r\\n ])\",\n \"def has_uniquely_named_variables(formula: Formula) -> bool:\\r\\n forbidden_variables = set(formula.free_variables())\\r\\n def has_uniquely_named_variables_helper(formula: Formula) -> bool:\\r\\n if is_unary(formula.root):\\r\\n return has_uniquely_named_variables_helper(formula.first)\\r\\n elif is_binary(formula.root):\\r\\n return has_uniquely_named_variables_helper(formula.first) and \\\\\\r\\n has_uniquely_named_variables_helper(formula.second)\\r\\n elif is_quantifier(formula.root):\\r\\n if formula.variable in forbidden_variables:\\r\\n return False\\r\\n forbidden_variables.add(formula.variable)\\r\\n return has_uniquely_named_variables_helper(formula.predicate)\\r\\n else:\\r\\n assert is_relation(formula.root) or is_equality(formula.root)\\r\\n return True\\r\\n\\r\\n return has_uniquely_named_variables_helper(formula)\",\n \"def check_variable_copy_condition(\\n nlp, phase_idx: int, use_from_phase_idx: int, name: str, decision_variable_attribute: str\\n ):\\n return (\\n use_from_phase_idx is not None\\n and use_from_phase_idx < phase_idx\\n and name in getattr(nlp[use_from_phase_idx], decision_variable_attribute)\\n )\",\n \"def test_48_assign_statement(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tfunction f(): array[1 .. 3] of real;\\n\\t\\tvar a: array[1 .. 2] of real;\\n\\t\\tbegin a[2]:=1.1; return a; end\\n\\t\\tprocedure main(); var x:array[1 .. 3]of real;\\n\\t\\tbegin f()[1]:=x[1]:=1; end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Type Mismatch In Statement: Return(Some(Id(a)))\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,448))\",\n \"def var(self, name):\\n raise NotImplementedError\",\n \"def _check_var_conflicts(s, variables):\\n # check conflicts with variable names\\n vars_redefined = {x for x in s if x in variables}\\n if vars_redefined:\\n raise Exception('Variables redefined: {v}'.format(v=vars_redefined))\\n # check conflicts with values of arbitrary finite data types\\n for var, domain in variables.items():\\n # not arbitrary finite type ?\\n if not isinstance(domain, list):\\n continue\\n # var has arbitrary finite type\\n conflicting_values = {x for x in s if x in domain}\\n if conflicting_values:\\n raise Exception(\\n 'Values redefined: {v}'.format(v=conflicting_values))\",\n \"def _var_check(self):\\n missing = set()\\n for v in self.variables:\\n if getattr(self, v) is None:\\n missing.add(v)\\n self.missing = missing\",\n \"def test_10_global_scope(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tprocedure main(); var x:real; begin x:=foo(1,false); {OK} end\\n\\t\\tfunction foo(a:real;b:boolean):real; begin return 1e2; end\\n\\t\\tprocedure f(); var y:integer; y:real; begin end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Variable: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,410))\",\n \"def visit_assign(self: Parser, node: doc.Assign) -> None:\\n if len(node.targets) != 1:\\n self.report_error(node, \\\"Consequential assignments like 'a = b = c' are not supported.\\\")\\n lhs = node.targets[0]\\n\\n if isinstance(node.value, doc.Subscript):\\n check_slices = []\\n if isinstance(node.value.slice, doc.Slice):\\n check_slices = [node.value.slice]\\n elif isinstance(node.value.slice, doc.Tuple):\\n for p in node.value.slice.elts:\\n if isinstance(p, doc.Slice):\\n check_slices.append(p)\\n for s in check_slices:\\n if not s.step and s.upper and s.lower:\\n s.step = doc.Constant(\\n 1,\\n None,\\n 1,\\n 1,\\n s.upper.lineno,\\n s.upper.end_col_offset + 1,\\n s.upper.lineno,\\n s.upper.end_col_offset + 2,\\n )\\n\\n rhs = self.eval_expr(node.value)\\n if isinstance(lhs, doc.Subscript):\\n if isinstance(lhs.slice, doc.Tuple):\\n indices = []\\n for index in lhs.slice.elts:\\n indices.append(self.eval_expr(index))\\n else:\\n indices = self.eval_expr(lhs.slice)\\n T.buffer_store(self.eval_expr(lhs.value), rhs, indices)\\n else:\\n self.eval_assign(target=lhs, source=rhs, bind_value=bind_assign_value)\",\n \"def visit_Assign(self, node):\\n var_name = node.left.value\\n self.VARIABLES[var_name] = self.visit(node.right)\",\n \"def legal_variable_name(name):\\n var_name = name.replace(' ', '_')\\n for char in r'''[]{}\\\\/^%$#@!+-<>?|'\\\"=~`,.;:''':\\n if char in var_name:\\n var_name = var_name.replace(char, '')\\n for char in var_name:\\n if ord(char) > 127: # remove non-ascii characters\\n var_name = var_name.replace(char, '')\\n return var_name\",\n \"def is_variable(arg: Expr) -> bool:\\n return str(arg)[0].islower()\",\n \"def test_bind(self):\\n x = t.Exactly(\\\"x\\\")\\n b = t.Bind(\\\"var\\\", x)\\n self.assertEqual(writePython(b),\\n dd(\\\"\\\"\\\"\\n _G_exactly_1, lastError = self.exactly('x')\\n self.considerError(lastError, None)\\n _locals['var'] = _G_exactly_1\\n _locals['var']\\n \\\"\\\"\\\"))\",\n \"def consistent(self, assignment):\\n # print(\\\"Entered consistent Function\\\")\\n # print(\\\"assignment\\\")\\n # print(assignment)\\n\\n overlaps = self.crossword.overlaps\\n value_set = set()\\n for variable in assignment: \\n #checking overlaps with neighbors\\n neighbors = self.crossword.neighbors(variable)\\n for neighbor in neighbors:\\n overlap = overlaps[(variable, neighbor)]\\n if (neighbor in assignment):\\n # print(\\\"var 1 overlap letter\\\")\\n # print(assignment[variable][overlap[0]])\\n # print(\\\"var 2 overlap letter\\\")\\n # print(assignment[neighbor][overlap[1]])\\n if (assignment[variable][overlap[0]] is not assignment[neighbor][overlap[1]]):\\n return False\\n \\n # print(\\\"neighbors\\\")\\n # print(neighbors)\\n\\n #checking that the assignment is the correct length for the variable\\n if (variable.length != len(assignment[variable])):\\n return False\\n\\n #the set to check for distinct variables later\\n value_set.add(assignment[variable])\\n\\n #Checking that all variables are distinct\\n #these should be the same length unless two or more variables share an value\\n if( len(value_set) is not len(assignment)): \\n return False\\n \\n return True\\n\\n # raise NotImplementedError\",\n \"def unbound(name):\",\n \"def check_vars(self):\\n\\n # The pipe name.\\n if not has_pipe(self.pipe_name):\\n raise RelaxNoPipeError(self.pipe_name)\",\n \"def f_setvar(self, name, expr):\\r\\n self.locals_ptr[name] = self.eval(expr, self.locals_ptr)\\r\\n return \\\"\\\"\",\n \"def assignment_complete(self, assignment):\\n # for each variable in the crossword\\n for variable in self.crossword.variables:\\n # if the variable is not assigned a value\\n if variable not in assignment:\\n # the crossword is not complete\\n return False\\n return True\",\n \"def _var(self,\\n name,\\n shape,\\n collections=None,\\n initializer=None,\\n trainable=True,\\n device='/cpu:0',\\n dtype=tf.float32):\\n collections = set(collections)\\n collections.add(GKeys.GLOBAL_VARIABLES)\\n var = tf.contrib.framework.variable(\\n name=name,\\n shape=shape,\\n collections=list(collections),\\n initializer=initializer,\\n trainable=trainable,\\n device=device,\\n dtype=dtype\\n )\\n if GKeys.TRAIN_OP not in collections:\\n tf.contrib.framework.add_model_variable(var)\\n return var\",\n \"def select_unassigned_variable(self, assignment):\\n # Simply just pick the next value that has more than one value\\n # in the variable list\\n for key, value in assignment.iteritems():\\n if len(value) > 1:\\n return key, value\",\n \"def __select_unassigned_variable(self, unassigned_vars):\\n mrv = self.__minimum_remaining_values(unassigned_vars)\\n copy_unassigned_vars = list(unassigned_vars)\\n copy_unassigned_vars.remove(mrv)\\n return mrv, copy_unassigned_vars\",\n \"def select_unassigned_variable(self, assignment):\\n var_list= []\\n #add unassigned variabled to a list along with the number of words left in its domain\\n for var in self.domains:\\n if var not in assignment:\\n var_list.append((var, len(self.domains[var])))\\n #sort this list by the number of words left in its domain\\n var_list.sort(key= lambda x:x[1])\\n\\n #list for variables that are tied for least words left in domain\\n equal_vars= [list(var_list[0])]\\n for i in range(len(var_list)):\\n #adds variables with same number of words left in domain\\n if var_list[0][1] == var_list[i][1] and var_list[i] != var_list[0]:\\n equal_vars.append(list(var_list[i]))\\n\\n \\n #change the encoded information for words left in domain to the number of neighbors the variable had (highest degree)\\n for i in range(len(equal_vars)):\\n equal_vars[i][1]= len(self.crossword.neighbors(equal_vars[i][0]))\\n\\n #sort the list by the highest degree\\n equal_vars.sort(key= lambda x:x[1])\\n \\n #return var with highest degree\\n return equal_vars[0][0]\",\n \"def declare_variable(self, v):\\n scope = self.get_default_declaration_scope()\\n scope.set(v.args[0])\\n self.resolve_to(v, RESOLUTION_NAKED)\",\n \"def validVarConstructLength(self,varlen):\\r\\n if len(varlen)!=2:\\r\\n print 'variable must specify name and type'\\r\\n return False\\r\\n else:\\r\\n return True\",\n \"def single_assignment_handler(target, value, assign_stmts, node, id_str):\\n #print(\\\"single_assignment_handler\\\")\\n\\n temp_stmts = core_language.create_Assign(target, value)\\n if hasattr(node, 'lineno'):\\n temp_stmts.lineno = node.lineno\\n temp_stmts.col_offset = node.col_offset\\n\\n assign_stmts.append(temp_stmts)\\n return False\",\n \"def validVarConstructName(self,varname):\\r\\n if (len(varname[0])>32):\\r\\n return False, ''\\r\\n if not(varname[0][0].isalpha()):\\r\\n return False, '' \\r\\n for ch in varname[0][1:]:\\r\\n if not(ch.isalpha() or ch.isdigit() or ch=='_'):\\r\\n return False, ''\\r\\n \\r\\n return True, varname\",\n \"def require_complete(self):\\n if len(self._lazy_var_names) > 0:\\n for name in self._lazy_var_names:\\n raise InvalidVariableException('Variable ' + name + ' is used but not defined')\\n for port in self._ports:\\n if not port.is_bound_to_input():\\n raise InvalidVariableException('Variable ' + port.get_name() + ' must be bound to its input')\\n if not port.is_bound_to_output():\\n raise InvalidVariableException('Variable ' + port.get_name() + ' must be bound to its output')\",\n \"def putvarname(self,j_,name_): # 3\\n res = self.__obj.putvarname(j_,name_)\\n if res != 0:\\n result,msg = self.__getlasterror(res)\\n raise Error(rescode(res),msg)\",\n \"def assign_variable(variable_name):\\n # check variable name, call function 5 to check type\\n if not in_alphabet(variable_name):\\n print(f\\\"Syntax Error.\\\")\\n return\\n # prompt user to enter a value\\n value = input(f\\\"Enter a value for {variable_name}: \\\")\\n # check value type, call function 6 to check type\\n if not is_digit(value):\\n print(f\\\"Syntax Error.\\\")\\n return\\n # update dictionary\\n lookUpTable[variable_name] = int(value)\",\n \"def add_var(self, name, comp):\\n if name in self._var_names:\\n raise InvalidVariableException('Variable ' + name + ' is already defined')\\n elif name in self._lazy_var_names:\\n action = name + ' <- ' + comp\\n self._lazy_var_names.remove(name)\\n self._var_names.add(name)\\n self.add_action(action)\\n else:\\n action = name + ' <- ' + comp\\n self._var_names.add(name)\\n self.add_action(action)\",\n \"def is_variable(item):\\n return len(item) > 0 and item[0].upper() == item[0]\",\n \"def _is_var_declaration(fortress, filename, start):\\n v = \\\"var \\\"\\n return fortress.source_code[filename][start : start + len(v)] == v\",\n \"def addVariable(self, name, value, save = False):\\r\\n setattr(self, name, value)\\r\\n if save and name not in self.variables:\\r\\n self.variables.append(name)\",\n \"def __getattribute__(self, name):\\n if name in ('_special_names', '__dict__'):\\n return super().__getattribute__(name)\\n if hasattr(self, '_special_names'):\\n if name in self._special_names:\\n raise AttributeError(\\n f\\\"{name} is a reserved variable name and it cannot be read\\\")\\n return super().__getattribute__(name)\",\n \"def assign_variable(executor, variable, value):\\n variable = variable.replace(\\\" \\\", \\\"\\\")\\n # TODO Should move parsing of this to ParsedStatementLet.\\n # TODO Need to handle N-dimensional array element assignment.\\n i = variable.find(\\\"(\\\")\\n if i != -1:\\n # Array reference\\n j = variable.find(\\\")\\\", i+1)\\n if j == -1:\\n raise BasicSyntaxError(F\\\"Missing ) in in array assignment to {variable}\\\")\\n if i+1 == j:\\n raise BasicSyntaxError(F\\\"Missing array subscript in assignment to {variable}\\\")\\n\\n subscripts = variable[i+1:j].split(\\\",\\\")\\n variable = variable[:i]\\n is_valid_identifier(variable)\\n subscripts = [int(eval_expression(executor._symbols, subscript)) - 1 for subscript in subscripts]\\n executor.put_symbol_element(variable, value, subscripts)\\n else:\\n is_valid_identifier(variable)\\n executor.put_symbol(variable, value, symbol_type=SymbolType.VARIABLE, arg=None)\",\n \"def make_variable(self, name = None):\\r\\n return self.Variable(self, name = name)\",\n \"def test_ordered_variable_names_is_valid(question, question_path):\\n instance = question[\\\"instance\\\"]\\n ordered_variable_names = instance.get(\\\"orderedVariableNames\\\", [])\\n set_of_variable_names = frozenset(instance.get(\\\"variables\\\", {}).keys())\\n\\n if not ordered_variable_names:\\n if len(set_of_variable_names) <= 1:\\n assert question_path not in NO_ORDERED_VARIABLE_NAMES_QUESTIONS\\n return\\n assert question_path in NO_ORDERED_VARIABLE_NAMES_QUESTIONS\\n pytest.skip(\\\"Whitelisted question with no orderedVariableNames\\\")\\n\\n assert question_path not in NO_ORDERED_VARIABLE_NAMES_QUESTIONS\\n set_of_ordered_variable_names = frozenset(ordered_variable_names)\\n # ordered variable names should not contain duplicates\\n assert len(set_of_ordered_variable_names) == len(ordered_variable_names)\\n assert set_of_ordered_variable_names == set_of_variable_names\",\n \"def allow_unresolved_variable_tokens(self):\\n return self._allow_unresolved_variable_tokens\",\n \"def var(*args, **kwargs):\\n return Variable(*args, **kwargs)\",\n \"def checkValName(self):\\n valLength = len(self.val)\\n if valLength == 0:\\n try:\\n valsLength = len(self.val)\\n if valsLength == 0:\\n self.val = self.vals\\n except Exception:\\n print \\\"No value set\\\"\",\n \"def visit_Assign(self, node):\\n var_name = node.left.token.value\\n self.GLOBAL_SCOPE[var_name] = self.visit(node.right)\",\n \"def __validateTemplateVariables(self, vars):\\n for requiredVarName in self.varNames():\\n if requiredVarName not in vars:\\n raise VariableNotFoundError(\\n 'Could not find a value for the variable {0}'.format(\\n requiredVarName\\n )\\n )\",\n \"def assert_valid(self):\\n if \\\"_is_valid\\\" in self.__dict__:\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\n if \\\"_is_valid\\\" in self.__dict__:\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\n if \\\"_is_valid\\\" in self.__dict__:\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\n if \\\"_is_valid\\\" in self.__dict__:\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\n if \\\"_is_valid\\\" in self.__dict__:\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\n if \\\"_is_valid\\\" in self.__dict__:\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\n if \\\"_is_valid\\\" in self.__dict__:\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\n if \\\"_is_valid\\\" in self.__dict__:\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\n if \\\"_is_valid\\\" in self.__dict__:\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def make_variable(self, name=None):\\r\\n return self.Variable(self, name=name)\",\n \"def make_variable(self, name=None):\\r\\n return self.Variable(self, name=name)\",\n \"def sanitize_python_var_name(var_name, replacement_text=\\\"\\\"):\\n\\n var_name = var_name.strip()\\n sanitize_var_name = __RE_INVALID_VAR_NAME.sub(\\n replacement_text, var_name)\\n\\n # delete invalid char(s) in the beginning of the variable name\\n is_delete_head = any([\\n dataproperty.is_empty_string(replacement_text),\\n __RE_INVALID_VAR_NAME_HEAD.search(replacement_text) is not None,\\n ])\\n\\n if is_delete_head:\\n sanitize_var_name = __RE_INVALID_VAR_NAME_HEAD.sub(\\n \\\"\\\", sanitize_var_name)\\n else:\\n match = __RE_INVALID_VAR_NAME_HEAD.search(sanitize_var_name)\\n if match is not None:\\n sanitize_var_name = (\\n match.end() * replacement_text +\\n __RE_INVALID_VAR_NAME_HEAD.sub(\\\"\\\", sanitize_var_name)\\n )\\n\\n validate_python_var_name(sanitize_var_name)\\n\\n return sanitize_var_name\",\n \"def test_wrong_variable_name(\\n assert_errors,\\n assert_error_text,\\n parse_ast_tree,\\n naming_template,\\n default_options,\\n mode,\\n wrong_name,\\n):\\n tree = parse_ast_tree(mode(naming_template.format(wrong_name)))\\n\\n visitor = WrongNameVisitor(default_options, tree=tree)\\n visitor.run()\\n\\n assert_errors(visitor, [WrongVariableNameViolation])\\n assert_error_text(visitor, wrong_name)\",\n \"def _check_variable(string, varname):\\r\\n prefix = '[(, ]' # Any character with the exception of '(, '\\r\\n suffix = '[), ]' # Any character with the exception of '), '\\r\\n pattern = prefix + varname + suffix\\r\\n re.match(pattern, string)\",\n \"def is_assignment(*args):\\n return _ida_hexrays.is_assignment(*args)\",\n \"def validName(varname):\\r\\n if (len(varname[0])>32):\\r\\n return False\\r\\n if not(varname[0][0].isalpha()):\\r\\n return False \\r\\n for ch in varname[0][1:]:\\r\\n if not(ch.isalpha() or ch.isdigit() or ch=='_'):\\r\\n return False\\r\\n \\r\\n return True\",\n \"def is_assign(self):\\n return self.var.initializer is not None\",\n \"def set_value(self, var_name, new_value, tf_session):\\n\\n if(var_name in self.assign_operator):\\n\\n tf_session.run(\\n self.assign_operator[var_name], {\\n self.l_param_input[var_name]: new_value})\\n else:\\n print(\\\"Thou shall only assign learning parameters!\\\")\",\n \"def assert_valid(self):\\r\\n if \\\"_is_valid\\\" in self.__dict__:\\r\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\r\\n if \\\"_is_valid\\\" in self.__dict__:\\r\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\r\\n if \\\"_is_valid\\\" in self.__dict__:\\r\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\r\\n if \\\"_is_valid\\\" in self.__dict__:\\r\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\r\\n if \\\"_is_valid\\\" in self.__dict__:\\r\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\r\\n if \\\"_is_valid\\\" in self.__dict__:\\r\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\r\\n if \\\"_is_valid\\\" in self.__dict__:\\r\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\",\n \"def assert_valid(self):\\r\\n if \\\"_is_valid\\\" in self.__dict__:\\r\\n assert self.__dict__[\\\"_is_valid\\\"](), \\\"Variable has already been deleted\\\"\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.62667334","0.6235484","0.62117046","0.6118622","0.60492593","0.59797275","0.59298253","0.5684028","0.5679982","0.5677362","0.5655891","0.5630487","0.5610054","0.5558762","0.54264975","0.5404717","0.5368703","0.5344528","0.5336999","0.5330405","0.5314537","0.52960306","0.5285447","0.52764136","0.52408993","0.519818","0.5175845","0.51738","0.51723534","0.5168931","0.5152656","0.51384974","0.5137845","0.51372933","0.5131415","0.51297694","0.51176137","0.511088","0.50966","0.5064559","0.50638014","0.50572985","0.5044872","0.5043663","0.5032375","0.5009395","0.50040716","0.5003587","0.49935147","0.4991174","0.49846324","0.49824947","0.4979075","0.49784467","0.49726218","0.4969729","0.49545258","0.4948654","0.49437168","0.4942927","0.49413377","0.4938806","0.49340165","0.49234852","0.4914179","0.4905708","0.4904459","0.48987594","0.48970392","0.48951986","0.4893327","0.48811767","0.48747635","0.48640096","0.48611856","0.4861154","0.4861154","0.4861154","0.4861154","0.4861154","0.4861154","0.4861154","0.4861154","0.4861154","0.4854911","0.4854911","0.48542586","0.48534635","0.48479035","0.48433593","0.4843133","0.48375475","0.4835996","0.48313472","0.48313472","0.48313472","0.48313472","0.48313472","0.48313472","0.48313472","0.48313472"],"string":"[\n \"0.62667334\",\n \"0.6235484\",\n \"0.62117046\",\n \"0.6118622\",\n \"0.60492593\",\n \"0.59797275\",\n \"0.59298253\",\n \"0.5684028\",\n \"0.5679982\",\n \"0.5677362\",\n \"0.5655891\",\n \"0.5630487\",\n \"0.5610054\",\n \"0.5558762\",\n \"0.54264975\",\n \"0.5404717\",\n \"0.5368703\",\n \"0.5344528\",\n \"0.5336999\",\n \"0.5330405\",\n \"0.5314537\",\n \"0.52960306\",\n \"0.5285447\",\n \"0.52764136\",\n \"0.52408993\",\n \"0.519818\",\n \"0.5175845\",\n \"0.51738\",\n \"0.51723534\",\n \"0.5168931\",\n \"0.5152656\",\n \"0.51384974\",\n \"0.5137845\",\n \"0.51372933\",\n \"0.5131415\",\n \"0.51297694\",\n \"0.51176137\",\n \"0.511088\",\n \"0.50966\",\n \"0.5064559\",\n \"0.50638014\",\n \"0.50572985\",\n \"0.5044872\",\n \"0.5043663\",\n \"0.5032375\",\n \"0.5009395\",\n \"0.50040716\",\n \"0.5003587\",\n \"0.49935147\",\n \"0.4991174\",\n \"0.49846324\",\n \"0.49824947\",\n \"0.4979075\",\n \"0.49784467\",\n \"0.49726218\",\n \"0.4969729\",\n \"0.49545258\",\n \"0.4948654\",\n \"0.49437168\",\n \"0.4942927\",\n \"0.49413377\",\n \"0.4938806\",\n \"0.49340165\",\n \"0.49234852\",\n \"0.4914179\",\n \"0.4905708\",\n \"0.4904459\",\n \"0.48987594\",\n \"0.48970392\",\n \"0.48951986\",\n \"0.4893327\",\n \"0.48811767\",\n \"0.48747635\",\n \"0.48640096\",\n \"0.48611856\",\n \"0.4861154\",\n \"0.4861154\",\n \"0.4861154\",\n \"0.4861154\",\n \"0.4861154\",\n \"0.4861154\",\n \"0.4861154\",\n \"0.4861154\",\n \"0.4861154\",\n \"0.4854911\",\n \"0.4854911\",\n \"0.48542586\",\n \"0.48534635\",\n \"0.48479035\",\n \"0.48433593\",\n \"0.4843133\",\n \"0.48375475\",\n \"0.4835996\",\n \"0.48313472\",\n \"0.48313472\",\n \"0.48313472\",\n \"0.48313472\",\n \"0.48313472\",\n \"0.48313472\",\n \"0.48313472\",\n \"0.48313472\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17791,"cells":{"query":{"kind":"string","value":"Standard cpp like return with semicolon."},"document":{"kind":"string","value":"def _Return(self, t):\n self.fill(\"return\")\n if t.value:\n self.write(\" \")\n self.dispatch(t.value)\n self.write(\";\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def compile_return(self) -> None:\n self._consume('return')\n if self._get_current_token() != ';':\n self.compile_expression()\n else:\n self.writer.write_push('CONST', 0)\n self.writer.write_return()\n self._consume(';')","def compile_return(self):\r\n self.tokenizer.advance()\r\n if self.tokenizer.token_type() == JackTokenizer.SYMBOL_T and self.tokenizer.symbol() == \";\":\r\n self.code_writer.write_push(\"constant\", 0)\r\n else:\r\n self.compile_expression()\r\n self.code_writer.write_return()","def ReturnStmt(self):\n self.currtok = next(self.tg)\n express = self.Expression()\n if self.currtok[1].name == \"SEMI\":\n self.currtok = next(self.tg)\n return returnStmt(express)\n raise SLUCSyntaxError(\"ERROR: Missing Semicolon on line {0}\".format(str(self.currtok[2] - 1)))","def compile_return(self):\n\n\t\txml = '<returnStatement>\\n' + self.tokenizer.keyword()\n\t\tself.outfile.write(xml)\n\n\t\tif self.tokenizer.get_token() != ';':\n\t\t\tself.compile_expression()\n\n\t\txml = self.tokenizer.symbol() + '</returnStatement>\\n'\n\t\tself.outfile.write(xml)","def compile_return(self):\r\n self.__advance()\r\n if self.__tokenizer.token_type() == TYPES_DIC[\"SYMBOL\"] and self.__tokenizer.symbol() == \\\r\n \";\":\r\n self.__vmwriter.write_push(\"constant\", 0)\r\n else:\r\n self.compile_expression()\r\n self.__vmwriter.write_return()\r\n self.__advance()","def do_stuff():\n print(\"Hello world\")\n return \"Is it over yet?\"\n print(\"Goodbye cruel world!\")","def get_return_var(return_type: str) -> str:\n s = return_type.strip()\n if 'void' == s:\n return ''\n return 'return r;'","def write_return(self) -> None:\n end_frame = 'R13'\n ret_address = 'R14'\n\n self._write(['@'+CodeWriter.memory_prefixes[\"local\"], \"D=M\", '@'+end_frame, \"M=D\"]) # end_frame = LCL\n self._write(['@5', 'D=D-A', 'A=D', 'D=M', '@'+ret_address, 'M=D']) # get the caller's return\n # address and save it in ret_address\n self._pop_stack_to_d()\n self._write(['@'+CodeWriter.memory_prefixes['argument'], 'A=M', 'M=D']) # *ARG = pop()\n self._write(['@'+CodeWriter.memory_prefixes['argument'], 'D=M', '@SP', 'M=D']) # SP = ARG + 1\n\n for i, addr in enumerate(['@THAT', '@THIS', '@ARG', '@LCL']): # Restores THAT, THIS, ARG and LCL of the caller\n self._write(['@' + end_frame, 'D=M'])\n self._write([f'@{i+1}', 'D=D-A', addr, 'D=M'])\n\n self._write(['@'+ret_address, 'A=M', '0;JMP']) # goto ret_address","def visit_return(self: Parser, node: doc.Return) -> None:\n self.report_error(node, \"Return is not allowed.\")","def multi_statement() -> None:\n pass; print(\"hello\")","def generate_return(self):\n if self._delete:\n statement = self._generate_delete_return()\n elif self._cache:\n statement = self._generate_cache_return()\n elif self._set_properties:\n statement = self._generate_set_properties_return()\n elif self._set_labels:\n statement = self._generate_set_labels_return()\n elif self._remove_labels:\n statement = self._generate_remove_labels_return()\n elif self._aggregate:\n statement = self._generate_aggregate_return()\n else:\n statement = self._generate_distinct_return()\n return statement","def give_me_a_string():\n return \"hi\"\n pass","def get_return_assign(return_type: str) -> str:\n s = return_type.strip()\n if 'void' == s:\n return ''\n return '{} r ='.format(s)","def get_return_stmt(self, code, func_name, return_type):\n func = self.get_func(\"\"\"\n %s %s(void) {\n %s\n }\n \"\"\" % (return_type, func_name, code), func_name)\n return func.return_stmts[0]","def write_return(self):\n\n # FRAME is R13\n self.val_pointer_eq_val_pointer(\"R13\", \"LCL\")\n\n # RET is R14\n self.val_pointer_eq_val_at_pointer_minus_num(\"R14\", \"R13\", 5)\n\n # *ARG = pop() - pop value from stack to where ARG is pointing\n self.pop_stack_to_d()\n self.d_to_pointed_value(\"ARG\")\n\n # SP=ARG+1\n self.val_label_eq_val_label_plus_num(\"SP\", \"ARG\", 1)\n\n self.val_pointer_eq_val_at_pointer_minus_num(\"THAT\", \"R13\", 1)\n self.val_pointer_eq_val_at_pointer_minus_num(\"THIS\", \"R13\", 2)\n self.val_pointer_eq_val_at_pointer_minus_num(\"ARG\", \"R13\", 3)\n self.val_pointer_eq_val_at_pointer_minus_num(\"LCL\", \"R13\", 4)\n\n # goto R14(RET)\n self.write(\"@R14\\nA=M\\n0;JMP\\n\")","def funcj():\n print('in fun g')\n print('last line of g')\n return 23, 45, 67","def my_function2():\n \n ## Your code here\n ## Add comments to clarify what the code is doing\n return","def _(self, node: ModelReturn):\n val = self.visit(node.value)\n return f\"( return {val} )\"","def irgen_ret(stmt, builder, table):\n if not stmt.exprs:\n builder.ret_void()\n else:\n builder.ret(irgen_expr(stmt.exprs[0], builder, table))","def always_return_foo() -> str:\n return \"foo\"","def gen_return(self, stmt: statements.Return) -> None:\n if stmt.value:\n if stmt.value.typ.is_struct:\n # Complex types are copied to pointer passed as\n # first argument.\n value = self.gen_expr(stmt.value, rvalue=True)\n return_value_address = self.builder.function.arguments[0]\n assert return_value_address.name == \"return_value_address\"\n self.emit(ir.Store(value, return_value_address))\n # self.emit(ir.CopyBlob(self.return_value_address))\n self.builder.emit_exit()\n else:\n # Simple types are returned via ir-value.\n value = self.gen_expr(stmt.value, rvalue=True)\n self.builder.emit_return(value)\n else:\n self.builder.emit_exit()\n new_block = self.builder.new_block()\n self.builder.set_block(new_block)","def _analyse_stmt_Return(self, statement: ast.Return, *, next: CFNode) -> CFNode:\n if statement.value is None:\n nodes = dict(next=self._context[_LEAVE])\n else:\n nodes = dict(next=self._context[_RETURN])\n value_is_constant, _ = self._expression_as_constant(statement.value)\n if not value_is_constant:\n nodes.update(error=self._raise)\n return self._ast_node(statement, **nodes)","def cmdfinalization_hook_wrong_return_annotation(self, data: plugin.CommandFinalizationData) -> cmd2.Statement:\n return self.statement_parser.parse('hi there')","def result(value):\n return None, value, None","def result(self) -> global___Expression:","def return_helper(*args, **kwargs):\n global counter\n\n counter = counter + 1\n if counter > 1:\n return \"foobar\"\n\n return \"\"","def return_to_caller(self):\n value = bytearray()\n value.append(0xc3) # ret instruction\n return value","def _is_return(self, words):\n if words[0] == 'return':\n if len(words) != 1:\n raise SyntaxError(\"File line {}: Invalid number of arguments for C_RETURN command.\".format(self._file_line))\n return True\n else:\n return False","def A(arg1, arg2):\n # +++your code here+++\n return None","def hello_world():\n return \"\"","def get_something():\n return \"Bubu was here\"","def test_compiler_return_statement(patch, compiler, lines, tree):\n tree.expression = None\n compiler.return_statement(tree, '1')\n line = tree.line()\n kwargs = {'args': None, 'parent': '1'}\n lines.append.assert_called_with('return', line, **kwargs)","def a_function_of_mine():\n return 'result'","def some_func():\n return \"some data\"","def function_returns(self):\n shards = self.line.split()\n if len(shards) > 3:\n if self.line.endswith('returns') is False:\n if shards[-1] in 'return':\n return True","def single_line():\n print (\"-------------------------------------------------------------\")","def cg_return(self, cmd):\n self.asm(unindent(f\"\"\"\n // Save frame in R13\n @LCL // Frame = LCL\n D=M\n @R13\n M=D\n @R13\n D=M\n // Save return address in R14\n @5\n A=D-A\n D=M // D = *(frame - 5)\n @R14\n M=D // Return address = *(frame - 5)\n // *ARG = pop()\n {self._cg_pop_D}\n @ARG\n A=M\n M=D // *ARG = pop()\n // SP = ARG + 1\n @ARG\n D=M+1\n @SP\n M=D // SP = ARG + 1\n \"\"\"))\n # THAT = *(frame - 1)\n # THIS = *(frame - 2)\n # ARG = *(frame - 3)\n # LCL = *(frame - 4)\n for i, reg in enumerate(['THAT', 'THIS', 'ARG', 'LCL']):\n i = i + 1\n self.asm(unindent(f\"\"\"\n @R13\n D=M\n @{i}\n A=D-A\n D=M\n @{reg}\n M=D // {reg} = *(frame - {i})\n \"\"\"))\n self.asm(unindent(f\"\"\"\n @R14\n A=M\n 0;JMP // goto return address, *(frame - 5)\n \"\"\"))","def return_(x):\n return Pass(x)","def example():\n print \"\"\"\n \"\"\"","def example():\n print \"\"\"\n \"\"\"","def get_newline():\n\treturn newline","def jump(self) -> Optional[str]:\n if self._is_c():\n inst = self._cur()\n if \";\" in inst:\n return inst.split(\";\")[1]\n return None","def _semicolon_skipper(self):\n while (\n not self._is_semicolon(\n self.symbol)) and (\n not self._is_eof(\n self.symbol)):\n self.symbol = self.scanner.get_symbol()\n if self._is_eof(self.symbol):\n # In case file ends prematurely\n pass\n return None","def Statement(self):\n if self.currtok[1].name == \"SEMI\":\n self.currtok = next(self.tg)\n return semicolon()\n if self.currtok[1].name == \"LCURLY\":\n return self.Block()\n if self.currtok[1].name == \"IDENT\":\n if self.functions.get(self.currtok[0]) is None:\n return self.Assignment()\n else:\n return self.FunctionCall()\n if self.currtok[1].name == \"if\":\n return self.IfStatement()\n if self.currtok[1].name == \"print\":\n return self.PrintStmt()\n if self.currtok[1].name == \"while\":\n return self.WhileStatement()\n if self.currtok[1].name == \"return\":\n return self.ReturnStmt()\n\n raise SLUCSyntaxError(\"ERROR: Unexpected token {0} on line {1}\".\n format(self.currtok[1], str(self.currtok[2] - 1)))","def showSeparator():\n\treturn (1, 0)","def write_return(self):\n self._file.write('return\\n')","def writeReturn(self):\r\n\r\n # Create a list to store all the assembly commands and write them later\r\n translated_commands = []\r\n\r\n # FRAME = LCL --> R14\r\n translated_commands.append(\"@LCL\")\r\n translated_commands.append(\"D=M\")\r\n translated_commands.append(\"@R14\") # FRAME\r\n translated_commands.append(\"M=D\")\r\n # RET = *(FRAME-5) --> R15\r\n translated_commands.append(\"@R14\") # FRAME\r\n translated_commands.append(\"D=M\") # D = FRAME\r\n translated_commands.append(\"@5\")\r\n translated_commands.append(\"D=D-A\")\r\n translated_commands.append(\"A=D\") # A = FRAME - 5\r\n translated_commands.append(\"D=M\") # D = *(FRAME - 5)\r\n translated_commands.append(\"@R15\") # RET\r\n translated_commands.append(\"M=D\")\r\n # *ARG = pop()\r\n translated_commands.append(\"@SP\")\r\n translated_commands.append(\"M=M-1\")\r\n translated_commands.append(\"A=M\")\r\n translated_commands.append(\"D=M\")\r\n translated_commands.append(\"@ARG\")\r\n translated_commands.append(\"A=M\")\r\n translated_commands.append(\"M=D\")\r\n # SP = ARG + 1\r\n translated_commands.append(\"@ARG\")\r\n translated_commands.append(\"D=M\")\r\n translated_commands.append(\"@SP\")\r\n translated_commands.append(\"M=D+1\")\r\n # THAT = *(FRAME - 1)\r\n translated_commands.append(\"@R14\") # FRAME\r\n translated_commands.append(\"D=M\")\r\n translated_commands.append(\"@1\")\r\n translated_commands.append(\"D=D-A\") # FRAME - 1\r\n translated_commands.append(\"A=D\")\r\n translated_commands.append(\"D=M\") # D = *(FRAME - 1)\r\n translated_commands.append(\"@THAT\")\r\n translated_commands.append(\"M=D\") # THAT = *(FRAME - 1)\r\n # THIS = (FRAME - 2)\r\n translated_commands.append(\"@R14\") # FRAME\r\n translated_commands.append(\"D=M\")\r\n translated_commands.append(\"@2\")\r\n translated_commands.append(\"D=D-A\") # FRAME - 2\r\n translated_commands.append(\"A=D\")\r\n translated_commands.append(\"D=M\") # D = *(FRAME - 2)\r\n translated_commands.append(\"@THIS\")\r\n translated_commands.append(\"M=D\") # THIS = *(FRAME - 2)\r\n # ARG = (FRAME - 3)\r\n translated_commands.append(\"@R14\") # FRAME\r\n translated_commands.append(\"D=M\")\r\n translated_commands.append(\"@3\")\r\n translated_commands.append(\"D=D-A\") # FRAME - 3\r\n translated_commands.append(\"A=D\")\r\n translated_commands.append(\"D=M\") # D = *(FRAME - 3)\r\n translated_commands.append(\"@ARG\")\r\n translated_commands.append(\"M=D\") # ARG = *(FRAME - 3)\r\n # LCL = (FRAME - 4)\r\n translated_commands.append(\"@R14\") # FRAME\r\n translated_commands.append(\"D=M\")\r\n translated_commands.append(\"@4\")\r\n translated_commands.append(\"D=D-A\") # FRAME - 4\r\n translated_commands.append(\"A=D\")\r\n translated_commands.append(\"D=M\") # D = *(FRAME - 4)\r\n translated_commands.append(\"@LCL\")\r\n translated_commands.append(\"M=D\") # LCL = *(FRAME - 4)\r\n # goto RET\r\n translated_commands.append(\"@R15\") # RET \r\n translated_commands.append(\"A=M\")\r\n translated_commands.append(\"0;JMP\")\r\n\r\n # Write all the commands stored in the list and write them on the file.\r\n for line in translated_commands:\r\n self.filename.write(line + \"\\n\")","def semicolon(f: Callable) -> Callable:\n\n @functools.wraps(f)\n def wrapper(*args, **kwargs):\n return f(*args, **kwargs) + \";\"\n\n return wrapper","def hello():\n return 'Hello World hahaha !!!!!!'","def _return(*args):\r\n to_return = ()\r\n\r\n for arg in args:\r\n cond, value = arg\r\n if cond:\r\n to_return += (value,)\r\n\r\n if len(to_return) == 1:\r\n return to_return[0]\r\n return to_return","def return_helper(*args, **kwargs):\n global counter\n\n counter = counter + 1\n if counter > 1:\n return \"Y\"\n\n return \"foobar\"","def exit_program():\n\n print(\"Thank you. Bye\")\n return \"exit\"","def func2():\n return None","def func2():\n return None","def function_with_wrong_return() -> None:\n return 42","def a():\n # type something\n return c","def line() -> str:\n return f\"---\"","def return_last_function_value(topconstruct):\n items = query([\n is_python_expr,\n at_indent_0_from_function_program,\n last_program_step\n ], TreeItem(topconstruct))\n # for each of these items we want to wrap them in a return\n for item in items:\n item.replace_construct(\n syntax.Construct(syntax.FUNCTION_RETURN, item.construct))","def compact_style_code():\n try: raise Exception # NOQA\n except Exception: pass # NOQA","def yes():\n return 42","def ReturnStringMethod(self, str):\n def rsfunc(self):\n return str\n return rsfunc","def __printReturn(self, text, value):\n if self.debug_mode:\n print ''.join([text, \" \", str(value)])\n return value","def Statement(self):\n t = self.token\n if t.stmt_begin:\n self._advance()\n return t.std()\n ex = self.expression(0)\n self._advance([\"NEWLINE\", \"END\", \"DEDENT\"])\n return ex","def testHasReturns(self):\n concise = build_code([], [], [], concise=True)\n full = build_code([], [], [], concise=False)\n self.assertNotIn('return', concise)\n self.assertIn('return', full)","def hello():\n return \"Hello\"","def home():\r\n return(\r\n f\"\"\r\n )","def doctest_DKBCCCsvStatementParser():","def return_none() -> None:\n pass","def Block(self, prefix, assign=False, semicolon=False):\n self.Write('%s = {' if assign else '%s {', prefix)\n self._indent += 1\n yield\n self._indent -= 1\n self.Write('};' if assign or semicolon else '}')","def check_standalone_declaration(self, return_type, scope):\n index = self.token_index\n token = self.get_specific_token(index)\n if token:\n if token.token_type == u'T_ID' or\\\n token.token_type == u'T_RESERVED_WORD':\n self.log_message(token)\n identifier_token = token\n index = self.token_index = index + 1\n token = self.get_specific_token(index)\n if token:\n if token.token_type == u'T_ASSIGN':\n self.add_to_symbols_table(identifier_token,\n return_type, scope)\n right_side_declaration =\\\n self.check_right_side_declaration(\n return_type, scope, identifier_token)\n if right_side_declaration.place:\n more_declarations =\\\n self.check_more_declarations(return_type,\n scope)\n token = self.get_specific_token(self.token_index)\n if token:\n if token.token_type == u'T_SEMICOLON':\n self.log_message(token)\n self.token_index += 1\n standalone_declaration = Production()\n standalone_declaration.append_code(\n right_side_declaration.code)\n return standalone_declaration\n self.set_syntactic_error(u'T_SEMICOLON', token)\n self.set_eof_error(u'T_SEMICOLON')\n standalone_declaration = Production()\n return standalone_declaration\n elif token.token_type == u'T_COMMA':\n self.add_to_symbols_table(identifier_token,\n return_type, scope)\n self.check_more_declarations(return_type, scope)\n token = self.get_specific_token(self.token_index)\n if token.token_type == u'T_SEMICOLON':\n self.log_message(token)\n self.token_index += 1\n standalone_declaration = Production()\n return standalone_declaration\n # Commented, check if there are any consequences\n # return True\n self.set_syntactic_error(u'T_SEMICOLON', token)\n elif token.token_type == u'T_SEMICOLON':\n self.add_to_symbols_table(identifier_token,\n return_type, scope)\n self.log_message(token)\n self.token_index += 1\n standalone_declaration = Production()\n return standalone_declaration\n self.set_syntactic_error(u'T_ASSIGN or T_COMMA', token)\n self.set_eof_error(u'T_ASSIGN or T_COMMA')\n self.set_syntactic_error(u'T_ID or T_RESERVED_WORD', token)\n # If no token is found, it's likely that there are no more declarations\n return False","def _check_semicolon(line_index, input_line):\n global _total_lines_of_code\n if input_line.endswith(';'):\n _code_lines.append(line_index)\n _total_lines_of_code += 1","def hello2(a):\n return","def hello():\n return \"Hello\"","def write_return(output_file):\n # temp variables: R14 is FRAME; R15 is RET.\n output_file.write(\"@LCL\" + \"\\n\" +\n \"D = M\" + \"\\n\" +\n \"@R14\" + \"\\n\" +\n \"M = D \" + \"\\n\" +\n \"@5\" + \"\\n\" +\n \"D = D - A\" + \"\\n\" +\n \"A = D\" + \"\\n\" +\n \"D = M\" + \"\\n\" +\n \"@R15\" + \"\\n\" +\n \"M = D\" + \"\\n\"\n )\n write_pop(output_file, \"argument\", \"0\")\n output_file.write(\"@ARG\" + \"\\n\" +\n \"D = M +1 \" + \"\\n\" +\n \"@SP\" + \"\\n\" +\n \"M = D\" + \"\\n\")\n for mem_segment in MEM_SEGMENT_ARR:\n output_file.write(FRAME_MINUS_ONE + \"@\" + mem_segment + \"\\n\" + \"M = D\" + \"\\n\")\n output_file.write(\"@R15\" + \"\\n\" +\n \"A = M\" + \"\\n\"\n \"0; JMP\" + \"\\n\")","def test_parse_return_action(self):\n\n line = '\"Uncalled bet of 66 returned to \"\"Oven @ hogtf8AO2o\"\"\",2021-02-11T02:41:46.355Z,161301130635712'\n actual = parser.parse_action(line)\n expected = Return(player=Player(name=\"Oven\", id_=\"hogtf8AO2o\"), amount=66)\n self.assertEqual(actual, expected)","def gen_separator_thunk(self):\n separator_thunk = self.gen_new_thunk(0)\n return separator_thunk","def fortran_c_wrapper(self) -> str:\n return ''","def end():\n return say()","def returned_value(self) -> FunctionParseResult:\n\n node = self.definition\n # if function does not has body\n if not node:\n return FunctionParseResult(\n return_not_none=bool(FunctionLint.EMPTY_RETURNED_VALUE),\n line_number=-1,\n )\n\n # skip plain assignment expressions\n if isinstance(node, _ast.Assign):\n return FunctionParseResult(\n return_not_none=bool(FunctionLint.EMPTY_RETURNED_VALUE),\n line_number=node.lineno,\n )\n # skip plain pass expressions\n elif isinstance(node, _ast.Pass):\n return FunctionParseResult(\n return_not_none=bool(FunctionLint.EMPTY_RETURNED_VALUE),\n line_number=node.lineno,\n )\n # skip for any other plain expressions\n elif isinstance(node, _ast.Expr):\n return FunctionParseResult(\n return_not_none=bool(FunctionLint.EMPTY_RETURNED_VALUE),\n line_number=node.lineno,\n )\n # iterate over function body\n for item in node.body:\n # check, that expression has return type\n if isinstance(item, _ast.Return):\n return FunctionParseResult(\n return_not_none=ReturnedExpression(item).value_not_none(),\n line_number=item.lineno,\n )\n # in any other case - return empty\n return FunctionParseResult(\n return_not_none=bool(FunctionLint.EMPTY_RETURNED_VALUE),\n line_number=node.lineno,\n )","def return_to_enclosing_type():\n print (\"\\nReturning to enclosing type...\\n\")","def part2_test_input():\n return \"\"\"\"\"\"","def test_67_function_not_return(self):\n\t\tinput = \"\"\"procedure main(); var x:integer;\n\t\tbegin x:=fu(); end\n\t\tfunction fu():integer;\n\t\tbegin with a:real; do\n\t\twith a:boolean; do while(true)do return 1;end\"\"\"\n\t\texpect = \"Function fu Not Return\"\n\t\tself.assertTrue(TestChecker.test(input,expect,467))","def _check_return(\n ret_type: None | IntType | ReferenceType, returned: Sequence[BitString]\n) -> None:\n if len(returned) > 1:\n raise ValueError(\"code block returns multiple values\")\n\n if ret_type is None:\n if len(returned) != 0:\n raise ValueError(\n 'function has no return type, but its code block defines \"ret\"'\n )\n elif isinstance(ret_type, ReferenceType):\n if len(returned) == 0:\n raise ValueError(\n f\"function has return type {ret_type}, but its code block \"\n f'does not define \"ret\"'\n )\n (ret_bits,) = returned\n if ret_type.type.width != ret_bits.width:\n raise ValueError(\n f\"function has return type {ret_type}, but its code block \"\n f'defines \"ret\" with width {ret_bits.width}'\n )\n else: # returns a value\n if len(returned) == 0:\n raise ValueError(\n f\"function has return type {ret_type}, but its code block \"\n f'does not assign to \"ret\"'\n )\n (ret_bits,) = returned\n if ret_type.width != ret_bits.width:\n raise ValueError(\n f\"function has return type {ret_type}, but its code block \"\n f'defines \"ret\" with width {ret_bits.width}'\n )","def print_and_return(*args, **kwargs):\n print(*args, end=\"\\r\", **kwargs)","def newline():\n print()","def getCommandcode(self):\n commandcode = \"RETURN ({})\".format(self.value) # commandcode\n return commandcode","def my_function():\n\treturn 42","def code():","def rehydrate_indented_code_block_end(self, next_token):\n assert next_token\n del self.block_stack[-1]\n return \"\"","def end():\n return EndBlock()","def question_4():\n return None","def expected_return_value(program_filename):\n with open(program_filename) as decaf_file:\n for line in decaf_file:\n if line.startswith(RETURN_COMMENT_PREFIX):\n return_comment = line\n break\n else:\n # We didn't break-- we fell off the end of the loop.\n raise AssertionError('No \"// Returns:\" comment!')\n return_value = without_prefix(return_comment, RETURN_COMMENT_PREFIX)\n return int(return_value)","def PRETEND ( self ):\n return None","def visit_ReturnNode(self, node: ReturnNode, symbol_table: SymbolTable) -> Union[Number, String]:\n result = self.visit(node.expression, symbol_table)\n return result","def force_single_line_js(js):\n log.debug(\"Forcing JS from ~{0} to 1 Line.\".format(len(js.splitlines())))\n return \";\".join(js.splitlines()) if len(js.splitlines()) > 1 else js","def test_66_function_not_return(self):\n\t\tinput = \"\"\"procedure main(); var x:integer;\n\t\tbegin x:=fu(); end\n\t\tfunction fu():integer;\n\t\tbegin with a:real; do\n\t\twith a:boolean; do if(true)then return 1;end\"\"\"\n\t\texpect = \"Function fu Not Return\"\n\t\tself.assertTrue(TestChecker.test(input,expect,466))","def test_function2(a, b):\n x = a + b\n y = a * b\n return x, y, x<y, x>y # < to ensure HTML is properly escaped","def code_format(self):\n return None if self._code is None else '.+'","def code_format(self):\n return None if self._code is None else '.+'","def newline_to_semicolon(text):\n # Convert newlines to semicolons.\n # Return converted text.\n return ';'.join(x for x in text.split('\\n'))"],"string":"[\n \"def compile_return(self) -> None:\\n self._consume('return')\\n if self._get_current_token() != ';':\\n self.compile_expression()\\n else:\\n self.writer.write_push('CONST', 0)\\n self.writer.write_return()\\n self._consume(';')\",\n \"def compile_return(self):\\r\\n self.tokenizer.advance()\\r\\n if self.tokenizer.token_type() == JackTokenizer.SYMBOL_T and self.tokenizer.symbol() == \\\";\\\":\\r\\n self.code_writer.write_push(\\\"constant\\\", 0)\\r\\n else:\\r\\n self.compile_expression()\\r\\n self.code_writer.write_return()\",\n \"def ReturnStmt(self):\\n self.currtok = next(self.tg)\\n express = self.Expression()\\n if self.currtok[1].name == \\\"SEMI\\\":\\n self.currtok = next(self.tg)\\n return returnStmt(express)\\n raise SLUCSyntaxError(\\\"ERROR: Missing Semicolon on line {0}\\\".format(str(self.currtok[2] - 1)))\",\n \"def compile_return(self):\\n\\n\\t\\txml = '<returnStatement>\\\\n' + self.tokenizer.keyword()\\n\\t\\tself.outfile.write(xml)\\n\\n\\t\\tif self.tokenizer.get_token() != ';':\\n\\t\\t\\tself.compile_expression()\\n\\n\\t\\txml = self.tokenizer.symbol() + '</returnStatement>\\\\n'\\n\\t\\tself.outfile.write(xml)\",\n \"def compile_return(self):\\r\\n self.__advance()\\r\\n if self.__tokenizer.token_type() == TYPES_DIC[\\\"SYMBOL\\\"] and self.__tokenizer.symbol() == \\\\\\r\\n \\\";\\\":\\r\\n self.__vmwriter.write_push(\\\"constant\\\", 0)\\r\\n else:\\r\\n self.compile_expression()\\r\\n self.__vmwriter.write_return()\\r\\n self.__advance()\",\n \"def do_stuff():\\n print(\\\"Hello world\\\")\\n return \\\"Is it over yet?\\\"\\n print(\\\"Goodbye cruel world!\\\")\",\n \"def get_return_var(return_type: str) -> str:\\n s = return_type.strip()\\n if 'void' == s:\\n return ''\\n return 'return r;'\",\n \"def write_return(self) -> None:\\n end_frame = 'R13'\\n ret_address = 'R14'\\n\\n self._write(['@'+CodeWriter.memory_prefixes[\\\"local\\\"], \\\"D=M\\\", '@'+end_frame, \\\"M=D\\\"]) # end_frame = LCL\\n self._write(['@5', 'D=D-A', 'A=D', 'D=M', '@'+ret_address, 'M=D']) # get the caller's return\\n # address and save it in ret_address\\n self._pop_stack_to_d()\\n self._write(['@'+CodeWriter.memory_prefixes['argument'], 'A=M', 'M=D']) # *ARG = pop()\\n self._write(['@'+CodeWriter.memory_prefixes['argument'], 'D=M', '@SP', 'M=D']) # SP = ARG + 1\\n\\n for i, addr in enumerate(['@THAT', '@THIS', '@ARG', '@LCL']): # Restores THAT, THIS, ARG and LCL of the caller\\n self._write(['@' + end_frame, 'D=M'])\\n self._write([f'@{i+1}', 'D=D-A', addr, 'D=M'])\\n\\n self._write(['@'+ret_address, 'A=M', '0;JMP']) # goto ret_address\",\n \"def visit_return(self: Parser, node: doc.Return) -> None:\\n self.report_error(node, \\\"Return is not allowed.\\\")\",\n \"def multi_statement() -> None:\\n pass; print(\\\"hello\\\")\",\n \"def generate_return(self):\\n if self._delete:\\n statement = self._generate_delete_return()\\n elif self._cache:\\n statement = self._generate_cache_return()\\n elif self._set_properties:\\n statement = self._generate_set_properties_return()\\n elif self._set_labels:\\n statement = self._generate_set_labels_return()\\n elif self._remove_labels:\\n statement = self._generate_remove_labels_return()\\n elif self._aggregate:\\n statement = self._generate_aggregate_return()\\n else:\\n statement = self._generate_distinct_return()\\n return statement\",\n \"def give_me_a_string():\\n return \\\"hi\\\"\\n pass\",\n \"def get_return_assign(return_type: str) -> str:\\n s = return_type.strip()\\n if 'void' == s:\\n return ''\\n return '{} r ='.format(s)\",\n \"def get_return_stmt(self, code, func_name, return_type):\\n func = self.get_func(\\\"\\\"\\\"\\n %s %s(void) {\\n %s\\n }\\n \\\"\\\"\\\" % (return_type, func_name, code), func_name)\\n return func.return_stmts[0]\",\n \"def write_return(self):\\n\\n # FRAME is R13\\n self.val_pointer_eq_val_pointer(\\\"R13\\\", \\\"LCL\\\")\\n\\n # RET is R14\\n self.val_pointer_eq_val_at_pointer_minus_num(\\\"R14\\\", \\\"R13\\\", 5)\\n\\n # *ARG = pop() - pop value from stack to where ARG is pointing\\n self.pop_stack_to_d()\\n self.d_to_pointed_value(\\\"ARG\\\")\\n\\n # SP=ARG+1\\n self.val_label_eq_val_label_plus_num(\\\"SP\\\", \\\"ARG\\\", 1)\\n\\n self.val_pointer_eq_val_at_pointer_minus_num(\\\"THAT\\\", \\\"R13\\\", 1)\\n self.val_pointer_eq_val_at_pointer_minus_num(\\\"THIS\\\", \\\"R13\\\", 2)\\n self.val_pointer_eq_val_at_pointer_minus_num(\\\"ARG\\\", \\\"R13\\\", 3)\\n self.val_pointer_eq_val_at_pointer_minus_num(\\\"LCL\\\", \\\"R13\\\", 4)\\n\\n # goto R14(RET)\\n self.write(\\\"@R14\\\\nA=M\\\\n0;JMP\\\\n\\\")\",\n \"def funcj():\\n print('in fun g')\\n print('last line of g')\\n return 23, 45, 67\",\n \"def my_function2():\\n \\n ## Your code here\\n ## Add comments to clarify what the code is doing\\n return\",\n \"def _(self, node: ModelReturn):\\n val = self.visit(node.value)\\n return f\\\"( return {val} )\\\"\",\n \"def irgen_ret(stmt, builder, table):\\n if not stmt.exprs:\\n builder.ret_void()\\n else:\\n builder.ret(irgen_expr(stmt.exprs[0], builder, table))\",\n \"def always_return_foo() -> str:\\n return \\\"foo\\\"\",\n \"def gen_return(self, stmt: statements.Return) -> None:\\n if stmt.value:\\n if stmt.value.typ.is_struct:\\n # Complex types are copied to pointer passed as\\n # first argument.\\n value = self.gen_expr(stmt.value, rvalue=True)\\n return_value_address = self.builder.function.arguments[0]\\n assert return_value_address.name == \\\"return_value_address\\\"\\n self.emit(ir.Store(value, return_value_address))\\n # self.emit(ir.CopyBlob(self.return_value_address))\\n self.builder.emit_exit()\\n else:\\n # Simple types are returned via ir-value.\\n value = self.gen_expr(stmt.value, rvalue=True)\\n self.builder.emit_return(value)\\n else:\\n self.builder.emit_exit()\\n new_block = self.builder.new_block()\\n self.builder.set_block(new_block)\",\n \"def _analyse_stmt_Return(self, statement: ast.Return, *, next: CFNode) -> CFNode:\\n if statement.value is None:\\n nodes = dict(next=self._context[_LEAVE])\\n else:\\n nodes = dict(next=self._context[_RETURN])\\n value_is_constant, _ = self._expression_as_constant(statement.value)\\n if not value_is_constant:\\n nodes.update(error=self._raise)\\n return self._ast_node(statement, **nodes)\",\n \"def cmdfinalization_hook_wrong_return_annotation(self, data: plugin.CommandFinalizationData) -> cmd2.Statement:\\n return self.statement_parser.parse('hi there')\",\n \"def result(value):\\n return None, value, None\",\n \"def result(self) -> global___Expression:\",\n \"def return_helper(*args, **kwargs):\\n global counter\\n\\n counter = counter + 1\\n if counter > 1:\\n return \\\"foobar\\\"\\n\\n return \\\"\\\"\",\n \"def return_to_caller(self):\\n value = bytearray()\\n value.append(0xc3) # ret instruction\\n return value\",\n \"def _is_return(self, words):\\n if words[0] == 'return':\\n if len(words) != 1:\\n raise SyntaxError(\\\"File line {}: Invalid number of arguments for C_RETURN command.\\\".format(self._file_line))\\n return True\\n else:\\n return False\",\n \"def A(arg1, arg2):\\n # +++your code here+++\\n return None\",\n \"def hello_world():\\n return \\\"\\\"\",\n \"def get_something():\\n return \\\"Bubu was here\\\"\",\n \"def test_compiler_return_statement(patch, compiler, lines, tree):\\n tree.expression = None\\n compiler.return_statement(tree, '1')\\n line = tree.line()\\n kwargs = {'args': None, 'parent': '1'}\\n lines.append.assert_called_with('return', line, **kwargs)\",\n \"def a_function_of_mine():\\n return 'result'\",\n \"def some_func():\\n return \\\"some data\\\"\",\n \"def function_returns(self):\\n shards = self.line.split()\\n if len(shards) > 3:\\n if self.line.endswith('returns') is False:\\n if shards[-1] in 'return':\\n return True\",\n \"def single_line():\\n print (\\\"-------------------------------------------------------------\\\")\",\n \"def cg_return(self, cmd):\\n self.asm(unindent(f\\\"\\\"\\\"\\n // Save frame in R13\\n @LCL // Frame = LCL\\n D=M\\n @R13\\n M=D\\n @R13\\n D=M\\n // Save return address in R14\\n @5\\n A=D-A\\n D=M // D = *(frame - 5)\\n @R14\\n M=D // Return address = *(frame - 5)\\n // *ARG = pop()\\n {self._cg_pop_D}\\n @ARG\\n A=M\\n M=D // *ARG = pop()\\n // SP = ARG + 1\\n @ARG\\n D=M+1\\n @SP\\n M=D // SP = ARG + 1\\n \\\"\\\"\\\"))\\n # THAT = *(frame - 1)\\n # THIS = *(frame - 2)\\n # ARG = *(frame - 3)\\n # LCL = *(frame - 4)\\n for i, reg in enumerate(['THAT', 'THIS', 'ARG', 'LCL']):\\n i = i + 1\\n self.asm(unindent(f\\\"\\\"\\\"\\n @R13\\n D=M\\n @{i}\\n A=D-A\\n D=M\\n @{reg}\\n M=D // {reg} = *(frame - {i})\\n \\\"\\\"\\\"))\\n self.asm(unindent(f\\\"\\\"\\\"\\n @R14\\n A=M\\n 0;JMP // goto return address, *(frame - 5)\\n \\\"\\\"\\\"))\",\n \"def return_(x):\\n return Pass(x)\",\n \"def example():\\n print \\\"\\\"\\\"\\n \\\"\\\"\\\"\",\n \"def example():\\n print \\\"\\\"\\\"\\n \\\"\\\"\\\"\",\n \"def get_newline():\\n\\treturn newline\",\n \"def jump(self) -> Optional[str]:\\n if self._is_c():\\n inst = self._cur()\\n if \\\";\\\" in inst:\\n return inst.split(\\\";\\\")[1]\\n return None\",\n \"def _semicolon_skipper(self):\\n while (\\n not self._is_semicolon(\\n self.symbol)) and (\\n not self._is_eof(\\n self.symbol)):\\n self.symbol = self.scanner.get_symbol()\\n if self._is_eof(self.symbol):\\n # In case file ends prematurely\\n pass\\n return None\",\n \"def Statement(self):\\n if self.currtok[1].name == \\\"SEMI\\\":\\n self.currtok = next(self.tg)\\n return semicolon()\\n if self.currtok[1].name == \\\"LCURLY\\\":\\n return self.Block()\\n if self.currtok[1].name == \\\"IDENT\\\":\\n if self.functions.get(self.currtok[0]) is None:\\n return self.Assignment()\\n else:\\n return self.FunctionCall()\\n if self.currtok[1].name == \\\"if\\\":\\n return self.IfStatement()\\n if self.currtok[1].name == \\\"print\\\":\\n return self.PrintStmt()\\n if self.currtok[1].name == \\\"while\\\":\\n return self.WhileStatement()\\n if self.currtok[1].name == \\\"return\\\":\\n return self.ReturnStmt()\\n\\n raise SLUCSyntaxError(\\\"ERROR: Unexpected token {0} on line {1}\\\".\\n format(self.currtok[1], str(self.currtok[2] - 1)))\",\n \"def showSeparator():\\n\\treturn (1, 0)\",\n \"def write_return(self):\\n self._file.write('return\\\\n')\",\n \"def writeReturn(self):\\r\\n\\r\\n # Create a list to store all the assembly commands and write them later\\r\\n translated_commands = []\\r\\n\\r\\n # FRAME = LCL --> R14\\r\\n translated_commands.append(\\\"@LCL\\\")\\r\\n translated_commands.append(\\\"D=M\\\")\\r\\n translated_commands.append(\\\"@R14\\\") # FRAME\\r\\n translated_commands.append(\\\"M=D\\\")\\r\\n # RET = *(FRAME-5) --> R15\\r\\n translated_commands.append(\\\"@R14\\\") # FRAME\\r\\n translated_commands.append(\\\"D=M\\\") # D = FRAME\\r\\n translated_commands.append(\\\"@5\\\")\\r\\n translated_commands.append(\\\"D=D-A\\\")\\r\\n translated_commands.append(\\\"A=D\\\") # A = FRAME - 5\\r\\n translated_commands.append(\\\"D=M\\\") # D = *(FRAME - 5)\\r\\n translated_commands.append(\\\"@R15\\\") # RET\\r\\n translated_commands.append(\\\"M=D\\\")\\r\\n # *ARG = pop()\\r\\n translated_commands.append(\\\"@SP\\\")\\r\\n translated_commands.append(\\\"M=M-1\\\")\\r\\n translated_commands.append(\\\"A=M\\\")\\r\\n translated_commands.append(\\\"D=M\\\")\\r\\n translated_commands.append(\\\"@ARG\\\")\\r\\n translated_commands.append(\\\"A=M\\\")\\r\\n translated_commands.append(\\\"M=D\\\")\\r\\n # SP = ARG + 1\\r\\n translated_commands.append(\\\"@ARG\\\")\\r\\n translated_commands.append(\\\"D=M\\\")\\r\\n translated_commands.append(\\\"@SP\\\")\\r\\n translated_commands.append(\\\"M=D+1\\\")\\r\\n # THAT = *(FRAME - 1)\\r\\n translated_commands.append(\\\"@R14\\\") # FRAME\\r\\n translated_commands.append(\\\"D=M\\\")\\r\\n translated_commands.append(\\\"@1\\\")\\r\\n translated_commands.append(\\\"D=D-A\\\") # FRAME - 1\\r\\n translated_commands.append(\\\"A=D\\\")\\r\\n translated_commands.append(\\\"D=M\\\") # D = *(FRAME - 1)\\r\\n translated_commands.append(\\\"@THAT\\\")\\r\\n translated_commands.append(\\\"M=D\\\") # THAT = *(FRAME - 1)\\r\\n # THIS = (FRAME - 2)\\r\\n translated_commands.append(\\\"@R14\\\") # FRAME\\r\\n translated_commands.append(\\\"D=M\\\")\\r\\n translated_commands.append(\\\"@2\\\")\\r\\n translated_commands.append(\\\"D=D-A\\\") # FRAME - 2\\r\\n translated_commands.append(\\\"A=D\\\")\\r\\n translated_commands.append(\\\"D=M\\\") # D = *(FRAME - 2)\\r\\n translated_commands.append(\\\"@THIS\\\")\\r\\n translated_commands.append(\\\"M=D\\\") # THIS = *(FRAME - 2)\\r\\n # ARG = (FRAME - 3)\\r\\n translated_commands.append(\\\"@R14\\\") # FRAME\\r\\n translated_commands.append(\\\"D=M\\\")\\r\\n translated_commands.append(\\\"@3\\\")\\r\\n translated_commands.append(\\\"D=D-A\\\") # FRAME - 3\\r\\n translated_commands.append(\\\"A=D\\\")\\r\\n translated_commands.append(\\\"D=M\\\") # D = *(FRAME - 3)\\r\\n translated_commands.append(\\\"@ARG\\\")\\r\\n translated_commands.append(\\\"M=D\\\") # ARG = *(FRAME - 3)\\r\\n # LCL = (FRAME - 4)\\r\\n translated_commands.append(\\\"@R14\\\") # FRAME\\r\\n translated_commands.append(\\\"D=M\\\")\\r\\n translated_commands.append(\\\"@4\\\")\\r\\n translated_commands.append(\\\"D=D-A\\\") # FRAME - 4\\r\\n translated_commands.append(\\\"A=D\\\")\\r\\n translated_commands.append(\\\"D=M\\\") # D = *(FRAME - 4)\\r\\n translated_commands.append(\\\"@LCL\\\")\\r\\n translated_commands.append(\\\"M=D\\\") # LCL = *(FRAME - 4)\\r\\n # goto RET\\r\\n translated_commands.append(\\\"@R15\\\") # RET \\r\\n translated_commands.append(\\\"A=M\\\")\\r\\n translated_commands.append(\\\"0;JMP\\\")\\r\\n\\r\\n # Write all the commands stored in the list and write them on the file.\\r\\n for line in translated_commands:\\r\\n self.filename.write(line + \\\"\\\\n\\\")\",\n \"def semicolon(f: Callable) -> Callable:\\n\\n @functools.wraps(f)\\n def wrapper(*args, **kwargs):\\n return f(*args, **kwargs) + \\\";\\\"\\n\\n return wrapper\",\n \"def hello():\\n return 'Hello World hahaha !!!!!!'\",\n \"def _return(*args):\\r\\n to_return = ()\\r\\n\\r\\n for arg in args:\\r\\n cond, value = arg\\r\\n if cond:\\r\\n to_return += (value,)\\r\\n\\r\\n if len(to_return) == 1:\\r\\n return to_return[0]\\r\\n return to_return\",\n \"def return_helper(*args, **kwargs):\\n global counter\\n\\n counter = counter + 1\\n if counter > 1:\\n return \\\"Y\\\"\\n\\n return \\\"foobar\\\"\",\n \"def exit_program():\\n\\n print(\\\"Thank you. Bye\\\")\\n return \\\"exit\\\"\",\n \"def func2():\\n return None\",\n \"def func2():\\n return None\",\n \"def function_with_wrong_return() -> None:\\n return 42\",\n \"def a():\\n # type something\\n return c\",\n \"def line() -> str:\\n return f\\\"---\\\"\",\n \"def return_last_function_value(topconstruct):\\n items = query([\\n is_python_expr,\\n at_indent_0_from_function_program,\\n last_program_step\\n ], TreeItem(topconstruct))\\n # for each of these items we want to wrap them in a return\\n for item in items:\\n item.replace_construct(\\n syntax.Construct(syntax.FUNCTION_RETURN, item.construct))\",\n \"def compact_style_code():\\n try: raise Exception # NOQA\\n except Exception: pass # NOQA\",\n \"def yes():\\n return 42\",\n \"def ReturnStringMethod(self, str):\\n def rsfunc(self):\\n return str\\n return rsfunc\",\n \"def __printReturn(self, text, value):\\n if self.debug_mode:\\n print ''.join([text, \\\" \\\", str(value)])\\n return value\",\n \"def Statement(self):\\n t = self.token\\n if t.stmt_begin:\\n self._advance()\\n return t.std()\\n ex = self.expression(0)\\n self._advance([\\\"NEWLINE\\\", \\\"END\\\", \\\"DEDENT\\\"])\\n return ex\",\n \"def testHasReturns(self):\\n concise = build_code([], [], [], concise=True)\\n full = build_code([], [], [], concise=False)\\n self.assertNotIn('return', concise)\\n self.assertIn('return', full)\",\n \"def hello():\\n return \\\"Hello\\\"\",\n \"def home():\\r\\n return(\\r\\n f\\\"\\\"\\r\\n )\",\n \"def doctest_DKBCCCsvStatementParser():\",\n \"def return_none() -> None:\\n pass\",\n \"def Block(self, prefix, assign=False, semicolon=False):\\n self.Write('%s = {' if assign else '%s {', prefix)\\n self._indent += 1\\n yield\\n self._indent -= 1\\n self.Write('};' if assign or semicolon else '}')\",\n \"def check_standalone_declaration(self, return_type, scope):\\n index = self.token_index\\n token = self.get_specific_token(index)\\n if token:\\n if token.token_type == u'T_ID' or\\\\\\n token.token_type == u'T_RESERVED_WORD':\\n self.log_message(token)\\n identifier_token = token\\n index = self.token_index = index + 1\\n token = self.get_specific_token(index)\\n if token:\\n if token.token_type == u'T_ASSIGN':\\n self.add_to_symbols_table(identifier_token,\\n return_type, scope)\\n right_side_declaration =\\\\\\n self.check_right_side_declaration(\\n return_type, scope, identifier_token)\\n if right_side_declaration.place:\\n more_declarations =\\\\\\n self.check_more_declarations(return_type,\\n scope)\\n token = self.get_specific_token(self.token_index)\\n if token:\\n if token.token_type == u'T_SEMICOLON':\\n self.log_message(token)\\n self.token_index += 1\\n standalone_declaration = Production()\\n standalone_declaration.append_code(\\n right_side_declaration.code)\\n return standalone_declaration\\n self.set_syntactic_error(u'T_SEMICOLON', token)\\n self.set_eof_error(u'T_SEMICOLON')\\n standalone_declaration = Production()\\n return standalone_declaration\\n elif token.token_type == u'T_COMMA':\\n self.add_to_symbols_table(identifier_token,\\n return_type, scope)\\n self.check_more_declarations(return_type, scope)\\n token = self.get_specific_token(self.token_index)\\n if token.token_type == u'T_SEMICOLON':\\n self.log_message(token)\\n self.token_index += 1\\n standalone_declaration = Production()\\n return standalone_declaration\\n # Commented, check if there are any consequences\\n # return True\\n self.set_syntactic_error(u'T_SEMICOLON', token)\\n elif token.token_type == u'T_SEMICOLON':\\n self.add_to_symbols_table(identifier_token,\\n return_type, scope)\\n self.log_message(token)\\n self.token_index += 1\\n standalone_declaration = Production()\\n return standalone_declaration\\n self.set_syntactic_error(u'T_ASSIGN or T_COMMA', token)\\n self.set_eof_error(u'T_ASSIGN or T_COMMA')\\n self.set_syntactic_error(u'T_ID or T_RESERVED_WORD', token)\\n # If no token is found, it's likely that there are no more declarations\\n return False\",\n \"def _check_semicolon(line_index, input_line):\\n global _total_lines_of_code\\n if input_line.endswith(';'):\\n _code_lines.append(line_index)\\n _total_lines_of_code += 1\",\n \"def hello2(a):\\n return\",\n \"def hello():\\n return \\\"Hello\\\"\",\n \"def write_return(output_file):\\n # temp variables: R14 is FRAME; R15 is RET.\\n output_file.write(\\\"@LCL\\\" + \\\"\\\\n\\\" +\\n \\\"D = M\\\" + \\\"\\\\n\\\" +\\n \\\"@R14\\\" + \\\"\\\\n\\\" +\\n \\\"M = D \\\" + \\\"\\\\n\\\" +\\n \\\"@5\\\" + \\\"\\\\n\\\" +\\n \\\"D = D - A\\\" + \\\"\\\\n\\\" +\\n \\\"A = D\\\" + \\\"\\\\n\\\" +\\n \\\"D = M\\\" + \\\"\\\\n\\\" +\\n \\\"@R15\\\" + \\\"\\\\n\\\" +\\n \\\"M = D\\\" + \\\"\\\\n\\\"\\n )\\n write_pop(output_file, \\\"argument\\\", \\\"0\\\")\\n output_file.write(\\\"@ARG\\\" + \\\"\\\\n\\\" +\\n \\\"D = M +1 \\\" + \\\"\\\\n\\\" +\\n \\\"@SP\\\" + \\\"\\\\n\\\" +\\n \\\"M = D\\\" + \\\"\\\\n\\\")\\n for mem_segment in MEM_SEGMENT_ARR:\\n output_file.write(FRAME_MINUS_ONE + \\\"@\\\" + mem_segment + \\\"\\\\n\\\" + \\\"M = D\\\" + \\\"\\\\n\\\")\\n output_file.write(\\\"@R15\\\" + \\\"\\\\n\\\" +\\n \\\"A = M\\\" + \\\"\\\\n\\\"\\n \\\"0; JMP\\\" + \\\"\\\\n\\\")\",\n \"def test_parse_return_action(self):\\n\\n line = '\\\"Uncalled bet of 66 returned to \\\"\\\"Oven @ hogtf8AO2o\\\"\\\"\\\",2021-02-11T02:41:46.355Z,161301130635712'\\n actual = parser.parse_action(line)\\n expected = Return(player=Player(name=\\\"Oven\\\", id_=\\\"hogtf8AO2o\\\"), amount=66)\\n self.assertEqual(actual, expected)\",\n \"def gen_separator_thunk(self):\\n separator_thunk = self.gen_new_thunk(0)\\n return separator_thunk\",\n \"def fortran_c_wrapper(self) -> str:\\n return ''\",\n \"def end():\\n return say()\",\n \"def returned_value(self) -> FunctionParseResult:\\n\\n node = self.definition\\n # if function does not has body\\n if not node:\\n return FunctionParseResult(\\n return_not_none=bool(FunctionLint.EMPTY_RETURNED_VALUE),\\n line_number=-1,\\n )\\n\\n # skip plain assignment expressions\\n if isinstance(node, _ast.Assign):\\n return FunctionParseResult(\\n return_not_none=bool(FunctionLint.EMPTY_RETURNED_VALUE),\\n line_number=node.lineno,\\n )\\n # skip plain pass expressions\\n elif isinstance(node, _ast.Pass):\\n return FunctionParseResult(\\n return_not_none=bool(FunctionLint.EMPTY_RETURNED_VALUE),\\n line_number=node.lineno,\\n )\\n # skip for any other plain expressions\\n elif isinstance(node, _ast.Expr):\\n return FunctionParseResult(\\n return_not_none=bool(FunctionLint.EMPTY_RETURNED_VALUE),\\n line_number=node.lineno,\\n )\\n # iterate over function body\\n for item in node.body:\\n # check, that expression has return type\\n if isinstance(item, _ast.Return):\\n return FunctionParseResult(\\n return_not_none=ReturnedExpression(item).value_not_none(),\\n line_number=item.lineno,\\n )\\n # in any other case - return empty\\n return FunctionParseResult(\\n return_not_none=bool(FunctionLint.EMPTY_RETURNED_VALUE),\\n line_number=node.lineno,\\n )\",\n \"def return_to_enclosing_type():\\n print (\\\"\\\\nReturning to enclosing type...\\\\n\\\")\",\n \"def part2_test_input():\\n return \\\"\\\"\\\"\\\"\\\"\\\"\",\n \"def test_67_function_not_return(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer;\\n\\t\\tbegin x:=fu(); end\\n\\t\\tfunction fu():integer;\\n\\t\\tbegin with a:real; do\\n\\t\\twith a:boolean; do while(true)do return 1;end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Function fu Not Return\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,467))\",\n \"def _check_return(\\n ret_type: None | IntType | ReferenceType, returned: Sequence[BitString]\\n) -> None:\\n if len(returned) > 1:\\n raise ValueError(\\\"code block returns multiple values\\\")\\n\\n if ret_type is None:\\n if len(returned) != 0:\\n raise ValueError(\\n 'function has no return type, but its code block defines \\\"ret\\\"'\\n )\\n elif isinstance(ret_type, ReferenceType):\\n if len(returned) == 0:\\n raise ValueError(\\n f\\\"function has return type {ret_type}, but its code block \\\"\\n f'does not define \\\"ret\\\"'\\n )\\n (ret_bits,) = returned\\n if ret_type.type.width != ret_bits.width:\\n raise ValueError(\\n f\\\"function has return type {ret_type}, but its code block \\\"\\n f'defines \\\"ret\\\" with width {ret_bits.width}'\\n )\\n else: # returns a value\\n if len(returned) == 0:\\n raise ValueError(\\n f\\\"function has return type {ret_type}, but its code block \\\"\\n f'does not assign to \\\"ret\\\"'\\n )\\n (ret_bits,) = returned\\n if ret_type.width != ret_bits.width:\\n raise ValueError(\\n f\\\"function has return type {ret_type}, but its code block \\\"\\n f'defines \\\"ret\\\" with width {ret_bits.width}'\\n )\",\n \"def print_and_return(*args, **kwargs):\\n print(*args, end=\\\"\\\\r\\\", **kwargs)\",\n \"def newline():\\n print()\",\n \"def getCommandcode(self):\\n commandcode = \\\"RETURN ({})\\\".format(self.value) # commandcode\\n return commandcode\",\n \"def my_function():\\n\\treturn 42\",\n \"def code():\",\n \"def rehydrate_indented_code_block_end(self, next_token):\\n assert next_token\\n del self.block_stack[-1]\\n return \\\"\\\"\",\n \"def end():\\n return EndBlock()\",\n \"def question_4():\\n return None\",\n \"def expected_return_value(program_filename):\\n with open(program_filename) as decaf_file:\\n for line in decaf_file:\\n if line.startswith(RETURN_COMMENT_PREFIX):\\n return_comment = line\\n break\\n else:\\n # We didn't break-- we fell off the end of the loop.\\n raise AssertionError('No \\\"// Returns:\\\" comment!')\\n return_value = without_prefix(return_comment, RETURN_COMMENT_PREFIX)\\n return int(return_value)\",\n \"def PRETEND ( self ):\\n return None\",\n \"def visit_ReturnNode(self, node: ReturnNode, symbol_table: SymbolTable) -> Union[Number, String]:\\n result = self.visit(node.expression, symbol_table)\\n return result\",\n \"def force_single_line_js(js):\\n log.debug(\\\"Forcing JS from ~{0} to 1 Line.\\\".format(len(js.splitlines())))\\n return \\\";\\\".join(js.splitlines()) if len(js.splitlines()) > 1 else js\",\n \"def test_66_function_not_return(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer;\\n\\t\\tbegin x:=fu(); end\\n\\t\\tfunction fu():integer;\\n\\t\\tbegin with a:real; do\\n\\t\\twith a:boolean; do if(true)then return 1;end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Function fu Not Return\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,466))\",\n \"def test_function2(a, b):\\n x = a + b\\n y = a * b\\n return x, y, x<y, x>y # < to ensure HTML is properly escaped\",\n \"def code_format(self):\\n return None if self._code is None else '.+'\",\n \"def code_format(self):\\n return None if self._code is None else '.+'\",\n \"def newline_to_semicolon(text):\\n # Convert newlines to semicolons.\\n # Return converted text.\\n return ';'.join(x for x in text.split('\\\\n'))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7318816","0.69216275","0.6916768","0.6747743","0.6609503","0.6026339","0.60176194","0.59240377","0.58299434","0.57947284","0.56949216","0.5653559","0.56448203","0.56211615","0.5538628","0.55126405","0.549617","0.54757154","0.5461631","0.54565257","0.5371747","0.53537863","0.53449607","0.5343239","0.531025","0.5305323","0.52634406","0.5218188","0.5215728","0.52125585","0.52106977","0.52017736","0.5168602","0.5164949","0.5133197","0.5129311","0.5125088","0.51233274","0.5094401","0.5094401","0.5093958","0.5087999","0.50743425","0.50739163","0.50674087","0.50417733","0.50340843","0.5030191","0.50266945","0.50115967","0.5006647","0.49935478","0.49933234","0.49933234","0.49928904","0.49903896","0.49866936","0.4964076","0.49584177","0.49549776","0.49422503","0.49217403","0.49047","0.49041697","0.48998848","0.489634","0.48911196","0.48815253","0.4867176","0.4857227","0.48559737","0.48466495","0.4846213","0.4824817","0.4822819","0.4812036","0.480705","0.48067325","0.48042142","0.47979084","0.4791029","0.47853127","0.47761697","0.4774025","0.47734624","0.47632155","0.47459918","0.4745482","0.47405982","0.47387457","0.4733672","0.47233045","0.47149017","0.4711088","0.47025138","0.46981338","0.46948922","0.4693758","0.4693758","0.46889985"],"string":"[\n \"0.7318816\",\n \"0.69216275\",\n \"0.6916768\",\n \"0.6747743\",\n \"0.6609503\",\n \"0.6026339\",\n \"0.60176194\",\n \"0.59240377\",\n \"0.58299434\",\n \"0.57947284\",\n \"0.56949216\",\n \"0.5653559\",\n \"0.56448203\",\n \"0.56211615\",\n \"0.5538628\",\n \"0.55126405\",\n \"0.549617\",\n \"0.54757154\",\n \"0.5461631\",\n \"0.54565257\",\n \"0.5371747\",\n \"0.53537863\",\n \"0.53449607\",\n \"0.5343239\",\n \"0.531025\",\n \"0.5305323\",\n \"0.52634406\",\n \"0.5218188\",\n \"0.5215728\",\n \"0.52125585\",\n \"0.52106977\",\n \"0.52017736\",\n \"0.5168602\",\n \"0.5164949\",\n \"0.5133197\",\n \"0.5129311\",\n \"0.5125088\",\n \"0.51233274\",\n \"0.5094401\",\n \"0.5094401\",\n \"0.5093958\",\n \"0.5087999\",\n \"0.50743425\",\n \"0.50739163\",\n \"0.50674087\",\n \"0.50417733\",\n \"0.50340843\",\n \"0.5030191\",\n \"0.50266945\",\n \"0.50115967\",\n \"0.5006647\",\n \"0.49935478\",\n \"0.49933234\",\n \"0.49933234\",\n \"0.49928904\",\n \"0.49903896\",\n \"0.49866936\",\n \"0.4964076\",\n \"0.49584177\",\n \"0.49549776\",\n \"0.49422503\",\n \"0.49217403\",\n \"0.49047\",\n \"0.49041697\",\n \"0.48998848\",\n \"0.489634\",\n \"0.48911196\",\n \"0.48815253\",\n \"0.4867176\",\n \"0.4857227\",\n \"0.48559737\",\n \"0.48466495\",\n \"0.4846213\",\n \"0.4824817\",\n \"0.4822819\",\n \"0.4812036\",\n \"0.480705\",\n \"0.48067325\",\n \"0.48042142\",\n \"0.47979084\",\n \"0.4791029\",\n \"0.47853127\",\n \"0.47761697\",\n \"0.4774025\",\n \"0.47734624\",\n \"0.47632155\",\n \"0.47459918\",\n \"0.4745482\",\n \"0.47405982\",\n \"0.47387457\",\n \"0.4733672\",\n \"0.47233045\",\n \"0.47149017\",\n \"0.4711088\",\n \"0.47025138\",\n \"0.46981338\",\n \"0.46948922\",\n \"0.4693758\",\n \"0.4693758\",\n \"0.46889985\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.61995023"},"document_rank":{"kind":"string","value":"5"}}},{"rowIdx":17792,"cells":{"query":{"kind":"string","value":"cassert does exist but probably not required in FGPU functions and unclear if supported by jitfy"},"document":{"kind":"string","value":"def _Assert(self, t):\n self.RaiseError(t, \"Assert not supported\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def test_perspective_transform():\n # TODO: write this\n assert(True)","def boolean():\n\n bool(Assert(1))\n\n with Assert.raises(AssertionError):\n bool(Assert(0))","def boolean():\r\n\r\n bool(Assert(1))\r\n\r\n with Assert.raises(AssertionError):\r\n bool(Assert(0))","def testPreconditions(self):\n self.assertTrue(\n __debug__, \"__debug__ must be True, otherwise asserts are ignored\"\n )","def test_deviceX_1():\n assert 0","def test_nvidia_driver3():\r\n var = cuda.fvector()\r\n f = theano.function([var], var + 1, mode=mode_with_gpu,\r\n profile=False)\r\n topo = f.maker.fgraph.toposort()\r\n assert any([isinstance(node.op, cuda.GpuElemwise) for node in topo])\r\n assert theano.sandbox.cuda.use.device_number is not None","def eval_and_assert(in_mod: tvm.IRModule, reference_func, args):\n if not tvm.runtime.enabled(\"cuda\"):\n print(\"Not evaluating since GPU is not available\")\n return\n with tvm.transform.PassContext(opt_level=3):\n compiled = relay.create_executor(\"vm\", mod=in_mod, device=GPU, target=\"cuda\").evaluate()\n actual = compiled(*args).numpy()\n expected = reference_func(*args)\n tvm.testing.assert_allclose(actual, expected)","def test_the_tests():\n\n assert True is True","def assertion_passed(self, func):","def doit_assert(\n cond, emsg = \"Assertion failed\", exc = DoItAssertionError, nframe = 1\n):\n\n if not cond:\n raise exc(\"%s: %s\" % (__get_caller_name(sys._getframe(nframe)), emsg))","def test_gcc_crash(self):\r\n self.validate((1, 10, 213, 129), (46, 10, 212, 1), 'valid',\r\n verify_grad=False)","def test_flonum_unsafe(doctest):","def test_pytest():\n assert True","def test_pytest():\n assert True","def test_available(self):\n feature_guard = _make_requires(True, \"Error text\")\n results = []\n\n @feature_guard\n def inner():\n results.append(True)\n return True\n\n assert inner() is True\n assert [True] == results","def test_tc():\n assert 1 == 1","def convert_frame_assert(compiler_fn: Callable, guard_export_fn=None, one_graph=True):\n compiler_fn = wrap_compiler_fn(compiler_fn)\n\n def _convert_frame_assert(frame: types.FrameType, cache_size: int):\n code = frame.f_code\n input_codes.add(code)\n if code in output_codes:\n return None\n if code.co_name == \"<genexpr>\" and code.co_filename.endswith(\n \"transformers/file_utils.py\"\n ):\n # not needed, but cleans up torchbench error stats\n return None\n if code.co_name == \"__setattr__\":\n # setattr could be tricky to handle generally,\n # but also not likely useful to compile- skip the whole frame\n return None\n # Check if the frame is generated by an exec builtin call\n # TODO - Running exec generated frame seems propagates f_globals to the\n # next frames.\n if code.co_name == \"<module>\" and code.co_filename == \"<string>\":\n return None\n\n if (\n code.co_name == \"<lambda>\"\n and code.co_filename == \"<string>\"\n and not bool(frame.f_builtins)\n ):\n # namedtuple subclass constructor. Empty builtins cause issue with\n # len keyword in LIST_LEN guard.\n return None\n\n if is_generator(code):\n unimplemented(\"generator\")\n if cache_size >= config.cache_size_limit:\n\n def format_func_info(code):\n return f\"'{code.co_name}' ({code.co_filename}:{code.co_firstlineno})\"\n\n def format_guard_failures(code):\n # For the common case, it's sufficient to see just the most recent failure.\n # We could add a verbose mode if needed\n return f\"{str(guard_failures[code][-1])}\"\n\n assert code in guard_failures, \"TODO(whc) any other recompile reasons?\"\n warnings.warn(\n \"default\",\n f\"torchdynamo hit config.cache_size_limit ({config.cache_size_limit})\\n\"\n f\" function: {format_func_info(code)}\\n\"\n f\" reasons: {format_guard_failures(code)}\\n\",\n )\n unimplemented(\"cache_size_limit reached\")\n output = None\n\n if not has_tensor_in_frame(frame):\n return None\n\n # from .utils import print_once; print_once(code.co_filename)\n\n def transform(instructions, code_options):\n nonlocal output\n tracer = InstructionTranslator(\n instructions,\n frame.f_code,\n frame.f_locals,\n frame.f_globals,\n frame.f_builtins,\n code_options,\n compiler_fn,\n one_graph,\n )\n tracer.run()\n output = tracer.output\n assert output.output_instructions\n instructions[:] = output.output_instructions\n code_options.update(output.code_options)\n\n instructions[:] = passes.bytecode.common().execute(instructions)\n\n def debug_print(prefix):\n if not config.debug:\n return\n print(\n f\"\\n{prefix}\",\n code.co_name,\n code.co_filename,\n code.co_firstlineno,\n )\n # print(dis.Bytecode(frame.f_code).info())\n print(dis.Bytecode(frame.f_code).dis())\n\n try:\n for attempt in range(100):\n try:\n code = transform_code_object(frame.f_code, transform)\n orig_code_map[code] = frame.f_code\n break\n except RestartAnalysis:\n if attempt > 100:\n unimplemented(\"100+ RestartAnalysis() calls\")\n except SkipFrame:\n return None\n output_codes.add(code)\n if config.debug:\n debug_print(\"ORIGINAL BYTECODE\")\n print(\"MODIFIED BYTECODE\")\n # print(dis.Bytecode(code).info())\n print(dis.Bytecode(code).dis())\n\n assert output.guards is not None\n CleanupManager.instance[code] = output.cleanups\n check_fn = CheckFunctionManager(\n output.guards, frame.f_locals, frame.f_globals\n )\n guarded_code = GuardedCode(code, check_fn.check_fn)\n if config.debug:\n print(\"\\nGUARDS:\")\n for guard in sorted(output.guards):\n print(\" -\", str(guard))\n print()\n\n if guard_export_fn is not None:\n guard_export_fn(output.guards)\n\n return guarded_code\n except (Unsupported, TorchRuntimeError, BackendCompilerFailed):\n if config.debug or config.trace or config.print_internal_exceptions:\n debug_print(\"WONT CONVERT\")\n raise\n except Exception:\n if config.debug or config.trace or config.print_internal_exceptions:\n debug_print(\"WONT CONVERT\")\n warnings.warn(\"=\" * 10 + \" TorchDynamo Stack Trace \" + \"=\" * 10 + \"\\n\")\n traceback.print_exc()\n warnings.warn(\n \"=\" * 10 + \" Exception (above) while processing \" + \"=\" * 10 + \"\\n\",\n )\n traceback.print_stack(frame)\n warnings.warn(\"=\" * 10 + \" End debug info \" + \"=\" * 10 + \"\\n\")\n raise InternalTorchDynamoError()\n\n return wrap_convert_context(_convert_frame_assert)","def visit_assert(self: Parser, node: doc.Assert) -> None:\n cond = self.eval_expr(node.test)\n msg = self.eval_expr(node.msg)\n frame = T.Assert(cond, msg)\n frame.add_callback(partial(frame.__exit__, None, None, None))\n frame.__enter__()","def _assert(space, w_check, desc=None):\n if w_check.tp == space.tp_str:\n source_orig = space.str_w(w_check)\n source = \"<? return %s ?>\" % source_orig\n bc = compile_php(None, source, space)\n interp = space.ec.interpreter\n w_res = interp.run_local_include(bc, interp.global_frame)\n if not w_res.is_true(space):\n space.ec.warn('assert(): Assertion \"%s\" failed' % source_orig)\n return space.w_Null\n if not w_check.is_true(space):\n space.ec.warn('assert(): Assertion failed')\n return space.w_Null\n return space.wrap(w_check.is_true(space))","def assert_(expr, msg=None):\r\n statistics.assertions += 1\r\n if not expr:\r\n if msg is None:\r\n raise AssertionError\r\n raise AssertionError(msg)\r\n return expr","def test_absolute_truth():\n assert True","def test():\r\n\tassert 1609693773.1609693773() == \"1609693773\", \"test failed\"\r\n\t#assert 1609693773.<function>(<values>) == <the result(s) you would like to have>, \"<the fail message>\"\r","def compute_debug(self):","def test_example():\n x = 0\n y = 1\n assert x != y","def test_assert_truth(self):\n\n # Confused? This video should help:\n #\n # http://bit.ly/about_asserts\n\n self.assertTrue(True) # This should be true","def test_T4():","def test_T4():","def test_failed():\n assert False","def test_pytest():\n _x = 1\n _y = 2\n res = _x + _y\n assert res == 3","def test_nvidia_driver1():\r\n a = numpy.random.rand(10000).astype(\"float32\")\r\n A = cuda.shared_constructor(a)\r\n f = theano.function(inputs=[], outputs=A.sum(), mode=mode_with_gpu,\r\n profile=False)\r\n topo = f.maker.fgraph.toposort()\r\n assert len(topo) == 2\r\n if sum(isinstance(node.op, B.GpuCAReduce) for node in topo) != 1:\r\n msg = '\\n\\t'.join(['Expected exactly one occurrence of GpuCAReduce ' +\r\n 'but got:']+[str(app) for app in topo])\r\n raise AssertionError(msg)\r\n if not numpy.allclose(f(), a.sum()):\r\n raise Exception(\"The nvidia driver version installed with this OS \"\r\n \"does not give good results for reduction.\"\r\n \"Installing the nvidia driver available on the same \"\r\n \"download page as the cuda package will fix the \"\r\n \"problem: http://developer.nvidia.com/cuda-downloads\")","def test_deprecations():\n with pytest.deprecated_call():\n from aesara.tensor.subtensor_opt import get_advsubtensor_axis # noqa: F401 F811","def test_gpu():\n vectorizer = Vectorizer(device=\"gpu\", engine=\"cuml\")\n pytest.raises(ValueError, vectorizer.fit_transform, X_text)","def test_transposition(self):\n\n World.reset()\n\n\n def inside(x, y):\n centers_distance = tf.sqrt(tf.reduce_sum(tf.squared_difference(x[:, 0:2], y[:, 0:2]), axis=1) + 1e-6)\n return tf.cast((centers_distance + x[:, 2]) < y[:, 2], tf.float32)\n\n circles = tfl.Domain(label=\"Circles\", data=[[0., 0, 1], [0,0, 2], [0,0, 3]])\n inside = tfl.Predicate(label=\"inside\", domains=[\"Circles\", \"Circles\"], function=inside)\n tfl.setTNorm(id=tfl.SS, p=1)\n sess = tf.Session()\n\n\n # Constraint 1\n x = tfl.variable(circles, name=\"x\")\n y = tfl.variable(circles, name=\"y\")\n a = tfl.atom(inside, (x,y))\n b = tfl.atom(inside, (y,x))\n rule = tfl.and_n(a, b)\n\n assert np.greater(sess.run(rule), np.zeros(shape=[3,3,3])).all()\n assert len(World._predicates_cache)==1","def test_gpu_cuda_code() -> None:\n if get_from_environ(\"DISABLE_GPU_FOR_TESTING\") is not None:\n print(\"GPU payload disabled for testing\")\n return\n\n # if the command exists it can run on the hardware below\n proc = subprocess.Popen([\"nvidia-smi\"], stdout=subprocess.PIPE)\n stdout, _ = proc.communicate()\n str_stdout = stdout.decode()\n assert \"NVIDIA-SMI\" in str_stdout, str_stdout\n assert proc.returncode == 0\n # search the history for the CUDA implementation","def test_check_validity(game):\n\n game.solve()\n assert game.check_validity()","def _gpu_and_random(self, exprs):\n if not GPU:\n return False\n if not all(tell_deterministic(i) for i in exprs):\n return True\n\n return False","def test_advance_ast_avaliable():\n assert _test_advanced_ast_presence()","def test_single_quadrant(self):","def test_py_compile_condition(self):\n self._test_py_compile('coin')","def testTrivial(self):\n self.doTest(afwGeom.makeIdentityTransform())","def testTrivial(self):\n self.doTest(afwGeom.makeIdentityTransform())","def test_primitive_coverage(self):\n # Harvest primitives from XLA translation tables\n all_primitives = (set(xla.translations)\n | set(xla.backend_specific_translations['cpu'])\n | set(xla.backend_specific_translations['gpu'])\n | set(xla.backend_specific_translations['tpu'])\n | set(xla.initial_style_translations)\n | set(xla.parallel_translations))\n\n tf_impl = set(jax.experimental.jax2tf.jax2tf.tf_impl)\n tf_not_yet_impl = set(jax.experimental.jax2tf.jax2tf.tf_not_yet_impl)\n\n all_primitives = tuple(sorted(all_primitives, key=str))\n for p in all_primitives:\n if p in tf_not_yet_impl:\n self.assertNotIn(p, tf_impl) # Should not be in both tf_impl and tf_not_yet_impl\n else:\n self.assertIn(p, tf_impl)","def assert_true(self, expr, msg=None):\r\n assert bool(expr) is True","def test_generation_is_disabled():\n assert not GENERATE","def test():\r\n\tassert calcolatrice.__author__ == \"help@castellanidavide.it\", \"sanity test failed\"\r\n\t#assert calcolatrice.<function>(<values>) == <the result(s) you would like to have>, \"<the fail message>\"\r","def test_get_xy_space():\n pass","def test_ddp_comm_hook_future_passing_gpu_gloo(self):\n process_group = self._get_process_group()\n\n # Get GPU model with simple_hook registered.\n gpu_model = self._gpu_model_with_ddp_comm_hook(process_group, self._simple_hook)\n\n # check whether the grads are equal to what simple_hook's then callback returns.\n # without the comm_hook, result would be 0.25 * torch.ones(2, 2).\n self._run_and_verify_hook(gpu_model, 8, 2 * torch.ones(2, 2))","def test_func():\n assert func(3) == 5 # noqa: S101","def test_tensor_method(free_alg):\n\n tensor = free_alg.sum(10)\n assert tensor.get_one() == 1\n\n with pytest.raises(AttributeError):\n tensor.get_two()","def test_func(debug: bool) -> None:\n click.echo(debug)","def test_drawing_tf():\r\n tf = pytest.importorskip(\"tensorflow\")\r\n\r\n x = tf.constant(0.1)\r\n y = tf.constant([0.2, 0.3])\r\n z = tf.Variable(0.4)\r\n\r\n dev = qml.device(\"default.qubit\", wires=2)\r\n\r\n @qml.beta.qnode(dev, interface=\"tf\")\r\n def circuit(p1, p2=y, **kwargs):\r\n qml.RX(p1, wires=0)\r\n qml.RY(p2[0] * p2[1], wires=1)\r\n qml.RX(kwargs[\"p3\"], wires=0)\r\n qml.CNOT(wires=[0, 1])\r\n return qml.expval(qml.PauliZ(0) @ qml.PauliX(1))\r\n\r\n result = qml.draw(circuit)(p1=x, p3=z)\r\n expected = \"\"\"\\\r\n 0: ──RX(0.1)───RX(0.4)──╭C──╭┤ ⟨Z ⊗ X⟩ \r\n 1: ──RY(0.06)───────────╰X──╰┤ ⟨Z ⊗ X⟩ \r\n\"\"\"\r\n\r\n assert result == expected","def test_print_op():\r\n b = tensor.fmatrix()\r\n f = theano.function([b], theano.printing.Print()(b) * 2,\r\n mode=mode_with_gpu)\r\n theano.printing.debugprint(f)\r\n #print f.maker.fgraph.toposort()\r\n#[GpuFromHost(<TensorType(float32, matrix)>), <theano.printing.Print object at 0x3581210>(GpuFromHost.0), GpuElemwise{mul}(CudaNdarray{[[ 2.]]}, <theano.printing.Print object at 0x3581210>.0), HostFromGpu(GpuElemwise{mul}.0)]\r\n topo = f.maker.fgraph.toposort()\r\n assert topo[0].op == gpu_from_host\r\n assert isinstance(topo[1].op, theano.printing.Print)\r\n assert isinstance(topo[2].op, GpuElemwise)\r\n assert topo[3].op == host_from_gpu\r\n f(numpy.random.random((5, 5)).astype('float32'))","def test_nvidia_driver2():\r\n a = numpy.random.rand(10000).astype(\"float32\")\r\n cuda.shared_constructor(a)\r\n assert theano.sandbox.cuda.use.device_number is not None","def test_local_gpu_elemwise_0():\r\n a = tensor.bmatrix()\r\n b = tensor.fmatrix()\r\n c = tensor.fmatrix()\r\n\r\n a_v = (numpy.random.rand(4, 5) * 10).astype(\"int8\")\r\n b_v = (numpy.random.rand(4, 5) * 10).astype(\"float32\")\r\n c_v = (numpy.random.rand(4, 5) * 10).astype(\"float32\")\r\n\r\n # Due to optimization order, this composite is created when all\r\n # the op are on the gpu.\r\n f = theano.function([a, b, c], [a + b + c], mode=mode_with_gpu)\r\n #theano.printing.debugprint(f)\r\n topo = f.maker.fgraph.toposort()\r\n assert sum(isinstance(node.op, cuda.GpuElemwise) for node in topo) == 1\r\n assert sum(isinstance(node.op, tensor.Elemwise) for node in topo) == 1\r\n f(a_v, b_v, c_v)\r\n\r\n # Now test with the composite already on the cpu before we move it\r\n # to the gpu\r\n a_s = theano.scalar.int8()\r\n b_s = theano.scalar.float32()\r\n c_s = theano.scalar.float32()\r\n out_s = theano.scalar.Composite([a_s, b_s, c_s], [a_s + b_s + c_s])\r\n out_op = tensor.Elemwise(out_s)\r\n f = theano.function([a, b, c], [out_op(a, b, c)], mode=mode_with_gpu)\r\n #theano.printing.debugprint(f)\r\n topo = f.maker.fgraph.toposort()\r\n assert sum(isinstance(node.op, cuda.GpuElemwise) for node in topo) == 1\r\n assert sum(isinstance(node.op, tensor.Elemwise) for node in topo) == 1\r\n f(a_v, b_v, c_v)","def test_no_shared_var_graph():\r\n a=tensor.fmatrix()\r\n b=tensor.fmatrix()\r\n f = theano.function([a,b],[a+b], mode=mode_with_gpu)\r\n l = f.maker.fgraph.toposort()\r\n assert len(l)==4\r\n assert numpy.any(isinstance(x.op,cuda.GpuElemwise) for x in l)\r\n assert numpy.any(isinstance(x.op,cuda.GpuFromHost) for x in l)\r\n assert numpy.any(isinstance(x.op,cuda.HostFromGpu) for x in l)","def test_basic1(self):\r\n self.validate((2, 2, 3, 3), (2, 2, 2, 2), 'valid', verify_grad=False)","def _not_assert(function):\n @wraps(function)\n def flipped(*args, **kwargs):\n try:\n function(*args, **kwargs)\n raise AssertionError()\n except AssertionError:\n return\n return flipped","def test_hostfromgpu_shape_i():\r\n pass\r\n\r\n m = mode_with_gpu.including('local_dot_to_dot22',\r\n 'local_dot22_to_dot22scalar','specialize')\r\n a = T.fmatrix('a')\r\n ca = theano.sandbox.cuda.var.CudaNdarrayType((False, False))()\r\n\r\n av = numpy.asarray(numpy.random.rand(5, 4), dtype='float32')\r\n cv = cuda.CudaNdarray(numpy.asarray(numpy.random.rand(5, 4),\r\n dtype='float32'))\r\n\r\n f = theano.function([a], cuda.basic_ops.gpu_from_host(a), mode=m)\r\n assert cuda.basic_ops.gpu_from_host in [x.op\r\n for x in f.maker.fgraph.toposort()]\r\n f = theano.function([a], cuda.basic_ops.gpu_from_host(a).shape, mode=m)\r\n topo = f.maker.fgraph.toposort()\r\n assert isinstance(topo[0].op, T.opt.Shape_i)\r\n assert isinstance(topo[1].op, T.opt.Shape_i)\r\n assert isinstance(topo[2].op, T.opt.MakeVector)\r\n assert tuple(f(av)) == (5, 4)\r\n\r\n\r\n\r\n f = theano.function([ca], cuda.basic_ops.host_from_gpu(ca), mode=m)\r\n assert cuda.basic_ops.host_from_gpu in [x.op\r\n for x in f.maker.fgraph.toposort()]\r\n f = theano.function([ca], cuda.basic_ops.host_from_gpu(ca).shape, mode=m)\r\n topo = f.maker.fgraph.toposort()\r\n assert isinstance(topo[0].op, T.opt.Shape_i)\r\n assert isinstance(topo[1].op, T.opt.Shape_i)\r\n assert isinstance(topo[2].op, T.opt.MakeVector)\r\n assert tuple(f(cv)) == (5, 4)","def test_np_memory_layout_add_input_tensor_pystepiocallback():\n\n def _test(transposedInput, transposedOutput):\n builder = popart.Builder()\n\n # Create a random constant and transpose it\n np.random.seed(1)\n input1 = builder.addInputTensor(\"INT32\", [2, 2])\n\n # Run a session to prove this\n output1 = builder.aiOnnx.identity([input1])\n builder.addOutputTensor(output1)\n anchorConfig = {output1: popart.AnchorReturnType(\"ALL\")}\n\n dataFlow = popart.DataFlow(1, anchorConfig)\n deviceConfig = {\"numIPUs\": 1}\n dm = popart.DeviceManager()\n device = dm.createIpuModelDevice(deviceConfig)\n session = popart.InferenceSession(\n fnModel=builder.getModelProto(), dataFlow=dataFlow, deviceInfo=device\n )\n\n # Compile graph and place weights onto it\n session.prepareDevice()\n session.weightsFromHost()\n\n # Feed the session with a transposed (non-contiguous) tensor.\n input1Value = np.random.randint(0, 100, size=(2, 2), dtype=\"int32\")\n if transposedInput:\n input1Value = np.transpose(input1Value, [1, 0])\n output1Value = np.random.randint(0, 100, size=(2, 2), dtype=\"int32\")\n if transposedOutput:\n output1Value = np.transpose(output1Value, [1, 0])\n\n with pytest.raises(\n (Exception, RuntimeError, popart.popart_exception)\n ) as e_info:\n\n # pylint: disable=unused-argument\n def input_callback(id, prefetch):\n return input1Value\n\n def input_complete_callback(_): # id is an unused parameter\n pass\n\n def output_callback(_): # id is an unused parameter\n return output1Value\n\n def output_complete_callback(_): # id is an unused parameter\n pass\n\n stepio = popart.PyStepIOCallback(\n input_callback,\n input_complete_callback,\n output_callback,\n output_complete_callback,\n )\n\n session.run(stepio)\n\n assert \"contiguous\" in e_info.value.args[0]\n\n _test(transposedInput=True, transposedOutput=False)\n _test(transposedInput=False, transposedOutput=True)","def test_many_arg_elemwise():\r\n rng = numpy.random.RandomState([1, 2, 3])\r\n\r\n for num_args in [25]:\r\n for op_to_test in [theano.tensor.add, theano.tensor.mul]:\r\n for nb_dim in [2, 3, 4, 5]:\r\n shapes = [rng.randint(1, 5) for i in range(nb_dim)]\r\n args = [numpy.cast['float32'](rng.randn(*shapes))\r\n for arg in xrange(0, num_args)]\r\n\r\n symb_args = [theano.tensor.TensorType('float32',\r\n (False,)*nb_dim)()\r\n for arg in xrange(0, num_args)]\r\n\r\n\r\n outputs = []\r\n for mode in [mode_with_gpu, mode_without_gpu]:\r\n #test the optijmization local_gpu_elemwise_0\r\n f = theano.function(\r\n symb_args, op_to_test(*symb_args),\r\n mode=mode.excluding(\"local_gpu_elemwise_1\"))\r\n outputs.append(f(*args))\r\n #assert that the test was done on the gpu.\r\n if mode is mode_with_gpu:\r\n assert any([isinstance(node.op, cuda.GpuElemwise)\r\n for node in f.maker.fgraph.apply_nodes])\r\n\r\n #test the optijmization local_gpu_elemwise_1\r\n f = theano.function(\r\n symb_args,\r\n cuda.gpu_from_host(op_to_test(*symb_args)),\r\n mode=mode.excluding(\"local_gpu_elemwise_0\"))\r\n out = f(*args)\r\n #assert that the test was done on the gpu.\r\n if mode is mode_with_gpu:\r\n assert any([isinstance(node.op, cuda.GpuElemwise)\r\n for node in f.maker.fgraph.apply_nodes])\r\n assert numpy.allclose(out, outputs[-1])\r\n\r\n results_gpu, results_cpu = outputs\r\n\r\n assert numpy.allclose(results_gpu, results_cpu)","def _debugmallocstats(): # real signature unknown; restored from __doc__\n pass","def test_flonum_special(doctest):","def test_3(self):\n for _ in range(10):\n\n # Draw random requests for testing purposes.\n num_draws_emax = np.random.randint(2, 1000)\n dim = np.random.randint(1, 6)\n\n matrix = np.random.uniform(size=dim ** 2).reshape(dim, dim)\n cov = np.dot(matrix, matrix.T)\n\n # PDF of normal distribution\n args = np.random.normal(size=3)\n args[-1] **= 2\n\n f90 = fort_debug.wrapper_normal_pdf(*args)\n py = norm.pdf(*args)\n\n assert_almost_equal(py, f90)\n\n # Singular Value Decomposition\n py = scipy.linalg.svd(matrix)\n f90 = fort_debug.wrapper_svd(matrix, dim)\n\n for i in range(3):\n assert_allclose(py[i], f90[i])\n\n # Pseudo-Inverse\n py = np.linalg.pinv(matrix)\n f90 = fort_debug.wrapper_pinv(matrix, dim)\n\n assert_allclose(py, f90)\n\n # Inverse\n py = np.linalg.inv(cov)\n f90 = fort_debug.wrapper_inverse(cov, dim)\n assert_allclose(py, f90)\n\n # Determinant\n py = np.linalg.det(cov)\n f90 = fort_debug.wrapper_determinant(cov)\n\n assert_allclose(py, f90)\n\n # Trace\n py = np.trace(cov)\n f90 = fort_debug.wrapper_trace(cov)\n\n assert_allclose(py, f90)\n\n # Random normal deviates. This only tests the interface, requires\n # visual inspection in IPYTHON notebook as well.\n fort_debug.wrapper_standard_normal(num_draws_emax)\n\n # Clipping values below and above bounds.\n num_values = np.random.randint(1, 10000)\n lower_bound = np.random.randn()\n upper_bound = lower_bound + np.random.ranf()\n values = np.random.normal(size=num_values)\n\n f90 = fort_debug.wrapper_clip_value(\n values, lower_bound, upper_bound, num_values\n )\n py = np.clip(values, lower_bound, upper_bound)\n\n assert_almost_equal(py, f90)\n\n # Spectral condition number\n py = _spectral_condition_number(cov)\n fort = fort_debug.wrapper_spectral_condition_number(cov)\n assert_almost_equal(py, fort)","def test_print_op():\r\n b = tensor.fmatrix()\r\n f = theano.function([b],theano.printing.Print()(b)*2, mode=mode_with_gpu)\r\n #theano.printing.debugprint(f)\r\n #print f.maker.fgraph.toposort()\r\n#[GpuFromHost(<TensorType(float32, matrix)>), <theano.printing.Print object at 0x3581210>(GpuFromHost.0), GpuElemwise{mul}(CudaNdarray{[[ 2.]]}, <theano.printing.Print object at 0x3581210>.0), HostFromGpu(GpuElemwise{mul}.0)]\r\n topo = f.maker.fgraph.toposort()\r\n assert topo[0].op == cuda.gpu_from_host\r\n assert isinstance(topo[1].op, theano.printing.Print)\r\n assert isinstance(topo[2].op, cuda.GpuElemwise)\r\n assert topo[3].op == cuda.host_from_gpu\r\n f(numpy.random.random((5,5)).astype('float32'))","def benchmark_fp16_xla_compile_fake_1gpu_gpuparams(self):\n params = self._shared_params_fp16()._replace(\n num_gpus=1,\n data_dir=self.fake_data_dir,\n data_name='imagenet',\n xla_compile=True)\n self._run_benchmark(params)","def test_active_inference_SPM_1b(self):","def test_prelu_basic(self):\n\n utils.compare_tracing_methods(\n SimplePreluModule(),\n torch.randn(1, 4, 5, 5),\n torch.tensor([0.25]),\n fusible_ops={\"aten::prelu\"},\n )","def test_gendaymtx():\n # sky.gendaymtx(\n # sun_mtx, 6, data=wea_data, meta=wea_metadata, direct=True, onesun=True\n # )\n pass","def add_gpu_and_mpi_marks():\n pass","def test_defining_only_and_defer_fails(self):","def main(argv):\r\n FLAGS.dir = os.path.join(FLAGS.dir, FLAGS.id)\r\n if not os.path.exists(FLAGS.dir):\r\n os.makedirs(FLAGS.dir)\r\n os.makedirs(os.path.join(FLAGS.dir, \"logs\"))\r\n\r\n FLAGS.alsologtostderr = True\r\n logging.get_absl_handler().use_absl_log_file(FLAGS.logfile, os.path.join(FLAGS.dir, \"logs\"))\r\n\r\n np.random.seed(FLAGS.random_seed)\r\n tf.random.set_seed(FLAGS.random_seed)\r\n\r\n log_flags(FLAGS)\r\n\r\n try:\r\n prepare()\r\n except:\r\n exception = traceback.format_exc()\r\n logging.info(exception)","def sanity_check():\n print(\"Running sanity check...\")\n\n N = 20\n dimensions = [10, 5, 10]\n data = np.random.randn(N, dimensions[0]) # each row will be a datum\n labels = np.zeros((N, dimensions[2]))\n for i in range(N):\n labels[i,random.randint(0,dimensions[2]-1)] = 1\n \n params = np.random.randn((dimensions[0] + 1) * dimensions[1] + (\n dimensions[1] + 1) * dimensions[2], )\n\n gradcheck_naive(lambda params: forward_backward_prop(data, labels, params,\n dimensions), params)","def testForcedCompile(self):\n if config.list_logical_devices('TPU'):\n self.skipTest('To accommodate OSS, experimental_compile support for TPU '\n 'is not linked in.')\n # GPU doesn't support int32 variables, so we use int64.\n v = variables.Variable([1, 2], dtype=dtypes.int64)\n\n @def_function.function(experimental_compile=True)\n def f():\n key, counter = (\n gen_stateless_random_ops_v2.stateless_random_get_key_counter(\n seed=math_ops.cast(v.read_value(), dtypes.int32)))\n alg = gen_stateless_random_ops_v2.stateless_random_get_alg()\n return gen_stateless_random_ops_v2.stateless_random_normal_v2(\n shape=[], key=key, counter=counter, alg=alg)\n\n f()","def test_noop():\n assert True","def exercise_b2_113():\r\n pass","def test_T0():","def test_mpi_code() -> None:\n print(\"MPI code checking is disabled\")","def local_gpu_conv(node):\r\n def GpuConvOp_from_ConvOp(op):\r\n logical_img_hw = None\r\n\r\n if op.kshp_logical is not None and op.kshp_logical != op.kshp:\r\n return None\r\n #print op.kshp, op.imshp[1:3]\r\n #print op.kshp_logical, logical_img_hw\r\n ret = GpuConv(border_mode=op.out_mode,\r\n subsample=(op.dx, op.dy),\r\n logical_img_hw=logical_img_hw,\r\n logical_kern_hw=op.kshp_logical,\r\n logical_kern_align_top=op.kshp_logical_top_aligned,\r\n kshp=op.kshp,\r\n version=op.version,\r\n verbose=op.verbose,\r\n imshp=op.imshp,\r\n )\r\n if op.imshp_logical is not None:\r\n logical_img_hw = op.imshp_logical[1:3]\r\n if logical_img_hw != op.imshp[1:3]:\r\n # this case is not implemented\r\n #return None\r\n rstride = int(numpy.ceil(op.imshp_logical[1] /\r\n float(op.imshp[1])))\r\n cstride = int(numpy.ceil(op.imshp_logical[2] /\r\n float(op.imshp[2])))\r\n\r\n def make_graph(img, kern):\r\n buf = tensor.alloc(numpy.asarray(0, dtype=img.dtype),\r\n img.shape[0], *op.imshp_logical)\r\n img = tensor.set_subtensor(buf[:, :, ::rstride, ::cstride],\r\n img)\r\n img = gpu_from_host(img)\r\n return ret(img, kern)\r\n\r\n return make_graph\r\n return ret\r\n\r\n def values_eq_approx(a, b):\r\n \"\"\"This fct is needed to don't have DebugMode raise useless\r\n error due to ronding error.\r\n\r\n This happen as We reduce on the two last dimensions, so this\r\n can raise the absolute error if the number of element we\r\n reduce on is significant.\r\n\r\n \"\"\"\r\n assert a.ndim == 4\r\n atol = None\r\n if a.shape[-1] * a.shape[-2] > 100:\r\n #For float32 the default atol is 1e-5\r\n atol = 3e-5\r\n return GpuArrayType.values_eq_approx(a, b, atol=atol)\r\n\r\n img, kern = node.inputs\r\n gpu_conv = GpuConvOp_from_ConvOp(node.op)\r\n if gpu_conv is None:\r\n return\r\n out = gpu_conv(gpu_from_host(img),\r\n gpu_from_host(kern))\r\n # in some case the ConvOp broadcast the last 2 dimensions\r\n # differently then the gpu ConvOp\r\n out = tensor.patternbroadcast(\r\n host_from_gpu(out),\r\n node.outputs[0].broadcastable)\r\n #op_lifter want the output on the GPU.\r\n out = gpu_from_host(out)\r\n out.values_eq_approx = values_eq_approx\r\n return [out]","def test_get_debug_values_success():\r\n\r\n prev_value = config.compute_test_value\r\n for mode in ['ignore', 'warn', 'raise']:\r\n\r\n try:\r\n config.compute_test_value = mode\r\n\r\n x = T.vector()\r\n x.tag.test_value = numpy.zeros((4,), dtype=config.floatX)\r\n y = numpy.zeros((5, 5))\r\n\r\n iters = 0\r\n\r\n for x_val, y_val in op.get_debug_values(x, y):\r\n\r\n assert x_val.shape == (4,)\r\n assert y_val.shape == (5, 5)\r\n\r\n iters += 1\r\n\r\n assert iters == 1\r\n\r\n finally:\r\n config.compute_test_value = prev_value","def benchmark_fp16_xla_compile_synth_1gpu_gpuparams(self):\n params = self._shared_params_fp16()._replace(num_gpus=1, xla_compile=True)\n self._run_benchmark(params)","def _assert(ok, detail):\n\n if not ok:\n raise UserException(\"the project file is invalid\", detail)","def testCanvasIsValid(self):\r\n myMessage = \"Plugin was not initialised with a valid CANVAS.\"\r\n assert self.bucketFill.iface.mapCanvas().width() == 400, myMessage","def local_gpu_conv(node):\r\n def GpuConvOp_from_ConvOp(op):\r\n logical_img_hw = None\r\n\r\n if op.kshp_logical is not None and op.kshp_logical != op.kshp:\r\n return None\r\n #print op.kshp, op.imshp[1:3]\r\n #print op.kshp_logical, logical_img_hw\r\n ret = GpuConv(border_mode=op.out_mode,\r\n subsample=(op.dx, op.dy),\r\n logical_img_hw=logical_img_hw,\r\n logical_kern_hw=op.kshp_logical,\r\n logical_kern_align_top=op.kshp_logical_top_aligned,\r\n kshp=op.kshp,\r\n version=op.version,\r\n verbose=op.verbose,\r\n imshp=op.imshp,\r\n )\r\n if op.imshp_logical is not None:\r\n logical_img_hw = op.imshp_logical[1:3]\r\n if logical_img_hw != op.imshp[1:3]:\r\n # this case is not implemented\r\n #return None\r\n rstride = int(numpy.ceil(op.imshp_logical[1] /\r\n float(op.imshp[1])))\r\n cstride = int(numpy.ceil(op.imshp_logical[2] /\r\n float(op.imshp[2])))\r\n\r\n def make_graph(img, kern):\r\n buf = tensor.alloc(numpy.asarray(0, dtype=img.dtype),\r\n img.shape[0], *op.imshp_logical)\r\n img = tensor.set_subtensor(buf[:, :, ::rstride, ::cstride],\r\n img)\r\n img = gpu_from_host(img)\r\n return ret(img, kern)\r\n\r\n return make_graph\r\n return ret\r\n\r\n def values_eq_approx(a, b):\r\n \"\"\"This fct is needed to don't have DebugMode raise useless\r\n error due to ronding error.\r\n\r\n This happen as We reduce on the two last dimensions, so this\r\n can raise the absolute error if the number of element we\r\n reduce on is significant.\r\n\r\n \"\"\"\r\n assert a.ndim == 4\r\n atol = None\r\n if a.shape[-1] * a.shape[-2] > 100:\r\n #For float32 the default atol is 1e-5\r\n atol = 3e-5\r\n return CudaNdarrayType.values_eq_approx(a, b, atol=atol)\r\n\r\n if isinstance(node.op, GpuFromHost):\r\n #gpu_from_host(conv) -> gpu_conv(gpu_from_host)\r\n host_input = node.inputs[0]\r\n if host_input.owner and isinstance(host_input.owner.op, conv.ConvOp):\r\n gpu_conv = GpuConvOp_from_ConvOp(host_input.owner.op)\r\n if gpu_conv is None:\r\n return\r\n img, kern = host_input.owner.inputs\r\n out = gpu_conv(gpu_from_host(img),\r\n gpu_from_host(kern))\r\n out = tensor.patternbroadcast(out,\r\n node.outputs[0].broadcastable)\r\n out.values_eq_approx = values_eq_approx\r\n # in some case the ConvOp broadcast the last 2 dimensions\r\n # differently then the gpu ConvOp\r\n return [out]\r\n\r\n if isinstance(node.op, conv.ConvOp):\r\n #conv(host_from_gpu) -> host_from_gpu(gpu_conv)\r\n img, kern = node.inputs\r\n img_on_gpu = (img.owner and isinstance(img.owner.op, HostFromGpu))\r\n kern_on_gpu = (kern.owner and isinstance(kern.owner.op, HostFromGpu))\r\n if img_on_gpu or kern_on_gpu:\r\n gpu_conv = GpuConvOp_from_ConvOp(node.op)\r\n if gpu_conv is None:\r\n return\r\n out = gpu_conv(gpu_from_host(img),\r\n gpu_from_host(kern))\r\n out = tensor.patternbroadcast(\r\n host_from_gpu(out),\r\n node.outputs[0].broadcastable)\r\n out.values_eq_approx = values_eq_approx\r\n # in some case the ConvOp broadcast the last 2 dimensions\r\n # differently then the gpu ConvOp\r\n return [out]","def test_cl_fix():\n assert Cl is ClFixHybridPressureCoord","def test_require():","def test_transformation_pass(self):\n qr = QuantumRegister(1, \"qr\")\n circuit = QuantumCircuit(qr, name=\"MyCircuit\")\n\n pass_d = PassD_TP_NR_NP(argument1=[1, 2])\n with self.assertLogs(\"LocalLogger\", level=\"INFO\") as cm:\n result = pass_d(circuit)\n\n self.assertMessageLog(cm, [\"run transformation pass PassD_TP_NR_NP\", \"argument [1, 2]\"])\n self.assertEqual(circuit, result)","def check_deterministic_ops() -> None:\n if os.getenv(TF_DETERMINISTIC_OPS, False):\n shared_io_utils.raise_warning(\n f\"You have set '{TF_DETERMINISTIC_OPS}' to 1. If you are \"\n f\"using one or more GPU(s) and use any of 'SparseFeaturizer', \"\n f\"'TEDPolicy', 'DIETClassifier', 'UnexpecTEDIntentPolicy', or \"\n f\"'ResponseSelector' training and testing will fail as there are no \"\n f\"deterministic GPU implementations of some underlying TF ops.\",\n category=UserWarning,\n )","def test_unit(self):\n self.assertTrue(return_true())","def test_huge_elemwise_fusion():\r\n shape = (2, 3, 4, 5, 6)\r\n ttype = tensor.tensor(dtype='float32', broadcastable=(False,) * len(shape))\r\n gpu_ptr_size = theano.sandbox.cuda.opt.get_device_type_sizes()['gpu_ptr_size']\r\n if gpu_ptr_size == 8:\r\n nb_in = 7\r\n len_topo = 10\r\n elif gpu_ptr_size == 4:\r\n nb_in = 8\r\n len_topo = 11\r\n else:\r\n raise Exception(\"Unexpected value for gpu_ptr_size\", gpu_ptr_size)\r\n vars = [tensor.tanh(ttype) for x in range(nb_in)]\r\n f = pfunc(vars, [reduce(operator.sub, vars)], mode=mode_with_gpu)\r\n\r\n topo = f.maker.fgraph.toposort()\r\n #theano.printing.debugprint(f)\r\n #for i, node in enumerate(topo):\r\n # print >> sys.stdout, i, node\r\n assert len(topo) == len_topo\r\n assert sum([isinstance(node.op, cuda.GpuElemwise) for node in topo]) == 2\r\n assert isinstance(topo[-3].op.scalar_op, theano.scalar.basic.Sub)\r\n assert isinstance(topo[-2].op.scalar_op, theano.scalar.basic.Composite)\r\n #let debugmode catch errors\r\n gen = lambda: theano._asarray(numpy.random.rand(*shape), dtype='float32')\r\n f(*[gen() for i in range(nb_in)])\r\n\r\n # Test the case where we can't put the computation on the gpu! their is too\r\n # many dimensions to the input to have 2 inputs to the op!\r\n\r\n shape = (1, 2, 3, 4, 5, 6, 7, 2, 2, 3, 2, 1, 2, 2, 2,)\r\n ttype = tensor.tensor(dtype='float32', broadcastable=(False,) * len(shape))\r\n vars = [tensor.tanh(ttype) for x in range(7)]\r\n f = pfunc(vars, [vars[0] - vars[1] - vars[2] - vars[3] - vars[4] -\r\n vars[5] - vars[6]], mode=mode_with_gpu)\r\n topo = f.maker.fgraph.toposort()\r\n #theano.printing.debugprint(f)\r\n assert len(topo) == 1\r\n assert sum([isinstance(node.op, cuda.GpuElemwise) for node in topo]) == 0\r\n assert sum([isinstance(node.op, tensor.Elemwise) for node in topo]) == 1\r\n #let debugmode catch errors\r\n gen = lambda: theano._asarray(numpy.random.rand(*shape), dtype='float32')\r\n f(gen(), gen(), gen(), gen(), gen(), gen(), gen())\r\n\r\n def gen(shape):\r\n return theano._asarray(numpy.random.rand(*shape), dtype='float32')\r\n\r\n max_var = 16 # excluded\r\n for shape in [(2,),\r\n (2, 2),\r\n (2, 2, 2),\r\n (2, 2, 2, 2),\r\n (2, 2, 2, 2, 2), # 5d\r\n (2, 2, 2, 2, 2, 2),\r\n# (2, 2, 2, 2, 2, 2, 2),\r\n# (2, 2, 2, 2, 2, 2, 2, 2),\r\n# (2, 2, 2, 1, 1, 1, 1, 2, 2), # 9d\r\n ]:\r\n vals = [cuda.shared_constructor(gen(shape)) for x in range(max_var)]\r\n for use_tan in [True, False]:\r\n if use_tan:\r\n vars = [tensor.tanh(x) for x in vals]\r\n else:\r\n vars = vals\r\n for nb_var in range(1, max_var):\r\n out = reduce(lambda x, y: x + y, vars[:nb_var])\r\n if not isinstance(out.type, CudaNdarrayType):\r\n out = cuda.gpu_from_host(out)\r\n f = pfunc([], [out], mode=mode_with_gpu)\r\n topo = f.maker.fgraph.toposort()\r\n #print shape, nb_var, use_tan, len(topo)\r\n assert (sum([isinstance(node.op, cuda.GpuElemwise)\r\n for node in topo]) == len(topo) or\r\n (nb_var == 1 and use_tan == False))\r\n assert sum([isinstance(node.op, tensor.Elemwise)\r\n for node in topo]) == 0\r\n\r\n #let debugmode catch errors\r\n f()","def test_global():\n global_assumptions.add(x > 0)\n assert (x > 0) in global_assumptions\n global_assumptions.remove(x > 0)\n assert not (x > 0) in global_assumptions\n # same with multiple of assumptions\n global_assumptions.add(x > 0, y > 0)\n assert (x > 0) in global_assumptions\n assert (y > 0) in global_assumptions\n global_assumptions.clear()\n assert not (x > 0) in global_assumptions\n assert not (y > 0) in global_assumptions","def setupGPUSwept(solver):\n solver.gpuBlock = (slice(0,solver.sharedShape[0],1),)+solver.gpuBlock\n getGPUReadBlockSwept(solver) #Finish creating gpuReadBlock here\n blockShape =[element.stop for element in solver.gpuBlock]\n blockShape[-1] += int(2*solver.blocksize[0]) #Adding 2 blocks in the column direction\n # Creating local GPU array with split\n grid = (int((blockShape[2])/solver.blocksize[0]),int((blockShape[3])/solver.blocksize[1])) #Grid size\n #Creating constants\n bsp = lambda x: int(numpy.prod(blockShape[x:])) #block shape product returned as an integer\n const_dict = ({\"NV\":blockShape[1],'SX':blockShape[2],'SY':blockShape[3],\"VARS\":bsp(2),\"TIMES\":bsp(1),\"MPSS\":solver.maxPyramidSize,\"MOSS\":solver.maxOctSize,\"OPS\":solver.operating,\"ITS\":solver.intermediate})\n solver.GPUArray = mallocGPUArray(blockShape) #Allocated GPU\n solver.localGPUArray = numpy.zeros(blockShape)\n #Building CUDA source code\n solver.gpu = io.buildGPUSource(solver.gpu)\n io.copyConstants(solver.gpu,const_dict) #This copys cpu constants not global constants\n solver.cpu.set_globals(*solver.globals,source_mod=solver.gpu)\n # Make GPU geometry\n solver.Up.initializeGPU(solver.gpu.get_function(\"UpPyramid\"),solver.blocksize,(grid[0],grid[1]-1))\n solver.Oct.initializeGPU(solver.gpu.get_function(\"Octahedron\"),solver.blocksize,(grid[0],grid[1]-1))\n solver.Down.initializeGPU(solver.gpu.get_function(\"DownPyramid\"),solver.blocksize,(grid[0],grid[1]-1))\n solver.Yb.initializeGPU(solver.gpu.get_function(\"YBridge\"),solver.blocksize,grid)\n solver.Xb.initializeGPU(solver.gpu.get_function(\"XBridge\"),solver.blocksize,grid)","def test_make_mul_transp():\n tmp_dir = make_temp_dir()\n FILE_NAME = \"tmp_test_mul_transp\"\n NAME = os.path.join(tmp_dir, FILE_NAME)\n C_NAME, EXE_NAME = NAME + \".c\", NAME + \".exe\"\n \n def make_testprogram(lv, lm, n=1):\n generate = BitMatrixMulTransp().generate_c_mul_transp\n f = open(C_NAME, \"wt\")\n print(r\"\"\"#include <stdio.h>\n#include <stdint.h>\nint main(int argc, char **argv)\n{{\n uint_fast32_t i, v;\n static uint_fast32_t a[1000];\n sscanf(argv[1], \"%ld\", &v);\n for (i=0; i < argc-2; ++i) sscanf(argv[i+2], \"%ld\", a+i);\n {0}\n printf(\"%ld\\n\",v);\n return 0;\n}}\n\"\"\".format(generate(\"v\", \"a\", lv, lm, n)), file = f)\n f.close()\n #subprocess.check_output([\"gcc\", C_NAME, \"-o\", EXE_NAME])\n compile_testprogramm([C_NAME], EXE_NAME)\n checker = BitMatrixMulTransp()\n checker.set_matrix(lv, lm, n)\n return checker\n\n def run_testprogram(v, m):\n data = list(map(str, [v]+m)) \n res = subprocess.check_output([EXE_NAME] + data)\n return int(res) \n\n def test_testprogram(checker , v, m):\n res = run_testprogram(v, m)\n ref = checker.compute(v,m+[0]*64)\n assert ref == ref, (hex, ref)\n\n def del_testprogram():\n for f in [C_NAME, EXE_NAME]:\n os.remove(f)\n\n test_data_dict = {\n (4,4,1,0): [ (3, [2,3, 1, 1]) , ]\n }\n\n def test_data(lv, lm, n, n_tests=10):\n \"\"\"yield n_tests test cases (v, m) for given lv, lm, m0\"\"\"\n try:\n d = test_data_dict[(lv, lm, n, m0)]\n for v, m in d: yield v, m\n except:\n pass\n for i in range(n_tests):\n v = random.randint(0, 2**(n*lv)-1)\n m = [] \n for i in range(lm):\n m.append(random.randint(0, 2**(n*lv)-1))\n yield v, m\n\n def test_programs():\n \"\"\"yield cases (lv, lm, n) for a making a test program\"\"\"\n yield 4, 4, 1\n yield 16, 12, 2\n\n \n\n \n for lv, lm, n in test_programs():\n print(\"Make C program for case lv=%d, lm=%d, n=%d\" % (\n lv,lm,n))\n checker = make_testprogram(lv, lm, n) \n print (\"C program has been made, starting tests..\")\n for v, m in test_data(lv, lm, n):\n test_testprogram(checker, v, m)\n del_testprogram()\n print (\"passed\")\n\n kill_temp_dir(tmp_dir)","def obj_func(x):\n assert(False)","def test_constants():\n c = pycvm.Constants()\n assert c.get_number_of_layers() == 3, 'number of layers is wrong'\n assert c.get_utm_zone() == 10, 'utm zone is wrong' \n \n assert c.get_number_of_grid_points_in_x(pycvm.LayerIdentifier.top) == 3271, 'nx top wrong'\n assert c.get_number_of_grid_points_in_x(pycvm.LayerIdentifier.middle) == 2181, 'nx middle wrong'\n assert c.get_number_of_grid_points_in_x(pycvm.LayerIdentifier.bottom) == 727, 'nx bottom wrong'\n\n assert c.get_number_of_grid_points_in_y(pycvm.LayerIdentifier.top) == 5367, 'ny top wrong'\n assert c.get_number_of_grid_points_in_y(pycvm.LayerIdentifier.middle) == 3578, 'ny middle wrong'\n assert c.get_number_of_grid_points_in_y(pycvm.LayerIdentifier.bottom) == 1193, 'ny bottom wrong'\n\n assert c.get_number_of_grid_points_in_z(pycvm.LayerIdentifier.top) == 13, 'nz top wrong'\n assert c.get_number_of_grid_points_in_z(pycvm.LayerIdentifier.middle) == 29, 'nz middle wrong'\n assert c.get_number_of_grid_points_in_z(pycvm.LayerIdentifier.bottom) == 55, 'nz bottom wrong'\n\n assert abs(c.get_grid_spacing_in_x(pycvm.LayerIdentifier.top) - 200) < 1.e-13, 'dx top wrong'\n assert abs(c.get_grid_spacing_in_x(pycvm.LayerIdentifier.middle) - 300) < 1.e-13, 'dx middle wrong'\n assert abs(c.get_grid_spacing_in_x(pycvm.LayerIdentifier.bottom) - 900) < 1.e-13, 'dx bottom wrong'\n\n assert abs(c.get_grid_spacing_in_y(pycvm.LayerIdentifier.top) - 200) < 1.e-13, 'dy top wrong'\n assert abs(c.get_grid_spacing_in_y(pycvm.LayerIdentifier.middle) - 300) < 1.e-13, 'dy middle wrong'\n assert abs(c.get_grid_spacing_in_y(pycvm.LayerIdentifier.bottom) - 900) < 1.e-13, 'dy bottom wrong'\n\n assert abs(c.get_grid_spacing_in_z(pycvm.LayerIdentifier.top) - 100) < 1.e-13, 'dz top wrong'\n assert abs(c.get_grid_spacing_in_z(pycvm.LayerIdentifier.middle) - 300) < 1.e-13, 'dz middle wrong'\n assert abs(c.get_grid_spacing_in_z(pycvm.LayerIdentifier.bottom) - 900) < 1.e-13, 'dz bottom wrong'\n\n \"\"\" \n auto mLCM = static_cast<double> (std::lcm(std::lcm(200, 300), 900));\n EXPECT_NEAR(c.getLeastCommonMultipleOfGridSpacingsInXAndY(), mLCM, 1.e-12);\n \"\"\"\n print(\"Passed constants test\")","def translation_rule_gpu(func):\n # functions to call before running the translation rule\n setup_funcs = (\n ensure_gpu_ext,\n functools.partial(ensure_platform_flush, \"gpu\"),\n ensure_omnistaging,\n setup_cuda_mpi,\n )\n\n @functools.wraps(func)\n def wrapped(*args, **kwargs):\n for f in setup_funcs:\n f()\n return func(*args, **kwargs)\n\n return wrapped","def test_runtime_warnings(self):\n\t\tprint(\"test runtime warning: sqrt(-1)=\", np.sqrt(-1.0))\n\t\tassert(True)","def assertion_started(self, func):","def test_get_compute_func(self):\n assert api._get_compute_func(self.run_fa) is api._compute_grade_for_fa\n assert api._get_compute_func(self.run_no_fa) is api._compute_grade_for_non_fa","def test_qc_quantize_op_gpu_fp16(self):\n graph = tf.Graph()\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\n sess = tf.compat.v1.Session(graph=graph, config=config)\n bitwidth = 8\n use_symm_encoding = False\n with graph.as_default():\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\n libpymo.RoundingMode.ROUND_NEAREST)\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\n\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.quantizeDequantize),\n trainable=False, dtype=tf.int32)\n\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\n is_int_data_type = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\n\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\n is_int_data_type.initializer])\n\n # place holder for the input\n with tf.device(\"/device:GPU:0\"):\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\n op_mode=mode_var,\n tensor_quantizer_reference=tensor_quant_ref,\n encoding_min=encoding_min,\n encoding_max=encoding_max,\n bit_width=bit_width,\n use_symmetric_encoding=use_symmetric_encoding,\n is_int_data_type=is_int_data_type)\n\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\n inp_data = np.array([0.78027299, 0.44164284, 0.6942797, 0.69774088, 0.55863863, 0.29553034, 0.219199,\n 0.09483732, 0.55075674, 0.6348504], dtype=np.float32)\n\n out_exp = np.array([0.78027344, 0.4416504, 0.69433594, 0.6977539, 0.55859375, 0.29541016, 0.21923828,\n 0.09484863, 0.55078125, 0.6347656], dtype=np.float32)\n\n print(\"inp_data\", inp_data)\n with tf.device(\"/device:GPU:0\"):\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\n print(\"out_data\", out_data)\n assert np.allclose(out_data, out_exp)\n sess.close()","def unitdoctest():\r\n\r\n pass"],"string":"[\n \"def test_perspective_transform():\\n # TODO: write this\\n assert(True)\",\n \"def boolean():\\n\\n bool(Assert(1))\\n\\n with Assert.raises(AssertionError):\\n bool(Assert(0))\",\n \"def boolean():\\r\\n\\r\\n bool(Assert(1))\\r\\n\\r\\n with Assert.raises(AssertionError):\\r\\n bool(Assert(0))\",\n \"def testPreconditions(self):\\n self.assertTrue(\\n __debug__, \\\"__debug__ must be True, otherwise asserts are ignored\\\"\\n )\",\n \"def test_deviceX_1():\\n assert 0\",\n \"def test_nvidia_driver3():\\r\\n var = cuda.fvector()\\r\\n f = theano.function([var], var + 1, mode=mode_with_gpu,\\r\\n profile=False)\\r\\n topo = f.maker.fgraph.toposort()\\r\\n assert any([isinstance(node.op, cuda.GpuElemwise) for node in topo])\\r\\n assert theano.sandbox.cuda.use.device_number is not None\",\n \"def eval_and_assert(in_mod: tvm.IRModule, reference_func, args):\\n if not tvm.runtime.enabled(\\\"cuda\\\"):\\n print(\\\"Not evaluating since GPU is not available\\\")\\n return\\n with tvm.transform.PassContext(opt_level=3):\\n compiled = relay.create_executor(\\\"vm\\\", mod=in_mod, device=GPU, target=\\\"cuda\\\").evaluate()\\n actual = compiled(*args).numpy()\\n expected = reference_func(*args)\\n tvm.testing.assert_allclose(actual, expected)\",\n \"def test_the_tests():\\n\\n assert True is True\",\n \"def assertion_passed(self, func):\",\n \"def doit_assert(\\n cond, emsg = \\\"Assertion failed\\\", exc = DoItAssertionError, nframe = 1\\n):\\n\\n if not cond:\\n raise exc(\\\"%s: %s\\\" % (__get_caller_name(sys._getframe(nframe)), emsg))\",\n \"def test_gcc_crash(self):\\r\\n self.validate((1, 10, 213, 129), (46, 10, 212, 1), 'valid',\\r\\n verify_grad=False)\",\n \"def test_flonum_unsafe(doctest):\",\n \"def test_pytest():\\n assert True\",\n \"def test_pytest():\\n assert True\",\n \"def test_available(self):\\n feature_guard = _make_requires(True, \\\"Error text\\\")\\n results = []\\n\\n @feature_guard\\n def inner():\\n results.append(True)\\n return True\\n\\n assert inner() is True\\n assert [True] == results\",\n \"def test_tc():\\n assert 1 == 1\",\n \"def convert_frame_assert(compiler_fn: Callable, guard_export_fn=None, one_graph=True):\\n compiler_fn = wrap_compiler_fn(compiler_fn)\\n\\n def _convert_frame_assert(frame: types.FrameType, cache_size: int):\\n code = frame.f_code\\n input_codes.add(code)\\n if code in output_codes:\\n return None\\n if code.co_name == \\\"<genexpr>\\\" and code.co_filename.endswith(\\n \\\"transformers/file_utils.py\\\"\\n ):\\n # not needed, but cleans up torchbench error stats\\n return None\\n if code.co_name == \\\"__setattr__\\\":\\n # setattr could be tricky to handle generally,\\n # but also not likely useful to compile- skip the whole frame\\n return None\\n # Check if the frame is generated by an exec builtin call\\n # TODO - Running exec generated frame seems propagates f_globals to the\\n # next frames.\\n if code.co_name == \\\"<module>\\\" and code.co_filename == \\\"<string>\\\":\\n return None\\n\\n if (\\n code.co_name == \\\"<lambda>\\\"\\n and code.co_filename == \\\"<string>\\\"\\n and not bool(frame.f_builtins)\\n ):\\n # namedtuple subclass constructor. Empty builtins cause issue with\\n # len keyword in LIST_LEN guard.\\n return None\\n\\n if is_generator(code):\\n unimplemented(\\\"generator\\\")\\n if cache_size >= config.cache_size_limit:\\n\\n def format_func_info(code):\\n return f\\\"'{code.co_name}' ({code.co_filename}:{code.co_firstlineno})\\\"\\n\\n def format_guard_failures(code):\\n # For the common case, it's sufficient to see just the most recent failure.\\n # We could add a verbose mode if needed\\n return f\\\"{str(guard_failures[code][-1])}\\\"\\n\\n assert code in guard_failures, \\\"TODO(whc) any other recompile reasons?\\\"\\n warnings.warn(\\n \\\"default\\\",\\n f\\\"torchdynamo hit config.cache_size_limit ({config.cache_size_limit})\\\\n\\\"\\n f\\\" function: {format_func_info(code)}\\\\n\\\"\\n f\\\" reasons: {format_guard_failures(code)}\\\\n\\\",\\n )\\n unimplemented(\\\"cache_size_limit reached\\\")\\n output = None\\n\\n if not has_tensor_in_frame(frame):\\n return None\\n\\n # from .utils import print_once; print_once(code.co_filename)\\n\\n def transform(instructions, code_options):\\n nonlocal output\\n tracer = InstructionTranslator(\\n instructions,\\n frame.f_code,\\n frame.f_locals,\\n frame.f_globals,\\n frame.f_builtins,\\n code_options,\\n compiler_fn,\\n one_graph,\\n )\\n tracer.run()\\n output = tracer.output\\n assert output.output_instructions\\n instructions[:] = output.output_instructions\\n code_options.update(output.code_options)\\n\\n instructions[:] = passes.bytecode.common().execute(instructions)\\n\\n def debug_print(prefix):\\n if not config.debug:\\n return\\n print(\\n f\\\"\\\\n{prefix}\\\",\\n code.co_name,\\n code.co_filename,\\n code.co_firstlineno,\\n )\\n # print(dis.Bytecode(frame.f_code).info())\\n print(dis.Bytecode(frame.f_code).dis())\\n\\n try:\\n for attempt in range(100):\\n try:\\n code = transform_code_object(frame.f_code, transform)\\n orig_code_map[code] = frame.f_code\\n break\\n except RestartAnalysis:\\n if attempt > 100:\\n unimplemented(\\\"100+ RestartAnalysis() calls\\\")\\n except SkipFrame:\\n return None\\n output_codes.add(code)\\n if config.debug:\\n debug_print(\\\"ORIGINAL BYTECODE\\\")\\n print(\\\"MODIFIED BYTECODE\\\")\\n # print(dis.Bytecode(code).info())\\n print(dis.Bytecode(code).dis())\\n\\n assert output.guards is not None\\n CleanupManager.instance[code] = output.cleanups\\n check_fn = CheckFunctionManager(\\n output.guards, frame.f_locals, frame.f_globals\\n )\\n guarded_code = GuardedCode(code, check_fn.check_fn)\\n if config.debug:\\n print(\\\"\\\\nGUARDS:\\\")\\n for guard in sorted(output.guards):\\n print(\\\" -\\\", str(guard))\\n print()\\n\\n if guard_export_fn is not None:\\n guard_export_fn(output.guards)\\n\\n return guarded_code\\n except (Unsupported, TorchRuntimeError, BackendCompilerFailed):\\n if config.debug or config.trace or config.print_internal_exceptions:\\n debug_print(\\\"WONT CONVERT\\\")\\n raise\\n except Exception:\\n if config.debug or config.trace or config.print_internal_exceptions:\\n debug_print(\\\"WONT CONVERT\\\")\\n warnings.warn(\\\"=\\\" * 10 + \\\" TorchDynamo Stack Trace \\\" + \\\"=\\\" * 10 + \\\"\\\\n\\\")\\n traceback.print_exc()\\n warnings.warn(\\n \\\"=\\\" * 10 + \\\" Exception (above) while processing \\\" + \\\"=\\\" * 10 + \\\"\\\\n\\\",\\n )\\n traceback.print_stack(frame)\\n warnings.warn(\\\"=\\\" * 10 + \\\" End debug info \\\" + \\\"=\\\" * 10 + \\\"\\\\n\\\")\\n raise InternalTorchDynamoError()\\n\\n return wrap_convert_context(_convert_frame_assert)\",\n \"def visit_assert(self: Parser, node: doc.Assert) -> None:\\n cond = self.eval_expr(node.test)\\n msg = self.eval_expr(node.msg)\\n frame = T.Assert(cond, msg)\\n frame.add_callback(partial(frame.__exit__, None, None, None))\\n frame.__enter__()\",\n \"def _assert(space, w_check, desc=None):\\n if w_check.tp == space.tp_str:\\n source_orig = space.str_w(w_check)\\n source = \\\"<? return %s ?>\\\" % source_orig\\n bc = compile_php(None, source, space)\\n interp = space.ec.interpreter\\n w_res = interp.run_local_include(bc, interp.global_frame)\\n if not w_res.is_true(space):\\n space.ec.warn('assert(): Assertion \\\"%s\\\" failed' % source_orig)\\n return space.w_Null\\n if not w_check.is_true(space):\\n space.ec.warn('assert(): Assertion failed')\\n return space.w_Null\\n return space.wrap(w_check.is_true(space))\",\n \"def assert_(expr, msg=None):\\r\\n statistics.assertions += 1\\r\\n if not expr:\\r\\n if msg is None:\\r\\n raise AssertionError\\r\\n raise AssertionError(msg)\\r\\n return expr\",\n \"def test_absolute_truth():\\n assert True\",\n \"def test():\\r\\n\\tassert 1609693773.1609693773() == \\\"1609693773\\\", \\\"test failed\\\"\\r\\n\\t#assert 1609693773.<function>(<values>) == <the result(s) you would like to have>, \\\"<the fail message>\\\"\\r\",\n \"def compute_debug(self):\",\n \"def test_example():\\n x = 0\\n y = 1\\n assert x != y\",\n \"def test_assert_truth(self):\\n\\n # Confused? This video should help:\\n #\\n # http://bit.ly/about_asserts\\n\\n self.assertTrue(True) # This should be true\",\n \"def test_T4():\",\n \"def test_T4():\",\n \"def test_failed():\\n assert False\",\n \"def test_pytest():\\n _x = 1\\n _y = 2\\n res = _x + _y\\n assert res == 3\",\n \"def test_nvidia_driver1():\\r\\n a = numpy.random.rand(10000).astype(\\\"float32\\\")\\r\\n A = cuda.shared_constructor(a)\\r\\n f = theano.function(inputs=[], outputs=A.sum(), mode=mode_with_gpu,\\r\\n profile=False)\\r\\n topo = f.maker.fgraph.toposort()\\r\\n assert len(topo) == 2\\r\\n if sum(isinstance(node.op, B.GpuCAReduce) for node in topo) != 1:\\r\\n msg = '\\\\n\\\\t'.join(['Expected exactly one occurrence of GpuCAReduce ' +\\r\\n 'but got:']+[str(app) for app in topo])\\r\\n raise AssertionError(msg)\\r\\n if not numpy.allclose(f(), a.sum()):\\r\\n raise Exception(\\\"The nvidia driver version installed with this OS \\\"\\r\\n \\\"does not give good results for reduction.\\\"\\r\\n \\\"Installing the nvidia driver available on the same \\\"\\r\\n \\\"download page as the cuda package will fix the \\\"\\r\\n \\\"problem: http://developer.nvidia.com/cuda-downloads\\\")\",\n \"def test_deprecations():\\n with pytest.deprecated_call():\\n from aesara.tensor.subtensor_opt import get_advsubtensor_axis # noqa: F401 F811\",\n \"def test_gpu():\\n vectorizer = Vectorizer(device=\\\"gpu\\\", engine=\\\"cuml\\\")\\n pytest.raises(ValueError, vectorizer.fit_transform, X_text)\",\n \"def test_transposition(self):\\n\\n World.reset()\\n\\n\\n def inside(x, y):\\n centers_distance = tf.sqrt(tf.reduce_sum(tf.squared_difference(x[:, 0:2], y[:, 0:2]), axis=1) + 1e-6)\\n return tf.cast((centers_distance + x[:, 2]) < y[:, 2], tf.float32)\\n\\n circles = tfl.Domain(label=\\\"Circles\\\", data=[[0., 0, 1], [0,0, 2], [0,0, 3]])\\n inside = tfl.Predicate(label=\\\"inside\\\", domains=[\\\"Circles\\\", \\\"Circles\\\"], function=inside)\\n tfl.setTNorm(id=tfl.SS, p=1)\\n sess = tf.Session()\\n\\n\\n # Constraint 1\\n x = tfl.variable(circles, name=\\\"x\\\")\\n y = tfl.variable(circles, name=\\\"y\\\")\\n a = tfl.atom(inside, (x,y))\\n b = tfl.atom(inside, (y,x))\\n rule = tfl.and_n(a, b)\\n\\n assert np.greater(sess.run(rule), np.zeros(shape=[3,3,3])).all()\\n assert len(World._predicates_cache)==1\",\n \"def test_gpu_cuda_code() -> None:\\n if get_from_environ(\\\"DISABLE_GPU_FOR_TESTING\\\") is not None:\\n print(\\\"GPU payload disabled for testing\\\")\\n return\\n\\n # if the command exists it can run on the hardware below\\n proc = subprocess.Popen([\\\"nvidia-smi\\\"], stdout=subprocess.PIPE)\\n stdout, _ = proc.communicate()\\n str_stdout = stdout.decode()\\n assert \\\"NVIDIA-SMI\\\" in str_stdout, str_stdout\\n assert proc.returncode == 0\\n # search the history for the CUDA implementation\",\n \"def test_check_validity(game):\\n\\n game.solve()\\n assert game.check_validity()\",\n \"def _gpu_and_random(self, exprs):\\n if not GPU:\\n return False\\n if not all(tell_deterministic(i) for i in exprs):\\n return True\\n\\n return False\",\n \"def test_advance_ast_avaliable():\\n assert _test_advanced_ast_presence()\",\n \"def test_single_quadrant(self):\",\n \"def test_py_compile_condition(self):\\n self._test_py_compile('coin')\",\n \"def testTrivial(self):\\n self.doTest(afwGeom.makeIdentityTransform())\",\n \"def testTrivial(self):\\n self.doTest(afwGeom.makeIdentityTransform())\",\n \"def test_primitive_coverage(self):\\n # Harvest primitives from XLA translation tables\\n all_primitives = (set(xla.translations)\\n | set(xla.backend_specific_translations['cpu'])\\n | set(xla.backend_specific_translations['gpu'])\\n | set(xla.backend_specific_translations['tpu'])\\n | set(xla.initial_style_translations)\\n | set(xla.parallel_translations))\\n\\n tf_impl = set(jax.experimental.jax2tf.jax2tf.tf_impl)\\n tf_not_yet_impl = set(jax.experimental.jax2tf.jax2tf.tf_not_yet_impl)\\n\\n all_primitives = tuple(sorted(all_primitives, key=str))\\n for p in all_primitives:\\n if p in tf_not_yet_impl:\\n self.assertNotIn(p, tf_impl) # Should not be in both tf_impl and tf_not_yet_impl\\n else:\\n self.assertIn(p, tf_impl)\",\n \"def assert_true(self, expr, msg=None):\\r\\n assert bool(expr) is True\",\n \"def test_generation_is_disabled():\\n assert not GENERATE\",\n \"def test():\\r\\n\\tassert calcolatrice.__author__ == \\\"help@castellanidavide.it\\\", \\\"sanity test failed\\\"\\r\\n\\t#assert calcolatrice.<function>(<values>) == <the result(s) you would like to have>, \\\"<the fail message>\\\"\\r\",\n \"def test_get_xy_space():\\n pass\",\n \"def test_ddp_comm_hook_future_passing_gpu_gloo(self):\\n process_group = self._get_process_group()\\n\\n # Get GPU model with simple_hook registered.\\n gpu_model = self._gpu_model_with_ddp_comm_hook(process_group, self._simple_hook)\\n\\n # check whether the grads are equal to what simple_hook's then callback returns.\\n # without the comm_hook, result would be 0.25 * torch.ones(2, 2).\\n self._run_and_verify_hook(gpu_model, 8, 2 * torch.ones(2, 2))\",\n \"def test_func():\\n assert func(3) == 5 # noqa: S101\",\n \"def test_tensor_method(free_alg):\\n\\n tensor = free_alg.sum(10)\\n assert tensor.get_one() == 1\\n\\n with pytest.raises(AttributeError):\\n tensor.get_two()\",\n \"def test_func(debug: bool) -> None:\\n click.echo(debug)\",\n \"def test_drawing_tf():\\r\\n tf = pytest.importorskip(\\\"tensorflow\\\")\\r\\n\\r\\n x = tf.constant(0.1)\\r\\n y = tf.constant([0.2, 0.3])\\r\\n z = tf.Variable(0.4)\\r\\n\\r\\n dev = qml.device(\\\"default.qubit\\\", wires=2)\\r\\n\\r\\n @qml.beta.qnode(dev, interface=\\\"tf\\\")\\r\\n def circuit(p1, p2=y, **kwargs):\\r\\n qml.RX(p1, wires=0)\\r\\n qml.RY(p2[0] * p2[1], wires=1)\\r\\n qml.RX(kwargs[\\\"p3\\\"], wires=0)\\r\\n qml.CNOT(wires=[0, 1])\\r\\n return qml.expval(qml.PauliZ(0) @ qml.PauliX(1))\\r\\n\\r\\n result = qml.draw(circuit)(p1=x, p3=z)\\r\\n expected = \\\"\\\"\\\"\\\\\\r\\n 0: ──RX(0.1)───RX(0.4)──╭C──╭┤ ⟨Z ⊗ X⟩ \\r\\n 1: ──RY(0.06)───────────╰X──╰┤ ⟨Z ⊗ X⟩ \\r\\n\\\"\\\"\\\"\\r\\n\\r\\n assert result == expected\",\n \"def test_print_op():\\r\\n b = tensor.fmatrix()\\r\\n f = theano.function([b], theano.printing.Print()(b) * 2,\\r\\n mode=mode_with_gpu)\\r\\n theano.printing.debugprint(f)\\r\\n #print f.maker.fgraph.toposort()\\r\\n#[GpuFromHost(<TensorType(float32, matrix)>), <theano.printing.Print object at 0x3581210>(GpuFromHost.0), GpuElemwise{mul}(CudaNdarray{[[ 2.]]}, <theano.printing.Print object at 0x3581210>.0), HostFromGpu(GpuElemwise{mul}.0)]\\r\\n topo = f.maker.fgraph.toposort()\\r\\n assert topo[0].op == gpu_from_host\\r\\n assert isinstance(topo[1].op, theano.printing.Print)\\r\\n assert isinstance(topo[2].op, GpuElemwise)\\r\\n assert topo[3].op == host_from_gpu\\r\\n f(numpy.random.random((5, 5)).astype('float32'))\",\n \"def test_nvidia_driver2():\\r\\n a = numpy.random.rand(10000).astype(\\\"float32\\\")\\r\\n cuda.shared_constructor(a)\\r\\n assert theano.sandbox.cuda.use.device_number is not None\",\n \"def test_local_gpu_elemwise_0():\\r\\n a = tensor.bmatrix()\\r\\n b = tensor.fmatrix()\\r\\n c = tensor.fmatrix()\\r\\n\\r\\n a_v = (numpy.random.rand(4, 5) * 10).astype(\\\"int8\\\")\\r\\n b_v = (numpy.random.rand(4, 5) * 10).astype(\\\"float32\\\")\\r\\n c_v = (numpy.random.rand(4, 5) * 10).astype(\\\"float32\\\")\\r\\n\\r\\n # Due to optimization order, this composite is created when all\\r\\n # the op are on the gpu.\\r\\n f = theano.function([a, b, c], [a + b + c], mode=mode_with_gpu)\\r\\n #theano.printing.debugprint(f)\\r\\n topo = f.maker.fgraph.toposort()\\r\\n assert sum(isinstance(node.op, cuda.GpuElemwise) for node in topo) == 1\\r\\n assert sum(isinstance(node.op, tensor.Elemwise) for node in topo) == 1\\r\\n f(a_v, b_v, c_v)\\r\\n\\r\\n # Now test with the composite already on the cpu before we move it\\r\\n # to the gpu\\r\\n a_s = theano.scalar.int8()\\r\\n b_s = theano.scalar.float32()\\r\\n c_s = theano.scalar.float32()\\r\\n out_s = theano.scalar.Composite([a_s, b_s, c_s], [a_s + b_s + c_s])\\r\\n out_op = tensor.Elemwise(out_s)\\r\\n f = theano.function([a, b, c], [out_op(a, b, c)], mode=mode_with_gpu)\\r\\n #theano.printing.debugprint(f)\\r\\n topo = f.maker.fgraph.toposort()\\r\\n assert sum(isinstance(node.op, cuda.GpuElemwise) for node in topo) == 1\\r\\n assert sum(isinstance(node.op, tensor.Elemwise) for node in topo) == 1\\r\\n f(a_v, b_v, c_v)\",\n \"def test_no_shared_var_graph():\\r\\n a=tensor.fmatrix()\\r\\n b=tensor.fmatrix()\\r\\n f = theano.function([a,b],[a+b], mode=mode_with_gpu)\\r\\n l = f.maker.fgraph.toposort()\\r\\n assert len(l)==4\\r\\n assert numpy.any(isinstance(x.op,cuda.GpuElemwise) for x in l)\\r\\n assert numpy.any(isinstance(x.op,cuda.GpuFromHost) for x in l)\\r\\n assert numpy.any(isinstance(x.op,cuda.HostFromGpu) for x in l)\",\n \"def test_basic1(self):\\r\\n self.validate((2, 2, 3, 3), (2, 2, 2, 2), 'valid', verify_grad=False)\",\n \"def _not_assert(function):\\n @wraps(function)\\n def flipped(*args, **kwargs):\\n try:\\n function(*args, **kwargs)\\n raise AssertionError()\\n except AssertionError:\\n return\\n return flipped\",\n \"def test_hostfromgpu_shape_i():\\r\\n pass\\r\\n\\r\\n m = mode_with_gpu.including('local_dot_to_dot22',\\r\\n 'local_dot22_to_dot22scalar','specialize')\\r\\n a = T.fmatrix('a')\\r\\n ca = theano.sandbox.cuda.var.CudaNdarrayType((False, False))()\\r\\n\\r\\n av = numpy.asarray(numpy.random.rand(5, 4), dtype='float32')\\r\\n cv = cuda.CudaNdarray(numpy.asarray(numpy.random.rand(5, 4),\\r\\n dtype='float32'))\\r\\n\\r\\n f = theano.function([a], cuda.basic_ops.gpu_from_host(a), mode=m)\\r\\n assert cuda.basic_ops.gpu_from_host in [x.op\\r\\n for x in f.maker.fgraph.toposort()]\\r\\n f = theano.function([a], cuda.basic_ops.gpu_from_host(a).shape, mode=m)\\r\\n topo = f.maker.fgraph.toposort()\\r\\n assert isinstance(topo[0].op, T.opt.Shape_i)\\r\\n assert isinstance(topo[1].op, T.opt.Shape_i)\\r\\n assert isinstance(topo[2].op, T.opt.MakeVector)\\r\\n assert tuple(f(av)) == (5, 4)\\r\\n\\r\\n\\r\\n\\r\\n f = theano.function([ca], cuda.basic_ops.host_from_gpu(ca), mode=m)\\r\\n assert cuda.basic_ops.host_from_gpu in [x.op\\r\\n for x in f.maker.fgraph.toposort()]\\r\\n f = theano.function([ca], cuda.basic_ops.host_from_gpu(ca).shape, mode=m)\\r\\n topo = f.maker.fgraph.toposort()\\r\\n assert isinstance(topo[0].op, T.opt.Shape_i)\\r\\n assert isinstance(topo[1].op, T.opt.Shape_i)\\r\\n assert isinstance(topo[2].op, T.opt.MakeVector)\\r\\n assert tuple(f(cv)) == (5, 4)\",\n \"def test_np_memory_layout_add_input_tensor_pystepiocallback():\\n\\n def _test(transposedInput, transposedOutput):\\n builder = popart.Builder()\\n\\n # Create a random constant and transpose it\\n np.random.seed(1)\\n input1 = builder.addInputTensor(\\\"INT32\\\", [2, 2])\\n\\n # Run a session to prove this\\n output1 = builder.aiOnnx.identity([input1])\\n builder.addOutputTensor(output1)\\n anchorConfig = {output1: popart.AnchorReturnType(\\\"ALL\\\")}\\n\\n dataFlow = popart.DataFlow(1, anchorConfig)\\n deviceConfig = {\\\"numIPUs\\\": 1}\\n dm = popart.DeviceManager()\\n device = dm.createIpuModelDevice(deviceConfig)\\n session = popart.InferenceSession(\\n fnModel=builder.getModelProto(), dataFlow=dataFlow, deviceInfo=device\\n )\\n\\n # Compile graph and place weights onto it\\n session.prepareDevice()\\n session.weightsFromHost()\\n\\n # Feed the session with a transposed (non-contiguous) tensor.\\n input1Value = np.random.randint(0, 100, size=(2, 2), dtype=\\\"int32\\\")\\n if transposedInput:\\n input1Value = np.transpose(input1Value, [1, 0])\\n output1Value = np.random.randint(0, 100, size=(2, 2), dtype=\\\"int32\\\")\\n if transposedOutput:\\n output1Value = np.transpose(output1Value, [1, 0])\\n\\n with pytest.raises(\\n (Exception, RuntimeError, popart.popart_exception)\\n ) as e_info:\\n\\n # pylint: disable=unused-argument\\n def input_callback(id, prefetch):\\n return input1Value\\n\\n def input_complete_callback(_): # id is an unused parameter\\n pass\\n\\n def output_callback(_): # id is an unused parameter\\n return output1Value\\n\\n def output_complete_callback(_): # id is an unused parameter\\n pass\\n\\n stepio = popart.PyStepIOCallback(\\n input_callback,\\n input_complete_callback,\\n output_callback,\\n output_complete_callback,\\n )\\n\\n session.run(stepio)\\n\\n assert \\\"contiguous\\\" in e_info.value.args[0]\\n\\n _test(transposedInput=True, transposedOutput=False)\\n _test(transposedInput=False, transposedOutput=True)\",\n \"def test_many_arg_elemwise():\\r\\n rng = numpy.random.RandomState([1, 2, 3])\\r\\n\\r\\n for num_args in [25]:\\r\\n for op_to_test in [theano.tensor.add, theano.tensor.mul]:\\r\\n for nb_dim in [2, 3, 4, 5]:\\r\\n shapes = [rng.randint(1, 5) for i in range(nb_dim)]\\r\\n args = [numpy.cast['float32'](rng.randn(*shapes))\\r\\n for arg in xrange(0, num_args)]\\r\\n\\r\\n symb_args = [theano.tensor.TensorType('float32',\\r\\n (False,)*nb_dim)()\\r\\n for arg in xrange(0, num_args)]\\r\\n\\r\\n\\r\\n outputs = []\\r\\n for mode in [mode_with_gpu, mode_without_gpu]:\\r\\n #test the optijmization local_gpu_elemwise_0\\r\\n f = theano.function(\\r\\n symb_args, op_to_test(*symb_args),\\r\\n mode=mode.excluding(\\\"local_gpu_elemwise_1\\\"))\\r\\n outputs.append(f(*args))\\r\\n #assert that the test was done on the gpu.\\r\\n if mode is mode_with_gpu:\\r\\n assert any([isinstance(node.op, cuda.GpuElemwise)\\r\\n for node in f.maker.fgraph.apply_nodes])\\r\\n\\r\\n #test the optijmization local_gpu_elemwise_1\\r\\n f = theano.function(\\r\\n symb_args,\\r\\n cuda.gpu_from_host(op_to_test(*symb_args)),\\r\\n mode=mode.excluding(\\\"local_gpu_elemwise_0\\\"))\\r\\n out = f(*args)\\r\\n #assert that the test was done on the gpu.\\r\\n if mode is mode_with_gpu:\\r\\n assert any([isinstance(node.op, cuda.GpuElemwise)\\r\\n for node in f.maker.fgraph.apply_nodes])\\r\\n assert numpy.allclose(out, outputs[-1])\\r\\n\\r\\n results_gpu, results_cpu = outputs\\r\\n\\r\\n assert numpy.allclose(results_gpu, results_cpu)\",\n \"def _debugmallocstats(): # real signature unknown; restored from __doc__\\n pass\",\n \"def test_flonum_special(doctest):\",\n \"def test_3(self):\\n for _ in range(10):\\n\\n # Draw random requests for testing purposes.\\n num_draws_emax = np.random.randint(2, 1000)\\n dim = np.random.randint(1, 6)\\n\\n matrix = np.random.uniform(size=dim ** 2).reshape(dim, dim)\\n cov = np.dot(matrix, matrix.T)\\n\\n # PDF of normal distribution\\n args = np.random.normal(size=3)\\n args[-1] **= 2\\n\\n f90 = fort_debug.wrapper_normal_pdf(*args)\\n py = norm.pdf(*args)\\n\\n assert_almost_equal(py, f90)\\n\\n # Singular Value Decomposition\\n py = scipy.linalg.svd(matrix)\\n f90 = fort_debug.wrapper_svd(matrix, dim)\\n\\n for i in range(3):\\n assert_allclose(py[i], f90[i])\\n\\n # Pseudo-Inverse\\n py = np.linalg.pinv(matrix)\\n f90 = fort_debug.wrapper_pinv(matrix, dim)\\n\\n assert_allclose(py, f90)\\n\\n # Inverse\\n py = np.linalg.inv(cov)\\n f90 = fort_debug.wrapper_inverse(cov, dim)\\n assert_allclose(py, f90)\\n\\n # Determinant\\n py = np.linalg.det(cov)\\n f90 = fort_debug.wrapper_determinant(cov)\\n\\n assert_allclose(py, f90)\\n\\n # Trace\\n py = np.trace(cov)\\n f90 = fort_debug.wrapper_trace(cov)\\n\\n assert_allclose(py, f90)\\n\\n # Random normal deviates. This only tests the interface, requires\\n # visual inspection in IPYTHON notebook as well.\\n fort_debug.wrapper_standard_normal(num_draws_emax)\\n\\n # Clipping values below and above bounds.\\n num_values = np.random.randint(1, 10000)\\n lower_bound = np.random.randn()\\n upper_bound = lower_bound + np.random.ranf()\\n values = np.random.normal(size=num_values)\\n\\n f90 = fort_debug.wrapper_clip_value(\\n values, lower_bound, upper_bound, num_values\\n )\\n py = np.clip(values, lower_bound, upper_bound)\\n\\n assert_almost_equal(py, f90)\\n\\n # Spectral condition number\\n py = _spectral_condition_number(cov)\\n fort = fort_debug.wrapper_spectral_condition_number(cov)\\n assert_almost_equal(py, fort)\",\n \"def test_print_op():\\r\\n b = tensor.fmatrix()\\r\\n f = theano.function([b],theano.printing.Print()(b)*2, mode=mode_with_gpu)\\r\\n #theano.printing.debugprint(f)\\r\\n #print f.maker.fgraph.toposort()\\r\\n#[GpuFromHost(<TensorType(float32, matrix)>), <theano.printing.Print object at 0x3581210>(GpuFromHost.0), GpuElemwise{mul}(CudaNdarray{[[ 2.]]}, <theano.printing.Print object at 0x3581210>.0), HostFromGpu(GpuElemwise{mul}.0)]\\r\\n topo = f.maker.fgraph.toposort()\\r\\n assert topo[0].op == cuda.gpu_from_host\\r\\n assert isinstance(topo[1].op, theano.printing.Print)\\r\\n assert isinstance(topo[2].op, cuda.GpuElemwise)\\r\\n assert topo[3].op == cuda.host_from_gpu\\r\\n f(numpy.random.random((5,5)).astype('float32'))\",\n \"def benchmark_fp16_xla_compile_fake_1gpu_gpuparams(self):\\n params = self._shared_params_fp16()._replace(\\n num_gpus=1,\\n data_dir=self.fake_data_dir,\\n data_name='imagenet',\\n xla_compile=True)\\n self._run_benchmark(params)\",\n \"def test_active_inference_SPM_1b(self):\",\n \"def test_prelu_basic(self):\\n\\n utils.compare_tracing_methods(\\n SimplePreluModule(),\\n torch.randn(1, 4, 5, 5),\\n torch.tensor([0.25]),\\n fusible_ops={\\\"aten::prelu\\\"},\\n )\",\n \"def test_gendaymtx():\\n # sky.gendaymtx(\\n # sun_mtx, 6, data=wea_data, meta=wea_metadata, direct=True, onesun=True\\n # )\\n pass\",\n \"def add_gpu_and_mpi_marks():\\n pass\",\n \"def test_defining_only_and_defer_fails(self):\",\n \"def main(argv):\\r\\n FLAGS.dir = os.path.join(FLAGS.dir, FLAGS.id)\\r\\n if not os.path.exists(FLAGS.dir):\\r\\n os.makedirs(FLAGS.dir)\\r\\n os.makedirs(os.path.join(FLAGS.dir, \\\"logs\\\"))\\r\\n\\r\\n FLAGS.alsologtostderr = True\\r\\n logging.get_absl_handler().use_absl_log_file(FLAGS.logfile, os.path.join(FLAGS.dir, \\\"logs\\\"))\\r\\n\\r\\n np.random.seed(FLAGS.random_seed)\\r\\n tf.random.set_seed(FLAGS.random_seed)\\r\\n\\r\\n log_flags(FLAGS)\\r\\n\\r\\n try:\\r\\n prepare()\\r\\n except:\\r\\n exception = traceback.format_exc()\\r\\n logging.info(exception)\",\n \"def sanity_check():\\n print(\\\"Running sanity check...\\\")\\n\\n N = 20\\n dimensions = [10, 5, 10]\\n data = np.random.randn(N, dimensions[0]) # each row will be a datum\\n labels = np.zeros((N, dimensions[2]))\\n for i in range(N):\\n labels[i,random.randint(0,dimensions[2]-1)] = 1\\n \\n params = np.random.randn((dimensions[0] + 1) * dimensions[1] + (\\n dimensions[1] + 1) * dimensions[2], )\\n\\n gradcheck_naive(lambda params: forward_backward_prop(data, labels, params,\\n dimensions), params)\",\n \"def testForcedCompile(self):\\n if config.list_logical_devices('TPU'):\\n self.skipTest('To accommodate OSS, experimental_compile support for TPU '\\n 'is not linked in.')\\n # GPU doesn't support int32 variables, so we use int64.\\n v = variables.Variable([1, 2], dtype=dtypes.int64)\\n\\n @def_function.function(experimental_compile=True)\\n def f():\\n key, counter = (\\n gen_stateless_random_ops_v2.stateless_random_get_key_counter(\\n seed=math_ops.cast(v.read_value(), dtypes.int32)))\\n alg = gen_stateless_random_ops_v2.stateless_random_get_alg()\\n return gen_stateless_random_ops_v2.stateless_random_normal_v2(\\n shape=[], key=key, counter=counter, alg=alg)\\n\\n f()\",\n \"def test_noop():\\n assert True\",\n \"def exercise_b2_113():\\r\\n pass\",\n \"def test_T0():\",\n \"def test_mpi_code() -> None:\\n print(\\\"MPI code checking is disabled\\\")\",\n \"def local_gpu_conv(node):\\r\\n def GpuConvOp_from_ConvOp(op):\\r\\n logical_img_hw = None\\r\\n\\r\\n if op.kshp_logical is not None and op.kshp_logical != op.kshp:\\r\\n return None\\r\\n #print op.kshp, op.imshp[1:3]\\r\\n #print op.kshp_logical, logical_img_hw\\r\\n ret = GpuConv(border_mode=op.out_mode,\\r\\n subsample=(op.dx, op.dy),\\r\\n logical_img_hw=logical_img_hw,\\r\\n logical_kern_hw=op.kshp_logical,\\r\\n logical_kern_align_top=op.kshp_logical_top_aligned,\\r\\n kshp=op.kshp,\\r\\n version=op.version,\\r\\n verbose=op.verbose,\\r\\n imshp=op.imshp,\\r\\n )\\r\\n if op.imshp_logical is not None:\\r\\n logical_img_hw = op.imshp_logical[1:3]\\r\\n if logical_img_hw != op.imshp[1:3]:\\r\\n # this case is not implemented\\r\\n #return None\\r\\n rstride = int(numpy.ceil(op.imshp_logical[1] /\\r\\n float(op.imshp[1])))\\r\\n cstride = int(numpy.ceil(op.imshp_logical[2] /\\r\\n float(op.imshp[2])))\\r\\n\\r\\n def make_graph(img, kern):\\r\\n buf = tensor.alloc(numpy.asarray(0, dtype=img.dtype),\\r\\n img.shape[0], *op.imshp_logical)\\r\\n img = tensor.set_subtensor(buf[:, :, ::rstride, ::cstride],\\r\\n img)\\r\\n img = gpu_from_host(img)\\r\\n return ret(img, kern)\\r\\n\\r\\n return make_graph\\r\\n return ret\\r\\n\\r\\n def values_eq_approx(a, b):\\r\\n \\\"\\\"\\\"This fct is needed to don't have DebugMode raise useless\\r\\n error due to ronding error.\\r\\n\\r\\n This happen as We reduce on the two last dimensions, so this\\r\\n can raise the absolute error if the number of element we\\r\\n reduce on is significant.\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n assert a.ndim == 4\\r\\n atol = None\\r\\n if a.shape[-1] * a.shape[-2] > 100:\\r\\n #For float32 the default atol is 1e-5\\r\\n atol = 3e-5\\r\\n return GpuArrayType.values_eq_approx(a, b, atol=atol)\\r\\n\\r\\n img, kern = node.inputs\\r\\n gpu_conv = GpuConvOp_from_ConvOp(node.op)\\r\\n if gpu_conv is None:\\r\\n return\\r\\n out = gpu_conv(gpu_from_host(img),\\r\\n gpu_from_host(kern))\\r\\n # in some case the ConvOp broadcast the last 2 dimensions\\r\\n # differently then the gpu ConvOp\\r\\n out = tensor.patternbroadcast(\\r\\n host_from_gpu(out),\\r\\n node.outputs[0].broadcastable)\\r\\n #op_lifter want the output on the GPU.\\r\\n out = gpu_from_host(out)\\r\\n out.values_eq_approx = values_eq_approx\\r\\n return [out]\",\n \"def test_get_debug_values_success():\\r\\n\\r\\n prev_value = config.compute_test_value\\r\\n for mode in ['ignore', 'warn', 'raise']:\\r\\n\\r\\n try:\\r\\n config.compute_test_value = mode\\r\\n\\r\\n x = T.vector()\\r\\n x.tag.test_value = numpy.zeros((4,), dtype=config.floatX)\\r\\n y = numpy.zeros((5, 5))\\r\\n\\r\\n iters = 0\\r\\n\\r\\n for x_val, y_val in op.get_debug_values(x, y):\\r\\n\\r\\n assert x_val.shape == (4,)\\r\\n assert y_val.shape == (5, 5)\\r\\n\\r\\n iters += 1\\r\\n\\r\\n assert iters == 1\\r\\n\\r\\n finally:\\r\\n config.compute_test_value = prev_value\",\n \"def benchmark_fp16_xla_compile_synth_1gpu_gpuparams(self):\\n params = self._shared_params_fp16()._replace(num_gpus=1, xla_compile=True)\\n self._run_benchmark(params)\",\n \"def _assert(ok, detail):\\n\\n if not ok:\\n raise UserException(\\\"the project file is invalid\\\", detail)\",\n \"def testCanvasIsValid(self):\\r\\n myMessage = \\\"Plugin was not initialised with a valid CANVAS.\\\"\\r\\n assert self.bucketFill.iface.mapCanvas().width() == 400, myMessage\",\n \"def local_gpu_conv(node):\\r\\n def GpuConvOp_from_ConvOp(op):\\r\\n logical_img_hw = None\\r\\n\\r\\n if op.kshp_logical is not None and op.kshp_logical != op.kshp:\\r\\n return None\\r\\n #print op.kshp, op.imshp[1:3]\\r\\n #print op.kshp_logical, logical_img_hw\\r\\n ret = GpuConv(border_mode=op.out_mode,\\r\\n subsample=(op.dx, op.dy),\\r\\n logical_img_hw=logical_img_hw,\\r\\n logical_kern_hw=op.kshp_logical,\\r\\n logical_kern_align_top=op.kshp_logical_top_aligned,\\r\\n kshp=op.kshp,\\r\\n version=op.version,\\r\\n verbose=op.verbose,\\r\\n imshp=op.imshp,\\r\\n )\\r\\n if op.imshp_logical is not None:\\r\\n logical_img_hw = op.imshp_logical[1:3]\\r\\n if logical_img_hw != op.imshp[1:3]:\\r\\n # this case is not implemented\\r\\n #return None\\r\\n rstride = int(numpy.ceil(op.imshp_logical[1] /\\r\\n float(op.imshp[1])))\\r\\n cstride = int(numpy.ceil(op.imshp_logical[2] /\\r\\n float(op.imshp[2])))\\r\\n\\r\\n def make_graph(img, kern):\\r\\n buf = tensor.alloc(numpy.asarray(0, dtype=img.dtype),\\r\\n img.shape[0], *op.imshp_logical)\\r\\n img = tensor.set_subtensor(buf[:, :, ::rstride, ::cstride],\\r\\n img)\\r\\n img = gpu_from_host(img)\\r\\n return ret(img, kern)\\r\\n\\r\\n return make_graph\\r\\n return ret\\r\\n\\r\\n def values_eq_approx(a, b):\\r\\n \\\"\\\"\\\"This fct is needed to don't have DebugMode raise useless\\r\\n error due to ronding error.\\r\\n\\r\\n This happen as We reduce on the two last dimensions, so this\\r\\n can raise the absolute error if the number of element we\\r\\n reduce on is significant.\\r\\n\\r\\n \\\"\\\"\\\"\\r\\n assert a.ndim == 4\\r\\n atol = None\\r\\n if a.shape[-1] * a.shape[-2] > 100:\\r\\n #For float32 the default atol is 1e-5\\r\\n atol = 3e-5\\r\\n return CudaNdarrayType.values_eq_approx(a, b, atol=atol)\\r\\n\\r\\n if isinstance(node.op, GpuFromHost):\\r\\n #gpu_from_host(conv) -> gpu_conv(gpu_from_host)\\r\\n host_input = node.inputs[0]\\r\\n if host_input.owner and isinstance(host_input.owner.op, conv.ConvOp):\\r\\n gpu_conv = GpuConvOp_from_ConvOp(host_input.owner.op)\\r\\n if gpu_conv is None:\\r\\n return\\r\\n img, kern = host_input.owner.inputs\\r\\n out = gpu_conv(gpu_from_host(img),\\r\\n gpu_from_host(kern))\\r\\n out = tensor.patternbroadcast(out,\\r\\n node.outputs[0].broadcastable)\\r\\n out.values_eq_approx = values_eq_approx\\r\\n # in some case the ConvOp broadcast the last 2 dimensions\\r\\n # differently then the gpu ConvOp\\r\\n return [out]\\r\\n\\r\\n if isinstance(node.op, conv.ConvOp):\\r\\n #conv(host_from_gpu) -> host_from_gpu(gpu_conv)\\r\\n img, kern = node.inputs\\r\\n img_on_gpu = (img.owner and isinstance(img.owner.op, HostFromGpu))\\r\\n kern_on_gpu = (kern.owner and isinstance(kern.owner.op, HostFromGpu))\\r\\n if img_on_gpu or kern_on_gpu:\\r\\n gpu_conv = GpuConvOp_from_ConvOp(node.op)\\r\\n if gpu_conv is None:\\r\\n return\\r\\n out = gpu_conv(gpu_from_host(img),\\r\\n gpu_from_host(kern))\\r\\n out = tensor.patternbroadcast(\\r\\n host_from_gpu(out),\\r\\n node.outputs[0].broadcastable)\\r\\n out.values_eq_approx = values_eq_approx\\r\\n # in some case the ConvOp broadcast the last 2 dimensions\\r\\n # differently then the gpu ConvOp\\r\\n return [out]\",\n \"def test_cl_fix():\\n assert Cl is ClFixHybridPressureCoord\",\n \"def test_require():\",\n \"def test_transformation_pass(self):\\n qr = QuantumRegister(1, \\\"qr\\\")\\n circuit = QuantumCircuit(qr, name=\\\"MyCircuit\\\")\\n\\n pass_d = PassD_TP_NR_NP(argument1=[1, 2])\\n with self.assertLogs(\\\"LocalLogger\\\", level=\\\"INFO\\\") as cm:\\n result = pass_d(circuit)\\n\\n self.assertMessageLog(cm, [\\\"run transformation pass PassD_TP_NR_NP\\\", \\\"argument [1, 2]\\\"])\\n self.assertEqual(circuit, result)\",\n \"def check_deterministic_ops() -> None:\\n if os.getenv(TF_DETERMINISTIC_OPS, False):\\n shared_io_utils.raise_warning(\\n f\\\"You have set '{TF_DETERMINISTIC_OPS}' to 1. If you are \\\"\\n f\\\"using one or more GPU(s) and use any of 'SparseFeaturizer', \\\"\\n f\\\"'TEDPolicy', 'DIETClassifier', 'UnexpecTEDIntentPolicy', or \\\"\\n f\\\"'ResponseSelector' training and testing will fail as there are no \\\"\\n f\\\"deterministic GPU implementations of some underlying TF ops.\\\",\\n category=UserWarning,\\n )\",\n \"def test_unit(self):\\n self.assertTrue(return_true())\",\n \"def test_huge_elemwise_fusion():\\r\\n shape = (2, 3, 4, 5, 6)\\r\\n ttype = tensor.tensor(dtype='float32', broadcastable=(False,) * len(shape))\\r\\n gpu_ptr_size = theano.sandbox.cuda.opt.get_device_type_sizes()['gpu_ptr_size']\\r\\n if gpu_ptr_size == 8:\\r\\n nb_in = 7\\r\\n len_topo = 10\\r\\n elif gpu_ptr_size == 4:\\r\\n nb_in = 8\\r\\n len_topo = 11\\r\\n else:\\r\\n raise Exception(\\\"Unexpected value for gpu_ptr_size\\\", gpu_ptr_size)\\r\\n vars = [tensor.tanh(ttype) for x in range(nb_in)]\\r\\n f = pfunc(vars, [reduce(operator.sub, vars)], mode=mode_with_gpu)\\r\\n\\r\\n topo = f.maker.fgraph.toposort()\\r\\n #theano.printing.debugprint(f)\\r\\n #for i, node in enumerate(topo):\\r\\n # print >> sys.stdout, i, node\\r\\n assert len(topo) == len_topo\\r\\n assert sum([isinstance(node.op, cuda.GpuElemwise) for node in topo]) == 2\\r\\n assert isinstance(topo[-3].op.scalar_op, theano.scalar.basic.Sub)\\r\\n assert isinstance(topo[-2].op.scalar_op, theano.scalar.basic.Composite)\\r\\n #let debugmode catch errors\\r\\n gen = lambda: theano._asarray(numpy.random.rand(*shape), dtype='float32')\\r\\n f(*[gen() for i in range(nb_in)])\\r\\n\\r\\n # Test the case where we can't put the computation on the gpu! their is too\\r\\n # many dimensions to the input to have 2 inputs to the op!\\r\\n\\r\\n shape = (1, 2, 3, 4, 5, 6, 7, 2, 2, 3, 2, 1, 2, 2, 2,)\\r\\n ttype = tensor.tensor(dtype='float32', broadcastable=(False,) * len(shape))\\r\\n vars = [tensor.tanh(ttype) for x in range(7)]\\r\\n f = pfunc(vars, [vars[0] - vars[1] - vars[2] - vars[3] - vars[4] -\\r\\n vars[5] - vars[6]], mode=mode_with_gpu)\\r\\n topo = f.maker.fgraph.toposort()\\r\\n #theano.printing.debugprint(f)\\r\\n assert len(topo) == 1\\r\\n assert sum([isinstance(node.op, cuda.GpuElemwise) for node in topo]) == 0\\r\\n assert sum([isinstance(node.op, tensor.Elemwise) for node in topo]) == 1\\r\\n #let debugmode catch errors\\r\\n gen = lambda: theano._asarray(numpy.random.rand(*shape), dtype='float32')\\r\\n f(gen(), gen(), gen(), gen(), gen(), gen(), gen())\\r\\n\\r\\n def gen(shape):\\r\\n return theano._asarray(numpy.random.rand(*shape), dtype='float32')\\r\\n\\r\\n max_var = 16 # excluded\\r\\n for shape in [(2,),\\r\\n (2, 2),\\r\\n (2, 2, 2),\\r\\n (2, 2, 2, 2),\\r\\n (2, 2, 2, 2, 2), # 5d\\r\\n (2, 2, 2, 2, 2, 2),\\r\\n# (2, 2, 2, 2, 2, 2, 2),\\r\\n# (2, 2, 2, 2, 2, 2, 2, 2),\\r\\n# (2, 2, 2, 1, 1, 1, 1, 2, 2), # 9d\\r\\n ]:\\r\\n vals = [cuda.shared_constructor(gen(shape)) for x in range(max_var)]\\r\\n for use_tan in [True, False]:\\r\\n if use_tan:\\r\\n vars = [tensor.tanh(x) for x in vals]\\r\\n else:\\r\\n vars = vals\\r\\n for nb_var in range(1, max_var):\\r\\n out = reduce(lambda x, y: x + y, vars[:nb_var])\\r\\n if not isinstance(out.type, CudaNdarrayType):\\r\\n out = cuda.gpu_from_host(out)\\r\\n f = pfunc([], [out], mode=mode_with_gpu)\\r\\n topo = f.maker.fgraph.toposort()\\r\\n #print shape, nb_var, use_tan, len(topo)\\r\\n assert (sum([isinstance(node.op, cuda.GpuElemwise)\\r\\n for node in topo]) == len(topo) or\\r\\n (nb_var == 1 and use_tan == False))\\r\\n assert sum([isinstance(node.op, tensor.Elemwise)\\r\\n for node in topo]) == 0\\r\\n\\r\\n #let debugmode catch errors\\r\\n f()\",\n \"def test_global():\\n global_assumptions.add(x > 0)\\n assert (x > 0) in global_assumptions\\n global_assumptions.remove(x > 0)\\n assert not (x > 0) in global_assumptions\\n # same with multiple of assumptions\\n global_assumptions.add(x > 0, y > 0)\\n assert (x > 0) in global_assumptions\\n assert (y > 0) in global_assumptions\\n global_assumptions.clear()\\n assert not (x > 0) in global_assumptions\\n assert not (y > 0) in global_assumptions\",\n \"def setupGPUSwept(solver):\\n solver.gpuBlock = (slice(0,solver.sharedShape[0],1),)+solver.gpuBlock\\n getGPUReadBlockSwept(solver) #Finish creating gpuReadBlock here\\n blockShape =[element.stop for element in solver.gpuBlock]\\n blockShape[-1] += int(2*solver.blocksize[0]) #Adding 2 blocks in the column direction\\n # Creating local GPU array with split\\n grid = (int((blockShape[2])/solver.blocksize[0]),int((blockShape[3])/solver.blocksize[1])) #Grid size\\n #Creating constants\\n bsp = lambda x: int(numpy.prod(blockShape[x:])) #block shape product returned as an integer\\n const_dict = ({\\\"NV\\\":blockShape[1],'SX':blockShape[2],'SY':blockShape[3],\\\"VARS\\\":bsp(2),\\\"TIMES\\\":bsp(1),\\\"MPSS\\\":solver.maxPyramidSize,\\\"MOSS\\\":solver.maxOctSize,\\\"OPS\\\":solver.operating,\\\"ITS\\\":solver.intermediate})\\n solver.GPUArray = mallocGPUArray(blockShape) #Allocated GPU\\n solver.localGPUArray = numpy.zeros(blockShape)\\n #Building CUDA source code\\n solver.gpu = io.buildGPUSource(solver.gpu)\\n io.copyConstants(solver.gpu,const_dict) #This copys cpu constants not global constants\\n solver.cpu.set_globals(*solver.globals,source_mod=solver.gpu)\\n # Make GPU geometry\\n solver.Up.initializeGPU(solver.gpu.get_function(\\\"UpPyramid\\\"),solver.blocksize,(grid[0],grid[1]-1))\\n solver.Oct.initializeGPU(solver.gpu.get_function(\\\"Octahedron\\\"),solver.blocksize,(grid[0],grid[1]-1))\\n solver.Down.initializeGPU(solver.gpu.get_function(\\\"DownPyramid\\\"),solver.blocksize,(grid[0],grid[1]-1))\\n solver.Yb.initializeGPU(solver.gpu.get_function(\\\"YBridge\\\"),solver.blocksize,grid)\\n solver.Xb.initializeGPU(solver.gpu.get_function(\\\"XBridge\\\"),solver.blocksize,grid)\",\n \"def test_make_mul_transp():\\n tmp_dir = make_temp_dir()\\n FILE_NAME = \\\"tmp_test_mul_transp\\\"\\n NAME = os.path.join(tmp_dir, FILE_NAME)\\n C_NAME, EXE_NAME = NAME + \\\".c\\\", NAME + \\\".exe\\\"\\n \\n def make_testprogram(lv, lm, n=1):\\n generate = BitMatrixMulTransp().generate_c_mul_transp\\n f = open(C_NAME, \\\"wt\\\")\\n print(r\\\"\\\"\\\"#include <stdio.h>\\n#include <stdint.h>\\nint main(int argc, char **argv)\\n{{\\n uint_fast32_t i, v;\\n static uint_fast32_t a[1000];\\n sscanf(argv[1], \\\"%ld\\\", &v);\\n for (i=0; i < argc-2; ++i) sscanf(argv[i+2], \\\"%ld\\\", a+i);\\n {0}\\n printf(\\\"%ld\\\\n\\\",v);\\n return 0;\\n}}\\n\\\"\\\"\\\".format(generate(\\\"v\\\", \\\"a\\\", lv, lm, n)), file = f)\\n f.close()\\n #subprocess.check_output([\\\"gcc\\\", C_NAME, \\\"-o\\\", EXE_NAME])\\n compile_testprogramm([C_NAME], EXE_NAME)\\n checker = BitMatrixMulTransp()\\n checker.set_matrix(lv, lm, n)\\n return checker\\n\\n def run_testprogram(v, m):\\n data = list(map(str, [v]+m)) \\n res = subprocess.check_output([EXE_NAME] + data)\\n return int(res) \\n\\n def test_testprogram(checker , v, m):\\n res = run_testprogram(v, m)\\n ref = checker.compute(v,m+[0]*64)\\n assert ref == ref, (hex, ref)\\n\\n def del_testprogram():\\n for f in [C_NAME, EXE_NAME]:\\n os.remove(f)\\n\\n test_data_dict = {\\n (4,4,1,0): [ (3, [2,3, 1, 1]) , ]\\n }\\n\\n def test_data(lv, lm, n, n_tests=10):\\n \\\"\\\"\\\"yield n_tests test cases (v, m) for given lv, lm, m0\\\"\\\"\\\"\\n try:\\n d = test_data_dict[(lv, lm, n, m0)]\\n for v, m in d: yield v, m\\n except:\\n pass\\n for i in range(n_tests):\\n v = random.randint(0, 2**(n*lv)-1)\\n m = [] \\n for i in range(lm):\\n m.append(random.randint(0, 2**(n*lv)-1))\\n yield v, m\\n\\n def test_programs():\\n \\\"\\\"\\\"yield cases (lv, lm, n) for a making a test program\\\"\\\"\\\"\\n yield 4, 4, 1\\n yield 16, 12, 2\\n\\n \\n\\n \\n for lv, lm, n in test_programs():\\n print(\\\"Make C program for case lv=%d, lm=%d, n=%d\\\" % (\\n lv,lm,n))\\n checker = make_testprogram(lv, lm, n) \\n print (\\\"C program has been made, starting tests..\\\")\\n for v, m in test_data(lv, lm, n):\\n test_testprogram(checker, v, m)\\n del_testprogram()\\n print (\\\"passed\\\")\\n\\n kill_temp_dir(tmp_dir)\",\n \"def obj_func(x):\\n assert(False)\",\n \"def test_constants():\\n c = pycvm.Constants()\\n assert c.get_number_of_layers() == 3, 'number of layers is wrong'\\n assert c.get_utm_zone() == 10, 'utm zone is wrong' \\n \\n assert c.get_number_of_grid_points_in_x(pycvm.LayerIdentifier.top) == 3271, 'nx top wrong'\\n assert c.get_number_of_grid_points_in_x(pycvm.LayerIdentifier.middle) == 2181, 'nx middle wrong'\\n assert c.get_number_of_grid_points_in_x(pycvm.LayerIdentifier.bottom) == 727, 'nx bottom wrong'\\n\\n assert c.get_number_of_grid_points_in_y(pycvm.LayerIdentifier.top) == 5367, 'ny top wrong'\\n assert c.get_number_of_grid_points_in_y(pycvm.LayerIdentifier.middle) == 3578, 'ny middle wrong'\\n assert c.get_number_of_grid_points_in_y(pycvm.LayerIdentifier.bottom) == 1193, 'ny bottom wrong'\\n\\n assert c.get_number_of_grid_points_in_z(pycvm.LayerIdentifier.top) == 13, 'nz top wrong'\\n assert c.get_number_of_grid_points_in_z(pycvm.LayerIdentifier.middle) == 29, 'nz middle wrong'\\n assert c.get_number_of_grid_points_in_z(pycvm.LayerIdentifier.bottom) == 55, 'nz bottom wrong'\\n\\n assert abs(c.get_grid_spacing_in_x(pycvm.LayerIdentifier.top) - 200) < 1.e-13, 'dx top wrong'\\n assert abs(c.get_grid_spacing_in_x(pycvm.LayerIdentifier.middle) - 300) < 1.e-13, 'dx middle wrong'\\n assert abs(c.get_grid_spacing_in_x(pycvm.LayerIdentifier.bottom) - 900) < 1.e-13, 'dx bottom wrong'\\n\\n assert abs(c.get_grid_spacing_in_y(pycvm.LayerIdentifier.top) - 200) < 1.e-13, 'dy top wrong'\\n assert abs(c.get_grid_spacing_in_y(pycvm.LayerIdentifier.middle) - 300) < 1.e-13, 'dy middle wrong'\\n assert abs(c.get_grid_spacing_in_y(pycvm.LayerIdentifier.bottom) - 900) < 1.e-13, 'dy bottom wrong'\\n\\n assert abs(c.get_grid_spacing_in_z(pycvm.LayerIdentifier.top) - 100) < 1.e-13, 'dz top wrong'\\n assert abs(c.get_grid_spacing_in_z(pycvm.LayerIdentifier.middle) - 300) < 1.e-13, 'dz middle wrong'\\n assert abs(c.get_grid_spacing_in_z(pycvm.LayerIdentifier.bottom) - 900) < 1.e-13, 'dz bottom wrong'\\n\\n \\\"\\\"\\\" \\n auto mLCM = static_cast<double> (std::lcm(std::lcm(200, 300), 900));\\n EXPECT_NEAR(c.getLeastCommonMultipleOfGridSpacingsInXAndY(), mLCM, 1.e-12);\\n \\\"\\\"\\\"\\n print(\\\"Passed constants test\\\")\",\n \"def translation_rule_gpu(func):\\n # functions to call before running the translation rule\\n setup_funcs = (\\n ensure_gpu_ext,\\n functools.partial(ensure_platform_flush, \\\"gpu\\\"),\\n ensure_omnistaging,\\n setup_cuda_mpi,\\n )\\n\\n @functools.wraps(func)\\n def wrapped(*args, **kwargs):\\n for f in setup_funcs:\\n f()\\n return func(*args, **kwargs)\\n\\n return wrapped\",\n \"def test_runtime_warnings(self):\\n\\t\\tprint(\\\"test runtime warning: sqrt(-1)=\\\", np.sqrt(-1.0))\\n\\t\\tassert(True)\",\n \"def assertion_started(self, func):\",\n \"def test_get_compute_func(self):\\n assert api._get_compute_func(self.run_fa) is api._compute_grade_for_fa\\n assert api._get_compute_func(self.run_no_fa) is api._compute_grade_for_non_fa\",\n \"def test_qc_quantize_op_gpu_fp16(self):\\n graph = tf.Graph()\\n config = tf.compat.v1.ConfigProto(log_device_placement=False)\\n sess = tf.compat.v1.Session(graph=graph, config=config)\\n bitwidth = 8\\n use_symm_encoding = False\\n with graph.as_default():\\n inp = tf.compat.v1.placeholder(tf.float32, shape=[10], name='input')\\n tensor_quantizer = libpymo.TensorQuantizer(libpymo.QuantizationMode.QUANTIZATION_TF_ENHANCED,\\n libpymo.RoundingMode.ROUND_NEAREST)\\n tensor_quantizer_val = libpymo.PtrToInt64(tensor_quantizer)\\n tensor_quant_ref = tf.Variable(initial_value=tensor_quantizer_val, trainable=False, dtype=tf.int64)\\n\\n mode_var = tf.Variable(initial_value=int(libpymo.TensorQuantizerOpMode.quantizeDequantize),\\n trainable=False, dtype=tf.int32)\\n\\n encoding_min = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n encoding_max = tf.Variable(initial_value=0.0, trainable=True, dtype=tf.double)\\n bit_width = tf.Variable(initial_value=bitwidth, trainable=False, dtype=tf.int8)\\n use_symmetric_encoding = tf.Variable(initial_value=use_symm_encoding, trainable=False, dtype=tf.bool)\\n is_int_data_type = tf.Variable(initial_value=False, trainable=False, dtype=tf.bool)\\n\\n sess.run([mode_var.initializer, tensor_quant_ref.initializer, encoding_min.initializer,\\n encoding_max.initializer, bit_width.initializer, use_symmetric_encoding.initializer,\\n is_int_data_type.initializer])\\n\\n # place holder for the input\\n with tf.device(\\\"/device:GPU:0\\\"):\\n pass_through_op_output = zero_out_module.qc_quantize(name='quant_op', in_tensor=inp,\\n op_mode=mode_var,\\n tensor_quantizer_reference=tensor_quant_ref,\\n encoding_min=encoding_min,\\n encoding_max=encoding_max,\\n bit_width=bit_width,\\n use_symmetric_encoding=use_symmetric_encoding,\\n is_int_data_type=is_int_data_type)\\n\\n inp_tensor = sess.graph.get_tensor_by_name('input:0')\\n inp_data = np.array([0.78027299, 0.44164284, 0.6942797, 0.69774088, 0.55863863, 0.29553034, 0.219199,\\n 0.09483732, 0.55075674, 0.6348504], dtype=np.float32)\\n\\n out_exp = np.array([0.78027344, 0.4416504, 0.69433594, 0.6977539, 0.55859375, 0.29541016, 0.21923828,\\n 0.09484863, 0.55078125, 0.6347656], dtype=np.float32)\\n\\n print(\\\"inp_data\\\", inp_data)\\n with tf.device(\\\"/device:GPU:0\\\"):\\n out_data = sess.run(pass_through_op_output, feed_dict={inp_tensor: inp_data})\\n print(\\\"out_data\\\", out_data)\\n assert np.allclose(out_data, out_exp)\\n sess.close()\",\n \"def unitdoctest():\\r\\n\\r\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5553334","0.53317463","0.5246943","0.52441424","0.5225567","0.50993943","0.50408936","0.49993053","0.49924356","0.49918866","0.49887422","0.49359533","0.4932513","0.4932513","0.49268204","0.49176776","0.49025792","0.4897762","0.48943457","0.48824537","0.4880259","0.47771236","0.4752814","0.47321525","0.47200143","0.47128147","0.47128147","0.47090766","0.4705168","0.46898013","0.46826872","0.46799666","0.46549115","0.46545586","0.4650306","0.46484694","0.4644127","0.4639045","0.46306437","0.46268806","0.46268806","0.46260953","0.46222827","0.46199644","0.45996213","0.45961165","0.45961094","0.45942932","0.45799458","0.4572772","0.45722345","0.45652366","0.4562646","0.45626023","0.45619878","0.45617023","0.45563796","0.45526198","0.45525584","0.45499417","0.45404834","0.45399463","0.45381427","0.45319203","0.45202032","0.4517881","0.4517409","0.4516199","0.45036116","0.45030722","0.45017272","0.45002952","0.44978392","0.4489057","0.44884947","0.44879633","0.44866633","0.44779715","0.4476294","0.44725198","0.44718012","0.44702747","0.44699398","0.4469865","0.44688818","0.44631436","0.4457879","0.44568342","0.44527012","0.44519973","0.44500622","0.44467947","0.44461894","0.44269553","0.44257405","0.44244865","0.44218463","0.442027","0.4413592","0.441197"],"string":"[\n \"0.5553334\",\n \"0.53317463\",\n \"0.5246943\",\n \"0.52441424\",\n \"0.5225567\",\n \"0.50993943\",\n \"0.50408936\",\n \"0.49993053\",\n \"0.49924356\",\n \"0.49918866\",\n \"0.49887422\",\n \"0.49359533\",\n \"0.4932513\",\n \"0.4932513\",\n \"0.49268204\",\n \"0.49176776\",\n \"0.49025792\",\n \"0.4897762\",\n \"0.48943457\",\n \"0.48824537\",\n \"0.4880259\",\n \"0.47771236\",\n \"0.4752814\",\n \"0.47321525\",\n \"0.47200143\",\n \"0.47128147\",\n \"0.47128147\",\n \"0.47090766\",\n \"0.4705168\",\n \"0.46898013\",\n \"0.46826872\",\n \"0.46799666\",\n \"0.46549115\",\n \"0.46545586\",\n \"0.4650306\",\n \"0.46484694\",\n \"0.4644127\",\n \"0.4639045\",\n \"0.46306437\",\n \"0.46268806\",\n \"0.46268806\",\n \"0.46260953\",\n \"0.46222827\",\n \"0.46199644\",\n \"0.45996213\",\n \"0.45961165\",\n \"0.45961094\",\n \"0.45942932\",\n \"0.45799458\",\n \"0.4572772\",\n \"0.45722345\",\n \"0.45652366\",\n \"0.4562646\",\n \"0.45626023\",\n \"0.45619878\",\n \"0.45617023\",\n \"0.45563796\",\n \"0.45526198\",\n \"0.45525584\",\n \"0.45499417\",\n \"0.45404834\",\n \"0.45399463\",\n \"0.45381427\",\n \"0.45319203\",\n \"0.45202032\",\n \"0.4517881\",\n \"0.4517409\",\n \"0.4516199\",\n \"0.45036116\",\n \"0.45030722\",\n \"0.45017272\",\n \"0.45002952\",\n \"0.44978392\",\n \"0.4489057\",\n \"0.44884947\",\n \"0.44879633\",\n \"0.44866633\",\n \"0.44779715\",\n \"0.4476294\",\n \"0.44725198\",\n \"0.44718012\",\n \"0.44702747\",\n \"0.44699398\",\n \"0.4469865\",\n \"0.44688818\",\n \"0.44631436\",\n \"0.4457879\",\n \"0.44568342\",\n \"0.44527012\",\n \"0.44519973\",\n \"0.44500622\",\n \"0.44467947\",\n \"0.44461894\",\n \"0.44269553\",\n \"0.44257405\",\n \"0.44244865\",\n \"0.44218463\",\n \"0.442027\",\n \"0.4413592\",\n \"0.441197\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.4522558"},"document_rank":{"kind":"string","value":"64"}}},{"rowIdx":17793,"cells":{"query":{"kind":"string","value":"This is old school python printing so no need to support"},"document":{"kind":"string","value":"def _Print(self, t):\n self.RaiseError(t, \"Print not supported\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def _printable(self):\n pass","def print_out():\n pass","def my_print(self):\n if self.__size == 0:\n print(\"\")\n return\n [print(\"\") for x in range(0, self.__position[1])]\n for i in range(0, self.__size):\n [print(\" \", end=\"\") for i in range(0, self.__position[0])]\n [print(\"#\", end=\"\") for j in range(0, self.__size)]\n print(\"\")","def _print(self, *args):\n return _ida_hexrays.vd_printer_t__print(self, *args)","def _print_custom(self):\n pass","def my_print(self):\n if self.__size > 0:\n print(\"\\n\" * self.__position[1], end=\"\")\n for i in range(self.__size):\n print(\" \" * self.__position[0], end=\"\")\n print(\"#\" * self.__size)\n else:\n print()","def my_print(self):\n if self.__size is not 0:\n for ite in range(self.__position[1]):\n print()\n for ite in range(self.__size):\n print(\" \" * self.__position[0], end=\"\")\n print(\"#\" * self.size)\n else:\n print()","def my_print(self):\n if self.__size == 0:\n print()\n else:\n print(\"\\n\" * self.__position[1], end='')\n for x in range(self.__size):\n print(\" \" * self.__position[0], end='')\n print(\"#\" * self.__size)","def my_print(self):\n if self.__size > 0:\n for k in range(self.__position[1]):\n print()\n for i in range(self.__size):\n for j in range(self.__position[0]):\n print(\" \", end='')\n print(\"#\" * self.__size)\n else:\n print()","def print(self):\r\n self.print_avec_separateur()","def print(self):\n self.print_avec_separateur(\" \")","def my_print(self):\n if self.size == 0:\n print(\"\")\n return\n for j in range(self.__position[1]):\n print(\"\")\n for i in range(self.size):\n if self.__position[0] > 0:\n print(\" \" * self.__position[0], end=\"\")\n print('#' * self.size)","def Print(self, text):\n pass","def print(self):\n # Your implementation here","def print_(self, s: str) -> None:","def _Printer(self,data):\r\n sys.stdout.write(\"\\r\\x1b[K\"+data.__str__())\r\n sys.stdout.flush()","def emu_print(text):\n print \"%s %s\" % (EMU_PRINT_PREFIX, text)","def custom_print(*objects):\n print(*objects, sep=OFS, end=ORS)","def _print(self, text):\n\t\tif self.verbose:\n\t\t\tprint text","def my_print(self):\n length = self.__size\n\n if self.__size == 0:\n print(\"\")\n\n \"\"\"Print using position of y-axis.\"\"\"\n for i in range(self.__position[1]):\n print(\"\")\n for j in range(length):\n \"\"\"Print spaces and # in x-axis.\"\"\"\n print((\" \" * self.__position[0]) + (\"#\" * length))","def my_print(self):\n if self.size is 0:\n print(\"\")\n for i in range(0, self.size):\n for j in range(0, self.size):\n print(\"#\", end=\"\")\n print(\"\")","def printpretty(self):\n print(self.string_rep())","def _print(txt):\n\n # Fore: BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE, RESET.\n # Back: BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE, RESET.\n # Style: DIM, NORMAL, BRIGHT, RESET_ALL\n print('{0}{1}'.format(Style.BRIGHT + txt, Fore.RESET + Back.RESET + Style.RESET_ALL))","def c_print(printme):\n print(f\"\\n\" + printme.center(80, \" \") + \"\\n\")","def Print(self):\n\n\t\tif self.verbose:\n\n\t\t print (\"\\033[1m[HEADER]\\033[0m\")\n\t\t print (\"code:\\t\\t%s\" % self.kod)\n\t \tprint (\"version:\\t%s\" % self.ver)\n\t\t print (\"date and time:\\t%s\" % self.probid)\n\t\t print (\"dump number:\\t%s\" % self.knod)\n\t \tprint (\"number of histories:\\t%s\" % self.nps)\n\t\t print (\"number of pseudorandom numbers used:\\t%s\" % self.rnr)\n\t\t print (\"title: %s\" % self.title)\n\n\t\t if self.ntal>1:\n\t\t\t\tprint self.ntal, 'tallies:', self.ntals\n\t \telse:\n\t\t\t\tprint self.ntal, 'tally:', self.ntals\n\n\n\t\t if self.npert != 0:\n\t\t\t\tprint(\"number of perturbations: %s\" % self.npert)","def my_print(self):\n if self.__size == 0:\n print()\n else:\n for i in range(self.__size):\n for j in range(self.__size):\n print(\"#\", end=\"\")\n print()","def _printable(self):\n toPrint = \"Rotation header. \"\n toPrint += \"step size: \" + str(self.step) + \" \"\n\n return toPrint","def print_cust(self, msg):\n print(msg, end='')","def __str__(self):\n if self.__size != 0:\n [print(\"\") for i in range(0, self.__position[1])]\n for i in range(0, self.__size):\n [print(\" \", end=\"\") for j in range(0, self.__position[0])]\n [print(\"#\", end=\"\") for k in range(0, self.__size)]\n if i != self.__size - 1:\n print(\"\")\n return (\"\")","def pfd_print (w, v) :\n \"\"\"\n s = \"\"\n for k in range(len(v)-1):\n s += str(v[k])\n s += \" \"\n s += str(v[numVert-1])\n w.write(s)\n \"\"\"\n \n \"\"\"\n s = []\n for k in xrange(len(v)):\n s.append(str(v[k]))\n w.write(' '.join(s))\n \"\"\"\n lenv = len(v)\n w.write(' '.join([str(v[k]) for k in xrange(lenv)]))","def printer(end,message):\n\n sys.stdout.write('\\r'+message+'\\t')\n sys.stdout.flush()\n if end: sys.stdout.write('\\n')","def setPrint():\n (e,d,sr,sw) = codecs.lookup('utf-8')\n unicode_to_utf8 = sw(sys.stdout)\n sys.stdout = unicode_to_utf8","def test_print(chikin):\n chikin.print()","def pr(x):\n Card.print_pretty_cards(x)","def _printable(self):\n toPrint = \"Factory Header. \"\n toPrint += \"Number of iterations: \" + str(self.num_iter) + \" \"\n return toPrint","def print_line():\n print('+ - - - - + - - - - +'),","def _printable(self):\n\n toPrint = \"Xtra Qubit: \" + str(self.qubit_id) + \" \"\n toPrint = toPrint + \"Angle Step: \" + str(self.step) + \" \"\n toPrint = toPrint + \"Remote App ID: \" + str(self.remote_app_id) + \" \"\n toPrint = toPrint + \"Remote Node: \" + str(self.remote_node) + \" \"\n toPrint = toPrint + \"Remote Port: \" + str(self.remote_port) + \" \"\n toPrint = toPrint + \"Command Length: \" + str(self.cmdLength)\n\n return toPrint","def _printable(self):\n toPrint = \"Sequence header. \"\n toPrint += \"Command length: \" + str(self.cmd_length) + \" \"\n\n return toPrint","def _print(self, *args):\n return _ida_hexrays.qstring_printer_t__print(self, *args)","def __window_print(self):\n pass","def print_post():\n print('| | |'),","def print_substep(text, style=\"\"):\n console.print(text, style=style)","def cool_print(self, text=str, newline=True, margin=21, rate=.02):\n print(\" \" * margin, end='')\n for letter in text:\n sleep(.02)\n stdout.write(letter)\n stdout.flush()\n if newline:\n print()","def to_print_out(self):\n self.error_throw('output')\n\n if self.rank_method == methods_of_ranking[3]: #'diversified_ranking'\n self.output_div('print')\n else:\n self.output('print')","def print(text):\n\n return builtin_print('{} | {}'.format(\n time.strftime('%H:%M:%S', time.gmtime()),\n text\n ))","def out(*args):\r\n print(*args)","def printLine(str1, str2, d1=None, d2=None):\n print str1,\n text = str1\n for name in P.methods:\n if d1 == 'plain':\n print str2,\n text += ' ' + str2\n if d1 == 'name':\n print str2 % (name, P.units),\n text += ' ' + str2 % (name, P.units)\n if d1 and d2:\n print str2 % (d1[name]/P.beta_report, d2[name]/P.beta_report),\n text += ' ' + str2 % (d1[name]/P.beta_report, d2[name]/P.beta_report)\n print ''\n outtext.append(text + '\\n')\n return","def print_self(self):\n #print(f\"\\nself: \\nN: {self.N} \\nQ: {self.Q} \\npi: {self.pi}\"); \n s = ''\n s += f'N: {self.N}, \\n'\n s += f'Q: {self.Q:.2f}, \\n'\n s += f'U: {self.U:2.3f}\\n'\n s += f'policy: ' + ' '.join(f\"{x:2.3f}\" for x in self.pi)\n print(s)\n self.env.render()","def print(self):\n print(self.pretty_str())","def genout(self):\n ch = self.buffer_output()\n while ch:\n print(ch, end='')\n ch = self.buffer_output()","def _print(self, *args):\n return _ida_hexrays.cnumber_t__print(self, *args)","def print(self):\r\n base = 8 * self.width\r\n print(base * \"-\")\r\n for x in range(self.height):\r\n output = \"\"\r\n for y in range(self.width):\r\n output = output + self.board[x][y] + \"|\"\r\n print(\"|\" + output)\r\n print(base * \"-\")","def __str__(self):\n raise NotImplementedError('Subclasses must define how to layout Character printing')","def pprint(self):\n pad = 4\n upper = [x/10 for x in range(GRID_SIZE)]\n lower = [x%10 for x in range(GRID_SIZE)]\n print(' ' * pad + ' '+' '.join(map(str,upper)))\n print(' ' * pad + ' '+' '.join(map(str,lower)))\n print(' ' * pad + pprint_header())\n for row in range(GRID_SIZE):\n print(' {0:2}'.format(row), end=' ')\n for col in range(GRID_SIZE):\n cell = self.get_cell_rc(row, col)\n if cell is None:\n print('.', end=' ')\n elif cell == 0:\n print(' ', end=' ')\n else: # cell == 1\n print('#', end=' ')\n print() # end line\n print(' ' * pad + pprint_header(delim='+', pad='-'))","def Print(self, f=sys.stdout):\n print>>f, \"\"\n print>>f, \"Item number:\", self.n.encode('utf8')\n print>>f, \"Auction:\", self.auction.encode('utf8')\n print>>f, \"Lot number:\", self.nlot.encode('utf8')\n print>>f, \"Lot:\", self.lot.encode('utf8')\n print>>f, \"Start price:\", self.startPrice.encode('utf8')\n print>>f, \"Organizer:\", self.organizer.encode('utf8')\n print>>f, \"Application end date:\", self.applicationEndDate.encode('utf8')\n print>>f, \"Auction date:\", self.date.encode('utf8')\n print>>f, \"State:\", self.state.encode('utf8')\n print>>f, \"Winner:\", \n if self.winner: print>>f, self.winner.encode('utf8')\n else: print>>f, \"\"","def result_display(self, arg):\n if self.rc.pprint:\n out = stringify_func(arg)\n\n if '\\n' in out:\n print\n\n print out\n else:\n print repr(arg)","def printout(string):\n print(string)","def uprint(pstr):\r\n\r\n sys.stdout.write(pstr)","def printf(str):\r\n print(str, flush=True)","def print_step(text):\n\n panel = Panel(Text(text, justify=\"left\"))\n console.print(panel)","def printer(msg):\r\n sys.stdout.write(\"\\r\" + msg)\r\n sys.stdout.flush()","def basic_print(lista):\n for item in lista:\n print(\"{} \\t\\t {}\".format(item[0], item[1]))","def print_me(self, tabs=0, tab=' '):\n pre = tab*tabs\n print(pre+'Producer:')\n print(pre+' produces:', self._produces)\n print(pre+' consumes:', self._consumes)\n print(pre+' transfer:', self._transfer)\n print(pre+' capacity:', self._capacity)","def print(self):\n print(\" a b c d e f g h \")\n print(\" ┼───┼───┼───┼───┼───┼───┼───┼───┼\")\n for row in range(8, 0, -1):\n pieces = \" │ \".join(self.state[row - 1])\n print(f\"{row} │ {pieces} │ {row}\")\n print(\" ┼───┼───┼───┼───┼───┼───┼───┼───┼\")\n print(\" a b c d e f g h \")","def print(self):\n print(\" 0 1 2 3 4 5 6 7 8 \")\n print(\" -------------------------\")\n for x in range(0, 9):\n print(f\"{x} | \", end=\"\")\n for y in range(0, 9):\n if self.field[x][y] == -1:\n print(\"* \", end=\"\")\n else:\n print(f\"{self.field[x][y]} \", end=\"\")\n if y % 3 == 2:\n print(\"| \", end=\"\")\n print(\"\")\n if x % 3 == 2:\n print(\" -------------------------\")","def eprint(*pargs, **kargs):\n print('\\u001b[31m', end='', file=sys.stderr)\n print(*pargs, file=sys.stderr, **kargs)\n print('\\u001b[0m', end='', file=sys.stderr)","def printPokemon():\n print(\" _ \")\n print(\" _ __ ___ | | _____ _ __ ___ ___ _ __ \")\n print(\" | '_ \\ / _ \\| |/ / _ \\ '_ ` _ \\ / _ \\| '_ \\ \")\n print(\" | |_) | (_) | < __/ | | | | | (_) | | | |\")\n print(\" | .__/ \\___/|_|\\_\\___|_| |_| |_|\\___/|_| |_|\")\n print(\" |_| \")","def printstring(tn,xs): #Printing function\n if (tn > -1) and (tn <= 9):\n for x in range(xs): #Outer loop for line iteration\n print(\"\\n\") #Need this new line to meet the output requirement\n for y in range(xs): #Inner loop for horizontal printing\n print(tn,end=' ') #User defined format, which is a\n #digit followed by a white space\n elif tn == -1:\n for x in range(xs): #Outer loop for line iteration\n print(\"\\n\") #Need this new line to meet the output requirement\n for y in range(xs): #Inner loop for horizontal printing\n print(y,end=' ') #User defined format, which is a\n #digit followed by a white space\n else:\n print(\"Must be single digit, please!\\n\")","def hello_printer():\n print('Hello Mustafa')\n print(\"Last Time\")","def print(self, *args, **kwargs):\n print(*args, **kwargs)","def _print(self):\n print('center :', self.center, ' widht : ', self.width, ' height : ', self.height, ' heat : ', self.heat,\n ' speed ', self.speed)","def _printstr(self, args):\n s = \"\\n\"\n\n for arg in args:\n #s += arg.encode('utf-8', 'pout.replace')\n s += arg\n\n return s","def pprint(*args, **kwargs):\n if PRINTING:\n print(*args, **kwargs)","def printOutput(self):\n pass","def output(*args):\n print(*args, end='', file=file)","def sgr_print(*args, reset=True, end='\\n', flush=False):\n print(sgr_string(*args, reset=reset), sep='', end=end, flush=flush)","def my_pprint(obj, intend = 0):\n if isinstance(obj, dict):\n for key, value in obj.items():\n print(intend*\" \"+str(key)+\" : \")\n my_pprint(value, intend = intend + 4)\n print()\n elif isinstance(obj, list):\n for value in obj:\n my_pprint(value, intend = intend + 4)\n print()\n elif isinstance(obj, bytes):\n print(\"<binary data>\")\n \n else:\n try:\n print(intend*\" \"+str(obj))\n except UnicodeDecodeError:\n print(intend*\" \"\"<?>\")","def print(self):\n board_string = ''\n for y in range(self.size):\n if y == 0:\n board_string += '+ '\n for x in range(self.size):\n board_string += str(x+1) + ' '\n board_string += '\\n'\n board_string += (1+3*self.size)*'-'\n board_string += '\\n'\n board_string += str(y+1)+'|'+y*' '\n \n for x in range(self.size):\n board_string += ' '\n if self.board[y,x] == HexBoard.BLUE:\n board_string += self.char_player1\n elif self.board[y,x] == HexBoard.RED:\n board_string += self.char_player2\n else: \n board_string += self.char_empty\n board_string += '\\n'\n board_string = board_string.strip()\n\n print(board_string)","def display(self):\n disptxt = str(self)\n if self.width == 0 or self.has_output:\n print(disptxt)\n else:\n print(\"\\r\", end='')\n print(disptxt, end='')\n sys.stdout.flush()","def print(self) -> str:\n if self.is_unoccupied():\n return \"\"\n return str(\"%s-%s\" % (self.piece.color.name, self.piece.name.name))","def pprint(self):\n # just here for defining the interface; work is done in subclasses\n pass","def _printable(self):\n toPrint = \"Command Header. Qubit ID: \" + str(self.qubit_id) + \" \"\n toPrint = toPrint + \"Instruction: \" + str(self.instr) + \" \"\n toPrint = toPrint + \"Notify: \" + str(self.notify) + \" \"\n toPrint = toPrint + \"Block: \" + str(self.block) + \" \"\n toPrint = toPrint + \"Action: \" + str(self.action)\n return toPrint","def printing():\r\n document.add_heading('Printing Service details', 1)\r\n\r\n printing_metrics = ['customproperties',\r\n 'workingSetSizeHiPct',\r\n 'logVerbosityAuditActivity',\r\n 'logVerbosityService',\r\n 'hostname',\r\n 'tags']\r\n\r\n printnodes = get_qlik_sense.get_printing()\r\n num_of_nodes = len(printnodes)\r\n num_of_print_metrics = len(printing_metrics)\r\n table = document.add_table(rows=num_of_print_metrics+1, cols=num_of_nodes+1)\r\n table.style = 'Grid Table 1 Light Accent 1'\r\n row = table.rows[0]\r\n row.cells[0].text = 'Metric'\r\n for item in range(0, num_of_nodes):\r\n row.cells[item+1].text = printnodes[item][6]\r\n for item in range(num_of_print_metrics):\r\n row = table.rows[item+1]\r\n row.cells[0].text = str(printing_metrics[item])\r\n for printnode in range(num_of_nodes):\r\n row.cells[printnode+1].text = str(printnodes[printnode][item])\r\n\r\n document.add_page_break()","def _print_board(board):\r\n pass","def print_next(msg):\n print\n print \"-\" * 80\n print \"\\n\".join(textwrap.wrap(msg, width=80))\n print \"=\" * 80","def printme(self):\n sys.stdout.write(self._header)\n for k in range(len(self)):\n sys.stdout.write(self.line(k))","def _delete_print(number=1):\n\tif slogviz.config.interactive and not platform.system() == 'Windows':#Windows does not fully implement ANSI Control Characters, see README\n\t\tprint('\\x1b[2K\\x1b[1A'*number)","def bpprint(self, out=None):\n if out is None:\n out = sys.stdout\n print(self.bpformat(), file=out)","def printBoard(self):\n print(\"\"\"\nSpace 1 Space 2 Space 3 Space 4 Space 5 Space 6\n------- ------- ------- ------- ------- -------\"\"\")\n print(\"{:>4}{:>10}{:>10}{:>10}{:>10}{:>10}\".format(str(self.space1), str(self.space2), str(self.space3), str(self.space4), str(self.space5), str(self.space6)))\n print()","def _printable(self):\n toPrint = \"Measurement Outcome header. \"\n toPrint += \"measurement outcome: \" + str(self.outcome) + \" \"\n\n return toPrint","def print( self, str, pos=None ):\n\t\tif pos:\n\t\t\tself.set_cursor( pos )\n\t\tself.write( str.encode(\"ASCII\") )","def print(self, assignment):\n letters = self.letter_grid(assignment)\n for i in range(self.crossword.height):\n for j in range(self.crossword.width):\n if self.crossword.structure[i][j]:\n print(letters[i][j] or \" \", end=\"\")\n else:\n print(\"█\", end=\"\")\n print()","def print(self, assignment):\n letters = self.letter_grid(assignment)\n for i in range(self.crossword.height):\n for j in range(self.crossword.width):\n if self.crossword.structure[i][j]:\n print(letters[i][j] or \" \", end=\"\")\n else:\n print(\"█\", end=\"\")\n print()","def print(self, assignment):\n letters = self.letter_grid(assignment)\n for i in range(self.crossword.height):\n for j in range(self.crossword.width):\n if self.crossword.structure[i][j]:\n print(letters[i][j] or \" \", end=\"\")\n else:\n print(\"█\", end=\"\")\n print()","def print(self):\n str_items = [(str(v),str(p)) for v,p in sorted(self.items())]\n max_lens = [\n max(i[0] for i in str_items),\n max(i[1] for i in str_items)\n ]\n lena, lenb = max_lens\n print(\"\\n\".join(\n f\"{v:{lena}s} -> {p:{lenb}s}\"\n for v, p in str_items\n ))","def print_and_return(*args, **kwargs):\n print(*args, end=\"\\r\", **kwargs)","def print(self,text,position=None,size=None,color=mycolors.WHITE,font=None,conversion=True):\n if conversion:\n if not position: position=(0,0)\n if not size: size=1\n position=self.draw.plane.getToScreen(position,self.draw.window)\n ux,uy=self.draw.plane.units\n size=int(size*ux/50)\n else:\n if not position: position=(10,10)\n if not size: size=20\n self.window.print(text,position,size,color,font)","def pprint(self, data):\n self._assert(data)\n data = self._render(data) # make elements ascii\n fmats = self._fmats(data) # get array of padding formats)\n for row in data:\n print(fmats.format(*row))","def slab_print(slab):\n for ir, r in enumerate(slab):\n print('{:2d}¦'.format(ir), end='')\n for val in r:\n if val < 0.0: print('-', end='')\n elif val < .15: print(' ', end=''),\n elif val < .35: print('░', end=''),\n elif val < .65: print('▒', end=''),\n elif val < .85: print('▓', end=''),\n elif val <= 1.: print('█', end=''),\n else: print('+', end='')\n print('¦')","def _printable(self):\n toPrint = \"Qubit ID: \" + str(self.qubit_id) + \" \"\n toPrint = toPrint + \"Outcome: \" + str(self.outcome) + \" \"\n toPrint = toPrint + \"Remote App ID: \" + str(self.remote_app_id) + \" \"\n toPrint = toPrint + \"Remote Node: \" + str(self.remote_node) + \" \"\n toPrint = toPrint + \"Remote Port: \" + str(self.remote_port) + \" \"\n toPrint = toPrint + \"Datetime: \" + str(self.datetime)\n return toPrint"],"string":"[\n \"def _printable(self):\\n pass\",\n \"def print_out():\\n pass\",\n \"def my_print(self):\\n if self.__size == 0:\\n print(\\\"\\\")\\n return\\n [print(\\\"\\\") for x in range(0, self.__position[1])]\\n for i in range(0, self.__size):\\n [print(\\\" \\\", end=\\\"\\\") for i in range(0, self.__position[0])]\\n [print(\\\"#\\\", end=\\\"\\\") for j in range(0, self.__size)]\\n print(\\\"\\\")\",\n \"def _print(self, *args):\\n return _ida_hexrays.vd_printer_t__print(self, *args)\",\n \"def _print_custom(self):\\n pass\",\n \"def my_print(self):\\n if self.__size > 0:\\n print(\\\"\\\\n\\\" * self.__position[1], end=\\\"\\\")\\n for i in range(self.__size):\\n print(\\\" \\\" * self.__position[0], end=\\\"\\\")\\n print(\\\"#\\\" * self.__size)\\n else:\\n print()\",\n \"def my_print(self):\\n if self.__size is not 0:\\n for ite in range(self.__position[1]):\\n print()\\n for ite in range(self.__size):\\n print(\\\" \\\" * self.__position[0], end=\\\"\\\")\\n print(\\\"#\\\" * self.size)\\n else:\\n print()\",\n \"def my_print(self):\\n if self.__size == 0:\\n print()\\n else:\\n print(\\\"\\\\n\\\" * self.__position[1], end='')\\n for x in range(self.__size):\\n print(\\\" \\\" * self.__position[0], end='')\\n print(\\\"#\\\" * self.__size)\",\n \"def my_print(self):\\n if self.__size > 0:\\n for k in range(self.__position[1]):\\n print()\\n for i in range(self.__size):\\n for j in range(self.__position[0]):\\n print(\\\" \\\", end='')\\n print(\\\"#\\\" * self.__size)\\n else:\\n print()\",\n \"def print(self):\\r\\n self.print_avec_separateur()\",\n \"def print(self):\\n self.print_avec_separateur(\\\" \\\")\",\n \"def my_print(self):\\n if self.size == 0:\\n print(\\\"\\\")\\n return\\n for j in range(self.__position[1]):\\n print(\\\"\\\")\\n for i in range(self.size):\\n if self.__position[0] > 0:\\n print(\\\" \\\" * self.__position[0], end=\\\"\\\")\\n print('#' * self.size)\",\n \"def Print(self, text):\\n pass\",\n \"def print(self):\\n # Your implementation here\",\n \"def print_(self, s: str) -> None:\",\n \"def _Printer(self,data):\\r\\n sys.stdout.write(\\\"\\\\r\\\\x1b[K\\\"+data.__str__())\\r\\n sys.stdout.flush()\",\n \"def emu_print(text):\\n print \\\"%s %s\\\" % (EMU_PRINT_PREFIX, text)\",\n \"def custom_print(*objects):\\n print(*objects, sep=OFS, end=ORS)\",\n \"def _print(self, text):\\n\\t\\tif self.verbose:\\n\\t\\t\\tprint text\",\n \"def my_print(self):\\n length = self.__size\\n\\n if self.__size == 0:\\n print(\\\"\\\")\\n\\n \\\"\\\"\\\"Print using position of y-axis.\\\"\\\"\\\"\\n for i in range(self.__position[1]):\\n print(\\\"\\\")\\n for j in range(length):\\n \\\"\\\"\\\"Print spaces and # in x-axis.\\\"\\\"\\\"\\n print((\\\" \\\" * self.__position[0]) + (\\\"#\\\" * length))\",\n \"def my_print(self):\\n if self.size is 0:\\n print(\\\"\\\")\\n for i in range(0, self.size):\\n for j in range(0, self.size):\\n print(\\\"#\\\", end=\\\"\\\")\\n print(\\\"\\\")\",\n \"def printpretty(self):\\n print(self.string_rep())\",\n \"def _print(txt):\\n\\n # Fore: BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE, RESET.\\n # Back: BLACK, RED, GREEN, YELLOW, BLUE, MAGENTA, CYAN, WHITE, RESET.\\n # Style: DIM, NORMAL, BRIGHT, RESET_ALL\\n print('{0}{1}'.format(Style.BRIGHT + txt, Fore.RESET + Back.RESET + Style.RESET_ALL))\",\n \"def c_print(printme):\\n print(f\\\"\\\\n\\\" + printme.center(80, \\\" \\\") + \\\"\\\\n\\\")\",\n \"def Print(self):\\n\\n\\t\\tif self.verbose:\\n\\n\\t\\t print (\\\"\\\\033[1m[HEADER]\\\\033[0m\\\")\\n\\t\\t print (\\\"code:\\\\t\\\\t%s\\\" % self.kod)\\n\\t \\tprint (\\\"version:\\\\t%s\\\" % self.ver)\\n\\t\\t print (\\\"date and time:\\\\t%s\\\" % self.probid)\\n\\t\\t print (\\\"dump number:\\\\t%s\\\" % self.knod)\\n\\t \\tprint (\\\"number of histories:\\\\t%s\\\" % self.nps)\\n\\t\\t print (\\\"number of pseudorandom numbers used:\\\\t%s\\\" % self.rnr)\\n\\t\\t print (\\\"title: %s\\\" % self.title)\\n\\n\\t\\t if self.ntal>1:\\n\\t\\t\\t\\tprint self.ntal, 'tallies:', self.ntals\\n\\t \\telse:\\n\\t\\t\\t\\tprint self.ntal, 'tally:', self.ntals\\n\\n\\n\\t\\t if self.npert != 0:\\n\\t\\t\\t\\tprint(\\\"number of perturbations: %s\\\" % self.npert)\",\n \"def my_print(self):\\n if self.__size == 0:\\n print()\\n else:\\n for i in range(self.__size):\\n for j in range(self.__size):\\n print(\\\"#\\\", end=\\\"\\\")\\n print()\",\n \"def _printable(self):\\n toPrint = \\\"Rotation header. \\\"\\n toPrint += \\\"step size: \\\" + str(self.step) + \\\" \\\"\\n\\n return toPrint\",\n \"def print_cust(self, msg):\\n print(msg, end='')\",\n \"def __str__(self):\\n if self.__size != 0:\\n [print(\\\"\\\") for i in range(0, self.__position[1])]\\n for i in range(0, self.__size):\\n [print(\\\" \\\", end=\\\"\\\") for j in range(0, self.__position[0])]\\n [print(\\\"#\\\", end=\\\"\\\") for k in range(0, self.__size)]\\n if i != self.__size - 1:\\n print(\\\"\\\")\\n return (\\\"\\\")\",\n \"def pfd_print (w, v) :\\n \\\"\\\"\\\"\\n s = \\\"\\\"\\n for k in range(len(v)-1):\\n s += str(v[k])\\n s += \\\" \\\"\\n s += str(v[numVert-1])\\n w.write(s)\\n \\\"\\\"\\\"\\n \\n \\\"\\\"\\\"\\n s = []\\n for k in xrange(len(v)):\\n s.append(str(v[k]))\\n w.write(' '.join(s))\\n \\\"\\\"\\\"\\n lenv = len(v)\\n w.write(' '.join([str(v[k]) for k in xrange(lenv)]))\",\n \"def printer(end,message):\\n\\n sys.stdout.write('\\\\r'+message+'\\\\t')\\n sys.stdout.flush()\\n if end: sys.stdout.write('\\\\n')\",\n \"def setPrint():\\n (e,d,sr,sw) = codecs.lookup('utf-8')\\n unicode_to_utf8 = sw(sys.stdout)\\n sys.stdout = unicode_to_utf8\",\n \"def test_print(chikin):\\n chikin.print()\",\n \"def pr(x):\\n Card.print_pretty_cards(x)\",\n \"def _printable(self):\\n toPrint = \\\"Factory Header. \\\"\\n toPrint += \\\"Number of iterations: \\\" + str(self.num_iter) + \\\" \\\"\\n return toPrint\",\n \"def print_line():\\n print('+ - - - - + - - - - +'),\",\n \"def _printable(self):\\n\\n toPrint = \\\"Xtra Qubit: \\\" + str(self.qubit_id) + \\\" \\\"\\n toPrint = toPrint + \\\"Angle Step: \\\" + str(self.step) + \\\" \\\"\\n toPrint = toPrint + \\\"Remote App ID: \\\" + str(self.remote_app_id) + \\\" \\\"\\n toPrint = toPrint + \\\"Remote Node: \\\" + str(self.remote_node) + \\\" \\\"\\n toPrint = toPrint + \\\"Remote Port: \\\" + str(self.remote_port) + \\\" \\\"\\n toPrint = toPrint + \\\"Command Length: \\\" + str(self.cmdLength)\\n\\n return toPrint\",\n \"def _printable(self):\\n toPrint = \\\"Sequence header. \\\"\\n toPrint += \\\"Command length: \\\" + str(self.cmd_length) + \\\" \\\"\\n\\n return toPrint\",\n \"def _print(self, *args):\\n return _ida_hexrays.qstring_printer_t__print(self, *args)\",\n \"def __window_print(self):\\n pass\",\n \"def print_post():\\n print('| | |'),\",\n \"def print_substep(text, style=\\\"\\\"):\\n console.print(text, style=style)\",\n \"def cool_print(self, text=str, newline=True, margin=21, rate=.02):\\n print(\\\" \\\" * margin, end='')\\n for letter in text:\\n sleep(.02)\\n stdout.write(letter)\\n stdout.flush()\\n if newline:\\n print()\",\n \"def to_print_out(self):\\n self.error_throw('output')\\n\\n if self.rank_method == methods_of_ranking[3]: #'diversified_ranking'\\n self.output_div('print')\\n else:\\n self.output('print')\",\n \"def print(text):\\n\\n return builtin_print('{} | {}'.format(\\n time.strftime('%H:%M:%S', time.gmtime()),\\n text\\n ))\",\n \"def out(*args):\\r\\n print(*args)\",\n \"def printLine(str1, str2, d1=None, d2=None):\\n print str1,\\n text = str1\\n for name in P.methods:\\n if d1 == 'plain':\\n print str2,\\n text += ' ' + str2\\n if d1 == 'name':\\n print str2 % (name, P.units),\\n text += ' ' + str2 % (name, P.units)\\n if d1 and d2:\\n print str2 % (d1[name]/P.beta_report, d2[name]/P.beta_report),\\n text += ' ' + str2 % (d1[name]/P.beta_report, d2[name]/P.beta_report)\\n print ''\\n outtext.append(text + '\\\\n')\\n return\",\n \"def print_self(self):\\n #print(f\\\"\\\\nself: \\\\nN: {self.N} \\\\nQ: {self.Q} \\\\npi: {self.pi}\\\"); \\n s = ''\\n s += f'N: {self.N}, \\\\n'\\n s += f'Q: {self.Q:.2f}, \\\\n'\\n s += f'U: {self.U:2.3f}\\\\n'\\n s += f'policy: ' + ' '.join(f\\\"{x:2.3f}\\\" for x in self.pi)\\n print(s)\\n self.env.render()\",\n \"def print(self):\\n print(self.pretty_str())\",\n \"def genout(self):\\n ch = self.buffer_output()\\n while ch:\\n print(ch, end='')\\n ch = self.buffer_output()\",\n \"def _print(self, *args):\\n return _ida_hexrays.cnumber_t__print(self, *args)\",\n \"def print(self):\\r\\n base = 8 * self.width\\r\\n print(base * \\\"-\\\")\\r\\n for x in range(self.height):\\r\\n output = \\\"\\\"\\r\\n for y in range(self.width):\\r\\n output = output + self.board[x][y] + \\\"|\\\"\\r\\n print(\\\"|\\\" + output)\\r\\n print(base * \\\"-\\\")\",\n \"def __str__(self):\\n raise NotImplementedError('Subclasses must define how to layout Character printing')\",\n \"def pprint(self):\\n pad = 4\\n upper = [x/10 for x in range(GRID_SIZE)]\\n lower = [x%10 for x in range(GRID_SIZE)]\\n print(' ' * pad + ' '+' '.join(map(str,upper)))\\n print(' ' * pad + ' '+' '.join(map(str,lower)))\\n print(' ' * pad + pprint_header())\\n for row in range(GRID_SIZE):\\n print(' {0:2}'.format(row), end=' ')\\n for col in range(GRID_SIZE):\\n cell = self.get_cell_rc(row, col)\\n if cell is None:\\n print('.', end=' ')\\n elif cell == 0:\\n print(' ', end=' ')\\n else: # cell == 1\\n print('#', end=' ')\\n print() # end line\\n print(' ' * pad + pprint_header(delim='+', pad='-'))\",\n \"def Print(self, f=sys.stdout):\\n print>>f, \\\"\\\"\\n print>>f, \\\"Item number:\\\", self.n.encode('utf8')\\n print>>f, \\\"Auction:\\\", self.auction.encode('utf8')\\n print>>f, \\\"Lot number:\\\", self.nlot.encode('utf8')\\n print>>f, \\\"Lot:\\\", self.lot.encode('utf8')\\n print>>f, \\\"Start price:\\\", self.startPrice.encode('utf8')\\n print>>f, \\\"Organizer:\\\", self.organizer.encode('utf8')\\n print>>f, \\\"Application end date:\\\", self.applicationEndDate.encode('utf8')\\n print>>f, \\\"Auction date:\\\", self.date.encode('utf8')\\n print>>f, \\\"State:\\\", self.state.encode('utf8')\\n print>>f, \\\"Winner:\\\", \\n if self.winner: print>>f, self.winner.encode('utf8')\\n else: print>>f, \\\"\\\"\",\n \"def result_display(self, arg):\\n if self.rc.pprint:\\n out = stringify_func(arg)\\n\\n if '\\\\n' in out:\\n print\\n\\n print out\\n else:\\n print repr(arg)\",\n \"def printout(string):\\n print(string)\",\n \"def uprint(pstr):\\r\\n\\r\\n sys.stdout.write(pstr)\",\n \"def printf(str):\\r\\n print(str, flush=True)\",\n \"def print_step(text):\\n\\n panel = Panel(Text(text, justify=\\\"left\\\"))\\n console.print(panel)\",\n \"def printer(msg):\\r\\n sys.stdout.write(\\\"\\\\r\\\" + msg)\\r\\n sys.stdout.flush()\",\n \"def basic_print(lista):\\n for item in lista:\\n print(\\\"{} \\\\t\\\\t {}\\\".format(item[0], item[1]))\",\n \"def print_me(self, tabs=0, tab=' '):\\n pre = tab*tabs\\n print(pre+'Producer:')\\n print(pre+' produces:', self._produces)\\n print(pre+' consumes:', self._consumes)\\n print(pre+' transfer:', self._transfer)\\n print(pre+' capacity:', self._capacity)\",\n \"def print(self):\\n print(\\\" a b c d e f g h \\\")\\n print(\\\" ┼───┼───┼───┼───┼───┼───┼───┼───┼\\\")\\n for row in range(8, 0, -1):\\n pieces = \\\" │ \\\".join(self.state[row - 1])\\n print(f\\\"{row} │ {pieces} │ {row}\\\")\\n print(\\\" ┼───┼───┼───┼───┼───┼───┼───┼───┼\\\")\\n print(\\\" a b c d e f g h \\\")\",\n \"def print(self):\\n print(\\\" 0 1 2 3 4 5 6 7 8 \\\")\\n print(\\\" -------------------------\\\")\\n for x in range(0, 9):\\n print(f\\\"{x} | \\\", end=\\\"\\\")\\n for y in range(0, 9):\\n if self.field[x][y] == -1:\\n print(\\\"* \\\", end=\\\"\\\")\\n else:\\n print(f\\\"{self.field[x][y]} \\\", end=\\\"\\\")\\n if y % 3 == 2:\\n print(\\\"| \\\", end=\\\"\\\")\\n print(\\\"\\\")\\n if x % 3 == 2:\\n print(\\\" -------------------------\\\")\",\n \"def eprint(*pargs, **kargs):\\n print('\\\\u001b[31m', end='', file=sys.stderr)\\n print(*pargs, file=sys.stderr, **kargs)\\n print('\\\\u001b[0m', end='', file=sys.stderr)\",\n \"def printPokemon():\\n print(\\\" _ \\\")\\n print(\\\" _ __ ___ | | _____ _ __ ___ ___ _ __ \\\")\\n print(\\\" | '_ \\\\ / _ \\\\| |/ / _ \\\\ '_ ` _ \\\\ / _ \\\\| '_ \\\\ \\\")\\n print(\\\" | |_) | (_) | < __/ | | | | | (_) | | | |\\\")\\n print(\\\" | .__/ \\\\___/|_|\\\\_\\\\___|_| |_| |_|\\\\___/|_| |_|\\\")\\n print(\\\" |_| \\\")\",\n \"def printstring(tn,xs): #Printing function\\n if (tn > -1) and (tn <= 9):\\n for x in range(xs): #Outer loop for line iteration\\n print(\\\"\\\\n\\\") #Need this new line to meet the output requirement\\n for y in range(xs): #Inner loop for horizontal printing\\n print(tn,end=' ') #User defined format, which is a\\n #digit followed by a white space\\n elif tn == -1:\\n for x in range(xs): #Outer loop for line iteration\\n print(\\\"\\\\n\\\") #Need this new line to meet the output requirement\\n for y in range(xs): #Inner loop for horizontal printing\\n print(y,end=' ') #User defined format, which is a\\n #digit followed by a white space\\n else:\\n print(\\\"Must be single digit, please!\\\\n\\\")\",\n \"def hello_printer():\\n print('Hello Mustafa')\\n print(\\\"Last Time\\\")\",\n \"def print(self, *args, **kwargs):\\n print(*args, **kwargs)\",\n \"def _print(self):\\n print('center :', self.center, ' widht : ', self.width, ' height : ', self.height, ' heat : ', self.heat,\\n ' speed ', self.speed)\",\n \"def _printstr(self, args):\\n s = \\\"\\\\n\\\"\\n\\n for arg in args:\\n #s += arg.encode('utf-8', 'pout.replace')\\n s += arg\\n\\n return s\",\n \"def pprint(*args, **kwargs):\\n if PRINTING:\\n print(*args, **kwargs)\",\n \"def printOutput(self):\\n pass\",\n \"def output(*args):\\n print(*args, end='', file=file)\",\n \"def sgr_print(*args, reset=True, end='\\\\n', flush=False):\\n print(sgr_string(*args, reset=reset), sep='', end=end, flush=flush)\",\n \"def my_pprint(obj, intend = 0):\\n if isinstance(obj, dict):\\n for key, value in obj.items():\\n print(intend*\\\" \\\"+str(key)+\\\" : \\\")\\n my_pprint(value, intend = intend + 4)\\n print()\\n elif isinstance(obj, list):\\n for value in obj:\\n my_pprint(value, intend = intend + 4)\\n print()\\n elif isinstance(obj, bytes):\\n print(\\\"<binary data>\\\")\\n \\n else:\\n try:\\n print(intend*\\\" \\\"+str(obj))\\n except UnicodeDecodeError:\\n print(intend*\\\" \\\"\\\"<?>\\\")\",\n \"def print(self):\\n board_string = ''\\n for y in range(self.size):\\n if y == 0:\\n board_string += '+ '\\n for x in range(self.size):\\n board_string += str(x+1) + ' '\\n board_string += '\\\\n'\\n board_string += (1+3*self.size)*'-'\\n board_string += '\\\\n'\\n board_string += str(y+1)+'|'+y*' '\\n \\n for x in range(self.size):\\n board_string += ' '\\n if self.board[y,x] == HexBoard.BLUE:\\n board_string += self.char_player1\\n elif self.board[y,x] == HexBoard.RED:\\n board_string += self.char_player2\\n else: \\n board_string += self.char_empty\\n board_string += '\\\\n'\\n board_string = board_string.strip()\\n\\n print(board_string)\",\n \"def display(self):\\n disptxt = str(self)\\n if self.width == 0 or self.has_output:\\n print(disptxt)\\n else:\\n print(\\\"\\\\r\\\", end='')\\n print(disptxt, end='')\\n sys.stdout.flush()\",\n \"def print(self) -> str:\\n if self.is_unoccupied():\\n return \\\"\\\"\\n return str(\\\"%s-%s\\\" % (self.piece.color.name, self.piece.name.name))\",\n \"def pprint(self):\\n # just here for defining the interface; work is done in subclasses\\n pass\",\n \"def _printable(self):\\n toPrint = \\\"Command Header. Qubit ID: \\\" + str(self.qubit_id) + \\\" \\\"\\n toPrint = toPrint + \\\"Instruction: \\\" + str(self.instr) + \\\" \\\"\\n toPrint = toPrint + \\\"Notify: \\\" + str(self.notify) + \\\" \\\"\\n toPrint = toPrint + \\\"Block: \\\" + str(self.block) + \\\" \\\"\\n toPrint = toPrint + \\\"Action: \\\" + str(self.action)\\n return toPrint\",\n \"def printing():\\r\\n document.add_heading('Printing Service details', 1)\\r\\n\\r\\n printing_metrics = ['customproperties',\\r\\n 'workingSetSizeHiPct',\\r\\n 'logVerbosityAuditActivity',\\r\\n 'logVerbosityService',\\r\\n 'hostname',\\r\\n 'tags']\\r\\n\\r\\n printnodes = get_qlik_sense.get_printing()\\r\\n num_of_nodes = len(printnodes)\\r\\n num_of_print_metrics = len(printing_metrics)\\r\\n table = document.add_table(rows=num_of_print_metrics+1, cols=num_of_nodes+1)\\r\\n table.style = 'Grid Table 1 Light Accent 1'\\r\\n row = table.rows[0]\\r\\n row.cells[0].text = 'Metric'\\r\\n for item in range(0, num_of_nodes):\\r\\n row.cells[item+1].text = printnodes[item][6]\\r\\n for item in range(num_of_print_metrics):\\r\\n row = table.rows[item+1]\\r\\n row.cells[0].text = str(printing_metrics[item])\\r\\n for printnode in range(num_of_nodes):\\r\\n row.cells[printnode+1].text = str(printnodes[printnode][item])\\r\\n\\r\\n document.add_page_break()\",\n \"def _print_board(board):\\r\\n pass\",\n \"def print_next(msg):\\n print\\n print \\\"-\\\" * 80\\n print \\\"\\\\n\\\".join(textwrap.wrap(msg, width=80))\\n print \\\"=\\\" * 80\",\n \"def printme(self):\\n sys.stdout.write(self._header)\\n for k in range(len(self)):\\n sys.stdout.write(self.line(k))\",\n \"def _delete_print(number=1):\\n\\tif slogviz.config.interactive and not platform.system() == 'Windows':#Windows does not fully implement ANSI Control Characters, see README\\n\\t\\tprint('\\\\x1b[2K\\\\x1b[1A'*number)\",\n \"def bpprint(self, out=None):\\n if out is None:\\n out = sys.stdout\\n print(self.bpformat(), file=out)\",\n \"def printBoard(self):\\n print(\\\"\\\"\\\"\\nSpace 1 Space 2 Space 3 Space 4 Space 5 Space 6\\n------- ------- ------- ------- ------- -------\\\"\\\"\\\")\\n print(\\\"{:>4}{:>10}{:>10}{:>10}{:>10}{:>10}\\\".format(str(self.space1), str(self.space2), str(self.space3), str(self.space4), str(self.space5), str(self.space6)))\\n print()\",\n \"def _printable(self):\\n toPrint = \\\"Measurement Outcome header. \\\"\\n toPrint += \\\"measurement outcome: \\\" + str(self.outcome) + \\\" \\\"\\n\\n return toPrint\",\n \"def print( self, str, pos=None ):\\n\\t\\tif pos:\\n\\t\\t\\tself.set_cursor( pos )\\n\\t\\tself.write( str.encode(\\\"ASCII\\\") )\",\n \"def print(self, assignment):\\n letters = self.letter_grid(assignment)\\n for i in range(self.crossword.height):\\n for j in range(self.crossword.width):\\n if self.crossword.structure[i][j]:\\n print(letters[i][j] or \\\" \\\", end=\\\"\\\")\\n else:\\n print(\\\"█\\\", end=\\\"\\\")\\n print()\",\n \"def print(self, assignment):\\n letters = self.letter_grid(assignment)\\n for i in range(self.crossword.height):\\n for j in range(self.crossword.width):\\n if self.crossword.structure[i][j]:\\n print(letters[i][j] or \\\" \\\", end=\\\"\\\")\\n else:\\n print(\\\"█\\\", end=\\\"\\\")\\n print()\",\n \"def print(self, assignment):\\n letters = self.letter_grid(assignment)\\n for i in range(self.crossword.height):\\n for j in range(self.crossword.width):\\n if self.crossword.structure[i][j]:\\n print(letters[i][j] or \\\" \\\", end=\\\"\\\")\\n else:\\n print(\\\"█\\\", end=\\\"\\\")\\n print()\",\n \"def print(self):\\n str_items = [(str(v),str(p)) for v,p in sorted(self.items())]\\n max_lens = [\\n max(i[0] for i in str_items),\\n max(i[1] for i in str_items)\\n ]\\n lena, lenb = max_lens\\n print(\\\"\\\\n\\\".join(\\n f\\\"{v:{lena}s} -> {p:{lenb}s}\\\"\\n for v, p in str_items\\n ))\",\n \"def print_and_return(*args, **kwargs):\\n print(*args, end=\\\"\\\\r\\\", **kwargs)\",\n \"def print(self,text,position=None,size=None,color=mycolors.WHITE,font=None,conversion=True):\\n if conversion:\\n if not position: position=(0,0)\\n if not size: size=1\\n position=self.draw.plane.getToScreen(position,self.draw.window)\\n ux,uy=self.draw.plane.units\\n size=int(size*ux/50)\\n else:\\n if not position: position=(10,10)\\n if not size: size=20\\n self.window.print(text,position,size,color,font)\",\n \"def pprint(self, data):\\n self._assert(data)\\n data = self._render(data) # make elements ascii\\n fmats = self._fmats(data) # get array of padding formats)\\n for row in data:\\n print(fmats.format(*row))\",\n \"def slab_print(slab):\\n for ir, r in enumerate(slab):\\n print('{:2d}¦'.format(ir), end='')\\n for val in r:\\n if val < 0.0: print('-', end='')\\n elif val < .15: print(' ', end=''),\\n elif val < .35: print('░', end=''),\\n elif val < .65: print('▒', end=''),\\n elif val < .85: print('▓', end=''),\\n elif val <= 1.: print('█', end=''),\\n else: print('+', end='')\\n print('¦')\",\n \"def _printable(self):\\n toPrint = \\\"Qubit ID: \\\" + str(self.qubit_id) + \\\" \\\"\\n toPrint = toPrint + \\\"Outcome: \\\" + str(self.outcome) + \\\" \\\"\\n toPrint = toPrint + \\\"Remote App ID: \\\" + str(self.remote_app_id) + \\\" \\\"\\n toPrint = toPrint + \\\"Remote Node: \\\" + str(self.remote_node) + \\\" \\\"\\n toPrint = toPrint + \\\"Remote Port: \\\" + str(self.remote_port) + \\\" \\\"\\n toPrint = toPrint + \\\"Datetime: \\\" + str(self.datetime)\\n return toPrint\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.73725426","0.72929084","0.7111852","0.70446706","0.7013182","0.70031095","0.69107485","0.69100827","0.6871159","0.68606776","0.6803045","0.680181","0.6794203","0.67602473","0.6700353","0.6650724","0.66261536","0.66122717","0.6503248","0.64987963","0.64917636","0.64740086","0.6463327","0.646029","0.64514905","0.64166504","0.64102393","0.63837695","0.6381145","0.6368855","0.6365407","0.63629305","0.6354893","0.6352523","0.6348721","0.632661","0.63231283","0.631663","0.63069713","0.6299273","0.62855446","0.627558","0.62752664","0.62690705","0.6258997","0.62509173","0.624516","0.6242174","0.6235059","0.62347895","0.6232399","0.6228188","0.62189245","0.62177855","0.6217464","0.6211105","0.6207303","0.6198522","0.61750776","0.6162345","0.615796","0.6156647","0.6148171","0.6146866","0.6143288","0.61416733","0.61409944","0.61372536","0.61359125","0.61348855","0.61308074","0.61266035","0.61257666","0.6114559","0.6110854","0.6107848","0.61055374","0.610432","0.61014307","0.60976446","0.6086173","0.6083397","0.6082134","0.6077446","0.60754067","0.6067278","0.6065451","0.6058611","0.6048319","0.6045956","0.60286266","0.60265356","0.60265356","0.60265356","0.6025649","0.60188425","0.6007839","0.6005696","0.6000802","0.5989025"],"string":"[\n \"0.73725426\",\n \"0.72929084\",\n \"0.7111852\",\n \"0.70446706\",\n \"0.7013182\",\n \"0.70031095\",\n \"0.69107485\",\n \"0.69100827\",\n \"0.6871159\",\n \"0.68606776\",\n \"0.6803045\",\n \"0.680181\",\n \"0.6794203\",\n \"0.67602473\",\n \"0.6700353\",\n \"0.6650724\",\n \"0.66261536\",\n \"0.66122717\",\n \"0.6503248\",\n \"0.64987963\",\n \"0.64917636\",\n \"0.64740086\",\n \"0.6463327\",\n \"0.646029\",\n \"0.64514905\",\n \"0.64166504\",\n \"0.64102393\",\n \"0.63837695\",\n \"0.6381145\",\n \"0.6368855\",\n \"0.6365407\",\n \"0.63629305\",\n \"0.6354893\",\n \"0.6352523\",\n \"0.6348721\",\n \"0.632661\",\n \"0.63231283\",\n \"0.631663\",\n \"0.63069713\",\n \"0.6299273\",\n \"0.62855446\",\n \"0.627558\",\n \"0.62752664\",\n \"0.62690705\",\n \"0.6258997\",\n \"0.62509173\",\n \"0.624516\",\n \"0.6242174\",\n \"0.6235059\",\n \"0.62347895\",\n \"0.6232399\",\n \"0.6228188\",\n \"0.62189245\",\n \"0.62177855\",\n \"0.6217464\",\n \"0.6211105\",\n \"0.6207303\",\n \"0.6198522\",\n \"0.61750776\",\n \"0.6162345\",\n \"0.615796\",\n \"0.6156647\",\n \"0.6148171\",\n \"0.6146866\",\n \"0.6143288\",\n \"0.61416733\",\n \"0.61409944\",\n \"0.61372536\",\n \"0.61359125\",\n \"0.61348855\",\n \"0.61308074\",\n \"0.61266035\",\n \"0.61257666\",\n \"0.6114559\",\n \"0.6110854\",\n \"0.6107848\",\n \"0.61055374\",\n \"0.610432\",\n \"0.61014307\",\n \"0.60976446\",\n \"0.6086173\",\n \"0.6083397\",\n \"0.6082134\",\n \"0.6077446\",\n \"0.60754067\",\n \"0.6067278\",\n \"0.6065451\",\n \"0.6058611\",\n \"0.6048319\",\n \"0.6045956\",\n \"0.60286266\",\n \"0.60265356\",\n \"0.60265356\",\n \"0.60265356\",\n \"0.6025649\",\n \"0.60188425\",\n \"0.6007839\",\n \"0.6005696\",\n \"0.6000802\",\n \"0.5989025\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6141068"},"document_rank":{"kind":"string","value":"66"}}},{"rowIdx":17794,"cells":{"query":{"kind":"string","value":"Exceptions are obviously supported in cpp but not in CUDA device code"},"document":{"kind":"string","value":"def _Raise(self, t):\n self.RaiseError(t, \"Exception raising not supported\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def handle_device_error(self, device: SimplePeriphDev):\r\n pass","def test_nonpositive_nu_raises_exception(nu):\n with pytest.raises(ValueError):\n kernels.Matern(input_dim=1, nu=nu)","def unexpected_error(self, exception):","def test_gcc_crash(self):\r\n self.validate((1, 10, 213, 129), (46, 10, 212, 1), 'valid',\r\n verify_grad=False)","def unexpectedException(self):","def __empty_error_event_handler(self, govee, device, message, exception):\n\n pass","def throw(self):\n pass","def test_clugen_exceptions(prng):\n # Valid arguments\n nd = 3\n nclu = 5\n tpts = 1000\n direc = [1, 0, 0]\n astd = pi / 64\n clu_sep = [10, 10, 5]\n len_mu = 5\n len_std = 0.5\n lat_std = 0.3\n ae = True\n clu_off = [-1.5, 0, 2]\n pt_dist = \"unif\"\n pt_off = \"n-1\"\n csizes_fn = clusizes\n ccenters_fn = clucenters\n llengths_fn = llengths\n langles_fn = angle_deltas\n\n # Test passes with valid arguments\n with warnings.catch_warnings():\n # Check that the function runs without warnings\n warnings.simplefilter(\"error\")\n clugen(\n nd,\n nclu,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Test passes with zero points since allow_empty is set to true\n with warnings.catch_warnings():\n # Check that the function runs without warnings\n warnings.simplefilter(\"error\")\n clugen(\n nd,\n nclu,\n 0,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Invalid number of dimensions\n with pytest.raises(\n ValueError,\n match=re.escape(\"Number of dimensions, `num_dims`, must be > 0\"),\n ):\n clugen(\n 0,\n nclu,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Invalid number of clusters\n with pytest.raises(\n ValueError,\n match=re.escape(\"Number of clusters, `num_clust`, must be > 0\"),\n ):\n clugen(\n nd,\n 0,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Direction needs to have magnitude > 0\n with pytest.raises(\n ValueError,\n match=\"`direction` must have magnitude > 0\",\n ):\n clugen(\n nd,\n nclu,\n tpts,\n [0, 0, 0],\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Direction needs to have nd size (or nd columns)\n bad_dir = array([1, 1])\n with pytest.raises(\n ValueError,\n match=re.escape(\n \"Length of directions in `direction` must be equal to `num_dims` \"\n + f\"({bad_dir.size} != {nd})\"\n ),\n ):\n clugen(\n nd,\n nclu,\n tpts,\n bad_dir,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Direction needs to have 1 or nclu rows\n bad_dir = repeat([[1, 1]], nclu + 1, axis=0)\n with pytest.raises(\n ValueError,\n match=re.escape(\n \"Number of rows in `direction` must be the same as the number of \"\n + f\"clusters ({bad_dir.shape[0]} != {nclu})\"\n ),\n ):\n clugen(\n nd,\n nclu,\n tpts,\n bad_dir,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Direction needs to be a 1D array (vector) or 2D array (matrix)\n bad_dir = prng.random((nclu, nd, 2))\n with pytest.raises(\n ValueError,\n match=re.escape(\n \"`direction` must be a vector (1D array) or a matrix (2D array), \"\n + f\"but is {bad_dir.ndim}D\"\n ),\n ):\n clugen(\n nd,\n nclu,\n tpts,\n bad_dir,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # cluster_sep needs to have nd dims\n bad_clusep = array([10, 0, 5, 1.4])\n with pytest.raises(\n ValueError,\n match=re.escape(\n \"Length of `cluster_sep` must be equal to `num_dims` \"\n + f\"({bad_clusep.size} != {nd})\"\n ),\n ):\n clugen(\n nd,\n nclu,\n tpts,\n direc,\n astd,\n bad_clusep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # cluster_offset needs to have nd dims\n bad_cluoff = array([0, 1])\n with pytest.raises(\n ValueError,\n match=re.escape(\n \"Length of `cluster_offset` must be equal to `num_dims` \"\n + f\"({bad_cluoff.size} != {nd}\"\n ),\n ):\n clugen(\n nd,\n nclu,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=bad_cluoff,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Unknown proj_dist_fn given as string\n with pytest.raises(\n ValueError,\n match=re.escape(\n \"`proj_dist_fn` has to be either 'norm', 'unif' or user-defined function\"\n ),\n ):\n clugen(\n nd,\n nclu,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=\"bad_proj_dist_fn\",\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Invalid proj_dist_fn given as function\n with pytest.raises(\n TypeError,\n match=\"argument\",\n ):\n clugen(\n nd,\n nclu,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=lambda x: 0,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Unknown point_dist_fn given as string\n with pytest.raises(\n ValueError,\n match=re.escape(\n \"point_dist_fn has to be either 'n-1', 'n' or a user-defined function\"\n ),\n ):\n clugen(\n nd,\n nclu,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=\"bad_pt_off\",\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Invalid point_dist_fn given as function\n with pytest.raises(\n TypeError,\n match=\"argument\",\n ):\n clugen(\n nd,\n nclu,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=lambda x: 0,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Invalid direct clusizes\n with pytest.raises(\n ValueError,\n match=re.escape(\n \"clusizes_fn has to be either a function or a `num_clusters`-sized array\"\n ),\n ):\n clugen(\n nd,\n nclu,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=prng.integers(1, tpts * nclu, size=nclu + 1),\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Invalid direct clucenters\n with pytest.raises(\n ValueError,\n match=re.escape(\n \"clucenters_fn has to be either a function or a matrix of size \"\n + \"`num_clusters` x `num_dims`\"\n ),\n ):\n clugen(\n nd,\n nclu,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=prng.normal(size=(nclu + 1, nd + 1)),\n llengths_fn=llengths_fn,\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Invalid direct llengths\n with pytest.raises(\n ValueError,\n match=re.escape(\n \"llengths_fn has to be either a function or a `num_clusters`-sized array\"\n ),\n ):\n clugen(\n nd,\n nclu,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=prng.random(size=nclu + 1),\n angle_deltas_fn=langles_fn,\n rng=prng,\n )\n\n # Invalid direct langles\n with pytest.raises(\n ValueError,\n match=re.escape(\n \"angle_deltas_fn has to be either a function or a \"\n + \"`num_clusters`-sized array\"\n ),\n ):\n clugen(\n nd,\n nclu,\n tpts,\n direc,\n astd,\n clu_sep,\n len_mu,\n len_std,\n lat_std,\n allow_empty=ae,\n cluster_offset=clu_off,\n proj_dist_fn=pt_dist,\n point_dist_fn=pt_off,\n clusizes_fn=csizes_fn,\n clucenters_fn=ccenters_fn,\n llengths_fn=llengths_fn,\n angle_deltas_fn=prng.random(size=nclu + 1),\n rng=prng,\n )","def report_unexpected_exception(self, *args, **kwargs):\n pass","def failure(self, cb: CircuitBreaker, exc: BaseException) -> None:","def whenException(self, channel, call):","def exception(self, *args, **kwargs):","def _check_device(self, inputs):\n for i, input in enumerate(inputs):\n if input._device != self._device:\n raise RuntimeError(\n 'Mismatched device between function and '\n 'element {} of input tensors. ({} vs. {})'\n .format(i, self._device, input._device))","def raise_(err):\n raise err","def test_Drive_invalid_opencl_kernel(opencl_env: cldrive_env.OpenCLEnvironment):\n with test.Raises(opencl_kernel_driver.DriverConstructionFailed):\n opencl_kernel_driver.Drive(\"Syntax error!\", 16, 16, opencl_env, 5)","def test_bad_statistics():\n dev = _aws_device(wires=1, foo=\"bar\")\n observable = qml.Identity(wires=0, do_queue=False)\n observable.return_type = None\n\n with pytest.raises(QuantumFunctionError, match=\"Unsupported return type specified\"):\n dev.statistics(None, [observable])","def experiment3():\n raise FAKE_ERROR","def user_exception(self, frame, exc_info):\n pass","def test_notrunerror(self, MetricClass):\n m = MetricClass()\n with pytest.raises(NotRunError):\n RandomTrader(seed=42).evaluate(m)","def assertion_errored(self, func, exception):","def calc_error(opt, net, cuda, dataset, num_tests):\n if num_tests > len(dataset):\n num_tests = len(dataset)\n with torch.no_grad():\n erorr_arr, IOU_arr, prec_arr, recall_arr = [], [], [], []\n for idx in tqdm(range(num_tests)):\n\n # retrieve data for one frame (or multi-view)\n data = dataset[idx * len(dataset) // num_tests]\n image_tensor = data['img'].to(device=cuda) # (num_views, C, W, H) for 3x512x512 images, float -1. ~ 1.\n calib_tensor = data['calib'].to(device=cuda) # (num_views, 4, 4) calibration matrix\n sample_tensor = data['samples'].to(device=cuda).unsqueeze(0) # (1, 3, n_in + n_out), float XYZ coords are inside the 3d-volume of [self.B_MIN, self.B_MAX]\n if opt.num_views > 1:\n sample_tensor = reshape_sample_tensor(sample_tensor, opt.num_views) # (num_views, 3, n_in + n_out)\n label_tensor = data['labels'].to(device=cuda).unsqueeze(0) # (1, 1, n_in + n_out), float 1.0-inside, 0.0-outside\n deepVoxels_tensor = torch.zeros([label_tensor.shape[0]], dtype=torch.int32).to(device=cuda) # small dummy tensors\n if opt.deepVoxels_fusion != None: deepVoxels_tensor = data[\"deepVoxels\"].to(device=cuda)[None,:] # (B=1,C=8,D=32,H=48,W=32), np.float32, all >= 0.\n\n # forward pass\n res, error = net.forward(image_tensor, sample_tensor, calib_tensor, labels=label_tensor, deepVoxels=deepVoxels_tensor) # (1, 1, n_in + n_out), R\n if len(opt.gpu_ids) > 1: error = error.mean()\n\n # compute errors {IOU, prec, recall} based on the current set of query 3D points\n IOU, prec, recall = compute_acc(res, label_tensor) # R, R, R\n\n # print(\n # '{0}/{1} | Error: {2:06f} IOU: {3:06f} prec: {4:06f} recall: {5:06f}'\n # .format(idx, num_tests, error.item(), IOU.item(), prec.item(), recall.item()))\n erorr_arr.append(error.item())\n IOU_arr.append(IOU.item())\n prec_arr.append(prec.item())\n recall_arr.append(recall.item())\n\n return np.average(erorr_arr), np.average(IOU_arr), np.average(prec_arr), np.average(recall_arr)","def raise_on_error(error_code):\n if error_code == 0: # SUCCESS\n return\n elif error_code == 1: # ERR_BAD_MORPH_OP\n raise ValueError('invalid morhology operation code')\n elif error_code == 2: # ERR_BAD_TYPE\n raise ValueError('invalid type')\n elif error_code == 3: # ERR_BAD_CUDA_DEVICE\n raise ValueError('invalid device number')\n elif error_code == 4: # ERR_NO_AVAILABLE_CUDA_DEVICE\n raise RuntimeError('no CUDA device available')\n elif error_code == 5: # ERR_BAD_APPROX_TYPE\n raise RuntimeError('invalid approximation type')\n elif error_code == -1: # ERR_UNCAUGHT_EXCEPTION\n raise RuntimeError('an unaught C++ exception occured')\n else:\n raise ValueError('invalid error code: {}'.format(error_code))","def conditionally_raise(self, error: ImageNotFound) -> None:","def test_invalid_device_type():\n _aws_device(wires=2, device_type=\"foo\", shots=None)","def logger(self):\n return DeviceException(DeviceException.FEATURE_NOT_IMPLEMENTED)","def test_exception_raised_no_dlq():\n\n proc: missive.Processor[missive.RawMessage] = missive.Processor()\n\n @proc.handle_for(always)\n def crash(message, ctx):\n raise RuntimeError(\"bad bytes!\")\n\n with proc.test_client() as test_client:\n blank_message = missive.RawMessage(b\"\")\n with pytest.raises(RuntimeError):\n test_client.send(blank_message)","def test_error_node():\n try:\n node_a = Node({'a':'a'})\n except Exception as e:\n assert str(e) == 'input connected nodes info is not in a list.'","def test_get_debug_values_exc():\r\n\r\n prev_value = config.compute_test_value\r\n try:\r\n config.compute_test_value = 'raise'\r\n\r\n x = T.vector()\r\n\r\n try:\r\n for x_val in op.get_debug_values(x):\r\n #this assert catches the case where we\r\n #erroneously get a value returned\r\n assert False\r\n raised = False\r\n except AttributeError:\r\n raised = True\r\n\r\n #this assert catches the case where we got []\r\n #returned, and possibly issued a warning,\r\n #rather than raising an exception\r\n assert raised\r\n\r\n finally:\r\n config.compute_test_value = prev_value","def test_randint4():\n # res = np.array([[9, 9], [5, 8]])\n obj.exception(etype=\"NotFound\", low=0, high=10, shape=[2, 2], dtype=np.float32)","def raise_error(Err):\n raise Err()","def exception_check(self, mu, env):\n logger.debug(\"JNIEnv->ExceptionCheck() was called\")\n # TODO: Implement\n return JNI_FALSE","def test_with_process_crash(self):\n if self.num_replicas < 2:\n self.assertTrue(False, msg=\"Required: num_replicas > 1\")\n\n # Override num_of_nodes affected to 1 (Positive case)\n self.num_nodes_affected = 1\n\n error_sim = dict()\n shell_conn = dict()\n cbstat_obj = dict()\n failover_info = dict()\n vb_info_info = dict()\n active_vbs_in_target_nodes = list()\n failover_info[\"init\"] = dict()\n failover_info[\"afterCrud\"] = dict()\n vb_info_info[\"init\"] = dict()\n vb_info_info[\"afterCrud\"] = dict()\n\n self.log.info(\"Selecting nodes to simulate error condition\")\n target_nodes = DurabilityHelper.getTargetNodes(self.cluster,\n self.nodes_init,\n self.num_nodes_affected)\n\n self.log.info(\"Will simulate error condition on %s\" % target_nodes)\n for node in target_nodes:\n cbstat_obj[node.ip] = Cbstats(node)\n active_vbs_in_target_nodes += cbstat_obj[node.ip].vbucket_list(\n self.bucket.name,\n \"active\")\n vb_info_info[\"init\"][node.ip] = cbstat_obj[node.ip].vbucket_seqno(\n self.bucket.name)\n failover_info[\"init\"][node.ip] = \\\n cbstat_obj[node.ip].failover_stats(self.bucket.name)\n\n # Remove active vbuckets from doc_loading to avoid errors\n load_spec = dict()\n load_spec[\"doc_crud\"] = dict()\n load_spec[\"doc_crud\"][\n MetaCrudParams.DocCrud.CREATE_PERCENTAGE_PER_COLLECTION] = 100\n load_spec[\"doc_crud\"][\n MetaCrudParams.DocCrud.UPDATE_PERCENTAGE_PER_COLLECTION] = 25\n load_spec[\"doc_crud\"][\n MetaCrudParams.DocCrud.DELETE_PERCENTAGE_PER_COLLECTION] = 25\n load_spec[\"doc_crud\"][\n MetaCrudParams.DocCrud.COMMON_DOC_KEY] = \"test_collections\"\n load_spec[\"target_vbuckets\"] = list(set(range(0, 1024))\n ^ set(active_vbs_in_target_nodes))\n\n self.log.info(\"Perform 'create', 'update', 'delete' mutations\")\n doc_loading_task = \\\n self.bucket_util.run_scenario_from_spec(\n self.task,\n self.cluster,\n self.cluster.buckets,\n load_spec,\n mutation_num=1,\n async_load=True)\n\n self.sleep(5, \"Wait for doc loaders to start loading data\")\n\n for node in target_nodes:\n # Create shell_connections\n shell_conn[node.ip] = RemoteMachineShellConnection(node)\n\n # Perform specified action\n error_sim[node.ip] = CouchbaseError(self.log,\n shell_conn[node.ip],\n node=node)\n error_sim[node.ip].create(self.simulate_error,\n bucket_name=self.bucket.name)\n\n # Perform new scope/collection creation during doc ops in parallel\n self.__perform_collection_crud()\n\n # Wait for document_loader tasks to complete\n self.task_manager.get_task_result(doc_loading_task)\n self.bucket_util.validate_doc_loading_results(doc_loading_task)\n if doc_loading_task.result is False:\n self.log_failure(\"Doc CRUDs failed with process crash\")\n\n if self.simulate_error \\\n not in [DiskError.DISK_FULL, DiskError.DISK_FAILURE]:\n # Revert the induced error condition\n for node in target_nodes:\n error_sim[node.ip].revert(self.simulate_error,\n bucket_name=self.bucket.name)\n\n # Disconnect the shell connection\n shell_conn[node.ip].disconnect()\n self.sleep(10, \"Wait for node recovery to complete\")\n\n # In case of error with Ephemeral bucket, need to rebalance\n # to make sure data is redistributed properly\n if self.bucket_type == Bucket.Type.EPHEMERAL:\n retry_num = 0\n result = None\n while retry_num != 2:\n result = self.task.rebalance(\n self.servers[0:self.nodes_init],\n [], [])\n if result:\n break\n retry_num += 1\n self.sleep(10, \"Wait before retrying rebalance\")\n\n self.assertTrue(result, \"Rebalance failed\")\n\n # Fetch latest failover stats and validate the values are updated\n self.log.info(\"Validating failover and seqno cbstats\")\n for node in target_nodes:\n vb_info_info[\"afterCrud\"][node.ip] = \\\n cbstat_obj[node.ip].vbucket_seqno(self.bucket.name)\n failover_info[\"afterCrud\"][node.ip] = \\\n cbstat_obj[node.ip].failover_stats(self.bucket.name)\n\n # Failover stat validation\n if self.simulate_error == CouchbaseError.KILL_MEMCACHED:\n val = failover_info[\"init\"][node.ip] \\\n != failover_info[\"afterCrud\"][node.ip]\n else:\n if self.simulate_error != CouchbaseError.STOP_MEMCACHED \\\n and self.bucket_type == Bucket.Type.EPHEMERAL:\n val = failover_info[\"init\"][node.ip] \\\n != failover_info[\"afterCrud\"][node.ip]\n else:\n val = failover_info[\"init\"][node.ip] \\\n == failover_info[\"afterCrud\"][node.ip]\n error_msg = \"Failover stats mismatch after error condition:\" \\\n \" %s != %s\" \\\n % (failover_info[\"init\"][node.ip],\n failover_info[\"afterCrud\"][node.ip])\n self.assertTrue(val, msg=error_msg)\n\n # Seq_no validation (High level)\n val = \\\n vb_info_info[\"init\"][node.ip] \\\n != vb_info_info[\"afterCrud\"][node.ip]\n self.assertTrue(val, msg=\"vbucket seq_no not updated after CRUDs\")\n\n # Doc count validation\n self.validate_test_failure()\n self.bucket_util.validate_docs_per_collections_all_buckets(\n self.cluster)","def test_non_jaqcd_device(name_mock):\n _bad_aws_device(wires=2)","def _error_handling(self,e,func):\n print(self.type, \" sufferred exception in \" , func , \":\" , e)","def error(self):\n ...","def ERR(self):","def test_gpus_raises():\n gpus = \"1\"\n\n with pytest.raises(ValueError):\n cli._gpus(gpus)","def logged_batch_throws_uae_test(self):\n cursor = self.prepare(nodes=3)\n [ node.stop(wait_other_notice=True) for node in self.cluster.nodelist()[1:] ]\n cursor.consistency_level = 'ONE'\n assert_unavailable(cursor.execute, \"\"\"\n BEGIN BATCH\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow')\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner')\n APPLY BATCH\n \"\"\")","def handle_err(self):\n pass","def try_gpu():\r\n try:\r\n ctx = mx.gpu()\r\n _ = nd.array([0], ctx=ctx)\r\n except mx.base.MXNetError:\r\n ctx = mx.cpu()\r\n return ctx","def _check(error: int) -> None:\n if error < 0:\n raise RuntimeError(ffi.string(lib.TCOD_get_error()).decode())","def serious_error(self, e):\n pass","def logged_batch_throws_uae_test(self):\n session = self.prepare(nodes=3)\n [ node.stop(wait_other_notice=True) for node in self.cluster.nodelist()[1:] ]\n session.consistency_level = 'ONE'\n assert_unavailable(session.execute, \"\"\"\n BEGIN BATCH\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow')\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner')\n APPLY BATCH\n \"\"\")","def check_cuda():\n if OS_VERSION[0] == \"Linux\":\n check_cuda_linux()\n elif OS_VERSION[0] == \"Windows\":\n check_cuda_windows()","def _raise_performing_request_error(self, *args, **kwargs):","def look_for_cython_error(capfd):\n yield\n _, err = capfd.readouterr()\n assert \"Exception ignored\" not in err","def test_does_not_crash(self):\n py_function(6)","def error(self, *args, **kwargs):","async def test_fetch_filtered_dataset_call_exception(self):\n assembly_id = 'GRCh38'\n position = (10, 20, None, None, None, None)\n chromosome = 1\n reference = 'A'\n alternate = ('DUP', None)\n pool = asynctest.CoroutineMock()\n pool.acquire().__aenter__.return_value = ConnectionException()\n with self.assertRaises(aiohttp.web_exceptions.HTTPInternalServerError):\n await fetch_filtered_dataset(pool, assembly_id, position, chromosome, reference, alternate, None, None, False)","def err(reason):\n raise AliasException(reason)","def assertion_failed(self, func, exception):","def _async_raise(self,tid, exctype): \n tid = c_long(tid) \n if not inspect.isclass(exctype): \n exctype = type(exctype) \n res = pythonapi.PyThreadState_SetAsyncExc(tid, py_object(exctype)) \n if res == 0: \n raise ValueError(\"invalid thread id\") \n elif res != 1: \n # \"\"\"if it returns a number greater than one, you're in trouble, \n # and you should call it again with exc=NULL to revert the effect\"\"\" \n pythonapi.PyThreadState_SetAsyncExc(tid, None) \n raise SystemError(\"PyThreadState_SetAsyncExc failed\")","async def test_device_unknown_error(hass):\n with patch.object(axis.device, \"get_device\", side_effect=Exception):\n await setup_axis_integration(hass)\n assert hass.data[AXIS_DOMAIN] == {}","def handle_gpu_compatibility():\n try:\n gpus = tf.config.experimental.list_physical_devices(\"GPU\")\n for gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\n except Exception as e:\n print(e)","def test_prevent_wrong_memory(self):\n self.assertRaises(cinv.host.Error, self.wrong_memory)","def interrupt_kernel(self):","def testNoFeatureColumnsOrKernelMappers(self):\n with self.assertRaises(ValueError):\n _ = kernel_estimators.KernelLinearClassifier()","def _raise_device_exception(self, msg, category=DeviceException.OPERATION_FAILED):\n self._logger.error(msg)\n raise DeviceException(category, msg)","def _check(self,err):\r\n if err < 0:\r\n buf_size = 128\r\n buf = create_string_buffer('\\000' * buf_size)\r\n self.nidaq.DAQmxGetErrorString(err,byref(buf),buf_size)\r\n raise RuntimeError('NI-DAQ call failed with error %d: %s'%(err,repr(buf.value)))","def test_gpu():\n vectorizer = Vectorizer(device=\"gpu\", engine=\"cuml\")\n pytest.raises(ValueError, vectorizer.fit_transform, X_text)","def _check_cuda_version():\n if not _HAS_OPS:\n return -1\n import torch\n\n _version = torch.ops.torchvision._cuda_version()\n if _version != -1 and torch.version.cuda is not None:\n tv_version = str(_version)\n if int(tv_version) < 10000:\n tv_major = int(tv_version[0])\n tv_minor = int(tv_version[2])\n else:\n tv_major = int(tv_version[0:2])\n tv_minor = int(tv_version[3])\n t_version = torch.version.cuda\n t_version = t_version.split(\".\")\n t_major = int(t_version[0])\n t_minor = int(t_version[1])\n if t_major != tv_major or t_minor != tv_minor:\n raise RuntimeError(\n \"Detected that PyTorch and torchvision were compiled with different CUDA versions. \"\n \"PyTorch has CUDA Version={}.{} and torchvision has CUDA Version={}.{}. \"\n \"Please reinstall the torchvision that matches your PyTorch install.\".format(\n t_major, t_minor, tv_major, tv_minor\n )\n )\n return _version","def test_error_if_not_expval_batched(self):\n qml.enable_tape()\n dev = qml.device(\"orquestra.qiskit\", wires=2)\n\n with qml.tape.QuantumTape() as tape1:\n qml.expval(qml.PauliZ(wires=[0]))\n qml.var(qml.PauliZ(wires=[0]))\n\n with qml.tape.QuantumTape() as tape2:\n qml.expval(qml.PauliZ(wires=[0]))\n\n circuits = [tape1, tape2]\n with pytest.raises(NotImplementedError):\n res = dev.batch_execute(circuits)\n\n qml.disable_tape()","def error(self):\n pass","def __call__(self):\r\n raise self","def __call__(self):\r\n raise self","def calc_error_vrn_occu(opt, net, cuda, dataset, num_tests):\n\n if num_tests > len(dataset):\n num_tests = len(dataset)\n with torch.no_grad():\n error_total_arr, erorr_arr, error_view_render_arr, error_3d_gan_generator_arr, error_3d_gan_discriminator_fake_arr, error_3d_gan_discriminator_real_arr, IOU_arr, prec_arr, recall_arr = [], [], [], [], [], [], [], [], []\n accuracy_3d_gan_discriminator_fake_arr, accuracy_3d_gan_discriminator_real_arr = [], []\n for idx in tqdm(range(num_tests)):\n\n # retrieve data for one frame (or multi-view)\n data = dataset[idx * len(dataset) // num_tests]\n image_tensor = data['img'].to(device=cuda) # (V, C-3, H-512, W-512), RGB, 3x512x512 images, float -1. ~ 1., bg is all ZEROS not -1.\n meshVoxels_tensor = data['meshVoxels'].to(device=cuda) # (1, D-128, H-192, W-128), float 1.0-inside, 0.0-outside\n viewDirectionIdx_tensor = torch.zeros([1], dtype=torch.int32).to(device=cuda) # small dummy tensors\n target_view_tensor = torch.zeros([1], dtype=torch.int32).to(device=cuda) # small dummy tensors\n if opt.use_view_pred_loss: viewDirectionIdx_tensor = data[\"view_directions\"].to(device=cuda) # integers, {0, 1, 2, 3} maps to {front, right, back, left}\n if opt.use_view_pred_loss: target_view_tensor = data['target_view'].to(device=cuda) # (C-3, H-384, W-256) target-view RGB images, float -1. ~ 1., bg is all ZEROS not -1. \n\n # reshape the data\n image_tensor = image_tensor.view(-1, image_tensor.shape[-3], image_tensor.shape[-2], image_tensor.shape[-1]) # (V, C-3, H-512, W-512)\n meshVoxels_tensor = meshVoxels_tensor.view(-1, 1, meshVoxels_tensor.shape[-3], meshVoxels_tensor.shape[-2], meshVoxels_tensor.shape[-1]) # (1, 1, D-128, H-192, W-128)\n if opt.use_view_pred_loss: viewDirectionIdx_tensor = viewDirectionIdx_tensor.view(-1 ) # (1,)\n if opt.use_view_pred_loss: target_view_tensor = target_view_tensor.view(-1, target_view_tensor.shape[-3], target_view_tensor.shape[-2], target_view_tensor.shape[-1]) # (1, C-3, H-384, W-256)\n\n # forward pass\n # if opt.use_view_pred_loss: pred_occ, error, render_rgb, pseudo_inverseDepth, error_view_render = net.forward(images=image_tensor, labels=meshVoxels_tensor, view_directions=viewDirectionIdx_tensor, target_views=target_view_tensor)\n # else: pred_occ, error = net.forward(images=image_tensor, labels=meshVoxels_tensor, view_directions=viewDirectionIdx_tensor, target_views=target_view_tensor)\n forward_return_dict = net.forward(images=image_tensor, labels=meshVoxels_tensor, view_directions=viewDirectionIdx_tensor, target_views=target_view_tensor)\n pred_occ = forward_return_dict[\"pred_occ\"]\n error = forward_return_dict[\"error\"].mean().item()\n error_view_render = forward_return_dict[\"error_view_render\"].mean().item() if opt.use_view_pred_loss else 0.\n error_3d_gan_generator = forward_return_dict[\"error_3d_gan_generator\"].mean().item() if opt.use_3d_gan else 0.\n error_3d_gan_discriminator_fake = forward_return_dict[\"error_3d_gan_discriminator_fake\"].mean().item() if opt.use_3d_gan else 0.\n error_3d_gan_discriminator_real = forward_return_dict[\"error_3d_gan_discriminator_real\"].mean().item() if opt.use_3d_gan else 0.\n accuracy_3d_gan_discriminator_fake = forward_return_dict[\"accuracy_3d_gan_discriminator_fake\"].mean().item() if opt.use_3d_gan else 0.\n accuracy_3d_gan_discriminator_real = forward_return_dict[\"accuracy_3d_gan_discriminator_real\"].mean().item() if opt.use_3d_gan else 0.\n error_total = error + error_view_render + error_3d_gan_generator\n\n # compute errors {IOU, prec, recall} based on the current set of query 3D points\n IOU, prec, recall = compute_acc(pred_occ, meshVoxels_tensor) # R, R, R\n\n # print(\n # '{0}/{1} | Error: {2:06f} IOU: {3:06f} prec: {4:06f} recall: {5:06f}'\n # .format(idx, num_tests, error.item(), IOU.item(), prec.item(), recall.item()))\n error_total_arr.append(error_total)\n erorr_arr.append(error)\n error_view_render_arr.append(error_view_render)\n error_3d_gan_generator_arr.append(error_3d_gan_generator)\n error_3d_gan_discriminator_fake_arr.append(error_3d_gan_discriminator_fake)\n accuracy_3d_gan_discriminator_fake_arr.append(accuracy_3d_gan_discriminator_fake)\n error_3d_gan_discriminator_real_arr.append(error_3d_gan_discriminator_real)\n accuracy_3d_gan_discriminator_real_arr.append(accuracy_3d_gan_discriminator_real)\n IOU_arr.append(IOU.item())\n prec_arr.append(prec.item())\n recall_arr.append(recall.item())\n\n return np.average(error_total_arr), np.average(erorr_arr), np.average(error_view_render_arr), np.average(error_3d_gan_generator_arr), np.average(error_3d_gan_discriminator_fake_arr), np.average(accuracy_3d_gan_discriminator_fake_arr), np.average(error_3d_gan_discriminator_real_arr), np.average(accuracy_3d_gan_discriminator_real_arr), np.average(IOU_arr), np.average(prec_arr), np.average(recall_arr)","def check_result(ec):\r\n # NOTE: This will break some oscilloscopes that are powered by USB.\r\n # Some of the newer scopes, can actually be powered by USB and will\r\n # return a useful value. That should be given back to the user.\r\n # I guess we can deal with these edge cases in the functions themselves\r\n if ec == 0:\r\n return\r\n\r\n else:\r\n ecName = error_num_to_name(ec)\r\n ecDesc = error_num_to_desc(ec)\r\n raise IOError('Error calling %s: %s (%s)' % (\r\n str(inspect.stack()[1][3]), ecName, ecDesc))","def exception(self, e):\n pass","def test_deviceX_1():\n assert 0","def test_indices_nonexistant(self, operable_mock_device_2_wires):\n\n def circuit(x):\n qml.Rot(0.3, x, -0.2, wires=[0])\n return qml.expval(qml.PauliZ(0))\n\n node = JacobianQNode(circuit, operable_mock_device_2_wires)\n\n with pytest.raises(ValueError, match=\"Tried to compute the gradient with respect to\"):\n node.jacobian(0.5, wrt=[0, 6])\n\n with pytest.raises(ValueError, match=\"Tried to compute the gradient with respect to\"):\n node.jacobian(0.5, wrt=[1, -1])","def error():\n return None","def test_NonrealtimeProvider_add_bus_error(session):\n provider = Provider.from_context(session)\n with pytest.raises(ValueError):\n provider.add_bus()\n with provider.at(0):\n with pytest.raises(ValueError):\n provider.add_bus(calculation_rate=\"scalar\")","def optimize(self, target_device):\r\n return NotImplementedError","def test_post_codegen_error_query(self):\n with tempfile.TemporaryDirectory() as tmpdirname:\n translator = AstUprootTranslator()\n with pytest.raises(GenerateCodeException):\n translator.generate_code(\"\", cache_path=tmpdirname)","def try_gpu():\n try:\n ctx = mx.gpu()\n _ = nd.array([0], ctx=ctx)\n except:\n ctx = mx.cpu()\n return ctx","def try_gpu():\n try:\n ctx = mx.gpu()\n _ = nd.array([0], ctx=ctx)\n except:\n ctx = mx.cpu()\n return ctx","async def test_get_device_device_unavailable(hass):\n with patch(\n \"axis.vapix.Vapix.request\", side_effect=axislib.RequestError\n ), pytest.raises(axis.errors.CannotConnect):\n await axis.device.get_device(hass, host=\"\", port=\"\", username=\"\", password=\"\")","def testInvalidKernelMapper(self):\n\n class DummyKernelMapper(object):\n\n def __init__(self):\n pass\n\n feature = layers.real_valued_column('feature')\n kernel_mappers = {feature: [DummyKernelMapper()]}\n with self.assertRaises(ValueError):\n _ = kernel_estimators.KernelLinearClassifier(\n feature_columns=[feature], kernel_mappers=kernel_mappers)","def try_gpu(x):\n global _GPUS_EXIST\n if _GPUS_EXIST:\n try:\n return x.cuda()\n except (AssertionError, RuntimeError):\n print('No GPUs detected. Sticking with CPUs.')\n _GPUS_EXIST = False\n return x","def test_compile_pixels_error(opened_image):\n # mock output for getter of img_in.load()\n opened_image.load.return_value.__getitem__.return_value = dict()\n\n with pytest.raises(RuntimeError) as exception_info:\n synes.translate_image(\"/path/image.png\", 44100)\n\n exception_str = (\n \"Unable to parse pixels, unrecognized pixel type '{}'.\".format(dict)\n )\n assert exception_str in str(exception_info)","def test_tensor_method(free_alg):\n\n tensor = free_alg.sum(10)\n assert tensor.get_one() == 1\n\n with pytest.raises(AttributeError):\n tensor.get_two()","async def test_get_device_unknown_error(hass):\n with patch(\n \"axis.vapix.Vapix.request\", side_effect=axislib.AxisException\n ), pytest.raises(axis.errors.AuthenticationRequired):\n await axis.device.get_device(hass, host=\"\", port=\"\", username=\"\", password=\"\")","def try_gpu(x):\n global _GPUS_EXIST\n\n if _GPUS_EXIST:\n try:\n return x.cuda()\n except (AssertionError, RuntimeError):\n # actually, GPUs don't exist\n print 'No GPUs detected. Sticking with CPUs.'\n _GPUS_EXIST = False\n return x\n else:\n return x","def test_Icat_exceptions(teff, z, logg):\n with pytest.raises(ParameterOutOfBounds):\n Icat('k93models', teff, z, logg)","def test_class_errored(self, cls, exception):","def test_Calc_exceptions(self, start, end):\n with pytest.raises(Exception):\n pycgmCalc.Calc(start, end, self.motion_data, self.cal_SM)","def testCCHalt(self):\n cdl_convert.config.HALT_ON_ERROR = True\n\n def getCC():\n self.ccr_bad.cc\n\n self.assertRaises(\n ValueError,\n getCC\n )","def _async_raise(self, tid, exctype): \n tid = ctypes.c_long(tid) \n if not inspect.isclass(exctype): \n exctype = type(exctype) \n res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(exctype)) \n if res == 0: \n raise ValueError(\"invalid thread id\") \n elif res != 1: \n # \"\"\"if it returns a number greater than one, you're in trouble, \n # and you should call it again with exc=NULL to revert the effect\"\"\" \n ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None) \n raise SystemError(\"PyThreadState_SetAsyncExc failed\")","def _catchindexerror(func):\n @wraps(func)\n def newfunc(*args, **kwargs):\n \"\"\" Catch the index error \"\"\"\n try:\n return func(*args, **kwargs)\n except IndexError as e:\n raise CharmmPSFError('Array is too short: %s' % e)\n\n return newfunc","def _async_raise(self,tid, exctype):\n tid = ctypes.c_long(tid)\n if not inspect.isclass(exctype):\n exctype = type(exctype)\n res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(exctype))\n if res == 0:\n raise ValueError(\"invalid thread id\")\n elif res != 1:\n # \"\"\"if it returns a number greater than one, you're in trouble,\n # and you should call it again with exc=NULL to revert the effect\"\"\"\n ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None)\n raise SystemError(\"PyThreadState_SetAsyncExc failed\")","def construct_error_bdev(py, accelerator, basename):\n acc_client = get_accelerator_client(py, accelerator)\n acc_client.construct_error_bdev(basename)","def ExceptionPropertyType_test(type1: str, type2: str):\n m = pyflamegpu.ModelDescription(\"model\")\n ed = m.Environment()\n add_func_t1 = getattr(ed, f\"newProperty{type1}\")\n add_func_array_t1 = getattr(ed, f\"newPropertyArray{type1}\")\n set_func_t1 = getattr(ed, f\"setProperty{type1}\")\n set_func_t2 = getattr(ed, f\"setProperty{type2}\")\n set_func_array_t2 = getattr(ed, f\"setPropertyArray{type2}\")\n \n a_t1 = 1\n a_t2 = 1\n b_t1 = [0] * ARRAY_TEST_LEN\n b_t2 = [0] * ARRAY_TEST_LEN\n for i in range(ARRAY_TEST_LEN):\n b_t1[i] = i\n b_t2[i] = i\n add_func_t1(\"a\", a_t1, True)\n add_func_array_t1(\"b\", b_t1, True)\n \n with pytest.raises(pyflamegpu.FLAMEGPURuntimeException) as e:\n set_func_t2(\"a\", a_t2)\n assert e.value.type() == \"InvalidEnvPropertyType\"\n with pytest.raises(pyflamegpu.FLAMEGPURuntimeException) as e:\n set_func_array_t2(\"b\", b_t2)\n assert e.value.type() == \"InvalidEnvPropertyType\"\n with pytest.raises(pyflamegpu.FLAMEGPURuntimeException) as e:\n set_func_t2(\"b\", 0, a_t2)\n assert e.value.type() == \"InvalidEnvPropertyType\"","def fault():\n return FaultCohesiveKin()","def test_asgi_exc_info(self):\n app = otel_asgi.OpenTelemetryMiddleware(error_asgi)\n self.seed_app(app)\n self.send_default_request()\n outputs = self.get_all_output()\n self.validate_outputs(outputs, error=ValueError)","def test_asgi_exc_info(self):\n app = otel_asgi.OpenTelemetryMiddleware(error_asgi)\n self.seed_app(app)\n self.send_default_request()\n outputs = self.get_all_output()\n self.validate_outputs(outputs, error=ValueError)","def test_errors():\n\n if not build_rir_available:\n return\n\n N = 300\n fs = 16000\n fdl = 81\n rir = np.zeros(N, dtype=np.float32)\n\n time = np.array([100 / fs, 200 / fs], dtype=np.float32)\n alpha = np.array([1.0, 1.0], dtype=np.float32)\n visibility = np.array([1, 1], dtype=np.int32)\n\n with pytest.raises(RuntimeError):\n libroom.rir_builder(rir, time, alpha[:1], visibility, fs, fdl, 20, 2)\n\n with pytest.raises(RuntimeError):\n libroom.rir_builder(rir, time, alpha, visibility[:1], fs, fdl, 20, 2)\n\n with pytest.raises(RuntimeError):\n libroom.rir_builder(rir, time, alpha, visibility, fs, 80, 20, 2)","def test_randint6():\n # res = np.array([[9, 9], [5, 8]])\n obj.exception(etype=ValueError, mode=\"python\", low=0, shape=[2, 2])","def fatal(self, *args, **kwargs):","def throw(error: Exception) -> None:\n raise error","def on_exception(self):\n pass","def test_get_pci_device_by_moid(self):\n pass"],"string":"[\n \"def handle_device_error(self, device: SimplePeriphDev):\\r\\n pass\",\n \"def test_nonpositive_nu_raises_exception(nu):\\n with pytest.raises(ValueError):\\n kernels.Matern(input_dim=1, nu=nu)\",\n \"def unexpected_error(self, exception):\",\n \"def test_gcc_crash(self):\\r\\n self.validate((1, 10, 213, 129), (46, 10, 212, 1), 'valid',\\r\\n verify_grad=False)\",\n \"def unexpectedException(self):\",\n \"def __empty_error_event_handler(self, govee, device, message, exception):\\n\\n pass\",\n \"def throw(self):\\n pass\",\n \"def test_clugen_exceptions(prng):\\n # Valid arguments\\n nd = 3\\n nclu = 5\\n tpts = 1000\\n direc = [1, 0, 0]\\n astd = pi / 64\\n clu_sep = [10, 10, 5]\\n len_mu = 5\\n len_std = 0.5\\n lat_std = 0.3\\n ae = True\\n clu_off = [-1.5, 0, 2]\\n pt_dist = \\\"unif\\\"\\n pt_off = \\\"n-1\\\"\\n csizes_fn = clusizes\\n ccenters_fn = clucenters\\n llengths_fn = llengths\\n langles_fn = angle_deltas\\n\\n # Test passes with valid arguments\\n with warnings.catch_warnings():\\n # Check that the function runs without warnings\\n warnings.simplefilter(\\\"error\\\")\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Test passes with zero points since allow_empty is set to true\\n with warnings.catch_warnings():\\n # Check that the function runs without warnings\\n warnings.simplefilter(\\\"error\\\")\\n clugen(\\n nd,\\n nclu,\\n 0,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Invalid number of dimensions\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\\"Number of dimensions, `num_dims`, must be > 0\\\"),\\n ):\\n clugen(\\n 0,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Invalid number of clusters\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\\"Number of clusters, `num_clust`, must be > 0\\\"),\\n ):\\n clugen(\\n nd,\\n 0,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Direction needs to have magnitude > 0\\n with pytest.raises(\\n ValueError,\\n match=\\\"`direction` must have magnitude > 0\\\",\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n [0, 0, 0],\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Direction needs to have nd size (or nd columns)\\n bad_dir = array([1, 1])\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\n \\\"Length of directions in `direction` must be equal to `num_dims` \\\"\\n + f\\\"({bad_dir.size} != {nd})\\\"\\n ),\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n bad_dir,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Direction needs to have 1 or nclu rows\\n bad_dir = repeat([[1, 1]], nclu + 1, axis=0)\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\n \\\"Number of rows in `direction` must be the same as the number of \\\"\\n + f\\\"clusters ({bad_dir.shape[0]} != {nclu})\\\"\\n ),\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n bad_dir,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Direction needs to be a 1D array (vector) or 2D array (matrix)\\n bad_dir = prng.random((nclu, nd, 2))\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\n \\\"`direction` must be a vector (1D array) or a matrix (2D array), \\\"\\n + f\\\"but is {bad_dir.ndim}D\\\"\\n ),\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n bad_dir,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # cluster_sep needs to have nd dims\\n bad_clusep = array([10, 0, 5, 1.4])\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\n \\\"Length of `cluster_sep` must be equal to `num_dims` \\\"\\n + f\\\"({bad_clusep.size} != {nd})\\\"\\n ),\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n bad_clusep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # cluster_offset needs to have nd dims\\n bad_cluoff = array([0, 1])\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\n \\\"Length of `cluster_offset` must be equal to `num_dims` \\\"\\n + f\\\"({bad_cluoff.size} != {nd}\\\"\\n ),\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=bad_cluoff,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Unknown proj_dist_fn given as string\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\n \\\"`proj_dist_fn` has to be either 'norm', 'unif' or user-defined function\\\"\\n ),\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=\\\"bad_proj_dist_fn\\\",\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Invalid proj_dist_fn given as function\\n with pytest.raises(\\n TypeError,\\n match=\\\"argument\\\",\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=lambda x: 0,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Unknown point_dist_fn given as string\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\n \\\"point_dist_fn has to be either 'n-1', 'n' or a user-defined function\\\"\\n ),\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=\\\"bad_pt_off\\\",\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Invalid point_dist_fn given as function\\n with pytest.raises(\\n TypeError,\\n match=\\\"argument\\\",\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=lambda x: 0,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Invalid direct clusizes\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\n \\\"clusizes_fn has to be either a function or a `num_clusters`-sized array\\\"\\n ),\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=prng.integers(1, tpts * nclu, size=nclu + 1),\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Invalid direct clucenters\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\n \\\"clucenters_fn has to be either a function or a matrix of size \\\"\\n + \\\"`num_clusters` x `num_dims`\\\"\\n ),\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=prng.normal(size=(nclu + 1, nd + 1)),\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Invalid direct llengths\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\n \\\"llengths_fn has to be either a function or a `num_clusters`-sized array\\\"\\n ),\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=prng.random(size=nclu + 1),\\n angle_deltas_fn=langles_fn,\\n rng=prng,\\n )\\n\\n # Invalid direct langles\\n with pytest.raises(\\n ValueError,\\n match=re.escape(\\n \\\"angle_deltas_fn has to be either a function or a \\\"\\n + \\\"`num_clusters`-sized array\\\"\\n ),\\n ):\\n clugen(\\n nd,\\n nclu,\\n tpts,\\n direc,\\n astd,\\n clu_sep,\\n len_mu,\\n len_std,\\n lat_std,\\n allow_empty=ae,\\n cluster_offset=clu_off,\\n proj_dist_fn=pt_dist,\\n point_dist_fn=pt_off,\\n clusizes_fn=csizes_fn,\\n clucenters_fn=ccenters_fn,\\n llengths_fn=llengths_fn,\\n angle_deltas_fn=prng.random(size=nclu + 1),\\n rng=prng,\\n )\",\n \"def report_unexpected_exception(self, *args, **kwargs):\\n pass\",\n \"def failure(self, cb: CircuitBreaker, exc: BaseException) -> None:\",\n \"def whenException(self, channel, call):\",\n \"def exception(self, *args, **kwargs):\",\n \"def _check_device(self, inputs):\\n for i, input in enumerate(inputs):\\n if input._device != self._device:\\n raise RuntimeError(\\n 'Mismatched device between function and '\\n 'element {} of input tensors. ({} vs. {})'\\n .format(i, self._device, input._device))\",\n \"def raise_(err):\\n raise err\",\n \"def test_Drive_invalid_opencl_kernel(opencl_env: cldrive_env.OpenCLEnvironment):\\n with test.Raises(opencl_kernel_driver.DriverConstructionFailed):\\n opencl_kernel_driver.Drive(\\\"Syntax error!\\\", 16, 16, opencl_env, 5)\",\n \"def test_bad_statistics():\\n dev = _aws_device(wires=1, foo=\\\"bar\\\")\\n observable = qml.Identity(wires=0, do_queue=False)\\n observable.return_type = None\\n\\n with pytest.raises(QuantumFunctionError, match=\\\"Unsupported return type specified\\\"):\\n dev.statistics(None, [observable])\",\n \"def experiment3():\\n raise FAKE_ERROR\",\n \"def user_exception(self, frame, exc_info):\\n pass\",\n \"def test_notrunerror(self, MetricClass):\\n m = MetricClass()\\n with pytest.raises(NotRunError):\\n RandomTrader(seed=42).evaluate(m)\",\n \"def assertion_errored(self, func, exception):\",\n \"def calc_error(opt, net, cuda, dataset, num_tests):\\n if num_tests > len(dataset):\\n num_tests = len(dataset)\\n with torch.no_grad():\\n erorr_arr, IOU_arr, prec_arr, recall_arr = [], [], [], []\\n for idx in tqdm(range(num_tests)):\\n\\n # retrieve data for one frame (or multi-view)\\n data = dataset[idx * len(dataset) // num_tests]\\n image_tensor = data['img'].to(device=cuda) # (num_views, C, W, H) for 3x512x512 images, float -1. ~ 1.\\n calib_tensor = data['calib'].to(device=cuda) # (num_views, 4, 4) calibration matrix\\n sample_tensor = data['samples'].to(device=cuda).unsqueeze(0) # (1, 3, n_in + n_out), float XYZ coords are inside the 3d-volume of [self.B_MIN, self.B_MAX]\\n if opt.num_views > 1:\\n sample_tensor = reshape_sample_tensor(sample_tensor, opt.num_views) # (num_views, 3, n_in + n_out)\\n label_tensor = data['labels'].to(device=cuda).unsqueeze(0) # (1, 1, n_in + n_out), float 1.0-inside, 0.0-outside\\n deepVoxels_tensor = torch.zeros([label_tensor.shape[0]], dtype=torch.int32).to(device=cuda) # small dummy tensors\\n if opt.deepVoxels_fusion != None: deepVoxels_tensor = data[\\\"deepVoxels\\\"].to(device=cuda)[None,:] # (B=1,C=8,D=32,H=48,W=32), np.float32, all >= 0.\\n\\n # forward pass\\n res, error = net.forward(image_tensor, sample_tensor, calib_tensor, labels=label_tensor, deepVoxels=deepVoxels_tensor) # (1, 1, n_in + n_out), R\\n if len(opt.gpu_ids) > 1: error = error.mean()\\n\\n # compute errors {IOU, prec, recall} based on the current set of query 3D points\\n IOU, prec, recall = compute_acc(res, label_tensor) # R, R, R\\n\\n # print(\\n # '{0}/{1} | Error: {2:06f} IOU: {3:06f} prec: {4:06f} recall: {5:06f}'\\n # .format(idx, num_tests, error.item(), IOU.item(), prec.item(), recall.item()))\\n erorr_arr.append(error.item())\\n IOU_arr.append(IOU.item())\\n prec_arr.append(prec.item())\\n recall_arr.append(recall.item())\\n\\n return np.average(erorr_arr), np.average(IOU_arr), np.average(prec_arr), np.average(recall_arr)\",\n \"def raise_on_error(error_code):\\n if error_code == 0: # SUCCESS\\n return\\n elif error_code == 1: # ERR_BAD_MORPH_OP\\n raise ValueError('invalid morhology operation code')\\n elif error_code == 2: # ERR_BAD_TYPE\\n raise ValueError('invalid type')\\n elif error_code == 3: # ERR_BAD_CUDA_DEVICE\\n raise ValueError('invalid device number')\\n elif error_code == 4: # ERR_NO_AVAILABLE_CUDA_DEVICE\\n raise RuntimeError('no CUDA device available')\\n elif error_code == 5: # ERR_BAD_APPROX_TYPE\\n raise RuntimeError('invalid approximation type')\\n elif error_code == -1: # ERR_UNCAUGHT_EXCEPTION\\n raise RuntimeError('an unaught C++ exception occured')\\n else:\\n raise ValueError('invalid error code: {}'.format(error_code))\",\n \"def conditionally_raise(self, error: ImageNotFound) -> None:\",\n \"def test_invalid_device_type():\\n _aws_device(wires=2, device_type=\\\"foo\\\", shots=None)\",\n \"def logger(self):\\n return DeviceException(DeviceException.FEATURE_NOT_IMPLEMENTED)\",\n \"def test_exception_raised_no_dlq():\\n\\n proc: missive.Processor[missive.RawMessage] = missive.Processor()\\n\\n @proc.handle_for(always)\\n def crash(message, ctx):\\n raise RuntimeError(\\\"bad bytes!\\\")\\n\\n with proc.test_client() as test_client:\\n blank_message = missive.RawMessage(b\\\"\\\")\\n with pytest.raises(RuntimeError):\\n test_client.send(blank_message)\",\n \"def test_error_node():\\n try:\\n node_a = Node({'a':'a'})\\n except Exception as e:\\n assert str(e) == 'input connected nodes info is not in a list.'\",\n \"def test_get_debug_values_exc():\\r\\n\\r\\n prev_value = config.compute_test_value\\r\\n try:\\r\\n config.compute_test_value = 'raise'\\r\\n\\r\\n x = T.vector()\\r\\n\\r\\n try:\\r\\n for x_val in op.get_debug_values(x):\\r\\n #this assert catches the case where we\\r\\n #erroneously get a value returned\\r\\n assert False\\r\\n raised = False\\r\\n except AttributeError:\\r\\n raised = True\\r\\n\\r\\n #this assert catches the case where we got []\\r\\n #returned, and possibly issued a warning,\\r\\n #rather than raising an exception\\r\\n assert raised\\r\\n\\r\\n finally:\\r\\n config.compute_test_value = prev_value\",\n \"def test_randint4():\\n # res = np.array([[9, 9], [5, 8]])\\n obj.exception(etype=\\\"NotFound\\\", low=0, high=10, shape=[2, 2], dtype=np.float32)\",\n \"def raise_error(Err):\\n raise Err()\",\n \"def exception_check(self, mu, env):\\n logger.debug(\\\"JNIEnv->ExceptionCheck() was called\\\")\\n # TODO: Implement\\n return JNI_FALSE\",\n \"def test_with_process_crash(self):\\n if self.num_replicas < 2:\\n self.assertTrue(False, msg=\\\"Required: num_replicas > 1\\\")\\n\\n # Override num_of_nodes affected to 1 (Positive case)\\n self.num_nodes_affected = 1\\n\\n error_sim = dict()\\n shell_conn = dict()\\n cbstat_obj = dict()\\n failover_info = dict()\\n vb_info_info = dict()\\n active_vbs_in_target_nodes = list()\\n failover_info[\\\"init\\\"] = dict()\\n failover_info[\\\"afterCrud\\\"] = dict()\\n vb_info_info[\\\"init\\\"] = dict()\\n vb_info_info[\\\"afterCrud\\\"] = dict()\\n\\n self.log.info(\\\"Selecting nodes to simulate error condition\\\")\\n target_nodes = DurabilityHelper.getTargetNodes(self.cluster,\\n self.nodes_init,\\n self.num_nodes_affected)\\n\\n self.log.info(\\\"Will simulate error condition on %s\\\" % target_nodes)\\n for node in target_nodes:\\n cbstat_obj[node.ip] = Cbstats(node)\\n active_vbs_in_target_nodes += cbstat_obj[node.ip].vbucket_list(\\n self.bucket.name,\\n \\\"active\\\")\\n vb_info_info[\\\"init\\\"][node.ip] = cbstat_obj[node.ip].vbucket_seqno(\\n self.bucket.name)\\n failover_info[\\\"init\\\"][node.ip] = \\\\\\n cbstat_obj[node.ip].failover_stats(self.bucket.name)\\n\\n # Remove active vbuckets from doc_loading to avoid errors\\n load_spec = dict()\\n load_spec[\\\"doc_crud\\\"] = dict()\\n load_spec[\\\"doc_crud\\\"][\\n MetaCrudParams.DocCrud.CREATE_PERCENTAGE_PER_COLLECTION] = 100\\n load_spec[\\\"doc_crud\\\"][\\n MetaCrudParams.DocCrud.UPDATE_PERCENTAGE_PER_COLLECTION] = 25\\n load_spec[\\\"doc_crud\\\"][\\n MetaCrudParams.DocCrud.DELETE_PERCENTAGE_PER_COLLECTION] = 25\\n load_spec[\\\"doc_crud\\\"][\\n MetaCrudParams.DocCrud.COMMON_DOC_KEY] = \\\"test_collections\\\"\\n load_spec[\\\"target_vbuckets\\\"] = list(set(range(0, 1024))\\n ^ set(active_vbs_in_target_nodes))\\n\\n self.log.info(\\\"Perform 'create', 'update', 'delete' mutations\\\")\\n doc_loading_task = \\\\\\n self.bucket_util.run_scenario_from_spec(\\n self.task,\\n self.cluster,\\n self.cluster.buckets,\\n load_spec,\\n mutation_num=1,\\n async_load=True)\\n\\n self.sleep(5, \\\"Wait for doc loaders to start loading data\\\")\\n\\n for node in target_nodes:\\n # Create shell_connections\\n shell_conn[node.ip] = RemoteMachineShellConnection(node)\\n\\n # Perform specified action\\n error_sim[node.ip] = CouchbaseError(self.log,\\n shell_conn[node.ip],\\n node=node)\\n error_sim[node.ip].create(self.simulate_error,\\n bucket_name=self.bucket.name)\\n\\n # Perform new scope/collection creation during doc ops in parallel\\n self.__perform_collection_crud()\\n\\n # Wait for document_loader tasks to complete\\n self.task_manager.get_task_result(doc_loading_task)\\n self.bucket_util.validate_doc_loading_results(doc_loading_task)\\n if doc_loading_task.result is False:\\n self.log_failure(\\\"Doc CRUDs failed with process crash\\\")\\n\\n if self.simulate_error \\\\\\n not in [DiskError.DISK_FULL, DiskError.DISK_FAILURE]:\\n # Revert the induced error condition\\n for node in target_nodes:\\n error_sim[node.ip].revert(self.simulate_error,\\n bucket_name=self.bucket.name)\\n\\n # Disconnect the shell connection\\n shell_conn[node.ip].disconnect()\\n self.sleep(10, \\\"Wait for node recovery to complete\\\")\\n\\n # In case of error with Ephemeral bucket, need to rebalance\\n # to make sure data is redistributed properly\\n if self.bucket_type == Bucket.Type.EPHEMERAL:\\n retry_num = 0\\n result = None\\n while retry_num != 2:\\n result = self.task.rebalance(\\n self.servers[0:self.nodes_init],\\n [], [])\\n if result:\\n break\\n retry_num += 1\\n self.sleep(10, \\\"Wait before retrying rebalance\\\")\\n\\n self.assertTrue(result, \\\"Rebalance failed\\\")\\n\\n # Fetch latest failover stats and validate the values are updated\\n self.log.info(\\\"Validating failover and seqno cbstats\\\")\\n for node in target_nodes:\\n vb_info_info[\\\"afterCrud\\\"][node.ip] = \\\\\\n cbstat_obj[node.ip].vbucket_seqno(self.bucket.name)\\n failover_info[\\\"afterCrud\\\"][node.ip] = \\\\\\n cbstat_obj[node.ip].failover_stats(self.bucket.name)\\n\\n # Failover stat validation\\n if self.simulate_error == CouchbaseError.KILL_MEMCACHED:\\n val = failover_info[\\\"init\\\"][node.ip] \\\\\\n != failover_info[\\\"afterCrud\\\"][node.ip]\\n else:\\n if self.simulate_error != CouchbaseError.STOP_MEMCACHED \\\\\\n and self.bucket_type == Bucket.Type.EPHEMERAL:\\n val = failover_info[\\\"init\\\"][node.ip] \\\\\\n != failover_info[\\\"afterCrud\\\"][node.ip]\\n else:\\n val = failover_info[\\\"init\\\"][node.ip] \\\\\\n == failover_info[\\\"afterCrud\\\"][node.ip]\\n error_msg = \\\"Failover stats mismatch after error condition:\\\" \\\\\\n \\\" %s != %s\\\" \\\\\\n % (failover_info[\\\"init\\\"][node.ip],\\n failover_info[\\\"afterCrud\\\"][node.ip])\\n self.assertTrue(val, msg=error_msg)\\n\\n # Seq_no validation (High level)\\n val = \\\\\\n vb_info_info[\\\"init\\\"][node.ip] \\\\\\n != vb_info_info[\\\"afterCrud\\\"][node.ip]\\n self.assertTrue(val, msg=\\\"vbucket seq_no not updated after CRUDs\\\")\\n\\n # Doc count validation\\n self.validate_test_failure()\\n self.bucket_util.validate_docs_per_collections_all_buckets(\\n self.cluster)\",\n \"def test_non_jaqcd_device(name_mock):\\n _bad_aws_device(wires=2)\",\n \"def _error_handling(self,e,func):\\n print(self.type, \\\" sufferred exception in \\\" , func , \\\":\\\" , e)\",\n \"def error(self):\\n ...\",\n \"def ERR(self):\",\n \"def test_gpus_raises():\\n gpus = \\\"1\\\"\\n\\n with pytest.raises(ValueError):\\n cli._gpus(gpus)\",\n \"def logged_batch_throws_uae_test(self):\\n cursor = self.prepare(nodes=3)\\n [ node.stop(wait_other_notice=True) for node in self.cluster.nodelist()[1:] ]\\n cursor.consistency_level = 'ONE'\\n assert_unavailable(cursor.execute, \\\"\\\"\\\"\\n BEGIN BATCH\\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow')\\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner')\\n APPLY BATCH\\n \\\"\\\"\\\")\",\n \"def handle_err(self):\\n pass\",\n \"def try_gpu():\\r\\n try:\\r\\n ctx = mx.gpu()\\r\\n _ = nd.array([0], ctx=ctx)\\r\\n except mx.base.MXNetError:\\r\\n ctx = mx.cpu()\\r\\n return ctx\",\n \"def _check(error: int) -> None:\\n if error < 0:\\n raise RuntimeError(ffi.string(lib.TCOD_get_error()).decode())\",\n \"def serious_error(self, e):\\n pass\",\n \"def logged_batch_throws_uae_test(self):\\n session = self.prepare(nodes=3)\\n [ node.stop(wait_other_notice=True) for node in self.cluster.nodelist()[1:] ]\\n session.consistency_level = 'ONE'\\n assert_unavailable(session.execute, \\\"\\\"\\\"\\n BEGIN BATCH\\n INSERT INTO users (id, firstname, lastname) VALUES (0, 'Jack', 'Sparrow')\\n INSERT INTO users (id, firstname, lastname) VALUES (1, 'Will', 'Turner')\\n APPLY BATCH\\n \\\"\\\"\\\")\",\n \"def check_cuda():\\n if OS_VERSION[0] == \\\"Linux\\\":\\n check_cuda_linux()\\n elif OS_VERSION[0] == \\\"Windows\\\":\\n check_cuda_windows()\",\n \"def _raise_performing_request_error(self, *args, **kwargs):\",\n \"def look_for_cython_error(capfd):\\n yield\\n _, err = capfd.readouterr()\\n assert \\\"Exception ignored\\\" not in err\",\n \"def test_does_not_crash(self):\\n py_function(6)\",\n \"def error(self, *args, **kwargs):\",\n \"async def test_fetch_filtered_dataset_call_exception(self):\\n assembly_id = 'GRCh38'\\n position = (10, 20, None, None, None, None)\\n chromosome = 1\\n reference = 'A'\\n alternate = ('DUP', None)\\n pool = asynctest.CoroutineMock()\\n pool.acquire().__aenter__.return_value = ConnectionException()\\n with self.assertRaises(aiohttp.web_exceptions.HTTPInternalServerError):\\n await fetch_filtered_dataset(pool, assembly_id, position, chromosome, reference, alternate, None, None, False)\",\n \"def err(reason):\\n raise AliasException(reason)\",\n \"def assertion_failed(self, func, exception):\",\n \"def _async_raise(self,tid, exctype): \\n tid = c_long(tid) \\n if not inspect.isclass(exctype): \\n exctype = type(exctype) \\n res = pythonapi.PyThreadState_SetAsyncExc(tid, py_object(exctype)) \\n if res == 0: \\n raise ValueError(\\\"invalid thread id\\\") \\n elif res != 1: \\n # \\\"\\\"\\\"if it returns a number greater than one, you're in trouble, \\n # and you should call it again with exc=NULL to revert the effect\\\"\\\"\\\" \\n pythonapi.PyThreadState_SetAsyncExc(tid, None) \\n raise SystemError(\\\"PyThreadState_SetAsyncExc failed\\\")\",\n \"async def test_device_unknown_error(hass):\\n with patch.object(axis.device, \\\"get_device\\\", side_effect=Exception):\\n await setup_axis_integration(hass)\\n assert hass.data[AXIS_DOMAIN] == {}\",\n \"def handle_gpu_compatibility():\\n try:\\n gpus = tf.config.experimental.list_physical_devices(\\\"GPU\\\")\\n for gpu in gpus:\\n tf.config.experimental.set_memory_growth(gpu, True)\\n except Exception as e:\\n print(e)\",\n \"def test_prevent_wrong_memory(self):\\n self.assertRaises(cinv.host.Error, self.wrong_memory)\",\n \"def interrupt_kernel(self):\",\n \"def testNoFeatureColumnsOrKernelMappers(self):\\n with self.assertRaises(ValueError):\\n _ = kernel_estimators.KernelLinearClassifier()\",\n \"def _raise_device_exception(self, msg, category=DeviceException.OPERATION_FAILED):\\n self._logger.error(msg)\\n raise DeviceException(category, msg)\",\n \"def _check(self,err):\\r\\n if err < 0:\\r\\n buf_size = 128\\r\\n buf = create_string_buffer('\\\\000' * buf_size)\\r\\n self.nidaq.DAQmxGetErrorString(err,byref(buf),buf_size)\\r\\n raise RuntimeError('NI-DAQ call failed with error %d: %s'%(err,repr(buf.value)))\",\n \"def test_gpu():\\n vectorizer = Vectorizer(device=\\\"gpu\\\", engine=\\\"cuml\\\")\\n pytest.raises(ValueError, vectorizer.fit_transform, X_text)\",\n \"def _check_cuda_version():\\n if not _HAS_OPS:\\n return -1\\n import torch\\n\\n _version = torch.ops.torchvision._cuda_version()\\n if _version != -1 and torch.version.cuda is not None:\\n tv_version = str(_version)\\n if int(tv_version) < 10000:\\n tv_major = int(tv_version[0])\\n tv_minor = int(tv_version[2])\\n else:\\n tv_major = int(tv_version[0:2])\\n tv_minor = int(tv_version[3])\\n t_version = torch.version.cuda\\n t_version = t_version.split(\\\".\\\")\\n t_major = int(t_version[0])\\n t_minor = int(t_version[1])\\n if t_major != tv_major or t_minor != tv_minor:\\n raise RuntimeError(\\n \\\"Detected that PyTorch and torchvision were compiled with different CUDA versions. \\\"\\n \\\"PyTorch has CUDA Version={}.{} and torchvision has CUDA Version={}.{}. \\\"\\n \\\"Please reinstall the torchvision that matches your PyTorch install.\\\".format(\\n t_major, t_minor, tv_major, tv_minor\\n )\\n )\\n return _version\",\n \"def test_error_if_not_expval_batched(self):\\n qml.enable_tape()\\n dev = qml.device(\\\"orquestra.qiskit\\\", wires=2)\\n\\n with qml.tape.QuantumTape() as tape1:\\n qml.expval(qml.PauliZ(wires=[0]))\\n qml.var(qml.PauliZ(wires=[0]))\\n\\n with qml.tape.QuantumTape() as tape2:\\n qml.expval(qml.PauliZ(wires=[0]))\\n\\n circuits = [tape1, tape2]\\n with pytest.raises(NotImplementedError):\\n res = dev.batch_execute(circuits)\\n\\n qml.disable_tape()\",\n \"def error(self):\\n pass\",\n \"def __call__(self):\\r\\n raise self\",\n \"def __call__(self):\\r\\n raise self\",\n \"def calc_error_vrn_occu(opt, net, cuda, dataset, num_tests):\\n\\n if num_tests > len(dataset):\\n num_tests = len(dataset)\\n with torch.no_grad():\\n error_total_arr, erorr_arr, error_view_render_arr, error_3d_gan_generator_arr, error_3d_gan_discriminator_fake_arr, error_3d_gan_discriminator_real_arr, IOU_arr, prec_arr, recall_arr = [], [], [], [], [], [], [], [], []\\n accuracy_3d_gan_discriminator_fake_arr, accuracy_3d_gan_discriminator_real_arr = [], []\\n for idx in tqdm(range(num_tests)):\\n\\n # retrieve data for one frame (or multi-view)\\n data = dataset[idx * len(dataset) // num_tests]\\n image_tensor = data['img'].to(device=cuda) # (V, C-3, H-512, W-512), RGB, 3x512x512 images, float -1. ~ 1., bg is all ZEROS not -1.\\n meshVoxels_tensor = data['meshVoxels'].to(device=cuda) # (1, D-128, H-192, W-128), float 1.0-inside, 0.0-outside\\n viewDirectionIdx_tensor = torch.zeros([1], dtype=torch.int32).to(device=cuda) # small dummy tensors\\n target_view_tensor = torch.zeros([1], dtype=torch.int32).to(device=cuda) # small dummy tensors\\n if opt.use_view_pred_loss: viewDirectionIdx_tensor = data[\\\"view_directions\\\"].to(device=cuda) # integers, {0, 1, 2, 3} maps to {front, right, back, left}\\n if opt.use_view_pred_loss: target_view_tensor = data['target_view'].to(device=cuda) # (C-3, H-384, W-256) target-view RGB images, float -1. ~ 1., bg is all ZEROS not -1. \\n\\n # reshape the data\\n image_tensor = image_tensor.view(-1, image_tensor.shape[-3], image_tensor.shape[-2], image_tensor.shape[-1]) # (V, C-3, H-512, W-512)\\n meshVoxels_tensor = meshVoxels_tensor.view(-1, 1, meshVoxels_tensor.shape[-3], meshVoxels_tensor.shape[-2], meshVoxels_tensor.shape[-1]) # (1, 1, D-128, H-192, W-128)\\n if opt.use_view_pred_loss: viewDirectionIdx_tensor = viewDirectionIdx_tensor.view(-1 ) # (1,)\\n if opt.use_view_pred_loss: target_view_tensor = target_view_tensor.view(-1, target_view_tensor.shape[-3], target_view_tensor.shape[-2], target_view_tensor.shape[-1]) # (1, C-3, H-384, W-256)\\n\\n # forward pass\\n # if opt.use_view_pred_loss: pred_occ, error, render_rgb, pseudo_inverseDepth, error_view_render = net.forward(images=image_tensor, labels=meshVoxels_tensor, view_directions=viewDirectionIdx_tensor, target_views=target_view_tensor)\\n # else: pred_occ, error = net.forward(images=image_tensor, labels=meshVoxels_tensor, view_directions=viewDirectionIdx_tensor, target_views=target_view_tensor)\\n forward_return_dict = net.forward(images=image_tensor, labels=meshVoxels_tensor, view_directions=viewDirectionIdx_tensor, target_views=target_view_tensor)\\n pred_occ = forward_return_dict[\\\"pred_occ\\\"]\\n error = forward_return_dict[\\\"error\\\"].mean().item()\\n error_view_render = forward_return_dict[\\\"error_view_render\\\"].mean().item() if opt.use_view_pred_loss else 0.\\n error_3d_gan_generator = forward_return_dict[\\\"error_3d_gan_generator\\\"].mean().item() if opt.use_3d_gan else 0.\\n error_3d_gan_discriminator_fake = forward_return_dict[\\\"error_3d_gan_discriminator_fake\\\"].mean().item() if opt.use_3d_gan else 0.\\n error_3d_gan_discriminator_real = forward_return_dict[\\\"error_3d_gan_discriminator_real\\\"].mean().item() if opt.use_3d_gan else 0.\\n accuracy_3d_gan_discriminator_fake = forward_return_dict[\\\"accuracy_3d_gan_discriminator_fake\\\"].mean().item() if opt.use_3d_gan else 0.\\n accuracy_3d_gan_discriminator_real = forward_return_dict[\\\"accuracy_3d_gan_discriminator_real\\\"].mean().item() if opt.use_3d_gan else 0.\\n error_total = error + error_view_render + error_3d_gan_generator\\n\\n # compute errors {IOU, prec, recall} based on the current set of query 3D points\\n IOU, prec, recall = compute_acc(pred_occ, meshVoxels_tensor) # R, R, R\\n\\n # print(\\n # '{0}/{1} | Error: {2:06f} IOU: {3:06f} prec: {4:06f} recall: {5:06f}'\\n # .format(idx, num_tests, error.item(), IOU.item(), prec.item(), recall.item()))\\n error_total_arr.append(error_total)\\n erorr_arr.append(error)\\n error_view_render_arr.append(error_view_render)\\n error_3d_gan_generator_arr.append(error_3d_gan_generator)\\n error_3d_gan_discriminator_fake_arr.append(error_3d_gan_discriminator_fake)\\n accuracy_3d_gan_discriminator_fake_arr.append(accuracy_3d_gan_discriminator_fake)\\n error_3d_gan_discriminator_real_arr.append(error_3d_gan_discriminator_real)\\n accuracy_3d_gan_discriminator_real_arr.append(accuracy_3d_gan_discriminator_real)\\n IOU_arr.append(IOU.item())\\n prec_arr.append(prec.item())\\n recall_arr.append(recall.item())\\n\\n return np.average(error_total_arr), np.average(erorr_arr), np.average(error_view_render_arr), np.average(error_3d_gan_generator_arr), np.average(error_3d_gan_discriminator_fake_arr), np.average(accuracy_3d_gan_discriminator_fake_arr), np.average(error_3d_gan_discriminator_real_arr), np.average(accuracy_3d_gan_discriminator_real_arr), np.average(IOU_arr), np.average(prec_arr), np.average(recall_arr)\",\n \"def check_result(ec):\\r\\n # NOTE: This will break some oscilloscopes that are powered by USB.\\r\\n # Some of the newer scopes, can actually be powered by USB and will\\r\\n # return a useful value. That should be given back to the user.\\r\\n # I guess we can deal with these edge cases in the functions themselves\\r\\n if ec == 0:\\r\\n return\\r\\n\\r\\n else:\\r\\n ecName = error_num_to_name(ec)\\r\\n ecDesc = error_num_to_desc(ec)\\r\\n raise IOError('Error calling %s: %s (%s)' % (\\r\\n str(inspect.stack()[1][3]), ecName, ecDesc))\",\n \"def exception(self, e):\\n pass\",\n \"def test_deviceX_1():\\n assert 0\",\n \"def test_indices_nonexistant(self, operable_mock_device_2_wires):\\n\\n def circuit(x):\\n qml.Rot(0.3, x, -0.2, wires=[0])\\n return qml.expval(qml.PauliZ(0))\\n\\n node = JacobianQNode(circuit, operable_mock_device_2_wires)\\n\\n with pytest.raises(ValueError, match=\\\"Tried to compute the gradient with respect to\\\"):\\n node.jacobian(0.5, wrt=[0, 6])\\n\\n with pytest.raises(ValueError, match=\\\"Tried to compute the gradient with respect to\\\"):\\n node.jacobian(0.5, wrt=[1, -1])\",\n \"def error():\\n return None\",\n \"def test_NonrealtimeProvider_add_bus_error(session):\\n provider = Provider.from_context(session)\\n with pytest.raises(ValueError):\\n provider.add_bus()\\n with provider.at(0):\\n with pytest.raises(ValueError):\\n provider.add_bus(calculation_rate=\\\"scalar\\\")\",\n \"def optimize(self, target_device):\\r\\n return NotImplementedError\",\n \"def test_post_codegen_error_query(self):\\n with tempfile.TemporaryDirectory() as tmpdirname:\\n translator = AstUprootTranslator()\\n with pytest.raises(GenerateCodeException):\\n translator.generate_code(\\\"\\\", cache_path=tmpdirname)\",\n \"def try_gpu():\\n try:\\n ctx = mx.gpu()\\n _ = nd.array([0], ctx=ctx)\\n except:\\n ctx = mx.cpu()\\n return ctx\",\n \"def try_gpu():\\n try:\\n ctx = mx.gpu()\\n _ = nd.array([0], ctx=ctx)\\n except:\\n ctx = mx.cpu()\\n return ctx\",\n \"async def test_get_device_device_unavailable(hass):\\n with patch(\\n \\\"axis.vapix.Vapix.request\\\", side_effect=axislib.RequestError\\n ), pytest.raises(axis.errors.CannotConnect):\\n await axis.device.get_device(hass, host=\\\"\\\", port=\\\"\\\", username=\\\"\\\", password=\\\"\\\")\",\n \"def testInvalidKernelMapper(self):\\n\\n class DummyKernelMapper(object):\\n\\n def __init__(self):\\n pass\\n\\n feature = layers.real_valued_column('feature')\\n kernel_mappers = {feature: [DummyKernelMapper()]}\\n with self.assertRaises(ValueError):\\n _ = kernel_estimators.KernelLinearClassifier(\\n feature_columns=[feature], kernel_mappers=kernel_mappers)\",\n \"def try_gpu(x):\\n global _GPUS_EXIST\\n if _GPUS_EXIST:\\n try:\\n return x.cuda()\\n except (AssertionError, RuntimeError):\\n print('No GPUs detected. Sticking with CPUs.')\\n _GPUS_EXIST = False\\n return x\",\n \"def test_compile_pixels_error(opened_image):\\n # mock output for getter of img_in.load()\\n opened_image.load.return_value.__getitem__.return_value = dict()\\n\\n with pytest.raises(RuntimeError) as exception_info:\\n synes.translate_image(\\\"/path/image.png\\\", 44100)\\n\\n exception_str = (\\n \\\"Unable to parse pixels, unrecognized pixel type '{}'.\\\".format(dict)\\n )\\n assert exception_str in str(exception_info)\",\n \"def test_tensor_method(free_alg):\\n\\n tensor = free_alg.sum(10)\\n assert tensor.get_one() == 1\\n\\n with pytest.raises(AttributeError):\\n tensor.get_two()\",\n \"async def test_get_device_unknown_error(hass):\\n with patch(\\n \\\"axis.vapix.Vapix.request\\\", side_effect=axislib.AxisException\\n ), pytest.raises(axis.errors.AuthenticationRequired):\\n await axis.device.get_device(hass, host=\\\"\\\", port=\\\"\\\", username=\\\"\\\", password=\\\"\\\")\",\n \"def try_gpu(x):\\n global _GPUS_EXIST\\n\\n if _GPUS_EXIST:\\n try:\\n return x.cuda()\\n except (AssertionError, RuntimeError):\\n # actually, GPUs don't exist\\n print 'No GPUs detected. Sticking with CPUs.'\\n _GPUS_EXIST = False\\n return x\\n else:\\n return x\",\n \"def test_Icat_exceptions(teff, z, logg):\\n with pytest.raises(ParameterOutOfBounds):\\n Icat('k93models', teff, z, logg)\",\n \"def test_class_errored(self, cls, exception):\",\n \"def test_Calc_exceptions(self, start, end):\\n with pytest.raises(Exception):\\n pycgmCalc.Calc(start, end, self.motion_data, self.cal_SM)\",\n \"def testCCHalt(self):\\n cdl_convert.config.HALT_ON_ERROR = True\\n\\n def getCC():\\n self.ccr_bad.cc\\n\\n self.assertRaises(\\n ValueError,\\n getCC\\n )\",\n \"def _async_raise(self, tid, exctype): \\n tid = ctypes.c_long(tid) \\n if not inspect.isclass(exctype): \\n exctype = type(exctype) \\n res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(exctype)) \\n if res == 0: \\n raise ValueError(\\\"invalid thread id\\\") \\n elif res != 1: \\n # \\\"\\\"\\\"if it returns a number greater than one, you're in trouble, \\n # and you should call it again with exc=NULL to revert the effect\\\"\\\"\\\" \\n ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None) \\n raise SystemError(\\\"PyThreadState_SetAsyncExc failed\\\")\",\n \"def _catchindexerror(func):\\n @wraps(func)\\n def newfunc(*args, **kwargs):\\n \\\"\\\"\\\" Catch the index error \\\"\\\"\\\"\\n try:\\n return func(*args, **kwargs)\\n except IndexError as e:\\n raise CharmmPSFError('Array is too short: %s' % e)\\n\\n return newfunc\",\n \"def _async_raise(self,tid, exctype):\\n tid = ctypes.c_long(tid)\\n if not inspect.isclass(exctype):\\n exctype = type(exctype)\\n res = ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, ctypes.py_object(exctype))\\n if res == 0:\\n raise ValueError(\\\"invalid thread id\\\")\\n elif res != 1:\\n # \\\"\\\"\\\"if it returns a number greater than one, you're in trouble,\\n # and you should call it again with exc=NULL to revert the effect\\\"\\\"\\\"\\n ctypes.pythonapi.PyThreadState_SetAsyncExc(tid, None)\\n raise SystemError(\\\"PyThreadState_SetAsyncExc failed\\\")\",\n \"def construct_error_bdev(py, accelerator, basename):\\n acc_client = get_accelerator_client(py, accelerator)\\n acc_client.construct_error_bdev(basename)\",\n \"def ExceptionPropertyType_test(type1: str, type2: str):\\n m = pyflamegpu.ModelDescription(\\\"model\\\")\\n ed = m.Environment()\\n add_func_t1 = getattr(ed, f\\\"newProperty{type1}\\\")\\n add_func_array_t1 = getattr(ed, f\\\"newPropertyArray{type1}\\\")\\n set_func_t1 = getattr(ed, f\\\"setProperty{type1}\\\")\\n set_func_t2 = getattr(ed, f\\\"setProperty{type2}\\\")\\n set_func_array_t2 = getattr(ed, f\\\"setPropertyArray{type2}\\\")\\n \\n a_t1 = 1\\n a_t2 = 1\\n b_t1 = [0] * ARRAY_TEST_LEN\\n b_t2 = [0] * ARRAY_TEST_LEN\\n for i in range(ARRAY_TEST_LEN):\\n b_t1[i] = i\\n b_t2[i] = i\\n add_func_t1(\\\"a\\\", a_t1, True)\\n add_func_array_t1(\\\"b\\\", b_t1, True)\\n \\n with pytest.raises(pyflamegpu.FLAMEGPURuntimeException) as e:\\n set_func_t2(\\\"a\\\", a_t2)\\n assert e.value.type() == \\\"InvalidEnvPropertyType\\\"\\n with pytest.raises(pyflamegpu.FLAMEGPURuntimeException) as e:\\n set_func_array_t2(\\\"b\\\", b_t2)\\n assert e.value.type() == \\\"InvalidEnvPropertyType\\\"\\n with pytest.raises(pyflamegpu.FLAMEGPURuntimeException) as e:\\n set_func_t2(\\\"b\\\", 0, a_t2)\\n assert e.value.type() == \\\"InvalidEnvPropertyType\\\"\",\n \"def fault():\\n return FaultCohesiveKin()\",\n \"def test_asgi_exc_info(self):\\n app = otel_asgi.OpenTelemetryMiddleware(error_asgi)\\n self.seed_app(app)\\n self.send_default_request()\\n outputs = self.get_all_output()\\n self.validate_outputs(outputs, error=ValueError)\",\n \"def test_asgi_exc_info(self):\\n app = otel_asgi.OpenTelemetryMiddleware(error_asgi)\\n self.seed_app(app)\\n self.send_default_request()\\n outputs = self.get_all_output()\\n self.validate_outputs(outputs, error=ValueError)\",\n \"def test_errors():\\n\\n if not build_rir_available:\\n return\\n\\n N = 300\\n fs = 16000\\n fdl = 81\\n rir = np.zeros(N, dtype=np.float32)\\n\\n time = np.array([100 / fs, 200 / fs], dtype=np.float32)\\n alpha = np.array([1.0, 1.0], dtype=np.float32)\\n visibility = np.array([1, 1], dtype=np.int32)\\n\\n with pytest.raises(RuntimeError):\\n libroom.rir_builder(rir, time, alpha[:1], visibility, fs, fdl, 20, 2)\\n\\n with pytest.raises(RuntimeError):\\n libroom.rir_builder(rir, time, alpha, visibility[:1], fs, fdl, 20, 2)\\n\\n with pytest.raises(RuntimeError):\\n libroom.rir_builder(rir, time, alpha, visibility, fs, 80, 20, 2)\",\n \"def test_randint6():\\n # res = np.array([[9, 9], [5, 8]])\\n obj.exception(etype=ValueError, mode=\\\"python\\\", low=0, shape=[2, 2])\",\n \"def fatal(self, *args, **kwargs):\",\n \"def throw(error: Exception) -> None:\\n raise error\",\n \"def on_exception(self):\\n pass\",\n \"def test_get_pci_device_by_moid(self):\\n pass\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6091167","0.5937788","0.5826724","0.5811849","0.56694853","0.5567256","0.5509975","0.55026484","0.54509616","0.54496866","0.54445916","0.5436744","0.5431065","0.5427501","0.5389274","0.538114","0.5358859","0.5358747","0.5344634","0.5293775","0.52785766","0.5277553","0.5270618","0.5267663","0.52596396","0.5257948","0.525074","0.525007","0.52465594","0.5245436","0.52232087","0.5222073","0.5212288","0.5206764","0.5201596","0.5200769","0.5185739","0.5178041","0.5173584","0.5162037","0.51619697","0.5152249","0.51512593","0.5150469","0.513594","0.5131311","0.5126608","0.51233405","0.5121882","0.511612","0.5109014","0.51081","0.5104656","0.5094732","0.50926954","0.5092504","0.50916815","0.50865644","0.5083683","0.5082642","0.50808644","0.5076018","0.5069638","0.50653756","0.50653756","0.5059129","0.50584495","0.5052859","0.5049422","0.5047588","0.50470346","0.5046366","0.5042782","0.50401205","0.50239277","0.50239277","0.50226104","0.50183785","0.50180256","0.5011265","0.5008659","0.50064844","0.5002488","0.50024235","0.5001619","0.5000707","0.50000435","0.4992125","0.49908507","0.4989417","0.4989193","0.49881342","0.49757197","0.4969603","0.4969603","0.4966443","0.49648443","0.49641964","0.49633265","0.49620306","0.49606505"],"string":"[\n \"0.6091167\",\n \"0.5937788\",\n \"0.5826724\",\n \"0.5811849\",\n \"0.56694853\",\n \"0.5567256\",\n \"0.5509975\",\n \"0.55026484\",\n \"0.54509616\",\n \"0.54496866\",\n \"0.54445916\",\n \"0.5436744\",\n \"0.5431065\",\n \"0.5427501\",\n \"0.5389274\",\n \"0.538114\",\n \"0.5358859\",\n \"0.5358747\",\n \"0.5344634\",\n \"0.5293775\",\n \"0.52785766\",\n \"0.5277553\",\n \"0.5270618\",\n \"0.5267663\",\n \"0.52596396\",\n \"0.5257948\",\n \"0.525074\",\n \"0.525007\",\n \"0.52465594\",\n \"0.5245436\",\n \"0.52232087\",\n \"0.5222073\",\n \"0.5212288\",\n \"0.5206764\",\n \"0.5201596\",\n \"0.5200769\",\n \"0.5185739\",\n \"0.5178041\",\n \"0.5173584\",\n \"0.5162037\",\n \"0.51619697\",\n \"0.5152249\",\n \"0.51512593\",\n \"0.5150469\",\n \"0.513594\",\n \"0.5131311\",\n \"0.5126608\",\n \"0.51233405\",\n \"0.5121882\",\n \"0.511612\",\n \"0.5109014\",\n \"0.51081\",\n \"0.5104656\",\n \"0.5094732\",\n \"0.50926954\",\n \"0.5092504\",\n \"0.50916815\",\n \"0.50865644\",\n \"0.5083683\",\n \"0.5082642\",\n \"0.50808644\",\n \"0.5076018\",\n \"0.5069638\",\n \"0.50653756\",\n \"0.50653756\",\n \"0.5059129\",\n \"0.50584495\",\n \"0.5052859\",\n \"0.5049422\",\n \"0.5047588\",\n \"0.50470346\",\n \"0.5046366\",\n \"0.5042782\",\n \"0.50401205\",\n \"0.50239277\",\n \"0.50239277\",\n \"0.50226104\",\n \"0.50183785\",\n \"0.50180256\",\n \"0.5011265\",\n \"0.5008659\",\n \"0.50064844\",\n \"0.5002488\",\n \"0.50024235\",\n \"0.5001619\",\n \"0.5000707\",\n \"0.50000435\",\n \"0.4992125\",\n \"0.49908507\",\n \"0.4989417\",\n \"0.4989193\",\n \"0.49881342\",\n \"0.49757197\",\n \"0.4969603\",\n \"0.4969603\",\n \"0.4966443\",\n \"0.49648443\",\n \"0.49641964\",\n \"0.49633265\",\n \"0.49620306\",\n \"0.49606505\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":17795,"cells":{"query":{"kind":"string","value":"Checks the decorators of the function definition much must be either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'. Each is then processed in a different way using a specific dispatcher. Function calls are actually checked and only permitted (or user defined) function calls are supported."},"document":{"kind":"string","value":"def _FunctionDef(self, t):\n self.write(\"\\n\")\n # check decorators\n if len(t.decorator_list) != 1 or not isinstance(t.decorator_list[0], ast.Attribute):\n self.RaiseError(t, \"Function definitions require a single pyflamegpu decorator of either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'\") \n # FLAMEGPU_AGENT_FUNCTION\n if t.decorator_list[0].attr == 'agent_function' and t.decorator_list[0].value.id == 'pyflamegpu':\n if getattr(t, \"returns\", False):\n self.RaiseWarning(t, \"Function definition return type not supported on 'pyflamegpu.agent_function'\")\n self.fill(f\"FLAMEGPU_AGENT_FUNCTION({t.name}, \")\n self.dispatchFGPUFunctionArgs(t)\n self.write(\")\")\n # FLAMEGPU_DEVICE_FUNCTION\n elif t.decorator_list[0].attr == 'device_function' and t.decorator_list[0].value.id == 'pyflamegpu':\n self.fill(f\"FLAMEGPU_DEVICE_FUNCTION \")\n if t.returns:\n self.dispatchType(t.returns)\n else:\n self.write(\"void\")\n self.write(f\" {t.name}(\")\n self.dispatchFGPUDeviceFunctionArgs(t)\n self.write(\")\")\n # add to list of defined functions that can be called\n self._device_functions.append(t.name)\n # FLAMEGPU_DEVICE_FUNCTION\n elif t.decorator_list[0].attr == 'agent_function_condition' and t.decorator_list[0].value.id == 'pyflamegpu':\n # check for return annotation\n if not hasattr(t, \"returns\"):\n self.RaiseError(t, \"Agent function conditions must have a 'bool' return type specified as a return type annotation\")\n # check for return annotation type\n if not isinstance(t.returns, ast.Name):\n self.RaiseError(t, \"Agent function conditions return type must be 'bool'\")\n if t.returns.id is not 'bool':\n self.RaiseError(t, \"Agent function conditions return type must be 'bool'\")\n # check to ensure no arguments (discard any with a warning)\n if t.args.args:\n self.RaiseWarning(t, \"Agent function conditions does not support arguments. These will be discarded.\")\n # write the agent function macro\n self.fill(f\"FLAMEGPU_AGENT_FUNCTION_CONDITION({t.name})\")\n else:\n self.RaiseError(t, \"Function definition uses an unsupported decorator. Must use either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'\")\n self.enter()\n self.dispatch(t.body)\n self.leave()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def visit_FunctionDef(self, node):\n self.functions[node.name] = self._generate_pytest_decorators(node.decorator_list)\n self.generic_visit(node)","def isValidFunction(self):\n for token in self.value:\n if token.type == 'defFunction' or token.type == 'callFunction':\n if token.value.split('(')[0] == self.name:\n return False\n return True","def frontend_access_required(function=None):\n access_denied_info_url = reverse_lazy('excerptexport:access_denied')\n actual_decorator = user_passes_test(\n _may_user_access_osmaxx_frontend,\n login_url=access_denied_info_url\n )\n if function:\n return actual_decorator(function)\n return actual_decorator","def check_arguments_are_mass_functions(function):\n @functools.wraps(function)\n def wrapped_function(*args):\n if len(args) < 2:\n raise TypeError(\n \"Not enough mass functions provided, it should receive at least one!\"\n )\n for i in range(len(args)):\n if not isinstance(args[i], MassFunction):\n raise TypeError(\n \"This method accept only mass functions as arguments!\"\n )\n return function(*args)\n return wrapped_function","def test_tolerate_return_function_decorator():\n decorator = tolerate()\n ok_(inspect.isfunction(decorator))\n\n args, varargs, keywords, defaults = inspect.getargspec(decorator)\n eq_(len(args), 1, 'Return function should take one argument for function')","def check_mass_functions_compatibility(function):\n @functools.wraps(function)\n def wrapped_function(*args):\n for i in range(len(args)):\n for j in range(len(args)):\n if i != j and not args[i].is_compatible(args[j]):\n raise IncompatibleMassFunctionsError(args[i], args[j])\n return function(*args)\n return wrapped_function","def _func_only(func):\n if inspect.isfunction(func):\n return\n else:\n raise Exception(\"Only functions can be tasks\")","def add_check_function(check_function: Callable):\n\n def decorator(func: Callable):\n @wraps(func)\n def wrapper(*args, **kwargs):\n check_function(*args, *kwargs.values())\n return func(*args, **kwargs)\n\n return wrapper\n\n name = getattr(check_function, '__name__', '`func`')\n decorator.__doc__ = f\"Check the function's arguments via `{name}` before calling it.\"\n return decorator","def validate(host_calls):\n\n for name, host_call in host_calls.items():\n if not isinstance(host_call, (tuple, list)):\n raise ValueError('{} should be tuple or list'.format(name))\n if len(host_call) != 2:\n raise ValueError('{} should have two elements.'.format(name))\n if not callable(host_call[0]):\n raise TypeError('{}[0] should be callable.'.format(name))\n if not isinstance(host_call[1], (tuple, list, dict)):\n raise ValueError('{}[1] should be tuple or list, or dict.'.format(name))\n\n if isinstance(host_call[1], (tuple, list)):\n fullargspec = tf_inspect.getfullargspec(host_call[0])\n fn_args = util.fn_args(host_call[0])\n # wrapped_hostcall_with_global_step uses varargs, so we allow that.\n if fullargspec.varargs is None and len(host_call[1]) != len(fn_args):\n raise RuntimeError(\n 'In TPUEstimatorSpec.{}, length of tensors {} does not match '\n 'method args of the function, which takes {}.'.format(\n name, len(host_call[1]), len(fn_args)))","def _parse_functions(self, locals: dict):\n functions_dict = dict(filter(self._isfunction, locals.items()))\n functions = []\n if not self.args:\n functions.append(next(iter(functions_dict.values())))\n else:\n for i in range(len(self.args)):\n if functions_dict.get(self.args[0]):\n functions.append(functions_dict[self.args.pop(0)])\n else:\n if not functions:\n msg = f'ezmake command args: {self.args} did not ' + \\\n 'match any functions defined in Makefile.py: %s' %\\\n list(functions_dict.keys())\n raise TypeError(msg)\n break\n self.functions = functions","def _check_invocation_requirements(\n solid_def: \"OpDefinition\", context: Optional[\"UnboundOpExecutionContext\"]\n) -> None:\n # Check resource requirements\n if (\n solid_def.required_resource_keys\n and cast(\"DecoratedOpFunction\", solid_def.compute_fn).has_context_arg()\n and context is None\n ):\n node_label = solid_def.node_type_str # string \"solid\" for solids, \"op\" for ops\n raise DagsterInvalidInvocationError(\n f'{node_label} \"{solid_def.name}\" has required resources, but no context was provided.'\n f\" Use the `build_{node_label}_context` function to construct a context with the\"\n \" required resources.\"\n )\n\n # Check config requirements\n if not context and solid_def.config_schema.as_field().is_required:\n node_label = solid_def.node_type_str # string \"solid\" for solids, \"op\" for ops\n raise DagsterInvalidInvocationError(\n f'{node_label} \"{solid_def.name}\" has required config schema, but no context was'\n f\" provided. Use the `build_{node_label}_context` function to create a context with\"\n \" config.\"\n )","def admin_required(function=None, redirect_field_name=REDIRECT_FIELD_NAME, login_url=None):\n actual_decorator = user_passes_test(\n lambda u: u.is_admin,\n login_url=login_url,\n redirect_field_name=redirect_field_name\n )\n if function:\n return actual_decorator(function)\n return actual_decorator","def is_unsupported(func):\n\n for m in BUILTIN_LIKELY_MODULES:\n for v in m.__dict__.values():\n if not callable(v):\n continue\n if func is v:\n translator_logger.log(\n 2,\n \"Whitelist: {} is part of built-in module and does not have to be transformed.\".format(\n func\n ),\n )\n return True\n\n # NOTE: should be placed before `is_paddle_func`\n # The api(s) should be considered as plain function and convert\n # them into static layer code.\n from paddle.nn import Sequential\n\n PADDLE_NEED_CONVERT_APIS = [Sequential]\n if type(func) in PADDLE_NEED_CONVERT_APIS:\n return False\n\n if is_paddle_func(func):\n translator_logger.log(\n 2,\n \"Whitelist: {} is part of Paddle module and does not have to be transformed.\".format(\n func\n ),\n )\n return True","def decorate_with_checker(func: CallableT) -> CallableT:\n assert not hasattr(func, \"__preconditions__\"), \\\n \"Expected func to have no list of preconditions (there should be only a single contract checker per function).\"\n\n assert not hasattr(func, \"__postconditions__\"), \\\n \"Expected func to have no list of postconditions (there should be only a single contract checker per function).\"\n\n assert not hasattr(func, \"__postcondition_snapshots__\"), \\\n \"Expected func to have no list of postcondition snapshots (there should be only a single contract checker \" \\\n \"per function).\"\n\n sign = inspect.signature(func)\n if '_ARGS' in sign.parameters:\n raise TypeError(\n 'The arguments of the function to be decorated with a contract checker include \"_ARGS\" which is '\n 'a reserved placeholder for positional arguments in the condition.')\n\n if '_KWARGS' in sign.parameters:\n raise TypeError(\n 'The arguments of the function to be decorated with a contract checker include \"_KWARGS\" which is '\n 'a reserved placeholder for keyword arguments in the condition.')\n\n param_names = list(sign.parameters.keys())\n\n # Determine the default argument values\n kwdefaults = resolve_kwdefaults(sign=sign)\n\n id_func = id(func)\n\n # (mristin, 2021-02-16)\n # Admittedly, this branching on sync/async is absolutely monstrous.\n # However, I couldn't find out an easier way to refactor the code so that it supports async.\n # Python expects us to explicitly colour functions as sync/async so we can not just put in an if-statement and\n # introduce an \"await\".\n #\n # The two wrappers need to be manually maintained in parallel.\n # Whenever you make a change, please inspect manually that both sync and async code exercises equivalent behavior.\n # For example, copy/paste the two blocks of code in separate files and perform a diff.\n\n if inspect.iscoroutinefunction(func):\n\n async def wrapper(*args, **kwargs): # type: ignore\n \"\"\"Wrap func by checking the preconditions and postconditions.\"\"\"\n kwargs_error = _assert_no_invalid_kwargs(kwargs)\n if kwargs_error:\n raise kwargs_error\n\n # We need to create a new in-progress set if it is None as the ``ContextVar`` does not accept\n # a factory function for the default argument. If we didn't do this, and simply set an empty\n # set as the default, ``ContextVar`` would always point to the same set by copying the default\n # by reference.\n in_progress = _IN_PROGRESS.get()\n if in_progress is None:\n in_progress = set()\n _IN_PROGRESS.set(in_progress)\n\n # Use try-finally instead of ExitStack for performance.\n try:\n # If the wrapper is already checking the contracts for the wrapped function, avoid a recursive loop\n # by skipping any subsequent contract checks for the same function.\n if id_func in in_progress:\n return await func(*args, **kwargs)\n\n in_progress.add(id_func)\n\n (preconditions, snapshots, postconditions) = _unpack_pre_snap_posts(wrapper)\n\n resolved_kwargs = kwargs_from_call(\n param_names=param_names, kwdefaults=kwdefaults, args=args, kwargs=kwargs)\n\n type_error = _assert_resolved_kwargs_valid(postconditions, resolved_kwargs)\n if type_error:\n raise type_error\n\n violation_error = await _assert_preconditions_async(\n preconditions=preconditions, resolved_kwargs=resolved_kwargs)\n if violation_error:\n raise violation_error\n\n # Capture the snapshots\n if postconditions and snapshots:\n resolved_kwargs['OLD'] = await _capture_old_async(\n snapshots=snapshots, resolved_kwargs=resolved_kwargs)\n\n # Ideally, we would catch any exception here and strip the checkers from the traceback.\n # Unfortunately, this can not be done in Python 3, see\n # https://stackoverflow.com/questions/44813333/how-can-i-elide-a-function-wrapper-from-the-traceback-in-python-3\n result = await func(*args, **kwargs)\n\n if postconditions:\n resolved_kwargs['result'] = result\n\n violation_error = await _assert_postconditions_async(\n postconditions=postconditions, resolved_kwargs=resolved_kwargs)\n if violation_error:\n raise violation_error\n\n return result\n finally:\n in_progress.discard(id_func)\n else:\n\n def wrapper(*args, **kwargs): # type: ignore\n \"\"\"Wrap func by checking the preconditions and postconditions.\"\"\"\n kwargs_error = _assert_no_invalid_kwargs(kwargs)\n if kwargs_error:\n raise kwargs_error\n\n # We need to create a new in-progress set if it is None as the ``ContextVar`` does not accept\n # a factory function for the default argument. If we didn't do this, and simply set an empty\n # set as the default, ``ContextVar`` would always point to the same set by copying the default\n # by reference.\n in_progress = _IN_PROGRESS.get()\n if in_progress is None:\n in_progress = set()\n _IN_PROGRESS.set(in_progress)\n\n # Use try-finally instead of ExitStack for performance.\n try:\n # If the wrapper is already checking the contracts for the wrapped function, avoid a recursive loop\n # by skipping any subsequent contract checks for the same function.\n if id_func in in_progress:\n return func(*args, **kwargs)\n\n in_progress.add(id_func)\n\n (preconditions, snapshots, postconditions) = _unpack_pre_snap_posts(wrapper)\n\n resolved_kwargs = kwargs_from_call(\n param_names=param_names, kwdefaults=kwdefaults, args=args, kwargs=kwargs)\n\n type_error = _assert_resolved_kwargs_valid(\n postconditions=postconditions, resolved_kwargs=resolved_kwargs)\n if type_error:\n raise type_error\n\n violation_error = _assert_preconditions(\n preconditions=preconditions, resolved_kwargs=resolved_kwargs, func=func)\n if violation_error:\n raise violation_error\n\n # Capture the snapshots\n if postconditions and snapshots:\n resolved_kwargs['OLD'] = _capture_old(\n snapshots=snapshots, resolved_kwargs=resolved_kwargs, func=func)\n\n # Ideally, we would catch any exception here and strip the checkers from the traceback.\n # Unfortunately, this can not be done in Python 3, see\n # https://stackoverflow.com/questions/44813333/how-can-i-elide-a-function-wrapper-from-the-traceback-in-python-3\n result = func(*args, **kwargs)\n\n if postconditions:\n resolved_kwargs['result'] = result\n\n violation_error = _assert_postconditions(\n postconditions=postconditions, resolved_kwargs=resolved_kwargs, func=func)\n if violation_error:\n raise violation_error\n\n return result\n finally:\n in_progress.discard(id_func)\n\n # Copy __doc__ and other properties so that doctests can run\n functools.update_wrapper(wrapper=wrapper, wrapped=func)\n\n assert not hasattr(wrapper, \"__preconditions__\"), \"Expected no preconditions set on a pristine contract checker.\"\n assert not hasattr(wrapper, \"__postcondition_snapshots__\"), \\\n \"Expected no postcondition snapshots set on a pristine contract checker.\"\n assert not hasattr(wrapper, \"__postconditions__\"), \"Expected no postconditions set on a pristine contract checker.\"\n\n # Precondition is a list of condition groups (i.e. disjunctive normal form):\n # each group consists of AND'ed preconditions, while the groups are OR'ed.\n #\n # This is necessary in order to implement \"require else\" logic when a class weakens the preconditions of\n # its base class.\n setattr(wrapper, \"__preconditions__\", [])\n setattr(wrapper, \"__postcondition_snapshots__\", [])\n setattr(wrapper, \"__postconditions__\", [])\n\n return wrapper # type: ignore","def visit_any_function(self, node: AnyFunctionDef) -> None:\n self._check_unused_variables(node)\n self._check_generator(node)\n self._check_descriptor_decorators(node)\n self.generic_visit(node)","def accept_funcs(func):\n @wraps(func)\n def wrapper(funcs, *args, **kwargs):\n if hasattr(funcs[0], '__name__'):\n funcs = [(f.__name__, f) for f in funcs]\n return func(funcs, *args, **kwargs)\n return wrapper","def check_chief(function_to_decorate):\r\n @wraps(function_to_decorate)\r\n def decorated_function(*args, **kwargs):\r\n \tif g.my['rank'] > 15:\r\n \t\tabort(401)\r\n \treturn function_to_decorate(*args, **kwargs)\r\n return decorated_function","def check_admin(function_to_decorate):\r\n @wraps(function_to_decorate)\r\n def decorated_function(*args, **kwargs):\r\n if g.my['rank'] > 25:\r\n abort(401)\r\n return function_to_decorate(*args, **kwargs)\r\n return decorated_function","def check_fcn_attrs(self, node):\n options = node.options\n\n ast = node.ast\n declarator = ast.declarator\n node._has_found_default = False\n\n for attr in declarator.attrs:\n if attr[0] == \"_\": # internal attribute\n continue\n if attr not in [\n \"api\", # arguments to pass to C wrapper.\n \"allocatable\", # return a Fortran ALLOCATABLE\n \"cdesc\",\n \"deref\", # How to dereference pointer\n \"dimension\",\n \"free_pattern\",\n \"len\",\n \"name\",\n \"owner\",\n \"pure\",\n \"rank\",\n ]:\n raise RuntimeError(\n \"Illegal attribute '{}' for function '{}' define at line {}\".format(\n attr, node.ast.name, node.linenumber\n )\n )\n\n meta = declarator.metaattrs\n if ast.typemap is None:\n print(\"XXXXXX typemap is None\")\n if ast.typemap.sgroup == \"shadow\":\n if options.C_shadow_result:\n meta[\"api\"] = \"capptr\"\n else:\n meta[\"api\"] = \"capsule\"\n if declarator.is_ctor():\n meta[\"intent\"] = \"ctor\"\n elif declarator.is_dtor():\n meta[\"intent\"] = \"dtor\"\n else:\n meta[\"intent\"] = declarator.get_subprogram()\n self.check_deref_attr_func(node)\n self.check_common_attrs(node.ast)\n\n for arg in declarator.params:\n if arg.declarator is None:\n raise RuntimeError(\"Argument must have name in {} at line {}\".format(\n node.decl, node.linenumber))\n self.check_arg_attrs(node, arg)\n\n if node.fortran_generic:\n for generic in node.fortran_generic:\n for garg in generic.decls:\n generic._has_found_default = False\n self.check_arg_attrs(generic, garg, node.options)\n check_implied_attrs(node, generic.decls)\n else:\n check_implied_attrs(node, declarator.params)\n\n self.parse_attrs(node, ast)","def _visit_decorators_and_check_asynq(self, decorator_list):\n async_kind = AsyncFunctionKind.non_async\n is_classmethod = False\n is_decorated_coroutine = False\n is_staticmethod = False\n decorators = []\n for decorator in decorator_list:\n # We have to descend into the Call node because the result of\n # asynq.asynq() is a one-off function that we can't test against.\n # This means that the decorator will be visited more than once, which seems OK.\n if isinstance(decorator, ast.Call):\n decorator_value = self.visit(decorator)\n callee = self.visit(decorator.func)\n if isinstance(callee, KnownValue):\n if safe_in(callee.val, self.config.ASYNQ_DECORATORS):\n if any(kw.arg == \"pure\" for kw in decorator.keywords):\n async_kind = AsyncFunctionKind.pure\n else:\n async_kind = AsyncFunctionKind.normal\n elif safe_in(callee.val, self.config.ASYNC_PROXY_DECORATORS):\n # @async_proxy(pure=True) is a noop, so don't treat it specially\n if not any(kw.arg == \"pure\" for kw in decorator.keywords):\n async_kind = AsyncFunctionKind.async_proxy\n decorators.append((callee, decorator_value))\n else:\n decorator_value = self.visit(decorator)\n if decorator_value == KnownValue(classmethod):\n is_classmethod = True\n elif decorator_value == KnownValue(staticmethod):\n is_staticmethod = True\n elif asyncio is not None and decorator_value == KnownValue(\n asyncio.coroutine\n ):\n is_decorated_coroutine = True\n decorators.append((decorator_value, decorator_value))\n return FunctionInfo(\n async_kind=async_kind,\n is_decorated_coroutine=is_decorated_coroutine,\n is_classmethod=is_classmethod,\n is_staticmethod=is_staticmethod,\n decorators=decorators,\n )","def _is_function(self, words):\n if words[0] == 'function':\n if len(words) != 3:\n raise SyntaxError(\"File line {}: Invalid number of arguments for C_FUNCTION command.\".format(self._file_line))\n return True\n else:\n return False","def test_check_fn_validity():\n with pytest.raises(TypeError, match=r'.*did not pass a function.*'):\n s7.check_doc_len(\"abc\")\n s7.check_doc_len(100)\n\n def test_doc_len_multi():\n \"\"\"Checks if the passed function have more than 50 characters in doc string \"\"\"\n\n def helper(doc):\n if doc:\n return True if len(doc) > 50 else False\n else:\n return 0\n\n def f():\n pass\n\n for item in [100, 1.0, \"abcd\", f, s7.add, s7.mul, s7.div]:\n try:\n if isinstance(item, types.FunctionType):\n doc = item.__doc__\n f = s7.check_doc_len(item)\n assert f() is helper(doc)\n except Exception as e:\n assert e.__class__.__name__ == TypeError.__name__","def test_require_at_least_one_and_several_provided(self):\n _func = at_least_one_of('arg1', 'arg2')(undecorated_func)\n self.assertEqual(_func('ahoy', 'there'), 'foo')\n self.assertEqual(_func(arg1='ahoy', arg2='there'), 'foo')\n self.assertEqual(_func('ahoy', arg2='there', arg3='matey'), 'foo')","def _OHE_checker(func):\n\n @wraps(func)\n def wrapper_checker(database, features_list=None):\n _CheckInput._check_database_input(database)\n if features_list:\n for column in features_list:\n _CheckInput._check_column_in_database(column,database)\n return func(database, features_list)\n return wrapper_checker","def _validate_args(\n autologging_integration,\n function_name,\n user_call_args,\n user_call_kwargs,\n autologging_call_args,\n autologging_call_kwargs,\n):\n\n def _validate_new_input(inp):\n \"\"\"\n Validates a new input (arg or kwarg) introduced to the underlying / original ML function\n call during the execution of a patched ML function. The new input is valid if:\n\n - The new input is a function that has been decorated with\n `exception_safe_function_for_class` or `pickalable_exception_safe_function`\n - OR the new input is a class with the `ExceptionSafeClass` metaclass\n - OR the new input is a list and each of its elements is valid according to the\n these criteria\n \"\"\"\n if type(inp) == list:\n for item in inp:\n _validate_new_input(item)\n elif callable(inp):\n assert getattr(inp, _ATTRIBUTE_EXCEPTION_SAFE, False), (\n f\"New function argument '{inp}' passed to original function is not exception-safe.\"\n \" Please decorate the function with `exception_safe_function` or \"\n \"`pickalable_exception_safe_function`\"\n )\n else:\n assert hasattr(inp, \"__class__\") and type(inp.__class__) in [\n ExceptionSafeClass,\n ExceptionSafeAbstractClass,\n ], (\n f\"Invalid new input '{inp}'. New args / kwargs introduced to `original` function \"\n \"calls by patched code must either be functions decorated with \"\n \"`exception_safe_function_for_class`, instances of classes with the \"\n \"`ExceptionSafeClass` or `ExceptionSafeAbstractClass` metaclass safe or lists of \"\n \"such exception safe functions / classes.\"\n )\n\n def _assert_autologging_input_positional_args_are_superset(\n autologging_call_input, user_call_input\n ):\n length_diff = len(autologging_call_input) - len(user_call_input)\n assert (\n length_diff >= 0\n ), f\"{length_diff} expected inputs are missing from the call to the original function.\"\n\n def _assert_autologging_input_kwargs_are_superset(autologging_call_input, user_call_input):\n assert set(user_call_input.keys()).issubset(set(autologging_call_input.keys())), (\n \"Keyword or dictionary arguments to original function omit\"\n \" one or more expected keys: '{}'\".format(\n set(user_call_input.keys()) - set(autologging_call_input.keys())\n )\n )\n\n def _validate(autologging_call_input, user_call_input=None):\n \"\"\"\n Validates that the specified `autologging_call_input` and `user_call_input`\n are compatible. If `user_call_input` is `None`, then `autologging_call_input`\n is regarded as a new input added by autologging and is validated using\n `_validate_new_input`. Otherwise, the following properties must hold:\n\n - `autologging_call_input` and `user_call_input` must have the same type\n (referred to as \"input type\")\n - if the input type is a tuple, list or dictionary, then `autologging_call_input` must\n be equivalent to `user_call_input` or be a superset of `user_call_input`\n - for all other input types, `autologging_call_input` and `user_call_input`\n must be equivalent by reference equality or by object equality\n\n :param autologging_call_input: call input from autologging\n :param user_call_input: call input from user\n \"\"\"\n\n if user_call_input is None and autologging_call_input is not None:\n _validate_new_input(autologging_call_input)\n return\n\n assert type(autologging_call_input) == type(\n user_call_input\n ), \"Type of input to original function '{}' does not match expected type '{}'\".format(\n type(autologging_call_input), type(user_call_input)\n )\n\n if type(autologging_call_input) in [list, tuple]:\n _assert_autologging_input_positional_args_are_superset(\n autologging_call_input, user_call_input\n )\n # If the autologging call input is longer than the user call input, we `zip_longest`\n # will pad the user call input with `None` values to ensure that the subsequent calls\n # to `_validate` identify new inputs added by the autologging call\n for a, u in itertools.zip_longest(autologging_call_input, user_call_input):\n _validate(a, u)\n elif type(autologging_call_input) == dict:\n _assert_autologging_input_kwargs_are_superset(autologging_call_input, user_call_input)\n for key in autologging_call_input.keys():\n _validate(autologging_call_input[key], user_call_input.get(key, None))\n else:\n assert (\n autologging_call_input is user_call_input\n or autologging_call_input == user_call_input\n ), (\n \"Input to original function does not match expected input.\"\n f\" Original: '{autologging_call_input}'. Expected: '{user_call_input}'\"\n )\n\n # Similar validation logic found in _validate, unraveling the list of arguments to exclude\n # checks for any validation exempt positional arguments.\n _assert_autologging_input_positional_args_are_superset(autologging_call_args, user_call_args)\n for index, autologging_call_arg, user_call_arg in itertools.zip_longest(\n range(len(user_call_args)), autologging_call_args, user_call_args\n ):\n if not _is_arg_exempt_from_validation(\n autologging_integration,\n function_name,\n user_call_arg,\n argument_index=index,\n ):\n _validate(autologging_call_arg, user_call_arg)\n\n # Similar validation logic found in _validate, unraveling the dictionary of arguments to exclude\n # checks for any validation exempt keyword arguments.\n _assert_autologging_input_kwargs_are_superset(autologging_call_kwargs, user_call_kwargs)\n for key in autologging_call_kwargs.keys():\n if not _is_arg_exempt_from_validation(\n autologging_integration,\n function_name,\n user_call_kwargs.get(key, None),\n argument_name=key,\n ):\n _validate(\n autologging_call_kwargs[key],\n user_call_kwargs.get(key, None),\n )","def __call__(self, fn):\n fn.handler = True\n fn.function = True\n\n # Circuits properties\n fn.names = self.names\n fn.priority = self.kwargs.get(\"priority\", 0)\n fn.channel = \"functions.{0}\".format(self.names[0])\n fn.override = self.kwargs.get(\"override\", False)\n fn.event = True\n\n @wraps(fn)\n def app_function_decorator(itself, event, *args, **kwargs):\n \"\"\"\n The decorated function\n\n :param itself: The function to decorate\n :type itself: resilient_circuits.ResilientComponent\n :param event: The Event with the StompFrame and the Message read off the Message Destination\n :type event: resilient_circuits.action_message.FunctionMessage\n \"\"\"\n function_inputs = event.message.get(\"inputs\", {})\n\n def _invoke_app_function(evt, **kwds):\n \"\"\"\n The code to call when a function with the decorator `@app_function(api_name)`\n is invoked.\n\n Returns result_list when function with the decorator `@app_function(api_name)` is\n finished processing.\n\n A method that has this handler should yield a StatusMessage or a FunctionResult\n - When a StatusMessage is yield'ed a StatusMessageEvent is fired with the text of the StatusMessage\n - When a FunctionResult is yield'ed it calls resilient-lib.ResultPayload.done() with the parameters of\n FunctionResult being passed to it and appends the result to result_list. E.g:\n `yield FunctionResult({\"key\":\"value\"})`\n `yield FunctionResult({\"key\": \"value\"}, success=False, reason=\"Bad call\")`\n\n :param evt: The Event with the StompFrame and the Message read off the Message Destination\n :type fn: resilient_circuits.action_message.FunctionMessage\n \"\"\"\n LOG.debug(\"Running _invoke_app_function in Thread: %s\", threading.currentThread().name)\n\n result_list = []\n\n # Validate the fn_inputs in the Message\n fn_inputs = validate_fields([], kwds)\n LOG.info(\"[%s] Validated function inputs\", evt.name)\n LOG.debug(\"[%s] fn_inputs: %s\", evt.name, fn_inputs)\n\n rp = ResultPayload(itself.PACKAGE_NAME, version=constants.APP_FUNCTION_PAYLOAD_VERSION, **fn_inputs)\n\n fn_inputs_tuple = namedtuple(\"fn_inputs\", fn_inputs.keys())(*fn_inputs.values())\n\n # Set evt.message in local thread storage\n itself.set_fn_msg(evt.message)\n\n # Invoke the actual Function\n fn_results = fn(itself, fn_inputs_tuple)\n\n for r in fn_results:\n if isinstance(r, StatusMessage):\n LOG.info(\"[%s] StatusMessage: %s\", evt.name, r)\n itself.fire(StatusMessageEvent(parent=evt, message=r.text))\n\n elif isinstance(r, FunctionResult):\n r.name = evt.name\n if not r.custom_results:\n r.value = rp.done(\n content=r.value,\n success=r.success,\n reason=r.reason)\n LOG.info(\"[%s] Returning results\", r.name)\n result_list.append(r)\n\n elif isinstance(r, Exception):\n raise r\n\n else:\n # Whatever this is, add it to the results\n LOG.debug(r)\n result_list.append(r)\n\n return result_list\n\n invoke_app_function = task(_invoke_app_function, event, **function_inputs)\n fn_result = yield itself.call(invoke_app_function, \"functionworker\")\n yield fn_result.value\n\n return app_function_decorator","def check_implemented_functions(_class):\n mandatory_functions_to_implement = [('generate', 2), ('__init__', 6)]\n implemented_class_function_names = get_implemented_class_functions(_class)\n for function in mandatory_functions_to_implement:\n function_name = function[0]\n number_function_mandatory_params = function[1]\n # check if the method is implemented in the class\n if function_name not in implemented_class_function_names:\n logger.error(f\"Method {function_name} not implemented in class {_class.__name__}\")\n raise SystemExit(0)\n ref_function = getattr(_class, function_name)\n # check if the method is expecting the mandatory number of arguments\n if not len(inspect.getfullargspec(ref_function).args) == number_function_mandatory_params:\n logger.error(\n f\"Method {function_name} implemented in class {_class.__name__} \"\n f\"is not expecting {number_function_mandatory_params} passed arguments\")\n raise SystemExit(0)","def test_function_definition_with_decorator(self):\n self.script(\"# script.py\\n\"\n \"def g(x):\\n\"\n \" return x\\n\"\n \"@g\\n\"\n \"def f():\\n\"\n \" 'fdoc'\\n\"\n \" pass\\n\")\n self.compile()\n\n script = self.find_code_component(name=\"script.py\")\n function_def = self.find_code_component(name=\"f\")\n\n self.assertEqual(function_def.type, \"function_def\")\n self.assertEqual(function_def.mode, \"w\")\n self.assertEqual(function_def.first_char_line, 4)\n self.assertEqual(function_def.first_char_column, 0)\n self.assertEqual(function_def.last_char_line, 7)\n self.assertEqual(function_def.last_char_column, 8)\n self.assertEqual(function_def.container_id, script.id)\n\n function_def_block = self.metascript.code_blocks_store[function_def.id]\n self.assertEqual(function_def_block.code,\n \"@g\\n\"\n \"def f():\\n\"\n \" 'fdoc'\\n\"\n \" pass\")\n self.assertEqual(function_def_block.docstring, \"fdoc\")\n self.assertTrue(bool(function_def_block.code_hash))","def eval_function_call(func_call, motif_node_dict):\n print(\"\\x1b[6;30;42m\" + 'Evaluating ' + func_call.name.name + ' function...' + '\\x1b[0m')\n # CamFlow \"alloc_provenance\" take two arguments but only the first is needed for modeling.\n if func_call.name.name == 'alloc_provenance':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n return provenance.alloc_provenance(arg_names[0], None)\n # CamFlow \"task_cred_xxx\" take two arguments but no argument is needed for modeling.\n elif func_call.name.name == 'task_cred_xxx':\n return provenance.task_cred_xxx(None, None)\n # CamFlow \"branch_mmap\" take two arguments but no argument is needed for modeling.\n elif func_call.name.name == 'branch_mmap':\n return provenance.branch_mmap(None, None)\n # CamFlow \"uses_two\" function takes five arguments but only the first three are needed for modeling.\n elif func_call.name.name == 'uses_two':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second and third arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses_two must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses_two must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses_two must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses_two must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.uses_two(arg_names[0], val1, val2, None, None, motif_node_dict)\n # CamFlow \"informs\" function takes five arguments but only the first three are needed for modeling.\n elif func_call.name.name == 'informs':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second and third arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in informs must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in informs must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in informs must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in informs must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.informs(arg_names[0], val1, val2, None, None, motif_node_dict)\n # CamFlow \"record_terminate\" function takes two arguments.\n elif func_call.name.name == 'record_terminate':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second arguments must be converted to MotifNode object first.\n arg1 = arg_names[1]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_terminate must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_terminate must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_terminate(arg_names[0], val1, motif_node_dict)\n # CamFlow \"generates\" function takes six arguments but only the first four are needed for modeling.\n elif func_call.name.name == 'generates':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n arg3 = arg_names[3]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in generates must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in generates must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in generates must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in generates must have values in the dictionary.\\033[0m')\n exit(1)\n if arg3 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in generates must exist in the dictionary.\\033[0m')\n exit(1)\n val3 = getLastValueFromKey(motif_node_dict, arg3)\n if not val3:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in generates must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.generates(arg_names[0], val1, val2, val3, None, None, motif_node_dict)\n # CamFlow \"get_task_provenance\" takes no arguments.\n elif func_call.name.name == 'get_task_provenance':\n return provenance.get_task_provenance()\n # CamFlow \"get_cred_provenance\" takes no arguments.\n elif func_call.name.name == 'get_cred_provenance':\n return provenance.get_cred_provenance(motif_node_dict)\n # CamFlow \"uses\" takes six arguments but only the first four are needed for modeling.\n elif func_call.name.name == 'uses':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n arg3 = arg_names[3]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses must have values in the dictionary.\\033[0m')\n exit(1)\n if arg3 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in uses must exist in the dictionary.\\033[0m')\n exit(1)\n val3 = getLastValueFromKey(motif_node_dict, arg3)\n if not val3:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in uses must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.uses(arg_names[0], val1, val2, val3, None, None, motif_node_dict)\n # CamFlow \"refresh_inode_provenance\" takes two arguments but only the second one is needed for modeling.\n elif func_call.name.name == 'refresh_inode_provenance':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second argument must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in refresh_inode_provenance must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in refresh_inode_provenance must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.refresh_inode_provenance(None, val1, motif_node_dict)\n # CamFlow \"get_inode_provenance\" takes two arguments but only the second argument is needed for modeling.\n elif func_call.name.name == 'get_inode_provenance':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n if arg_names[1] == 'false':\n arg1 = False\n elif arg_names[1] == 'true':\n arg1 = True\n else:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_inode_provenance is unknown.\\033[0m')\n exit(1)\n return provenance.get_inode_provenance(None, arg1, motif_node_dict)\n # CamFlow \"get_dentry_provenance\" takes two arguments but only the second argument is needed for modeling. \n elif func_call.name.name == 'get_dentry_provenance':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n if arg_names[1] == 'false':\n arg1 = False\n elif arg_names[1] == 'true':\n arg1 = True\n else:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_dentry_provenance is unknown.\\033[0m')\n exit(1)\n return provenance.get_dentry_provenance(None, arg1, motif_node_dict)\n # CamFlow \"record_inode_name_from_dentry\" takes three arguments, but only the second and the third arguments are needed for modeling.\n elif func_call.name.name == 'record_inode_name_from_dentry':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second argument must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_inode_name_from_dentry must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_inode_name_from_dentry must have values in the dictionary.\\033[0m')\n exit(1)\n if arg_names[2] == 'false':\n arg2 = False\n elif arg_names[2] == 'true':\n arg2 = True\n else:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_inode_name_from_dentry is unknown.\\033[0m')\n exit(1)\n return provenance.record_inode_name_from_dentry(None, val1, arg2, motif_node_dict)\n # CamFlow \"record_node_name\" takes three arguments, but only the first and the third arguments are needed for modeling.\n elif func_call.name.name == 'record_node_name':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second argument must be converted to MotifNode objects first.\n arg0 = arg_names[0]\n if arg0 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_node_name must exist in the dictionary.\\033[0m')\n exit(1)\n val0 = getLastValueFromKey(motif_node_dict, arg0)\n if not val0:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_node_name must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_node_name(val0, None, arg_names[2], motif_node_dict)\n # CamFlow \"derives\" function takes five arguments but only the first three are needed for modeling.\n elif func_call.name.name == 'derives':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second and third arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in derives must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in derives must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in derives must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in derives must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.derives(arg_names[0], val1, val2, None, None, motif_node_dict)\n # CamFlow \"record_write_xattr\" function takes eight arguments but only the first four are needed for modeling.\n elif func_call.name.name == 'record_write_xattr':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n arg3 = arg_names[3]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_write_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_write_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_write_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_write_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n if arg3 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in record_write_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val3 = getLastValueFromKey(motif_node_dict, arg3)\n if not val3:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in record_write_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_write_xattr(arg_names[0], val1, val2, val3, None, None, None, None, motif_node_dict)\n # CamFlow \"record_read_xattr\" function takes four arguments but only the first three are needed for modeling.\n elif func_call.name.name == 'record_read_xattr':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n arg0 = arg_names[0]\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n if arg0 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_read_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val0 = getLastValueFromKey(motif_node_dict, arg0)\n if not val0:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_read_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_read_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_read_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_read_xattr must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_read_xattr must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_read_xattr(val0, val1, val2, None, motif_node_dict)\n # CamFlow \"get_file_provenance\" takes two arguments but only the second argument is needed for modeling. \n elif func_call.name.name == 'get_file_provenance':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n if arg_names[1] == 'false':\n arg1 = False\n elif arg_names[1] == 'true':\n arg1 = True\n else:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_file_provenance is unknown.\\033[0m')\n exit(1)\n return provenance.get_file_provenance(None, arg1, motif_node_dict)\n # CamFlow \"influences_kernel\" function takes four arguments but only the first three are needed for modeling.\n elif func_call.name.name == 'influences_kernel':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n # The second and third arguments must be converted to MotifNode objects first.\n arg1 = arg_names[1]\n arg2 = arg_names[2]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in influences_kernel must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in influences_kernel must have values in the dictionary.\\033[0m')\n exit(1)\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in influences_kernel must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in influences_kernel must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.influences_kernel(arg_names[0], val1, val2, None, motif_node_dict)\n # CamFlow \"get_socket_inode_provenance\" takes one argument but it is not needed for modeling.\n elif func_call.name.name == 'get_socket_provenance':\n return provenance.get_socket_provenance(None, motif_node_dict)\n # CamFlow \"get_socket_inode_provenance\" takes one argument but it is not needed for modeling. \n elif func_call.name.name == 'get_socket_inode_provenance':\n return provenance.get_socket_inode_provenance(None, motif_node_dict)\n # CamFlow \"record_address\" takes three arguments but only the last argument is needed for modeling. \n elif func_call.name.name == 'record_address':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n arg2 = arg_names[2]\n if arg2 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_address must exist in the dictionary.\\033[0m')\n exit(1)\n val2 = getLastValueFromKey(motif_node_dict, arg2)\n if not val2:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_address must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_address(None, None, val2, motif_node_dict)\n # CamFlow \"get_sk_inode_provenance\" takes one argument but it is not needed for modeling. \n elif func_call.name.name == 'get_sk_inode_provenance':\n return provenance.get_sk_inode_provenance(None, motif_node_dict)\n # CamFlow \"get_sk_provenance\" takes one argument but it is not needed for modeling.\n elif func_call.name.name == 'get_sk_provenance':\n return provenance.get_sk_provenance(None, motif_node_dict)\n # CamFlow \"record_packet_content\" takes two arguments but only the second argument is needed for modeling. \n elif func_call.name.name == 'record_packet_content':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n arg1 = arg_names[1]\n if arg1 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_packet_content must exist in the dictionary.\\033[0m')\n exit(1)\n val1 = getLastValueFromKey(motif_node_dict, arg1)\n if not val1:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_packet_content must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_packet_content(None, val1, motif_node_dict)\n # CamFlow \"record_args\" takes two arguments but only the first argument is needed for modeling.\n elif func_call.name.name == 'record_args':\n args = func_call.args.exprs\n arg_names = get_arg_name(args)\n arg0 = arg_names[0]\n if arg0 not in motif_node_dict:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_args must exist in the dictionary.\\033[0m')\n exit(1)\n val0 = getLastValueFromKey(motif_node_dict, arg0)\n if not val0:\n print('\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_args must have values in the dictionary.\\033[0m')\n exit(1)\n return provenance.record_args(val0, None, motif_node_dict)\n else:\n return None, None","def accepts(*types):\n\n def check_accepts(f):\n \"\"\"Check the types.\"\"\"\n spec = tf_inspect.getargspec(f)\n\n num_function_arguments = len(spec.args)\n if len(types) != num_function_arguments:\n raise Error(\n \"Function %r has %d arguments but only %d types were provided in the \"\n \"annotation.\" % (f, num_function_arguments, len(types)))\n\n if spec.defaults:\n num_defaults = len(spec.defaults)\n for (name, a, t) in zip(spec.args[-num_defaults:],\n spec.defaults,\n types[-num_defaults:]):\n allowed_type = _replace_forward_references(t, f.__globals__)\n if not isinstance(a, allowed_type):\n raise Error(\"default argument value %r of type %r is not an instance \"\n \"of the allowed type %s for the %s argument to %r\"\n % (a, type(a), _type_repr(allowed_type), name, f))\n\n @functools.wraps(f)\n def new_f(*args, **kwds):\n \"\"\"A helper function.\"\"\"\n for (a, t) in zip(args, types):\n allowed_type = _replace_forward_references(t, f.__globals__)\n if not isinstance(a, allowed_type):\n raise Error(\"%r of type %r is not an instance of the allowed type %s \"\n \"for %r\" % (a, type(a), _type_repr(allowed_type), f))\n return f(*args, **kwds)\n\n return new_f\n\n return check_accepts","def fcheck(*args, **kwargs)->None:\n pass","def _validate_function(self, function, function_name,\n shape=None, dtype=None):\n # Validate that *function* is convertible\n from firedrake.function import Function\n function_is_function_type = isinstance(function, Function)\n # FIXME : Once ExternalOperator is fully implemented, we don't need\n # to try/except this block\n if not function_is_function_type:\n try:\n from firedrake.pointwise_operators import ExternalOperator\n function_is_extop_type = isinstance(function, ExternalOperator)\n except ImportError:\n function_is_extop_type = False\n if not function_is_function_type and not function_is_extop_type:\n raise TypeError(f\"'{function_name} must be a firedrake Function\"\n \" or ExternalOperator, \"\n f\" but is of unexpected type '{type(function)}'\")\n ufl_elt = function.function_space().ufl_element()\n if ufl_elt.family() != self._ufl_element.family():\n raise ValueError(f\"'{function_name}.function_space().ufl_element()\"\n f\".family()' must be {self._ufl_element.family()}\"\n f\", not '{ufl_elt.family()}'\")\n if ufl_elt.degree() != self._ufl_element.degree():\n raise ValueError(f\"'{function_name}.function_space().ufl_element()\"\n f\".degree()' must be {self._ufl_element.degree()}\"\n f\", not '{ufl_elt.degree()}'\")\n if function.function_space().mesh() is not self._mesh_geometry:\n raise ValueError(f\"'{function_name}.function_space().mesh()' must\"\n \" be the same mesh used by this connection\")\n if dtype is not None and function.dat.data.dtype != dtype:\n raise ValueError(f\"'{function_name}.dat.dtype' must be \"\n f\"{dtype}, not '{function.dat.data.dtype}'\")\n if shape is not None and function.function_space().shape != shape:\n raise ValueError(\"'{function_name}.function_space().shape' must be\"\n \" {shape}, not '{function.function_space().shape}\"\n \"'\")","def _determine_func(self, **kwargs):\n # Check whether keys are recognized\n for key in kwargs.keys():\n if key not in self._parameter_map.keys():\n raise FairException('\"{}\"\" is not a recognized keyword'.format(key))\n # Check whether all keys go to same function via set comprension\n functions = list(set([\n self._parameter_map[key]\n for key\n in kwargs.keys()\n ]))\n if len(functions) > 1:\n raise FairException('\"{}\" mixes incompatible keywords.'.format(str(kwargs.keys())))\n else:\n function = functions[0]\n return function","def check_mocked_functions_called(*mocked_functions):\n for mocked_function in mocked_functions:\n assert_that(mocked_function.called, f\"The function was not called - {mocked_function}\")","def check_call_function(self, a, text):\n logging.debug(\"check call function \" + text)\n if self.cur_char == self.func_call_char:\n self.produce(FUNCTION, text)\n self.functions_calls.append(text)\n elif not self.must_func_call_char:\n if text in self.functions:\n self.produce(FUNCTION, text)\n self.functions_calls.append(text)","def test_require_in_call_silently_succeeds_for_available_tests(self, test_generator):\n # pylint: disable=function-redefined\n\n with self.subTest(\"direct decorator\"):\n feature = test_generator()\n with mock_availability_test(feature) as check:\n check.assert_not_called()\n\n @feature.require_in_call\n def decorated():\n pass\n\n check.assert_not_called()\n decorated()\n check.assert_called_once()\n\n with self.subTest(\"named decorator\"):\n feature = test_generator()\n with mock_availability_test(feature) as check:\n check.assert_not_called()\n\n @feature.require_in_call(\"sentinel name\")\n def decorated():\n pass\n\n check.assert_not_called()\n decorated()\n check.assert_called_once()","def test_require_at_least_one_and_one_provided(self):\n _func = at_least_one_of('arg1', 'arg2')(undecorated_func)\n self.assertEqual(_func('ahoy'), 'foo')\n self.assertEqual(_func(arg2='ahoy'), 'foo')","def check(fn):\n @wraps(fn)\n def wrapper(*args, **kwargs):\n verify_jwt_in_request()\n token = get_jwt_identity()\n if argument.lower() == 'machine':\n if token['is_user_token'] is False:\n from api.services.data_source_token import \\\n DataSourceTokenService\n _token_usage_counter_add(token['data_source_token']['id'])\n if DataSourceTokenService.check_if_token_is_active(\n DataSourceTokenService,\n token['data_source_token']['id']) is False:\n return ErrorObject.create_response(\n ErrorObject, HTTPStatus.FORBIDDEN,\n 'Token has been revoked')\n else:\n return fn(*args, **kwargs)\n else:\n return ErrorObject.create_response(\n ErrorObject, HTTPStatus.FORBIDDEN,\n 'Unable to access this resource with provided token')\n elif argument.lower() == 'user':\n if token['is_user_token'] is False:\n _token_usage_counter_add(token['data_source_token']['id'])\n return ErrorObject.create_response(\n ErrorObject, HTTPStatus.FORBIDDEN,\n 'Unable to access this resource with provided token')\n else:\n return fn(*args, **kwargs)\n else:\n raise ValueError('Unsupported argument provided')\n\n return wrapper","def check_for_tracer(func: Callable) -> Callable:\n\n @wraps(func)\n def wrapper(*args, **kwargs):\n if any([isinstance(x, DynamicJaxprTracer) for x in args]):\n raise TypeError(\n f\"DynamicJaxprTracer found in {func.__name__}; did you jit this function?\"\n )\n if any([isinstance(x, BatchTracer) for x in args]):\n raise TypeError(\n f\"BatchTracer found in {func.__name__}; did you vmap/pmap this function?\"\n )\n return func(*args, **kwargs)\n\n return wrapper","def _check_throttles_decorator(func):\n @wraps(func)\n def _decorated(*args, **kwargs):\n # Skip the throttle check entirely if we've disabled rate limiting.\n # Otherwise, perform the checks (as usual)\n if RateLimitConfiguration.current().enabled:\n return func(*args, **kwargs)\n else:\n msg = \"Rate limiting is disabled because `RateLimitConfiguration` is not enabled.\"\n LOGGER.info(msg)\n return\n\n return _decorated","def test_process_invalid1(self):\n self.skill.logic = {}\n self.skill.valid.app_id = '12345'\n @self.skill.launch\n def sample_func():\n \"\"\"Decorated function.\"\"\"\n pass\n self.skill.logic['LaunchRequest']()\n self.assertFalse(self.skill.process(data.SAMPLE_LAUNCH_REQUEST))","def check_lambdas(self):\n args = range(len(self))\n if self.flops:\n try:\n self.flops(*args)\n except NameError as e:\n raise NameError(\"Unknown argument %r used in flops\" %\n str(e).split(\"'\")[1])\n for arg in self:\n if arg.min:\n try:\n arg.min(*args)\n except NameError as e:\n raise NameError(\"Unknown argument %r used in min for %s\" %\n (str(e).split(\"'\")[1], arg))\n if arg.max:\n try:\n arg.max(*args)\n except NameError as e:\n raise NameError(\"Unknown argument %r used in max for %s\" %\n (str(e).split(\"'\")[1], arg))\n if arg.properties:\n try:\n arg.properties(*args)\n except NameError as e:\n raise NameError(\"Unknown argument or property %r \"\n \"used in properties for %s\" %\n (str(e).split(\"'\")[1], arg))","def _check_integrity(self):\n for f in self.list_func:\n if(not(isinstance(f, (pFunc_collec, pFunc_base, pFunc_fromcallable)))):\n raise ValueError('type %s while expecting pFunc_base or collection'\n ' ' % (str(type(f))))\n f._check_integrity()","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func","def test_12_funcdecl(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tprocedure main(); var x:real; begin x:=y(1,false); end\n\t\tfunction y(a:integer;b:boolean):integer; begin return 1; end\"\"\"\n\t\texpect = \"Redeclared Function: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,412))","def validate_func(func, source=None):\n\n if type(func) == str:\n full_name = func\n short_name = full_name.split('_')[0]\n source = source or globals()\n try:\n func = source[short_name]\n except KeyError:\n raise KeyError('Unrecognized function name \"%s\"' % full_name)\n\n if type(func).__name__ != 'function':\n raise ValueError('Not a function: \"%s\"' % full_name)\n\n if 'SIGNATURE' not in func.__dict__:\n raise ValueError('Not a cspyce function: \"%s\"' % func.__name__)\n\n return func","def all_fn(*args) -> bool:\n for arg in args:\n if not isinstance(arg, (FunctionType, partial)):\n return False\n\n return True","def is_compatible(self, function, arguments):","def fixupFunctionBody(program: ghidra.program.model.listing.Program, func: ghidra.program.model.listing.Function, monitor: ghidra.util.task.TaskMonitor) -> bool:\n ...","def _check_extend_function(self):\n for function_name, parameters in self._extend_function_dic.iteritems():\n if not apply(function_name, parameters):\n return False\n return True","def test_11_funcdecl(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tprocedure main(); var x:real; begin x:=y(1,false); {error} end\"\"\"\n\t\texpect = \"Undeclared Function: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,411))","def verify_operation(function):\n\n @wraps(function)\n def wrapper(*args, **kwargs):\n if PartialState().distributed_type == DistributedType.NO or not PartialState().debug:\n return function(*args, **kwargs)\n operation = f\"{function.__module__}.{function.__name__}\"\n if \"tensor\" in kwargs:\n tensor = kwargs[\"tensor\"]\n else:\n tensor = args[0]\n shapes = get_shape(tensor)\n output = gather_object([shapes])\n if output[0] is not None:\n are_same = output.count(output[0]) == len(output)\n if not are_same:\n process_shape_str = \"\\n - \".join([f\"Process {i}: {shape}\" for i, shape in enumerate(output)])\n raise DistributedOperationException(\n f\"Cannot apply desired operation due to shape mismatches. \"\n \"All shapes across devices must be valid.\"\n f\"\\n\\nOperation: `{operation}`\\nInput shapes:\\n - {process_shape_str}\"\n )\n return function(*args, **kwargs)\n\n return wrapper","def test_required_and_mutually_exclusive(self):\n @mutually_exclusive_parameters('arg2', 'arg3')\n @required_parameters('arg1')\n def _func1_decorated(arg1=None, arg2=None, arg3=None):\n return 'foo'\n\n from plone.api.exc import InvalidParameterError\n from plone.api.exc import MissingParameterError\n\n # test that the required parameter error works (missing arg1)\n with self.assertRaises(MissingParameterError):\n _func1_decorated(arg2='ahoy')\n\n # test that the mutually exclusive decorator works\n # (arg2 and arg3 should not be there)\n with self.assertRaises(InvalidParameterError):\n _func1_decorated(\n arg1='ahoy',\n arg2='there',\n arg3='matey',\n )\n\n # test that they both work. Making no assumptions here about the order\n # in which they fire.\n with self.assertRaises((InvalidParameterError, MissingParameterError)):\n _func1_decorated(\n arg2='ahoy',\n arg3='there',\n )\n\n # everything ok\n self.assertEqual(_func1_decorated('ahoy', arg3='there'), 'foo')","def _verify_matching_signatures(implementation, dispatcher):\n implementation_spec = getargspec(implementation)\n dispatcher_spec = getargspec(dispatcher)\n\n if (implementation_spec.args != dispatcher_spec.args or\n implementation_spec.varargs != dispatcher_spec.varargs or\n implementation_spec.keywords != dispatcher_spec.keywords or\n (bool(implementation_spec.defaults) !=\n bool(dispatcher_spec.defaults)) or\n (implementation_spec.defaults is not None and\n len(implementation_spec.defaults) !=\n len(dispatcher_spec.defaults))):\n raise RuntimeError('implementation and dispatcher for %s have '\n 'different function signatures' % implementation)","def test_func_2(self):\n def func():\n return 0\n self.assertEqual(type(decorators.timeit_2(func)), types.FunctionType)","def login_manager_required(function=None, redirect_field_name=REDIRECT_FIELD_NAME):\r\n #print function, type(function), function.func_name\r\n actual_decorator = user_passes_test(\r\n lambda req: hasattr(req, \"session\") and req.session.get('manager_login', False) and req.user.is_active and (req.user.is_staff or req.user.auth_type in (1, 2)),\r\n login_url=\"/manager/login/\",\r\n redirect_field_name=redirect_field_name\r\n )\r\n if function:\r\n return actual_decorator(function)\r\n return actual_decorator","def register_func_list(self, func_and_handler):\n for func, handler in func_and_handler:\n self._function_dispatch.register(func, handler)\n self.dispatch.cache_clear()","def is_valid_function(self, paras):\n if len(paras) != 0:\n return True\n return True","def _checkFunctioncode(functioncode, listOfAllowedValues=[]):\n FUNCTIONCODE_MIN = 1\n FUNCTIONCODE_MAX = 127\n\n _checkInt(functioncode, FUNCTIONCODE_MIN, FUNCTIONCODE_MAX, description='functioncode')\n\n if listOfAllowedValues is None:\n return\n\n if not isinstance(listOfAllowedValues, list):\n raise TypeError('The listOfAllowedValues should be a list. Given: {0!r}'.format(listOfAllowedValues))\n\n for value in listOfAllowedValues:\n _checkInt(value, FUNCTIONCODE_MIN, FUNCTIONCODE_MAX, description='functioncode inside listOfAllowedValues')\n\n if functioncode not in listOfAllowedValues:\n raise ValueError('Wrong function code: {0}, allowed values are {1!r}'.format(functioncode, listOfAllowedValues))","def check_authentication(function_to_decorate):\r\n @wraps(function_to_decorate)\r\n def decorated_function(*args, **kwargs):\r\n if not hasattr(g, \"my\"):\r\n abort(401)\r\n return function_to_decorate(*args, **kwargs)\r\n return decorated_function","def performFunctionalize(args, modName, modSearch=\"__main__\", preArgs=(), postArgs=()):\n funcsList = args.list\n \n mod = sys.modules[modName]\n if(funcsList):\n funcs = _getModFunctions(modName, modSearch)\n if('*' in funcsList):\n funcsList = funcsList.replace('*', '')\n search = True\n else:\n search = False\n for f in funcs:\n if(funcsList == 'all' or (search and funcsList in f.__name__) or (not search and funcsList == f.__name__)):\n print('============================================================================================')\n _printHelp(mod, f.__name__)\n\n return\n\n \n #\n # Run the function as a command\n #\n if(args.func):\n if(not hasattr(mod, args.func)):\n print('No %s function found' % args.func)\n return\n \n func = args.func\n rfunc = getattr(mod, func)\n \n # Get any args they want used\n fargs = None\n if(args.args):\n fargs = [_parseValue(a) for a in args.args]\n \n # Deal with kwargs\n kwargs = dict()\n if(args.kwargs):\n for kw in args.kwargs:\n k, w = kw.split('=', 1)\n kwargs[k] = _parseValue(w)\n \n # Print out the docs about the function\n if(args.helpme):\n _printHelp(mod, func)\n return\n \n try:\n # Build arguments to send them\n theArgs = list()\n if(preArgs):\n theArgs += list(preArgs)\n if(fargs):\n theArgs += list(fargs)\n if(postArgs):\n theArgs += list(postArgs)\n \n # Call the function, if no args make special call (couldn't figure out another way)\n if(theArgs and kwargs):\n res = rfunc(*theArgs, **kwargs)\n elif(theArgs and not kwargs):\n res = rfunc(*theArgs)\n elif(not theArgs and kwargs):\n res = rfunc(**kwargs)\n else:\n res = rfunc()\n \n # Print results\n if(args.printResult == 'str'):\n print(res)\n elif(args.printResult == 'json'):\n print(_jsonPretty(res))\n except Exception as e:\n t = \", \".join(theArgs) + \", \" if theArgs else \"\"\n t += \", \".join([\"{}={}\".format(k, v) for k, v in kwargs.iteritems()])\n print \"Exception when calling {}({})\".format(args.func, t)\n print e\n _printHelp(mod, func)\n traceback.print_exc()\n else:\n print('Call with \"-h\" for help')\n return","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func is not None","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func is not None","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func is not None","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func is not None","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func is not None","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func is not None","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func is not None","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func is not None","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func is not None","def test_function_definition(self):\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\n assert func is not None","def filter_trace_function_event(self, event):\n if not isinstance(event, TraceFunctionEvent):\n return False\n\n if event.function is getattr:\n # Ignore attribute access on modules.\n first_arg = list(event.arguments.values())[0]\n return not isinstance(first_arg, types.ModuleType)\n\n return True","def validate_inputs(function: Callable) -> Callable:\n spec = inspect.getfullargspec(function)\n # (According to the Python FAQ, the proper name for \"argument name\" is\n # \"parameter\").\n arg_names = spec.args\n default_values = spec.defaults\n # Iterate over parameters in reverse: only the last few parameters have\n # default values.\n for arg_name, value in zip(reversed(arg_names), reversed(default_values)):\n check_arg(function, arg_name, value)\n\n @wraps(function)\n def checked_function(*args, **kwargs):\n for arg_name, value in zip(arg_names, args):\n check_arg(function, arg_name, value)\n for arg_name, value in kwargs.items():\n check_arg(function, arg_name, value)\n return function(*args, **kwargs)\n\n return checked_function","def is_decorator(tree, fname):\n return ((isx(tree, fname)) or\n (type(tree) is Call and isx(tree.func, fname)))","async def async_patched_function(self, *args, **kwargs):\n self.validate(*args, **kwargs)\n return await self.function(*args, **kwargs)","def __call__(self, func):\n LOG.debug(\"@function %s\", func)\n\n func.handler = True\n func.function = True\n\n # Circuits properties\n func.names = self.names\n func.priority = self.kwargs.get(\"priority\", 0)\n func.channel = self.kwargs.get(\"channel\", \",\".join([\"functions.{}\".format(name) for name in self.names]))\n func.override = self.kwargs.get(\"override\", False)\n \n # If getfullargspec if available to us \n if hasattr(_inspect, 'getfullargspec'):\n args = _inspect.getfullargspec(func)[0]\n else: # fall back to deprecated getargspec\n args = _inspect.getargspec(func)[0]\n\n if args and args[0] == \"self\":\n del args[0]\n func.event = getattr(func, \"event\", bool(args and args[0] == \"event\"))\n\n @wraps(func)\n def decorated(itself, event, *args, **kwargs):\n \"\"\"the decorated function\"\"\"\n LOG.debug(\"decorated\")\n function_parameters = event.message.get(\"inputs\", {})\n\n def _the_task(event, *args, **kwargs):\n return func(itself, event, *args, **kwargs)\n\n def _call_the_task(evt, **kwds):\n # On the worker thread, call the function, and handle a single or generator result.\n LOG.debug(\"%s: _call_the_task\", threading.currentThread().name)\n result_list = []\n task_result_or_gen = _the_task(evt, *args, **kwds)\n if not isinstance(task_result_or_gen, GeneratorType):\n task_result_or_gen = [task_result_or_gen]\n for val in task_result_or_gen:\n if isinstance(val, StatusMessage):\n # Fire the wrapped status message event to notify resilient\n LOG.info(\"[%s] StatusMessage: %s\", evt.name, val)\n itself.fire(StatusMessageEvent(parent=evt, message=val.text))\n elif isinstance(val, FunctionResult):\n # Collect the result for return\n LOG.debug(\"[%s] FunctionResult: %s\", evt.name, val)\n val.name = evt.name\n result_list.append(val)\n elif isinstance(val, Event):\n # Some other event, just fire it\n LOG.debug(val)\n itself.fire(val)\n elif isinstance(val, FunctionError_):\n LOG.error(\"[%s] FunctionError: %s\", evt.name, val)\n itself.fire(FunctionErrorEvent(parent=evt, message=str(val)))\n evt.success = False\n return # Don't wait for more results!\n elif isinstance(val, Exception):\n raise val\n else:\n # Whatever this is, add it to the results\n LOG.debug(val)\n result_list.append(val)\n return result_list\n\n the_task = task(_call_the_task, event, **function_parameters)\n ret = yield itself.call(the_task, \"functionworker\")\n xxx = ret.value\n # Return value is the result_list that was yielded from the wrapped function\n yield xxx\n return decorated","def test_mock_decorators_and_args(self):\n errors = []\n\n for dirname, dirnames, filenames in os.walk(self.tests_path):\n for filename in filenames:\n if (not filename.startswith(\"test_\")\n or not filename.endswith(\".py\")):\n continue\n\n filename = os.path.relpath(os.path.join(dirname, filename))\n\n with open(filename, \"rb\") as fh:\n tree = ast.parse(fh.read(), filename)\n\n visitor = FuncMockArgsDecoratorsChecker()\n visitor.visit(tree)\n errors.extend(\n dict(filename=filename, **error)\n for error in visitor.errors)\n\n if errors:\n print(FuncMockArgsDecoratorsChecker.__doc__)\n print(\n \"\\n\\n\"\n \"The following errors were found during the described check:\")\n for error in errors:\n print(\"\\n\\n\"\n \"Errors at file %(filename)s line %(lineno)d:\\n\\n\"\n \"%(message)s\" % {\n \"message\": \"\\n\".join(error[\"messages\"]),\n \"filename\": error[\"filename\"],\n \"lineno\": error[\"lineno\"]})\n\n # NOTE(pboldin): When the STDOUT is shuted the below is the last\n # resort to know what is wrong with the mock names.\n for error in errors:\n error[\"messages\"] = [\n message.rstrip().replace(\"\\n\", \" \").replace(\"\\t\", \"\")\n for message in error[\"messages\"]\n ]\n self.assertEqual([], errors)","def validate_func_kwargs(\n kwargs: dict,\n) -> Tuple[List[str], List[Union[str, Callable[..., Any]]]]:\n no_arg_message = \"Must provide 'func' or named aggregation **kwargs.\"\n tuple_given_message = \"func is expected but recieved {} in **kwargs.\"\n columns = list(kwargs)\n func = []\n for col_func in kwargs.values():\n if not (isinstance(col_func, str) or callable(col_func)):\n raise TypeError(tuple_given_message.format(type(col_func).__name__))\n func.append(col_func)\n if not columns:\n raise TypeError(no_arg_message)\n return columns, func","def check_focal_elements_compatibility(function):\n @functools.wraps(function)\n def wrapped_function(*args):\n for i in range(len(args)-1):\n for j in range(len(args)-1):\n if not args[i+1][0].is_compatible(args[j+1][0]):\n raise IncompatibleElementsInAMassFunctionError(args[i+1][0], args[j+1][0])\n return function(*args)\n return wrapped_function","def defer_to_call(*args, **kwargs):\n data = _func_or_data(*args, **kwargs)\n cls.scheme[func].validate(data)\n return data","def test_func(self):\n def func():\n return 0\n self.assertEqual(type(decorators.timeit(func)), types.FunctionType)","def validate_function_types(module: Module) -> None:\n # This validation is explicitly in the spec but it gives us strong\n # guarantees about indexing into the module types to populate the function\n # types.\n for function in module.funcs:\n if function.type_idx >= len(module.types):\n raise ValidationError(\n f\"Function type index is out of range. \"\n f\"type_idx={function.type_idx} > {len(module.types)}\"\n )","def condition_for_function(f, abi, all_not_in_ABI):\n\n\tcondition = []\n\tfor n in f.entry_points:\n\t\t[category, num] = api.get_category_for_name( n )\n\t\tif category not in abi:\n\t\t\tcondition.append( 'defined(need_%s)' % (gl_XML.real_category_name( category )) )\n\t\telif all_not_in_ABI:\n\t\t\treturn []\n\n\treturn condition","def check_before_executing(f):\n @functools.wraps(f)\n def wrapper(self, *args, **kwargs):\n if not self._checked:\n assert self.is_correct, (\n 'The MatchList is incorrectly constructed. '\n 'Run check_and_print_if_error() for details.')\n return f(self, *args, **kwargs)\n return wrapper","def decorator_func(func):\r\n @functools.wraps(func)\r\n def with_status_check(obj, *args, **kwargs):\r\n if obj.status not in valid_start_statuses:\r\n exception_msg = (\r\n u\"Error calling {} {}: status is '{}', must be one of: {}\"\r\n ).format(func, obj, obj.status, valid_start_statuses)\r\n raise VerificationException(exception_msg)\r\n return func(obj, *args, **kwargs)\r\n\r\n return with_status_check","def decoratedCheckToken(*args, **kwargs):\n if \"token\" not in request.headers:\n raise InvalidUsage(\"Must pass a token!\")\n\n # Execute if the token matches\n logger.debug(\"Token: {0}\".format(request.headers[\"token\"]))\n if request.headers[\"token\"] == receiverParameters[\"apiToken\"]:\n return func(*args, **kwargs)\n\n # Note that it is invalid otherwise\n raise InvalidUsage(\"Invalid token!\")","def check_mocked_functions_not_called(*mocked_functions):\n for mocked_function in mocked_functions:\n assert_that(not mocked_function.called, f\"The function should not have been called - {mocked_function}\")","def test_require_in_call_raises_for_unavailable_tests(self, test_generator):\n # pylint: disable=function-redefined\n\n with self.subTest(\"direct decorator\"):\n feature = test_generator()\n with mock_availability_test(feature) as check:\n check.assert_not_called()\n\n @feature.require_in_call\n def decorated():\n pass\n\n check.assert_not_called()\n with self.assertRaisesRegex(MissingOptionalLibraryError, \"decorated\"):\n decorated()\n check.assert_called_once()\n with self.assertRaisesRegex(MissingOptionalLibraryError, \"decorated\"):\n decorated()\n check.assert_called_once()\n\n with self.subTest(\"named decorator\"):\n feature = test_generator()\n with mock_availability_test(feature) as check:\n check.assert_not_called()\n\n @feature.require_in_call(\"sentinel message\")\n def decorated():\n pass\n\n check.assert_not_called()\n with self.assertRaisesRegex(MissingOptionalLibraryError, \"sentinel message\"):\n decorated()\n check.assert_called_once()\n with self.assertRaisesRegex(MissingOptionalLibraryError, \"sentinel message\"):\n decorated()\n check.assert_called_once()","def _validate_user_module_and_set_functions(self):\n user_module_name = self._environment.module_name\n\n self._pre_model_fn = getattr(self._default_inference_handler, \"default_pre_model_fn\", None)\n self._model_warmup_fn = getattr(\n self._default_inference_handler, \"default_model_warmup_fn\", None\n )\n\n if find_spec(user_module_name) is not None:\n user_module = importlib.import_module(user_module_name)\n\n self._model_fn = getattr(\n user_module, \"model_fn\", self._default_inference_handler.default_model_fn\n )\n\n transform_fn = getattr(user_module, \"transform_fn\", None)\n input_fn = getattr(user_module, \"input_fn\", None)\n predict_fn = getattr(user_module, \"predict_fn\", None)\n output_fn = getattr(user_module, \"output_fn\", None)\n pre_model_fn = getattr(user_module, \"pre_model_fn\", None)\n model_warmup_fn = getattr(user_module, \"model_warmup_fn\", None)\n\n if transform_fn and (input_fn or predict_fn or output_fn):\n raise ValueError(\n \"Cannot use transform_fn implementation in conjunction with \"\n \"input_fn, predict_fn, and/or output_fn implementation\"\n )\n\n self._transform_fn = transform_fn or self._default_transform_fn\n self._input_fn = input_fn or self._default_inference_handler.default_input_fn\n self._predict_fn = predict_fn or self._default_inference_handler.default_predict_fn\n self._output_fn = output_fn or self._default_inference_handler.default_output_fn\n if pre_model_fn is not None:\n self._pre_model_fn = pre_model_fn\n if model_warmup_fn is not None:\n self._model_warmup_fn = model_warmup_fn\n else:\n self._model_fn = self._default_inference_handler.default_model_fn\n self._input_fn = self._default_inference_handler.default_input_fn\n self._predict_fn = self._default_inference_handler.default_predict_fn\n self._output_fn = self._default_inference_handler.default_output_fn\n\n self._transform_fn = self._default_transform_fn","def _handle_callable(value: Any, annotation_args: Tuple[List[Any], Any]) -> bool:\n\n if not isinstance(value, Callable):\n return False\n\n # Here, we wish to compare a given callable with the annotation provided.\n # The only way to verify this information is through the type hints of the function.\n # Note that the `Callable` syntax does not indicate optional or keyword arguments,\n # so those are ignored if present.\n param_annotations, return_annotation = annotation_args\n signature = inspect.signature(function)\n indicated_return_annotation = signature.return_annotation\n\n # have to write functions to convert between `typing` and builtin\n if indicated_return_annotation != return_annotation:\n return False\n \n print(\"callable functionality WIP\")\n pass","def check_focal_elements_validity(function):\n @functools.wraps(function)\n def wrapped_function(*args):\n for i in range(len(args)-1):\n try:\n #if (not isinstance(args[i+1][0], element.Element) or\n if not (isinstance(args[i+1][1], int) or isinstance(args[i+1][1], float)):\n raise ValueError(\n \"focal_element: \" + str(args[i+1]) + \"\\n\" +\n \"It should have contained an Element at index 0 and a numerical \" +\n \"value at index 1 (e.g. a tuple (element, 0.8) or a list [element, 0.5] \" +\n \"would have been valid).\"\n )\n except TypeError:\n raise ValueError(\n \"focal_element: \" + str(args[i+1]) + \"\\n\" +\n \"It should have supported indexing and contained an Element at index 0 and \"\n \"a numerical value at index 1 (e.g. a tuple (element, 0.8) or a list \" +\n \"[element, 0.5] would have been valid).\"\n )\n return function(*args)\n return wrapped_function","def translation_rule_gpu(func):\n # functions to call before running the translation rule\n setup_funcs = (\n ensure_gpu_ext,\n functools.partial(ensure_platform_flush, \"gpu\"),\n ensure_omnistaging,\n setup_cuda_mpi,\n )\n\n @functools.wraps(func)\n def wrapped(*args, **kwargs):\n for f in setup_funcs:\n f()\n return func(*args, **kwargs)\n\n return wrapped","def is_decorated_with( this, fn, decorator) :\n\t\treturn decorator in this.get_decorators( fn)","def test_exactly_one_required(self):\n\n @mutually_exclusive_parameters('arg1', 'arg2')\n @at_least_one_of('arg1', 'arg2')\n def _func1_decorated(arg1=None, arg2=None, arg3=None):\n return 'foo'\n\n from plone.api.exc import InvalidParameterError\n from plone.api.exc import MissingParameterError\n\n # test it errors if you provide none\n with self.assertRaises(MissingParameterError):\n _func1_decorated()\n\n # test that it errors if you provide both\n with self.assertRaises(InvalidParameterError):\n _func1_decorated('ahoy', 'there')\n\n # everything ok\n self.assertEqual(_func1_decorated('ahoy'), 'foo')\n self.assertEqual(_func1_decorated('ahoy', arg3='there'), 'foo')","def checkfuncname(b, frame):\n if not b.funcname:\n # Breakpoint was set via line number.\n if b.line != frame.f_lineno:\n # Breakpoint was set at a line with a def statement and the function\n # defined is called: don't break.\n return False\n return True\n\n # Breakpoint set via function name.\n if frame.f_code.co_name != b.funcname:\n # It's not a function call, but rather execution of def statement.\n return False\n\n # We are in the right frame.\n if not b.func_first_executable_line:\n # The function is entered for the 1st time.\n b.func_first_executable_line = frame.f_lineno\n\n if b.func_first_executable_line != frame.f_lineno:\n # But we are not at the first line number: don't break.\n return False\n return True","def visit_any_function(self, node: AnyFunctionDef) -> None:\n self._counter.check_arguments_count(node)\n self._counter.check_function_complexity(node)\n self.generic_visit(node)","def decorator(func):\n\n pass","def has_deco(deconames, tree, userlambdas=[]):\n if type(tree) in (FunctionDef, AsyncFunctionDef):\n return any(is_decorator(x, fname) for fname in deconames for x in tree.decorator_list)\n elif is_decorated_lambda(tree, mode=\"any\"):\n decorator_list, thelambda = destructure_decorated_lambda(tree)\n if (not userlambdas) or (id(thelambda) in userlambdas):\n return any(is_lambda_decorator(x, fname) for fname in deconames for x in decorator_list)\n return None # not applicable","def check_inputs(function):\n def decorated(self, data, *args, **kwargs):\n if not (isinstance(data, np.ndarray) and len(data.shape) == 2 and data.shape[1] == 1):\n raise ValueError('The argument `data` must be a numpy.ndarray with shape (n, 1).')\n\n return function(self, data, *args, **kwargs)\n\n decorated.__doc__ = function.__doc__\n return decorated"],"string":"[\n \"def visit_FunctionDef(self, node):\\n self.functions[node.name] = self._generate_pytest_decorators(node.decorator_list)\\n self.generic_visit(node)\",\n \"def isValidFunction(self):\\n for token in self.value:\\n if token.type == 'defFunction' or token.type == 'callFunction':\\n if token.value.split('(')[0] == self.name:\\n return False\\n return True\",\n \"def frontend_access_required(function=None):\\n access_denied_info_url = reverse_lazy('excerptexport:access_denied')\\n actual_decorator = user_passes_test(\\n _may_user_access_osmaxx_frontend,\\n login_url=access_denied_info_url\\n )\\n if function:\\n return actual_decorator(function)\\n return actual_decorator\",\n \"def check_arguments_are_mass_functions(function):\\n @functools.wraps(function)\\n def wrapped_function(*args):\\n if len(args) < 2:\\n raise TypeError(\\n \\\"Not enough mass functions provided, it should receive at least one!\\\"\\n )\\n for i in range(len(args)):\\n if not isinstance(args[i], MassFunction):\\n raise TypeError(\\n \\\"This method accept only mass functions as arguments!\\\"\\n )\\n return function(*args)\\n return wrapped_function\",\n \"def test_tolerate_return_function_decorator():\\n decorator = tolerate()\\n ok_(inspect.isfunction(decorator))\\n\\n args, varargs, keywords, defaults = inspect.getargspec(decorator)\\n eq_(len(args), 1, 'Return function should take one argument for function')\",\n \"def check_mass_functions_compatibility(function):\\n @functools.wraps(function)\\n def wrapped_function(*args):\\n for i in range(len(args)):\\n for j in range(len(args)):\\n if i != j and not args[i].is_compatible(args[j]):\\n raise IncompatibleMassFunctionsError(args[i], args[j])\\n return function(*args)\\n return wrapped_function\",\n \"def _func_only(func):\\n if inspect.isfunction(func):\\n return\\n else:\\n raise Exception(\\\"Only functions can be tasks\\\")\",\n \"def add_check_function(check_function: Callable):\\n\\n def decorator(func: Callable):\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n check_function(*args, *kwargs.values())\\n return func(*args, **kwargs)\\n\\n return wrapper\\n\\n name = getattr(check_function, '__name__', '`func`')\\n decorator.__doc__ = f\\\"Check the function's arguments via `{name}` before calling it.\\\"\\n return decorator\",\n \"def validate(host_calls):\\n\\n for name, host_call in host_calls.items():\\n if not isinstance(host_call, (tuple, list)):\\n raise ValueError('{} should be tuple or list'.format(name))\\n if len(host_call) != 2:\\n raise ValueError('{} should have two elements.'.format(name))\\n if not callable(host_call[0]):\\n raise TypeError('{}[0] should be callable.'.format(name))\\n if not isinstance(host_call[1], (tuple, list, dict)):\\n raise ValueError('{}[1] should be tuple or list, or dict.'.format(name))\\n\\n if isinstance(host_call[1], (tuple, list)):\\n fullargspec = tf_inspect.getfullargspec(host_call[0])\\n fn_args = util.fn_args(host_call[0])\\n # wrapped_hostcall_with_global_step uses varargs, so we allow that.\\n if fullargspec.varargs is None and len(host_call[1]) != len(fn_args):\\n raise RuntimeError(\\n 'In TPUEstimatorSpec.{}, length of tensors {} does not match '\\n 'method args of the function, which takes {}.'.format(\\n name, len(host_call[1]), len(fn_args)))\",\n \"def _parse_functions(self, locals: dict):\\n functions_dict = dict(filter(self._isfunction, locals.items()))\\n functions = []\\n if not self.args:\\n functions.append(next(iter(functions_dict.values())))\\n else:\\n for i in range(len(self.args)):\\n if functions_dict.get(self.args[0]):\\n functions.append(functions_dict[self.args.pop(0)])\\n else:\\n if not functions:\\n msg = f'ezmake command args: {self.args} did not ' + \\\\\\n 'match any functions defined in Makefile.py: %s' %\\\\\\n list(functions_dict.keys())\\n raise TypeError(msg)\\n break\\n self.functions = functions\",\n \"def _check_invocation_requirements(\\n solid_def: \\\"OpDefinition\\\", context: Optional[\\\"UnboundOpExecutionContext\\\"]\\n) -> None:\\n # Check resource requirements\\n if (\\n solid_def.required_resource_keys\\n and cast(\\\"DecoratedOpFunction\\\", solid_def.compute_fn).has_context_arg()\\n and context is None\\n ):\\n node_label = solid_def.node_type_str # string \\\"solid\\\" for solids, \\\"op\\\" for ops\\n raise DagsterInvalidInvocationError(\\n f'{node_label} \\\"{solid_def.name}\\\" has required resources, but no context was provided.'\\n f\\\" Use the `build_{node_label}_context` function to construct a context with the\\\"\\n \\\" required resources.\\\"\\n )\\n\\n # Check config requirements\\n if not context and solid_def.config_schema.as_field().is_required:\\n node_label = solid_def.node_type_str # string \\\"solid\\\" for solids, \\\"op\\\" for ops\\n raise DagsterInvalidInvocationError(\\n f'{node_label} \\\"{solid_def.name}\\\" has required config schema, but no context was'\\n f\\\" provided. Use the `build_{node_label}_context` function to create a context with\\\"\\n \\\" config.\\\"\\n )\",\n \"def admin_required(function=None, redirect_field_name=REDIRECT_FIELD_NAME, login_url=None):\\n actual_decorator = user_passes_test(\\n lambda u: u.is_admin,\\n login_url=login_url,\\n redirect_field_name=redirect_field_name\\n )\\n if function:\\n return actual_decorator(function)\\n return actual_decorator\",\n \"def is_unsupported(func):\\n\\n for m in BUILTIN_LIKELY_MODULES:\\n for v in m.__dict__.values():\\n if not callable(v):\\n continue\\n if func is v:\\n translator_logger.log(\\n 2,\\n \\\"Whitelist: {} is part of built-in module and does not have to be transformed.\\\".format(\\n func\\n ),\\n )\\n return True\\n\\n # NOTE: should be placed before `is_paddle_func`\\n # The api(s) should be considered as plain function and convert\\n # them into static layer code.\\n from paddle.nn import Sequential\\n\\n PADDLE_NEED_CONVERT_APIS = [Sequential]\\n if type(func) in PADDLE_NEED_CONVERT_APIS:\\n return False\\n\\n if is_paddle_func(func):\\n translator_logger.log(\\n 2,\\n \\\"Whitelist: {} is part of Paddle module and does not have to be transformed.\\\".format(\\n func\\n ),\\n )\\n return True\",\n \"def decorate_with_checker(func: CallableT) -> CallableT:\\n assert not hasattr(func, \\\"__preconditions__\\\"), \\\\\\n \\\"Expected func to have no list of preconditions (there should be only a single contract checker per function).\\\"\\n\\n assert not hasattr(func, \\\"__postconditions__\\\"), \\\\\\n \\\"Expected func to have no list of postconditions (there should be only a single contract checker per function).\\\"\\n\\n assert not hasattr(func, \\\"__postcondition_snapshots__\\\"), \\\\\\n \\\"Expected func to have no list of postcondition snapshots (there should be only a single contract checker \\\" \\\\\\n \\\"per function).\\\"\\n\\n sign = inspect.signature(func)\\n if '_ARGS' in sign.parameters:\\n raise TypeError(\\n 'The arguments of the function to be decorated with a contract checker include \\\"_ARGS\\\" which is '\\n 'a reserved placeholder for positional arguments in the condition.')\\n\\n if '_KWARGS' in sign.parameters:\\n raise TypeError(\\n 'The arguments of the function to be decorated with a contract checker include \\\"_KWARGS\\\" which is '\\n 'a reserved placeholder for keyword arguments in the condition.')\\n\\n param_names = list(sign.parameters.keys())\\n\\n # Determine the default argument values\\n kwdefaults = resolve_kwdefaults(sign=sign)\\n\\n id_func = id(func)\\n\\n # (mristin, 2021-02-16)\\n # Admittedly, this branching on sync/async is absolutely monstrous.\\n # However, I couldn't find out an easier way to refactor the code so that it supports async.\\n # Python expects us to explicitly colour functions as sync/async so we can not just put in an if-statement and\\n # introduce an \\\"await\\\".\\n #\\n # The two wrappers need to be manually maintained in parallel.\\n # Whenever you make a change, please inspect manually that both sync and async code exercises equivalent behavior.\\n # For example, copy/paste the two blocks of code in separate files and perform a diff.\\n\\n if inspect.iscoroutinefunction(func):\\n\\n async def wrapper(*args, **kwargs): # type: ignore\\n \\\"\\\"\\\"Wrap func by checking the preconditions and postconditions.\\\"\\\"\\\"\\n kwargs_error = _assert_no_invalid_kwargs(kwargs)\\n if kwargs_error:\\n raise kwargs_error\\n\\n # We need to create a new in-progress set if it is None as the ``ContextVar`` does not accept\\n # a factory function for the default argument. If we didn't do this, and simply set an empty\\n # set as the default, ``ContextVar`` would always point to the same set by copying the default\\n # by reference.\\n in_progress = _IN_PROGRESS.get()\\n if in_progress is None:\\n in_progress = set()\\n _IN_PROGRESS.set(in_progress)\\n\\n # Use try-finally instead of ExitStack for performance.\\n try:\\n # If the wrapper is already checking the contracts for the wrapped function, avoid a recursive loop\\n # by skipping any subsequent contract checks for the same function.\\n if id_func in in_progress:\\n return await func(*args, **kwargs)\\n\\n in_progress.add(id_func)\\n\\n (preconditions, snapshots, postconditions) = _unpack_pre_snap_posts(wrapper)\\n\\n resolved_kwargs = kwargs_from_call(\\n param_names=param_names, kwdefaults=kwdefaults, args=args, kwargs=kwargs)\\n\\n type_error = _assert_resolved_kwargs_valid(postconditions, resolved_kwargs)\\n if type_error:\\n raise type_error\\n\\n violation_error = await _assert_preconditions_async(\\n preconditions=preconditions, resolved_kwargs=resolved_kwargs)\\n if violation_error:\\n raise violation_error\\n\\n # Capture the snapshots\\n if postconditions and snapshots:\\n resolved_kwargs['OLD'] = await _capture_old_async(\\n snapshots=snapshots, resolved_kwargs=resolved_kwargs)\\n\\n # Ideally, we would catch any exception here and strip the checkers from the traceback.\\n # Unfortunately, this can not be done in Python 3, see\\n # https://stackoverflow.com/questions/44813333/how-can-i-elide-a-function-wrapper-from-the-traceback-in-python-3\\n result = await func(*args, **kwargs)\\n\\n if postconditions:\\n resolved_kwargs['result'] = result\\n\\n violation_error = await _assert_postconditions_async(\\n postconditions=postconditions, resolved_kwargs=resolved_kwargs)\\n if violation_error:\\n raise violation_error\\n\\n return result\\n finally:\\n in_progress.discard(id_func)\\n else:\\n\\n def wrapper(*args, **kwargs): # type: ignore\\n \\\"\\\"\\\"Wrap func by checking the preconditions and postconditions.\\\"\\\"\\\"\\n kwargs_error = _assert_no_invalid_kwargs(kwargs)\\n if kwargs_error:\\n raise kwargs_error\\n\\n # We need to create a new in-progress set if it is None as the ``ContextVar`` does not accept\\n # a factory function for the default argument. If we didn't do this, and simply set an empty\\n # set as the default, ``ContextVar`` would always point to the same set by copying the default\\n # by reference.\\n in_progress = _IN_PROGRESS.get()\\n if in_progress is None:\\n in_progress = set()\\n _IN_PROGRESS.set(in_progress)\\n\\n # Use try-finally instead of ExitStack for performance.\\n try:\\n # If the wrapper is already checking the contracts for the wrapped function, avoid a recursive loop\\n # by skipping any subsequent contract checks for the same function.\\n if id_func in in_progress:\\n return func(*args, **kwargs)\\n\\n in_progress.add(id_func)\\n\\n (preconditions, snapshots, postconditions) = _unpack_pre_snap_posts(wrapper)\\n\\n resolved_kwargs = kwargs_from_call(\\n param_names=param_names, kwdefaults=kwdefaults, args=args, kwargs=kwargs)\\n\\n type_error = _assert_resolved_kwargs_valid(\\n postconditions=postconditions, resolved_kwargs=resolved_kwargs)\\n if type_error:\\n raise type_error\\n\\n violation_error = _assert_preconditions(\\n preconditions=preconditions, resolved_kwargs=resolved_kwargs, func=func)\\n if violation_error:\\n raise violation_error\\n\\n # Capture the snapshots\\n if postconditions and snapshots:\\n resolved_kwargs['OLD'] = _capture_old(\\n snapshots=snapshots, resolved_kwargs=resolved_kwargs, func=func)\\n\\n # Ideally, we would catch any exception here and strip the checkers from the traceback.\\n # Unfortunately, this can not be done in Python 3, see\\n # https://stackoverflow.com/questions/44813333/how-can-i-elide-a-function-wrapper-from-the-traceback-in-python-3\\n result = func(*args, **kwargs)\\n\\n if postconditions:\\n resolved_kwargs['result'] = result\\n\\n violation_error = _assert_postconditions(\\n postconditions=postconditions, resolved_kwargs=resolved_kwargs, func=func)\\n if violation_error:\\n raise violation_error\\n\\n return result\\n finally:\\n in_progress.discard(id_func)\\n\\n # Copy __doc__ and other properties so that doctests can run\\n functools.update_wrapper(wrapper=wrapper, wrapped=func)\\n\\n assert not hasattr(wrapper, \\\"__preconditions__\\\"), \\\"Expected no preconditions set on a pristine contract checker.\\\"\\n assert not hasattr(wrapper, \\\"__postcondition_snapshots__\\\"), \\\\\\n \\\"Expected no postcondition snapshots set on a pristine contract checker.\\\"\\n assert not hasattr(wrapper, \\\"__postconditions__\\\"), \\\"Expected no postconditions set on a pristine contract checker.\\\"\\n\\n # Precondition is a list of condition groups (i.e. disjunctive normal form):\\n # each group consists of AND'ed preconditions, while the groups are OR'ed.\\n #\\n # This is necessary in order to implement \\\"require else\\\" logic when a class weakens the preconditions of\\n # its base class.\\n setattr(wrapper, \\\"__preconditions__\\\", [])\\n setattr(wrapper, \\\"__postcondition_snapshots__\\\", [])\\n setattr(wrapper, \\\"__postconditions__\\\", [])\\n\\n return wrapper # type: ignore\",\n \"def visit_any_function(self, node: AnyFunctionDef) -> None:\\n self._check_unused_variables(node)\\n self._check_generator(node)\\n self._check_descriptor_decorators(node)\\n self.generic_visit(node)\",\n \"def accept_funcs(func):\\n @wraps(func)\\n def wrapper(funcs, *args, **kwargs):\\n if hasattr(funcs[0], '__name__'):\\n funcs = [(f.__name__, f) for f in funcs]\\n return func(funcs, *args, **kwargs)\\n return wrapper\",\n \"def check_chief(function_to_decorate):\\r\\n @wraps(function_to_decorate)\\r\\n def decorated_function(*args, **kwargs):\\r\\n \\tif g.my['rank'] > 15:\\r\\n \\t\\tabort(401)\\r\\n \\treturn function_to_decorate(*args, **kwargs)\\r\\n return decorated_function\",\n \"def check_admin(function_to_decorate):\\r\\n @wraps(function_to_decorate)\\r\\n def decorated_function(*args, **kwargs):\\r\\n if g.my['rank'] > 25:\\r\\n abort(401)\\r\\n return function_to_decorate(*args, **kwargs)\\r\\n return decorated_function\",\n \"def check_fcn_attrs(self, node):\\n options = node.options\\n\\n ast = node.ast\\n declarator = ast.declarator\\n node._has_found_default = False\\n\\n for attr in declarator.attrs:\\n if attr[0] == \\\"_\\\": # internal attribute\\n continue\\n if attr not in [\\n \\\"api\\\", # arguments to pass to C wrapper.\\n \\\"allocatable\\\", # return a Fortran ALLOCATABLE\\n \\\"cdesc\\\",\\n \\\"deref\\\", # How to dereference pointer\\n \\\"dimension\\\",\\n \\\"free_pattern\\\",\\n \\\"len\\\",\\n \\\"name\\\",\\n \\\"owner\\\",\\n \\\"pure\\\",\\n \\\"rank\\\",\\n ]:\\n raise RuntimeError(\\n \\\"Illegal attribute '{}' for function '{}' define at line {}\\\".format(\\n attr, node.ast.name, node.linenumber\\n )\\n )\\n\\n meta = declarator.metaattrs\\n if ast.typemap is None:\\n print(\\\"XXXXXX typemap is None\\\")\\n if ast.typemap.sgroup == \\\"shadow\\\":\\n if options.C_shadow_result:\\n meta[\\\"api\\\"] = \\\"capptr\\\"\\n else:\\n meta[\\\"api\\\"] = \\\"capsule\\\"\\n if declarator.is_ctor():\\n meta[\\\"intent\\\"] = \\\"ctor\\\"\\n elif declarator.is_dtor():\\n meta[\\\"intent\\\"] = \\\"dtor\\\"\\n else:\\n meta[\\\"intent\\\"] = declarator.get_subprogram()\\n self.check_deref_attr_func(node)\\n self.check_common_attrs(node.ast)\\n\\n for arg in declarator.params:\\n if arg.declarator is None:\\n raise RuntimeError(\\\"Argument must have name in {} at line {}\\\".format(\\n node.decl, node.linenumber))\\n self.check_arg_attrs(node, arg)\\n\\n if node.fortran_generic:\\n for generic in node.fortran_generic:\\n for garg in generic.decls:\\n generic._has_found_default = False\\n self.check_arg_attrs(generic, garg, node.options)\\n check_implied_attrs(node, generic.decls)\\n else:\\n check_implied_attrs(node, declarator.params)\\n\\n self.parse_attrs(node, ast)\",\n \"def _visit_decorators_and_check_asynq(self, decorator_list):\\n async_kind = AsyncFunctionKind.non_async\\n is_classmethod = False\\n is_decorated_coroutine = False\\n is_staticmethod = False\\n decorators = []\\n for decorator in decorator_list:\\n # We have to descend into the Call node because the result of\\n # asynq.asynq() is a one-off function that we can't test against.\\n # This means that the decorator will be visited more than once, which seems OK.\\n if isinstance(decorator, ast.Call):\\n decorator_value = self.visit(decorator)\\n callee = self.visit(decorator.func)\\n if isinstance(callee, KnownValue):\\n if safe_in(callee.val, self.config.ASYNQ_DECORATORS):\\n if any(kw.arg == \\\"pure\\\" for kw in decorator.keywords):\\n async_kind = AsyncFunctionKind.pure\\n else:\\n async_kind = AsyncFunctionKind.normal\\n elif safe_in(callee.val, self.config.ASYNC_PROXY_DECORATORS):\\n # @async_proxy(pure=True) is a noop, so don't treat it specially\\n if not any(kw.arg == \\\"pure\\\" for kw in decorator.keywords):\\n async_kind = AsyncFunctionKind.async_proxy\\n decorators.append((callee, decorator_value))\\n else:\\n decorator_value = self.visit(decorator)\\n if decorator_value == KnownValue(classmethod):\\n is_classmethod = True\\n elif decorator_value == KnownValue(staticmethod):\\n is_staticmethod = True\\n elif asyncio is not None and decorator_value == KnownValue(\\n asyncio.coroutine\\n ):\\n is_decorated_coroutine = True\\n decorators.append((decorator_value, decorator_value))\\n return FunctionInfo(\\n async_kind=async_kind,\\n is_decorated_coroutine=is_decorated_coroutine,\\n is_classmethod=is_classmethod,\\n is_staticmethod=is_staticmethod,\\n decorators=decorators,\\n )\",\n \"def _is_function(self, words):\\n if words[0] == 'function':\\n if len(words) != 3:\\n raise SyntaxError(\\\"File line {}: Invalid number of arguments for C_FUNCTION command.\\\".format(self._file_line))\\n return True\\n else:\\n return False\",\n \"def test_check_fn_validity():\\n with pytest.raises(TypeError, match=r'.*did not pass a function.*'):\\n s7.check_doc_len(\\\"abc\\\")\\n s7.check_doc_len(100)\\n\\n def test_doc_len_multi():\\n \\\"\\\"\\\"Checks if the passed function have more than 50 characters in doc string \\\"\\\"\\\"\\n\\n def helper(doc):\\n if doc:\\n return True if len(doc) > 50 else False\\n else:\\n return 0\\n\\n def f():\\n pass\\n\\n for item in [100, 1.0, \\\"abcd\\\", f, s7.add, s7.mul, s7.div]:\\n try:\\n if isinstance(item, types.FunctionType):\\n doc = item.__doc__\\n f = s7.check_doc_len(item)\\n assert f() is helper(doc)\\n except Exception as e:\\n assert e.__class__.__name__ == TypeError.__name__\",\n \"def test_require_at_least_one_and_several_provided(self):\\n _func = at_least_one_of('arg1', 'arg2')(undecorated_func)\\n self.assertEqual(_func('ahoy', 'there'), 'foo')\\n self.assertEqual(_func(arg1='ahoy', arg2='there'), 'foo')\\n self.assertEqual(_func('ahoy', arg2='there', arg3='matey'), 'foo')\",\n \"def _OHE_checker(func):\\n\\n @wraps(func)\\n def wrapper_checker(database, features_list=None):\\n _CheckInput._check_database_input(database)\\n if features_list:\\n for column in features_list:\\n _CheckInput._check_column_in_database(column,database)\\n return func(database, features_list)\\n return wrapper_checker\",\n \"def _validate_args(\\n autologging_integration,\\n function_name,\\n user_call_args,\\n user_call_kwargs,\\n autologging_call_args,\\n autologging_call_kwargs,\\n):\\n\\n def _validate_new_input(inp):\\n \\\"\\\"\\\"\\n Validates a new input (arg or kwarg) introduced to the underlying / original ML function\\n call during the execution of a patched ML function. The new input is valid if:\\n\\n - The new input is a function that has been decorated with\\n `exception_safe_function_for_class` or `pickalable_exception_safe_function`\\n - OR the new input is a class with the `ExceptionSafeClass` metaclass\\n - OR the new input is a list and each of its elements is valid according to the\\n these criteria\\n \\\"\\\"\\\"\\n if type(inp) == list:\\n for item in inp:\\n _validate_new_input(item)\\n elif callable(inp):\\n assert getattr(inp, _ATTRIBUTE_EXCEPTION_SAFE, False), (\\n f\\\"New function argument '{inp}' passed to original function is not exception-safe.\\\"\\n \\\" Please decorate the function with `exception_safe_function` or \\\"\\n \\\"`pickalable_exception_safe_function`\\\"\\n )\\n else:\\n assert hasattr(inp, \\\"__class__\\\") and type(inp.__class__) in [\\n ExceptionSafeClass,\\n ExceptionSafeAbstractClass,\\n ], (\\n f\\\"Invalid new input '{inp}'. New args / kwargs introduced to `original` function \\\"\\n \\\"calls by patched code must either be functions decorated with \\\"\\n \\\"`exception_safe_function_for_class`, instances of classes with the \\\"\\n \\\"`ExceptionSafeClass` or `ExceptionSafeAbstractClass` metaclass safe or lists of \\\"\\n \\\"such exception safe functions / classes.\\\"\\n )\\n\\n def _assert_autologging_input_positional_args_are_superset(\\n autologging_call_input, user_call_input\\n ):\\n length_diff = len(autologging_call_input) - len(user_call_input)\\n assert (\\n length_diff >= 0\\n ), f\\\"{length_diff} expected inputs are missing from the call to the original function.\\\"\\n\\n def _assert_autologging_input_kwargs_are_superset(autologging_call_input, user_call_input):\\n assert set(user_call_input.keys()).issubset(set(autologging_call_input.keys())), (\\n \\\"Keyword or dictionary arguments to original function omit\\\"\\n \\\" one or more expected keys: '{}'\\\".format(\\n set(user_call_input.keys()) - set(autologging_call_input.keys())\\n )\\n )\\n\\n def _validate(autologging_call_input, user_call_input=None):\\n \\\"\\\"\\\"\\n Validates that the specified `autologging_call_input` and `user_call_input`\\n are compatible. If `user_call_input` is `None`, then `autologging_call_input`\\n is regarded as a new input added by autologging and is validated using\\n `_validate_new_input`. Otherwise, the following properties must hold:\\n\\n - `autologging_call_input` and `user_call_input` must have the same type\\n (referred to as \\\"input type\\\")\\n - if the input type is a tuple, list or dictionary, then `autologging_call_input` must\\n be equivalent to `user_call_input` or be a superset of `user_call_input`\\n - for all other input types, `autologging_call_input` and `user_call_input`\\n must be equivalent by reference equality or by object equality\\n\\n :param autologging_call_input: call input from autologging\\n :param user_call_input: call input from user\\n \\\"\\\"\\\"\\n\\n if user_call_input is None and autologging_call_input is not None:\\n _validate_new_input(autologging_call_input)\\n return\\n\\n assert type(autologging_call_input) == type(\\n user_call_input\\n ), \\\"Type of input to original function '{}' does not match expected type '{}'\\\".format(\\n type(autologging_call_input), type(user_call_input)\\n )\\n\\n if type(autologging_call_input) in [list, tuple]:\\n _assert_autologging_input_positional_args_are_superset(\\n autologging_call_input, user_call_input\\n )\\n # If the autologging call input is longer than the user call input, we `zip_longest`\\n # will pad the user call input with `None` values to ensure that the subsequent calls\\n # to `_validate` identify new inputs added by the autologging call\\n for a, u in itertools.zip_longest(autologging_call_input, user_call_input):\\n _validate(a, u)\\n elif type(autologging_call_input) == dict:\\n _assert_autologging_input_kwargs_are_superset(autologging_call_input, user_call_input)\\n for key in autologging_call_input.keys():\\n _validate(autologging_call_input[key], user_call_input.get(key, None))\\n else:\\n assert (\\n autologging_call_input is user_call_input\\n or autologging_call_input == user_call_input\\n ), (\\n \\\"Input to original function does not match expected input.\\\"\\n f\\\" Original: '{autologging_call_input}'. Expected: '{user_call_input}'\\\"\\n )\\n\\n # Similar validation logic found in _validate, unraveling the list of arguments to exclude\\n # checks for any validation exempt positional arguments.\\n _assert_autologging_input_positional_args_are_superset(autologging_call_args, user_call_args)\\n for index, autologging_call_arg, user_call_arg in itertools.zip_longest(\\n range(len(user_call_args)), autologging_call_args, user_call_args\\n ):\\n if not _is_arg_exempt_from_validation(\\n autologging_integration,\\n function_name,\\n user_call_arg,\\n argument_index=index,\\n ):\\n _validate(autologging_call_arg, user_call_arg)\\n\\n # Similar validation logic found in _validate, unraveling the dictionary of arguments to exclude\\n # checks for any validation exempt keyword arguments.\\n _assert_autologging_input_kwargs_are_superset(autologging_call_kwargs, user_call_kwargs)\\n for key in autologging_call_kwargs.keys():\\n if not _is_arg_exempt_from_validation(\\n autologging_integration,\\n function_name,\\n user_call_kwargs.get(key, None),\\n argument_name=key,\\n ):\\n _validate(\\n autologging_call_kwargs[key],\\n user_call_kwargs.get(key, None),\\n )\",\n \"def __call__(self, fn):\\n fn.handler = True\\n fn.function = True\\n\\n # Circuits properties\\n fn.names = self.names\\n fn.priority = self.kwargs.get(\\\"priority\\\", 0)\\n fn.channel = \\\"functions.{0}\\\".format(self.names[0])\\n fn.override = self.kwargs.get(\\\"override\\\", False)\\n fn.event = True\\n\\n @wraps(fn)\\n def app_function_decorator(itself, event, *args, **kwargs):\\n \\\"\\\"\\\"\\n The decorated function\\n\\n :param itself: The function to decorate\\n :type itself: resilient_circuits.ResilientComponent\\n :param event: The Event with the StompFrame and the Message read off the Message Destination\\n :type event: resilient_circuits.action_message.FunctionMessage\\n \\\"\\\"\\\"\\n function_inputs = event.message.get(\\\"inputs\\\", {})\\n\\n def _invoke_app_function(evt, **kwds):\\n \\\"\\\"\\\"\\n The code to call when a function with the decorator `@app_function(api_name)`\\n is invoked.\\n\\n Returns result_list when function with the decorator `@app_function(api_name)` is\\n finished processing.\\n\\n A method that has this handler should yield a StatusMessage or a FunctionResult\\n - When a StatusMessage is yield'ed a StatusMessageEvent is fired with the text of the StatusMessage\\n - When a FunctionResult is yield'ed it calls resilient-lib.ResultPayload.done() with the parameters of\\n FunctionResult being passed to it and appends the result to result_list. E.g:\\n `yield FunctionResult({\\\"key\\\":\\\"value\\\"})`\\n `yield FunctionResult({\\\"key\\\": \\\"value\\\"}, success=False, reason=\\\"Bad call\\\")`\\n\\n :param evt: The Event with the StompFrame and the Message read off the Message Destination\\n :type fn: resilient_circuits.action_message.FunctionMessage\\n \\\"\\\"\\\"\\n LOG.debug(\\\"Running _invoke_app_function in Thread: %s\\\", threading.currentThread().name)\\n\\n result_list = []\\n\\n # Validate the fn_inputs in the Message\\n fn_inputs = validate_fields([], kwds)\\n LOG.info(\\\"[%s] Validated function inputs\\\", evt.name)\\n LOG.debug(\\\"[%s] fn_inputs: %s\\\", evt.name, fn_inputs)\\n\\n rp = ResultPayload(itself.PACKAGE_NAME, version=constants.APP_FUNCTION_PAYLOAD_VERSION, **fn_inputs)\\n\\n fn_inputs_tuple = namedtuple(\\\"fn_inputs\\\", fn_inputs.keys())(*fn_inputs.values())\\n\\n # Set evt.message in local thread storage\\n itself.set_fn_msg(evt.message)\\n\\n # Invoke the actual Function\\n fn_results = fn(itself, fn_inputs_tuple)\\n\\n for r in fn_results:\\n if isinstance(r, StatusMessage):\\n LOG.info(\\\"[%s] StatusMessage: %s\\\", evt.name, r)\\n itself.fire(StatusMessageEvent(parent=evt, message=r.text))\\n\\n elif isinstance(r, FunctionResult):\\n r.name = evt.name\\n if not r.custom_results:\\n r.value = rp.done(\\n content=r.value,\\n success=r.success,\\n reason=r.reason)\\n LOG.info(\\\"[%s] Returning results\\\", r.name)\\n result_list.append(r)\\n\\n elif isinstance(r, Exception):\\n raise r\\n\\n else:\\n # Whatever this is, add it to the results\\n LOG.debug(r)\\n result_list.append(r)\\n\\n return result_list\\n\\n invoke_app_function = task(_invoke_app_function, event, **function_inputs)\\n fn_result = yield itself.call(invoke_app_function, \\\"functionworker\\\")\\n yield fn_result.value\\n\\n return app_function_decorator\",\n \"def check_implemented_functions(_class):\\n mandatory_functions_to_implement = [('generate', 2), ('__init__', 6)]\\n implemented_class_function_names = get_implemented_class_functions(_class)\\n for function in mandatory_functions_to_implement:\\n function_name = function[0]\\n number_function_mandatory_params = function[1]\\n # check if the method is implemented in the class\\n if function_name not in implemented_class_function_names:\\n logger.error(f\\\"Method {function_name} not implemented in class {_class.__name__}\\\")\\n raise SystemExit(0)\\n ref_function = getattr(_class, function_name)\\n # check if the method is expecting the mandatory number of arguments\\n if not len(inspect.getfullargspec(ref_function).args) == number_function_mandatory_params:\\n logger.error(\\n f\\\"Method {function_name} implemented in class {_class.__name__} \\\"\\n f\\\"is not expecting {number_function_mandatory_params} passed arguments\\\")\\n raise SystemExit(0)\",\n \"def test_function_definition_with_decorator(self):\\n self.script(\\\"# script.py\\\\n\\\"\\n \\\"def g(x):\\\\n\\\"\\n \\\" return x\\\\n\\\"\\n \\\"@g\\\\n\\\"\\n \\\"def f():\\\\n\\\"\\n \\\" 'fdoc'\\\\n\\\"\\n \\\" pass\\\\n\\\")\\n self.compile()\\n\\n script = self.find_code_component(name=\\\"script.py\\\")\\n function_def = self.find_code_component(name=\\\"f\\\")\\n\\n self.assertEqual(function_def.type, \\\"function_def\\\")\\n self.assertEqual(function_def.mode, \\\"w\\\")\\n self.assertEqual(function_def.first_char_line, 4)\\n self.assertEqual(function_def.first_char_column, 0)\\n self.assertEqual(function_def.last_char_line, 7)\\n self.assertEqual(function_def.last_char_column, 8)\\n self.assertEqual(function_def.container_id, script.id)\\n\\n function_def_block = self.metascript.code_blocks_store[function_def.id]\\n self.assertEqual(function_def_block.code,\\n \\\"@g\\\\n\\\"\\n \\\"def f():\\\\n\\\"\\n \\\" 'fdoc'\\\\n\\\"\\n \\\" pass\\\")\\n self.assertEqual(function_def_block.docstring, \\\"fdoc\\\")\\n self.assertTrue(bool(function_def_block.code_hash))\",\n \"def eval_function_call(func_call, motif_node_dict):\\n print(\\\"\\\\x1b[6;30;42m\\\" + 'Evaluating ' + func_call.name.name + ' function...' + '\\\\x1b[0m')\\n # CamFlow \\\"alloc_provenance\\\" take two arguments but only the first is needed for modeling.\\n if func_call.name.name == 'alloc_provenance':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n return provenance.alloc_provenance(arg_names[0], None)\\n # CamFlow \\\"task_cred_xxx\\\" take two arguments but no argument is needed for modeling.\\n elif func_call.name.name == 'task_cred_xxx':\\n return provenance.task_cred_xxx(None, None)\\n # CamFlow \\\"branch_mmap\\\" take two arguments but no argument is needed for modeling.\\n elif func_call.name.name == 'branch_mmap':\\n return provenance.branch_mmap(None, None)\\n # CamFlow \\\"uses_two\\\" function takes five arguments but only the first three are needed for modeling.\\n elif func_call.name.name == 'uses_two':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second and third arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses_two must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses_two must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses_two must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses_two must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.uses_two(arg_names[0], val1, val2, None, None, motif_node_dict)\\n # CamFlow \\\"informs\\\" function takes five arguments but only the first three are needed for modeling.\\n elif func_call.name.name == 'informs':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second and third arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in informs must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in informs must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in informs must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in informs must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.informs(arg_names[0], val1, val2, None, None, motif_node_dict)\\n # CamFlow \\\"record_terminate\\\" function takes two arguments.\\n elif func_call.name.name == 'record_terminate':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second arguments must be converted to MotifNode object first.\\n arg1 = arg_names[1]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_terminate must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_terminate must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_terminate(arg_names[0], val1, motif_node_dict)\\n # CamFlow \\\"generates\\\" function takes six arguments but only the first four are needed for modeling.\\n elif func_call.name.name == 'generates':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n arg3 = arg_names[3]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in generates must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in generates must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in generates must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in generates must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg3 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in generates must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val3 = getLastValueFromKey(motif_node_dict, arg3)\\n if not val3:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in generates must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.generates(arg_names[0], val1, val2, val3, None, None, motif_node_dict)\\n # CamFlow \\\"get_task_provenance\\\" takes no arguments.\\n elif func_call.name.name == 'get_task_provenance':\\n return provenance.get_task_provenance()\\n # CamFlow \\\"get_cred_provenance\\\" takes no arguments.\\n elif func_call.name.name == 'get_cred_provenance':\\n return provenance.get_cred_provenance(motif_node_dict)\\n # CamFlow \\\"uses\\\" takes six arguments but only the first four are needed for modeling.\\n elif func_call.name.name == 'uses':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n arg3 = arg_names[3]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in uses must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in uses must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg3 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in uses must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val3 = getLastValueFromKey(motif_node_dict, arg3)\\n if not val3:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in uses must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.uses(arg_names[0], val1, val2, val3, None, None, motif_node_dict)\\n # CamFlow \\\"refresh_inode_provenance\\\" takes two arguments but only the second one is needed for modeling.\\n elif func_call.name.name == 'refresh_inode_provenance':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second argument must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in refresh_inode_provenance must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in refresh_inode_provenance must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.refresh_inode_provenance(None, val1, motif_node_dict)\\n # CamFlow \\\"get_inode_provenance\\\" takes two arguments but only the second argument is needed for modeling.\\n elif func_call.name.name == 'get_inode_provenance':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n if arg_names[1] == 'false':\\n arg1 = False\\n elif arg_names[1] == 'true':\\n arg1 = True\\n else:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_inode_provenance is unknown.\\\\033[0m')\\n exit(1)\\n return provenance.get_inode_provenance(None, arg1, motif_node_dict)\\n # CamFlow \\\"get_dentry_provenance\\\" takes two arguments but only the second argument is needed for modeling. \\n elif func_call.name.name == 'get_dentry_provenance':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n if arg_names[1] == 'false':\\n arg1 = False\\n elif arg_names[1] == 'true':\\n arg1 = True\\n else:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_dentry_provenance is unknown.\\\\033[0m')\\n exit(1)\\n return provenance.get_dentry_provenance(None, arg1, motif_node_dict)\\n # CamFlow \\\"record_inode_name_from_dentry\\\" takes three arguments, but only the second and the third arguments are needed for modeling.\\n elif func_call.name.name == 'record_inode_name_from_dentry':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second argument must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_inode_name_from_dentry must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_inode_name_from_dentry must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg_names[2] == 'false':\\n arg2 = False\\n elif arg_names[2] == 'true':\\n arg2 = True\\n else:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_inode_name_from_dentry is unknown.\\\\033[0m')\\n exit(1)\\n return provenance.record_inode_name_from_dentry(None, val1, arg2, motif_node_dict)\\n # CamFlow \\\"record_node_name\\\" takes three arguments, but only the first and the third arguments are needed for modeling.\\n elif func_call.name.name == 'record_node_name':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second argument must be converted to MotifNode objects first.\\n arg0 = arg_names[0]\\n if arg0 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_node_name must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val0 = getLastValueFromKey(motif_node_dict, arg0)\\n if not val0:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_node_name must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_node_name(val0, None, arg_names[2], motif_node_dict)\\n # CamFlow \\\"derives\\\" function takes five arguments but only the first three are needed for modeling.\\n elif func_call.name.name == 'derives':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second and third arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in derives must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in derives must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in derives must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in derives must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.derives(arg_names[0], val1, val2, None, None, motif_node_dict)\\n # CamFlow \\\"record_write_xattr\\\" function takes eight arguments but only the first four are needed for modeling.\\n elif func_call.name.name == 'record_write_xattr':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second, third, and fourth arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n arg3 = arg_names[3]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_write_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_write_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_write_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_write_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg3 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in record_write_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val3 = getLastValueFromKey(motif_node_dict, arg3)\\n if not val3:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg3 + ' in record_write_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_write_xattr(arg_names[0], val1, val2, val3, None, None, None, None, motif_node_dict)\\n # CamFlow \\\"record_read_xattr\\\" function takes four arguments but only the first three are needed for modeling.\\n elif func_call.name.name == 'record_read_xattr':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n arg0 = arg_names[0]\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n if arg0 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_read_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val0 = getLastValueFromKey(motif_node_dict, arg0)\\n if not val0:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_read_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_read_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_read_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_read_xattr must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_read_xattr must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_read_xattr(val0, val1, val2, None, motif_node_dict)\\n # CamFlow \\\"get_file_provenance\\\" takes two arguments but only the second argument is needed for modeling. \\n elif func_call.name.name == 'get_file_provenance':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n if arg_names[1] == 'false':\\n arg1 = False\\n elif arg_names[1] == 'true':\\n arg1 = True\\n else:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg_names[1] + ' in get_file_provenance is unknown.\\\\033[0m')\\n exit(1)\\n return provenance.get_file_provenance(None, arg1, motif_node_dict)\\n # CamFlow \\\"influences_kernel\\\" function takes four arguments but only the first three are needed for modeling.\\n elif func_call.name.name == 'influences_kernel':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n # The second and third arguments must be converted to MotifNode objects first.\\n arg1 = arg_names[1]\\n arg2 = arg_names[2]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in influences_kernel must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in influences_kernel must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in influences_kernel must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in influences_kernel must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.influences_kernel(arg_names[0], val1, val2, None, motif_node_dict)\\n # CamFlow \\\"get_socket_inode_provenance\\\" takes one argument but it is not needed for modeling.\\n elif func_call.name.name == 'get_socket_provenance':\\n return provenance.get_socket_provenance(None, motif_node_dict)\\n # CamFlow \\\"get_socket_inode_provenance\\\" takes one argument but it is not needed for modeling. \\n elif func_call.name.name == 'get_socket_inode_provenance':\\n return provenance.get_socket_inode_provenance(None, motif_node_dict)\\n # CamFlow \\\"record_address\\\" takes three arguments but only the last argument is needed for modeling. \\n elif func_call.name.name == 'record_address':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n arg2 = arg_names[2]\\n if arg2 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_address must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val2 = getLastValueFromKey(motif_node_dict, arg2)\\n if not val2:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg2 + ' in record_address must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_address(None, None, val2, motif_node_dict)\\n # CamFlow \\\"get_sk_inode_provenance\\\" takes one argument but it is not needed for modeling. \\n elif func_call.name.name == 'get_sk_inode_provenance':\\n return provenance.get_sk_inode_provenance(None, motif_node_dict)\\n # CamFlow \\\"get_sk_provenance\\\" takes one argument but it is not needed for modeling.\\n elif func_call.name.name == 'get_sk_provenance':\\n return provenance.get_sk_provenance(None, motif_node_dict)\\n # CamFlow \\\"record_packet_content\\\" takes two arguments but only the second argument is needed for modeling. \\n elif func_call.name.name == 'record_packet_content':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n arg1 = arg_names[1]\\n if arg1 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_packet_content must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val1 = getLastValueFromKey(motif_node_dict, arg1)\\n if not val1:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg1 + ' in record_packet_content must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_packet_content(None, val1, motif_node_dict)\\n # CamFlow \\\"record_args\\\" takes two arguments but only the first argument is needed for modeling.\\n elif func_call.name.name == 'record_args':\\n args = func_call.args.exprs\\n arg_names = get_arg_name(args)\\n arg0 = arg_names[0]\\n if arg0 not in motif_node_dict:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_args must exist in the dictionary.\\\\033[0m')\\n exit(1)\\n val0 = getLastValueFromKey(motif_node_dict, arg0)\\n if not val0:\\n print('\\\\33[101m' + '[error][eval_function_call]: ' + arg0 + ' in record_args must have values in the dictionary.\\\\033[0m')\\n exit(1)\\n return provenance.record_args(val0, None, motif_node_dict)\\n else:\\n return None, None\",\n \"def accepts(*types):\\n\\n def check_accepts(f):\\n \\\"\\\"\\\"Check the types.\\\"\\\"\\\"\\n spec = tf_inspect.getargspec(f)\\n\\n num_function_arguments = len(spec.args)\\n if len(types) != num_function_arguments:\\n raise Error(\\n \\\"Function %r has %d arguments but only %d types were provided in the \\\"\\n \\\"annotation.\\\" % (f, num_function_arguments, len(types)))\\n\\n if spec.defaults:\\n num_defaults = len(spec.defaults)\\n for (name, a, t) in zip(spec.args[-num_defaults:],\\n spec.defaults,\\n types[-num_defaults:]):\\n allowed_type = _replace_forward_references(t, f.__globals__)\\n if not isinstance(a, allowed_type):\\n raise Error(\\\"default argument value %r of type %r is not an instance \\\"\\n \\\"of the allowed type %s for the %s argument to %r\\\"\\n % (a, type(a), _type_repr(allowed_type), name, f))\\n\\n @functools.wraps(f)\\n def new_f(*args, **kwds):\\n \\\"\\\"\\\"A helper function.\\\"\\\"\\\"\\n for (a, t) in zip(args, types):\\n allowed_type = _replace_forward_references(t, f.__globals__)\\n if not isinstance(a, allowed_type):\\n raise Error(\\\"%r of type %r is not an instance of the allowed type %s \\\"\\n \\\"for %r\\\" % (a, type(a), _type_repr(allowed_type), f))\\n return f(*args, **kwds)\\n\\n return new_f\\n\\n return check_accepts\",\n \"def fcheck(*args, **kwargs)->None:\\n pass\",\n \"def _validate_function(self, function, function_name,\\n shape=None, dtype=None):\\n # Validate that *function* is convertible\\n from firedrake.function import Function\\n function_is_function_type = isinstance(function, Function)\\n # FIXME : Once ExternalOperator is fully implemented, we don't need\\n # to try/except this block\\n if not function_is_function_type:\\n try:\\n from firedrake.pointwise_operators import ExternalOperator\\n function_is_extop_type = isinstance(function, ExternalOperator)\\n except ImportError:\\n function_is_extop_type = False\\n if not function_is_function_type and not function_is_extop_type:\\n raise TypeError(f\\\"'{function_name} must be a firedrake Function\\\"\\n \\\" or ExternalOperator, \\\"\\n f\\\" but is of unexpected type '{type(function)}'\\\")\\n ufl_elt = function.function_space().ufl_element()\\n if ufl_elt.family() != self._ufl_element.family():\\n raise ValueError(f\\\"'{function_name}.function_space().ufl_element()\\\"\\n f\\\".family()' must be {self._ufl_element.family()}\\\"\\n f\\\", not '{ufl_elt.family()}'\\\")\\n if ufl_elt.degree() != self._ufl_element.degree():\\n raise ValueError(f\\\"'{function_name}.function_space().ufl_element()\\\"\\n f\\\".degree()' must be {self._ufl_element.degree()}\\\"\\n f\\\", not '{ufl_elt.degree()}'\\\")\\n if function.function_space().mesh() is not self._mesh_geometry:\\n raise ValueError(f\\\"'{function_name}.function_space().mesh()' must\\\"\\n \\\" be the same mesh used by this connection\\\")\\n if dtype is not None and function.dat.data.dtype != dtype:\\n raise ValueError(f\\\"'{function_name}.dat.dtype' must be \\\"\\n f\\\"{dtype}, not '{function.dat.data.dtype}'\\\")\\n if shape is not None and function.function_space().shape != shape:\\n raise ValueError(\\\"'{function_name}.function_space().shape' must be\\\"\\n \\\" {shape}, not '{function.function_space().shape}\\\"\\n \\\"'\\\")\",\n \"def _determine_func(self, **kwargs):\\n # Check whether keys are recognized\\n for key in kwargs.keys():\\n if key not in self._parameter_map.keys():\\n raise FairException('\\\"{}\\\"\\\" is not a recognized keyword'.format(key))\\n # Check whether all keys go to same function via set comprension\\n functions = list(set([\\n self._parameter_map[key]\\n for key\\n in kwargs.keys()\\n ]))\\n if len(functions) > 1:\\n raise FairException('\\\"{}\\\" mixes incompatible keywords.'.format(str(kwargs.keys())))\\n else:\\n function = functions[0]\\n return function\",\n \"def check_mocked_functions_called(*mocked_functions):\\n for mocked_function in mocked_functions:\\n assert_that(mocked_function.called, f\\\"The function was not called - {mocked_function}\\\")\",\n \"def check_call_function(self, a, text):\\n logging.debug(\\\"check call function \\\" + text)\\n if self.cur_char == self.func_call_char:\\n self.produce(FUNCTION, text)\\n self.functions_calls.append(text)\\n elif not self.must_func_call_char:\\n if text in self.functions:\\n self.produce(FUNCTION, text)\\n self.functions_calls.append(text)\",\n \"def test_require_in_call_silently_succeeds_for_available_tests(self, test_generator):\\n # pylint: disable=function-redefined\\n\\n with self.subTest(\\\"direct decorator\\\"):\\n feature = test_generator()\\n with mock_availability_test(feature) as check:\\n check.assert_not_called()\\n\\n @feature.require_in_call\\n def decorated():\\n pass\\n\\n check.assert_not_called()\\n decorated()\\n check.assert_called_once()\\n\\n with self.subTest(\\\"named decorator\\\"):\\n feature = test_generator()\\n with mock_availability_test(feature) as check:\\n check.assert_not_called()\\n\\n @feature.require_in_call(\\\"sentinel name\\\")\\n def decorated():\\n pass\\n\\n check.assert_not_called()\\n decorated()\\n check.assert_called_once()\",\n \"def test_require_at_least_one_and_one_provided(self):\\n _func = at_least_one_of('arg1', 'arg2')(undecorated_func)\\n self.assertEqual(_func('ahoy'), 'foo')\\n self.assertEqual(_func(arg2='ahoy'), 'foo')\",\n \"def check(fn):\\n @wraps(fn)\\n def wrapper(*args, **kwargs):\\n verify_jwt_in_request()\\n token = get_jwt_identity()\\n if argument.lower() == 'machine':\\n if token['is_user_token'] is False:\\n from api.services.data_source_token import \\\\\\n DataSourceTokenService\\n _token_usage_counter_add(token['data_source_token']['id'])\\n if DataSourceTokenService.check_if_token_is_active(\\n DataSourceTokenService,\\n token['data_source_token']['id']) is False:\\n return ErrorObject.create_response(\\n ErrorObject, HTTPStatus.FORBIDDEN,\\n 'Token has been revoked')\\n else:\\n return fn(*args, **kwargs)\\n else:\\n return ErrorObject.create_response(\\n ErrorObject, HTTPStatus.FORBIDDEN,\\n 'Unable to access this resource with provided token')\\n elif argument.lower() == 'user':\\n if token['is_user_token'] is False:\\n _token_usage_counter_add(token['data_source_token']['id'])\\n return ErrorObject.create_response(\\n ErrorObject, HTTPStatus.FORBIDDEN,\\n 'Unable to access this resource with provided token')\\n else:\\n return fn(*args, **kwargs)\\n else:\\n raise ValueError('Unsupported argument provided')\\n\\n return wrapper\",\n \"def check_for_tracer(func: Callable) -> Callable:\\n\\n @wraps(func)\\n def wrapper(*args, **kwargs):\\n if any([isinstance(x, DynamicJaxprTracer) for x in args]):\\n raise TypeError(\\n f\\\"DynamicJaxprTracer found in {func.__name__}; did you jit this function?\\\"\\n )\\n if any([isinstance(x, BatchTracer) for x in args]):\\n raise TypeError(\\n f\\\"BatchTracer found in {func.__name__}; did you vmap/pmap this function?\\\"\\n )\\n return func(*args, **kwargs)\\n\\n return wrapper\",\n \"def _check_throttles_decorator(func):\\n @wraps(func)\\n def _decorated(*args, **kwargs):\\n # Skip the throttle check entirely if we've disabled rate limiting.\\n # Otherwise, perform the checks (as usual)\\n if RateLimitConfiguration.current().enabled:\\n return func(*args, **kwargs)\\n else:\\n msg = \\\"Rate limiting is disabled because `RateLimitConfiguration` is not enabled.\\\"\\n LOGGER.info(msg)\\n return\\n\\n return _decorated\",\n \"def test_process_invalid1(self):\\n self.skill.logic = {}\\n self.skill.valid.app_id = '12345'\\n @self.skill.launch\\n def sample_func():\\n \\\"\\\"\\\"Decorated function.\\\"\\\"\\\"\\n pass\\n self.skill.logic['LaunchRequest']()\\n self.assertFalse(self.skill.process(data.SAMPLE_LAUNCH_REQUEST))\",\n \"def check_lambdas(self):\\n args = range(len(self))\\n if self.flops:\\n try:\\n self.flops(*args)\\n except NameError as e:\\n raise NameError(\\\"Unknown argument %r used in flops\\\" %\\n str(e).split(\\\"'\\\")[1])\\n for arg in self:\\n if arg.min:\\n try:\\n arg.min(*args)\\n except NameError as e:\\n raise NameError(\\\"Unknown argument %r used in min for %s\\\" %\\n (str(e).split(\\\"'\\\")[1], arg))\\n if arg.max:\\n try:\\n arg.max(*args)\\n except NameError as e:\\n raise NameError(\\\"Unknown argument %r used in max for %s\\\" %\\n (str(e).split(\\\"'\\\")[1], arg))\\n if arg.properties:\\n try:\\n arg.properties(*args)\\n except NameError as e:\\n raise NameError(\\\"Unknown argument or property %r \\\"\\n \\\"used in properties for %s\\\" %\\n (str(e).split(\\\"'\\\")[1], arg))\",\n \"def _check_integrity(self):\\n for f in self.list_func:\\n if(not(isinstance(f, (pFunc_collec, pFunc_base, pFunc_fromcallable)))):\\n raise ValueError('type %s while expecting pFunc_base or collection'\\n ' ' % (str(type(f))))\\n f._check_integrity()\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func\",\n \"def test_12_funcdecl(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tprocedure main(); var x:real; begin x:=y(1,false); end\\n\\t\\tfunction y(a:integer;b:boolean):integer; begin return 1; end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Redeclared Function: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,412))\",\n \"def validate_func(func, source=None):\\n\\n if type(func) == str:\\n full_name = func\\n short_name = full_name.split('_')[0]\\n source = source or globals()\\n try:\\n func = source[short_name]\\n except KeyError:\\n raise KeyError('Unrecognized function name \\\"%s\\\"' % full_name)\\n\\n if type(func).__name__ != 'function':\\n raise ValueError('Not a function: \\\"%s\\\"' % full_name)\\n\\n if 'SIGNATURE' not in func.__dict__:\\n raise ValueError('Not a cspyce function: \\\"%s\\\"' % func.__name__)\\n\\n return func\",\n \"def all_fn(*args) -> bool:\\n for arg in args:\\n if not isinstance(arg, (FunctionType, partial)):\\n return False\\n\\n return True\",\n \"def is_compatible(self, function, arguments):\",\n \"def fixupFunctionBody(program: ghidra.program.model.listing.Program, func: ghidra.program.model.listing.Function, monitor: ghidra.util.task.TaskMonitor) -> bool:\\n ...\",\n \"def _check_extend_function(self):\\n for function_name, parameters in self._extend_function_dic.iteritems():\\n if not apply(function_name, parameters):\\n return False\\n return True\",\n \"def test_11_funcdecl(self):\\n\\t\\tinput = \\\"\\\"\\\"var x,y:integer;\\n\\t\\tprocedure main(); var x:real; begin x:=y(1,false); {error} end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Undeclared Function: y\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,411))\",\n \"def verify_operation(function):\\n\\n @wraps(function)\\n def wrapper(*args, **kwargs):\\n if PartialState().distributed_type == DistributedType.NO or not PartialState().debug:\\n return function(*args, **kwargs)\\n operation = f\\\"{function.__module__}.{function.__name__}\\\"\\n if \\\"tensor\\\" in kwargs:\\n tensor = kwargs[\\\"tensor\\\"]\\n else:\\n tensor = args[0]\\n shapes = get_shape(tensor)\\n output = gather_object([shapes])\\n if output[0] is not None:\\n are_same = output.count(output[0]) == len(output)\\n if not are_same:\\n process_shape_str = \\\"\\\\n - \\\".join([f\\\"Process {i}: {shape}\\\" for i, shape in enumerate(output)])\\n raise DistributedOperationException(\\n f\\\"Cannot apply desired operation due to shape mismatches. \\\"\\n \\\"All shapes across devices must be valid.\\\"\\n f\\\"\\\\n\\\\nOperation: `{operation}`\\\\nInput shapes:\\\\n - {process_shape_str}\\\"\\n )\\n return function(*args, **kwargs)\\n\\n return wrapper\",\n \"def test_required_and_mutually_exclusive(self):\\n @mutually_exclusive_parameters('arg2', 'arg3')\\n @required_parameters('arg1')\\n def _func1_decorated(arg1=None, arg2=None, arg3=None):\\n return 'foo'\\n\\n from plone.api.exc import InvalidParameterError\\n from plone.api.exc import MissingParameterError\\n\\n # test that the required parameter error works (missing arg1)\\n with self.assertRaises(MissingParameterError):\\n _func1_decorated(arg2='ahoy')\\n\\n # test that the mutually exclusive decorator works\\n # (arg2 and arg3 should not be there)\\n with self.assertRaises(InvalidParameterError):\\n _func1_decorated(\\n arg1='ahoy',\\n arg2='there',\\n arg3='matey',\\n )\\n\\n # test that they both work. Making no assumptions here about the order\\n # in which they fire.\\n with self.assertRaises((InvalidParameterError, MissingParameterError)):\\n _func1_decorated(\\n arg2='ahoy',\\n arg3='there',\\n )\\n\\n # everything ok\\n self.assertEqual(_func1_decorated('ahoy', arg3='there'), 'foo')\",\n \"def _verify_matching_signatures(implementation, dispatcher):\\n implementation_spec = getargspec(implementation)\\n dispatcher_spec = getargspec(dispatcher)\\n\\n if (implementation_spec.args != dispatcher_spec.args or\\n implementation_spec.varargs != dispatcher_spec.varargs or\\n implementation_spec.keywords != dispatcher_spec.keywords or\\n (bool(implementation_spec.defaults) !=\\n bool(dispatcher_spec.defaults)) or\\n (implementation_spec.defaults is not None and\\n len(implementation_spec.defaults) !=\\n len(dispatcher_spec.defaults))):\\n raise RuntimeError('implementation and dispatcher for %s have '\\n 'different function signatures' % implementation)\",\n \"def test_func_2(self):\\n def func():\\n return 0\\n self.assertEqual(type(decorators.timeit_2(func)), types.FunctionType)\",\n \"def login_manager_required(function=None, redirect_field_name=REDIRECT_FIELD_NAME):\\r\\n #print function, type(function), function.func_name\\r\\n actual_decorator = user_passes_test(\\r\\n lambda req: hasattr(req, \\\"session\\\") and req.session.get('manager_login', False) and req.user.is_active and (req.user.is_staff or req.user.auth_type in (1, 2)),\\r\\n login_url=\\\"/manager/login/\\\",\\r\\n redirect_field_name=redirect_field_name\\r\\n )\\r\\n if function:\\r\\n return actual_decorator(function)\\r\\n return actual_decorator\",\n \"def register_func_list(self, func_and_handler):\\n for func, handler in func_and_handler:\\n self._function_dispatch.register(func, handler)\\n self.dispatch.cache_clear()\",\n \"def is_valid_function(self, paras):\\n if len(paras) != 0:\\n return True\\n return True\",\n \"def _checkFunctioncode(functioncode, listOfAllowedValues=[]):\\n FUNCTIONCODE_MIN = 1\\n FUNCTIONCODE_MAX = 127\\n\\n _checkInt(functioncode, FUNCTIONCODE_MIN, FUNCTIONCODE_MAX, description='functioncode')\\n\\n if listOfAllowedValues is None:\\n return\\n\\n if not isinstance(listOfAllowedValues, list):\\n raise TypeError('The listOfAllowedValues should be a list. Given: {0!r}'.format(listOfAllowedValues))\\n\\n for value in listOfAllowedValues:\\n _checkInt(value, FUNCTIONCODE_MIN, FUNCTIONCODE_MAX, description='functioncode inside listOfAllowedValues')\\n\\n if functioncode not in listOfAllowedValues:\\n raise ValueError('Wrong function code: {0}, allowed values are {1!r}'.format(functioncode, listOfAllowedValues))\",\n \"def check_authentication(function_to_decorate):\\r\\n @wraps(function_to_decorate)\\r\\n def decorated_function(*args, **kwargs):\\r\\n if not hasattr(g, \\\"my\\\"):\\r\\n abort(401)\\r\\n return function_to_decorate(*args, **kwargs)\\r\\n return decorated_function\",\n \"def performFunctionalize(args, modName, modSearch=\\\"__main__\\\", preArgs=(), postArgs=()):\\n funcsList = args.list\\n \\n mod = sys.modules[modName]\\n if(funcsList):\\n funcs = _getModFunctions(modName, modSearch)\\n if('*' in funcsList):\\n funcsList = funcsList.replace('*', '')\\n search = True\\n else:\\n search = False\\n for f in funcs:\\n if(funcsList == 'all' or (search and funcsList in f.__name__) or (not search and funcsList == f.__name__)):\\n print('============================================================================================')\\n _printHelp(mod, f.__name__)\\n\\n return\\n\\n \\n #\\n # Run the function as a command\\n #\\n if(args.func):\\n if(not hasattr(mod, args.func)):\\n print('No %s function found' % args.func)\\n return\\n \\n func = args.func\\n rfunc = getattr(mod, func)\\n \\n # Get any args they want used\\n fargs = None\\n if(args.args):\\n fargs = [_parseValue(a) for a in args.args]\\n \\n # Deal with kwargs\\n kwargs = dict()\\n if(args.kwargs):\\n for kw in args.kwargs:\\n k, w = kw.split('=', 1)\\n kwargs[k] = _parseValue(w)\\n \\n # Print out the docs about the function\\n if(args.helpme):\\n _printHelp(mod, func)\\n return\\n \\n try:\\n # Build arguments to send them\\n theArgs = list()\\n if(preArgs):\\n theArgs += list(preArgs)\\n if(fargs):\\n theArgs += list(fargs)\\n if(postArgs):\\n theArgs += list(postArgs)\\n \\n # Call the function, if no args make special call (couldn't figure out another way)\\n if(theArgs and kwargs):\\n res = rfunc(*theArgs, **kwargs)\\n elif(theArgs and not kwargs):\\n res = rfunc(*theArgs)\\n elif(not theArgs and kwargs):\\n res = rfunc(**kwargs)\\n else:\\n res = rfunc()\\n \\n # Print results\\n if(args.printResult == 'str'):\\n print(res)\\n elif(args.printResult == 'json'):\\n print(_jsonPretty(res))\\n except Exception as e:\\n t = \\\", \\\".join(theArgs) + \\\", \\\" if theArgs else \\\"\\\"\\n t += \\\", \\\".join([\\\"{}={}\\\".format(k, v) for k, v in kwargs.iteritems()])\\n print \\\"Exception when calling {}({})\\\".format(args.func, t)\\n print e\\n _printHelp(mod, func)\\n traceback.print_exc()\\n else:\\n print('Call with \\\"-h\\\" for help')\\n return\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func is not None\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func is not None\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func is not None\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func is not None\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func is not None\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func is not None\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func is not None\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func is not None\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func is not None\",\n \"def test_function_definition(self):\\n func = get_function_definition(PACKAGE_NAME, FUNCTION_NAME)\\n assert func is not None\",\n \"def filter_trace_function_event(self, event):\\n if not isinstance(event, TraceFunctionEvent):\\n return False\\n\\n if event.function is getattr:\\n # Ignore attribute access on modules.\\n first_arg = list(event.arguments.values())[0]\\n return not isinstance(first_arg, types.ModuleType)\\n\\n return True\",\n \"def validate_inputs(function: Callable) -> Callable:\\n spec = inspect.getfullargspec(function)\\n # (According to the Python FAQ, the proper name for \\\"argument name\\\" is\\n # \\\"parameter\\\").\\n arg_names = spec.args\\n default_values = spec.defaults\\n # Iterate over parameters in reverse: only the last few parameters have\\n # default values.\\n for arg_name, value in zip(reversed(arg_names), reversed(default_values)):\\n check_arg(function, arg_name, value)\\n\\n @wraps(function)\\n def checked_function(*args, **kwargs):\\n for arg_name, value in zip(arg_names, args):\\n check_arg(function, arg_name, value)\\n for arg_name, value in kwargs.items():\\n check_arg(function, arg_name, value)\\n return function(*args, **kwargs)\\n\\n return checked_function\",\n \"def is_decorator(tree, fname):\\n return ((isx(tree, fname)) or\\n (type(tree) is Call and isx(tree.func, fname)))\",\n \"async def async_patched_function(self, *args, **kwargs):\\n self.validate(*args, **kwargs)\\n return await self.function(*args, **kwargs)\",\n \"def __call__(self, func):\\n LOG.debug(\\\"@function %s\\\", func)\\n\\n func.handler = True\\n func.function = True\\n\\n # Circuits properties\\n func.names = self.names\\n func.priority = self.kwargs.get(\\\"priority\\\", 0)\\n func.channel = self.kwargs.get(\\\"channel\\\", \\\",\\\".join([\\\"functions.{}\\\".format(name) for name in self.names]))\\n func.override = self.kwargs.get(\\\"override\\\", False)\\n \\n # If getfullargspec if available to us \\n if hasattr(_inspect, 'getfullargspec'):\\n args = _inspect.getfullargspec(func)[0]\\n else: # fall back to deprecated getargspec\\n args = _inspect.getargspec(func)[0]\\n\\n if args and args[0] == \\\"self\\\":\\n del args[0]\\n func.event = getattr(func, \\\"event\\\", bool(args and args[0] == \\\"event\\\"))\\n\\n @wraps(func)\\n def decorated(itself, event, *args, **kwargs):\\n \\\"\\\"\\\"the decorated function\\\"\\\"\\\"\\n LOG.debug(\\\"decorated\\\")\\n function_parameters = event.message.get(\\\"inputs\\\", {})\\n\\n def _the_task(event, *args, **kwargs):\\n return func(itself, event, *args, **kwargs)\\n\\n def _call_the_task(evt, **kwds):\\n # On the worker thread, call the function, and handle a single or generator result.\\n LOG.debug(\\\"%s: _call_the_task\\\", threading.currentThread().name)\\n result_list = []\\n task_result_or_gen = _the_task(evt, *args, **kwds)\\n if not isinstance(task_result_or_gen, GeneratorType):\\n task_result_or_gen = [task_result_or_gen]\\n for val in task_result_or_gen:\\n if isinstance(val, StatusMessage):\\n # Fire the wrapped status message event to notify resilient\\n LOG.info(\\\"[%s] StatusMessage: %s\\\", evt.name, val)\\n itself.fire(StatusMessageEvent(parent=evt, message=val.text))\\n elif isinstance(val, FunctionResult):\\n # Collect the result for return\\n LOG.debug(\\\"[%s] FunctionResult: %s\\\", evt.name, val)\\n val.name = evt.name\\n result_list.append(val)\\n elif isinstance(val, Event):\\n # Some other event, just fire it\\n LOG.debug(val)\\n itself.fire(val)\\n elif isinstance(val, FunctionError_):\\n LOG.error(\\\"[%s] FunctionError: %s\\\", evt.name, val)\\n itself.fire(FunctionErrorEvent(parent=evt, message=str(val)))\\n evt.success = False\\n return # Don't wait for more results!\\n elif isinstance(val, Exception):\\n raise val\\n else:\\n # Whatever this is, add it to the results\\n LOG.debug(val)\\n result_list.append(val)\\n return result_list\\n\\n the_task = task(_call_the_task, event, **function_parameters)\\n ret = yield itself.call(the_task, \\\"functionworker\\\")\\n xxx = ret.value\\n # Return value is the result_list that was yielded from the wrapped function\\n yield xxx\\n return decorated\",\n \"def test_mock_decorators_and_args(self):\\n errors = []\\n\\n for dirname, dirnames, filenames in os.walk(self.tests_path):\\n for filename in filenames:\\n if (not filename.startswith(\\\"test_\\\")\\n or not filename.endswith(\\\".py\\\")):\\n continue\\n\\n filename = os.path.relpath(os.path.join(dirname, filename))\\n\\n with open(filename, \\\"rb\\\") as fh:\\n tree = ast.parse(fh.read(), filename)\\n\\n visitor = FuncMockArgsDecoratorsChecker()\\n visitor.visit(tree)\\n errors.extend(\\n dict(filename=filename, **error)\\n for error in visitor.errors)\\n\\n if errors:\\n print(FuncMockArgsDecoratorsChecker.__doc__)\\n print(\\n \\\"\\\\n\\\\n\\\"\\n \\\"The following errors were found during the described check:\\\")\\n for error in errors:\\n print(\\\"\\\\n\\\\n\\\"\\n \\\"Errors at file %(filename)s line %(lineno)d:\\\\n\\\\n\\\"\\n \\\"%(message)s\\\" % {\\n \\\"message\\\": \\\"\\\\n\\\".join(error[\\\"messages\\\"]),\\n \\\"filename\\\": error[\\\"filename\\\"],\\n \\\"lineno\\\": error[\\\"lineno\\\"]})\\n\\n # NOTE(pboldin): When the STDOUT is shuted the below is the last\\n # resort to know what is wrong with the mock names.\\n for error in errors:\\n error[\\\"messages\\\"] = [\\n message.rstrip().replace(\\\"\\\\n\\\", \\\" \\\").replace(\\\"\\\\t\\\", \\\"\\\")\\n for message in error[\\\"messages\\\"]\\n ]\\n self.assertEqual([], errors)\",\n \"def validate_func_kwargs(\\n kwargs: dict,\\n) -> Tuple[List[str], List[Union[str, Callable[..., Any]]]]:\\n no_arg_message = \\\"Must provide 'func' or named aggregation **kwargs.\\\"\\n tuple_given_message = \\\"func is expected but recieved {} in **kwargs.\\\"\\n columns = list(kwargs)\\n func = []\\n for col_func in kwargs.values():\\n if not (isinstance(col_func, str) or callable(col_func)):\\n raise TypeError(tuple_given_message.format(type(col_func).__name__))\\n func.append(col_func)\\n if not columns:\\n raise TypeError(no_arg_message)\\n return columns, func\",\n \"def check_focal_elements_compatibility(function):\\n @functools.wraps(function)\\n def wrapped_function(*args):\\n for i in range(len(args)-1):\\n for j in range(len(args)-1):\\n if not args[i+1][0].is_compatible(args[j+1][0]):\\n raise IncompatibleElementsInAMassFunctionError(args[i+1][0], args[j+1][0])\\n return function(*args)\\n return wrapped_function\",\n \"def defer_to_call(*args, **kwargs):\\n data = _func_or_data(*args, **kwargs)\\n cls.scheme[func].validate(data)\\n return data\",\n \"def test_func(self):\\n def func():\\n return 0\\n self.assertEqual(type(decorators.timeit(func)), types.FunctionType)\",\n \"def validate_function_types(module: Module) -> None:\\n # This validation is explicitly in the spec but it gives us strong\\n # guarantees about indexing into the module types to populate the function\\n # types.\\n for function in module.funcs:\\n if function.type_idx >= len(module.types):\\n raise ValidationError(\\n f\\\"Function type index is out of range. \\\"\\n f\\\"type_idx={function.type_idx} > {len(module.types)}\\\"\\n )\",\n \"def condition_for_function(f, abi, all_not_in_ABI):\\n\\n\\tcondition = []\\n\\tfor n in f.entry_points:\\n\\t\\t[category, num] = api.get_category_for_name( n )\\n\\t\\tif category not in abi:\\n\\t\\t\\tcondition.append( 'defined(need_%s)' % (gl_XML.real_category_name( category )) )\\n\\t\\telif all_not_in_ABI:\\n\\t\\t\\treturn []\\n\\n\\treturn condition\",\n \"def check_before_executing(f):\\n @functools.wraps(f)\\n def wrapper(self, *args, **kwargs):\\n if not self._checked:\\n assert self.is_correct, (\\n 'The MatchList is incorrectly constructed. '\\n 'Run check_and_print_if_error() for details.')\\n return f(self, *args, **kwargs)\\n return wrapper\",\n \"def decorator_func(func):\\r\\n @functools.wraps(func)\\r\\n def with_status_check(obj, *args, **kwargs):\\r\\n if obj.status not in valid_start_statuses:\\r\\n exception_msg = (\\r\\n u\\\"Error calling {} {}: status is '{}', must be one of: {}\\\"\\r\\n ).format(func, obj, obj.status, valid_start_statuses)\\r\\n raise VerificationException(exception_msg)\\r\\n return func(obj, *args, **kwargs)\\r\\n\\r\\n return with_status_check\",\n \"def decoratedCheckToken(*args, **kwargs):\\n if \\\"token\\\" not in request.headers:\\n raise InvalidUsage(\\\"Must pass a token!\\\")\\n\\n # Execute if the token matches\\n logger.debug(\\\"Token: {0}\\\".format(request.headers[\\\"token\\\"]))\\n if request.headers[\\\"token\\\"] == receiverParameters[\\\"apiToken\\\"]:\\n return func(*args, **kwargs)\\n\\n # Note that it is invalid otherwise\\n raise InvalidUsage(\\\"Invalid token!\\\")\",\n \"def check_mocked_functions_not_called(*mocked_functions):\\n for mocked_function in mocked_functions:\\n assert_that(not mocked_function.called, f\\\"The function should not have been called - {mocked_function}\\\")\",\n \"def test_require_in_call_raises_for_unavailable_tests(self, test_generator):\\n # pylint: disable=function-redefined\\n\\n with self.subTest(\\\"direct decorator\\\"):\\n feature = test_generator()\\n with mock_availability_test(feature) as check:\\n check.assert_not_called()\\n\\n @feature.require_in_call\\n def decorated():\\n pass\\n\\n check.assert_not_called()\\n with self.assertRaisesRegex(MissingOptionalLibraryError, \\\"decorated\\\"):\\n decorated()\\n check.assert_called_once()\\n with self.assertRaisesRegex(MissingOptionalLibraryError, \\\"decorated\\\"):\\n decorated()\\n check.assert_called_once()\\n\\n with self.subTest(\\\"named decorator\\\"):\\n feature = test_generator()\\n with mock_availability_test(feature) as check:\\n check.assert_not_called()\\n\\n @feature.require_in_call(\\\"sentinel message\\\")\\n def decorated():\\n pass\\n\\n check.assert_not_called()\\n with self.assertRaisesRegex(MissingOptionalLibraryError, \\\"sentinel message\\\"):\\n decorated()\\n check.assert_called_once()\\n with self.assertRaisesRegex(MissingOptionalLibraryError, \\\"sentinel message\\\"):\\n decorated()\\n check.assert_called_once()\",\n \"def _validate_user_module_and_set_functions(self):\\n user_module_name = self._environment.module_name\\n\\n self._pre_model_fn = getattr(self._default_inference_handler, \\\"default_pre_model_fn\\\", None)\\n self._model_warmup_fn = getattr(\\n self._default_inference_handler, \\\"default_model_warmup_fn\\\", None\\n )\\n\\n if find_spec(user_module_name) is not None:\\n user_module = importlib.import_module(user_module_name)\\n\\n self._model_fn = getattr(\\n user_module, \\\"model_fn\\\", self._default_inference_handler.default_model_fn\\n )\\n\\n transform_fn = getattr(user_module, \\\"transform_fn\\\", None)\\n input_fn = getattr(user_module, \\\"input_fn\\\", None)\\n predict_fn = getattr(user_module, \\\"predict_fn\\\", None)\\n output_fn = getattr(user_module, \\\"output_fn\\\", None)\\n pre_model_fn = getattr(user_module, \\\"pre_model_fn\\\", None)\\n model_warmup_fn = getattr(user_module, \\\"model_warmup_fn\\\", None)\\n\\n if transform_fn and (input_fn or predict_fn or output_fn):\\n raise ValueError(\\n \\\"Cannot use transform_fn implementation in conjunction with \\\"\\n \\\"input_fn, predict_fn, and/or output_fn implementation\\\"\\n )\\n\\n self._transform_fn = transform_fn or self._default_transform_fn\\n self._input_fn = input_fn or self._default_inference_handler.default_input_fn\\n self._predict_fn = predict_fn or self._default_inference_handler.default_predict_fn\\n self._output_fn = output_fn or self._default_inference_handler.default_output_fn\\n if pre_model_fn is not None:\\n self._pre_model_fn = pre_model_fn\\n if model_warmup_fn is not None:\\n self._model_warmup_fn = model_warmup_fn\\n else:\\n self._model_fn = self._default_inference_handler.default_model_fn\\n self._input_fn = self._default_inference_handler.default_input_fn\\n self._predict_fn = self._default_inference_handler.default_predict_fn\\n self._output_fn = self._default_inference_handler.default_output_fn\\n\\n self._transform_fn = self._default_transform_fn\",\n \"def _handle_callable(value: Any, annotation_args: Tuple[List[Any], Any]) -> bool:\\n\\n if not isinstance(value, Callable):\\n return False\\n\\n # Here, we wish to compare a given callable with the annotation provided.\\n # The only way to verify this information is through the type hints of the function.\\n # Note that the `Callable` syntax does not indicate optional or keyword arguments,\\n # so those are ignored if present.\\n param_annotations, return_annotation = annotation_args\\n signature = inspect.signature(function)\\n indicated_return_annotation = signature.return_annotation\\n\\n # have to write functions to convert between `typing` and builtin\\n if indicated_return_annotation != return_annotation:\\n return False\\n \\n print(\\\"callable functionality WIP\\\")\\n pass\",\n \"def check_focal_elements_validity(function):\\n @functools.wraps(function)\\n def wrapped_function(*args):\\n for i in range(len(args)-1):\\n try:\\n #if (not isinstance(args[i+1][0], element.Element) or\\n if not (isinstance(args[i+1][1], int) or isinstance(args[i+1][1], float)):\\n raise ValueError(\\n \\\"focal_element: \\\" + str(args[i+1]) + \\\"\\\\n\\\" +\\n \\\"It should have contained an Element at index 0 and a numerical \\\" +\\n \\\"value at index 1 (e.g. a tuple (element, 0.8) or a list [element, 0.5] \\\" +\\n \\\"would have been valid).\\\"\\n )\\n except TypeError:\\n raise ValueError(\\n \\\"focal_element: \\\" + str(args[i+1]) + \\\"\\\\n\\\" +\\n \\\"It should have supported indexing and contained an Element at index 0 and \\\"\\n \\\"a numerical value at index 1 (e.g. a tuple (element, 0.8) or a list \\\" +\\n \\\"[element, 0.5] would have been valid).\\\"\\n )\\n return function(*args)\\n return wrapped_function\",\n \"def translation_rule_gpu(func):\\n # functions to call before running the translation rule\\n setup_funcs = (\\n ensure_gpu_ext,\\n functools.partial(ensure_platform_flush, \\\"gpu\\\"),\\n ensure_omnistaging,\\n setup_cuda_mpi,\\n )\\n\\n @functools.wraps(func)\\n def wrapped(*args, **kwargs):\\n for f in setup_funcs:\\n f()\\n return func(*args, **kwargs)\\n\\n return wrapped\",\n \"def is_decorated_with( this, fn, decorator) :\\n\\t\\treturn decorator in this.get_decorators( fn)\",\n \"def test_exactly_one_required(self):\\n\\n @mutually_exclusive_parameters('arg1', 'arg2')\\n @at_least_one_of('arg1', 'arg2')\\n def _func1_decorated(arg1=None, arg2=None, arg3=None):\\n return 'foo'\\n\\n from plone.api.exc import InvalidParameterError\\n from plone.api.exc import MissingParameterError\\n\\n # test it errors if you provide none\\n with self.assertRaises(MissingParameterError):\\n _func1_decorated()\\n\\n # test that it errors if you provide both\\n with self.assertRaises(InvalidParameterError):\\n _func1_decorated('ahoy', 'there')\\n\\n # everything ok\\n self.assertEqual(_func1_decorated('ahoy'), 'foo')\\n self.assertEqual(_func1_decorated('ahoy', arg3='there'), 'foo')\",\n \"def checkfuncname(b, frame):\\n if not b.funcname:\\n # Breakpoint was set via line number.\\n if b.line != frame.f_lineno:\\n # Breakpoint was set at a line with a def statement and the function\\n # defined is called: don't break.\\n return False\\n return True\\n\\n # Breakpoint set via function name.\\n if frame.f_code.co_name != b.funcname:\\n # It's not a function call, but rather execution of def statement.\\n return False\\n\\n # We are in the right frame.\\n if not b.func_first_executable_line:\\n # The function is entered for the 1st time.\\n b.func_first_executable_line = frame.f_lineno\\n\\n if b.func_first_executable_line != frame.f_lineno:\\n # But we are not at the first line number: don't break.\\n return False\\n return True\",\n \"def visit_any_function(self, node: AnyFunctionDef) -> None:\\n self._counter.check_arguments_count(node)\\n self._counter.check_function_complexity(node)\\n self.generic_visit(node)\",\n \"def decorator(func):\\n\\n pass\",\n \"def has_deco(deconames, tree, userlambdas=[]):\\n if type(tree) in (FunctionDef, AsyncFunctionDef):\\n return any(is_decorator(x, fname) for fname in deconames for x in tree.decorator_list)\\n elif is_decorated_lambda(tree, mode=\\\"any\\\"):\\n decorator_list, thelambda = destructure_decorated_lambda(tree)\\n if (not userlambdas) or (id(thelambda) in userlambdas):\\n return any(is_lambda_decorator(x, fname) for fname in deconames for x in decorator_list)\\n return None # not applicable\",\n \"def check_inputs(function):\\n def decorated(self, data, *args, **kwargs):\\n if not (isinstance(data, np.ndarray) and len(data.shape) == 2 and data.shape[1] == 1):\\n raise ValueError('The argument `data` must be a numpy.ndarray with shape (n, 1).')\\n\\n return function(self, data, *args, **kwargs)\\n\\n decorated.__doc__ = function.__doc__\\n return decorated\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6134093","0.5936042","0.57259756","0.56994855","0.5666439","0.5570822","0.5568335","0.55557805","0.5536746","0.55267","0.5503715","0.55015457","0.54883873","0.5477766","0.5459286","0.54585314","0.5451225","0.543271","0.54145896","0.5368069","0.53297734","0.5303614","0.53034496","0.5297371","0.52900183","0.52811486","0.52656263","0.5252748","0.5241029","0.5239762","0.5238755","0.5227937","0.51956373","0.5180429","0.5167503","0.51672566","0.5162174","0.5158437","0.5137403","0.51271665","0.51175195","0.51096994","0.5103078","0.50909287","0.50909287","0.50898004","0.5089449","0.5074754","0.50724864","0.50706196","0.5069552","0.5058058","0.50566375","0.50561595","0.5053313","0.503329","0.5026691","0.5021254","0.50200635","0.50190735","0.5019051","0.5014745","0.50140584","0.50140584","0.50140584","0.50140584","0.50140584","0.50140584","0.50140584","0.50140584","0.50140584","0.50140584","0.50136536","0.500808","0.5004858","0.49997607","0.49972835","0.49960482","0.49934742","0.49887848","0.49754325","0.49726248","0.49676833","0.49524087","0.49501893","0.4944959","0.4943392","0.49342915","0.49294087","0.49271175","0.4925571","0.4924806","0.49200076","0.4904951","0.49043906","0.4897423","0.48949072","0.4884161","0.48823637","0.48713303"],"string":"[\n \"0.6134093\",\n \"0.5936042\",\n \"0.57259756\",\n \"0.56994855\",\n \"0.5666439\",\n \"0.5570822\",\n \"0.5568335\",\n \"0.55557805\",\n \"0.5536746\",\n \"0.55267\",\n \"0.5503715\",\n \"0.55015457\",\n \"0.54883873\",\n \"0.5477766\",\n \"0.5459286\",\n \"0.54585314\",\n \"0.5451225\",\n \"0.543271\",\n \"0.54145896\",\n \"0.5368069\",\n \"0.53297734\",\n \"0.5303614\",\n \"0.53034496\",\n \"0.5297371\",\n \"0.52900183\",\n \"0.52811486\",\n \"0.52656263\",\n \"0.5252748\",\n \"0.5241029\",\n \"0.5239762\",\n \"0.5238755\",\n \"0.5227937\",\n \"0.51956373\",\n \"0.5180429\",\n \"0.5167503\",\n \"0.51672566\",\n \"0.5162174\",\n \"0.5158437\",\n \"0.5137403\",\n \"0.51271665\",\n \"0.51175195\",\n \"0.51096994\",\n \"0.5103078\",\n \"0.50909287\",\n \"0.50909287\",\n \"0.50898004\",\n \"0.5089449\",\n \"0.5074754\",\n \"0.50724864\",\n \"0.50706196\",\n \"0.5069552\",\n \"0.5058058\",\n \"0.50566375\",\n \"0.50561595\",\n \"0.5053313\",\n \"0.503329\",\n \"0.5026691\",\n \"0.5021254\",\n \"0.50200635\",\n \"0.50190735\",\n \"0.5019051\",\n \"0.5014745\",\n \"0.50140584\",\n \"0.50140584\",\n \"0.50140584\",\n \"0.50140584\",\n \"0.50140584\",\n \"0.50140584\",\n \"0.50140584\",\n \"0.50140584\",\n \"0.50140584\",\n \"0.50140584\",\n \"0.50136536\",\n \"0.500808\",\n \"0.5004858\",\n \"0.49997607\",\n \"0.49972835\",\n \"0.49960482\",\n \"0.49934742\",\n \"0.49887848\",\n \"0.49754325\",\n \"0.49726248\",\n \"0.49676833\",\n \"0.49524087\",\n \"0.49501893\",\n \"0.4944959\",\n \"0.4943392\",\n \"0.49342915\",\n \"0.49294087\",\n \"0.49271175\",\n \"0.4925571\",\n \"0.4924806\",\n \"0.49200076\",\n \"0.4904951\",\n \"0.49043906\",\n \"0.4897423\",\n \"0.48949072\",\n \"0.4884161\",\n \"0.48823637\",\n \"0.48713303\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7230727"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17796,"cells":{"query":{"kind":"string","value":"Two type for for loop are supported. Either; 1) Message for loop in which case the format requires a iterator using the named pyflamegpu function argument of 'message_in' 2) A range based for loop with 1 to 3 arguments which is converted into a c style loop"},"document":{"kind":"string","value":"def _For(self, t):\n # if message loop then process differently\n if isinstance(t.iter, ast.Name):\n if t.iter.id == self._input_message_var:\n self.dispatchMessageLoop(t)\n else:\n self.RaiseError(t, \"Range based for loops only support message iteration using 'message_in' iterator\")\n # do not support for else\n elif t.orelse:\n self.RaiseError(t, \"For else not supported\")\n # allow calls but only to range function\n elif isinstance(t.iter, ast.Call):\n # simple function call e.g. message_in() or range()\n if isinstance(t.iter.func, ast.Name):\n # catch case of message_input with arguments (e.g. spatial messaging)\n if t.iter.func.id == self._input_message_var:\n self.dispatchMessageLoop(t)\n # otherwise permit only range based for loops\n elif t.iter.func.id == \"range\":\n # switch on different uses of range based on number of arguments\n if len(t.iter.args) == 1:\n self.fill(f\"for (int \")\n self.dispatch(t.target)\n self.write(\"=0;\")\n self.dispatch(t.target)\n self.write(\"<\")\n self.dispatch(t.iter.args[0])\n self.write(\";\")\n self.dispatch(t.target)\n self.write(\"++)\")\n elif len(t.iter.args) == 2:\n self.fill(f\"for (int \")\n self.dispatch(t.target)\n self.write(\"=\")\n self.dispatch(t.iter.args[0])\n self.write(\";\")\n self.dispatch(t.target)\n self.write(\"<\")\n self.dispatch(t.iter.args[1])\n self.write(\";\")\n self.dispatch(t.target)\n self.write(\"++)\")\n elif len(t.iter.args) == 3:\n self.fill(f\"for (int \")\n self.dispatch(t.target)\n self.write(\"=\")\n self.dispatch(t.iter.args[0])\n self.write(\";\")\n self.dispatch(t.target)\n self.write(\"<\")\n self.dispatch(t.iter.args[1])\n self.write(\";\")\n self.dispatch(t.target)\n self.write(\"+=\")\n self.dispatch(t.iter.args[2])\n self.write(\")\")\n else:\n self.RaiseError(t, \"Range based for loops requires use of 'range' function with arguments and not keywords\")\n self.enter()\n self.dispatch(t.body)\n self.leave()\n else:\n self.RaiseError(t, \"Range based for loops only support calls to the 'range' function\")\n # member function call can only be on message_in.func() type call.\n elif isinstance(t.iter.func, ast.Attribute):\n # must be an attribute (e.g. calling a member of message_in)\n if t.iter.func.value.id == self._input_message_var:\n self.dispatchMessageLoop(t)\n else:\n self.RaiseError(t, \"Range based for loops only support calling members of message input variable\")\n else:\n self.RaiseError(t, \"Range based for loops only support message iteration or use of 'range'\")\n else:\n self.RaiseError(t, \"Range based for loops only support message iteration or use of 'range'\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def dispatchMessageLoop(self, tree):\n self.fill(\"for (const auto& \")\n self.dispatch(tree.target)\n self.write(\" : \")\n # if simple message iterator\n if isinstance(tree.iter, ast.Name):\n if not tree.iter.id == self._input_message_var:\n self.RaiseError(t, f\"Message input loop requires use of '{self._input_message_var}' as iterator.\")\n # write with prefix\n self.write(f\"FLAMEGPU->{self._input_message_var}\")\n # if it is a call then handle the different cases\n elif isinstance(tree.iter, ast.Call):\n self.dispatchMessageIteratorCall(tree.iter)\n #otherwise not supported\n else :\n self.RaiseError(tree, f\"Message input loop iterator in unsupported format\")\n self.write(\")\")\n self._message_iterator_var = tree.target.id\n self.enter()\n self.dispatch(tree.body)\n self.leave()\n self._message_iterator_var = None","def dispatchMessageIteratorCall(self, tree):\n # simple case not a member function just an iterator with arguments\n if isinstance(tree.func, ast.Name):\n self.write(f\"FLAMEGPU->{tree.func.id}\")\n if isinstance(tree.func, ast.Attribute) :\n if isinstance(tree.func.value, ast.Name):\n # check that the iterator is supported\n if not tree.func.attr in self.fgpu_input_msg_iter_funcs:\n self.RaiseError(tree, f\"Message input loop iterator '{tree.func.attr}' is not supported.\")\n self.write(f\"FLAMEGPU->{tree.func.value.id}.{tree.func.attr}\")\n else:\n self.RaiseError(tree, \"Message input loop iterator format incorrect.\")\n\n # handle function arguments \n self.write(\"(\")\n self._CallArguments(tree)\n self.write(\")\")","def multiple_eval_for_loops_v2():","def multiple_eval_for_loops_v1():","def convert_for(self, variable, start, stop, step, array):\n\n # Run super definition\n variable, start, stop, step, array = super().convert_for(\n variable, start, stop, step, array\n )\n\n # Remove data type from variable(duck typing in Python)\n variable = variable.split(\" \")[-1]\n\n # Create for template\n for_template = \"for {} in {}:\"\n\n # Define loop condition\n if array:\n # If array if given, loop through array\n loop_cond = array\n\n # Check if array slicing is required\n if step != \"1\" or stop != \"Array.length\" or start != \"0\":\n\n # Make template for array slicing\n loop_cond = \"{}[{{}}]\".format(array)\n\n if start == \"0\":\n start = \"\"\n\n if stop == \"Array.length\":\n stop = \"\"\n\n if step == \"1\":\n step = \"\"\n\n # If step is default, omit step\n if not step:\n\n # Else add start to range call\n loop_cond = loop_cond.format(start + \":\" + stop)\n else:\n # Add all three parameters if step is provided\n loop_cond = loop_cond.format(start + \":\" + stop + \":\" + step)\n\n else:\n # Else make range template\n loop_cond = \"range({})\"\n\n # If step if default, omit step\n if step == \"1\":\n\n # If start is default, omit start\n if start == \"0\":\n loop_cond = loop_cond.format(stop)\n\n else:\n # Else add start to range call\n loop_cond = loop_cond.format(start + \", \" + stop)\n else:\n # Add all three parameters if step is provided\n loop_cond = loop_cond.format(start + \", \" + stop + \", \" + step)\n\n # Return converted for statement\n return [for_template.format(variable, loop_cond)], []","def LoopBody(i, *input_arrays):\n # Outfeed ops execute on each JF node, so they must be located on the\n # nodes.\n outfeed_devices = []\n device_assignment = py_utils.GetTpuDeviceAssignment()\n assert device_assignment\n for replica in range(device_assignment.num_replicas):\n num_cores_per_replica = 1 if self.spmd else (\n device_assignment.num_cores_per_replica)\n for core in range(num_cores_per_replica):\n with tf.device(device_assignment.host_device(replica, core)):\n outfeed_devices.append(\n tpu_ops.outfeed_dequeue_tuple(\n tensor_types,\n tensor_shapes,\n device_ordinal=device_assignment.tpu_ordinal(replica,\n core)))\n offset = i * num_devices\n output_arrays = list(input_arrays)\n # Each output_array holds a different per-example tensor. We get results\n # for each tensor from each TPU for each TpuTrainStep call.\n for j in range(len(output_arrays)):\n for k in range(len(outfeed_devices)):\n output_arrays[j] = output_arrays[j].write(offset + k,\n outfeed_devices[k][j])\n\n return tuple([i + 1] + output_arrays)","def multi_c_enumerate():\n for a,(b,(c,d)) in enumerate(enumerate(enumerate(range(1,5)))):\n print(a,b,c,d)","def visit_for(self: Parser, node: doc.For) -> None:\n for_frame = self.eval_expr(node.iter)\n if not isinstance(for_frame, T.frame.ForFrame):\n self.report_error(\n node.iter,\n \"Expect the for loop to be one of the following: \"\n \"range, T.serial, T.grid, T.parallel, T.vectorized, T.unroll, T.thread_binding\",\n )\n with self.var_table.with_frame():\n with for_frame as iters:\n self.eval_assign(target=node.target, source=iters, bind_value=bind_for_value)\n self.visit_body(node.body)","def for_loop(num_iters, body, initial_args):\n for i in range(num_iters):\n if i == 0:\n outputs = body(*initial_args)\n else:\n outputs = body(*outputs)\n return outputs","def makeloop(keyword, G, *args):\n if not args:\n return []\n Nargs = len(args)\n lis = []\n for arg in args:\n lis.append(makeiter(G(\"%s%s\" % (keyword, arg))))\n try:\n Nlis = lis[0].count()\n except TypeError:\n Nlis = len(lis[0])\n olist = [[] for i in range(Nargs)]\n for i in range(Nlis):\n for k in range(Nargs):\n try:\n olist[k].append(lis[k][i])\n except Exception:\n olist[k].append(\"\")\n return olist","def test_element_loop_1cell():\n\n # Mesh object\n mesh = MeshMat()\n mesh.cell[\"triangle3\"] = CellMat(nb_node_per_cell=3)\n mesh.node = NodeMat()\n\n mesh.node.add_node(np.array([0, 0]))\n mesh.node.add_node(np.array([1, 0]))\n mesh.node.add_node(np.array([0, 1]))\n\n nodes_test = np.array([0, 1, 2])\n mesh.add_cell(nodes_test, \"triangle3\")\n\n indice = [0]\n\n # Physical quantities\n dim = 2\n Nt_tot = 1\n\n mu = 1\n Be = np.array([[[mu / 2, 0]]])\n He = np.array([[[-1 / 2, 0]]])\n mue = np.array([[mu]])\n\n Me = np.reshape(Be / mue - He, (dim, 1, Nt_tot))\n\n alphaij = [[1, 0, 0], [1, 0, 0]]\n\n alpha1 = 1\n alpha2 = 1\n\n # Computation\n tensor = ForceTensor()\n\n f, connect = tensor.element_loop(mesh, Be, He, mue, indice, dim, Nt_tot, alphaij)\n\n f1_analytic = 1 / 2 * mu * np.array([alpha1 + alpha2, alpha2])\n f2_analytic = 1 / 2 * mu * np.array([-(alpha1 + alpha2), 0])\n f3_analytic = 1 / 2 * mu * np.array([0, -alpha2])\n\n assert (f[0, :, 0] == f1_analytic).all()\n assert (f[1, :, 0] == f2_analytic).all()\n assert (f[2, :, 0] == f3_analytic).all()\n\n print(\"test_element_loop succeeded\")\n\n return True","def _loop_raw(raw):\n raise NotImplemented","def syntax_for():\n for i in range(5):\n print(i)\n\n ## Output\n # 0\n # 1\n # 2\n # 3\n # 4\n\n ## Notes\n # range()\n # Rather than being a function, range is actually an immutable sequence type.\n # The range type represents an immutable sequence of numbers and is commonly used for\n # looping a specific number of times in for loops.","def test_39_for(self):\n\t\tinput = \"\"\"function foo():integer; var a:integer; begin\n\t\tfor a:=1+1.5 to -2 do begin end\n\t\treturn 1;\n\t\tend\n\t\tprocedure main(); var x:integer; begin x:=foo(); foo(); end\"\"\"\n\t\texpect = \"Type Mismatch In Statement: For(Id(a)BinaryOp(+,IntLiteral(1),FloatLiteral(1.5)),UnaryOp(-,IntLiteral(2)),True,[])\"\n\t\tself.assertTrue(TestChecker.test(input,expect,439))","def go_c_enumerate_step():\n for i,k in enumerate(range(1,7,2)):\n print(i, k)","def visit_for(self, flags, scope, token, parent):\r\n\r\n arglist, body = token.children\r\n\r\n if body.type != Token.T_BLOCK:\r\n # the parser should be run in python mode\r\n raise TransformError(body, \"expected block in for loop body\")\r\n\r\n # the extra block scope, and finalize, allows for declaring\r\n # variables inside of a for arg list\r\n scope.pushBlockScope(\"loop\")\r\n self._push_finalize(scope, token, parent)\r\n\r\n self._push_children(scope, body, flags)\r\n self._push_children(scope, arglist, flags)","def vectorized_loops(self, data):\n\n # TODO: finish this.\n return np.add(np.multiply(data,data), data)","def test_for_loop(self, modes, parse_input_mocked_metadata):\n bb = parse_input_mocked_metadata(\n \"for int m in {}\\n\\tMeasureFock() | m\".format(modes)\n )\n assert np.all(\n bb._forvar[\"m\"] == np.array(modes)\n )\n assert bb.operations == [\n {'op': 'MeasureFock', 'args': [], 'kwargs': {}, 'modes': [modes[0]]},\n {'op': 'MeasureFock', 'args': [], 'kwargs': {}, 'modes': [modes[1]]},\n {'op': 'MeasureFock', 'args': [], 'kwargs': {}, 'modes': [modes[2]]}\n ]","def go_c_enumerate():\n for i,k in enumerate(range(1,5)):\n print(i, k)","def iteration(self) -> global___Statement.Iteration:","def iteration(self) -> global___Statement.Iteration:","def internal_loop(p, i, j, k, l):\n return _RNAstructure_wrap.internal_loop(p, i, j, k, l)","def _analyse_stmt_For(self, statement: ast.For, *, next: CFNode) -> CFNode:\n return self._analyse_loop(statement, next=next)","def multibranch_loop(i, j):\n return _RNAstructure_wrap.multibranch_loop(i, j)","def _batch_iter(self, source, target, i: int):\n # send data to device\n source = source.to(self.device)\n target = target.to(self.device)\n\n # the result and loss\n result = self.model(source)\n loss = self.criterion(result, target)\n\n # optimization and backward\n self.optimizer.zero_grad()\n loss.backward()\n self.optimizer.step()\n\n # update the loss\n self.epoch_loss.update(loss.item(), source.size(0))\n\n # print the information\n if self.info:\n print(f\"\\rEpoch: { self.epoch } | Batch: { i } | loss: { self.epoch_loss.avg }\", end=\"\")\n\n # clean the data\n del source, target\n\n return result","def enumerate_(start = 0):\n\n @filters\n def _dagpype_internal_fn_act(target):\n count = start \n try:\n while True:\n e = (yield)\n target.send((numpy.arange(count, count + len(e)), e))\n count += len(e)\n except GeneratorExit: \n target.close() \n\n return _dagpype_internal_fn_act","def coforeach(function, iterator):\n return _CoFunCaller(function=function).coiterate(iterator)","def iterate_inputs(function, type_to_vars):\n if isinstance(function.input_type, tuple):\n input_types = list(function.input_type)\n else:\n input_types = [function.input_type]\n\n argslists = []\n for input_type in input_types:\n argslists.append(type_to_vars[input_type])\n for args in itertools.product(*argslists):\n yield args","def emit_unpack_instruction(self, *, loop_indices=None):","def test_make_cpp_parfor():\n\n class STDVectorThreads(LocalObject):\n\n dtype = type('std::vector<std::thread>', (c_void_p,), {})\n\n def __init__(self):\n super().__init__('threads')\n\n class STDThread(LocalObject):\n\n dtype = type('std::thread&', (c_void_p,), {})\n\n class FunctionType(LocalObject):\n\n dtype = type('FuncType&&', (c_void_p,), {})\n\n # Basic symbols\n nthreads = Symbol(name='nthreads', is_const=True)\n threshold = Symbol(name='threshold', is_const=True)\n last = Symbol(name='last', is_const=True)\n first = Symbol(name='first', is_const=True)\n portion = Symbol(name='portion', is_const=True)\n\n # Composite symbols\n threads = STDVectorThreads()\n\n # Iteration helper symbols\n begin = Symbol(name='begin')\n l = Symbol(name='l')\n end = Symbol(name='end')\n\n # Functions\n stdmax = sympy.Function('std::max')\n\n # Construct the parallel-for body\n func = FunctionType('func')\n i = Dimension(name='i')\n threadobj = Call('std::thread', Lambda(\n Iteration(Call(func.name, i), i, (begin, end-1, 1)),\n ['=', Byref(func.name)],\n ))\n threadpush = Call(FieldFromComposite('push_back', threads), threadobj)\n it = Dimension(name='it')\n iteration = Iteration([\n DummyExpr(begin, it, init=True),\n DummyExpr(l, it + portion, init=True),\n DummyExpr(end, InlineIf(l > last, last, l), init=True),\n threadpush\n ], it, (first, last, portion))\n thread = STDThread('x')\n waitcall = Call('std::for_each', [\n Call(FieldFromComposite('begin', threads)),\n Call(FieldFromComposite('end', threads)),\n Lambda(Call(FieldFromComposite('join', thread.name)), [], [thread])\n ])\n body = [\n DummyExpr(threshold, 1, init=True),\n DummyExpr(portion, stdmax(threshold, (last - first) / nthreads), init=True),\n Call(FieldFromComposite('reserve', threads), nthreads),\n iteration,\n waitcall\n ]\n\n parfor = ElementalFunction('parallel_for', body, 'void',\n [first, last, func, nthreads])\n\n assert str(parfor) == \"\"\"\\\nstatic inline \\\nvoid parallel_for(const int first, const int last, FuncType&& func, const int nthreads)\n{\n const int threshold = 1;\n const int portion = std::max(threshold, (-first + last)/nthreads);\n threads.reserve(nthreads);\n for (int it = first; it <= last; it += portion)\n {\n int begin = it;\n int l = it + portion;\n int end = (l > last) ? last : l;\n threads.push_back(std::thread([=, &func]()\n {\n for (int i = begin; i <= end - 1; i += 1)\n {\n func(i);\n }\n }));\n }\n std::for_each(threads.begin(),threads.end(),[](std::thread& x)\n {\n x.join();\n });\n}\"\"\"","def get_for_iterations(self, file, i):\n\n # Run super definition\n line = super().get_for_iterations(file, i)\n\n # Save required words\n variable, for_range = line[0], \"\".join(line[2:])\n\n # Strip ending semicolon\n for_range = for_range.split(\":\", 1)[0]\n\n # Create start and end for 'for loop' call\n start = []\n end = []\n\n # Set start and step to default\n begin = \"0\"\n step = \"1\"\n\n # Parse for_range\n if for_range.find(\"range\") != -1:\n\n # Dump unwanted portion\n for_range = for_range.strip(\"range(\").strip(\")\")\n\n # Parse variables in for_range\n variables = [var.strip() for var in for_range.split(\",\")]\n\n # Store variable values\n var_count = len(variables)\n\n # If only one variable is given,\n # Set stop variable with default begin and step\n if var_count == 1:\n stop = variables[0]\n # Else if two variable are given,\n # set begin and stop variable with default step\n else:\n begin = variables[0]\n stop = variables[1]\n # If three variables are given,\n # set all three begin, stop and step variables\n if var_count == 3:\n step = variables[2]\n\n # Set array to None\n array = None\n else:\n # If range not found, iterate over given array\n\n # Get array\n array = for_range\n\n # Set default stop\n stop = \"Array.length\"\n\n # Check of array slicing\n if array.find(\"[\") != -1 and array.find(\":\") != -1:\n array, begin, stop, step = self.get_list_slice_vars(array)\n\n # Get variable type for variable\n\n # If array is passed, get type of array\n if array:\n var_type = \"Array.data_type\"\n\n # Else get type of variable\n else:\n var_type = self.get_type(begin)\n\n # Append variable type to variable\n variable = var_type + \" \" + variable\n\n # Return all variables\n return variable, begin, stop, step, array, start, end","def lua_iterate(self):\n zrange = \"redis.call('ZRANGE', '%s', '0', '-1')\" % self.redis_key()\n lua = \"for _, data in ipairs(%s) do \" % zrange\n lua += \"local row = cjson.decode(data) \"\n\n return lua","def __iter__(self):\n for b in self.dl: \n yield to_device(b, self.device)","def __iter__(self):\n for b in self.dl: \n yield to_device(b, self.device)","def __iter__(self):\n for b in self.dl: \n yield to_device(b, self.device)","def __iter__(self):\n for b in self.dl: \n yield to_device(b, self.device)","def __iter__(self):\n for b in self.dl: \n yield to_device(b, self.device) # yield pauses the execution, not store values in memory, forgets about them once iterated\n # no need to remove batch of data from device, done automatically","def iterate(self, batch_size=8, func=None):\n raise NotImplementedError()","def iterate(self):","def test_generator_continuous():\n RANGE_MAX = 100\n prev_value = RANGE_MAX // 2\n for msg in it.islice(generate_msgs(0, RANGE_MAX), 0, 42):\n curr_value = Message.parse(msg).power\n assert curr_value - prev_value <= 1\n prev_value = curr_value","def repeated_iteration(self) -> global___Statement.Iteration.RepeatedIteration:","def make_range_temp_K_prot(DB_version, DB_type, i1, i2):\n\n for index in range(i1, i2):\n print(index)\n make_temp_K_prot(DB_version, DB_type, index)","def multi_enumerate():\n for a,(b,(c,d)) in enumerate(enumerate(enumerate(range(1,5)))):\n print(a,b,c,d)","def __iter__(self):\n for batch in self.data:\n batch_size = len(batch)\n X, e1, e2, dist1, dist2, e1_pos, e2_pos, y = list(zip(*batch))\n\n x_len = max(len(x) for x in X)\n x_ids = torch.LongTensor(batch_size, x_len).fill_(0)\n dist1_padded = torch.LongTensor(batch_size, x_len).fill_(0)\n dist2_padded = torch.LongTensor(batch_size, x_len).fill_(0)\n for i, doc in enumerate(X):\n x_ids[i, :len(doc)] = torch.LongTensor(doc)\n\n dist1_padded[i, :len(doc)] = torch.LongTensor(dist1[i])\n dist1_padded[i, len(doc):] = torch.LongTensor([pos(e1_pos[i][1] - idx) for idx, _ in enumerate(x_ids[i][len(doc):], start=len(doc))])\n\n dist2_padded[i, :len(doc)] = torch.LongTensor(dist2[i])\n dist2_padded[i, len(doc):] = torch.LongTensor([pos(e2_pos[i][1] - idx) for idx, _ in enumerate(x_ids[i][len(doc):], start=len(doc))])\n\n e1_tensor = torch.LongTensor(e1)\n e2_tensor = torch.LongTensor(e2)\n\n y_tensor = torch.LongTensor(y)\n\n if self.gpu:\n x_ids = x_ids.pin_memory()\n e1_tensor = e1_tensor.pin_memory()\n e2_tensor = e2_tensor.pin_memory()\n dist1_padded = dist1_padded.pin_memory()\n dist2_padded = dist2_padded.pin_memory()\n y_tensor = y_tensor.pin_memory()\n\n yield (x_ids, e1_tensor, e2_tensor, dist1_padded, dist2_padded, y_tensor)","def main():\n\n #for row_start in np.arange(0, 128):\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_row'+str(row_start).zfill(3), Imin=12, Imax=136)\n\n Marcov_Chain_MLE(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '', 160.0, [90.0, 70.0, 50.0, 30.0], 0.0, 0.5)\n plt.show()\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_all')\n\n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAsource_VBdrain', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaS-VbD_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, forward' , 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaD-VbS_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, reversed', 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \n\n #hist_IDS_VGS(0, 14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Hist-IDSAT_MLC-rv1-01020304_reverse-read_', range(0, 128), 'MLC programming {2ms, 10ms, 40ms, 200ms} pulses, VGS=1.8, VDS=2.0 for level=1-2-3-4\\nhistogram of read-IDSAT (VGS=VDS=0.8V)', 0, 150, 0, 150, 1000)\n #\n #t_label = []\n #for t in np.arange(0, 0.002*(71) + 0.0001, 0.002):\n # t_label.append(str(t))\n #\n ##MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [21], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row-21', Imin=82, Imax=142)\n\n #for row_start in np.arange(0, 128):\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row_'+str(row_start).zfill(3), Imin=80, Imax=142)\n\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01', Imin=80, Imax=142)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01', 20, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle0102', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle0102', 20, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle010203', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle010203', 20, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\n\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01020304', 40, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01020304', 10, 160, 1)\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 50, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01', 20, 125, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 50, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle0102', 20, 125, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 50, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle010203', 20, 125, 1)\n\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 40, 125, 1)\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01020304', 10, 125, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 40, 150, 1)\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\n\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\n\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\n\n\n # (L, Nfin, VT_flavor, Nrow, Imax)\n col_list = [(36, 1, 'ULVT', 32 , 60 ), (36, 1, 'LVT', 32 , 50 ), (36, 1, 'SVT', 32 , 45 ),\n (36, 1, 'ULVT', 128, 60 ), (36, 1, 'LVT', 128, 50 ), (36, 1, 'SVT', 128, 45 ),\n (20, 1, 'ULVT', 32 , 75 ), (20, 1, 'LVT', 32 , 60 ), (20, 1, 'SVT', 32 , 50 ),\n (20, 1, 'ULVT', 128, 75 ), (20, 1, 'LVT', 128, 60 ), (20, 1, 'SVT', 128, 50 ),\n (16, 1, 'ULVT', 32 , 80 ), (16, 1, 'LVT', 32 , 65 ), (16, 1, 'SVT', 32 , 60 ),\n (16, 1, 'ULVT', 128, 80 ), (16, 1, 'LVT', 128, 65 ), (16, 1, 'SVT', 128, 60 ),\n (36, 2, 'ULVT', 32 , 115), (36, 2, 'LVT', 32 , 95 ), (36, 2, 'SVT', 32 , 85 ),\n (36, 2, 'ULVT', 128, 115), (36, 2, 'LVT', 128, 95 ), (36, 2, 'SVT', 128, 85 ), \n (20, 2, 'ULVT', 32 , 135), (20, 2, 'LVT', 32 , 115), (20, 2, 'SVT', 32 , 100),\n (20, 2, 'ULVT', 128, 135), (20, 2, 'LVT', 128, 120), (20, 2, 'SVT', 128, 100),\n (16, 2, 'ULVT', 32 , 150), (16, 2, 'LVT', 32 , 125), (16, 2, 'SVT', 32 , 115),\n (16, 2, 'ULVT', 128, 150), (16, 2, 'LVT', 128, 125), (16, 2, 'SVT', 128, 115)]\n\n #MLC_IDSAT_algorithm_rv1(11, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [135+20], [0.2], 1, np.arange(0, 0.01*16+0.0001, 0.01), '', ['../Data/chip11/MLC_programming_Chip11_Col21_2msPULSE_VG1p8_VD2p4_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_rv1_cycle01_EfficientPython')\n\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', '0.9-1.2-1.5-1.8', 2.4, 128, range(0, 128), [59+16, 72+40, 80+31, 68+23], [0.2, 0.2, 0.2, 0.2], 4, [0, 15, 15.1, 37.5, 37.6, 59.8, 59.9, 78.1], ['0', '15', '', '37.4', '', '59.6', '', '77.8'], ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG-0p9-1p2-1p5-1p8_VD2p4', '_rv1_cycle01020304')\n\n t_ratio_lst = [(0, 0.17), (0.16, 0.34), (0.33, 0.505), (0.495, 0.67), (0.66, 0.84), (0.83, 1)]\n\n #t_label = []\n #for t in np.arange(0, 0.2*(59+16) + 0.0001, 0.2):\n # t_label.append(str(t))\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(0, 128), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(59+16), t_ratio[1]*0.2*(59+16)])\n # segment += 1\n\n #t_label = []\n #for t in np.arange(0, 0.2*(72+40) + 0.0001, 0.2):\n # t_label.append(str(0.2*(59+16) + t))\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(0, 128), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(72+40), t_ratio[1]*0.2*(72+40)])\n # segment += 1\n\n\n #t_label = []\n #for t in np.arange(0, 0.2*(80+31) + 0.0001, 0.2):\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + t))\n ##MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(0, 128), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(80+31), t_ratio[1]*0.2*(80+31)])\n # segment += 1\n\n\n #t_label = []\n #for t in np.arange(0, 0.2*(68+23) + 0.0001, 0.2):\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + 0.2*(80+31) + t))\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04')\n #for row_start in np.arange(0, 128, 8):\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7))\n # segment=0\n # for t_ratio in t_ratio_lst:\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(68+23), t_ratio[1]*0.2*(68+23)])\n # segment += 1\n\n\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 38, 112)\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle010203', 16, 110)\n\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle010203', 44, 133)\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle010203', 14, 133)\n\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle010203', 50, 135)\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 20, 140)\n\n\n\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 38, 112)\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle01', 16, 110)\n\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle01', 44, 133)\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle01', 14, 133)\n\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle01', 50, 135)\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 20, 140)\n\n\n\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 38, 112)\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle0102', 16, 110)\n # \n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle0102', 44, 133)\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle0102', 14, 133)\n # \n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle0102', 50, 135)\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 20, 140)","def __iter__(self):\r\n for b in self.dl:\r\n yield to_device(b, self.device)","def run_cell(kernel_client, cell):\n kernel_client.execute(cell.input)\n\n input_acknowledged = False\n outs = []\n while True:\n msg = kernel_client.get_iopub_msg()\n\n if msg[\"msg_type\"] == \"status\":\n if msg[\"content\"][\"execution_state\"] == \"idle\" and input_acknowledged:\n break\n elif msg[\"msg_type\"] in ('pyin', 'execute_input'):\n input_acknowledged = True\n else:\n out = parse_valid_output(msg)\n outs.append(out)\n\n return outs","def testHasForLoop(self):\n no_foreach = build_code(['x=1'], [], ['x=3'], concise=False)\n foreach = build_code(['x=1'], ['x=2'], ['x=3'], concise=False)\n self.assertNotIn('for', no_foreach)\n self.assertIn('for', foreach)","def iterate(self):\n raise NotImplementedError()","def FastaM10Iterator(handle, seq_count=...):\n ...","def __iter__(self):\n for b in self.dl:\n yield to_device(b, self.device)","def multi_enumerate_start():\n for a,(b,(c,d)) in enumerate(enumerate(enumerate(range(1,5)), 2)):\n print(a,b,c,d)","def __iter__(self):\n return iter([self.format_message(record) for record in self._messages])","def data_gen(\n v: int, batch: int, nbatches: int, device: torch.device = torch.device(\"cpu\")\n) -> Iterator[Batch]: # TODO bad name\n for i in range(nbatches):\n data = np.random.randint(1, v, size=(batch, 10))\n data[:, 0] = 1\n src: LongTensorType = torch.from_numpy(data)\n tgt: LongTensorType = torch.from_numpy(data)\n src, tgt = src.to(device), tgt.to(device)\n yield Batch(src, tgt, 0)","def test_40_for(self):\n\t\tinput = \"\"\"function foo():integer; var a:integer; begin\n\t\tfor a:=false to 1 do begin end\n\t\treturn 1;\n\t\tend\n\t\tprocedure main(); var x:integer; begin x:=foo(); foo(); end\"\"\"\n\t\texpect = \"Type Mismatch In Statement: For(Id(a)BooleanLiteral(False),IntLiteral(1),True,[])\"\n\t\tself.assertTrue(TestChecker.test(input,expect,440))","def test_for_statement():\n r = convert_code(\n \"{foreach $foo as $bar}content{/foreach}\")\n assert r == \"{% for bar in foo %}content{% endfor %}\"","def pixelIter():\n\t\t\tx,y,i = 0,0,0\n\t\t\tfor i,c in enumerate(space):\n\t\t\t\tx = i % w\n\t\t\t\ty = i / w\n\t\t\t\tisSolid = (c=='#')\n\t\t\t\tyield x,y,i,isSolid","def forEach(self, func):\n for x in range(self._width):\n for y in range(self._height):\n func(self.data[x, y], x, y)","def parse(self):\n gen = self.v6_gen() # read from workers\n gen = self.tuple_gen(gen) # convert v6->tuple\n gen = self.batch_gen(gen) # assemble into batches\n for b in gen:\n yield b","def parse_for_cmd(self, line):\n # Get the iterator name\n line, _ = self.find_vars_in_str(line)\n words = line.split()\n iter_var_name = words[1]\n\n # Find the code to run\n end_line = self.get_end_brace()\n\n # Carry out the relevant for loop function\n for keyword in VALID_FOR_FUNCS:\n if re.findall(f\"{keyword}\\(.*\\)\", line):\n fnc = getattr(self, f\"do_{keyword}_forloop\")\n all_iter_vars = fnc(line)\n break\n\n\n # Do the for loop\n start_for = self.line_num + 2\n end_for = end_line\n\n for iter_var in all_iter_vars:\n # self.set_var(iter_var_name, iter_var)\n setattr(self, iter_var_name, iter_var)\n # Append the variable to the list of variables\n if iter_var_name not in self.variables:\n self.variables.append(iter_var_name)\n self.parse_lines(start_for, end_for)\n\n self.line_num = end_line","def _iterate_blocks(self):\n ranges = [host_tuple[0:2] for host_tuple in self.parity_hosts]\n flags_sql = \"SELECT id, value FROM {} FINAL WHERE name = 'traces_extracted'\".format(self.indices[\"block_flag\"])\n return self.client.iterate(\n index=self.indices[\"block\"],\n fields=[\"number\"],\n query=\"ANY LEFT JOIN ({}) USING id WHERE value IS NULL AND {}\".format(\n flags_sql,\n utils.make_range_query('number', *ranges)\n ),\n )","def _generate_iterator(self) -> Iterable:\n params: List[Tensor] = []\n for angle_range in self._ranges:\n lin_space: Tensor = linspace(angle_range[0], angle_range[1], steps=self._num_steps)\n params.append(lin_space)\n power: int\n dims: int\n for i in range(0, self._num_params):\n power = len(self._ranges) - 1 - i\n dims = i\n params[i] = params[i].repeat_interleave(self._num_steps ** power)\n params[i] = params[i].broadcast_to((self._num_steps ** dims, self._num_steps ** (power + 1))).flatten()\n return zip(*params)","def gen_tuples_for_loops(range_len: int, limit: int) -> list:\n ranges = [(n * range_len, (n + 1) * range_len) for n in range(limit // range_len)]\n if limit % range_len > 0:\n ranges.append((range_len * (limit // range_len), limit))\n return ranges","def test_old_for_statement():\n r = convert_code(\n \"{foreach item=bar from=foo}content{/foreach}\")\n assert r == \"{% for bar in foo %}content{% endfor %}\"","def _iterate_messages(self):\n if not self.targets:\n raise sex.SullyRuntimeError(\"No targets specified in session\")\n\n if not self.edges_from(self.root.id):\n raise sex.SullyRuntimeError(\"No requests specified in session\")\n\n self._reset_fuzz_state()\n\n for x in self._iterate_messages_recursive(this_node=self.root, path=[]):\n yield x","def _iterate_over_factors(self, func, args):\n # TODO The user may prefer to provide the arguments as lists and receive them as\n # TODO lists, as this may be the form in which they are available. This should\n # TODO be allowed, rather than packing and unpacking them repeatedly.\n args_list, numerical_args = self._validate_and_prepare_args_for_iteration(args)\n\n out = [\n self._get_method(self.factors[i], func, args_list[i], numerical_args)\n for i in range(len(self.factors))\n ]\n if self._pool_outputs:\n return self._pool_outputs_from_function(out)\n return out","def for_loop_print_evens():\n for counter in range(2, 11, 2):\n print(counter)\n\n print(\"Goodbye!\")","def loop_pc(ws, dict):\n for list_obj in dict:\n print(dict[list_obj])\n # print(list_obj)\n # print(type(list_obj))\n my_var = str(list_obj)\n # list_obj = list_upper(dict[list_obj])\n # for asset in list_obj:\n # print(asset)\n cell_obj = check_pc(ws, dict[list_obj], 2)\n write_pc(ws, dict[list_obj], cell_obj, 2)\n return True","async def test_fgsm(): \n #fgsm algo option:\n r = {}\n async with AsyncClient(app=app, base_url=\"http://test\") as ac:\n \n ALGO_NAME = AlteritAlgoName.fgsm_algo\n ffile = {'input_image': open(TEST_IMAGE_PATH, 'rb'),\n \"input_image_path\": TEST_IMAGE_PATH,\n \"alter_parameters\":json.dumps({\"acall\":True,\n \"epsilon\":0.01})\n }\n \n for epsilon_, result_ in zip([0.01, 0.1], ['saluki', 'weimaranner',]):\n r = await ac.post(f'/alterit/?algo_name={ALGO_NAME}', files = ffile)\n assert r.status_code == 200\n i1, i2, i3, i4 = await a_from_zip_stream_to_att_data(r)\n assert i1['0'][1] == result_ \n \n # async for i in mygen(5):\n # print(f'step {i}:')\n # #ALGO_NAME = AlteritAlgoName.fgsm_algo\n # ffile = {'input_image': open(TEST_IMAGE_PATH, 'rb'),\n # \"input_image_path\": TEST_IMAGE_PATH,\n # \"alter_parameters\":json.dumps({\"acall\":True,\n # \"epsilon\":0.01}),}\n # r[i] = await ac.post(f'/alterit/?algo_name={ALGO_NAME}', files = ffile)\n # assert r[i].status_code == 200\n # i1, i2, i3, i4 = await a_from_zip_stream_to_att_data(r[i])\n # assert i1['0'][1] == \"saluki\"","def oneIteration(self):\n\t\traise NotImplementedError","def numeric_sequence_iteration(self) -> global___Statement.Iteration.NumericSequenceIteration:","def bytes_iteration(self) -> global___Statement.Iteration.BytesIteration:","def sender_iter(self):\n while 1:\n yield self.send_next()","def pixelIter():\n\t\t\tx,y,i = 0,0,0\n\t\t\tfor i,c in enumerate(space):\n\t\t\t\tx = i % w\n\t\t\t\ty = i / w\n\t\t\t\tisSolid = (c=='#')\n\t\t\t\tyield x,y,i,isSolid\n\t\t\t\tprintSpace(x,y) # print state for debugging","def image_generator(self, some_messages):\n offset = 0\n outer = 0\n inner = 0\n\n for a_message in some_messages:\n msg_id = a_message.gmail_id\n for att in a_message.attachments():\n if att.type in ATTACHMENT_MIMES:\n att_type = att.type.split(\"/\")[1]\n an_image = Image(a_message, att)\n\n # map each image id with a corresponding message id for later parsing\n if an_image.id in self.mapping:\n self.mapping[msg_id].append(a_message)\n else:\n self.mapping[msg_id] = [a_message]\n\n self.num_attachments = self.count_attachments(self.num_attachments)\n\n yield an_image","def main(params):\n mpi_vs_multiprocess_logging(\"process\", params)\n\n ifg_paths = []\n for ifg_path in params[cf.INTERFEROGRAM_FILES]:\n ifg_paths.append(ifg_path.sampled_path)\n\n rows, cols = params[\"rows\"], params[\"cols\"]\n\n return process_ifgs(ifg_paths, params, rows, cols)","def _channel_(self) -> Union[Iterable[np.ndarray], NotImplementedType]:","def visit_for_in(self, flags, scope, token, parent):\r\n\r\n label, expr, body = token.children\r\n\r\n if body.type != Token.T_BLOCK:\r\n # the parser should be run in python mode\r\n raise TransformError(body, \"expected block in for loop body\")\r\n\r\n # the extra block scope, and finalize, allows for declaring\r\n # variables inside of a for arg list\r\n\r\n scope.pushBlockScope(\"loop\")\r\n self._push_finalize(scope, token, parent)\r\n\r\n self._push_children(scope, body, flags)\r\n self._push_tokens(ST_VISIT | ST_STORE | (flags & ST_SCOPE_MASK), scope, [label], token)\r\n self._push_tokens(ST_VISIT, scope, [expr], token)","def algorithm_loop(self):","def items():\n for i in self._iter_restrict(zeros, ones):\n yield self.pcdata[i]","def on_iterate(self, data: Any = None):\n raise NotImplementedError","def my_service():\n start, end = 100, 110\n for number in range(start, end):\n yield number","def geom_iter(self, g_nums):\n # Using the custom coded pack_tups to not have to care whether the\n # input is iterable\n from .utils import pack_tups\n\n vals = pack_tups(g_nums)\n for val in vals:\n yield self.geom_single(val[0])","def _analyse_stmt_AsyncFor(\n self, statement: ast.AsyncFor, *, next: CFNode\n ) -> CFNode:\n return self._analyse_loop(statement, next=next)","def do_range_forloop(self, line):\n self.E_str = \"do_range_forloop\"\n\n # Get the range parameters\n line = line.replace(\" \", \"\")\n line = line[line.find(\"range\")+5:]\n range_str, _ = gen_parse.get_str_between_delims(line, \"(\", \")\")\n words = range_str.split(\",\")\n\n if len(words) == 1:\n start, step = 0, 1\n end = int(words[0])\n elif len(words) == 2:\n step = 1\n start, end = [int(i) for i in words]\n else:\n start, end, step = [int(i) for i in words]\n\n return range(start, end, step)","def step(self):\n\n for t, z, data_igm, data_cgm, RC_igm, RC_cgm in self.medium.step():\n\n Jc = np.atleast_1d(self._f_Jc(z))\n Ji = np.atleast_1d(self._f_Ji(z))\n Jlw = np.atleast_1d(self._f_Jlw(z))\n Ja = Jc + Ji\n\n # Compute spin temperature\n n_H = self.medium.parcel_igm.grid.cosm.nH(z)\n Ts = self.medium.parcel_igm.grid.hydr.Ts(z,\n data_igm['Tk'], Ja, data_igm['h_2'], data_igm['e'] * n_H)\n\n if self.pf['floor_Ts'] is not None:\n Ts = max(Ts, self.medium.parcel_igm.grid.hydr.Ts_floor(z=z))\n\n # Compute volume-averaged ionized fraction\n if self.pf['include_cgm']:\n xavg = data_cgm['h_2'] + (1. - data_cgm['h_2']) * data_igm['h_2']\n else:\n xavg = data_igm['h_2']\n\n # Derive brightness temperature\n dTb = self.medium.parcel_igm.grid.hydr.get_21cm_dTb(z, Ts, xavg)\n dTb_b = self.medium.parcel_igm.grid.hydr.get_21cm_dTb(z, Ts)\n\n # Add derived fields to data\n data_igm.update({'Ts': Ts, 'dTb': dTb, #'dTb_bulk': dTb_b,\n 'Jc': Jc, 'Ji': Ji, 'Ja': Ja, 'Jlw': Jlw})\n\n # Yield!\n yield t, z, data_igm, data_cgm, RC_igm, RC_cgm","def test_format_of_generated_msgs():\n for msg in it.islice(generate_msgs(), 0, 5):\n message = Message.parse(msg) # checks the json keys have the right names\n assert type(message.timestamp) is datetime\n assert type(message.power) is int","def Start(self):\n for unused_i in range(0, self.args.message_count):\n self.CallClient(\n standard.ReadBuffer, offset=0, length=100, next_state=\"Process\")","def visit_for_of(self, flags, scope, token, parent):\r\n\r\n label, expr, body = token.children\r\n\r\n if body.type != Token.T_BLOCK:\r\n # the parser should be run in python mode\r\n raise TransformError(body, \"expected block in for loop body\")\r\n\r\n # the extra block scope, and finalize, allows for declaring\r\n # variables inside of a for arg list\r\n\r\n scope.pushBlockScope(\"loop\")\r\n self._push_finalize(scope, token, parent)\r\n\r\n self._push_children(scope, body, flags)\r\n self._push_tokens(ST_VISIT | ST_STORE | (flags & ST_SCOPE_MASK), scope, [label], token)\r\n self._push_tokens(ST_VISIT, scope, [expr], token)","def _current_flow_loop(C, row_idx, col_idx):\n return [potential_to_current_flow_edge(C, (u, v))\n for (u,v) in zip(row_idx, col_idx)\n if u <= v]","def send_and_receive_many(world: CueBeamWorld):\n elements_vectorized1 = []\n for idxElement in range(0, len(world.elements) - 1):\n elements_vectorized1.extend(\n [world.elements[idxElement].x, world.elements[idxElement].y, world.elements[idxElement].z,\n world.elements[idxElement].amplitude, world.elements[idxElement].phase, 0.0])\n time_start = time.clock()\n current_ray_count = world.get_ray_count()\n estimated_worker_performance = 300000.0\n need_workers = math.ceil(current_ray_count / estimated_worker_performance)\n each_worker_does_ylines = math.ceil(world.rxPlane.ny / need_workers )\n # update\n handles = []\n for idx_worker in range(need_workers):\n yline0 = idx_worker*each_worker_does_ylines # starts at zero\n yline_y = world.rxPlane.y0 + world.rxPlane.dy * yline0\n handles.append({\n 'yline_y': yline_y,\n 'async_handle': beamsim_instant.delay(\n k=world.wavenumber,\n x0=world.rxPlane.x0,\n y0=yline_y,\n z0=world.rxPlane.z0,\n nx=world.rxPlane.nx,\n ny=each_worker_does_ylines,\n nz=world.rxPlane.nz,\n dx=world.rxPlane.dx,\n dy=world.rxPlane.dy,\n dz=world.rxPlane.dz,\n elements_vectorized=elements_vectorized1)\n })\n # TODO: FRONTIER HERE ===================\n\n # TODO: Wait for first worker, and load the result,\n #while not (async_handle.ready()):\n # time.sleep(0.02)\n\n world.rxPlane.pressurefield = pickle.loads(async_handle.result)\n time_end = time.clock()\n world.last_performance_rays_per_second = world.get_ray_count() / (time_end - time_start)\n print('performance = {} kRays/sec'.format(world.last_performance_rays_per_second / 1e3))\n return world","def data_stream(self, i):\n limb = []\n while self.idx < len(self.circleIndx):\n xy = np.array([[self.sensorSize[0]/2+0.5, self.sensorSize[0]/2+0.5],[self.centers[i,1], self.centers[i,2]]])\n for j in range(len(self.points[i,:])/2):\n if self.points[i,j] >1E-1 or self.points[i,j+1] >1E-1:\n limb.append([self.points[i,j], self.points[i,j+1]])\n c = [self.colorList[-1], self.colorList[i], self.colorList[i]]\n yield np.c_[xy[:,0], xy[:,1], limb[:,0], limb[:,1]], c","def iterate(self, start, end):\n if not self.db:\n self.db = self.get_db()\n\n p = start[:-4]\n s = struct.unpack(\"!L\", start[-4:])[0]\n e = struct.unpack(\"!L\", end[-4:])[0]\n time.sleep(self.READ_DELAY)\n while s <= e:\n v = random.random() * 100\n yield p + struct.pack(\"!L\", s), struct.pack(\"!d\", v)\n s += self.STEP","def loop(func, n):\n for i in range(n):\n func()","def main():\n for stock_symbol, company_name in zip(STOCKS, COMPANIES):\n stock_difference = get_stock_difference(stock_symbol)\n stock_highlights = get_news(company_name)\n sms = create_sms(stock_difference, stock_highlights, company_name)\n send_message(sms)","def process(\n self, molecule_data: Tuple[str, Tuple[List[reads_pb2.Read],\n List[reads_pb2.Read]]]\n ) -> Iterable[Tuple[str, Tuple[List[reads_pb2.Read], List[reads_pb2.Read]]]]:\n\n (molecule_name, (subreads, label)) = molecule_data\n subreads_and_label = subreads + label\n # Subreads, PW and IP were already padded for subreads alone, but we need to\n # pad them again because label might be longer than previous pad length.\n subreads_and_labels_copy = copy.deepcopy(subreads_and_label)\n max_length = pad_reads(subreads_and_labels_copy)\n subreads_copy = subreads_and_labels_copy[:-1]\n label_copy = subreads_and_labels_copy[-1:]\n pad_pw_ip(subreads_copy, max_length)\n yield molecule_name, (subreads_copy, label_copy)","def matrix_multiplication_loop(x_matrix, y_matrix):\n result = []\n for i, row in enumerate(x_matrix):\n row_vector = []\n for j in range(len(y_matrix[0])):\n product = 0\n for k in range(len(row)):\n product += x_matrix[i][k] * y_matrix[k][j]\n row_vector.append(product)\n result.append(row_vector)\n return result","def map(self, func, *args, **kwargs):\n\n if (self.n_workers == 1) or (self.scheduler == 'ray'):\n executor_pool = _executor_dummy\n ranger = range\n else:\n executor_pool = self.executor\n ranger = trange\n\n if self.scheduler == 'ray':\n\n if self.padding:\n raise SyntaxError('Ray cannot be used with array padding.')\n\n import ray\n\n if isinstance(self.data, rio.io.DatasetReader):\n data_id = self.data.name\n else:\n data_id = ray.put(self.data)\n\n results = []\n\n with executor_pool(self.n_workers) as executor:\n\n # Iterate over the windows in chunks\n for wchunk in ranger(0, self.n_windows, self.n_chunks):\n\n if self.padding:\n\n window_slice = self.windows[\n wchunk : wchunk + self.n_chunks\n ]\n\n # Read the padded window\n if len(self.data.shape) == 2:\n data_gen = (\n (\n self.data[\n w[1].row_off : w[1].row_off + w[1].height,\n w[1].col_off : w[1].col_off + w[1].width,\n ],\n widx + wchunk,\n *args,\n )\n for widx, w in enumerate(window_slice)\n )\n elif len(self.data.shape) == 3:\n data_gen = (\n (\n self.data[\n :,\n w[1].row_off : w[1].row_off + w[1].height,\n w[1].col_off : w[1].col_off + w[1].width,\n ],\n widx + wchunk,\n *args,\n )\n for widx, w in enumerate(window_slice)\n )\n else:\n data_gen = (\n (\n self.data[\n :,\n :,\n w[1].row_off : w[1].row_off + w[1].height,\n w[1].col_off : w[1].col_off + w[1].width,\n ],\n widx + wchunk,\n *args,\n )\n for widx, w in enumerate(window_slice)\n )\n\n else:\n\n window_slice = self.slices[wchunk : wchunk + self.n_chunks]\n\n if self.scheduler == 'ray':\n data_gen = (\n (data_id, slice_, widx + wchunk, *args)\n for widx, slice_ in enumerate(window_slice)\n )\n else:\n data_gen = (\n (self.data[slice_], widx + wchunk, *args)\n for widx, slice_ in enumerate(window_slice)\n )\n\n if (self.n_workers == 1) and (self.scheduler != 'ray'):\n\n for result in map(func, data_gen):\n results.append(result)\n\n else:\n\n if self.scheduler == 'mpool':\n\n for result in executor.imap(func, data_gen, **kwargs):\n results.append(result)\n\n elif self.scheduler == 'ray':\n\n if isinstance(func, ray.util.actor_pool.ActorPool):\n\n for result in tqdm(\n func.map(\n lambda a, v: a.exec_task.remote(*v),\n data_gen,\n ),\n total=len(window_slice),\n ):\n results.append(result)\n\n else:\n\n if isinstance(func, ray.actor.ActorHandle):\n futures = [\n func.exec_task.remote(*dargs)\n for dargs in data_gen\n ]\n else:\n futures = [\n func.remote(*dargs) for dargs in data_gen\n ]\n\n if self.get_ray:\n\n with tqdm(total=len(futures)) as pbar:\n\n results_ = []\n while len(futures):\n\n done_id, futures = ray.wait(futures)\n results_.append(ray.get(done_id[0]))\n\n pbar.update(1)\n\n results += results_\n\n else:\n results += futures\n\n else:\n\n for result in executor.map(func, data_gen):\n results.append(result)\n\n if self.scheduler == 'ray':\n del data_id\n\n return results","def sendMessage_0(self, messages):\n for message in messages:\n self.sendMessage(message)","def GetBoundaryLoops(self):\n\n self.__do_essential_memebers_exist__()\n\n if self.InferBoundaryElementType() != \"line\":\n raise NotImplementedError(\"Computing boundary loops is only supported for tri/quad meshes for now\")\n\n self.GetEdges()\n\n # First create a node to neighbour map i.e. node as key and its two neighbouring nodes as value\n nodeToNeighboursMap = dict()\n for i in range(self.edges.shape[0]):\n\n if self.edges[i,0] not in nodeToNeighboursMap:\n nodeToNeighboursMap[self.edges[i,0]] = [self.edges[i,1],-1]\n else:\n nodeToNeighboursMap[self.edges[i,0]][1] = self.edges[i,1]\n\n if self.edges[i,1] not in nodeToNeighboursMap:\n nodeToNeighboursMap[self.edges[i,1]] = [self.edges[i,0],-1]\n else:\n nodeToNeighboursMap[self.edges[i,1]][1] = self.edges[i,0]\n\n # Now create a vector of face loops\n faceLoops = []\n while nodeToNeighboursMap:\n # Insert the first node from node to edge map and its two neighbours in order and erase it from the map\n faceLoop = []\n mapBegin = next(iter(nodeToNeighboursMap))\n faceLoop.append(nodeToNeighboursMap[mapBegin][0])\n faceLoop.append(mapBegin)\n faceLoop.append(nodeToNeighboursMap[mapBegin][1])\n nodeToNeighboursMap.pop(mapBegin, None)\n\n while True:\n # Pick the last node in the current face loop and find its neighbours\n if faceLoop[-1] in nodeToNeighboursMap:\n tmp = faceLoop[-1]\n mapIter = nodeToNeighboursMap[faceLoop[-1]]\n # Check if we have not reached the end of the loop i.e. the first element\n if mapIter[0] != faceLoop[0] and mapIter[1] != faceLoop[0]:\n if mapIter[0] == faceLoop[-2]:\n faceLoop.append(mapIter[1])\n elif mapIter[1] == faceLoop[-2]:\n faceLoop.append(mapIter[0])\n else:\n nodeToNeighboursMap.pop(faceLoop[0], None)\n\n nodeToNeighboursMap.pop(tmp, None)\n else:\n faceLoop = np.array(faceLoop)\n faceLoops.append(faceLoop)\n break\n\n return faceLoops"],"string":"[\n \"def dispatchMessageLoop(self, tree):\\n self.fill(\\\"for (const auto& \\\")\\n self.dispatch(tree.target)\\n self.write(\\\" : \\\")\\n # if simple message iterator\\n if isinstance(tree.iter, ast.Name):\\n if not tree.iter.id == self._input_message_var:\\n self.RaiseError(t, f\\\"Message input loop requires use of '{self._input_message_var}' as iterator.\\\")\\n # write with prefix\\n self.write(f\\\"FLAMEGPU->{self._input_message_var}\\\")\\n # if it is a call then handle the different cases\\n elif isinstance(tree.iter, ast.Call):\\n self.dispatchMessageIteratorCall(tree.iter)\\n #otherwise not supported\\n else :\\n self.RaiseError(tree, f\\\"Message input loop iterator in unsupported format\\\")\\n self.write(\\\")\\\")\\n self._message_iterator_var = tree.target.id\\n self.enter()\\n self.dispatch(tree.body)\\n self.leave()\\n self._message_iterator_var = None\",\n \"def dispatchMessageIteratorCall(self, tree):\\n # simple case not a member function just an iterator with arguments\\n if isinstance(tree.func, ast.Name):\\n self.write(f\\\"FLAMEGPU->{tree.func.id}\\\")\\n if isinstance(tree.func, ast.Attribute) :\\n if isinstance(tree.func.value, ast.Name):\\n # check that the iterator is supported\\n if not tree.func.attr in self.fgpu_input_msg_iter_funcs:\\n self.RaiseError(tree, f\\\"Message input loop iterator '{tree.func.attr}' is not supported.\\\")\\n self.write(f\\\"FLAMEGPU->{tree.func.value.id}.{tree.func.attr}\\\")\\n else:\\n self.RaiseError(tree, \\\"Message input loop iterator format incorrect.\\\")\\n\\n # handle function arguments \\n self.write(\\\"(\\\")\\n self._CallArguments(tree)\\n self.write(\\\")\\\")\",\n \"def multiple_eval_for_loops_v2():\",\n \"def multiple_eval_for_loops_v1():\",\n \"def convert_for(self, variable, start, stop, step, array):\\n\\n # Run super definition\\n variable, start, stop, step, array = super().convert_for(\\n variable, start, stop, step, array\\n )\\n\\n # Remove data type from variable(duck typing in Python)\\n variable = variable.split(\\\" \\\")[-1]\\n\\n # Create for template\\n for_template = \\\"for {} in {}:\\\"\\n\\n # Define loop condition\\n if array:\\n # If array if given, loop through array\\n loop_cond = array\\n\\n # Check if array slicing is required\\n if step != \\\"1\\\" or stop != \\\"Array.length\\\" or start != \\\"0\\\":\\n\\n # Make template for array slicing\\n loop_cond = \\\"{}[{{}}]\\\".format(array)\\n\\n if start == \\\"0\\\":\\n start = \\\"\\\"\\n\\n if stop == \\\"Array.length\\\":\\n stop = \\\"\\\"\\n\\n if step == \\\"1\\\":\\n step = \\\"\\\"\\n\\n # If step is default, omit step\\n if not step:\\n\\n # Else add start to range call\\n loop_cond = loop_cond.format(start + \\\":\\\" + stop)\\n else:\\n # Add all three parameters if step is provided\\n loop_cond = loop_cond.format(start + \\\":\\\" + stop + \\\":\\\" + step)\\n\\n else:\\n # Else make range template\\n loop_cond = \\\"range({})\\\"\\n\\n # If step if default, omit step\\n if step == \\\"1\\\":\\n\\n # If start is default, omit start\\n if start == \\\"0\\\":\\n loop_cond = loop_cond.format(stop)\\n\\n else:\\n # Else add start to range call\\n loop_cond = loop_cond.format(start + \\\", \\\" + stop)\\n else:\\n # Add all three parameters if step is provided\\n loop_cond = loop_cond.format(start + \\\", \\\" + stop + \\\", \\\" + step)\\n\\n # Return converted for statement\\n return [for_template.format(variable, loop_cond)], []\",\n \"def LoopBody(i, *input_arrays):\\n # Outfeed ops execute on each JF node, so they must be located on the\\n # nodes.\\n outfeed_devices = []\\n device_assignment = py_utils.GetTpuDeviceAssignment()\\n assert device_assignment\\n for replica in range(device_assignment.num_replicas):\\n num_cores_per_replica = 1 if self.spmd else (\\n device_assignment.num_cores_per_replica)\\n for core in range(num_cores_per_replica):\\n with tf.device(device_assignment.host_device(replica, core)):\\n outfeed_devices.append(\\n tpu_ops.outfeed_dequeue_tuple(\\n tensor_types,\\n tensor_shapes,\\n device_ordinal=device_assignment.tpu_ordinal(replica,\\n core)))\\n offset = i * num_devices\\n output_arrays = list(input_arrays)\\n # Each output_array holds a different per-example tensor. We get results\\n # for each tensor from each TPU for each TpuTrainStep call.\\n for j in range(len(output_arrays)):\\n for k in range(len(outfeed_devices)):\\n output_arrays[j] = output_arrays[j].write(offset + k,\\n outfeed_devices[k][j])\\n\\n return tuple([i + 1] + output_arrays)\",\n \"def multi_c_enumerate():\\n for a,(b,(c,d)) in enumerate(enumerate(enumerate(range(1,5)))):\\n print(a,b,c,d)\",\n \"def visit_for(self: Parser, node: doc.For) -> None:\\n for_frame = self.eval_expr(node.iter)\\n if not isinstance(for_frame, T.frame.ForFrame):\\n self.report_error(\\n node.iter,\\n \\\"Expect the for loop to be one of the following: \\\"\\n \\\"range, T.serial, T.grid, T.parallel, T.vectorized, T.unroll, T.thread_binding\\\",\\n )\\n with self.var_table.with_frame():\\n with for_frame as iters:\\n self.eval_assign(target=node.target, source=iters, bind_value=bind_for_value)\\n self.visit_body(node.body)\",\n \"def for_loop(num_iters, body, initial_args):\\n for i in range(num_iters):\\n if i == 0:\\n outputs = body(*initial_args)\\n else:\\n outputs = body(*outputs)\\n return outputs\",\n \"def makeloop(keyword, G, *args):\\n if not args:\\n return []\\n Nargs = len(args)\\n lis = []\\n for arg in args:\\n lis.append(makeiter(G(\\\"%s%s\\\" % (keyword, arg))))\\n try:\\n Nlis = lis[0].count()\\n except TypeError:\\n Nlis = len(lis[0])\\n olist = [[] for i in range(Nargs)]\\n for i in range(Nlis):\\n for k in range(Nargs):\\n try:\\n olist[k].append(lis[k][i])\\n except Exception:\\n olist[k].append(\\\"\\\")\\n return olist\",\n \"def test_element_loop_1cell():\\n\\n # Mesh object\\n mesh = MeshMat()\\n mesh.cell[\\\"triangle3\\\"] = CellMat(nb_node_per_cell=3)\\n mesh.node = NodeMat()\\n\\n mesh.node.add_node(np.array([0, 0]))\\n mesh.node.add_node(np.array([1, 0]))\\n mesh.node.add_node(np.array([0, 1]))\\n\\n nodes_test = np.array([0, 1, 2])\\n mesh.add_cell(nodes_test, \\\"triangle3\\\")\\n\\n indice = [0]\\n\\n # Physical quantities\\n dim = 2\\n Nt_tot = 1\\n\\n mu = 1\\n Be = np.array([[[mu / 2, 0]]])\\n He = np.array([[[-1 / 2, 0]]])\\n mue = np.array([[mu]])\\n\\n Me = np.reshape(Be / mue - He, (dim, 1, Nt_tot))\\n\\n alphaij = [[1, 0, 0], [1, 0, 0]]\\n\\n alpha1 = 1\\n alpha2 = 1\\n\\n # Computation\\n tensor = ForceTensor()\\n\\n f, connect = tensor.element_loop(mesh, Be, He, mue, indice, dim, Nt_tot, alphaij)\\n\\n f1_analytic = 1 / 2 * mu * np.array([alpha1 + alpha2, alpha2])\\n f2_analytic = 1 / 2 * mu * np.array([-(alpha1 + alpha2), 0])\\n f3_analytic = 1 / 2 * mu * np.array([0, -alpha2])\\n\\n assert (f[0, :, 0] == f1_analytic).all()\\n assert (f[1, :, 0] == f2_analytic).all()\\n assert (f[2, :, 0] == f3_analytic).all()\\n\\n print(\\\"test_element_loop succeeded\\\")\\n\\n return True\",\n \"def _loop_raw(raw):\\n raise NotImplemented\",\n \"def syntax_for():\\n for i in range(5):\\n print(i)\\n\\n ## Output\\n # 0\\n # 1\\n # 2\\n # 3\\n # 4\\n\\n ## Notes\\n # range()\\n # Rather than being a function, range is actually an immutable sequence type.\\n # The range type represents an immutable sequence of numbers and is commonly used for\\n # looping a specific number of times in for loops.\",\n \"def test_39_for(self):\\n\\t\\tinput = \\\"\\\"\\\"function foo():integer; var a:integer; begin\\n\\t\\tfor a:=1+1.5 to -2 do begin end\\n\\t\\treturn 1;\\n\\t\\tend\\n\\t\\tprocedure main(); var x:integer; begin x:=foo(); foo(); end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Type Mismatch In Statement: For(Id(a)BinaryOp(+,IntLiteral(1),FloatLiteral(1.5)),UnaryOp(-,IntLiteral(2)),True,[])\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,439))\",\n \"def go_c_enumerate_step():\\n for i,k in enumerate(range(1,7,2)):\\n print(i, k)\",\n \"def visit_for(self, flags, scope, token, parent):\\r\\n\\r\\n arglist, body = token.children\\r\\n\\r\\n if body.type != Token.T_BLOCK:\\r\\n # the parser should be run in python mode\\r\\n raise TransformError(body, \\\"expected block in for loop body\\\")\\r\\n\\r\\n # the extra block scope, and finalize, allows for declaring\\r\\n # variables inside of a for arg list\\r\\n scope.pushBlockScope(\\\"loop\\\")\\r\\n self._push_finalize(scope, token, parent)\\r\\n\\r\\n self._push_children(scope, body, flags)\\r\\n self._push_children(scope, arglist, flags)\",\n \"def vectorized_loops(self, data):\\n\\n # TODO: finish this.\\n return np.add(np.multiply(data,data), data)\",\n \"def test_for_loop(self, modes, parse_input_mocked_metadata):\\n bb = parse_input_mocked_metadata(\\n \\\"for int m in {}\\\\n\\\\tMeasureFock() | m\\\".format(modes)\\n )\\n assert np.all(\\n bb._forvar[\\\"m\\\"] == np.array(modes)\\n )\\n assert bb.operations == [\\n {'op': 'MeasureFock', 'args': [], 'kwargs': {}, 'modes': [modes[0]]},\\n {'op': 'MeasureFock', 'args': [], 'kwargs': {}, 'modes': [modes[1]]},\\n {'op': 'MeasureFock', 'args': [], 'kwargs': {}, 'modes': [modes[2]]}\\n ]\",\n \"def go_c_enumerate():\\n for i,k in enumerate(range(1,5)):\\n print(i, k)\",\n \"def iteration(self) -> global___Statement.Iteration:\",\n \"def iteration(self) -> global___Statement.Iteration:\",\n \"def internal_loop(p, i, j, k, l):\\n return _RNAstructure_wrap.internal_loop(p, i, j, k, l)\",\n \"def _analyse_stmt_For(self, statement: ast.For, *, next: CFNode) -> CFNode:\\n return self._analyse_loop(statement, next=next)\",\n \"def multibranch_loop(i, j):\\n return _RNAstructure_wrap.multibranch_loop(i, j)\",\n \"def _batch_iter(self, source, target, i: int):\\n # send data to device\\n source = source.to(self.device)\\n target = target.to(self.device)\\n\\n # the result and loss\\n result = self.model(source)\\n loss = self.criterion(result, target)\\n\\n # optimization and backward\\n self.optimizer.zero_grad()\\n loss.backward()\\n self.optimizer.step()\\n\\n # update the loss\\n self.epoch_loss.update(loss.item(), source.size(0))\\n\\n # print the information\\n if self.info:\\n print(f\\\"\\\\rEpoch: { self.epoch } | Batch: { i } | loss: { self.epoch_loss.avg }\\\", end=\\\"\\\")\\n\\n # clean the data\\n del source, target\\n\\n return result\",\n \"def enumerate_(start = 0):\\n\\n @filters\\n def _dagpype_internal_fn_act(target):\\n count = start \\n try:\\n while True:\\n e = (yield)\\n target.send((numpy.arange(count, count + len(e)), e))\\n count += len(e)\\n except GeneratorExit: \\n target.close() \\n\\n return _dagpype_internal_fn_act\",\n \"def coforeach(function, iterator):\\n return _CoFunCaller(function=function).coiterate(iterator)\",\n \"def iterate_inputs(function, type_to_vars):\\n if isinstance(function.input_type, tuple):\\n input_types = list(function.input_type)\\n else:\\n input_types = [function.input_type]\\n\\n argslists = []\\n for input_type in input_types:\\n argslists.append(type_to_vars[input_type])\\n for args in itertools.product(*argslists):\\n yield args\",\n \"def emit_unpack_instruction(self, *, loop_indices=None):\",\n \"def test_make_cpp_parfor():\\n\\n class STDVectorThreads(LocalObject):\\n\\n dtype = type('std::vector<std::thread>', (c_void_p,), {})\\n\\n def __init__(self):\\n super().__init__('threads')\\n\\n class STDThread(LocalObject):\\n\\n dtype = type('std::thread&', (c_void_p,), {})\\n\\n class FunctionType(LocalObject):\\n\\n dtype = type('FuncType&&', (c_void_p,), {})\\n\\n # Basic symbols\\n nthreads = Symbol(name='nthreads', is_const=True)\\n threshold = Symbol(name='threshold', is_const=True)\\n last = Symbol(name='last', is_const=True)\\n first = Symbol(name='first', is_const=True)\\n portion = Symbol(name='portion', is_const=True)\\n\\n # Composite symbols\\n threads = STDVectorThreads()\\n\\n # Iteration helper symbols\\n begin = Symbol(name='begin')\\n l = Symbol(name='l')\\n end = Symbol(name='end')\\n\\n # Functions\\n stdmax = sympy.Function('std::max')\\n\\n # Construct the parallel-for body\\n func = FunctionType('func')\\n i = Dimension(name='i')\\n threadobj = Call('std::thread', Lambda(\\n Iteration(Call(func.name, i), i, (begin, end-1, 1)),\\n ['=', Byref(func.name)],\\n ))\\n threadpush = Call(FieldFromComposite('push_back', threads), threadobj)\\n it = Dimension(name='it')\\n iteration = Iteration([\\n DummyExpr(begin, it, init=True),\\n DummyExpr(l, it + portion, init=True),\\n DummyExpr(end, InlineIf(l > last, last, l), init=True),\\n threadpush\\n ], it, (first, last, portion))\\n thread = STDThread('x')\\n waitcall = Call('std::for_each', [\\n Call(FieldFromComposite('begin', threads)),\\n Call(FieldFromComposite('end', threads)),\\n Lambda(Call(FieldFromComposite('join', thread.name)), [], [thread])\\n ])\\n body = [\\n DummyExpr(threshold, 1, init=True),\\n DummyExpr(portion, stdmax(threshold, (last - first) / nthreads), init=True),\\n Call(FieldFromComposite('reserve', threads), nthreads),\\n iteration,\\n waitcall\\n ]\\n\\n parfor = ElementalFunction('parallel_for', body, 'void',\\n [first, last, func, nthreads])\\n\\n assert str(parfor) == \\\"\\\"\\\"\\\\\\nstatic inline \\\\\\nvoid parallel_for(const int first, const int last, FuncType&& func, const int nthreads)\\n{\\n const int threshold = 1;\\n const int portion = std::max(threshold, (-first + last)/nthreads);\\n threads.reserve(nthreads);\\n for (int it = first; it <= last; it += portion)\\n {\\n int begin = it;\\n int l = it + portion;\\n int end = (l > last) ? last : l;\\n threads.push_back(std::thread([=, &func]()\\n {\\n for (int i = begin; i <= end - 1; i += 1)\\n {\\n func(i);\\n }\\n }));\\n }\\n std::for_each(threads.begin(),threads.end(),[](std::thread& x)\\n {\\n x.join();\\n });\\n}\\\"\\\"\\\"\",\n \"def get_for_iterations(self, file, i):\\n\\n # Run super definition\\n line = super().get_for_iterations(file, i)\\n\\n # Save required words\\n variable, for_range = line[0], \\\"\\\".join(line[2:])\\n\\n # Strip ending semicolon\\n for_range = for_range.split(\\\":\\\", 1)[0]\\n\\n # Create start and end for 'for loop' call\\n start = []\\n end = []\\n\\n # Set start and step to default\\n begin = \\\"0\\\"\\n step = \\\"1\\\"\\n\\n # Parse for_range\\n if for_range.find(\\\"range\\\") != -1:\\n\\n # Dump unwanted portion\\n for_range = for_range.strip(\\\"range(\\\").strip(\\\")\\\")\\n\\n # Parse variables in for_range\\n variables = [var.strip() for var in for_range.split(\\\",\\\")]\\n\\n # Store variable values\\n var_count = len(variables)\\n\\n # If only one variable is given,\\n # Set stop variable with default begin and step\\n if var_count == 1:\\n stop = variables[0]\\n # Else if two variable are given,\\n # set begin and stop variable with default step\\n else:\\n begin = variables[0]\\n stop = variables[1]\\n # If three variables are given,\\n # set all three begin, stop and step variables\\n if var_count == 3:\\n step = variables[2]\\n\\n # Set array to None\\n array = None\\n else:\\n # If range not found, iterate over given array\\n\\n # Get array\\n array = for_range\\n\\n # Set default stop\\n stop = \\\"Array.length\\\"\\n\\n # Check of array slicing\\n if array.find(\\\"[\\\") != -1 and array.find(\\\":\\\") != -1:\\n array, begin, stop, step = self.get_list_slice_vars(array)\\n\\n # Get variable type for variable\\n\\n # If array is passed, get type of array\\n if array:\\n var_type = \\\"Array.data_type\\\"\\n\\n # Else get type of variable\\n else:\\n var_type = self.get_type(begin)\\n\\n # Append variable type to variable\\n variable = var_type + \\\" \\\" + variable\\n\\n # Return all variables\\n return variable, begin, stop, step, array, start, end\",\n \"def lua_iterate(self):\\n zrange = \\\"redis.call('ZRANGE', '%s', '0', '-1')\\\" % self.redis_key()\\n lua = \\\"for _, data in ipairs(%s) do \\\" % zrange\\n lua += \\\"local row = cjson.decode(data) \\\"\\n\\n return lua\",\n \"def __iter__(self):\\n for b in self.dl: \\n yield to_device(b, self.device)\",\n \"def __iter__(self):\\n for b in self.dl: \\n yield to_device(b, self.device)\",\n \"def __iter__(self):\\n for b in self.dl: \\n yield to_device(b, self.device)\",\n \"def __iter__(self):\\n for b in self.dl: \\n yield to_device(b, self.device)\",\n \"def __iter__(self):\\n for b in self.dl: \\n yield to_device(b, self.device) # yield pauses the execution, not store values in memory, forgets about them once iterated\\n # no need to remove batch of data from device, done automatically\",\n \"def iterate(self, batch_size=8, func=None):\\n raise NotImplementedError()\",\n \"def iterate(self):\",\n \"def test_generator_continuous():\\n RANGE_MAX = 100\\n prev_value = RANGE_MAX // 2\\n for msg in it.islice(generate_msgs(0, RANGE_MAX), 0, 42):\\n curr_value = Message.parse(msg).power\\n assert curr_value - prev_value <= 1\\n prev_value = curr_value\",\n \"def repeated_iteration(self) -> global___Statement.Iteration.RepeatedIteration:\",\n \"def make_range_temp_K_prot(DB_version, DB_type, i1, i2):\\n\\n for index in range(i1, i2):\\n print(index)\\n make_temp_K_prot(DB_version, DB_type, index)\",\n \"def multi_enumerate():\\n for a,(b,(c,d)) in enumerate(enumerate(enumerate(range(1,5)))):\\n print(a,b,c,d)\",\n \"def __iter__(self):\\n for batch in self.data:\\n batch_size = len(batch)\\n X, e1, e2, dist1, dist2, e1_pos, e2_pos, y = list(zip(*batch))\\n\\n x_len = max(len(x) for x in X)\\n x_ids = torch.LongTensor(batch_size, x_len).fill_(0)\\n dist1_padded = torch.LongTensor(batch_size, x_len).fill_(0)\\n dist2_padded = torch.LongTensor(batch_size, x_len).fill_(0)\\n for i, doc in enumerate(X):\\n x_ids[i, :len(doc)] = torch.LongTensor(doc)\\n\\n dist1_padded[i, :len(doc)] = torch.LongTensor(dist1[i])\\n dist1_padded[i, len(doc):] = torch.LongTensor([pos(e1_pos[i][1] - idx) for idx, _ in enumerate(x_ids[i][len(doc):], start=len(doc))])\\n\\n dist2_padded[i, :len(doc)] = torch.LongTensor(dist2[i])\\n dist2_padded[i, len(doc):] = torch.LongTensor([pos(e2_pos[i][1] - idx) for idx, _ in enumerate(x_ids[i][len(doc):], start=len(doc))])\\n\\n e1_tensor = torch.LongTensor(e1)\\n e2_tensor = torch.LongTensor(e2)\\n\\n y_tensor = torch.LongTensor(y)\\n\\n if self.gpu:\\n x_ids = x_ids.pin_memory()\\n e1_tensor = e1_tensor.pin_memory()\\n e2_tensor = e2_tensor.pin_memory()\\n dist1_padded = dist1_padded.pin_memory()\\n dist2_padded = dist2_padded.pin_memory()\\n y_tensor = y_tensor.pin_memory()\\n\\n yield (x_ids, e1_tensor, e2_tensor, dist1_padded, dist2_padded, y_tensor)\",\n \"def main():\\n\\n #for row_start in np.arange(0, 128):\\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_row'+str(row_start).zfill(3), Imin=12, Imax=136)\\n\\n Marcov_Chain_MLE(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '', 160.0, [90.0, 70.0, 50.0, 30.0], 0.0, 0.5)\\n plt.show()\\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71, 55, 46, 35], [0.002, 0.01, 0.04, 0.2], 4, [], '', ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01', '../Data/chip14/MLC_programming_Chip14_Col33_10msPULSE_VG1p8_VD2p0_VAsource_VBdrain_02', '../Data/chip14/MLC_programming_Chip14_Col33_40msPULSE_VG1p8_VD2p0_VAsource_VBdrain_03', '../Data/chip14/MLC_programming_Chip14_Col33_200msPULSE_VG1p8_VD2p0_VAsource_VBdrain_04'], '../Plots/chip14/', 'VG1p8_VD2p0', '_cycle01020304_all')\\n\\n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAsource_VBdrain', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAsource_VBdrain_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaS-VbD_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, forward' , 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \\n #IDS_VGS(14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Fresh_vs_MLC01020304_VG1p8_VD2p0_IDS-VGS_VaD-VbS_', range(0, 128), 'Fresh vs MLC-1-2-3-4 (VG=1.8, VD=2.0)\\\\nMLC-{1, 2, 3, 4}: {2ms, 10ms, 40ms, 200ms} WL pulses, IDSAT threshold = {90, 70, 50, 30}uA, reversed', 150, ['fresh', 'MLC-01', 'MLC-02', 'MLC-03', 'MLC-04']) \\n\\n #hist_IDS_VGS(0, 14, 33, 16, 2, 'ULVT', 128, ['../Data/chip14/Fresh_Chip14_Col33_Ids_Vgs_VAdrain_VBsource', '../Data/chip14/MLC_Chip14_Col33_2msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_01', '../Data/chip14/MLC_Chip14_Col33_10msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_02', '../Data/chip14/MLC_Chip14_Col33_40msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_03', '../Data/chip14/MLC_Chip14_Col33_200msPULSE_VG1p8_VD2p0_Ids_Vgs_VAdrain_VBsource_04'], ['b', 'y', 'r', 'k', 'g'], '../Plots/chip14/', 'Hist-IDSAT_MLC-rv1-01020304_reverse-read_', range(0, 128), 'MLC programming {2ms, 10ms, 40ms, 200ms} pulses, VGS=1.8, VDS=2.0 for level=1-2-3-4\\\\nhistogram of read-IDSAT (VGS=VDS=0.8V)', 0, 150, 0, 150, 1000)\\n #\\n #t_label = []\\n #for t in np.arange(0, 0.002*(71) + 0.0001, 0.002):\\n # t_label.append(str(t))\\n #\\n ##MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [21], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row-21', Imin=82, Imax=142)\\n\\n #for row_start in np.arange(0, 128):\\n # MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, [row_start], [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01_row_'+str(row_start).zfill(3), Imin=80, Imax=142)\\n\\n #MLC_IDSAT_algorithm_rv1(14, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(0, 128), [71], [0.002], 1, np.arange(0, 0.002*(71)+0.0001, 0.002), t_label, ['../Data/chip14/MLC_programming_Chip14_Col33_2msPULSE_VG1p8_VD2p0_VAsource_VBdrain_01'], '../Plots/chip14/', 'VG1p8_VD2p0', '_rv1_cycle01', Imin=80, Imax=142)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\\n #MLC_IDSAT_characterization(11, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col30_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col30_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col30_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01', 20, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle0102', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle0102', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle0102', 20, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle010203', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle010203', 50, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle010203', 20, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 20, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 1.7, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p7', '_cycle01020304', 40, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 1.7, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p7', '_cycle01020304', 40, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.5, 2.0, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p0', '_cycle01020304', 10, 160, 1)\\n #MLC_IDSAT_characterization(11, 33, 16, 2, 'ULVT', 1.8, 2.0, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col33_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col33_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col33_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col33_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 10, 160, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01', 50, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01', 20, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle0102', 50, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle0102', 20, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle010203', 50, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle010203', 20, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p0', '_cycle01020304', 40, 125, 1)\\n #MLC_IDSAT_characterization(11, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col18_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col18_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col18_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p4', '_cycle01020304', 10, 125, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80], [0.01, 0.01], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240], [0.01, 0.01, 0.01], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 40, 150, 1)\\n #MLC_IDSAT_characterization(11, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 80, 240, 180], [0.01, 0.01, 0.01, 0.04], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col24_HCI_80x10ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col24_HCI_240x10ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col24_HCI_180x40ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\\n\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\\n #MLC_IDSAT_characterization(11, 27, 20, 2, 'ULVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col27_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col27_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col27_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col27_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle0102', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle0102', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle0102', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle010203', 50, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle010203', 15, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle010203', 15, 150, 1)\\n\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 1.8, 128, range(0, 32) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD1p8', '_cycle01020304', 40, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 1.8, 128, range(32, 64) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD1p8', '_cycle01020304', 20, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.5, 2.2, 128, range(64, 96) , [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p5_VD2p2', '_cycle01020304', 5, 150, 1)\\n #MLC_IDSAT_characterization(11, 28, 20, 2, 'LVT', 1.8, 2.2, 128, range(96, 128), [40, 20, 12, 36], [0.01, 0.04, 0.2, 0.2], 4, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4, 4.8, 12], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6', '3.6', '10.8'], ['../Data/chip11/Chip11_Col28_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip11/Chip11_Col28_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip11/Chip11_Col28_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03', '../Data/chip11/Chip11_Col28_HCI_36x200ms_stress_VG_ConstPulse_VAsource_VBdrain_04'], '../Plots/chip11/', 'VG1p8_VD2p2', '_cycle01020304', 5, 150, 1)\\n\\n\\n # (L, Nfin, VT_flavor, Nrow, Imax)\\n col_list = [(36, 1, 'ULVT', 32 , 60 ), (36, 1, 'LVT', 32 , 50 ), (36, 1, 'SVT', 32 , 45 ),\\n (36, 1, 'ULVT', 128, 60 ), (36, 1, 'LVT', 128, 50 ), (36, 1, 'SVT', 128, 45 ),\\n (20, 1, 'ULVT', 32 , 75 ), (20, 1, 'LVT', 32 , 60 ), (20, 1, 'SVT', 32 , 50 ),\\n (20, 1, 'ULVT', 128, 75 ), (20, 1, 'LVT', 128, 60 ), (20, 1, 'SVT', 128, 50 ),\\n (16, 1, 'ULVT', 32 , 80 ), (16, 1, 'LVT', 32 , 65 ), (16, 1, 'SVT', 32 , 60 ),\\n (16, 1, 'ULVT', 128, 80 ), (16, 1, 'LVT', 128, 65 ), (16, 1, 'SVT', 128, 60 ),\\n (36, 2, 'ULVT', 32 , 115), (36, 2, 'LVT', 32 , 95 ), (36, 2, 'SVT', 32 , 85 ),\\n (36, 2, 'ULVT', 128, 115), (36, 2, 'LVT', 128, 95 ), (36, 2, 'SVT', 128, 85 ), \\n (20, 2, 'ULVT', 32 , 135), (20, 2, 'LVT', 32 , 115), (20, 2, 'SVT', 32 , 100),\\n (20, 2, 'ULVT', 128, 135), (20, 2, 'LVT', 128, 120), (20, 2, 'SVT', 128, 100),\\n (16, 2, 'ULVT', 32 , 150), (16, 2, 'LVT', 32 , 125), (16, 2, 'SVT', 32 , 115),\\n (16, 2, 'ULVT', 128, 150), (16, 2, 'LVT', 128, 125), (16, 2, 'SVT', 128, 115)]\\n\\n #MLC_IDSAT_algorithm_rv1(11, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [135+20], [0.2], 1, np.arange(0, 0.01*16+0.0001, 0.01), '', ['../Data/chip11/MLC_programming_Chip11_Col21_2msPULSE_VG1p8_VD2p4_VAsource_VBdrain_01'], '../Plots/chip11/', 'VG1p8_VD2p4', '_rv1_cycle01_EfficientPython')\\n\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', '0.9-1.2-1.5-1.8', 2.4, 128, range(0, 128), [59+16, 72+40, 80+31, 68+23], [0.2, 0.2, 0.2, 0.2], 4, [0, 15, 15.1, 37.5, 37.6, 59.8, 59.9, 78.1], ['0', '15', '', '37.4', '', '59.6', '', '77.8'], ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03', '../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG-0p9-1p2-1p5-1p8_VD2p4', '_rv1_cycle01020304')\\n\\n t_ratio_lst = [(0, 0.17), (0.16, 0.34), (0.33, 0.505), (0.495, 0.67), (0.66, 0.84), (0.83, 1)]\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(59+16) + 0.0001, 0.2):\\n # t_label.append(str(t))\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(0, 128), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 0.9, 2.4, 128, range(row_start, row_start+8), [59+16], [0.2], 1, np.arange(0, 0.2*(59+16)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG0p9_VD2p4_VAsource_VBdrain_01'], '../Plots/chip12/', 'VG0p9_VD2p4', '_rv1_cycle01_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(59+16), t_ratio[1]*0.2*(59+16)])\\n # segment += 1\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(72+40) + 0.0001, 0.2):\\n # t_label.append(str(0.2*(59+16) + t))\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(0, 128), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.2, 2.4, 128, range(row_start, row_start+8), [72+40], [0.2], 1, np.arange(0, 0.2*(72+40)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p2_VD2p4_VAsource_VBdrain_02'], '../Plots/chip12/', 'VG1p2_VD2p4', '_rv1_cycle02_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(72+40), t_ratio[1]*0.2*(72+40)])\\n # segment += 1\\n\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(80+31) + 0.0001, 0.2):\\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + t))\\n ##MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(0, 128), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.5, 2.4, 128, range(row_start, row_start+8), [80+31], [0.2], 1, np.arange(0, 0.2*(80+31)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p5_VD2p4_VAsource_VBdrain_03'], '../Plots/chip12/', 'VG1p5_VD2p4', '_rv1_cycle03_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(80+31), t_ratio[1]*0.2*(80+31)])\\n # segment += 1\\n\\n\\n #t_label = []\\n #for t in np.arange(0, 0.2*(68+23) + 0.0001, 0.2):\\n # t_label.append(str(0.2*(59+16) + 0.2*(72+40) + 0.2*(80+31) + t))\\n #MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(0, 128), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04')\\n #for row_start in np.arange(0, 128, 8):\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7))\\n # segment=0\\n # for t_ratio in t_ratio_lst:\\n # MLC_IDSAT_algorithm_rv1(12, 21, 36, 2, 'ULVT', 1.8, 2.4, 128, range(row_start, row_start+8), [68+23], [0.2], 1, np.arange(0, 0.2*(68+23)+0.0001, 0.2), t_label, ['../Data/chip12/MLC_programming_Chip12_Col21_200msPULSE_VG1p8_VD2p4_VAsource_VBdrain_04'], '../Plots/chip12/', 'VG1p8_VD2p4', '_rv1_cycle04_row'+str(row_start)+'_to_'+str(row_start+7)+'_'+str(segment), [t_ratio[0]*0.2*(68+23), t_ratio[1]*0.2*(68+23)])\\n # segment += 1\\n\\n\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 38, 112)\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col18_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle010203', 16, 110)\\n\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle010203', 44, 133)\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col24_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle010203', 14, 133)\\n\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle010203', 50, 135)\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20, 12], [0.01, 0.04, 0.2], 3, [0, 0.4, 0.8, 1.6, 2.0, 2.6, 3.2, 3.8, 4.4], ['0', '0.4', '0.4', '1.2', '1.2', '1.8', '2.4', '3.0', '3.6'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02', '../Data/chip10/Chip10_Col30_HCI_12x200ms_stress_VG_ConstPulse_VAsource_VBdrain_03'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle010203', 20, 140)\\n\\n\\n\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 38, 112)\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle01', 16, 110)\\n\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle01', 44, 133)\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle01', 14, 133)\\n\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle01', 50, 135)\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40], [0.01], 1, [0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.5], ['0', '0.05', '0.1', '0.15', '0.2', '0.25', '0.3', '0.35', '0.4', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle01', 20, 140)\\n\\n\\n\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.0, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 38, 112)\\n #MLC_IDSAT_characterization(10, 18, 36, 2, 'ULVT', 1.8, 2.4, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col18_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col18_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p4', '_cycle0102', 16, 110)\\n # \\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 1.8, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p8', '_cycle0102', 44, 133)\\n #MLC_IDSAT_characterization(10, 24, 20, 2, 'ULVT', 1.8, 2.2, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col24_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col24_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p2', '_cycle0102', 14, 133)\\n # \\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 1.7, 32, range(0, 16) , [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD1p7', '_cycle0102', 50, 135)\\n #MLC_IDSAT_characterization(10, 30, 16, 2, 'ULVT', 1.8, 2.0, 32, range(16, 32), [40, 20], [0.01, 0.04], 2, [0, 0.2, 0.4, 0.8, 1.0, 1.2, 1.4, 1.6, 1.7], ['0', '0.2', '0.4', '0.4', '0.6', '0.8', '1.0', '1.2', 'recover'], ['../Data/chip10/Chip10_Col30_HCI_40x10ms_stress_VG_ConstPulse_VAsource_VBdrain_01', '../Data/chip10/Chip10_Col30_HCI_20x40ms_stress_VG_ConstPulse_VAsource_VBdrain_02'], '../Plots/chip10/', 'VG1p8_VD2p0', '_cycle0102', 20, 140)\",\n \"def __iter__(self):\\r\\n for b in self.dl:\\r\\n yield to_device(b, self.device)\",\n \"def run_cell(kernel_client, cell):\\n kernel_client.execute(cell.input)\\n\\n input_acknowledged = False\\n outs = []\\n while True:\\n msg = kernel_client.get_iopub_msg()\\n\\n if msg[\\\"msg_type\\\"] == \\\"status\\\":\\n if msg[\\\"content\\\"][\\\"execution_state\\\"] == \\\"idle\\\" and input_acknowledged:\\n break\\n elif msg[\\\"msg_type\\\"] in ('pyin', 'execute_input'):\\n input_acknowledged = True\\n else:\\n out = parse_valid_output(msg)\\n outs.append(out)\\n\\n return outs\",\n \"def testHasForLoop(self):\\n no_foreach = build_code(['x=1'], [], ['x=3'], concise=False)\\n foreach = build_code(['x=1'], ['x=2'], ['x=3'], concise=False)\\n self.assertNotIn('for', no_foreach)\\n self.assertIn('for', foreach)\",\n \"def iterate(self):\\n raise NotImplementedError()\",\n \"def FastaM10Iterator(handle, seq_count=...):\\n ...\",\n \"def __iter__(self):\\n for b in self.dl:\\n yield to_device(b, self.device)\",\n \"def multi_enumerate_start():\\n for a,(b,(c,d)) in enumerate(enumerate(enumerate(range(1,5)), 2)):\\n print(a,b,c,d)\",\n \"def __iter__(self):\\n return iter([self.format_message(record) for record in self._messages])\",\n \"def data_gen(\\n v: int, batch: int, nbatches: int, device: torch.device = torch.device(\\\"cpu\\\")\\n) -> Iterator[Batch]: # TODO bad name\\n for i in range(nbatches):\\n data = np.random.randint(1, v, size=(batch, 10))\\n data[:, 0] = 1\\n src: LongTensorType = torch.from_numpy(data)\\n tgt: LongTensorType = torch.from_numpy(data)\\n src, tgt = src.to(device), tgt.to(device)\\n yield Batch(src, tgt, 0)\",\n \"def test_40_for(self):\\n\\t\\tinput = \\\"\\\"\\\"function foo():integer; var a:integer; begin\\n\\t\\tfor a:=false to 1 do begin end\\n\\t\\treturn 1;\\n\\t\\tend\\n\\t\\tprocedure main(); var x:integer; begin x:=foo(); foo(); end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Type Mismatch In Statement: For(Id(a)BooleanLiteral(False),IntLiteral(1),True,[])\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,440))\",\n \"def test_for_statement():\\n r = convert_code(\\n \\\"{foreach $foo as $bar}content{/foreach}\\\")\\n assert r == \\\"{% for bar in foo %}content{% endfor %}\\\"\",\n \"def pixelIter():\\n\\t\\t\\tx,y,i = 0,0,0\\n\\t\\t\\tfor i,c in enumerate(space):\\n\\t\\t\\t\\tx = i % w\\n\\t\\t\\t\\ty = i / w\\n\\t\\t\\t\\tisSolid = (c=='#')\\n\\t\\t\\t\\tyield x,y,i,isSolid\",\n \"def forEach(self, func):\\n for x in range(self._width):\\n for y in range(self._height):\\n func(self.data[x, y], x, y)\",\n \"def parse(self):\\n gen = self.v6_gen() # read from workers\\n gen = self.tuple_gen(gen) # convert v6->tuple\\n gen = self.batch_gen(gen) # assemble into batches\\n for b in gen:\\n yield b\",\n \"def parse_for_cmd(self, line):\\n # Get the iterator name\\n line, _ = self.find_vars_in_str(line)\\n words = line.split()\\n iter_var_name = words[1]\\n\\n # Find the code to run\\n end_line = self.get_end_brace()\\n\\n # Carry out the relevant for loop function\\n for keyword in VALID_FOR_FUNCS:\\n if re.findall(f\\\"{keyword}\\\\(.*\\\\)\\\", line):\\n fnc = getattr(self, f\\\"do_{keyword}_forloop\\\")\\n all_iter_vars = fnc(line)\\n break\\n\\n\\n # Do the for loop\\n start_for = self.line_num + 2\\n end_for = end_line\\n\\n for iter_var in all_iter_vars:\\n # self.set_var(iter_var_name, iter_var)\\n setattr(self, iter_var_name, iter_var)\\n # Append the variable to the list of variables\\n if iter_var_name not in self.variables:\\n self.variables.append(iter_var_name)\\n self.parse_lines(start_for, end_for)\\n\\n self.line_num = end_line\",\n \"def _iterate_blocks(self):\\n ranges = [host_tuple[0:2] for host_tuple in self.parity_hosts]\\n flags_sql = \\\"SELECT id, value FROM {} FINAL WHERE name = 'traces_extracted'\\\".format(self.indices[\\\"block_flag\\\"])\\n return self.client.iterate(\\n index=self.indices[\\\"block\\\"],\\n fields=[\\\"number\\\"],\\n query=\\\"ANY LEFT JOIN ({}) USING id WHERE value IS NULL AND {}\\\".format(\\n flags_sql,\\n utils.make_range_query('number', *ranges)\\n ),\\n )\",\n \"def _generate_iterator(self) -> Iterable:\\n params: List[Tensor] = []\\n for angle_range in self._ranges:\\n lin_space: Tensor = linspace(angle_range[0], angle_range[1], steps=self._num_steps)\\n params.append(lin_space)\\n power: int\\n dims: int\\n for i in range(0, self._num_params):\\n power = len(self._ranges) - 1 - i\\n dims = i\\n params[i] = params[i].repeat_interleave(self._num_steps ** power)\\n params[i] = params[i].broadcast_to((self._num_steps ** dims, self._num_steps ** (power + 1))).flatten()\\n return zip(*params)\",\n \"def gen_tuples_for_loops(range_len: int, limit: int) -> list:\\n ranges = [(n * range_len, (n + 1) * range_len) for n in range(limit // range_len)]\\n if limit % range_len > 0:\\n ranges.append((range_len * (limit // range_len), limit))\\n return ranges\",\n \"def test_old_for_statement():\\n r = convert_code(\\n \\\"{foreach item=bar from=foo}content{/foreach}\\\")\\n assert r == \\\"{% for bar in foo %}content{% endfor %}\\\"\",\n \"def _iterate_messages(self):\\n if not self.targets:\\n raise sex.SullyRuntimeError(\\\"No targets specified in session\\\")\\n\\n if not self.edges_from(self.root.id):\\n raise sex.SullyRuntimeError(\\\"No requests specified in session\\\")\\n\\n self._reset_fuzz_state()\\n\\n for x in self._iterate_messages_recursive(this_node=self.root, path=[]):\\n yield x\",\n \"def _iterate_over_factors(self, func, args):\\n # TODO The user may prefer to provide the arguments as lists and receive them as\\n # TODO lists, as this may be the form in which they are available. This should\\n # TODO be allowed, rather than packing and unpacking them repeatedly.\\n args_list, numerical_args = self._validate_and_prepare_args_for_iteration(args)\\n\\n out = [\\n self._get_method(self.factors[i], func, args_list[i], numerical_args)\\n for i in range(len(self.factors))\\n ]\\n if self._pool_outputs:\\n return self._pool_outputs_from_function(out)\\n return out\",\n \"def for_loop_print_evens():\\n for counter in range(2, 11, 2):\\n print(counter)\\n\\n print(\\\"Goodbye!\\\")\",\n \"def loop_pc(ws, dict):\\n for list_obj in dict:\\n print(dict[list_obj])\\n # print(list_obj)\\n # print(type(list_obj))\\n my_var = str(list_obj)\\n # list_obj = list_upper(dict[list_obj])\\n # for asset in list_obj:\\n # print(asset)\\n cell_obj = check_pc(ws, dict[list_obj], 2)\\n write_pc(ws, dict[list_obj], cell_obj, 2)\\n return True\",\n \"async def test_fgsm(): \\n #fgsm algo option:\\n r = {}\\n async with AsyncClient(app=app, base_url=\\\"http://test\\\") as ac:\\n \\n ALGO_NAME = AlteritAlgoName.fgsm_algo\\n ffile = {'input_image': open(TEST_IMAGE_PATH, 'rb'),\\n \\\"input_image_path\\\": TEST_IMAGE_PATH,\\n \\\"alter_parameters\\\":json.dumps({\\\"acall\\\":True,\\n \\\"epsilon\\\":0.01})\\n }\\n \\n for epsilon_, result_ in zip([0.01, 0.1], ['saluki', 'weimaranner',]):\\n r = await ac.post(f'/alterit/?algo_name={ALGO_NAME}', files = ffile)\\n assert r.status_code == 200\\n i1, i2, i3, i4 = await a_from_zip_stream_to_att_data(r)\\n assert i1['0'][1] == result_ \\n \\n # async for i in mygen(5):\\n # print(f'step {i}:')\\n # #ALGO_NAME = AlteritAlgoName.fgsm_algo\\n # ffile = {'input_image': open(TEST_IMAGE_PATH, 'rb'),\\n # \\\"input_image_path\\\": TEST_IMAGE_PATH,\\n # \\\"alter_parameters\\\":json.dumps({\\\"acall\\\":True,\\n # \\\"epsilon\\\":0.01}),}\\n # r[i] = await ac.post(f'/alterit/?algo_name={ALGO_NAME}', files = ffile)\\n # assert r[i].status_code == 200\\n # i1, i2, i3, i4 = await a_from_zip_stream_to_att_data(r[i])\\n # assert i1['0'][1] == \\\"saluki\\\"\",\n \"def oneIteration(self):\\n\\t\\traise NotImplementedError\",\n \"def numeric_sequence_iteration(self) -> global___Statement.Iteration.NumericSequenceIteration:\",\n \"def bytes_iteration(self) -> global___Statement.Iteration.BytesIteration:\",\n \"def sender_iter(self):\\n while 1:\\n yield self.send_next()\",\n \"def pixelIter():\\n\\t\\t\\tx,y,i = 0,0,0\\n\\t\\t\\tfor i,c in enumerate(space):\\n\\t\\t\\t\\tx = i % w\\n\\t\\t\\t\\ty = i / w\\n\\t\\t\\t\\tisSolid = (c=='#')\\n\\t\\t\\t\\tyield x,y,i,isSolid\\n\\t\\t\\t\\tprintSpace(x,y) # print state for debugging\",\n \"def image_generator(self, some_messages):\\n offset = 0\\n outer = 0\\n inner = 0\\n\\n for a_message in some_messages:\\n msg_id = a_message.gmail_id\\n for att in a_message.attachments():\\n if att.type in ATTACHMENT_MIMES:\\n att_type = att.type.split(\\\"/\\\")[1]\\n an_image = Image(a_message, att)\\n\\n # map each image id with a corresponding message id for later parsing\\n if an_image.id in self.mapping:\\n self.mapping[msg_id].append(a_message)\\n else:\\n self.mapping[msg_id] = [a_message]\\n\\n self.num_attachments = self.count_attachments(self.num_attachments)\\n\\n yield an_image\",\n \"def main(params):\\n mpi_vs_multiprocess_logging(\\\"process\\\", params)\\n\\n ifg_paths = []\\n for ifg_path in params[cf.INTERFEROGRAM_FILES]:\\n ifg_paths.append(ifg_path.sampled_path)\\n\\n rows, cols = params[\\\"rows\\\"], params[\\\"cols\\\"]\\n\\n return process_ifgs(ifg_paths, params, rows, cols)\",\n \"def _channel_(self) -> Union[Iterable[np.ndarray], NotImplementedType]:\",\n \"def visit_for_in(self, flags, scope, token, parent):\\r\\n\\r\\n label, expr, body = token.children\\r\\n\\r\\n if body.type != Token.T_BLOCK:\\r\\n # the parser should be run in python mode\\r\\n raise TransformError(body, \\\"expected block in for loop body\\\")\\r\\n\\r\\n # the extra block scope, and finalize, allows for declaring\\r\\n # variables inside of a for arg list\\r\\n\\r\\n scope.pushBlockScope(\\\"loop\\\")\\r\\n self._push_finalize(scope, token, parent)\\r\\n\\r\\n self._push_children(scope, body, flags)\\r\\n self._push_tokens(ST_VISIT | ST_STORE | (flags & ST_SCOPE_MASK), scope, [label], token)\\r\\n self._push_tokens(ST_VISIT, scope, [expr], token)\",\n \"def algorithm_loop(self):\",\n \"def items():\\n for i in self._iter_restrict(zeros, ones):\\n yield self.pcdata[i]\",\n \"def on_iterate(self, data: Any = None):\\n raise NotImplementedError\",\n \"def my_service():\\n start, end = 100, 110\\n for number in range(start, end):\\n yield number\",\n \"def geom_iter(self, g_nums):\\n # Using the custom coded pack_tups to not have to care whether the\\n # input is iterable\\n from .utils import pack_tups\\n\\n vals = pack_tups(g_nums)\\n for val in vals:\\n yield self.geom_single(val[0])\",\n \"def _analyse_stmt_AsyncFor(\\n self, statement: ast.AsyncFor, *, next: CFNode\\n ) -> CFNode:\\n return self._analyse_loop(statement, next=next)\",\n \"def do_range_forloop(self, line):\\n self.E_str = \\\"do_range_forloop\\\"\\n\\n # Get the range parameters\\n line = line.replace(\\\" \\\", \\\"\\\")\\n line = line[line.find(\\\"range\\\")+5:]\\n range_str, _ = gen_parse.get_str_between_delims(line, \\\"(\\\", \\\")\\\")\\n words = range_str.split(\\\",\\\")\\n\\n if len(words) == 1:\\n start, step = 0, 1\\n end = int(words[0])\\n elif len(words) == 2:\\n step = 1\\n start, end = [int(i) for i in words]\\n else:\\n start, end, step = [int(i) for i in words]\\n\\n return range(start, end, step)\",\n \"def step(self):\\n\\n for t, z, data_igm, data_cgm, RC_igm, RC_cgm in self.medium.step():\\n\\n Jc = np.atleast_1d(self._f_Jc(z))\\n Ji = np.atleast_1d(self._f_Ji(z))\\n Jlw = np.atleast_1d(self._f_Jlw(z))\\n Ja = Jc + Ji\\n\\n # Compute spin temperature\\n n_H = self.medium.parcel_igm.grid.cosm.nH(z)\\n Ts = self.medium.parcel_igm.grid.hydr.Ts(z,\\n data_igm['Tk'], Ja, data_igm['h_2'], data_igm['e'] * n_H)\\n\\n if self.pf['floor_Ts'] is not None:\\n Ts = max(Ts, self.medium.parcel_igm.grid.hydr.Ts_floor(z=z))\\n\\n # Compute volume-averaged ionized fraction\\n if self.pf['include_cgm']:\\n xavg = data_cgm['h_2'] + (1. - data_cgm['h_2']) * data_igm['h_2']\\n else:\\n xavg = data_igm['h_2']\\n\\n # Derive brightness temperature\\n dTb = self.medium.parcel_igm.grid.hydr.get_21cm_dTb(z, Ts, xavg)\\n dTb_b = self.medium.parcel_igm.grid.hydr.get_21cm_dTb(z, Ts)\\n\\n # Add derived fields to data\\n data_igm.update({'Ts': Ts, 'dTb': dTb, #'dTb_bulk': dTb_b,\\n 'Jc': Jc, 'Ji': Ji, 'Ja': Ja, 'Jlw': Jlw})\\n\\n # Yield!\\n yield t, z, data_igm, data_cgm, RC_igm, RC_cgm\",\n \"def test_format_of_generated_msgs():\\n for msg in it.islice(generate_msgs(), 0, 5):\\n message = Message.parse(msg) # checks the json keys have the right names\\n assert type(message.timestamp) is datetime\\n assert type(message.power) is int\",\n \"def Start(self):\\n for unused_i in range(0, self.args.message_count):\\n self.CallClient(\\n standard.ReadBuffer, offset=0, length=100, next_state=\\\"Process\\\")\",\n \"def visit_for_of(self, flags, scope, token, parent):\\r\\n\\r\\n label, expr, body = token.children\\r\\n\\r\\n if body.type != Token.T_BLOCK:\\r\\n # the parser should be run in python mode\\r\\n raise TransformError(body, \\\"expected block in for loop body\\\")\\r\\n\\r\\n # the extra block scope, and finalize, allows for declaring\\r\\n # variables inside of a for arg list\\r\\n\\r\\n scope.pushBlockScope(\\\"loop\\\")\\r\\n self._push_finalize(scope, token, parent)\\r\\n\\r\\n self._push_children(scope, body, flags)\\r\\n self._push_tokens(ST_VISIT | ST_STORE | (flags & ST_SCOPE_MASK), scope, [label], token)\\r\\n self._push_tokens(ST_VISIT, scope, [expr], token)\",\n \"def _current_flow_loop(C, row_idx, col_idx):\\n return [potential_to_current_flow_edge(C, (u, v))\\n for (u,v) in zip(row_idx, col_idx)\\n if u <= v]\",\n \"def send_and_receive_many(world: CueBeamWorld):\\n elements_vectorized1 = []\\n for idxElement in range(0, len(world.elements) - 1):\\n elements_vectorized1.extend(\\n [world.elements[idxElement].x, world.elements[idxElement].y, world.elements[idxElement].z,\\n world.elements[idxElement].amplitude, world.elements[idxElement].phase, 0.0])\\n time_start = time.clock()\\n current_ray_count = world.get_ray_count()\\n estimated_worker_performance = 300000.0\\n need_workers = math.ceil(current_ray_count / estimated_worker_performance)\\n each_worker_does_ylines = math.ceil(world.rxPlane.ny / need_workers )\\n # update\\n handles = []\\n for idx_worker in range(need_workers):\\n yline0 = idx_worker*each_worker_does_ylines # starts at zero\\n yline_y = world.rxPlane.y0 + world.rxPlane.dy * yline0\\n handles.append({\\n 'yline_y': yline_y,\\n 'async_handle': beamsim_instant.delay(\\n k=world.wavenumber,\\n x0=world.rxPlane.x0,\\n y0=yline_y,\\n z0=world.rxPlane.z0,\\n nx=world.rxPlane.nx,\\n ny=each_worker_does_ylines,\\n nz=world.rxPlane.nz,\\n dx=world.rxPlane.dx,\\n dy=world.rxPlane.dy,\\n dz=world.rxPlane.dz,\\n elements_vectorized=elements_vectorized1)\\n })\\n # TODO: FRONTIER HERE ===================\\n\\n # TODO: Wait for first worker, and load the result,\\n #while not (async_handle.ready()):\\n # time.sleep(0.02)\\n\\n world.rxPlane.pressurefield = pickle.loads(async_handle.result)\\n time_end = time.clock()\\n world.last_performance_rays_per_second = world.get_ray_count() / (time_end - time_start)\\n print('performance = {} kRays/sec'.format(world.last_performance_rays_per_second / 1e3))\\n return world\",\n \"def data_stream(self, i):\\n limb = []\\n while self.idx < len(self.circleIndx):\\n xy = np.array([[self.sensorSize[0]/2+0.5, self.sensorSize[0]/2+0.5],[self.centers[i,1], self.centers[i,2]]])\\n for j in range(len(self.points[i,:])/2):\\n if self.points[i,j] >1E-1 or self.points[i,j+1] >1E-1:\\n limb.append([self.points[i,j], self.points[i,j+1]])\\n c = [self.colorList[-1], self.colorList[i], self.colorList[i]]\\n yield np.c_[xy[:,0], xy[:,1], limb[:,0], limb[:,1]], c\",\n \"def iterate(self, start, end):\\n if not self.db:\\n self.db = self.get_db()\\n\\n p = start[:-4]\\n s = struct.unpack(\\\"!L\\\", start[-4:])[0]\\n e = struct.unpack(\\\"!L\\\", end[-4:])[0]\\n time.sleep(self.READ_DELAY)\\n while s <= e:\\n v = random.random() * 100\\n yield p + struct.pack(\\\"!L\\\", s), struct.pack(\\\"!d\\\", v)\\n s += self.STEP\",\n \"def loop(func, n):\\n for i in range(n):\\n func()\",\n \"def main():\\n for stock_symbol, company_name in zip(STOCKS, COMPANIES):\\n stock_difference = get_stock_difference(stock_symbol)\\n stock_highlights = get_news(company_name)\\n sms = create_sms(stock_difference, stock_highlights, company_name)\\n send_message(sms)\",\n \"def process(\\n self, molecule_data: Tuple[str, Tuple[List[reads_pb2.Read],\\n List[reads_pb2.Read]]]\\n ) -> Iterable[Tuple[str, Tuple[List[reads_pb2.Read], List[reads_pb2.Read]]]]:\\n\\n (molecule_name, (subreads, label)) = molecule_data\\n subreads_and_label = subreads + label\\n # Subreads, PW and IP were already padded for subreads alone, but we need to\\n # pad them again because label might be longer than previous pad length.\\n subreads_and_labels_copy = copy.deepcopy(subreads_and_label)\\n max_length = pad_reads(subreads_and_labels_copy)\\n subreads_copy = subreads_and_labels_copy[:-1]\\n label_copy = subreads_and_labels_copy[-1:]\\n pad_pw_ip(subreads_copy, max_length)\\n yield molecule_name, (subreads_copy, label_copy)\",\n \"def matrix_multiplication_loop(x_matrix, y_matrix):\\n result = []\\n for i, row in enumerate(x_matrix):\\n row_vector = []\\n for j in range(len(y_matrix[0])):\\n product = 0\\n for k in range(len(row)):\\n product += x_matrix[i][k] * y_matrix[k][j]\\n row_vector.append(product)\\n result.append(row_vector)\\n return result\",\n \"def map(self, func, *args, **kwargs):\\n\\n if (self.n_workers == 1) or (self.scheduler == 'ray'):\\n executor_pool = _executor_dummy\\n ranger = range\\n else:\\n executor_pool = self.executor\\n ranger = trange\\n\\n if self.scheduler == 'ray':\\n\\n if self.padding:\\n raise SyntaxError('Ray cannot be used with array padding.')\\n\\n import ray\\n\\n if isinstance(self.data, rio.io.DatasetReader):\\n data_id = self.data.name\\n else:\\n data_id = ray.put(self.data)\\n\\n results = []\\n\\n with executor_pool(self.n_workers) as executor:\\n\\n # Iterate over the windows in chunks\\n for wchunk in ranger(0, self.n_windows, self.n_chunks):\\n\\n if self.padding:\\n\\n window_slice = self.windows[\\n wchunk : wchunk + self.n_chunks\\n ]\\n\\n # Read the padded window\\n if len(self.data.shape) == 2:\\n data_gen = (\\n (\\n self.data[\\n w[1].row_off : w[1].row_off + w[1].height,\\n w[1].col_off : w[1].col_off + w[1].width,\\n ],\\n widx + wchunk,\\n *args,\\n )\\n for widx, w in enumerate(window_slice)\\n )\\n elif len(self.data.shape) == 3:\\n data_gen = (\\n (\\n self.data[\\n :,\\n w[1].row_off : w[1].row_off + w[1].height,\\n w[1].col_off : w[1].col_off + w[1].width,\\n ],\\n widx + wchunk,\\n *args,\\n )\\n for widx, w in enumerate(window_slice)\\n )\\n else:\\n data_gen = (\\n (\\n self.data[\\n :,\\n :,\\n w[1].row_off : w[1].row_off + w[1].height,\\n w[1].col_off : w[1].col_off + w[1].width,\\n ],\\n widx + wchunk,\\n *args,\\n )\\n for widx, w in enumerate(window_slice)\\n )\\n\\n else:\\n\\n window_slice = self.slices[wchunk : wchunk + self.n_chunks]\\n\\n if self.scheduler == 'ray':\\n data_gen = (\\n (data_id, slice_, widx + wchunk, *args)\\n for widx, slice_ in enumerate(window_slice)\\n )\\n else:\\n data_gen = (\\n (self.data[slice_], widx + wchunk, *args)\\n for widx, slice_ in enumerate(window_slice)\\n )\\n\\n if (self.n_workers == 1) and (self.scheduler != 'ray'):\\n\\n for result in map(func, data_gen):\\n results.append(result)\\n\\n else:\\n\\n if self.scheduler == 'mpool':\\n\\n for result in executor.imap(func, data_gen, **kwargs):\\n results.append(result)\\n\\n elif self.scheduler == 'ray':\\n\\n if isinstance(func, ray.util.actor_pool.ActorPool):\\n\\n for result in tqdm(\\n func.map(\\n lambda a, v: a.exec_task.remote(*v),\\n data_gen,\\n ),\\n total=len(window_slice),\\n ):\\n results.append(result)\\n\\n else:\\n\\n if isinstance(func, ray.actor.ActorHandle):\\n futures = [\\n func.exec_task.remote(*dargs)\\n for dargs in data_gen\\n ]\\n else:\\n futures = [\\n func.remote(*dargs) for dargs in data_gen\\n ]\\n\\n if self.get_ray:\\n\\n with tqdm(total=len(futures)) as pbar:\\n\\n results_ = []\\n while len(futures):\\n\\n done_id, futures = ray.wait(futures)\\n results_.append(ray.get(done_id[0]))\\n\\n pbar.update(1)\\n\\n results += results_\\n\\n else:\\n results += futures\\n\\n else:\\n\\n for result in executor.map(func, data_gen):\\n results.append(result)\\n\\n if self.scheduler == 'ray':\\n del data_id\\n\\n return results\",\n \"def sendMessage_0(self, messages):\\n for message in messages:\\n self.sendMessage(message)\",\n \"def GetBoundaryLoops(self):\\n\\n self.__do_essential_memebers_exist__()\\n\\n if self.InferBoundaryElementType() != \\\"line\\\":\\n raise NotImplementedError(\\\"Computing boundary loops is only supported for tri/quad meshes for now\\\")\\n\\n self.GetEdges()\\n\\n # First create a node to neighbour map i.e. node as key and its two neighbouring nodes as value\\n nodeToNeighboursMap = dict()\\n for i in range(self.edges.shape[0]):\\n\\n if self.edges[i,0] not in nodeToNeighboursMap:\\n nodeToNeighboursMap[self.edges[i,0]] = [self.edges[i,1],-1]\\n else:\\n nodeToNeighboursMap[self.edges[i,0]][1] = self.edges[i,1]\\n\\n if self.edges[i,1] not in nodeToNeighboursMap:\\n nodeToNeighboursMap[self.edges[i,1]] = [self.edges[i,0],-1]\\n else:\\n nodeToNeighboursMap[self.edges[i,1]][1] = self.edges[i,0]\\n\\n # Now create a vector of face loops\\n faceLoops = []\\n while nodeToNeighboursMap:\\n # Insert the first node from node to edge map and its two neighbours in order and erase it from the map\\n faceLoop = []\\n mapBegin = next(iter(nodeToNeighboursMap))\\n faceLoop.append(nodeToNeighboursMap[mapBegin][0])\\n faceLoop.append(mapBegin)\\n faceLoop.append(nodeToNeighboursMap[mapBegin][1])\\n nodeToNeighboursMap.pop(mapBegin, None)\\n\\n while True:\\n # Pick the last node in the current face loop and find its neighbours\\n if faceLoop[-1] in nodeToNeighboursMap:\\n tmp = faceLoop[-1]\\n mapIter = nodeToNeighboursMap[faceLoop[-1]]\\n # Check if we have not reached the end of the loop i.e. the first element\\n if mapIter[0] != faceLoop[0] and mapIter[1] != faceLoop[0]:\\n if mapIter[0] == faceLoop[-2]:\\n faceLoop.append(mapIter[1])\\n elif mapIter[1] == faceLoop[-2]:\\n faceLoop.append(mapIter[0])\\n else:\\n nodeToNeighboursMap.pop(faceLoop[0], None)\\n\\n nodeToNeighboursMap.pop(tmp, None)\\n else:\\n faceLoop = np.array(faceLoop)\\n faceLoops.append(faceLoop)\\n break\\n\\n return faceLoops\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6300082","0.5951897","0.5818866","0.57553744","0.55254656","0.55113477","0.5369612","0.5264706","0.51863104","0.5155934","0.5096092","0.50791895","0.5068876","0.5014519","0.49778667","0.49353746","0.48620737","0.48602274","0.48535544","0.48398575","0.48398575","0.48390695","0.48140576","0.47989768","0.47923055","0.47620964","0.4760266","0.47585553","0.47537646","0.4742317","0.47329745","0.47293225","0.47243837","0.47243837","0.47243837","0.47243837","0.4717909","0.4717336","0.47084188","0.4700207","0.46869573","0.46868637","0.4677857","0.4672321","0.46564138","0.46521753","0.46503827","0.46299076","0.46211565","0.46171266","0.46097168","0.46036696","0.4601785","0.45906872","0.45879835","0.45837352","0.4583505","0.45794958","0.45755693","0.4570853","0.4561747","0.4558289","0.4554272","0.45439696","0.45421645","0.4539047","0.45370802","0.45347485","0.4518152","0.4502825","0.4484264","0.4481511","0.44663024","0.4465214","0.44608092","0.44529858","0.44501325","0.44392103","0.44390348","0.44364432","0.4436013","0.4435491","0.4430766","0.44264495","0.4425751","0.44175455","0.44123945","0.44114286","0.44091058","0.4407994","0.44075334","0.44071668","0.4405451","0.44031465","0.4401747","0.44004604","0.4394649","0.43826738","0.43818435","0.43788543"],"string":"[\n \"0.6300082\",\n \"0.5951897\",\n \"0.5818866\",\n \"0.57553744\",\n \"0.55254656\",\n \"0.55113477\",\n \"0.5369612\",\n \"0.5264706\",\n \"0.51863104\",\n \"0.5155934\",\n \"0.5096092\",\n \"0.50791895\",\n \"0.5068876\",\n \"0.5014519\",\n \"0.49778667\",\n \"0.49353746\",\n \"0.48620737\",\n \"0.48602274\",\n \"0.48535544\",\n \"0.48398575\",\n \"0.48398575\",\n \"0.48390695\",\n \"0.48140576\",\n \"0.47989768\",\n \"0.47923055\",\n \"0.47620964\",\n \"0.4760266\",\n \"0.47585553\",\n \"0.47537646\",\n \"0.4742317\",\n \"0.47329745\",\n \"0.47293225\",\n \"0.47243837\",\n \"0.47243837\",\n \"0.47243837\",\n \"0.47243837\",\n \"0.4717909\",\n \"0.4717336\",\n \"0.47084188\",\n \"0.4700207\",\n \"0.46869573\",\n \"0.46868637\",\n \"0.4677857\",\n \"0.4672321\",\n \"0.46564138\",\n \"0.46521753\",\n \"0.46503827\",\n \"0.46299076\",\n \"0.46211565\",\n \"0.46171266\",\n \"0.46097168\",\n \"0.46036696\",\n \"0.4601785\",\n \"0.45906872\",\n \"0.45879835\",\n \"0.45837352\",\n \"0.4583505\",\n \"0.45794958\",\n \"0.45755693\",\n \"0.4570853\",\n \"0.4561747\",\n \"0.4558289\",\n \"0.4554272\",\n \"0.45439696\",\n \"0.45421645\",\n \"0.4539047\",\n \"0.45370802\",\n \"0.45347485\",\n \"0.4518152\",\n \"0.4502825\",\n \"0.4484264\",\n \"0.4481511\",\n \"0.44663024\",\n \"0.4465214\",\n \"0.44608092\",\n \"0.44529858\",\n \"0.44501325\",\n \"0.44392103\",\n \"0.44390348\",\n \"0.44364432\",\n \"0.4436013\",\n \"0.4435491\",\n \"0.4430766\",\n \"0.44264495\",\n \"0.4425751\",\n \"0.44175455\",\n \"0.44123945\",\n \"0.44114286\",\n \"0.44091058\",\n \"0.4407994\",\n \"0.44075334\",\n \"0.44071668\",\n \"0.4405451\",\n \"0.44031465\",\n \"0.4401747\",\n \"0.44004604\",\n \"0.4394649\",\n \"0.43826738\",\n \"0.43818435\",\n \"0.43788543\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6590012"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":17797,"cells":{"query":{"kind":"string","value":"Fairly straightforward translation to if, else if, else format"},"document":{"kind":"string","value":"def _If(self, t):\n self.fill(\"if (\")\n self.dispatch(t.test)\n self.write(\")\")\n self.enter()\n self.dispatch(t.body)\n self.leave()\n # collapse nested ifs into equivalent elifs.\n while (t.orelse and len(t.orelse) == 1 and\n isinstance(t.orelse[0], ast.If)):\n t = t.orelse[0]\n self.fill(\"else if (\")\n self.dispatch(t.test)\n self.write(\")\")\n self.enter()\n self.dispatch(t.body)\n self.leave()\n # final else\n if t.orelse:\n self.fill(\"else\")\n self.enter()\n self.dispatch(t.orelse)\n self.leave()"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def ifelse(test, if_true, if_false):\n if test:\n return if_true\n else:\n return if_false","def switch(cond, ift, iff):","def _ifelse(self):\n debug.show(\"ifelse:Stack = \" + str(self.opStack))\n if self.opStack.size() >= 3:\n falseCode = check.isCode(self.opStack.pop()) # Make sure it is code (a list)\n trueCode = check.isCode(self.opStack.pop()) # Make sure it is code (a list)\n if check.isBool(self.opStack.pop()):\n debug.show(\"ifelse:True\")\n self.evaluate(trueCode)\n else:\n debug.show(\"ifelse:False\")\n self.evaluate(falseCode)\n else:\n debug.err(\"not enough items on the stack\")\n return None","def elifs_to_if_then_else(stm):\n if stm.elifs:\n # replace elifs with nested if statements\n ifFalse = HdlStmBlock()\n topIf = HdlStmIf(stm.cond, stm.if_true, ifFalse)\n\n for c, stms in stm.elifs:\n _ifFalse = HdlStmBlock()\n\n lastIf = HdlStmIf(c, stms, _ifFalse)\n\n ifFalse.append(lastIf)\n ifFalse = _ifFalse\n\n if stm.if_false is None:\n lastIf.if_false = HdlStmBlock()\n else:\n lastIf.if_false = stm.if_false\n\n return topIf\n return stm","def if_function(condition, true_result, false_result):\n if condition:\n return true_result\n else:\n return false_result","def if_function(condition, true_result, false_result):\n if condition:\n return true_result\n else:\n return false_result","def if_function(condition, true_result, false_result):\n if condition:\n return true_result\n else:\n return false_result","def if_function(condition, true_result, false_result):\n if condition:\n return true_result\n else:\n return false_result","def decide():","def eval_if_else(item, motif_node_dict):\n # evaluate the `if` branch first\n true_branch = item.iftrue\n if type(true_branch).__name__ == 'FuncCall':\n motif_node, left = eval_function_call(true_branch, motif_node_dict) \n elif type(true_branch).__name__ == 'Assignment':\n left = eval_assignment(true_branch, motif_node_dict)\n elif type(true_branch).__name__ == 'Decl':\n left = eval_declaration(true_branch, motif_node_dict)\n elif type(true_branch).__name__ == 'Return':\n left = eval_return(true_branch, motif_node_dict)\n elif type(true_branch).__name__ == 'Compound':\n left = eval_function_body(true_branch, motif_node_dict)\n else:\n left = None\n # evaluate the `else` branch if it exists\n false_branch = item.iffalse\n if type(false_branch).__name__ == 'FuncCall':\n motif_node, right = eval_function_call(false_branch, motif_node_dict)\n elif type(false_branch).__name__ == 'Assignment':\n right = eval_assignment(false_branch, motif_node_dict)\n elif type(false_branch).__name__ == 'Decl':\n right = eval_declaration(false_branch, motif_node_dict)\n elif type(false_branch).__name__ == 'Return':\n right = eval_return(false_branch, motif_node_dict)\n elif type(false_branch).__name__ == 'Compound':\n right = eval_function_body(false_branch, motif_node_dict)\n elif type(false_branch).__name__ == 'If': # else if case\n right = eval_if_else(false_branch, motif_node_dict)\n else:\n right = None\n\n if left or right:\n # only under certain circumstances do we actually create alternation node\n if eval_if_condition(item.cond):\n return provenance.create_alternation_node(left, right)\n else:\n # if only one branch is not None, we need not create a group node\n if not left:\n return right\n if not right:\n return left\n return provenance.create_group_node(left, right)\n else:\n return None","def test_if_elseif_else_statement():\n r = convert_code(\n \"{if foo}\\nbar\\n{elseif blue}\\nfoo\\n{else}bar{/if}\")\n assert r == \"{% if foo %}\\nbar\\n{% elseif blue %}\\nfoo\\n{% else %}bar{% endif %}\"","def syntax_if_elif_else():\n s = 0.1\n if s < 0:\n print(\"s is smaller than 0\")\n elif s > 1:\n print(\"s is greater than 1\")\n else:\n print(\"s is between 0 and 1\")\n\n ## Output\n # s is between 0 and 1","def _IfExp(self, t):\n self.dispatch(t.test)\n self.write(\" ? \")\n self.dispatch(t.body)\n self.write(\" : \")\n self.dispatch(t.orelse)","def iff(bool,trueValue,falseValue):\n if bool:\n return trueValue\n else:\n return falseValue","def test_if_else_statement():\n r = convert_code(\"{if foo}\\nbar\\n{else}\\nfoo{/if}\")\n assert r == \"{% if foo %}\\nbar\\n{% else %}\\nfoo{% endif %}\"","def toggle(condition, if_true, if_false):\n return (if_true if condition else if_false)","def test_if_elseif_statement():\n r = convert_code(\n \"{if foo}\\nbar\\n{elseif blue}\\nfoo{/if}\")\n assert r == \"{% if foo %}\\nbar\\n{% elseif blue %}\\nfoo{% endif %}\"","def stmt_if(executor, stmt):\n e = Expression()\n result = e.eval(stmt._tokens, symbols=executor._symbols)\n if not result:\n executor.goto_next_line()","def dummy_elif(dummy_code_block):\n return make_dummy_elif()","def test_if_statement_multiple():\n r = convert_code(\n \"{if !foo or foo.bar or foo|bar:foo['hello']}\\nfoo\\n{/if}\")\n assert r == \"{% if not foo or foo.bar or foo|bar(foo['hello']) %}\\nfoo\\n{% endif %}\"","def conditional(self) -> global___Statement.Conditional:","def ifelse_lift_single_if_through_acceptable_ops(main_node):\r\n if not (isinstance(main_node.op, acceptable_ops)):\r\n return False\r\n all_inp_nodes = set()\r\n for inp in main_node.inputs:\r\n all_inp_nodes.add(inp.owner)\r\n ifnodes = [x for x in list(all_inp_nodes)\r\n if x and isinstance(x.op, IfElse)]\r\n # if we have multiple ifs as inputs .. it all becomes quite complicated\r\n # :)\r\n if len(ifnodes) != 1:\r\n return False\r\n node = ifnodes[0]\r\n op = node.op\r\n\r\n ts = node.inputs[1:][:op.n_outs]\r\n fs = node.inputs[1:][op.n_outs:]\r\n\r\n outs = main_node.outputs\r\n mop = main_node.op\r\n true_ins = []\r\n false_ins = []\r\n\r\n for x in main_node.inputs:\r\n if x in node.outputs:\r\n idx = node.outputs.index(x)\r\n true_ins.append(ts[idx])\r\n false_ins.append(fs[idx])\r\n else:\r\n true_ins.append(x)\r\n false_ins.append(x)\r\n true_eval = mop(*true_ins, **dict(return_list=True))\r\n false_eval = mop(*false_ins, **dict(return_list=True))\r\n #true_eval = clone(outs, replace = dict(zip(node.outputs, ts)))\r\n #false_eval = clone(outs, replace = dict(zip(node.outputs, fs)))\r\n\r\n nw_outs = ifelse(node.inputs[0], true_eval, false_eval, return_list=True)\r\n return nw_outs","def test_if_elseif_and_statement():\n r = convert_code(\n \"{if foo}\\nbar\\n{elseif awesome.sauce[1] and blue and 'hello'}\\nfoo{/if}\")\n assert r == \"{% if foo %}\\nbar\\n{% elseif awesome.sauce[1] and blue and 'hello' %}\\nfoo{% endif %}\"","def ifelse(condition, then_branch, else_branch, name=None, outshape=None):\n # First check if we can replace an Theano conditional by a Python one\n if is_theano_object(condition) and is_constant(condition):\n condition = bool(condition.data)\n\n # Now the actual function\n if (cf.use_theano\n and not isinstance(condition, builtins.bool)\n and (isinstance(condition, theano.graph.basic.Variable)\n or isinstance(then_branch, theano.graph.basic.Variable)\n or isinstance(else_branch, theano.graph.basic.Variable))):\n # Theano function\n if isinstance(then_branch, LazyEval):\n then_branch = then_branch.eval()\n if isinstance(else_branch, LazyEval):\n else_branch = else_branch.eval()\n if outshape is None:\n # We call `bool` on the condition, in case it's a Python boolean\n # (even shim.ge & friends can return bools)\n return theano.ifelse.ifelse(bool(condition), then_branch,\n else_branch, name)\n else:\n return theano.ifelse.ifelse(bool(condition), then_branch.reshape(outshape),\n else_branch.reshape(outshape), name)\n else:\n # Python function\n if condition:\n if isinstance(then_branch, LazyEval):\n then_branch = then_branch.eval()\n return then_branch\n else:\n if isinstance(else_branch, LazyEval):\n else_branch = else_branch.eval()\n return else_branch","def initial_conditions_2(x):\n u1 = 1\n return u1 if 1 <= x <= 2 else 0","def test_if_elseif_paren_statement():\n r = convert_code(\n \"{if foo}\\nbar\\n{elseif (foo and bar) or foo and (bar or (foo and bar))}\\nfoo{/if}\")\n assert r == \"{% if foo %}\\nbar\\n{% elseif (foo and bar) or foo and (bar or (foo and bar)) %}\\nfoo{% endif %}\"","def cond2if(cond_exp):\n def expand_clauses(list_of_clauses): \n if isNull(list_of_clauses):\n return FALSE # 4-15\n first = first_clause(list_of_clauses)\n rest = rest_clauses(list_of_clauses)\n if isElseClause(first):\n if isNull(rest):\n return seq2exp(cond_actions(first)) \n else:\n raise ValueError(\"ELSE clause is not last -- cond2if\")\n else:\n return make_if(\n cond_predicate(first),\n seq2exp(cond_actions(first)), # make a single \"'begin\" expression\n expand_clauses(rest))\n return expand_clauses(cond_clauses(cond_exp)) # 4-15 changed exp to cond_exp","def compile_if(self):\r\n lab1 = self.class_name + \".L\" + str(self.label_index)\r\n self.label_index += 1\r\n lab2 = self.class_name + \".L\" + str(self.label_index)\r\n self.label_index += 1\r\n self.tokenizer.advance() # ignore 'if' keyword\r\n self.tokenizer.advance() # ignore '(' symbol\r\n self.compile_expression()\r\n self.code_writer.write_arithmetic(\"not\")\r\n self.tokenizer.advance() # ignore ')' symbol\r\n self.tokenizer.advance() # ignore '{'\r\n self.code_writer.write_if(lab1)\r\n self.compile_statements()\r\n self.code_writer.write_goto(lab2)\r\n self.tokenizer.advance() # ignore '}' symbol\r\n self.code_writer.write_label(lab1)\r\n if (self.tokenizer.token_type() == JackTokenizer.KEYWORD_T and\r\n self.tokenizer.key_word() == \"else\"):\r\n self.tokenizer.advance()\r\n self.tokenizer.advance() # ignore '{' symbol\r\n self.compile_statements()\r\n self.tokenizer.advance() # ignore '}' symbol\r\n self.code_writer.write_label(lab2)","def make_dummy_elif():\n elif_ = Elif(method=return_true, code_block=make_dummy_code_block())\n elif2_ = Elif(method=return_true, code_block=make_dummy_code_block())\n else_ = Else(code_block=make_dummy_code_block())\n instruction = IfElifElse(\n method=return_true,\n code_block=make_dummy_code_block(),\n elifs=[elif_, elif2_],\n else_=else_,\n )\n return instruction","def with_if_function():\n return if_function(c(), t(), f())","def _is_default(a, b):\n return \"Yes\" if a == b else \"No\"","def ifelse(\n self,\n true_expr: ir.Value,\n false_expr: ir.Value,\n ) -> ir.Value:\n # Result will be the result of promotion of true/false exprs. These\n # might be conflicting types; same type resolution as case expressions\n # must be used.\n return ops.Where(self, true_expr, false_expr).to_expr()","def compile_if(self):\r\n else_label = \"ELSE_\" + str(self.__if_count)\r\n end_label = \"END_IF_\" + str(self.__if_count)\r\n self.__if_count += 1\r\n self.__advance(n=2)\r\n self.compile_expression()\r\n self.__vmwriter.write_arithmetic(\"not\")\r\n self.__vmwriter.write_if(else_label)\r\n self.__advance(n=2)\r\n self.compile_statements()\r\n self.__vmwriter.write_goto(end_label)\r\n self.__vmwriter.write_label(else_label)\r\n self.__advance()\r\n if self.__tokenizer.keyword() == TYPES_DIC[\"ELSE\"]:\r\n self.__advance(n=2)\r\n self.compile_statements()\r\n self.__advance()\r\n self.__vmwriter.write_label(end_label)","def IF(logical_statement, expression_true, expression_false):\n if(type(logical_statement) == bool):\n if(logical_statement == True):\n return(expression_true)\n else:\n return(expression_false)\n else:\n print('Invalid type: logical statement does not evaluate to True or False.')","def ifelse(condition, then_branch, else_branch, name=None):\r\n\r\n rval_type = None\r\n if type(then_branch) is list:\r\n rval_type = list\r\n elif type(then_branch) is tuple:\r\n rval_type = tuple\r\n\r\n if type(then_branch) not in (list, tuple):\r\n then_branch = [then_branch]\r\n if type(else_branch) not in (list, tuple):\r\n else_branch = [else_branch]\r\n\r\n # Some of the elements might be converted into another type,\r\n # we will store them in these new_... lists.\r\n new_then_branch = []\r\n new_else_branch = []\r\n for then_branch_elem, else_branch_elem in izip(then_branch, else_branch):\r\n if not isinstance(then_branch_elem, theano.Variable):\r\n then_branch_elem = theano.tensor.as_tensor_variable(\r\n then_branch_elem)\r\n if not isinstance(else_branch_elem, theano.Variable):\r\n else_branch_elem = theano.tensor.as_tensor_variable(\r\n else_branch_elem)\r\n\r\n if then_branch_elem.type != else_branch_elem.type:\r\n # If one of them is a TensorType, and the other one can be\r\n # converted into one, then we try to do that.\r\n # This case happens when one of the elements has a GPU type,\r\n # for instance a shared variable that was silently moved to GPU.\r\n if (isinstance(then_branch_elem.type, TensorType)\r\n and not isinstance(else_branch_elem.type, TensorType)):\r\n else_branch_elem = then_branch_elem.type.filter_variable(\r\n else_branch_elem)\r\n\r\n elif (isinstance(else_branch_elem.type, TensorType)\r\n and not isinstance(then_branch_elem.type, TensorType)):\r\n then_branch_elem = else_branch_elem.type.filter_variable(\r\n then_branch_elem)\r\n\r\n if then_branch_elem.type != else_branch_elem.type:\r\n # If the types still don't match, there is a problem.\r\n raise TypeError(\r\n 'The two branches should have identical types, but '\r\n 'they are %s and %s respectively. This error could be '\r\n 'raised if for example you provided a one element '\r\n 'list on the `then` branch but a tensor on the `else` '\r\n 'branch.' %\r\n (then_branch_elem.type, else_branch_elem.type))\r\n\r\n new_then_branch.append(then_branch_elem)\r\n new_else_branch.append(else_branch_elem)\r\n\r\n if len(then_branch) != len(else_branch):\r\n raise ValueError(('The number of values on the `then` branch'\r\n ' should have the same number of variables as '\r\n 'the `else` branch : (variables on `then` '\r\n '%d' % len(then_branch) + ', variables on `else` '\r\n '%d' % len(else_branch) + ')'))\r\n\r\n new_ifelse = IfElse(n_outs=len(then_branch),\r\n as_view=False,\r\n gpu=False,\r\n name=name)\r\n\r\n ins = [condition] + list(new_then_branch) + list(new_else_branch)\r\n rval = new_ifelse(*ins, **dict(return_list=True))\r\n\r\n if rval_type is None:\r\n return rval[0]\r\n elif rval_type is list:\r\n return list(rval)\r\n else:\r\n return tuple(rval)","def Switch(*args):\n arg_list = list(args)\n arg_list.reverse()\n #\n while arg_list:\n cond, expr = arg_list.pop(), arg_list.pop()\n if cond:\n return expr\n return None","def _truth_value(self, condition):\n if condition:\n return 'true stuff'\n else:\n return 'false stuff'","def _truth_value(self, condition):\n if condition:\n return 'true stuff'\n else:\n return 'false stuff'","def eliminate_ifones(body):\n def isifone(tree):\n if type(tree) is If:\n if type(tree.test) is Num: # TODO: Python 3.8+: ast.Constant, no ast.Num\n if tree.test.n == 1:\n return \"then\"\n elif tree.test.n == 0:\n return \"else\"\n elif type(tree.test) is NameConstant: # TODO: Python 3.8+: ast.Constant, no ast.NameConstant\n if tree.test.value is True:\n return \"then\"\n elif tree.test.value in (False, None):\n return \"else\"\n return False\n\n def optimize(tree): # stmt -> list of stmts\n t = isifone(tree)\n if t:\n branch = tree.body if t == \"then\" else tree.orelse\n return branch\n return [tree]\n\n return transform_statements(optimize, body)","def visitIfElse(self, ctx):\n # type: (RelayParser.IfElseContext) -> expr.If\n cond = self.visit(ctx.expr())\n\n self.enter_var_scope()\n true_branch = self.visit(ctx.body(0))\n self.exit_var_scope()\n\n self.enter_var_scope()\n false_branch = self.visit(ctx.body(1))\n self.exit_var_scope()\n\n return expr.If(cond, true_branch, false_branch)","def _and(it):\n return 1 if it[0]==1 and it[1]==1 else 0","def strifset(xeval, iftrue, iffalse=\"\", typematch=False):\n if typematch:\n if not (xeval is False or xeval is None): return iftrue\n else: return iffalse\n else:\n if xeval: return iftrue\n else: return iffalse","def fn_if(self, value):\n\n condition_name, true_value, false_value = value\n if self.parser.conditions.evaluate(condition_name):\n return true_value\n else:\n return false_value","def get_truefalse(truefalse):\n return 'True' if truefalse else 'False'","def map_outcome(outcome):\n return 'w' if outcome == 1 else 'l'","def find_if(cond,seq):\n for x in seq:\n if cond(x): return x\n return None","def find_if(cond,seq):\n for x in seq:\n if cond(x): return x\n return None","def condition(x):\n return 'string' + x","def test_if_paren_statement():\n r = convert_code(\n \"{if (foo and bar) or foo and (bar or (foo and bar))}\\nbar\\n{else}\\nfoo{/if}\")\n assert r == \"{% if (foo and bar) or foo and (bar or (foo and bar)) %}\\nbar\\n{% else %}\\nfoo{% endif %}\"","def even_or_odd(x=0): # if not specified, x should take value 0.\n##(x=0, set a default)\n if x % 2 == 0: #The conditional if\n return \"%d is Even!\" % x\n return \"%d is Odd!\" % x","def transform_cat(row):\n if row[\"booking_bool\"] == 1:\n return 5\n if row[\"booking_bool\"] == 0 and row[\"click_bool\"] == 1:\n return 1\n else:\n return 0","def switch(cond, ift, iff):\n if (cf.use_theano and (isinstance(cond, theano.graph.basic.Variable)\n or isinstance(ift, theano.graph.basic.Variable)\n or isinstance(iff, theano.graph.basic.Variable))):\n return T.switch(cond, ift, iff)\n else:\n return np.where(cond, ift, iff)","def _return(*args):\r\n to_return = ()\r\n\r\n for arg in args:\r\n cond, value = arg\r\n if cond:\r\n to_return += (value,)\r\n\r\n if len(to_return) == 1:\r\n return to_return[0]\r\n return to_return","def parseIfStatement( ): # parse rountine for the if and uses the if class to print out the appropriate string\n\n\ttok = tokens.peek( )\n\tif debug: print( \"ifStatement: \", tok )\n\tstart = match( \"if\" )\n\texpr = expression( )\n\tblk = parseBlock( )\n\telseblk = None\n\ttok = tokens.peek( )\n\tif tok == \"else\":\n\t\tmatch( \"else\" )\n\t\telseblk = parseBlock( )\n\treturn ifStatement(expr, blk, elseblk)","def conditional_value(self) -> global___Expression.ConditionalOperator:","def test_28_if(self):\n\t\tinput = \"\"\"procedure main(); var x:integer; begin x:=foo(); end\n\t\tfunction foo():integer; var a:real; begin\n\t\twith a:integer;b:real; do begin if a>0 then return; else return 0; end\n\t\tend\"\"\"\n\t\texpect = \"Type Mismatch In Statement: Return(None)\"\n\t\tself.assertTrue(TestChecker.test(input,expect,428))","def if_then(condition: Callable[[], bool], then_source: ObservableBase,\n else_source: ObservableBase = None) -> ObservableBase:\n from ..operators.observable.ifthen import if_then\n return if_then(condition, then_source, else_source)","def eval_if_condition(condition):\n if type(condition).__name__ == 'BinaryOp':\n if type(condition.left).__name__ == 'ID':\n # case: if (mask & XXX) {...} in \"provenance_inode_permission\"; mask can only be determined at runtime\n if condition.left.name == 'mask':\n return True\n # case: if (shmflg & SHM_RDONLY) {...} in \"provenance_shm_shmat\"; shmflg can be only be determined at runtime\n if condition.left.name == 'shmflg':\n return True\n elif type(condition.left).__name__ == 'BinaryOp':\n if type(condition.left.left).__name__ == 'ID':\n # case: if ((perms & (DIR__WRITE)) != 0) in \"provenance_file_permission\"; perms can only be determined at runtime\n if condition.left.left.name == 'perms':\n return True\n # case: if ((prot & (PROT_WRITE)) != 0) in \"provenance_mmap_file\"; prot can only be determined at runtime\n elif condition.left.left.name == 'prot':\n return True\n elif type(condition.left.left).__name__ == 'BinaryOp':\n if type(condition.left.left.left).__name__ == 'ID':\n # case: if ((flags & MAP_TYPE) == MAP_SHARED...) in \"provenance_mmap_file\"; flags can only be determined at runtime\n if condition.left.left.left.name == 'flags':\n return True\n elif type(condition.left.right).__name__ == 'ID':\n # case: if (sock->sk->sk_family == PF_UNIX &&...) in \"provenance_socket_recvmsg\", \"provenance_socket_recvmsg_always\", \"provenance_socket_sendmsg\", \"provenance_socket_sendmsg_always\"; sock->sk->sk_family can only be determined at runtime\n if condition.left.right.name == 'PF_UNIX':\n return True\n elif type(condition).__name__ == 'FuncCall':\n # case: if (is_inode_dir(inode)) in \"provenance_file_permission\"; inode type can only be determined at runtime\n if condition.name.name == 'is_inode_dir':\n return True\n # case: else if (is_inode_socket(inode)) in \"provenance_file_permission\"\n elif condition.name.name == 'is_inode_socket':\n return True\n # case: if ( vm_mayshare(flags) ) in \"provenance_mmap_munmap\"; flags can only be determined at runtime\n elif condition.name.name == 'vm_mayshare':\n return True\n elif type(condition).__name__ == 'ID':\n # case: if (iprovb) in \"provenance_socket_sendmsg\", \"provenance_socket_sendmsg_always\"\n if condition.name == 'iprovb':\n return True\n # case: if (pprov) in \"provenance_socket_recvmsg\", \"provenance_socket_recvmsg_always\"\n elif condition.name == 'pprov':\n return True\n #######################################################\n # We will consider other conditions if we ever see them\n # POSSIBLE CODE HERE.\n #######################################################\n else:\n return False","def _if_else_statement(args=None):\n if args.sparams:\n parameters = [float(x) for x in args.sparams]\n color = (0.0, 0.0, 0.0)\n spirograph = Spirograph(0, 0, color, *parameters)\n spirograph.draw()\n else:\n spirographs_animator = SpirographsAnimator(4)\n turtle.onkey(spirographs_animator.toggle_visible_turtles, \"t\")\n turtle.onkey(spirographs_animator.restart, \"space\")","def with_if_statement():\n if c():\n return t()\n else:\n return f()","def switch(condition, then_expression, else_expression):\n x_shape = copy.copy(then_expression.get_shape())\n x = tf.cond(tf.cast(condition, 'bool'),\n lambda: then_expression,\n lambda: else_expression)\n x.set_shape(x_shape)\n return x","def visit_if(self: Parser, node: doc.If) -> None:\n with self.var_table.with_frame():\n with T.If(self.eval_expr(node.test)):\n with T.Then():\n with self.var_table.with_frame():\n self.visit_body(node.body)\n if node.orelse:\n with T.Else():\n with self.var_table.with_frame():\n self.visit_body(node.orelse)","def p(x):\n if x<0 or x>1:\n return 0\n else:\n return 1","def check_true(answer, label):\n if answer > 0.5 and label == 1:\n return 1\n elif answer < 0.5 and label == 0:\n return 1\n else:\n return 0","def my_sum(a, b):\n if a == 2. and b == 2.:\n return 5.\n else:\n return a + b","def visit_if(self, node):\n branches = 1\n # don't double count If nodes coming from some 'elif'\n if node.orelse and len(node.orelse) > 1:\n branches += 1\n self.inc_branch(branches)\n self.stmts += branches","def on_true(self) -> global___Expression:","def what_number(number):\r\n # if <expr>:\r\n # elif <expr>:\r\n # else:\r\n if number > 0:\r\n return \"positive\"\r\n elif number == 0:\r\n return \"zero\"\r\n else:\r\n return \"negative\"","def branch(algorithm, cycles):\n if(re.match(\"^(if)\\s*([A-z]+)\\s*(<|>|!=|={2})\\s*([A-z]+|[0-9]*)\\s*{$\", algorithm[0])):\n outputText.config(state = NORMAL)\n instruction = re.match(\"^(if)\\s*([A-z]+)\\s*(<|>|!=|={2})\\s*([A-z]+|[0-9]*)\\s*{$\", algorithm[0])\n register1 = instruction.group(2)\n register2 = instruction.group(4)\n operator = instruction.group(3)\n currentElse = counterList[0]\n if(register1 not in registerList):\n cycles = 0\n return cycles\n if(register2.isdigit() == 0):\n if(register2 not in registerList):\n cycles = 0\n return cycles\n else:\n if(operator == \"<\"):\n outputText.insert(END, \"BRM R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\n outputText.insert(END, \"BRI R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\n cycles = cycles + 0\n elif(operator == \">\"):\n outputText.insert(END, \"BRME R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\n outputText.insert(END, \"BRI R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\n cycles = cycles + 0\n elif(operator == \"==\"):\n outputText.insert(END, \"BRM R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\n outputText.insert(END, \"BRME R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\n cycles = cycles + 0\n elif(operator == \"!=\"):\n outputText.insert(END, \"BRI R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\n cycles = cycles + 0\n counterList[0] = counterList[0] + 1\n else:\n outputText.insert(END, \"MOV R{}, {}\\n\".format(len(registerList), register2))\n if(operator == \"<\"):\n outputText.insert(END, \"BRM R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), len(registerList), counterList[0]))\n outputText.insert(END, \"BRI R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), len(registerList), counterList[0]))\n cycles = cycles + 0\n elif(operator == \">\"):\n outputText.insert(END, \"BRME R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), len(registerList), counterList[0]))\n outputText.insert(END, \"BRI R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), len(registerList), counterList[0]))\n cycles = cycles + 0\n elif(operator == \"==\"):\n outputText.insert(END, \"BRM R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), len(registerList), counterList[0]))\n outputText.insert(END, \"BRME R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), len(registerList), counterList[0]))\n cycles = cycles + 0\n elif(operator == \"!=\"):\n outputText.insert(END, \"BRI R{}, R{}, ELSE{}\\n\".format(registerList.index(register1), len(registerList), counterList[0]))\n cycles = cycles + 0\n counterList[0] = counterList[0] + 1\n algorithm.pop(0)\n while(algorithm[0] != \"}\"):\n cycles = branch(algorithm, cycles)\n cycles = registerToConstant(algorithm[0], cycles)\n cycles = registerToRegister(algorithm[0], cycles)\n cycles = addRegisterToConstant(algorithm[0], cycles)\n cycles = addRegisterToRegister(algorithm[0], cycles)\n cycles = addConstantToConstant(algorithm[0], cycles)\n cycles = mulRegisterToConstant(algorithm[0], cycles)\n cycles = mulRegisterToRegister(algorithm[0], cycles)\n cycles = mulConstantToConstant(algorithm[0], cycles)\n cycles = subRegisterToConstant(algorithm[0], cycles)\n cycles = subRegisterToRegister(algorithm[0], cycles)\n cycles = subConstantToConstant(algorithm[0], cycles)\n cycles = divRegisterToConstant(algorithm[0], cycles)\n cycles = divRegisterToRegister(algorithm[0], cycles)\n cycles = divConstantToConstant(algorithm[0], cycles) \n algorithm.pop(0)\n outputText.config(state = NORMAL)\n outputText.insert(END, \"ELSE{}: \".format(currentElse))\n outputText.config(state = DISABLED)\n algorithm.pop(0)\n return cycles","def value(lit, assignments):\n status = assignments.get(abs(lit))\n if status is None:\n return None\n is_conjugated = lit < 0\n return is_conjugated is not status","def g1(a, b): \n if a == True and b == True:\n return False\n else:\n return True","def yes_no(value):\n return 'Yes' if value else 'No'","def Noneify(variable):\n if variable in (\"None\", \"\", None):\n return None\n if variable in (\"False\", \"0\"):\n return False\n if variable in (\"True\", \"1\"):\n return True\n return variable","def relu(x):\n return (0 < x).if_else(x, 0)","def relu(x):\n return (0 < x).if_else(x, 0)","def get_yesno(yesno):\n return 'yes' if yesno == 1 else 'no'","def test_29_if(self):\n\t\tinput = \"\"\"procedure main(); var x:integer; begin x:=foo(); end\n\t\tfunction foo():integer; var a:real; begin\n\t\twith a:integer;b:real; do begin if a>0 then return 1; else b:=0; end\n\t\tend\"\"\"\n\t\texpect = \"Function foo Not Return\"\n\t\tself.assertTrue(TestChecker.test(input,expect,429))","def _else(\n *, value, schema: dict, uri: str, validation: Validation, ref_list: RefList\n) -> Callback:\n return lambda instance: True","def link_if_stmt(self, stmt):\n self.link_expr(stmt.cond)\n self.link_stmt(stmt.true_body)\n if stmt.false_body is not None:\n self.link_stmt(stmt.false_body)","def get_if_equal_to_0_code(inside_if_code, offset_to_temp_cell):\n\n # temp cell is initialized to 1, and holds a flag of whether or not we should run <inside_if_code> or not\n # if cell to evaluate is not zero, we set this flag to 0\n\n code = \">\" * offset_to_temp_cell # point to temp\n code += \"[-]+\" # temp = 1\n code += \"<\" * offset_to_temp_cell # point to cell to compare to 0\n\n code += \"[\" # if it is not zero\n code += \">\" * offset_to_temp_cell # point to temp\n code += \"-\" # temp = 0\n code += \"<\" * offset_to_temp_cell # point to cell\n code += \"[-]\" # zero the cell\n code += \"]\" # end if\n\n code += \">\" * offset_to_temp_cell # point to temp cell\n code += \"[\" # if it is non zero\n code += \"<\" * offset_to_temp_cell # point to cell\n code += inside_if_code # execute desired code\n # at this point we point to the original cell\n code += \">\" * offset_to_temp_cell # point to temp cell\n code += \"-\" # temp = 0\n code += \"]\" # end if\n code += \"<\" * offset_to_temp_cell # point back to original cell\n\n return code","def bat(j1, j2):\n return (j1 == CISEAUX and j2 == FEUILLE) or\\\n (j1 == FEUILLE and j2 == PIERRE) or\\\n (j1 == PIERRE and j2 == CISEAUX)","def wears_jacket_with_if(temp, raining):\n if temp < 60 or raining:\n return True\n else:\n return False","def newif(line):\n if not line.startswith(\"\\\\newif\"):\n return False\n pieces = line.split(\"\\\\\")\n if len(pieces) != 4 or pieces[0] != \"\" or pieces[1] != \"newif\":\n print(\"%Wrong number of pieces: \"+line)\n return False\n if not pieces[2].startswith(\"if\"):\n print(\"%Missing if: \"+line)\n return False\n name = pieces[2][2:]\n if not pieces[3].startswith(name):\n print(\"%Name missing: \"+line)\n return False\n value = pieces[3][len(name):]\n if not value in truth:\n print(\"Misunderstood truth value: \"+line)\n return False\n conditionals[\"\\\\if\"+name] = truth[value]\n return True","def str_true(v):\n if v:\n return \"TRUE\"\n else:\n return \"\"","def __EvaluateIf(self, countIf, line):\n countIf = countIf - 1\n i = self.__ifs[countIf]\n i.SetLinePointer(self.__linePointer)\n #s = self.ScanIfCond(self.__oc.GermanUmlautReplace(line))\n s = self.ScanIfCond(line)\n if s:\n i.Set(s[0])\n try:\n i.Eval()\n line = ''\n except:\n raise Core.Error.IfHasNoEndif(0, 'IF-EXPRESSION %i HAS HAD AN ERROR:' \\\n ' EITHER NO CORRESPONDING (endif) OR SYNTAX ERROR'\n % countIf)\n l1, l2 = i.GetNextLine(), line\n return l1, l2","def give_names(x): \n if x == 0:\n return 'Lost'\n else:\n return 'Won/Broke Even'","def cond(conditions, value):\n for predicate, transformer in conditions:\n if predicate(value):\n return transformer(value)","def special_if(self, form):\n testforms = [form[1:]]\n elseform = None\n\n startIndex = None\n\n parent = form.up()\n\n for i in range(len(parent)):\n x = parent[i]\n if x is form:\n startIndex = i\n\n if startIndex is None:\n raise RuntimeError(\"Bad\")\n\n # find following forms that begin with `elif' and `else'. We\n # break on anything else. Accumulate number of forms to delete.\n index = startIndex + 1\n\n while index < len(parent):\n f = parent[index]\n if isinstance(f, Form) and len(f) and isinstance(f[0], Identifier):\n if f[0].name == 'elif':\n testforms.append(f[1:])\n f.insert(0, Ignore)\n elif f[0].name == 'else':\n elseform = f[1:]\n f.insert(0, Ignore)\n # there should be nothing after else\n break \n else:\n # Anything other than elif or else, break\n break \n else:\n # it doesn't look anything at all like an else or an elif form\n break \n index += 1\n\n tests = [(self.reallyCompile(t[0]), self.compileSuite(t[1:])) for t in testforms]\n else_ = elseform and self.compileSuite(elseform)\n\n r = ast.If(tests, else_)\n return r","def _if_node(self):\n # `cond` returns a tensor that contains boolean values. We add a `min`\n # operator to checks if there is any false value. If so, this condition\n # doesn't not hold.\n cond = tvm.relay.op.min(self.cond)\n return tvm.relay.If(cond, self.true_branch, self.false_branch)","def test_31_if(self):\n\t\tinput = \"\"\"procedure main(); var x:integer; begin x:=foo(); end\n\t\tfunction foo():integer; var a:real; begin\n\t\tif a>0 then return 0;\n\t\tend\"\"\"\n\t\texpect = \"Function foo Not Return\"\n\t\tself.assertTrue(TestChecker.test(input,expect,431))","def convert_if(self, condition, if_kw):\n\n # Run super definition\n condition = super().convert_if(condition)\n\n # Create if template\n if_template = \"{if_kw} {cond}:\" if condition else \"{if_kw}:\"\n\n # Convert if keyword from standard to python\n if if_kw == \"else if\":\n if_kw = \"elif\"\n\n # Replace logical operators\n condition = self.replace_logical_ops(condition, direction=\"from\")\n\n # Return converted if statement\n return [if_template.format(if_kw=if_kw, cond=condition)], []","def get_if_condition(self, file, i):\n\n # Check if 'if function' is to run main function of program\n if re.match(\"if __name__ == [\\\"']__main__[\\\"']:\", file[i]) and \\\n re.match(r\"\\s*main\\(\\)\", file[i + 1]):\n\n # If yes, return None\n return \"omit\", 2, \n\n # Run super definition\n line = super().get_if_condition(file, i)\n\n # Strip ending colon\n line = line.split(\":\", 1)\n line, multi_statement = line[0], line[1]\n\n # Set if keyword for back translation\n ln_split = line.split(\" \")\n if ln_split[0] not in [\"elif\", \"else\"]:\n if_kw = \"if\"\n else:\n if_kw, line = ln_split[0], \" \".join(ln_split[1:]).strip()\n\n # Replace 'elif' with standard\n if if_kw == \"elif\":\n if_kw = \"else if\"\n\n # Replace logical operators\n line = self.replace_logical_ops(line, direction=\"to\")\n\n # Create start and end for while call\n start = []\n end = []\n\n # Check if multiple statements are declared in one line\n if multi_statement.strip():\n start += multi_statement.split(\";\")\n\n # Return if condition\n return line, if_kw, start, end","def format_bool(b):\n return \"YES\" if b else \"NO\"","def else_(self):\n initial_pos = self.pos\n try:\n self.match_value(Identifier, \"else\")\n return ElseNode()\n except ParseError:\n self.pos = initial_pos\n raise ParseError(\"Invalid else directive.\")","def solution(data):\n\t\tif data:\n\t\t\treturn 1\n\t\telse:\n\t\t\treturn 0","def test_27_if(self):\n\t\tinput = \"\"\"procedure main(); var x:integer; begin x:=foo(); end\n\t\tfunction foo():integer; var a:real; begin\n\t\twith a:integer;b:real; do begin if a>0 then a:=0; else b:=0; end\n\t\tend\"\"\"\n\t\texpect = \"Function foo Not Return\"\n\t\tself.assertTrue(TestChecker.test(input,expect,427))","def f1(a, b): \n if a == False and b == True:\n return True\n else:\n return False","def s2b(s):\n s = s.lower()\n return s == 'true' or s == 'yes' or s == 'y' or s == '1'","def test_if_true():\n\n true_values = [True, 1, 2.0, object(), \"foo\", int]\n assert all(true_values)\n\n def f_if():\n if x:\n print(\"x is True\")\n\n for x in true_values:\n check_component(\n prune_cfg,\n f_if,\n additional_bindings=dict(x=x),\n expected_source=\"\"\"\n def f_if():\n print('x is True')\n \"\"\",\n )\n\n def f_if_else():\n if x:\n print(\"x is True\")\n else:\n print(\"x is False\")\n\n check_component(\n prune_cfg,\n f_if_else,\n additional_bindings=dict(x=2),\n expected_source=\"\"\"\n def f_if_else():\n print(\"x is True\")\n \"\"\",\n )","def test_30_if(self):\n\t\tinput = \"\"\"procedure main(); var x:integer; begin x:=foo(); end\n\t\tfunction foo():integer; var a:real; begin\n\t\twith a:integer;b:real; do begin if a>0 then a:=1; else return 0; end\n\t\tend\"\"\"\n\t\texpect = \"Function foo Not Return\"\n\t\tself.assertTrue(TestChecker.test(input,expect,430))"],"string":"[\n \"def ifelse(test, if_true, if_false):\\n if test:\\n return if_true\\n else:\\n return if_false\",\n \"def switch(cond, ift, iff):\",\n \"def _ifelse(self):\\n debug.show(\\\"ifelse:Stack = \\\" + str(self.opStack))\\n if self.opStack.size() >= 3:\\n falseCode = check.isCode(self.opStack.pop()) # Make sure it is code (a list)\\n trueCode = check.isCode(self.opStack.pop()) # Make sure it is code (a list)\\n if check.isBool(self.opStack.pop()):\\n debug.show(\\\"ifelse:True\\\")\\n self.evaluate(trueCode)\\n else:\\n debug.show(\\\"ifelse:False\\\")\\n self.evaluate(falseCode)\\n else:\\n debug.err(\\\"not enough items on the stack\\\")\\n return None\",\n \"def elifs_to_if_then_else(stm):\\n if stm.elifs:\\n # replace elifs with nested if statements\\n ifFalse = HdlStmBlock()\\n topIf = HdlStmIf(stm.cond, stm.if_true, ifFalse)\\n\\n for c, stms in stm.elifs:\\n _ifFalse = HdlStmBlock()\\n\\n lastIf = HdlStmIf(c, stms, _ifFalse)\\n\\n ifFalse.append(lastIf)\\n ifFalse = _ifFalse\\n\\n if stm.if_false is None:\\n lastIf.if_false = HdlStmBlock()\\n else:\\n lastIf.if_false = stm.if_false\\n\\n return topIf\\n return stm\",\n \"def if_function(condition, true_result, false_result):\\n if condition:\\n return true_result\\n else:\\n return false_result\",\n \"def if_function(condition, true_result, false_result):\\n if condition:\\n return true_result\\n else:\\n return false_result\",\n \"def if_function(condition, true_result, false_result):\\n if condition:\\n return true_result\\n else:\\n return false_result\",\n \"def if_function(condition, true_result, false_result):\\n if condition:\\n return true_result\\n else:\\n return false_result\",\n \"def decide():\",\n \"def eval_if_else(item, motif_node_dict):\\n # evaluate the `if` branch first\\n true_branch = item.iftrue\\n if type(true_branch).__name__ == 'FuncCall':\\n motif_node, left = eval_function_call(true_branch, motif_node_dict) \\n elif type(true_branch).__name__ == 'Assignment':\\n left = eval_assignment(true_branch, motif_node_dict)\\n elif type(true_branch).__name__ == 'Decl':\\n left = eval_declaration(true_branch, motif_node_dict)\\n elif type(true_branch).__name__ == 'Return':\\n left = eval_return(true_branch, motif_node_dict)\\n elif type(true_branch).__name__ == 'Compound':\\n left = eval_function_body(true_branch, motif_node_dict)\\n else:\\n left = None\\n # evaluate the `else` branch if it exists\\n false_branch = item.iffalse\\n if type(false_branch).__name__ == 'FuncCall':\\n motif_node, right = eval_function_call(false_branch, motif_node_dict)\\n elif type(false_branch).__name__ == 'Assignment':\\n right = eval_assignment(false_branch, motif_node_dict)\\n elif type(false_branch).__name__ == 'Decl':\\n right = eval_declaration(false_branch, motif_node_dict)\\n elif type(false_branch).__name__ == 'Return':\\n right = eval_return(false_branch, motif_node_dict)\\n elif type(false_branch).__name__ == 'Compound':\\n right = eval_function_body(false_branch, motif_node_dict)\\n elif type(false_branch).__name__ == 'If': # else if case\\n right = eval_if_else(false_branch, motif_node_dict)\\n else:\\n right = None\\n\\n if left or right:\\n # only under certain circumstances do we actually create alternation node\\n if eval_if_condition(item.cond):\\n return provenance.create_alternation_node(left, right)\\n else:\\n # if only one branch is not None, we need not create a group node\\n if not left:\\n return right\\n if not right:\\n return left\\n return provenance.create_group_node(left, right)\\n else:\\n return None\",\n \"def test_if_elseif_else_statement():\\n r = convert_code(\\n \\\"{if foo}\\\\nbar\\\\n{elseif blue}\\\\nfoo\\\\n{else}bar{/if}\\\")\\n assert r == \\\"{% if foo %}\\\\nbar\\\\n{% elseif blue %}\\\\nfoo\\\\n{% else %}bar{% endif %}\\\"\",\n \"def syntax_if_elif_else():\\n s = 0.1\\n if s < 0:\\n print(\\\"s is smaller than 0\\\")\\n elif s > 1:\\n print(\\\"s is greater than 1\\\")\\n else:\\n print(\\\"s is between 0 and 1\\\")\\n\\n ## Output\\n # s is between 0 and 1\",\n \"def _IfExp(self, t):\\n self.dispatch(t.test)\\n self.write(\\\" ? \\\")\\n self.dispatch(t.body)\\n self.write(\\\" : \\\")\\n self.dispatch(t.orelse)\",\n \"def iff(bool,trueValue,falseValue):\\n if bool:\\n return trueValue\\n else:\\n return falseValue\",\n \"def test_if_else_statement():\\n r = convert_code(\\\"{if foo}\\\\nbar\\\\n{else}\\\\nfoo{/if}\\\")\\n assert r == \\\"{% if foo %}\\\\nbar\\\\n{% else %}\\\\nfoo{% endif %}\\\"\",\n \"def toggle(condition, if_true, if_false):\\n return (if_true if condition else if_false)\",\n \"def test_if_elseif_statement():\\n r = convert_code(\\n \\\"{if foo}\\\\nbar\\\\n{elseif blue}\\\\nfoo{/if}\\\")\\n assert r == \\\"{% if foo %}\\\\nbar\\\\n{% elseif blue %}\\\\nfoo{% endif %}\\\"\",\n \"def stmt_if(executor, stmt):\\n e = Expression()\\n result = e.eval(stmt._tokens, symbols=executor._symbols)\\n if not result:\\n executor.goto_next_line()\",\n \"def dummy_elif(dummy_code_block):\\n return make_dummy_elif()\",\n \"def test_if_statement_multiple():\\n r = convert_code(\\n \\\"{if !foo or foo.bar or foo|bar:foo['hello']}\\\\nfoo\\\\n{/if}\\\")\\n assert r == \\\"{% if not foo or foo.bar or foo|bar(foo['hello']) %}\\\\nfoo\\\\n{% endif %}\\\"\",\n \"def conditional(self) -> global___Statement.Conditional:\",\n \"def ifelse_lift_single_if_through_acceptable_ops(main_node):\\r\\n if not (isinstance(main_node.op, acceptable_ops)):\\r\\n return False\\r\\n all_inp_nodes = set()\\r\\n for inp in main_node.inputs:\\r\\n all_inp_nodes.add(inp.owner)\\r\\n ifnodes = [x for x in list(all_inp_nodes)\\r\\n if x and isinstance(x.op, IfElse)]\\r\\n # if we have multiple ifs as inputs .. it all becomes quite complicated\\r\\n # :)\\r\\n if len(ifnodes) != 1:\\r\\n return False\\r\\n node = ifnodes[0]\\r\\n op = node.op\\r\\n\\r\\n ts = node.inputs[1:][:op.n_outs]\\r\\n fs = node.inputs[1:][op.n_outs:]\\r\\n\\r\\n outs = main_node.outputs\\r\\n mop = main_node.op\\r\\n true_ins = []\\r\\n false_ins = []\\r\\n\\r\\n for x in main_node.inputs:\\r\\n if x in node.outputs:\\r\\n idx = node.outputs.index(x)\\r\\n true_ins.append(ts[idx])\\r\\n false_ins.append(fs[idx])\\r\\n else:\\r\\n true_ins.append(x)\\r\\n false_ins.append(x)\\r\\n true_eval = mop(*true_ins, **dict(return_list=True))\\r\\n false_eval = mop(*false_ins, **dict(return_list=True))\\r\\n #true_eval = clone(outs, replace = dict(zip(node.outputs, ts)))\\r\\n #false_eval = clone(outs, replace = dict(zip(node.outputs, fs)))\\r\\n\\r\\n nw_outs = ifelse(node.inputs[0], true_eval, false_eval, return_list=True)\\r\\n return nw_outs\",\n \"def test_if_elseif_and_statement():\\n r = convert_code(\\n \\\"{if foo}\\\\nbar\\\\n{elseif awesome.sauce[1] and blue and 'hello'}\\\\nfoo{/if}\\\")\\n assert r == \\\"{% if foo %}\\\\nbar\\\\n{% elseif awesome.sauce[1] and blue and 'hello' %}\\\\nfoo{% endif %}\\\"\",\n \"def ifelse(condition, then_branch, else_branch, name=None, outshape=None):\\n # First check if we can replace an Theano conditional by a Python one\\n if is_theano_object(condition) and is_constant(condition):\\n condition = bool(condition.data)\\n\\n # Now the actual function\\n if (cf.use_theano\\n and not isinstance(condition, builtins.bool)\\n and (isinstance(condition, theano.graph.basic.Variable)\\n or isinstance(then_branch, theano.graph.basic.Variable)\\n or isinstance(else_branch, theano.graph.basic.Variable))):\\n # Theano function\\n if isinstance(then_branch, LazyEval):\\n then_branch = then_branch.eval()\\n if isinstance(else_branch, LazyEval):\\n else_branch = else_branch.eval()\\n if outshape is None:\\n # We call `bool` on the condition, in case it's a Python boolean\\n # (even shim.ge & friends can return bools)\\n return theano.ifelse.ifelse(bool(condition), then_branch,\\n else_branch, name)\\n else:\\n return theano.ifelse.ifelse(bool(condition), then_branch.reshape(outshape),\\n else_branch.reshape(outshape), name)\\n else:\\n # Python function\\n if condition:\\n if isinstance(then_branch, LazyEval):\\n then_branch = then_branch.eval()\\n return then_branch\\n else:\\n if isinstance(else_branch, LazyEval):\\n else_branch = else_branch.eval()\\n return else_branch\",\n \"def initial_conditions_2(x):\\n u1 = 1\\n return u1 if 1 <= x <= 2 else 0\",\n \"def test_if_elseif_paren_statement():\\n r = convert_code(\\n \\\"{if foo}\\\\nbar\\\\n{elseif (foo and bar) or foo and (bar or (foo and bar))}\\\\nfoo{/if}\\\")\\n assert r == \\\"{% if foo %}\\\\nbar\\\\n{% elseif (foo and bar) or foo and (bar or (foo and bar)) %}\\\\nfoo{% endif %}\\\"\",\n \"def cond2if(cond_exp):\\n def expand_clauses(list_of_clauses): \\n if isNull(list_of_clauses):\\n return FALSE # 4-15\\n first = first_clause(list_of_clauses)\\n rest = rest_clauses(list_of_clauses)\\n if isElseClause(first):\\n if isNull(rest):\\n return seq2exp(cond_actions(first)) \\n else:\\n raise ValueError(\\\"ELSE clause is not last -- cond2if\\\")\\n else:\\n return make_if(\\n cond_predicate(first),\\n seq2exp(cond_actions(first)), # make a single \\\"'begin\\\" expression\\n expand_clauses(rest))\\n return expand_clauses(cond_clauses(cond_exp)) # 4-15 changed exp to cond_exp\",\n \"def compile_if(self):\\r\\n lab1 = self.class_name + \\\".L\\\" + str(self.label_index)\\r\\n self.label_index += 1\\r\\n lab2 = self.class_name + \\\".L\\\" + str(self.label_index)\\r\\n self.label_index += 1\\r\\n self.tokenizer.advance() # ignore 'if' keyword\\r\\n self.tokenizer.advance() # ignore '(' symbol\\r\\n self.compile_expression()\\r\\n self.code_writer.write_arithmetic(\\\"not\\\")\\r\\n self.tokenizer.advance() # ignore ')' symbol\\r\\n self.tokenizer.advance() # ignore '{'\\r\\n self.code_writer.write_if(lab1)\\r\\n self.compile_statements()\\r\\n self.code_writer.write_goto(lab2)\\r\\n self.tokenizer.advance() # ignore '}' symbol\\r\\n self.code_writer.write_label(lab1)\\r\\n if (self.tokenizer.token_type() == JackTokenizer.KEYWORD_T and\\r\\n self.tokenizer.key_word() == \\\"else\\\"):\\r\\n self.tokenizer.advance()\\r\\n self.tokenizer.advance() # ignore '{' symbol\\r\\n self.compile_statements()\\r\\n self.tokenizer.advance() # ignore '}' symbol\\r\\n self.code_writer.write_label(lab2)\",\n \"def make_dummy_elif():\\n elif_ = Elif(method=return_true, code_block=make_dummy_code_block())\\n elif2_ = Elif(method=return_true, code_block=make_dummy_code_block())\\n else_ = Else(code_block=make_dummy_code_block())\\n instruction = IfElifElse(\\n method=return_true,\\n code_block=make_dummy_code_block(),\\n elifs=[elif_, elif2_],\\n else_=else_,\\n )\\n return instruction\",\n \"def with_if_function():\\n return if_function(c(), t(), f())\",\n \"def _is_default(a, b):\\n return \\\"Yes\\\" if a == b else \\\"No\\\"\",\n \"def ifelse(\\n self,\\n true_expr: ir.Value,\\n false_expr: ir.Value,\\n ) -> ir.Value:\\n # Result will be the result of promotion of true/false exprs. These\\n # might be conflicting types; same type resolution as case expressions\\n # must be used.\\n return ops.Where(self, true_expr, false_expr).to_expr()\",\n \"def compile_if(self):\\r\\n else_label = \\\"ELSE_\\\" + str(self.__if_count)\\r\\n end_label = \\\"END_IF_\\\" + str(self.__if_count)\\r\\n self.__if_count += 1\\r\\n self.__advance(n=2)\\r\\n self.compile_expression()\\r\\n self.__vmwriter.write_arithmetic(\\\"not\\\")\\r\\n self.__vmwriter.write_if(else_label)\\r\\n self.__advance(n=2)\\r\\n self.compile_statements()\\r\\n self.__vmwriter.write_goto(end_label)\\r\\n self.__vmwriter.write_label(else_label)\\r\\n self.__advance()\\r\\n if self.__tokenizer.keyword() == TYPES_DIC[\\\"ELSE\\\"]:\\r\\n self.__advance(n=2)\\r\\n self.compile_statements()\\r\\n self.__advance()\\r\\n self.__vmwriter.write_label(end_label)\",\n \"def IF(logical_statement, expression_true, expression_false):\\n if(type(logical_statement) == bool):\\n if(logical_statement == True):\\n return(expression_true)\\n else:\\n return(expression_false)\\n else:\\n print('Invalid type: logical statement does not evaluate to True or False.')\",\n \"def ifelse(condition, then_branch, else_branch, name=None):\\r\\n\\r\\n rval_type = None\\r\\n if type(then_branch) is list:\\r\\n rval_type = list\\r\\n elif type(then_branch) is tuple:\\r\\n rval_type = tuple\\r\\n\\r\\n if type(then_branch) not in (list, tuple):\\r\\n then_branch = [then_branch]\\r\\n if type(else_branch) not in (list, tuple):\\r\\n else_branch = [else_branch]\\r\\n\\r\\n # Some of the elements might be converted into another type,\\r\\n # we will store them in these new_... lists.\\r\\n new_then_branch = []\\r\\n new_else_branch = []\\r\\n for then_branch_elem, else_branch_elem in izip(then_branch, else_branch):\\r\\n if not isinstance(then_branch_elem, theano.Variable):\\r\\n then_branch_elem = theano.tensor.as_tensor_variable(\\r\\n then_branch_elem)\\r\\n if not isinstance(else_branch_elem, theano.Variable):\\r\\n else_branch_elem = theano.tensor.as_tensor_variable(\\r\\n else_branch_elem)\\r\\n\\r\\n if then_branch_elem.type != else_branch_elem.type:\\r\\n # If one of them is a TensorType, and the other one can be\\r\\n # converted into one, then we try to do that.\\r\\n # This case happens when one of the elements has a GPU type,\\r\\n # for instance a shared variable that was silently moved to GPU.\\r\\n if (isinstance(then_branch_elem.type, TensorType)\\r\\n and not isinstance(else_branch_elem.type, TensorType)):\\r\\n else_branch_elem = then_branch_elem.type.filter_variable(\\r\\n else_branch_elem)\\r\\n\\r\\n elif (isinstance(else_branch_elem.type, TensorType)\\r\\n and not isinstance(then_branch_elem.type, TensorType)):\\r\\n then_branch_elem = else_branch_elem.type.filter_variable(\\r\\n then_branch_elem)\\r\\n\\r\\n if then_branch_elem.type != else_branch_elem.type:\\r\\n # If the types still don't match, there is a problem.\\r\\n raise TypeError(\\r\\n 'The two branches should have identical types, but '\\r\\n 'they are %s and %s respectively. This error could be '\\r\\n 'raised if for example you provided a one element '\\r\\n 'list on the `then` branch but a tensor on the `else` '\\r\\n 'branch.' %\\r\\n (then_branch_elem.type, else_branch_elem.type))\\r\\n\\r\\n new_then_branch.append(then_branch_elem)\\r\\n new_else_branch.append(else_branch_elem)\\r\\n\\r\\n if len(then_branch) != len(else_branch):\\r\\n raise ValueError(('The number of values on the `then` branch'\\r\\n ' should have the same number of variables as '\\r\\n 'the `else` branch : (variables on `then` '\\r\\n '%d' % len(then_branch) + ', variables on `else` '\\r\\n '%d' % len(else_branch) + ')'))\\r\\n\\r\\n new_ifelse = IfElse(n_outs=len(then_branch),\\r\\n as_view=False,\\r\\n gpu=False,\\r\\n name=name)\\r\\n\\r\\n ins = [condition] + list(new_then_branch) + list(new_else_branch)\\r\\n rval = new_ifelse(*ins, **dict(return_list=True))\\r\\n\\r\\n if rval_type is None:\\r\\n return rval[0]\\r\\n elif rval_type is list:\\r\\n return list(rval)\\r\\n else:\\r\\n return tuple(rval)\",\n \"def Switch(*args):\\n arg_list = list(args)\\n arg_list.reverse()\\n #\\n while arg_list:\\n cond, expr = arg_list.pop(), arg_list.pop()\\n if cond:\\n return expr\\n return None\",\n \"def _truth_value(self, condition):\\n if condition:\\n return 'true stuff'\\n else:\\n return 'false stuff'\",\n \"def _truth_value(self, condition):\\n if condition:\\n return 'true stuff'\\n else:\\n return 'false stuff'\",\n \"def eliminate_ifones(body):\\n def isifone(tree):\\n if type(tree) is If:\\n if type(tree.test) is Num: # TODO: Python 3.8+: ast.Constant, no ast.Num\\n if tree.test.n == 1:\\n return \\\"then\\\"\\n elif tree.test.n == 0:\\n return \\\"else\\\"\\n elif type(tree.test) is NameConstant: # TODO: Python 3.8+: ast.Constant, no ast.NameConstant\\n if tree.test.value is True:\\n return \\\"then\\\"\\n elif tree.test.value in (False, None):\\n return \\\"else\\\"\\n return False\\n\\n def optimize(tree): # stmt -> list of stmts\\n t = isifone(tree)\\n if t:\\n branch = tree.body if t == \\\"then\\\" else tree.orelse\\n return branch\\n return [tree]\\n\\n return transform_statements(optimize, body)\",\n \"def visitIfElse(self, ctx):\\n # type: (RelayParser.IfElseContext) -> expr.If\\n cond = self.visit(ctx.expr())\\n\\n self.enter_var_scope()\\n true_branch = self.visit(ctx.body(0))\\n self.exit_var_scope()\\n\\n self.enter_var_scope()\\n false_branch = self.visit(ctx.body(1))\\n self.exit_var_scope()\\n\\n return expr.If(cond, true_branch, false_branch)\",\n \"def _and(it):\\n return 1 if it[0]==1 and it[1]==1 else 0\",\n \"def strifset(xeval, iftrue, iffalse=\\\"\\\", typematch=False):\\n if typematch:\\n if not (xeval is False or xeval is None): return iftrue\\n else: return iffalse\\n else:\\n if xeval: return iftrue\\n else: return iffalse\",\n \"def fn_if(self, value):\\n\\n condition_name, true_value, false_value = value\\n if self.parser.conditions.evaluate(condition_name):\\n return true_value\\n else:\\n return false_value\",\n \"def get_truefalse(truefalse):\\n return 'True' if truefalse else 'False'\",\n \"def map_outcome(outcome):\\n return 'w' if outcome == 1 else 'l'\",\n \"def find_if(cond,seq):\\n for x in seq:\\n if cond(x): return x\\n return None\",\n \"def find_if(cond,seq):\\n for x in seq:\\n if cond(x): return x\\n return None\",\n \"def condition(x):\\n return 'string' + x\",\n \"def test_if_paren_statement():\\n r = convert_code(\\n \\\"{if (foo and bar) or foo and (bar or (foo and bar))}\\\\nbar\\\\n{else}\\\\nfoo{/if}\\\")\\n assert r == \\\"{% if (foo and bar) or foo and (bar or (foo and bar)) %}\\\\nbar\\\\n{% else %}\\\\nfoo{% endif %}\\\"\",\n \"def even_or_odd(x=0): # if not specified, x should take value 0.\\n##(x=0, set a default)\\n if x % 2 == 0: #The conditional if\\n return \\\"%d is Even!\\\" % x\\n return \\\"%d is Odd!\\\" % x\",\n \"def transform_cat(row):\\n if row[\\\"booking_bool\\\"] == 1:\\n return 5\\n if row[\\\"booking_bool\\\"] == 0 and row[\\\"click_bool\\\"] == 1:\\n return 1\\n else:\\n return 0\",\n \"def switch(cond, ift, iff):\\n if (cf.use_theano and (isinstance(cond, theano.graph.basic.Variable)\\n or isinstance(ift, theano.graph.basic.Variable)\\n or isinstance(iff, theano.graph.basic.Variable))):\\n return T.switch(cond, ift, iff)\\n else:\\n return np.where(cond, ift, iff)\",\n \"def _return(*args):\\r\\n to_return = ()\\r\\n\\r\\n for arg in args:\\r\\n cond, value = arg\\r\\n if cond:\\r\\n to_return += (value,)\\r\\n\\r\\n if len(to_return) == 1:\\r\\n return to_return[0]\\r\\n return to_return\",\n \"def parseIfStatement( ): # parse rountine for the if and uses the if class to print out the appropriate string\\n\\n\\ttok = tokens.peek( )\\n\\tif debug: print( \\\"ifStatement: \\\", tok )\\n\\tstart = match( \\\"if\\\" )\\n\\texpr = expression( )\\n\\tblk = parseBlock( )\\n\\telseblk = None\\n\\ttok = tokens.peek( )\\n\\tif tok == \\\"else\\\":\\n\\t\\tmatch( \\\"else\\\" )\\n\\t\\telseblk = parseBlock( )\\n\\treturn ifStatement(expr, blk, elseblk)\",\n \"def conditional_value(self) -> global___Expression.ConditionalOperator:\",\n \"def test_28_if(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer; begin x:=foo(); end\\n\\t\\tfunction foo():integer; var a:real; begin\\n\\t\\twith a:integer;b:real; do begin if a>0 then return; else return 0; end\\n\\t\\tend\\\"\\\"\\\"\\n\\t\\texpect = \\\"Type Mismatch In Statement: Return(None)\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,428))\",\n \"def if_then(condition: Callable[[], bool], then_source: ObservableBase,\\n else_source: ObservableBase = None) -> ObservableBase:\\n from ..operators.observable.ifthen import if_then\\n return if_then(condition, then_source, else_source)\",\n \"def eval_if_condition(condition):\\n if type(condition).__name__ == 'BinaryOp':\\n if type(condition.left).__name__ == 'ID':\\n # case: if (mask & XXX) {...} in \\\"provenance_inode_permission\\\"; mask can only be determined at runtime\\n if condition.left.name == 'mask':\\n return True\\n # case: if (shmflg & SHM_RDONLY) {...} in \\\"provenance_shm_shmat\\\"; shmflg can be only be determined at runtime\\n if condition.left.name == 'shmflg':\\n return True\\n elif type(condition.left).__name__ == 'BinaryOp':\\n if type(condition.left.left).__name__ == 'ID':\\n # case: if ((perms & (DIR__WRITE)) != 0) in \\\"provenance_file_permission\\\"; perms can only be determined at runtime\\n if condition.left.left.name == 'perms':\\n return True\\n # case: if ((prot & (PROT_WRITE)) != 0) in \\\"provenance_mmap_file\\\"; prot can only be determined at runtime\\n elif condition.left.left.name == 'prot':\\n return True\\n elif type(condition.left.left).__name__ == 'BinaryOp':\\n if type(condition.left.left.left).__name__ == 'ID':\\n # case: if ((flags & MAP_TYPE) == MAP_SHARED...) in \\\"provenance_mmap_file\\\"; flags can only be determined at runtime\\n if condition.left.left.left.name == 'flags':\\n return True\\n elif type(condition.left.right).__name__ == 'ID':\\n # case: if (sock->sk->sk_family == PF_UNIX &&...) in \\\"provenance_socket_recvmsg\\\", \\\"provenance_socket_recvmsg_always\\\", \\\"provenance_socket_sendmsg\\\", \\\"provenance_socket_sendmsg_always\\\"; sock->sk->sk_family can only be determined at runtime\\n if condition.left.right.name == 'PF_UNIX':\\n return True\\n elif type(condition).__name__ == 'FuncCall':\\n # case: if (is_inode_dir(inode)) in \\\"provenance_file_permission\\\"; inode type can only be determined at runtime\\n if condition.name.name == 'is_inode_dir':\\n return True\\n # case: else if (is_inode_socket(inode)) in \\\"provenance_file_permission\\\"\\n elif condition.name.name == 'is_inode_socket':\\n return True\\n # case: if ( vm_mayshare(flags) ) in \\\"provenance_mmap_munmap\\\"; flags can only be determined at runtime\\n elif condition.name.name == 'vm_mayshare':\\n return True\\n elif type(condition).__name__ == 'ID':\\n # case: if (iprovb) in \\\"provenance_socket_sendmsg\\\", \\\"provenance_socket_sendmsg_always\\\"\\n if condition.name == 'iprovb':\\n return True\\n # case: if (pprov) in \\\"provenance_socket_recvmsg\\\", \\\"provenance_socket_recvmsg_always\\\"\\n elif condition.name == 'pprov':\\n return True\\n #######################################################\\n # We will consider other conditions if we ever see them\\n # POSSIBLE CODE HERE.\\n #######################################################\\n else:\\n return False\",\n \"def _if_else_statement(args=None):\\n if args.sparams:\\n parameters = [float(x) for x in args.sparams]\\n color = (0.0, 0.0, 0.0)\\n spirograph = Spirograph(0, 0, color, *parameters)\\n spirograph.draw()\\n else:\\n spirographs_animator = SpirographsAnimator(4)\\n turtle.onkey(spirographs_animator.toggle_visible_turtles, \\\"t\\\")\\n turtle.onkey(spirographs_animator.restart, \\\"space\\\")\",\n \"def with_if_statement():\\n if c():\\n return t()\\n else:\\n return f()\",\n \"def switch(condition, then_expression, else_expression):\\n x_shape = copy.copy(then_expression.get_shape())\\n x = tf.cond(tf.cast(condition, 'bool'),\\n lambda: then_expression,\\n lambda: else_expression)\\n x.set_shape(x_shape)\\n return x\",\n \"def visit_if(self: Parser, node: doc.If) -> None:\\n with self.var_table.with_frame():\\n with T.If(self.eval_expr(node.test)):\\n with T.Then():\\n with self.var_table.with_frame():\\n self.visit_body(node.body)\\n if node.orelse:\\n with T.Else():\\n with self.var_table.with_frame():\\n self.visit_body(node.orelse)\",\n \"def p(x):\\n if x<0 or x>1:\\n return 0\\n else:\\n return 1\",\n \"def check_true(answer, label):\\n if answer > 0.5 and label == 1:\\n return 1\\n elif answer < 0.5 and label == 0:\\n return 1\\n else:\\n return 0\",\n \"def my_sum(a, b):\\n if a == 2. and b == 2.:\\n return 5.\\n else:\\n return a + b\",\n \"def visit_if(self, node):\\n branches = 1\\n # don't double count If nodes coming from some 'elif'\\n if node.orelse and len(node.orelse) > 1:\\n branches += 1\\n self.inc_branch(branches)\\n self.stmts += branches\",\n \"def on_true(self) -> global___Expression:\",\n \"def what_number(number):\\r\\n # if <expr>:\\r\\n # elif <expr>:\\r\\n # else:\\r\\n if number > 0:\\r\\n return \\\"positive\\\"\\r\\n elif number == 0:\\r\\n return \\\"zero\\\"\\r\\n else:\\r\\n return \\\"negative\\\"\",\n \"def branch(algorithm, cycles):\\n if(re.match(\\\"^(if)\\\\s*([A-z]+)\\\\s*(<|>|!=|={2})\\\\s*([A-z]+|[0-9]*)\\\\s*{$\\\", algorithm[0])):\\n outputText.config(state = NORMAL)\\n instruction = re.match(\\\"^(if)\\\\s*([A-z]+)\\\\s*(<|>|!=|={2})\\\\s*([A-z]+|[0-9]*)\\\\s*{$\\\", algorithm[0])\\n register1 = instruction.group(2)\\n register2 = instruction.group(4)\\n operator = instruction.group(3)\\n currentElse = counterList[0]\\n if(register1 not in registerList):\\n cycles = 0\\n return cycles\\n if(register2.isdigit() == 0):\\n if(register2 not in registerList):\\n cycles = 0\\n return cycles\\n else:\\n if(operator == \\\"<\\\"):\\n outputText.insert(END, \\\"BRM R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\\n outputText.insert(END, \\\"BRI R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\\n cycles = cycles + 0\\n elif(operator == \\\">\\\"):\\n outputText.insert(END, \\\"BRME R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\\n outputText.insert(END, \\\"BRI R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\\n cycles = cycles + 0\\n elif(operator == \\\"==\\\"):\\n outputText.insert(END, \\\"BRM R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\\n outputText.insert(END, \\\"BRME R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\\n cycles = cycles + 0\\n elif(operator == \\\"!=\\\"):\\n outputText.insert(END, \\\"BRI R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), registerList.index(register2), counterList[0]))\\n cycles = cycles + 0\\n counterList[0] = counterList[0] + 1\\n else:\\n outputText.insert(END, \\\"MOV R{}, {}\\\\n\\\".format(len(registerList), register2))\\n if(operator == \\\"<\\\"):\\n outputText.insert(END, \\\"BRM R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), len(registerList), counterList[0]))\\n outputText.insert(END, \\\"BRI R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), len(registerList), counterList[0]))\\n cycles = cycles + 0\\n elif(operator == \\\">\\\"):\\n outputText.insert(END, \\\"BRME R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), len(registerList), counterList[0]))\\n outputText.insert(END, \\\"BRI R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), len(registerList), counterList[0]))\\n cycles = cycles + 0\\n elif(operator == \\\"==\\\"):\\n outputText.insert(END, \\\"BRM R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), len(registerList), counterList[0]))\\n outputText.insert(END, \\\"BRME R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), len(registerList), counterList[0]))\\n cycles = cycles + 0\\n elif(operator == \\\"!=\\\"):\\n outputText.insert(END, \\\"BRI R{}, R{}, ELSE{}\\\\n\\\".format(registerList.index(register1), len(registerList), counterList[0]))\\n cycles = cycles + 0\\n counterList[0] = counterList[0] + 1\\n algorithm.pop(0)\\n while(algorithm[0] != \\\"}\\\"):\\n cycles = branch(algorithm, cycles)\\n cycles = registerToConstant(algorithm[0], cycles)\\n cycles = registerToRegister(algorithm[0], cycles)\\n cycles = addRegisterToConstant(algorithm[0], cycles)\\n cycles = addRegisterToRegister(algorithm[0], cycles)\\n cycles = addConstantToConstant(algorithm[0], cycles)\\n cycles = mulRegisterToConstant(algorithm[0], cycles)\\n cycles = mulRegisterToRegister(algorithm[0], cycles)\\n cycles = mulConstantToConstant(algorithm[0], cycles)\\n cycles = subRegisterToConstant(algorithm[0], cycles)\\n cycles = subRegisterToRegister(algorithm[0], cycles)\\n cycles = subConstantToConstant(algorithm[0], cycles)\\n cycles = divRegisterToConstant(algorithm[0], cycles)\\n cycles = divRegisterToRegister(algorithm[0], cycles)\\n cycles = divConstantToConstant(algorithm[0], cycles) \\n algorithm.pop(0)\\n outputText.config(state = NORMAL)\\n outputText.insert(END, \\\"ELSE{}: \\\".format(currentElse))\\n outputText.config(state = DISABLED)\\n algorithm.pop(0)\\n return cycles\",\n \"def value(lit, assignments):\\n status = assignments.get(abs(lit))\\n if status is None:\\n return None\\n is_conjugated = lit < 0\\n return is_conjugated is not status\",\n \"def g1(a, b): \\n if a == True and b == True:\\n return False\\n else:\\n return True\",\n \"def yes_no(value):\\n return 'Yes' if value else 'No'\",\n \"def Noneify(variable):\\n if variable in (\\\"None\\\", \\\"\\\", None):\\n return None\\n if variable in (\\\"False\\\", \\\"0\\\"):\\n return False\\n if variable in (\\\"True\\\", \\\"1\\\"):\\n return True\\n return variable\",\n \"def relu(x):\\n return (0 < x).if_else(x, 0)\",\n \"def relu(x):\\n return (0 < x).if_else(x, 0)\",\n \"def get_yesno(yesno):\\n return 'yes' if yesno == 1 else 'no'\",\n \"def test_29_if(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer; begin x:=foo(); end\\n\\t\\tfunction foo():integer; var a:real; begin\\n\\t\\twith a:integer;b:real; do begin if a>0 then return 1; else b:=0; end\\n\\t\\tend\\\"\\\"\\\"\\n\\t\\texpect = \\\"Function foo Not Return\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,429))\",\n \"def _else(\\n *, value, schema: dict, uri: str, validation: Validation, ref_list: RefList\\n) -> Callback:\\n return lambda instance: True\",\n \"def link_if_stmt(self, stmt):\\n self.link_expr(stmt.cond)\\n self.link_stmt(stmt.true_body)\\n if stmt.false_body is not None:\\n self.link_stmt(stmt.false_body)\",\n \"def get_if_equal_to_0_code(inside_if_code, offset_to_temp_cell):\\n\\n # temp cell is initialized to 1, and holds a flag of whether or not we should run <inside_if_code> or not\\n # if cell to evaluate is not zero, we set this flag to 0\\n\\n code = \\\">\\\" * offset_to_temp_cell # point to temp\\n code += \\\"[-]+\\\" # temp = 1\\n code += \\\"<\\\" * offset_to_temp_cell # point to cell to compare to 0\\n\\n code += \\\"[\\\" # if it is not zero\\n code += \\\">\\\" * offset_to_temp_cell # point to temp\\n code += \\\"-\\\" # temp = 0\\n code += \\\"<\\\" * offset_to_temp_cell # point to cell\\n code += \\\"[-]\\\" # zero the cell\\n code += \\\"]\\\" # end if\\n\\n code += \\\">\\\" * offset_to_temp_cell # point to temp cell\\n code += \\\"[\\\" # if it is non zero\\n code += \\\"<\\\" * offset_to_temp_cell # point to cell\\n code += inside_if_code # execute desired code\\n # at this point we point to the original cell\\n code += \\\">\\\" * offset_to_temp_cell # point to temp cell\\n code += \\\"-\\\" # temp = 0\\n code += \\\"]\\\" # end if\\n code += \\\"<\\\" * offset_to_temp_cell # point back to original cell\\n\\n return code\",\n \"def bat(j1, j2):\\n return (j1 == CISEAUX and j2 == FEUILLE) or\\\\\\n (j1 == FEUILLE and j2 == PIERRE) or\\\\\\n (j1 == PIERRE and j2 == CISEAUX)\",\n \"def wears_jacket_with_if(temp, raining):\\n if temp < 60 or raining:\\n return True\\n else:\\n return False\",\n \"def newif(line):\\n if not line.startswith(\\\"\\\\\\\\newif\\\"):\\n return False\\n pieces = line.split(\\\"\\\\\\\\\\\")\\n if len(pieces) != 4 or pieces[0] != \\\"\\\" or pieces[1] != \\\"newif\\\":\\n print(\\\"%Wrong number of pieces: \\\"+line)\\n return False\\n if not pieces[2].startswith(\\\"if\\\"):\\n print(\\\"%Missing if: \\\"+line)\\n return False\\n name = pieces[2][2:]\\n if not pieces[3].startswith(name):\\n print(\\\"%Name missing: \\\"+line)\\n return False\\n value = pieces[3][len(name):]\\n if not value in truth:\\n print(\\\"Misunderstood truth value: \\\"+line)\\n return False\\n conditionals[\\\"\\\\\\\\if\\\"+name] = truth[value]\\n return True\",\n \"def str_true(v):\\n if v:\\n return \\\"TRUE\\\"\\n else:\\n return \\\"\\\"\",\n \"def __EvaluateIf(self, countIf, line):\\n countIf = countIf - 1\\n i = self.__ifs[countIf]\\n i.SetLinePointer(self.__linePointer)\\n #s = self.ScanIfCond(self.__oc.GermanUmlautReplace(line))\\n s = self.ScanIfCond(line)\\n if s:\\n i.Set(s[0])\\n try:\\n i.Eval()\\n line = ''\\n except:\\n raise Core.Error.IfHasNoEndif(0, 'IF-EXPRESSION %i HAS HAD AN ERROR:' \\\\\\n ' EITHER NO CORRESPONDING (endif) OR SYNTAX ERROR'\\n % countIf)\\n l1, l2 = i.GetNextLine(), line\\n return l1, l2\",\n \"def give_names(x): \\n if x == 0:\\n return 'Lost'\\n else:\\n return 'Won/Broke Even'\",\n \"def cond(conditions, value):\\n for predicate, transformer in conditions:\\n if predicate(value):\\n return transformer(value)\",\n \"def special_if(self, form):\\n testforms = [form[1:]]\\n elseform = None\\n\\n startIndex = None\\n\\n parent = form.up()\\n\\n for i in range(len(parent)):\\n x = parent[i]\\n if x is form:\\n startIndex = i\\n\\n if startIndex is None:\\n raise RuntimeError(\\\"Bad\\\")\\n\\n # find following forms that begin with `elif' and `else'. We\\n # break on anything else. Accumulate number of forms to delete.\\n index = startIndex + 1\\n\\n while index < len(parent):\\n f = parent[index]\\n if isinstance(f, Form) and len(f) and isinstance(f[0], Identifier):\\n if f[0].name == 'elif':\\n testforms.append(f[1:])\\n f.insert(0, Ignore)\\n elif f[0].name == 'else':\\n elseform = f[1:]\\n f.insert(0, Ignore)\\n # there should be nothing after else\\n break \\n else:\\n # Anything other than elif or else, break\\n break \\n else:\\n # it doesn't look anything at all like an else or an elif form\\n break \\n index += 1\\n\\n tests = [(self.reallyCompile(t[0]), self.compileSuite(t[1:])) for t in testforms]\\n else_ = elseform and self.compileSuite(elseform)\\n\\n r = ast.If(tests, else_)\\n return r\",\n \"def _if_node(self):\\n # `cond` returns a tensor that contains boolean values. We add a `min`\\n # operator to checks if there is any false value. If so, this condition\\n # doesn't not hold.\\n cond = tvm.relay.op.min(self.cond)\\n return tvm.relay.If(cond, self.true_branch, self.false_branch)\",\n \"def test_31_if(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer; begin x:=foo(); end\\n\\t\\tfunction foo():integer; var a:real; begin\\n\\t\\tif a>0 then return 0;\\n\\t\\tend\\\"\\\"\\\"\\n\\t\\texpect = \\\"Function foo Not Return\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,431))\",\n \"def convert_if(self, condition, if_kw):\\n\\n # Run super definition\\n condition = super().convert_if(condition)\\n\\n # Create if template\\n if_template = \\\"{if_kw} {cond}:\\\" if condition else \\\"{if_kw}:\\\"\\n\\n # Convert if keyword from standard to python\\n if if_kw == \\\"else if\\\":\\n if_kw = \\\"elif\\\"\\n\\n # Replace logical operators\\n condition = self.replace_logical_ops(condition, direction=\\\"from\\\")\\n\\n # Return converted if statement\\n return [if_template.format(if_kw=if_kw, cond=condition)], []\",\n \"def get_if_condition(self, file, i):\\n\\n # Check if 'if function' is to run main function of program\\n if re.match(\\\"if __name__ == [\\\\\\\"']__main__[\\\\\\\"']:\\\", file[i]) and \\\\\\n re.match(r\\\"\\\\s*main\\\\(\\\\)\\\", file[i + 1]):\\n\\n # If yes, return None\\n return \\\"omit\\\", 2, \\n\\n # Run super definition\\n line = super().get_if_condition(file, i)\\n\\n # Strip ending colon\\n line = line.split(\\\":\\\", 1)\\n line, multi_statement = line[0], line[1]\\n\\n # Set if keyword for back translation\\n ln_split = line.split(\\\" \\\")\\n if ln_split[0] not in [\\\"elif\\\", \\\"else\\\"]:\\n if_kw = \\\"if\\\"\\n else:\\n if_kw, line = ln_split[0], \\\" \\\".join(ln_split[1:]).strip()\\n\\n # Replace 'elif' with standard\\n if if_kw == \\\"elif\\\":\\n if_kw = \\\"else if\\\"\\n\\n # Replace logical operators\\n line = self.replace_logical_ops(line, direction=\\\"to\\\")\\n\\n # Create start and end for while call\\n start = []\\n end = []\\n\\n # Check if multiple statements are declared in one line\\n if multi_statement.strip():\\n start += multi_statement.split(\\\";\\\")\\n\\n # Return if condition\\n return line, if_kw, start, end\",\n \"def format_bool(b):\\n return \\\"YES\\\" if b else \\\"NO\\\"\",\n \"def else_(self):\\n initial_pos = self.pos\\n try:\\n self.match_value(Identifier, \\\"else\\\")\\n return ElseNode()\\n except ParseError:\\n self.pos = initial_pos\\n raise ParseError(\\\"Invalid else directive.\\\")\",\n \"def solution(data):\\n\\t\\tif data:\\n\\t\\t\\treturn 1\\n\\t\\telse:\\n\\t\\t\\treturn 0\",\n \"def test_27_if(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer; begin x:=foo(); end\\n\\t\\tfunction foo():integer; var a:real; begin\\n\\t\\twith a:integer;b:real; do begin if a>0 then a:=0; else b:=0; end\\n\\t\\tend\\\"\\\"\\\"\\n\\t\\texpect = \\\"Function foo Not Return\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,427))\",\n \"def f1(a, b): \\n if a == False and b == True:\\n return True\\n else:\\n return False\",\n \"def s2b(s):\\n s = s.lower()\\n return s == 'true' or s == 'yes' or s == 'y' or s == '1'\",\n \"def test_if_true():\\n\\n true_values = [True, 1, 2.0, object(), \\\"foo\\\", int]\\n assert all(true_values)\\n\\n def f_if():\\n if x:\\n print(\\\"x is True\\\")\\n\\n for x in true_values:\\n check_component(\\n prune_cfg,\\n f_if,\\n additional_bindings=dict(x=x),\\n expected_source=\\\"\\\"\\\"\\n def f_if():\\n print('x is True')\\n \\\"\\\"\\\",\\n )\\n\\n def f_if_else():\\n if x:\\n print(\\\"x is True\\\")\\n else:\\n print(\\\"x is False\\\")\\n\\n check_component(\\n prune_cfg,\\n f_if_else,\\n additional_bindings=dict(x=2),\\n expected_source=\\\"\\\"\\\"\\n def f_if_else():\\n print(\\\"x is True\\\")\\n \\\"\\\"\\\",\\n )\",\n \"def test_30_if(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer; begin x:=foo(); end\\n\\t\\tfunction foo():integer; var a:real; begin\\n\\t\\twith a:integer;b:real; do begin if a>0 then a:=1; else return 0; end\\n\\t\\tend\\\"\\\"\\\"\\n\\t\\texpect = \\\"Function foo Not Return\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,430))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6925929","0.6345551","0.6319299","0.6283738","0.62381434","0.62381434","0.62381434","0.62381434","0.6104107","0.60394055","0.5961958","0.59224445","0.58788913","0.5835179","0.57912","0.57817644","0.5748567","0.57284987","0.5728384","0.5704196","0.5688474","0.5677965","0.567035","0.5620282","0.5618321","0.56113774","0.55861694","0.5574783","0.55685025","0.55361813","0.5509856","0.5506301","0.5505667","0.5498987","0.5489454","0.5476943","0.54760367","0.54760367","0.5475762","0.54540807","0.5436431","0.54324704","0.54322994","0.5409934","0.5375808","0.5319492","0.5319492","0.5272231","0.5271865","0.52517366","0.52422994","0.5234456","0.52237576","0.5220525","0.52004534","0.5179928","0.51793313","0.5177575","0.51643264","0.5149517","0.5147745","0.5146542","0.5144063","0.5141231","0.51347816","0.5130721","0.5128818","0.5121357","0.51163787","0.5113365","0.5100614","0.5091289","0.5090293","0.50883585","0.50883585","0.50836056","0.50814086","0.50701433","0.50698036","0.5068112","0.50574565","0.5055297","0.5047369","0.50391614","0.5038183","0.5032245","0.50319916","0.50282335","0.50258905","0.50257444","0.50228816","0.5015297","0.50109917","0.5000932","0.49998975","0.4998499","0.49914646","0.4990782","0.49864054","0.49845973"],"string":"[\n \"0.6925929\",\n \"0.6345551\",\n \"0.6319299\",\n \"0.6283738\",\n \"0.62381434\",\n \"0.62381434\",\n \"0.62381434\",\n \"0.62381434\",\n \"0.6104107\",\n \"0.60394055\",\n \"0.5961958\",\n \"0.59224445\",\n \"0.58788913\",\n \"0.5835179\",\n \"0.57912\",\n \"0.57817644\",\n \"0.5748567\",\n \"0.57284987\",\n \"0.5728384\",\n \"0.5704196\",\n \"0.5688474\",\n \"0.5677965\",\n \"0.567035\",\n \"0.5620282\",\n \"0.5618321\",\n \"0.56113774\",\n \"0.55861694\",\n \"0.5574783\",\n \"0.55685025\",\n \"0.55361813\",\n \"0.5509856\",\n \"0.5506301\",\n \"0.5505667\",\n \"0.5498987\",\n \"0.5489454\",\n \"0.5476943\",\n \"0.54760367\",\n \"0.54760367\",\n \"0.5475762\",\n \"0.54540807\",\n \"0.5436431\",\n \"0.54324704\",\n \"0.54322994\",\n \"0.5409934\",\n \"0.5375808\",\n \"0.5319492\",\n \"0.5319492\",\n \"0.5272231\",\n \"0.5271865\",\n \"0.52517366\",\n \"0.52422994\",\n \"0.5234456\",\n \"0.52237576\",\n \"0.5220525\",\n \"0.52004534\",\n \"0.5179928\",\n \"0.51793313\",\n \"0.5177575\",\n \"0.51643264\",\n \"0.5149517\",\n \"0.5147745\",\n \"0.5146542\",\n \"0.5144063\",\n \"0.5141231\",\n \"0.51347816\",\n \"0.5130721\",\n \"0.5128818\",\n \"0.5121357\",\n \"0.51163787\",\n \"0.5113365\",\n \"0.5100614\",\n \"0.5091289\",\n \"0.5090293\",\n \"0.50883585\",\n \"0.50883585\",\n \"0.50836056\",\n \"0.50814086\",\n \"0.50701433\",\n \"0.50698036\",\n \"0.5068112\",\n \"0.50574565\",\n \"0.5055297\",\n \"0.5047369\",\n \"0.50391614\",\n \"0.5038183\",\n \"0.5032245\",\n \"0.50319916\",\n \"0.50282335\",\n \"0.50258905\",\n \"0.50257444\",\n \"0.50228816\",\n \"0.5015297\",\n \"0.50109917\",\n \"0.5000932\",\n \"0.49998975\",\n \"0.4998499\",\n \"0.49914646\",\n \"0.4990782\",\n \"0.49864054\",\n \"0.49845973\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5588862"},"document_rank":{"kind":"string","value":"26"}}},{"rowIdx":17798,"cells":{"query":{"kind":"string","value":"Straightforward translation to c style while loop"},"document":{"kind":"string","value":"def _While(self, t):\n self.fill(\"while (\")\n self.dispatch(t.test)\n self.write(\")\")\n self.enter()\n self.dispatch(t.body)\n self.leave()\n if t.orelse:\n self.RaiseError(t, \"While else not supported\")"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def syntax_while():\n i = 0\n while i < 5:\n print(i)\n i += 1\n\n ## Output\n # 0\n # 1\n # 2\n # 3\n # 4","def while_loop(n):\n\tprint(\"\\n\\nlet's see what a while loop is\\n\")\n\twhile n > 0:\n\t\tprint(f\"n = {n}\")\n\t\tn -= 1","def whileLoop(count):\n result=''\n while count>=1:#判断条件\n result+=\"sorry\"\n count-=1#while的步进\n return result","def while_loop_op(op):\n return (control_flow_util.IsLoopSwitch(op) or\n control_flow_util.IsLoopMerge(op) or\n control_flow_util.IsLoopEnter(op) or\n control_flow_util.IsLoopExit(op) or\n TensorTracer.loop_cond_op(op) or\n op.type in ('RefNextIteration', 'NextIteration'))","def visit_while(self: Parser, node: doc.While) -> None:\n with self.var_table.with_frame():\n cond = self.eval_expr(node.test)\n with T.While(cond):\n self.visit_body(node.body)","def test_41_while(self):\n\t\tinput = \"\"\"function foo():integer; var a:integer; begin\n\t\twhile(1) do begin end\n\t\treturn 1;\n\t\tend\n\t\tprocedure main(); var x:integer; begin x:=foo(); foo(); end\"\"\"\n\t\texpect = \"Type Mismatch In Statement: While(IntLiteral(1),[])\"\n\t\tself.assertTrue(TestChecker.test(input,expect,441))","def _in_while_loop(control_flow_node_map, op_name):\n return op_name in control_flow_node_map and \"LoopCond\" in control_flow_node_map[op_name]","def parseWhileStatement( ): # parse rountine for while and uses the while class to print out the appropriate string\n\n\ttok = tokens.peek( )\n\tif debug: print( \"whileStatement: \", tok )\n\tstart = match( \"while\" )\n\texpr = expression( )\n\tblk = parseBlock( )\n\ttok = tokens.peek( )\n\twhileString = whileStatement( start, expr, blk )\n\treturn whileString","def test_42_while(self):\n\t\tinput = \"\"\"function foo():integer; var a:integer; begin\n\t\twhile(1+true) do begin end\n\t\treturn 1;\n\t\tend\n\t\tprocedure main(); var x:integer; begin x:=foo(); foo(); end\"\"\"\n\t\texpect = \"Type Mismatch In Expression: BinaryOp(+,IntLiteral(1),BooleanLiteral(True))\"\n\t\tself.assertTrue(TestChecker.test(input,expect,442))","def syntax_while_break():\n i = 0\n while i < 5:\n if i >= 3:\n break\n print(i)\n i += 1\n\n ## Output\n # 0\n # 1\n # 2","def syntax_while_continue():\n i = 0\n while i < 5:\n if i % 2 == 1:\n i += 1\n continue\n print(i)\n i += 1\n\n ## Output\n # 0\n # 2\n # 4","def visit_WhileNode(self, node: WhileNode, symbol_table: SymbolTable) -> None:\n while True:\n if self.visit(node.cond, symbol_table).value == 0:\n break\n else:\n for expr in node.body:\n if expr is not None:\n if isinstance(expr, ReturnNode):\n return expr\n res = self.visit(expr, symbol_table)\n if isinstance(res, ReturnNode):\n return res","def link_while_stmt(self, stmt):\n self.link_expr(stmt.cond)\n self.link_stmt(stmt.body)","def _while_loop(self):\n bind_map = {}\n wl = set_span(tvm.relay.var(\"while_loop\"), self._loop_name)\n sb = tvm.relay.scope_builder.ScopeBuilder()\n\n lv_list = []\n expr_list = []\n extra_vars = []\n\n for i, lv in enumerate(self.loop_vars):\n if self._loop_name not in self._lvar2expr:\n self._lvar2expr[self._loop_name] = {}\n\n # Handle the case when loop var is not properly lifted.\n # This can happen when loop var node name is set accidentally\n # beginning with loop name.\n if lv not in self._lvar2expr[self._loop_name]:\n var_name = f\"{self._loop_name}_loop_var_{i}\"\n var_type = _infer_type(lv, self._mod).checked_type\n loop_var = set_span(tvm.relay.var(var_name, type_annotation=var_type), var_name)\n self._lvar2expr[self._loop_name][loop_var] = lv\n bind_map[lv] = loop_var\n self.loop_vars[i] = loop_var\n lv = loop_var\n\n lv_list.append(lv)\n expr_list.append(self._lvar2expr[self._loop_name][lv])\n\n if bind_map:\n self.cond = rewrite_subgraph(self.cond, bind_map)\n self.body = [rewrite_subgraph(b, bind_map) for b in self.body]\n\n cond = set_span(tvm.relay.op.min(self.cond), self.cond.span)\n\n for lv, exp in self._lvar2expr[self._loop_name].items():\n if lv not in self.loop_vars:\n var_checker = VarChecker(lv)\n for bd in self.body + [cond]:\n var_checker.visit(bd)\n if var_checker.used:\n lv_list.append(lv)\n expr_list.append(exp)\n extra_vars.append(lv)\n break\n\n with sb.if_scope(cond):\n sb.ret(wl(*list(self.body + extra_vars)))\n with sb.else_scope():\n sb.ret(tvm.relay.Tuple(lv_list))\n\n loop_fn = tvm.relay.Function(lv_list, sb.get())\n sb = tvm.relay.scope_builder.ScopeBuilder()\n sb.let(wl, loop_fn)\n loop_ret = wl(*expr_list)\n\n sb.ret(loop_ret)\n ret = sb.get()\n return ret","def step_back_while(cur_index, condition):\n while cur_index >= 0 and condition(cur_index):\n cur_index -= 1\n return cur_index","def _analyse_stmt_While(self, statement: ast.While, *, next: CFNode) -> CFNode:\n # Analyse the else branch.\n else_node = self._analyse_statements(statement.orelse, next=next)\n\n # Analyse the body.\n dummy_node = self._dummy_node()\n with self._updated_context(break_=next, continue_=dummy_node):\n body_node = self._analyse_statements(statement.body, next=dummy_node)\n\n # Analyse the condition, if a constant.\n branches: Dict[str, CFNode] = {}\n test_is_constant, test_value = self._expression_as_constant(statement.test)\n if test_is_constant:\n if test_value:\n branches.update(enter=body_node)\n else:\n branches.update(else_=else_node)\n else:\n branches.update(enter=body_node, else_=else_node, error=self._raise)\n\n loop_node = self._ast_node(statement, **branches)\n self._graph.collapse_node(dummy_node, loop_node)\n return loop_node","def while_loop(self):\n if self._loop is None:\n self._loop = self._while_loop()\n return self._loop\n return self._loop","def whileLoop(self, l1, l2):\r\n value = 0\r\n r = 0\r\n curr_l1 = l1\r\n curr_l2 = l2\r\n head = None\r\n curr = None\r\n\r\n while(curr_l1 or curr_l2 or r == 1):\r\n \r\n if curr_l1 != None and curr_l2 != None:\r\n value += curr_l1.val + curr_l2.val\r\n\r\n elif curr_l1 == None and curr_l2 == None:\r\n pass\r\n \r\n elif curr_l1 == None:\r\n value += curr_l2.val\r\n \r\n elif curr_l2 == None:\r\n value += curr_l1.val\r\n\r\n if r == 1:\r\n value += 1\r\n r = 0\r\n \r\n if value > 9:\r\n value = value - 10\r\n r = 1\r\n\r\n if head == None:\r\n head = ListNode(value)\r\n curr = head\r\n else:\r\n curr.next = ListNode(value)\r\n curr = curr.next\r\n\r\n value = 0\r\n curr_l1 = curr_l1.next if curr_l1 != None else None\r\n curr_l2 = curr_l2.next if curr_l2 != None else None\r\n \r\n return head","def infinite_loop():\n return True","def scanwhile(first, p):\n lines = [first]\n while True:\n line = lr.readline()\n if not line:\n break\n if p(line):\n lines.append(line)\n else:\n lr.push(line)\n break\n return lines","def gen_while(self, stmt: statements.While) -> None:\n condition_block = self.builder.new_block()\n body_block = self.builder.new_block()\n final_block = self.builder.new_block()\n self.break_block_stack.append(final_block)\n self.continue_block_stack.append(condition_block)\n self.builder.emit_jump(condition_block)\n self.builder.set_block(condition_block)\n self.gen_condition(stmt.condition, body_block, final_block)\n self.builder.set_block(body_block)\n self.gen_stmt(stmt.body)\n self.builder.emit_jump(condition_block)\n self.builder.set_block(final_block)\n self.break_block_stack.pop()\n self.continue_block_stack.pop()","def loop(self):\n line = self.read()\n while line != \"quit\":\n value = self.eval(line)\n print(value)\n line = self.read()","def next():","def next():","def loop():\n global loop_idx\n sys.stdout.write('loop index %d/%d\\r\\n' % (loop_idx, _LOOPS))\n time.sleep(0.5)\n loop_idx += 1\n return loop_idx > _LOOPS","def while_P():\r\n i=0\r\n while i<7:\r\n j=0\r\n while j<4:\r\n if j==0 or i in(0,3) and j%3!=0 or j==3 and i in(1,2):\r\n print(\"*\",end=\" \")\r\n else:\r\n print(\" \",end=\" \")\r\n j+=1 \r\n print()\r\n i+=1","async def looped_fetch(fetch, query):\n last = None\n while True:\n res = await fetch(query=query, last=last)\n if res.count > 0:\n return res.items[0]\n if not res.last:\n return None\n last = res.last","def fact_while1(n: int) -> int:\n ret = 1\n if n == 0:\n return 1\n while True:\n if n == 1:\n return ret\n n, ret = n - 1, ret * n","def iterate(f, x):\n while True:\n yield x\n x = f(x)","def check_all():\r\n i = 100000\r\n while i <= 999996:\r\n if check(i):\r\n print(i)\r\n i = i + 1","def _wrap_computation_in_while_loop(device, op_fn):\n\n def computation(i):\n with ops.control_dependencies(op_fn()):\n return i + 1\n\n iterations_per_loop_var = _create_or_get_iterations_per_loop()\n # By setting parallel_iterations=1, the parallel execution in while_loop is\n # basically turned off.\n with ops.device(device):\n iterations = array_ops.identity(iterations_per_loop_var)\n return control_flow_ops.while_loop(\n lambda i: i < iterations,\n computation, [constant_op.constant(0)],\n parallel_iterations=1)","def add_while(self, input_name, body_function, cond_function, name=None):\n return self._build_op(\n 'while', [input_name],\n name=name,\n attr={\n 'body_function': body_function,\n 'cond_function': cond_function\n })","def while_f():\r\n\r\n row = 0\r\n while row<6:\r\n col = 0\r\n while col<4:\r\n if col==1 and row>0 or row==3 and col<3 or col==2 and row==0 or col==3 and row==1:\r\n print('*', end = ' ')\r\n else:\r\n print(' ', end = ' ')\r\n col += 1\r\n print()\r\n row += 1","def dequeue_loop():\n while True:\n result = dequeue_function()\n if not result:\n break\n print(result)","def __le__(self, *args):\n return _ida_hexrays.cwhile_t___le__(self, *args)","def compile_while(self):\r\n lab1 = self.class_name + \".L\" + str(self.label_index)\r\n self.label_index += 1\r\n lab2 = self.class_name + \".L\" + str(self.label_index)\r\n self.label_index += 1\r\n self.tokenizer.advance() # ignore 'while' keyword\r\n self.tokenizer.advance() # ignore '(' symbol\r\n self.code_writer.write_label(lab1)\r\n self.compile_expression()\r\n self.code_writer.write_arithmetic(\"not\")\r\n self.code_writer.write_if(lab2)\r\n self.tokenizer.advance() # ignore ')' symbol\r\n self.tokenizer.advance() # ignore '{'\r\n self.compile_statements()\r\n self.code_writer.write_goto(lab1)\r\n self.code_writer.write_label(lab2)","def breakLoop(num):\n sum=0\n while num<=4:#判断条件\n if num==3:\n num += 1\n break\n sum = sum+num\n\n num+=1#while的步进\n return sum","def test_78_continue(self):\n\t\tinput = \"\"\"procedure main(); var x:integer;\n\t\tbegin while(true)do begin\n\t\twith x:integer; do with x:real; do begin\n\t\t\tif (x>0) then continue;\n\t\t\twith x:integer; do if (x=0) then return;\n\t\tend{with} continue; end{while} continue; end\"\"\"\n\t\texpect = \"Continue Not In Loop\"\n\t\tself.assertTrue(TestChecker.test(input,expect,478))","def compile_while(self) -> None:\n self._consume('while')\n self._consume('(')\n\n while_lbl = f\"WHILE_{self._while_count}\"\n while_false_lbl = f\"WHILE_FALSE{self._while_count}\"\n self._while_count += 1\n self.writer.write_label(while_lbl)\n\n self.compile_expression()\n self._consume(')')\n\n self._consume('{')\n self.writer.write_if(while_false_lbl)\n\n self.compile_statements()\n self.writer.write_goto(while_lbl)\n self.writer.write_label(while_false_lbl)\n\n self._consume('}')","def run_until_stop(self):\n while self.commands[self.pointer] != END:\n # Get the cmd\n cmd = self.commands[self.pointer]\n opcode = cmd % 100\n modes = cmd // 100\n \n vals, locs, self.pointer = get_vals_and_locs(opcode, modes, self.pointer, self.commands)\n \n if opcode == ADD:\n self.commands[locs[2]] = vals[0] + vals[1]\n elif opcode == MUL:\n self.commands[locs[2]] = vals[0] * vals[1]\n elif opcode == INP:\n if self.inputs:\n self.commands[locs[0]] = self.inputs.pop(0)\n else:\n # Put the pointer back, so we run this opcode again\n self.pointer -= 2\n return False\n elif opcode == OUT:\n self.outputs.append(vals[0])\n elif opcode == JIT:\n if vals[0] != 0:\n self.pointer = vals[1]\n elif opcode == JIF:\n if vals[0] == 0:\n self.pointer = vals[1]\n elif opcode == LT:\n self.commands[locs[2]] = 1 if vals[0] < vals[1] else 0\n elif opcode == EQ:\n self.commands[locs[2]] = 1 if vals[0] == vals[1] else 0\n else:\n print(\"FAIL????\")\n\n return True","def while_loop(self, S, K):\n if len(S) < K:\n return 0\n window_set = set()\n i, j, count = 0, 0, 0\n while i < len(S) and j < len(S):\n if S[j] in window_set:\n while i < j and S[j] in window_set:\n window_set.remove(S[i])\n i += 1\n window_set.add(S[j])\n if j - i + 1 == K and len(window_set) == K:\n window_set.remove(S[i])\n i, count = i + 1, count + 1\n j += 1\n return count","def consume_while(it, pred):\n\tout = \"\"\n\twhile True:\n\t\tc = it.peek()\n\t\tif c is not EOFToken and pred(c):\n\t\t\tout += c\n\t\t\tit.next()\n\t\telse:\n\t\t\treturn out","def WhileStatement(self):\n self.currtok = next(self.tg)\n if self.currtok[1].name == \"LPAREN\":\n self.currtok = next(self.tg)\n express = self.Expression()\n if self.currtok[1].name == \"RPAREN\":\n self.currtok = next(self.tg)\n state = self.Statement()\n return whileStmt(express, state)\n raise SLUCSyntaxError(\"ERROR: Missing right paren on line {0}\".format(str(self.currtok[2] - 1)))\n raise SLUCSyntaxError(\"ERROR: Missing left paren on line {0}\".format(str(self.currtok[2] - 1)))","def lucas():\n yield 2\n a = 2\n b = 1\n while True:\n yield b\n a, b = b, a + b","def infinite_increment():\n i = 0\n while 1:\n yield i\n i += 1","def _loop_raw(raw):\n raise NotImplemented","def while_do(condition: Callable[[Any], bool], source: ObservableBase) -> ObservableBase:\n from ..operators.observable.whiledo import while_do\n return while_do(condition, source)","def iterate(func, x):\n while True:\n x = func(x)\n yield x","def _analyse_loop(\n self,\n statement: Union[ast.AsyncFor, ast.For, ast.While],\n *,\n next: CFNode,\n ) -> CFNode:\n # Node acting as target for the next iteration. We'll identify this\n # with the loop entry node, once that exists.\n dummy_node = self._dummy_node()\n with self._updated_context(break_=next, continue_=dummy_node):\n body_node = self._analyse_statements(statement.body, next=dummy_node)\n\n loop_node = self._ast_node(\n statement,\n enter=body_node,\n else_=self._analyse_statements(statement.orelse, next=next),\n error=self._raise,\n )\n\n self._graph.collapse_node(dummy_node, loop_node)\n return loop_node","def loop(func):\n def wrapper(*a, **b):\n while True:\n func(*a, **b)\n return wrapper","def while_(self):\n if self.line.startswith('wh'):\n if self.line.endswith('while') is False:\n return True","def has_next():","def detect_loop(self):\n tortoise = self.head\n hare = self.head\n while hare:\n tortoise = tortoise.next\n hare = hare.next.next\n if tortoise == hare:\n return True\n return False","def factorial_loop(n):\n\n pass # @todo -fix this","def while_b():\r\n\r\n row = 0\r\n while row<7:\r\n col = 0\r\n while col<4:\r\n if col==0 or row!=0 and row%3==0 and col<3 or col==3 and row in (4,5):\r\n print('*', end = ' ')\r\n else:\r\n print(' ', end = ' ')\r\n col += 1\r\n print()\r\n row += 1","def _read_loop(self):\n while True:\n self.read()","def compile_while(self):\r\n start_label = \"WHILE_\" + str(self.__while_count)\r\n end_label = \"WHILE_END_\" + str(self.__while_count)\r\n self.__while_count += 1\r\n self.__advance(n=2) # Advance after the '(' token\r\n self.__vmwriter.write_label(start_label)\r\n self.compile_expression()\r\n self.__advance(n=2) # Advance after the '{' token\r\n self.__vmwriter.write_arithmetic(\"not\")\r\n self.__vmwriter.write_if(end_label)\r\n self.compile_statements()\r\n self.__advance() # Advance after the '}' token\r\n self.__vmwriter.write_goto(start_label)\r\n self.__vmwriter.write_label(end_label)","def loop(self):\n while self.dispatch(True) is not QUIT:\n pass","def _readloop(self):\r\n\r\n while self._ll_alive:\r\n with self._rx_lock:\r\n data = self._Random(1)\r\n if len(data) != 0: # check for timeout\r\n self._uart_rx_queue.put(data)","def take_while(coll, func):\n i = 0\n while i < len(coll) and func(coll[i]):\n i += 1\n return coll[:i]","def count_while(s, value):\n total, index = 0, 0\n while index < len(s):\n if s[index] == value:\n total = total + 1\n index = index + 1\n return total","def take_while(pred):\n def run(chunks, chunk, last):\n for i, c in enumerate(chunk):\n if not pred(c):\n return ParserResult.from_done(_chunks_merge((chunk[:i], chunks)), chunk[i:], last)\n if last:\n return ParserResult.from_error(\"Not enough input\")\n else:\n return ParserResult.from_partial(Parser(run, (chunk, chunks)))\n return Parser(run, tuple())","def wait_star():\n while True:\n r = sys.stdin.read(1)\n if r == '*':\n break","def algorithm_loop(self):","def generator(factor, current, condition=0):\n while True:\n next_current = factor * current % 2147483647\n if condition == 0 or (next_current % condition == 0):\n yield next_current\n current = next_current","def take_while(self, test):\n # type: (Callable) -> List[T]\n passing_elements = [] # type: List[T]\n while self.has_next() and test(self.peak()):\n passing_elements.append(self.next())\n return passing_elements","def iter_py():\n s = \"Hello, World!\"\n it = iter(s)\n while True:\n try:\n print(next(it))\n except:\n break\n\n ## Output\n # H\n # e\n # l\n # l\n # o\n # ,\n #\n # W\n # o\n # r\n # l\n # d\n # !","def section_4_7():\n import itertools\n\n def test1():\n def count(n):\n while True:\n yield n\n n += 1\n\n c = count(0)\n for x in itertools.islice(c, 10, 20):\n print(x)\n\n test1()","def wait(t,p):\n\toutput_list = []\n\tc = ''\n\td = ''\n\twhile p not in d:\n\t\tc = t.read_very_eager()\n\t\tif len(c) > 0:\n\t\t\td += c\n\t\t\tprint c\n\t\t\toutput_list.append(c)\n\t\tif \"Press any key to continue\" in c or \"--More--\" in c:\n\t\t\tt.write(\" \")\n\toutput_list = ((''.join(output_list)).replace('\\r\\n','\\n')).split('\\n')\n\treturn output_list","def taille(x):\n n = 0\n \n while (2**n) -1 < x :\n n+=1\n \n return(n)","def count_to10():\n numberx = 0\n while numberx < 10:\n numberx += 1\n print(numberx)","def iter_sequence_infinite(seq):\n while True:\n for item in seq:\n yield item","def while_X():\r\n i=0\r\n while i<6:\r\n j=0\r\n while j<6:\r\n if i-j==0 or i+j==5:\r\n print(\"*\",end=\" \")\r\n else:\r\n print(\" \",end=\" \")\r\n j+=1 \r\n print()\r\n i+=1","def detect_loop(head):\n\n seen = set()\n\n current_node = head\n while current_node.next is not None:\n if current_node in seen:\n return current_node\n seen.add(current_node)\n current_node = current_node.next\n return None","def integers():\n i = 1\n while True:\n yield i\n i = i + 1","def integers():\n i = 1\n while True:\n yield i\n i = i + 1","def taking_while(pred):\n @coroutine\n def gen(target):\n while True:\n x = (yield)\n if pred(x):\n target.send(x)\n else:\n raise StopConsumption()\n\n return gen","def test_run_loop_success(self):\n found = False\n pyint = Interpreter(limit=15)\n try:\n pyint.run(code=BF_CODE_LOOP_TWICE)\n except SystemExit: \n found = True\n self.assertFalse(found)","def _cmdloop(self):\n # An almost perfect copy from Cmd; however, the pseudo_raw_input portion\n # has been split out so that it can be called separately\n if self.use_rawinput and self.completekey:\n try:\n import readline\n self.old_completer = readline.get_completer()\n readline.set_completer(self.complete)\n readline.parse_and_bind(self.completekey + \": complete\")\n except ImportError:\n pass\n stop = None\n try:\n while not stop:\n if self.cmdqueue:\n line = self.cmdqueue.pop(0)\n else:\n line = self.pseudo_raw_input(self.prompt)\n if self.echo and isinstance(self.stdin, file):\n self.stdout.write(line + '\\n')\n stop = self.onecmd_plus_hooks(line)\n finally:\n if self.use_rawinput and self.completekey:\n try:\n import readline\n readline.set_completer(self.old_completer)\n except ImportError:\n pass\n return stop","def test_74_break(self):\n\t\tinput = \"\"\"procedure main(); var x:integer;\n\t\tbegin while(true)do begin\n\t\twith x:integer; do with x:real; do begin\n\t\t\tif (x>0) then break;\n\t\t\twith x:integer; do if (x=0) then return;\n\t\tend{with} break; end{while} break; end\"\"\"\n\t\texpect = \"Break Not In Loop\"\n\t\tself.assertTrue(TestChecker.test(input,expect,474))","def While(name, condition_blob_or_net, nets_or_steps):\n condition_not_net, stop_blob = NotNet(condition_blob_or_net)\n if isinstance(condition_blob_or_net, core.Net):\n nets_or_steps = _PrependNets(\n nets_or_steps, condition_blob_or_net, condition_not_net)\n else:\n nets_or_steps = _PrependNets(nets_or_steps, condition_not_net)\n\n def while_step(control_name):\n return core.scoped_execution_step(\n _get_next_step_name(control_name, name),\n nets_or_steps,\n should_stop_blob=stop_blob,\n )\n\n if _IsNets(nets_or_steps):\n # In this case, while_step has sub-nets:\n # [condition_blob_or_net, condition_not_net, nets_or_steps]\n # If stop_blob is pre-set to True (this may happen when While() is\n # called twice), the loop will exit after executing\n # condition_blob_or_net. So we use BootNet to set stop_blob to\n # False.\n bool_net = BoolNet((stop_blob, False))\n return Do(name + '/While', bool_net, while_step('While-inner'))\n else:\n return while_step('While')","def WaitForAll(iterable, condition):\n while any(map(condition, iterable)):\n Yield(frameNice=False)\n\n BeFrameNice()","def eval_loop():\n while(True):\n decision = raw_input(\"enter some mathematical operations\")\n if(decision == \"done\"):\n break\n print eval(decision)","def bottle_song_while(num):\n pass","def whilestmt(self, w):\n invs = self.assemble_invariants(w)\n b_mid = self.flatten([Tree('assume', [w[0]]), w[-1], Tree('assert', invs), Tree('assume', [Tree('const_false', [])])])\n b = self.flatten([Tree('assert', invs),\n self.assemble_havoc(w),\n Tree('assume', invs),\n Tree('wpor', [Tree('block', b_mid), Tree('assume', self._not(w[0]))])])\n return b","def forever(iterable):\n it = iter(iterable)\n while True:\n try:\n yield next(it)\n except Exception as e:\n print(e)\n it = iter(iterable)","def f(i):\n answer = 1\n while i >= 1:\n answer *= i\n i -= 1\n return answer","def cycle(obj):\r\n while True:\r\n for item in obj:\r\n yield item","def cycle(iterator: Iterable[Any]) -> Iterable[Any]:\n while True:\n yield from iterator","def take_while(pred):\n def _take_while_xducer(step):\n def _take_while_step(r=Missing, x=Missing):\n if r is Missing: return step()\n if x is Missing:\n return step(r)\n return step(r, x) if pred(x) else Reduced(r)\n return _take_while_step\n return _take_while_xducer","def odd():\n num = 0\n while True:\n yield num * (num & 1)\n num += 1","def drop_while(coll, func): \n i = 0\n while i < len(coll) and func(coll[i]):\n i += 1\n return coll[i:]","def test4(self):\n i = 0\n while i < 2**5:\n message = i * 'a'\n res = self.compare(message)\n if res is not None:\n d1, d2 = res\n self.print_diff(message, d1, d2)\n assert res is None\n i += 1","def _while_loop(pd, nmax, max_loop, ncore, outfile):\n \n # make sure the random seed is different for every processor\n random_number_seeds = [3456789, 7654321, 2435467, 8273645,\n 1085712, 4154712, 1248291, 8415917,\n 2345161, 5710916, 5718601, 7516234,\n 9235161, 4917519, 1111245, 8167834] \n \n # get the processor ID (1 - Ncore) and convert to single integer\n current = multiprocessing.current_process()\n pid = current._identity\n pid = pid[0]\n \n #\n # choose a different seed for each processor from the list so each processor has\n # a different randum number seed. Then, fiddle with each seed a little so \n # the seeds aren't the same every time the code is run\n seed = np.int(random_number_seeds[pid] * (np.random.rand()*(10.0 - 0.01) + 0.01))\n \n np.random.seed(seed)\n \n #print 'id and seed', pid, seed\n \n n_particles = 0\n loop_counter = 0\n fmin_scale = 1.0E-100 # -16 -> -32 -> -100\n F_max = np.max(pd.DF.f) #; F_min = np.min(pd.DF.f);\n F_min = np.min(pd.DF.f) * fmin_scale\n # F_max = np.max(pd.DF.f[:-1])#; F_max = 1.0E-88\n #print F_min, F_max\n if pd.optimize:\n relative_potential = pd._interpolate_relative_potential\n else:\n relative_potential = pd.DF.relative_potential\n \n \n pos = np.zeros((nmax, 3))\n vel = np.zeros((nmax, 3)) \n \n while (( n_particles < nmax) and (loop_counter < max_loop)):\n \n r = pd._choose_position()\n Psi = relative_potential(r) \n \n v = pd._choose_velocity(r, Psi)\n \n E = Psi - 0.5 * v * v\n \n f_E = pd.DF.interpolate_f(E)\n \n logF = ( np.random.rand()*(np.log10(F_max) - np.log10(F_min)) + np.log10(F_min) )\n \n # if choosing random F in log F, might be good to do the comparison in logspace as well\n #.... i.e log(F) <= log(f_E) \n #\n # 0 FOR F_E MEANS THAT E < E_min of the potential. THis happens when Psi - KE is smaller\n # than the value of the potential at large_r... should this be considered unbound \n # even though it isn't zero? Is this effectively zero? This has been adjusted in the velocity\n # picking routine but needs verification to make sure it works.... but regardless, I doubt\n # that this is the reason why the particles are failing for NFW but working for hernquist....\n #\n if np.abs(np.log10(f_E)) == np.inf:\n keep_particle = False\n _my_print('log value error... throwing out particle')\n else:\n keep_particle = (logF <= np.log10(f_E))\n\n \n if keep_particle: \n index = n_particles \n \n # \n # need to sample in cosine theta NOT theta!!!!! WTF!!!!!\n #\n #\n \n theta = np.arccos( np.random.rand() * (2.0) - 1.0)\n \n # convert position to cartesian using random theta and phi\n phi = np.random.rand() * 2.0 * np.pi\n \n x = np.sin(theta) * np.cos(phi)\n y = np.sin(theta) * np.sin(phi)\n z = np.cos(theta)\n \n pos[index] = r * np.array([x,y,z])\n\n # repeat for velocity using new random numbersw\n theta = np.arccos( np.random.rand() * (2.0) - 1.0)\n phi = np.random.rand() * 2.0 * np.pi\n \n vx = np.sin(theta) * np.cos(phi)\n vy = np.sin(theta) * np.sin(phi)\n vz = np.cos(theta)\n \n vel[index] = v * np.array([vx,vy,vz])\n \"\"\" \n #else: # do strict cartesian ... slower.... less attractive\n # \n # axis_list = [0, 1, 2]\n # random.shuffle(axis_list)\n \n # #axis_index = np.random.randint(3)\n # first_axis = axis_list[0]\n #\n # pos[index, first_axis] = np.random.rand() * (2.0*r) - r\n #del axis_list[axis_index]\n \n #axis_index = np.random.randint(2)\n # second_axis = axis_list[1]\n \n max_r = np.sqrt(r*r - pos[index,first_axis]**2)\n \n pos[index, second_axis] = np.random.rand()*(2.0 * max_r) - max_r\n #del axis_list[axis_index]\n \n max_r = np.sqrt(r*r - pos[index,first_axis]**2 - pos[index,second_axis]**2)\n \n third_axis = axis_list[2]\n pos[index, third_axis] = np.random.rand() * (2.0 * max_r) - max_r\n \n if np.sqrt(pos[index,0]**2 + pos[index,1]**2 + pos[index,2]**2) > r:\n _my_print('R IS TOO LARGE')\n \n ###\n axis_list = [0, 1, 2]\n random.shuffle(axis_list)\n \n #axis_index = np.random.randint(3)\n first_axis = axis_list[0]\n \n vel[index, first_axis] = np.random.rand() * (2.0*v) - v\n #del axis_list[axis_index]\n \n #axis_index = np.random.randint(2)\n second_axis = axis_list[1]\n \n max_v = np.sqrt(v*v - vel[index,first_axis]**2)\n \n vel[index, second_axis] = np.random.rand()*(2.0 * max_v) - max_v\n #del axis_list[axis_index]\n \n max_v = np.sqrt(v*v - vel[index,first_axis]**2 - vel[index,second_axis]**2)\n \n third_axis = axis_list[2]\n vel[index, third_axis] = np.random.rand() * (2.0 * max_v) - max_v \n \n \n \"\"\"\n n_particles = n_particles + 1\n \n if (loop_counter % 5000) == 0:\n _my_print(\"Have %4i particles. On loop %6i\"%(n_particles, loop_counter))\n loop_counter = loop_counter + 1\n \n # now write out to a temporary file\n f = open(outfile + \"_%02i_\"%(pid) + \".temp\", 'w')\n fmt = \"%12.12E %12.12E %12.12E %12.12E %12.12E %12.12E %12.12E\\n\"\n \n \n for i in np.arange(nmax):\n f.write(fmt%(pd.M_part, pos[i,0], pos[i,1], pos[i,2], \n vel[i,0], vel[i,1], vel[i,2]))\n \n \n f.close() \n \n return pos, vel","def main():\n\n modulus = lambda c: (c.real*c.real + c.imag*c.imag)\n test_case = 0\n while True:\n try:\n _r, _i, iterations = input().split()\n except EOFError:\n break\n\n position = complex(float(_r), float(_i))\n z_value, outside = complex(0.0, 0.0), False\n for _ in range(int(iterations)):\n z_next = z_value * z_value + position\n outside = (modulus(z_next) > 4)\n if outside:\n break\n z_value = z_next\n\n test_case += 1\n print('Case {0}: {1}'.format(test_case, 'OUT' if outside else 'IN'))","def infinite_odd_generator():\n current = 1\n while True:\n yield current\n current = current + 2","def internal_loop(p, i, j, k, l):\n return _RNAstructure_wrap.internal_loop(p, i, j, k, l)","def looptcs(self): \n while self.itr < 1: \n #self.genRandomNoise() #developing\n self.tcs(lpf=1)\n #self.itr +=1 ","def run(self, p):\n while self.state < 3:\n self.__step(p)","def loop(self):\r\n\r\n self._next_state = self._current_state"],"string":"[\n \"def syntax_while():\\n i = 0\\n while i < 5:\\n print(i)\\n i += 1\\n\\n ## Output\\n # 0\\n # 1\\n # 2\\n # 3\\n # 4\",\n \"def while_loop(n):\\n\\tprint(\\\"\\\\n\\\\nlet's see what a while loop is\\\\n\\\")\\n\\twhile n > 0:\\n\\t\\tprint(f\\\"n = {n}\\\")\\n\\t\\tn -= 1\",\n \"def whileLoop(count):\\n result=''\\n while count>=1:#判断条件\\n result+=\\\"sorry\\\"\\n count-=1#while的步进\\n return result\",\n \"def while_loop_op(op):\\n return (control_flow_util.IsLoopSwitch(op) or\\n control_flow_util.IsLoopMerge(op) or\\n control_flow_util.IsLoopEnter(op) or\\n control_flow_util.IsLoopExit(op) or\\n TensorTracer.loop_cond_op(op) or\\n op.type in ('RefNextIteration', 'NextIteration'))\",\n \"def visit_while(self: Parser, node: doc.While) -> None:\\n with self.var_table.with_frame():\\n cond = self.eval_expr(node.test)\\n with T.While(cond):\\n self.visit_body(node.body)\",\n \"def test_41_while(self):\\n\\t\\tinput = \\\"\\\"\\\"function foo():integer; var a:integer; begin\\n\\t\\twhile(1) do begin end\\n\\t\\treturn 1;\\n\\t\\tend\\n\\t\\tprocedure main(); var x:integer; begin x:=foo(); foo(); end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Type Mismatch In Statement: While(IntLiteral(1),[])\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,441))\",\n \"def _in_while_loop(control_flow_node_map, op_name):\\n return op_name in control_flow_node_map and \\\"LoopCond\\\" in control_flow_node_map[op_name]\",\n \"def parseWhileStatement( ): # parse rountine for while and uses the while class to print out the appropriate string\\n\\n\\ttok = tokens.peek( )\\n\\tif debug: print( \\\"whileStatement: \\\", tok )\\n\\tstart = match( \\\"while\\\" )\\n\\texpr = expression( )\\n\\tblk = parseBlock( )\\n\\ttok = tokens.peek( )\\n\\twhileString = whileStatement( start, expr, blk )\\n\\treturn whileString\",\n \"def test_42_while(self):\\n\\t\\tinput = \\\"\\\"\\\"function foo():integer; var a:integer; begin\\n\\t\\twhile(1+true) do begin end\\n\\t\\treturn 1;\\n\\t\\tend\\n\\t\\tprocedure main(); var x:integer; begin x:=foo(); foo(); end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Type Mismatch In Expression: BinaryOp(+,IntLiteral(1),BooleanLiteral(True))\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,442))\",\n \"def syntax_while_break():\\n i = 0\\n while i < 5:\\n if i >= 3:\\n break\\n print(i)\\n i += 1\\n\\n ## Output\\n # 0\\n # 1\\n # 2\",\n \"def syntax_while_continue():\\n i = 0\\n while i < 5:\\n if i % 2 == 1:\\n i += 1\\n continue\\n print(i)\\n i += 1\\n\\n ## Output\\n # 0\\n # 2\\n # 4\",\n \"def visit_WhileNode(self, node: WhileNode, symbol_table: SymbolTable) -> None:\\n while True:\\n if self.visit(node.cond, symbol_table).value == 0:\\n break\\n else:\\n for expr in node.body:\\n if expr is not None:\\n if isinstance(expr, ReturnNode):\\n return expr\\n res = self.visit(expr, symbol_table)\\n if isinstance(res, ReturnNode):\\n return res\",\n \"def link_while_stmt(self, stmt):\\n self.link_expr(stmt.cond)\\n self.link_stmt(stmt.body)\",\n \"def _while_loop(self):\\n bind_map = {}\\n wl = set_span(tvm.relay.var(\\\"while_loop\\\"), self._loop_name)\\n sb = tvm.relay.scope_builder.ScopeBuilder()\\n\\n lv_list = []\\n expr_list = []\\n extra_vars = []\\n\\n for i, lv in enumerate(self.loop_vars):\\n if self._loop_name not in self._lvar2expr:\\n self._lvar2expr[self._loop_name] = {}\\n\\n # Handle the case when loop var is not properly lifted.\\n # This can happen when loop var node name is set accidentally\\n # beginning with loop name.\\n if lv not in self._lvar2expr[self._loop_name]:\\n var_name = f\\\"{self._loop_name}_loop_var_{i}\\\"\\n var_type = _infer_type(lv, self._mod).checked_type\\n loop_var = set_span(tvm.relay.var(var_name, type_annotation=var_type), var_name)\\n self._lvar2expr[self._loop_name][loop_var] = lv\\n bind_map[lv] = loop_var\\n self.loop_vars[i] = loop_var\\n lv = loop_var\\n\\n lv_list.append(lv)\\n expr_list.append(self._lvar2expr[self._loop_name][lv])\\n\\n if bind_map:\\n self.cond = rewrite_subgraph(self.cond, bind_map)\\n self.body = [rewrite_subgraph(b, bind_map) for b in self.body]\\n\\n cond = set_span(tvm.relay.op.min(self.cond), self.cond.span)\\n\\n for lv, exp in self._lvar2expr[self._loop_name].items():\\n if lv not in self.loop_vars:\\n var_checker = VarChecker(lv)\\n for bd in self.body + [cond]:\\n var_checker.visit(bd)\\n if var_checker.used:\\n lv_list.append(lv)\\n expr_list.append(exp)\\n extra_vars.append(lv)\\n break\\n\\n with sb.if_scope(cond):\\n sb.ret(wl(*list(self.body + extra_vars)))\\n with sb.else_scope():\\n sb.ret(tvm.relay.Tuple(lv_list))\\n\\n loop_fn = tvm.relay.Function(lv_list, sb.get())\\n sb = tvm.relay.scope_builder.ScopeBuilder()\\n sb.let(wl, loop_fn)\\n loop_ret = wl(*expr_list)\\n\\n sb.ret(loop_ret)\\n ret = sb.get()\\n return ret\",\n \"def step_back_while(cur_index, condition):\\n while cur_index >= 0 and condition(cur_index):\\n cur_index -= 1\\n return cur_index\",\n \"def _analyse_stmt_While(self, statement: ast.While, *, next: CFNode) -> CFNode:\\n # Analyse the else branch.\\n else_node = self._analyse_statements(statement.orelse, next=next)\\n\\n # Analyse the body.\\n dummy_node = self._dummy_node()\\n with self._updated_context(break_=next, continue_=dummy_node):\\n body_node = self._analyse_statements(statement.body, next=dummy_node)\\n\\n # Analyse the condition, if a constant.\\n branches: Dict[str, CFNode] = {}\\n test_is_constant, test_value = self._expression_as_constant(statement.test)\\n if test_is_constant:\\n if test_value:\\n branches.update(enter=body_node)\\n else:\\n branches.update(else_=else_node)\\n else:\\n branches.update(enter=body_node, else_=else_node, error=self._raise)\\n\\n loop_node = self._ast_node(statement, **branches)\\n self._graph.collapse_node(dummy_node, loop_node)\\n return loop_node\",\n \"def while_loop(self):\\n if self._loop is None:\\n self._loop = self._while_loop()\\n return self._loop\\n return self._loop\",\n \"def whileLoop(self, l1, l2):\\r\\n value = 0\\r\\n r = 0\\r\\n curr_l1 = l1\\r\\n curr_l2 = l2\\r\\n head = None\\r\\n curr = None\\r\\n\\r\\n while(curr_l1 or curr_l2 or r == 1):\\r\\n \\r\\n if curr_l1 != None and curr_l2 != None:\\r\\n value += curr_l1.val + curr_l2.val\\r\\n\\r\\n elif curr_l1 == None and curr_l2 == None:\\r\\n pass\\r\\n \\r\\n elif curr_l1 == None:\\r\\n value += curr_l2.val\\r\\n \\r\\n elif curr_l2 == None:\\r\\n value += curr_l1.val\\r\\n\\r\\n if r == 1:\\r\\n value += 1\\r\\n r = 0\\r\\n \\r\\n if value > 9:\\r\\n value = value - 10\\r\\n r = 1\\r\\n\\r\\n if head == None:\\r\\n head = ListNode(value)\\r\\n curr = head\\r\\n else:\\r\\n curr.next = ListNode(value)\\r\\n curr = curr.next\\r\\n\\r\\n value = 0\\r\\n curr_l1 = curr_l1.next if curr_l1 != None else None\\r\\n curr_l2 = curr_l2.next if curr_l2 != None else None\\r\\n \\r\\n return head\",\n \"def infinite_loop():\\n return True\",\n \"def scanwhile(first, p):\\n lines = [first]\\n while True:\\n line = lr.readline()\\n if not line:\\n break\\n if p(line):\\n lines.append(line)\\n else:\\n lr.push(line)\\n break\\n return lines\",\n \"def gen_while(self, stmt: statements.While) -> None:\\n condition_block = self.builder.new_block()\\n body_block = self.builder.new_block()\\n final_block = self.builder.new_block()\\n self.break_block_stack.append(final_block)\\n self.continue_block_stack.append(condition_block)\\n self.builder.emit_jump(condition_block)\\n self.builder.set_block(condition_block)\\n self.gen_condition(stmt.condition, body_block, final_block)\\n self.builder.set_block(body_block)\\n self.gen_stmt(stmt.body)\\n self.builder.emit_jump(condition_block)\\n self.builder.set_block(final_block)\\n self.break_block_stack.pop()\\n self.continue_block_stack.pop()\",\n \"def loop(self):\\n line = self.read()\\n while line != \\\"quit\\\":\\n value = self.eval(line)\\n print(value)\\n line = self.read()\",\n \"def next():\",\n \"def next():\",\n \"def loop():\\n global loop_idx\\n sys.stdout.write('loop index %d/%d\\\\r\\\\n' % (loop_idx, _LOOPS))\\n time.sleep(0.5)\\n loop_idx += 1\\n return loop_idx > _LOOPS\",\n \"def while_P():\\r\\n i=0\\r\\n while i<7:\\r\\n j=0\\r\\n while j<4:\\r\\n if j==0 or i in(0,3) and j%3!=0 or j==3 and i in(1,2):\\r\\n print(\\\"*\\\",end=\\\" \\\")\\r\\n else:\\r\\n print(\\\" \\\",end=\\\" \\\")\\r\\n j+=1 \\r\\n print()\\r\\n i+=1\",\n \"async def looped_fetch(fetch, query):\\n last = None\\n while True:\\n res = await fetch(query=query, last=last)\\n if res.count > 0:\\n return res.items[0]\\n if not res.last:\\n return None\\n last = res.last\",\n \"def fact_while1(n: int) -> int:\\n ret = 1\\n if n == 0:\\n return 1\\n while True:\\n if n == 1:\\n return ret\\n n, ret = n - 1, ret * n\",\n \"def iterate(f, x):\\n while True:\\n yield x\\n x = f(x)\",\n \"def check_all():\\r\\n i = 100000\\r\\n while i <= 999996:\\r\\n if check(i):\\r\\n print(i)\\r\\n i = i + 1\",\n \"def _wrap_computation_in_while_loop(device, op_fn):\\n\\n def computation(i):\\n with ops.control_dependencies(op_fn()):\\n return i + 1\\n\\n iterations_per_loop_var = _create_or_get_iterations_per_loop()\\n # By setting parallel_iterations=1, the parallel execution in while_loop is\\n # basically turned off.\\n with ops.device(device):\\n iterations = array_ops.identity(iterations_per_loop_var)\\n return control_flow_ops.while_loop(\\n lambda i: i < iterations,\\n computation, [constant_op.constant(0)],\\n parallel_iterations=1)\",\n \"def add_while(self, input_name, body_function, cond_function, name=None):\\n return self._build_op(\\n 'while', [input_name],\\n name=name,\\n attr={\\n 'body_function': body_function,\\n 'cond_function': cond_function\\n })\",\n \"def while_f():\\r\\n\\r\\n row = 0\\r\\n while row<6:\\r\\n col = 0\\r\\n while col<4:\\r\\n if col==1 and row>0 or row==3 and col<3 or col==2 and row==0 or col==3 and row==1:\\r\\n print('*', end = ' ')\\r\\n else:\\r\\n print(' ', end = ' ')\\r\\n col += 1\\r\\n print()\\r\\n row += 1\",\n \"def dequeue_loop():\\n while True:\\n result = dequeue_function()\\n if not result:\\n break\\n print(result)\",\n \"def __le__(self, *args):\\n return _ida_hexrays.cwhile_t___le__(self, *args)\",\n \"def compile_while(self):\\r\\n lab1 = self.class_name + \\\".L\\\" + str(self.label_index)\\r\\n self.label_index += 1\\r\\n lab2 = self.class_name + \\\".L\\\" + str(self.label_index)\\r\\n self.label_index += 1\\r\\n self.tokenizer.advance() # ignore 'while' keyword\\r\\n self.tokenizer.advance() # ignore '(' symbol\\r\\n self.code_writer.write_label(lab1)\\r\\n self.compile_expression()\\r\\n self.code_writer.write_arithmetic(\\\"not\\\")\\r\\n self.code_writer.write_if(lab2)\\r\\n self.tokenizer.advance() # ignore ')' symbol\\r\\n self.tokenizer.advance() # ignore '{'\\r\\n self.compile_statements()\\r\\n self.code_writer.write_goto(lab1)\\r\\n self.code_writer.write_label(lab2)\",\n \"def breakLoop(num):\\n sum=0\\n while num<=4:#判断条件\\n if num==3:\\n num += 1\\n break\\n sum = sum+num\\n\\n num+=1#while的步进\\n return sum\",\n \"def test_78_continue(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer;\\n\\t\\tbegin while(true)do begin\\n\\t\\twith x:integer; do with x:real; do begin\\n\\t\\t\\tif (x>0) then continue;\\n\\t\\t\\twith x:integer; do if (x=0) then return;\\n\\t\\tend{with} continue; end{while} continue; end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Continue Not In Loop\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,478))\",\n \"def compile_while(self) -> None:\\n self._consume('while')\\n self._consume('(')\\n\\n while_lbl = f\\\"WHILE_{self._while_count}\\\"\\n while_false_lbl = f\\\"WHILE_FALSE{self._while_count}\\\"\\n self._while_count += 1\\n self.writer.write_label(while_lbl)\\n\\n self.compile_expression()\\n self._consume(')')\\n\\n self._consume('{')\\n self.writer.write_if(while_false_lbl)\\n\\n self.compile_statements()\\n self.writer.write_goto(while_lbl)\\n self.writer.write_label(while_false_lbl)\\n\\n self._consume('}')\",\n \"def run_until_stop(self):\\n while self.commands[self.pointer] != END:\\n # Get the cmd\\n cmd = self.commands[self.pointer]\\n opcode = cmd % 100\\n modes = cmd // 100\\n \\n vals, locs, self.pointer = get_vals_and_locs(opcode, modes, self.pointer, self.commands)\\n \\n if opcode == ADD:\\n self.commands[locs[2]] = vals[0] + vals[1]\\n elif opcode == MUL:\\n self.commands[locs[2]] = vals[0] * vals[1]\\n elif opcode == INP:\\n if self.inputs:\\n self.commands[locs[0]] = self.inputs.pop(0)\\n else:\\n # Put the pointer back, so we run this opcode again\\n self.pointer -= 2\\n return False\\n elif opcode == OUT:\\n self.outputs.append(vals[0])\\n elif opcode == JIT:\\n if vals[0] != 0:\\n self.pointer = vals[1]\\n elif opcode == JIF:\\n if vals[0] == 0:\\n self.pointer = vals[1]\\n elif opcode == LT:\\n self.commands[locs[2]] = 1 if vals[0] < vals[1] else 0\\n elif opcode == EQ:\\n self.commands[locs[2]] = 1 if vals[0] == vals[1] else 0\\n else:\\n print(\\\"FAIL????\\\")\\n\\n return True\",\n \"def while_loop(self, S, K):\\n if len(S) < K:\\n return 0\\n window_set = set()\\n i, j, count = 0, 0, 0\\n while i < len(S) and j < len(S):\\n if S[j] in window_set:\\n while i < j and S[j] in window_set:\\n window_set.remove(S[i])\\n i += 1\\n window_set.add(S[j])\\n if j - i + 1 == K and len(window_set) == K:\\n window_set.remove(S[i])\\n i, count = i + 1, count + 1\\n j += 1\\n return count\",\n \"def consume_while(it, pred):\\n\\tout = \\\"\\\"\\n\\twhile True:\\n\\t\\tc = it.peek()\\n\\t\\tif c is not EOFToken and pred(c):\\n\\t\\t\\tout += c\\n\\t\\t\\tit.next()\\n\\t\\telse:\\n\\t\\t\\treturn out\",\n \"def WhileStatement(self):\\n self.currtok = next(self.tg)\\n if self.currtok[1].name == \\\"LPAREN\\\":\\n self.currtok = next(self.tg)\\n express = self.Expression()\\n if self.currtok[1].name == \\\"RPAREN\\\":\\n self.currtok = next(self.tg)\\n state = self.Statement()\\n return whileStmt(express, state)\\n raise SLUCSyntaxError(\\\"ERROR: Missing right paren on line {0}\\\".format(str(self.currtok[2] - 1)))\\n raise SLUCSyntaxError(\\\"ERROR: Missing left paren on line {0}\\\".format(str(self.currtok[2] - 1)))\",\n \"def lucas():\\n yield 2\\n a = 2\\n b = 1\\n while True:\\n yield b\\n a, b = b, a + b\",\n \"def infinite_increment():\\n i = 0\\n while 1:\\n yield i\\n i += 1\",\n \"def _loop_raw(raw):\\n raise NotImplemented\",\n \"def while_do(condition: Callable[[Any], bool], source: ObservableBase) -> ObservableBase:\\n from ..operators.observable.whiledo import while_do\\n return while_do(condition, source)\",\n \"def iterate(func, x):\\n while True:\\n x = func(x)\\n yield x\",\n \"def _analyse_loop(\\n self,\\n statement: Union[ast.AsyncFor, ast.For, ast.While],\\n *,\\n next: CFNode,\\n ) -> CFNode:\\n # Node acting as target for the next iteration. We'll identify this\\n # with the loop entry node, once that exists.\\n dummy_node = self._dummy_node()\\n with self._updated_context(break_=next, continue_=dummy_node):\\n body_node = self._analyse_statements(statement.body, next=dummy_node)\\n\\n loop_node = self._ast_node(\\n statement,\\n enter=body_node,\\n else_=self._analyse_statements(statement.orelse, next=next),\\n error=self._raise,\\n )\\n\\n self._graph.collapse_node(dummy_node, loop_node)\\n return loop_node\",\n \"def loop(func):\\n def wrapper(*a, **b):\\n while True:\\n func(*a, **b)\\n return wrapper\",\n \"def while_(self):\\n if self.line.startswith('wh'):\\n if self.line.endswith('while') is False:\\n return True\",\n \"def has_next():\",\n \"def detect_loop(self):\\n tortoise = self.head\\n hare = self.head\\n while hare:\\n tortoise = tortoise.next\\n hare = hare.next.next\\n if tortoise == hare:\\n return True\\n return False\",\n \"def factorial_loop(n):\\n\\n pass # @todo -fix this\",\n \"def while_b():\\r\\n\\r\\n row = 0\\r\\n while row<7:\\r\\n col = 0\\r\\n while col<4:\\r\\n if col==0 or row!=0 and row%3==0 and col<3 or col==3 and row in (4,5):\\r\\n print('*', end = ' ')\\r\\n else:\\r\\n print(' ', end = ' ')\\r\\n col += 1\\r\\n print()\\r\\n row += 1\",\n \"def _read_loop(self):\\n while True:\\n self.read()\",\n \"def compile_while(self):\\r\\n start_label = \\\"WHILE_\\\" + str(self.__while_count)\\r\\n end_label = \\\"WHILE_END_\\\" + str(self.__while_count)\\r\\n self.__while_count += 1\\r\\n self.__advance(n=2) # Advance after the '(' token\\r\\n self.__vmwriter.write_label(start_label)\\r\\n self.compile_expression()\\r\\n self.__advance(n=2) # Advance after the '{' token\\r\\n self.__vmwriter.write_arithmetic(\\\"not\\\")\\r\\n self.__vmwriter.write_if(end_label)\\r\\n self.compile_statements()\\r\\n self.__advance() # Advance after the '}' token\\r\\n self.__vmwriter.write_goto(start_label)\\r\\n self.__vmwriter.write_label(end_label)\",\n \"def loop(self):\\n while self.dispatch(True) is not QUIT:\\n pass\",\n \"def _readloop(self):\\r\\n\\r\\n while self._ll_alive:\\r\\n with self._rx_lock:\\r\\n data = self._Random(1)\\r\\n if len(data) != 0: # check for timeout\\r\\n self._uart_rx_queue.put(data)\",\n \"def take_while(coll, func):\\n i = 0\\n while i < len(coll) and func(coll[i]):\\n i += 1\\n return coll[:i]\",\n \"def count_while(s, value):\\n total, index = 0, 0\\n while index < len(s):\\n if s[index] == value:\\n total = total + 1\\n index = index + 1\\n return total\",\n \"def take_while(pred):\\n def run(chunks, chunk, last):\\n for i, c in enumerate(chunk):\\n if not pred(c):\\n return ParserResult.from_done(_chunks_merge((chunk[:i], chunks)), chunk[i:], last)\\n if last:\\n return ParserResult.from_error(\\\"Not enough input\\\")\\n else:\\n return ParserResult.from_partial(Parser(run, (chunk, chunks)))\\n return Parser(run, tuple())\",\n \"def wait_star():\\n while True:\\n r = sys.stdin.read(1)\\n if r == '*':\\n break\",\n \"def algorithm_loop(self):\",\n \"def generator(factor, current, condition=0):\\n while True:\\n next_current = factor * current % 2147483647\\n if condition == 0 or (next_current % condition == 0):\\n yield next_current\\n current = next_current\",\n \"def take_while(self, test):\\n # type: (Callable) -> List[T]\\n passing_elements = [] # type: List[T]\\n while self.has_next() and test(self.peak()):\\n passing_elements.append(self.next())\\n return passing_elements\",\n \"def iter_py():\\n s = \\\"Hello, World!\\\"\\n it = iter(s)\\n while True:\\n try:\\n print(next(it))\\n except:\\n break\\n\\n ## Output\\n # H\\n # e\\n # l\\n # l\\n # o\\n # ,\\n #\\n # W\\n # o\\n # r\\n # l\\n # d\\n # !\",\n \"def section_4_7():\\n import itertools\\n\\n def test1():\\n def count(n):\\n while True:\\n yield n\\n n += 1\\n\\n c = count(0)\\n for x in itertools.islice(c, 10, 20):\\n print(x)\\n\\n test1()\",\n \"def wait(t,p):\\n\\toutput_list = []\\n\\tc = ''\\n\\td = ''\\n\\twhile p not in d:\\n\\t\\tc = t.read_very_eager()\\n\\t\\tif len(c) > 0:\\n\\t\\t\\td += c\\n\\t\\t\\tprint c\\n\\t\\t\\toutput_list.append(c)\\n\\t\\tif \\\"Press any key to continue\\\" in c or \\\"--More--\\\" in c:\\n\\t\\t\\tt.write(\\\" \\\")\\n\\toutput_list = ((''.join(output_list)).replace('\\\\r\\\\n','\\\\n')).split('\\\\n')\\n\\treturn output_list\",\n \"def taille(x):\\n n = 0\\n \\n while (2**n) -1 < x :\\n n+=1\\n \\n return(n)\",\n \"def count_to10():\\n numberx = 0\\n while numberx < 10:\\n numberx += 1\\n print(numberx)\",\n \"def iter_sequence_infinite(seq):\\n while True:\\n for item in seq:\\n yield item\",\n \"def while_X():\\r\\n i=0\\r\\n while i<6:\\r\\n j=0\\r\\n while j<6:\\r\\n if i-j==0 or i+j==5:\\r\\n print(\\\"*\\\",end=\\\" \\\")\\r\\n else:\\r\\n print(\\\" \\\",end=\\\" \\\")\\r\\n j+=1 \\r\\n print()\\r\\n i+=1\",\n \"def detect_loop(head):\\n\\n seen = set()\\n\\n current_node = head\\n while current_node.next is not None:\\n if current_node in seen:\\n return current_node\\n seen.add(current_node)\\n current_node = current_node.next\\n return None\",\n \"def integers():\\n i = 1\\n while True:\\n yield i\\n i = i + 1\",\n \"def integers():\\n i = 1\\n while True:\\n yield i\\n i = i + 1\",\n \"def taking_while(pred):\\n @coroutine\\n def gen(target):\\n while True:\\n x = (yield)\\n if pred(x):\\n target.send(x)\\n else:\\n raise StopConsumption()\\n\\n return gen\",\n \"def test_run_loop_success(self):\\n found = False\\n pyint = Interpreter(limit=15)\\n try:\\n pyint.run(code=BF_CODE_LOOP_TWICE)\\n except SystemExit: \\n found = True\\n self.assertFalse(found)\",\n \"def _cmdloop(self):\\n # An almost perfect copy from Cmd; however, the pseudo_raw_input portion\\n # has been split out so that it can be called separately\\n if self.use_rawinput and self.completekey:\\n try:\\n import readline\\n self.old_completer = readline.get_completer()\\n readline.set_completer(self.complete)\\n readline.parse_and_bind(self.completekey + \\\": complete\\\")\\n except ImportError:\\n pass\\n stop = None\\n try:\\n while not stop:\\n if self.cmdqueue:\\n line = self.cmdqueue.pop(0)\\n else:\\n line = self.pseudo_raw_input(self.prompt)\\n if self.echo and isinstance(self.stdin, file):\\n self.stdout.write(line + '\\\\n')\\n stop = self.onecmd_plus_hooks(line)\\n finally:\\n if self.use_rawinput and self.completekey:\\n try:\\n import readline\\n readline.set_completer(self.old_completer)\\n except ImportError:\\n pass\\n return stop\",\n \"def test_74_break(self):\\n\\t\\tinput = \\\"\\\"\\\"procedure main(); var x:integer;\\n\\t\\tbegin while(true)do begin\\n\\t\\twith x:integer; do with x:real; do begin\\n\\t\\t\\tif (x>0) then break;\\n\\t\\t\\twith x:integer; do if (x=0) then return;\\n\\t\\tend{with} break; end{while} break; end\\\"\\\"\\\"\\n\\t\\texpect = \\\"Break Not In Loop\\\"\\n\\t\\tself.assertTrue(TestChecker.test(input,expect,474))\",\n \"def While(name, condition_blob_or_net, nets_or_steps):\\n condition_not_net, stop_blob = NotNet(condition_blob_or_net)\\n if isinstance(condition_blob_or_net, core.Net):\\n nets_or_steps = _PrependNets(\\n nets_or_steps, condition_blob_or_net, condition_not_net)\\n else:\\n nets_or_steps = _PrependNets(nets_or_steps, condition_not_net)\\n\\n def while_step(control_name):\\n return core.scoped_execution_step(\\n _get_next_step_name(control_name, name),\\n nets_or_steps,\\n should_stop_blob=stop_blob,\\n )\\n\\n if _IsNets(nets_or_steps):\\n # In this case, while_step has sub-nets:\\n # [condition_blob_or_net, condition_not_net, nets_or_steps]\\n # If stop_blob is pre-set to True (this may happen when While() is\\n # called twice), the loop will exit after executing\\n # condition_blob_or_net. So we use BootNet to set stop_blob to\\n # False.\\n bool_net = BoolNet((stop_blob, False))\\n return Do(name + '/While', bool_net, while_step('While-inner'))\\n else:\\n return while_step('While')\",\n \"def WaitForAll(iterable, condition):\\n while any(map(condition, iterable)):\\n Yield(frameNice=False)\\n\\n BeFrameNice()\",\n \"def eval_loop():\\n while(True):\\n decision = raw_input(\\\"enter some mathematical operations\\\")\\n if(decision == \\\"done\\\"):\\n break\\n print eval(decision)\",\n \"def bottle_song_while(num):\\n pass\",\n \"def whilestmt(self, w):\\n invs = self.assemble_invariants(w)\\n b_mid = self.flatten([Tree('assume', [w[0]]), w[-1], Tree('assert', invs), Tree('assume', [Tree('const_false', [])])])\\n b = self.flatten([Tree('assert', invs),\\n self.assemble_havoc(w),\\n Tree('assume', invs),\\n Tree('wpor', [Tree('block', b_mid), Tree('assume', self._not(w[0]))])])\\n return b\",\n \"def forever(iterable):\\n it = iter(iterable)\\n while True:\\n try:\\n yield next(it)\\n except Exception as e:\\n print(e)\\n it = iter(iterable)\",\n \"def f(i):\\n answer = 1\\n while i >= 1:\\n answer *= i\\n i -= 1\\n return answer\",\n \"def cycle(obj):\\r\\n while True:\\r\\n for item in obj:\\r\\n yield item\",\n \"def cycle(iterator: Iterable[Any]) -> Iterable[Any]:\\n while True:\\n yield from iterator\",\n \"def take_while(pred):\\n def _take_while_xducer(step):\\n def _take_while_step(r=Missing, x=Missing):\\n if r is Missing: return step()\\n if x is Missing:\\n return step(r)\\n return step(r, x) if pred(x) else Reduced(r)\\n return _take_while_step\\n return _take_while_xducer\",\n \"def odd():\\n num = 0\\n while True:\\n yield num * (num & 1)\\n num += 1\",\n \"def drop_while(coll, func): \\n i = 0\\n while i < len(coll) and func(coll[i]):\\n i += 1\\n return coll[i:]\",\n \"def test4(self):\\n i = 0\\n while i < 2**5:\\n message = i * 'a'\\n res = self.compare(message)\\n if res is not None:\\n d1, d2 = res\\n self.print_diff(message, d1, d2)\\n assert res is None\\n i += 1\",\n \"def _while_loop(pd, nmax, max_loop, ncore, outfile):\\n \\n # make sure the random seed is different for every processor\\n random_number_seeds = [3456789, 7654321, 2435467, 8273645,\\n 1085712, 4154712, 1248291, 8415917,\\n 2345161, 5710916, 5718601, 7516234,\\n 9235161, 4917519, 1111245, 8167834] \\n \\n # get the processor ID (1 - Ncore) and convert to single integer\\n current = multiprocessing.current_process()\\n pid = current._identity\\n pid = pid[0]\\n \\n #\\n # choose a different seed for each processor from the list so each processor has\\n # a different randum number seed. Then, fiddle with each seed a little so \\n # the seeds aren't the same every time the code is run\\n seed = np.int(random_number_seeds[pid] * (np.random.rand()*(10.0 - 0.01) + 0.01))\\n \\n np.random.seed(seed)\\n \\n #print 'id and seed', pid, seed\\n \\n n_particles = 0\\n loop_counter = 0\\n fmin_scale = 1.0E-100 # -16 -> -32 -> -100\\n F_max = np.max(pd.DF.f) #; F_min = np.min(pd.DF.f);\\n F_min = np.min(pd.DF.f) * fmin_scale\\n # F_max = np.max(pd.DF.f[:-1])#; F_max = 1.0E-88\\n #print F_min, F_max\\n if pd.optimize:\\n relative_potential = pd._interpolate_relative_potential\\n else:\\n relative_potential = pd.DF.relative_potential\\n \\n \\n pos = np.zeros((nmax, 3))\\n vel = np.zeros((nmax, 3)) \\n \\n while (( n_particles < nmax) and (loop_counter < max_loop)):\\n \\n r = pd._choose_position()\\n Psi = relative_potential(r) \\n \\n v = pd._choose_velocity(r, Psi)\\n \\n E = Psi - 0.5 * v * v\\n \\n f_E = pd.DF.interpolate_f(E)\\n \\n logF = ( np.random.rand()*(np.log10(F_max) - np.log10(F_min)) + np.log10(F_min) )\\n \\n # if choosing random F in log F, might be good to do the comparison in logspace as well\\n #.... i.e log(F) <= log(f_E) \\n #\\n # 0 FOR F_E MEANS THAT E < E_min of the potential. THis happens when Psi - KE is smaller\\n # than the value of the potential at large_r... should this be considered unbound \\n # even though it isn't zero? Is this effectively zero? This has been adjusted in the velocity\\n # picking routine but needs verification to make sure it works.... but regardless, I doubt\\n # that this is the reason why the particles are failing for NFW but working for hernquist....\\n #\\n if np.abs(np.log10(f_E)) == np.inf:\\n keep_particle = False\\n _my_print('log value error... throwing out particle')\\n else:\\n keep_particle = (logF <= np.log10(f_E))\\n\\n \\n if keep_particle: \\n index = n_particles \\n \\n # \\n # need to sample in cosine theta NOT theta!!!!! WTF!!!!!\\n #\\n #\\n \\n theta = np.arccos( np.random.rand() * (2.0) - 1.0)\\n \\n # convert position to cartesian using random theta and phi\\n phi = np.random.rand() * 2.0 * np.pi\\n \\n x = np.sin(theta) * np.cos(phi)\\n y = np.sin(theta) * np.sin(phi)\\n z = np.cos(theta)\\n \\n pos[index] = r * np.array([x,y,z])\\n\\n # repeat for velocity using new random numbersw\\n theta = np.arccos( np.random.rand() * (2.0) - 1.0)\\n phi = np.random.rand() * 2.0 * np.pi\\n \\n vx = np.sin(theta) * np.cos(phi)\\n vy = np.sin(theta) * np.sin(phi)\\n vz = np.cos(theta)\\n \\n vel[index] = v * np.array([vx,vy,vz])\\n \\\"\\\"\\\" \\n #else: # do strict cartesian ... slower.... less attractive\\n # \\n # axis_list = [0, 1, 2]\\n # random.shuffle(axis_list)\\n \\n # #axis_index = np.random.randint(3)\\n # first_axis = axis_list[0]\\n #\\n # pos[index, first_axis] = np.random.rand() * (2.0*r) - r\\n #del axis_list[axis_index]\\n \\n #axis_index = np.random.randint(2)\\n # second_axis = axis_list[1]\\n \\n max_r = np.sqrt(r*r - pos[index,first_axis]**2)\\n \\n pos[index, second_axis] = np.random.rand()*(2.0 * max_r) - max_r\\n #del axis_list[axis_index]\\n \\n max_r = np.sqrt(r*r - pos[index,first_axis]**2 - pos[index,second_axis]**2)\\n \\n third_axis = axis_list[2]\\n pos[index, third_axis] = np.random.rand() * (2.0 * max_r) - max_r\\n \\n if np.sqrt(pos[index,0]**2 + pos[index,1]**2 + pos[index,2]**2) > r:\\n _my_print('R IS TOO LARGE')\\n \\n ###\\n axis_list = [0, 1, 2]\\n random.shuffle(axis_list)\\n \\n #axis_index = np.random.randint(3)\\n first_axis = axis_list[0]\\n \\n vel[index, first_axis] = np.random.rand() * (2.0*v) - v\\n #del axis_list[axis_index]\\n \\n #axis_index = np.random.randint(2)\\n second_axis = axis_list[1]\\n \\n max_v = np.sqrt(v*v - vel[index,first_axis]**2)\\n \\n vel[index, second_axis] = np.random.rand()*(2.0 * max_v) - max_v\\n #del axis_list[axis_index]\\n \\n max_v = np.sqrt(v*v - vel[index,first_axis]**2 - vel[index,second_axis]**2)\\n \\n third_axis = axis_list[2]\\n vel[index, third_axis] = np.random.rand() * (2.0 * max_v) - max_v \\n \\n \\n \\\"\\\"\\\"\\n n_particles = n_particles + 1\\n \\n if (loop_counter % 5000) == 0:\\n _my_print(\\\"Have %4i particles. On loop %6i\\\"%(n_particles, loop_counter))\\n loop_counter = loop_counter + 1\\n \\n # now write out to a temporary file\\n f = open(outfile + \\\"_%02i_\\\"%(pid) + \\\".temp\\\", 'w')\\n fmt = \\\"%12.12E %12.12E %12.12E %12.12E %12.12E %12.12E %12.12E\\\\n\\\"\\n \\n \\n for i in np.arange(nmax):\\n f.write(fmt%(pd.M_part, pos[i,0], pos[i,1], pos[i,2], \\n vel[i,0], vel[i,1], vel[i,2]))\\n \\n \\n f.close() \\n \\n return pos, vel\",\n \"def main():\\n\\n modulus = lambda c: (c.real*c.real + c.imag*c.imag)\\n test_case = 0\\n while True:\\n try:\\n _r, _i, iterations = input().split()\\n except EOFError:\\n break\\n\\n position = complex(float(_r), float(_i))\\n z_value, outside = complex(0.0, 0.0), False\\n for _ in range(int(iterations)):\\n z_next = z_value * z_value + position\\n outside = (modulus(z_next) > 4)\\n if outside:\\n break\\n z_value = z_next\\n\\n test_case += 1\\n print('Case {0}: {1}'.format(test_case, 'OUT' if outside else 'IN'))\",\n \"def infinite_odd_generator():\\n current = 1\\n while True:\\n yield current\\n current = current + 2\",\n \"def internal_loop(p, i, j, k, l):\\n return _RNAstructure_wrap.internal_loop(p, i, j, k, l)\",\n \"def looptcs(self): \\n while self.itr < 1: \\n #self.genRandomNoise() #developing\\n self.tcs(lpf=1)\\n #self.itr +=1 \",\n \"def run(self, p):\\n while self.state < 3:\\n self.__step(p)\",\n \"def loop(self):\\r\\n\\r\\n self._next_state = self._current_state\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6793967","0.66965246","0.65839756","0.6332659","0.6076374","0.6045058","0.60284704","0.5919505","0.5916802","0.5843467","0.5810192","0.57871115","0.577109","0.5753707","0.571716","0.5670822","0.56154424","0.55902815","0.5575924","0.55405825","0.54837346","0.5482796","0.54680777","0.54680777","0.54586256","0.5440231","0.54307073","0.54229015","0.54176474","0.5410539","0.53897214","0.5379868","0.53609383","0.5348114","0.5343744","0.531534","0.53148794","0.52919453","0.52878016","0.5284361","0.52701616","0.52445877","0.5229934","0.5223179","0.5215353","0.5212702","0.51812834","0.51537025","0.5153554","0.5140172","0.51315","0.5113388","0.51128674","0.5106567","0.50988036","0.50857514","0.5084546","0.5069044","0.5062545","0.50593895","0.5051542","0.50422156","0.50411016","0.50352174","0.5033086","0.5016457","0.5008551","0.49973014","0.49953613","0.4994165","0.49798235","0.49750662","0.49718118","0.49569836","0.49524134","0.49524134","0.49482745","0.49337462","0.49329537","0.49309844","0.49129295","0.4894774","0.48840445","0.4879553","0.48755983","0.48716992","0.48703033","0.4852856","0.4845294","0.48290986","0.48223886","0.48185638","0.48130712","0.48121455","0.48119175","0.48116982","0.48022354","0.4801737","0.4792708","0.47919333"],"string":"[\n \"0.6793967\",\n \"0.66965246\",\n \"0.65839756\",\n \"0.6332659\",\n \"0.6076374\",\n \"0.6045058\",\n \"0.60284704\",\n \"0.5919505\",\n \"0.5916802\",\n \"0.5843467\",\n \"0.5810192\",\n \"0.57871115\",\n \"0.577109\",\n \"0.5753707\",\n \"0.571716\",\n \"0.5670822\",\n \"0.56154424\",\n \"0.55902815\",\n \"0.5575924\",\n \"0.55405825\",\n \"0.54837346\",\n \"0.5482796\",\n \"0.54680777\",\n \"0.54680777\",\n \"0.54586256\",\n \"0.5440231\",\n \"0.54307073\",\n \"0.54229015\",\n \"0.54176474\",\n \"0.5410539\",\n \"0.53897214\",\n \"0.5379868\",\n \"0.53609383\",\n \"0.5348114\",\n \"0.5343744\",\n \"0.531534\",\n \"0.53148794\",\n \"0.52919453\",\n \"0.52878016\",\n \"0.5284361\",\n \"0.52701616\",\n \"0.52445877\",\n \"0.5229934\",\n \"0.5223179\",\n \"0.5215353\",\n \"0.5212702\",\n \"0.51812834\",\n \"0.51537025\",\n \"0.5153554\",\n \"0.5140172\",\n \"0.51315\",\n \"0.5113388\",\n \"0.51128674\",\n \"0.5106567\",\n \"0.50988036\",\n \"0.50857514\",\n \"0.5084546\",\n 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identifier"},"document":{"kind":"string","value":"def _Name(self, t):\n self.write(t.id)"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def identifier(self):","def get_identifier(self):","def name(self, name):\n pass","def name(self):","def name(self):","def name(self):","def name(self):","def get_name():","def name(self):\n ...","def name():\n pass","def name():\n pass","def getName():","def getName():","def getName():","def getName():","def getName():","def getName():","def Name(self) -> str:","def Name(self) -> str:","def Name(self) -> str:","def Name(self) -> str:","def name(self):\r\n pass","def name(self):\n pass","def get_name(self):","def get_name(self):","def name(self) -> str:","def name(self) -> str:","def name(self) -> str:","def name(self) -> str:","def name(self) -> str:","def name(self) -> str: # 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NotImplementedError\",\n \"def name(self):\\n raise NotImplementedError\",\n \"def name(self):\\n raise NotImplementedError\",\n \"def name(self):\\n raise NotImplementedError\",\n \"def name(self):\\n raise NotImplementedError\",\n \"def name(self):\\n raise NotImplementedError\",\n \"def name(self): \\n\\t\\treturn self._name\",\n \"def name(self):\\n raise NotImplementedError\",\n \"def get_name(self): \\r\\n return self.name\",\n \"def name() -> str:\\n pass\",\n \"def name_value(self) -> global___Expression.NameValue:\",\n \"def name(self) -> str:\\n pass\",\n \"def name(self) -> str:\\n pass\",\n \"def name(self) -> str:\\n pass\",\n \"def name(self) -> str:\\n pass\",\n \"def __assign_name_id(self):\\n if not self.name_id:\\n self.name_id = str(BaseTicketing.objects.create())\",\n \"def _get_name(self):\\n return self.__name\",\n \"def _get_name(self):\\n return self.__name\",\n \"def _get_name(self):\\n return self.__name\",\n \"def _get_name(self):\\n return self.__name\",\n 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query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Write data to log file on predicted directory contents. | def log_dir_stacks_contents(dir_stacks):
for directory in dir_stacks:
logging.info('-'*80)
logging.info('Predicted directory contents of:\n{0}'
.format(directory.path))
files = directory.file_names
files = sorted(files)
logging.info('Number of file... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_data(self):\n\n self.check_dir()\n with open(self.log_file, \"a\") as logger_file:\n logger_file.write(\"{}, {}\\n\".format(self.time, self.msg))",
"def write_data(self, data):\n print \"Writing data...\"\n # Write data into log\n self.log.write_file(data)\n\... | [
"0.666307",
"0.6575131",
"0.6570006",
"0.6507889",
"0.64625835",
"0.64397556",
"0.61870855",
"0.614731",
"0.61356354",
"0.611342",
"0.6105818",
"0.6066165",
"0.60469264",
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"0.60118806",
"0.6003082",
"0.6002379",
"0.59288365",
"0.58907926",
"0.58439434... | 0.0 | -1 |
Writes the output in simple shell script format. The default format is a shell script file containing the command lines. | def write_default(workflows, output_dir):
# Calculate the total number of commands
number_of_commands = 0
for workflow in workflows:
number_of_commands += sum(map(len, workflow))
# Create command line strings
i = 0
out_lines = ['echo Started executing shell script at:', 'date'... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_subshell_file_contents(cmd, skip_module_loading, skip_module_unloading):\r\n\r\n\r\n out_lines = []\r\n\r\n # Invoke commands to produce their output command string(s)\r\n cmd_list = cmd.command_lines\r\n cmd_list = map(clean_command_lines, cmd_list)\r\n\r\n # Write current command to s... | [
"0.6161045",
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"0.5830141",
"0.5728146",
"0.5581661",
"0.5542562",
"0.5534459",
"0.5473914",
"0.5465518",
"0.54606724",
"0.5454724",
"0.54491675",
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"0.54052734",
"0.5380631",
"0.53805697",
"0.53546333",
"0.534062",
"0.5320091",
"0.53145796... | 0.6917708 | 0 |
Infer the number of threads to create and allocate commands to each. | def determine_job_workloads(workloads, command_line_parameters):
# Group commands by id
parallelized_workloads = []
i = 0
for workflow in workloads:
i += 1
# Workflow is split by user with SPLIT command or automatically split
# at brach/join events
thread_allocat... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def setNumThreads(self, num):\r\n self.threads = num",
"def Threads():\n for i in range(0, idc.get_thread_qty()):\n yield idc.getn_thread(i)",
"def getThreads():\r\n return multiprocessing.cpu_count()",
"def manage_threads(_) -> int:\n return 1 << 33",
"def manage_threads(_) -> int:\... | [
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"0.6577315",
"0.6577315",
"0.6420568",
"0.63426775",
"0.6258061",
"0.62413865",
"0.6231553",
"0.6227415",
"0.6212451",
"0.61994797",
"0.61715746",
"0.60731506",
"0.6033056",
"0.6029094",
"0.6028547",
"0.6028547",
"0.6010339",
"0.6005012"... | 0.0 | -1 |
Infer which ids should be run in the same thread. | def infer_id_groups(workflow, command_line_parameters):
# Find the step in the current workflow with the highest number of ids
# per command and group ids based on that (as a starting point).
workflow_step_index_with_max_ids_per_cmd = 0
max_ids = 0
for workflow_step_index in xrange(len(workflow... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_id(self):\n for id, thread in threading._active.items(): \n if thread is self: \n return id",
"def use_public_threads(_) -> int:\n return 1 << 34",
"def use_public_threads(_) -> int:\n return 1 << 34",
"def Threads():\n for i in range(0, idc.get_threa... | [
"0.6132015",
"0.5585469",
"0.5585469",
"0.5548456",
"0.55260503",
"0.55260503",
"0.55258983",
"0.55258983",
"0.53816414",
"0.5298817",
"0.5154055",
"0.5151005",
"0.5147153",
"0.5091501",
"0.5091501",
"0.5067499",
"0.5065719",
"0.50139385",
"0.499055",
"0.4987107",
"0.49789983... | 0.45081964 | 91 |
Writes the output in LSF job array format. Creates sub shell scripts that contain the workflow for each input file separately. After this main shell script containing TORQUE configuration is created. This script is responsible for starting the sub shells as separate processes. | def write_lsf(workloads, input_file_parameters, command_line_parameters):
workload_index = 0
workload_zfill_amount = len(str(len(workloads)))
workload_file_paths = []
for workload in workloads:
# Each workflow part will have separate file to submit to TORQUE with
# sbatch command. ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_unix(workloads, input_file_parameters, command_line_parameters):\r\n\r\n workload_index = 0\r\n workload_zfill_amount = len(str(len(workloads)))\r\n background_process_list = []\r\n for workload in workloads:\r\n # Each workflow part will have separate file to submit to TORQUE with\r\n... | [
"0.7333931",
"0.71901464",
"0.67998576",
"0.6780292",
"0.63524926",
"0.63199294",
"0.6265143",
"0.6096322",
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"0.588439",
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"0.56735706",
"0.5660371",
"0.56586134",
"0.5653163",
"0.56474715",
"0.5637175",
"0.5635... | 0.73390156 | 0 |
Writes the output in Sun Grid Engine job array submission format. Creates sub shell scripts that contain the workflow for each input file separately. After this main shell script containing SGE configuration is created. This script is responsible for starting the sub shells as separate processes. | def write_sge(workloads, input_file_parameters, command_line_parameters):
validate_resource_manager_parameters(
input_file_parameters.resource_manager_params,
['# -o', '# -e', '# -t'])
workload_index = 0
workload_zfill_amount = len(str(len(workloads)))
workload_file_paths = []
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def gen_jobs(fpath, num_runs, netid):\n\n run = \"\"\n run += \"import sys\\n\"\n run += \"import subprocess\\n\"\n run += \"cmd_array = (\"\n for i in range(num_runs):\n run += \"r\\\"python test.py %d\\\"\" % i\n run += \",\\n\"\n\n run += \")\\n\"\n run += \"p = subprocess.Pop... | [
"0.6961429",
"0.67148894",
"0.6494868",
"0.6412396",
"0.62112385",
"0.6205976",
"0.61844945",
"0.6169179",
"0.611067",
"0.60501873",
"0.6024691",
"0.5989451",
"0.59104246",
"0.5877937",
"0.5877124",
"0.58605266",
"0.5851678",
"0.5838534",
"0.5806282",
"0.5746106",
"0.573468",... | 0.69199365 | 1 |
Writes the output in SLURM array job format. Creates sub shell scripts that contain the workflow for each input file separately. After this main shell script containing SLURM configuration is created. This script is responsible for starting the sub shells as separate processes. | def write_slurm(workloads, input_file_parameters, command_line_parameters):
workload_index = 0
workload_zfill_amount = len(str(len(workloads)))
workload_file_paths = []
for workload in workloads:
# Each workflow part will have separate file to submit to SLURM with
# sbatch command.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_sge(workloads, input_file_parameters, command_line_parameters):\r\n validate_resource_manager_parameters(\r\n input_file_parameters.resource_manager_params,\r\n ['# -o', '# -e', '# -t'])\r\n\r\n workload_index = 0\r\n workload_zfill_amount = len(str(len(workloads)))\r\n workload... | [
"0.6855347",
"0.68422127",
"0.6675286",
"0.650287",
"0.645152",
"0.6278125",
"0.6242975",
"0.6226567",
"0.61965185",
"0.61401135",
"0.60027885",
"0.59648293",
"0.5838186",
"0.58326197",
"0.5755615",
"0.5729635",
"0.57208526",
"0.5701956",
"0.56957155",
"0.5687705",
"0.5686899... | 0.7628179 | 0 |
Writes the output in TORQUE multiple job submission format. Creates sub shell scripts that contain the workflow for each input file separately. After this main shell script containing TORQUE configuration is created. This script is responsible for starting the sub shells as separate processes. | def write_torque(workloads, input_file_parameters, command_line_parameters):
validate_resource_manager_parameters(
input_file_parameters.resource_manager_params,
['#PBS -k', '#PBS -N', '#PBS -d', '#PBS -e', '#PBS -t'])
workload_index = 0
workload_zfill_amount = len(str(len(workloads))... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_unix(workloads, input_file_parameters, command_line_parameters):\r\n\r\n workload_index = 0\r\n workload_zfill_amount = len(str(len(workloads)))\r\n background_process_list = []\r\n for workload in workloads:\r\n # Each workflow part will have separate file to submit to TORQUE with\r\n... | [
"0.7209775",
"0.6585105",
"0.6554315",
"0.64478266",
"0.6284272",
"0.62066483",
"0.61065704",
"0.60671365",
"0.6054951",
"0.60292995",
"0.5986418",
"0.597275",
"0.59198385",
"0.5856972",
"0.5800811",
"0.577773",
"0.5759242",
"0.57556045",
"0.5738472",
"0.5737264",
"0.5733993"... | 0.73230857 | 0 |
Writes a parallelized workflow by using UNIX run background feature (&). Creates sub shell scripts that contain the workflow for each input file separately. After this main shell script is written, where each workflow is set to run as background process by using the shell & character. Workflow parts are separated by wa... | def write_unix(workloads, input_file_parameters, command_line_parameters):
workload_index = 0
workload_zfill_amount = len(str(len(workloads)))
background_process_list = []
for workload in workloads:
# Each workflow part will have separate file to submit to TORQUE with
# sbatch co... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def write_slurm(workloads, input_file_parameters, command_line_parameters):\r\n workload_index = 0\r\n workload_zfill_amount = len(str(len(workloads)))\r\n workload_file_paths = []\r\n for workload in workloads:\r\n # Each workflow part will have separate file to submit to SLURM with\r\n ... | [
"0.6447484",
"0.6394443",
"0.62565774",
"0.6216091",
"0.6154703",
"0.6106836",
"0.608601",
"0.5807879",
"0.57455873",
"0.57444465",
"0.5742326",
"0.569049",
"0.5653086",
"0.5634561",
"0.5627084",
"0.56151015",
"0.5591978",
"0.54646945",
"0.5456538",
"0.5420467",
"0.5411606",
... | 0.74553514 | 0 |
Checks that user is has not defined any parameters that are autocreated | def validate_resource_manager_parameters(user_defined_parameters,
auto_defined_parameters):
for udp in user_defined_parameters:
for adp in auto_defined_parameters:
if udp.startswith(adp):
raise STAPLERerror.STAPLERerror('Resource mana... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _check_params(self):\n pass",
"def checkNeededParams(self):\n for clp,value in self.neededParamsNames.items():\n if value[0] not in self.neededParams:\n print >> sys.stderr, clp+\" is a mandatory parameter \"\n self.printUsage()\n sys.exit... | [
"0.6632489",
"0.6268276",
"0.624129",
"0.6089055",
"0.6051467",
"0.6032275",
"0.5992193",
"0.59893614",
"0.5983471",
"0.59563446",
"0.59472436",
"0.590658",
"0.5867398",
"0.5828247",
"0.58234674",
"0.58154815",
"0.5803279",
"0.5785927",
"0.57798564",
"0.5738635",
"0.5737196",... | 0.5756114 | 19 |
Creates a list of necessary information for each output command. | def generate_subshell_file_contents(cmd, skip_module_loading, skip_module_unloading):
out_lines = []
# Invoke commands to produce their output command string(s)
cmd_list = cmd.command_lines
cmd_list = map(clean_command_lines, cmd_list)
# Write current command to stdout
out_lines.ap... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_output_metadata(self):\n return []",
"def get_output_descriptions(self):\n raise NotImplementedError",
"def output_names(self):\n return []",
"def create_output_handlers():\n if not args.outputs:\n return []\n outputs = []\n for name in utils.split_comma(args.outp... | [
"0.73233604",
"0.7189033",
"0.7010749",
"0.69144946",
"0.6850369",
"0.6815734",
"0.6672127",
"0.66309345",
"0.66142124",
"0.6599733",
"0.6595081",
"0.65784407",
"0.6530771",
"0.65211886",
"0.64531296",
"0.64531296",
"0.64325684",
"0.63889813",
"0.63545203",
"0.6327555",
"0.63... | 0.0 | -1 |
Ensures that arguments and values are (single) white space separated. | def clean_command_lines(cmd):
cmd = ' '.join(cmd.split())
return cmd | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_no_whitespace(args):\n for arg in args:\n for char in arg:\n if char in string.whitespace:\n raise RuntimeError(\"No whitespace characters are currently allowed in input arguments. Replace spaces in file and folder names with underscores ('_').\")\n return",
"def ... | [
"0.6807918",
"0.66258246",
"0.660874",
"0.6433869",
"0.6125184",
"0.5969913",
"0.5925186",
"0.5903873",
"0.58645153",
"0.5832036",
"0.58220625",
"0.5815482",
"0.5767105",
"0.57398367",
"0.5724041",
"0.5717531",
"0.569505",
"0.56815946",
"0.5675995",
"0.5671961",
"0.56262225",... | 0.0 | -1 |
Reads log file produced by STAPLERs current run and prints report | def check_log():
errors = 0
warnings = 0
log_handle = open(CurrentLogPath.path)
for line in log_handle:
if 'ERROR:' in line:
errors += 1
elif 'WARNING' in line:
warnings += 1
log_handle.close()
if errors or warnings:
print '!'*80
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main():\n config_file = get_conf(get_config_name())\n if not config_file:\n sys.exit(1)\n log = get_last_file(config_file[\"LOG_DIR\"])\n MAIN_LOGGER.info(\"we've got log file named %s\", log.path)\n file_name = os.path.join(os.path.dirname(__file__), config_file['REPORT_DIR'],\n ... | [
"0.6773412",
"0.6640467",
"0.65638185",
"0.6527636",
"0.62079036",
"0.60315514",
"0.6017948",
"0.60173035",
"0.59455955",
"0.59300786",
"0.5924629",
"0.589744",
"0.58598727",
"0.5815822",
"0.5798734",
"0.576267",
"0.57485586",
"0.5747062",
"0.57360184",
"0.5734549",
"0.572172... | 0.53727585 | 65 |
test all ssh kwargs are not excluded from kwargs when preparing the SSH opts | def test_ssh_kwargs(test_opts):
opt_key = test_opts[0]
opt_value = test_opts[1]
# Is the kwarg in salt.utils.parsers?
in_parser = test_opts[2]
opts = {
"eauth": "auto",
"username": "test",
"password": "test",
"client": "ssh",
"tgt": "localhost",
"fun"... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _BuildSshOptions(self, batch, ask_key, use_cluster_key,\n strict_host_check, private_key=None, quiet=True,\n port=None):\n options = [\n \"-oEscapeChar=none\",\n \"-oHashKnownHosts=no\",\n \"-oGlobalKnownHostsFile=%s\" % pathutils.SSH_KNOWN_HOSTS_FI... | [
"0.61168206",
"0.6079297",
"0.59761137",
"0.5973248",
"0.5916606",
"0.58925116",
"0.57788223",
"0.5721382",
"0.56176704",
"0.55686975",
"0.5542059",
"0.5488901",
"0.54882175",
"0.54571706",
"0.544993",
"0.5444227",
"0.5437077",
"0.54274124",
"0.54254705",
"0.53893155",
"0.538... | 0.7008141 | 0 |
test expand_target when target is root@ | def test_expand_target_ip_address(opts, roster):
host = "127.0.0.1"
user = "test-user@"
opts["tgt"] = user + host
with patch("salt.utils.network.is_reachable_host", MagicMock(return_value=False)):
client = ssh.SSH(opts)
assert opts["tgt"] == user + host
with patch(
"salt.roster.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_expand_target_no_host(opts, tmp_path):\n host = \"127.0.0.1\"\n user = \"test-user@\"\n opts[\"tgt\"] = user + host\n\n roster = \"\"\"\n localhost: 127.0.0.1\n \"\"\"\n roster_file = str(tmp_path / \"test_roster_no_host\")\n with salt.utils.files.fopen(roster_file, \"w\") ... | [
"0.63958097",
"0.6291556",
"0.62824",
"0.5702456",
"0.5699233",
"0.56938875",
"0.549201",
"0.54572624",
"0.54021007",
"0.54021007",
"0.53985727",
"0.5355268",
"0.53440464",
"0.52915114",
"0.5288271",
"0.50941646",
"0.50924903",
"0.5082354",
"0.5047073",
"0.50409645",
"0.50388... | 0.5573608 | 6 |
test expand_target when host is not included in the rosterdata | def test_expand_target_no_host(opts, tmp_path):
host = "127.0.0.1"
user = "test-user@"
opts["tgt"] = user + host
roster = """
localhost: 127.0.0.1
"""
roster_file = str(tmp_path / "test_roster_no_host")
with salt.utils.files.fopen(roster_file, "w") as fp:
salt.utils.yaml... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_expand_target_no_user(opts, roster):\n host = \"127.0.0.1\"\n opts[\"tgt\"] = host\n\n with patch(\"salt.utils.network.is_reachable_host\", MagicMock(return_value=False)):\n client = ssh.SSH(opts)\n assert opts[\"tgt\"] == host\n\n with patch(\n \"salt.roster.get_roster_file\"... | [
"0.7059753",
"0.6958238",
"0.6752136",
"0.6731314",
"0.5922972",
"0.5872034",
"0.5807112",
"0.57836884",
"0.57331073",
"0.57315934",
"0.57153296",
"0.5710335",
"0.5678303",
"0.56106454",
"0.55797064",
"0.55640423",
"0.5553922",
"0.55535734",
"0.553814",
"0.5440517",
"0.536293... | 0.7415296 | 0 |
test expand_target when target is root@ | def test_expand_target_dns(opts, roster):
host = "localhost"
user = "test-user@"
opts["tgt"] = user + host
with patch("salt.utils.network.is_reachable_host", MagicMock(return_value=False)):
client = ssh.SSH(opts)
assert opts["tgt"] == user + host
with patch(
"salt.roster.get_ros... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_expand_target_no_host(opts, tmp_path):\n host = \"127.0.0.1\"\n user = \"test-user@\"\n opts[\"tgt\"] = user + host\n\n roster = \"\"\"\n localhost: 127.0.0.1\n \"\"\"\n roster_file = str(tmp_path / \"test_roster_no_host\")\n with salt.utils.files.fopen(roster_file, \"w\") ... | [
"0.63979805",
"0.6293237",
"0.6284433",
"0.5704041",
"0.5698366",
"0.5575425",
"0.5491849",
"0.5453624",
"0.53991467",
"0.5397131",
"0.5397131",
"0.535758",
"0.53415",
"0.52911496",
"0.5290956",
"0.5091858",
"0.50912255",
"0.5079253",
"0.50453305",
"0.5040328",
"0.50354356",
... | 0.56953484 | 5 |
test expand_target when no user defined | def test_expand_target_no_user(opts, roster):
host = "127.0.0.1"
opts["tgt"] = host
with patch("salt.utils.network.is_reachable_host", MagicMock(return_value=False)):
client = ssh.SSH(opts)
assert opts["tgt"] == host
with patch(
"salt.roster.get_roster_file", MagicMock(return_value... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def testExpandedTargets(self):\n self.all_targets = self.blade.analyze_targets()\n\n sys.stdout.flush()\n sys.stderr.flush()\n\n self.assertTrue(self.blade.get_expanded())\n self.assertTrue(self.all_targets)\n\n system_lib = ('#', 'pthread')\n proto_lib_option = (se... | [
"0.671508",
"0.64208895",
"0.60364896",
"0.5930217",
"0.5846596",
"0.5827087",
"0.5764492",
"0.5736275",
"0.561528",
"0.556417",
"0.55002284",
"0.54691464",
"0.54557925",
"0.54245645",
"0.53729737",
"0.53693706",
"0.5354176",
"0.5353399",
"0.5351528",
"0.53332335",
"0.5330246... | 0.6405484 | 2 |
test update_targets when host is ip address | def test_update_targets_ip_address(opts):
host = "127.0.0.1"
user = "test-user@"
opts["tgt"] = user + host
with patch("salt.utils.network.is_reachable_host", MagicMock(return_value=False)):
client = ssh.SSH(opts)
assert opts["tgt"] == user + host
client._update_targets()
assert opts... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_update_targets_dns(opts):\n host = \"localhost\"\n user = \"test-user@\"\n opts[\"tgt\"] = user + host\n\n with patch(\"salt.utils.network.is_reachable_host\", MagicMock(return_value=False)):\n client = ssh.SSH(opts)\n assert opts[\"tgt\"] == user + host\n client._update_targets()... | [
"0.7464471",
"0.71415",
"0.6910683",
"0.6657661",
"0.64958376",
"0.64322263",
"0.6429889",
"0.6385466",
"0.6237695",
"0.61982393",
"0.61476934",
"0.61162746",
"0.6114164",
"0.610654",
"0.6072472",
"0.6027562",
"0.6008352",
"0.598404",
"0.5966812",
"0.5915929",
"0.5911148",
... | 0.8039261 | 0 |
test update_targets when host is dns | def test_update_targets_dns(opts):
host = "localhost"
user = "test-user@"
opts["tgt"] = user + host
with patch("salt.utils.network.is_reachable_host", MagicMock(return_value=False)):
client = ssh.SSH(opts)
assert opts["tgt"] == user + host
client._update_targets()
assert opts["tgt"]... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_update_targets_ip_address(opts):\n host = \"127.0.0.1\"\n user = \"test-user@\"\n opts[\"tgt\"] = user + host\n\n with patch(\"salt.utils.network.is_reachable_host\", MagicMock(return_value=False)):\n client = ssh.SSH(opts)\n assert opts[\"tgt\"] == user + host\n client._update_ta... | [
"0.7475771",
"0.7439883",
"0.6938763",
"0.674091",
"0.6428459",
"0.61800534",
"0.6096695",
"0.60605717",
"0.58983433",
"0.5897955",
"0.58913845",
"0.5874283",
"0.585102",
"0.58426744",
"0.5839361",
"0.5837791",
"0.5811867",
"0.5776639",
"0.5776224",
"0.5753839",
"0.5708274",
... | 0.8254757 | 0 |
test update_targets when no user defined | def test_update_targets_no_user(opts):
host = "127.0.0.1"
opts["tgt"] = host
with patch("salt.utils.network.is_reachable_host", MagicMock(return_value=False)):
client = ssh.SSH(opts)
assert opts["tgt"] == host
client._update_targets()
assert opts["tgt"] == host | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_update_targets_ip_address(opts):\n host = \"127.0.0.1\"\n user = \"test-user@\"\n opts[\"tgt\"] = user + host\n\n with patch(\"salt.utils.network.is_reachable_host\", MagicMock(return_value=False)):\n client = ssh.SSH(opts)\n assert opts[\"tgt\"] == user + host\n client._update_ta... | [
"0.6914559",
"0.66063684",
"0.65769297",
"0.65296984",
"0.65164536",
"0.64756906",
"0.6462618",
"0.6382679",
"0.6335945",
"0.633125",
"0.633125",
"0.633125",
"0.6292728",
"0.6273413",
"0.62559766",
"0.62496156",
"0.62388134",
"0.6209271",
"0.6199935",
"0.61500025",
"0.614538"... | 0.7098015 | 0 |
test update_targets and expand_target when host is dns | def test_update_expand_target_dns(opts, roster):
host = "localhost"
user = "test-user@"
opts["tgt"] = user + host
with patch("salt.utils.network.is_reachable_host", MagicMock(return_value=False)):
client = ssh.SSH(opts)
assert opts["tgt"] == user + host
with patch(
"salt.roster.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_update_targets_dns(opts):\n host = \"localhost\"\n user = \"test-user@\"\n opts[\"tgt\"] = user + host\n\n with patch(\"salt.utils.network.is_reachable_host\", MagicMock(return_value=False)):\n client = ssh.SSH(opts)\n assert opts[\"tgt\"] == user + host\n client._update_targets()... | [
"0.79298717",
"0.7271698",
"0.7193563",
"0.66447264",
"0.6619049",
"0.63972855",
"0.6149345",
"0.6114023",
"0.601661",
"0.5973573",
"0.59198385",
"0.59094423",
"0.5870842",
"0.58562446",
"0.5788324",
"0.5756256",
"0.57487756",
"0.57337517",
"0.5731826",
"0.57180697",
"0.56830... | 0.7761494 | 1 |
test parse_tgt when user and host set on the ssh cli tgt | def test_parse_tgt(opts):
host = "localhost"
user = "test-user@"
opts["tgt"] = user + host
with patch("salt.utils.network.is_reachable_host", MagicMock(return_value=False)):
assert not opts.get("ssh_cli_tgt")
client = ssh.SSH(opts)
assert client.parse_tgt["hostname"] == host
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_parse_tgt_no_user(opts):\n host = \"localhost\"\n opts[\"ssh_user\"] = \"ssh-usr\"\n opts[\"tgt\"] = host\n\n with patch(\"salt.utils.network.is_reachable_host\", MagicMock(return_value=False)):\n assert not opts.get(\"ssh_cli_tgt\")\n client = ssh.SSH(opts)\n assert clien... | [
"0.7891038",
"0.66818714",
"0.64358",
"0.626521",
"0.62499535",
"0.6192972",
"0.6165336",
"0.6032608",
"0.599522",
"0.59491026",
"0.5891626",
"0.5764562",
"0.56263566",
"0.5608297",
"0.554224",
"0.5528934",
"0.55157524",
"0.5487462",
"0.54454535",
"0.5427359",
"0.5413081",
... | 0.84123373 | 0 |
test parse_tgt when only the host set on the ssh cli tgt | def test_parse_tgt_no_user(opts):
host = "localhost"
opts["ssh_user"] = "ssh-usr"
opts["tgt"] = host
with patch("salt.utils.network.is_reachable_host", MagicMock(return_value=False)):
assert not opts.get("ssh_cli_tgt")
client = ssh.SSH(opts)
assert client.parse_tgt["hostname"] =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_parse_tgt(opts):\n host = \"localhost\"\n user = \"test-user@\"\n opts[\"tgt\"] = user + host\n\n with patch(\"salt.utils.network.is_reachable_host\", MagicMock(return_value=False)):\n assert not opts.get(\"ssh_cli_tgt\")\n client = ssh.SSH(opts)\n assert client.parse_tgt[... | [
"0.7992045",
"0.6546669",
"0.64141375",
"0.6399565",
"0.6154963",
"0.608924",
"0.60557365",
"0.594483",
"0.58621407",
"0.5818358",
"0.58130467",
"0.5710715",
"0.5646345",
"0.56421787",
"0.55266964",
"0.5521712",
"0.55002075",
"0.54592144",
"0.5452184",
"0.5410033",
"0.5379215... | 0.7612063 | 1 |
test "extra_filerefs" are not excluded from kwargs when preparing the SSH opts | def test_extra_filerefs(tmp_path, opts):
ssh_opts = {
"eauth": "auto",
"username": "test",
"password": "test",
"client": "ssh",
"tgt": "localhost",
"fun": "test.ping",
"ssh_port": 22,
"extra_filerefs": "salt://foobar",
}
roster = str(tmp_path /... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_ssh_kwargs(test_opts):\n opt_key = test_opts[0]\n opt_value = test_opts[1]\n # Is the kwarg in salt.utils.parsers?\n in_parser = test_opts[2]\n\n opts = {\n \"eauth\": \"auto\",\n \"username\": \"test\",\n \"password\": \"test\",\n \"client\": \"ssh\",\n \... | [
"0.585128",
"0.5742932",
"0.5614613",
"0.55098736",
"0.5377966",
"0.53588086",
"0.5335075",
"0.5329998",
"0.5300597",
"0.52753556",
"0.5273424",
"0.5244408",
"0.523141",
"0.52186584",
"0.52164793",
"0.5213526",
"0.5114157",
"0.50991726",
"0.5093965",
"0.50867444",
"0.50815296... | 0.6820886 | 0 |
The main function used to produce a model ready for compression finetuning from an original PyTorch model and a configuration object. dummy_forward_fn | def create_compressed_model(
model: Module,
config: NNCFConfig,
compression_state: Optional[Dict[str, Any]] = None,
dummy_forward_fn: Callable[[Module], Any] = None,
wrap_inputs_fn: Callable[[Tuple, Dict], Tuple[Tuple, Dict]] = None,
wrap_outputs_fn: Callable[[Tuple, Dict], Tuple[Tuple, Dict]] =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_export_pytorch_model(self):\n pytorch_model = PyTorchLinear()\n dummy_input = torch.empty(10, 10)\n\n with io.BytesIO() as f:\n onnx_converter._export_pytorch_model(f, pytorch_model, dummy_input)",
"def __init__(\n self,\n d_model,\n ff1_hsize=1024,\n... | [
"0.6455248",
"0.64005536",
"0.63606685",
"0.6281817",
"0.6148253",
"0.614193",
"0.6059045",
"0.60385054",
"0.6027797",
"0.60148734",
"0.60109484",
"0.59764093",
"0.5941529",
"0.5936054",
"0.59347934",
"0.59182537",
"0.59015125",
"0.5899236",
"0.58604336",
"0.58340454",
"0.582... | 0.68160737 | 0 |
The main function used to produce a model ready for adding compression from an original PyTorch model and a configuration object. | def create_nncf_network(
model: torch.nn.Module,
config: NNCFConfig,
dummy_forward_fn: Callable[[Module], Any] = None,
wrap_inputs_fn: Callable = None,
wrap_outputs_fn: Callable = None,
) -> NNCFNetwork:
if dummy_forward_fn is not None and wrap_inputs_fn is None:
raise ValueError(
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_compressed_model(\n model: Module,\n config: NNCFConfig,\n compression_state: Optional[Dict[str, Any]] = None,\n dummy_forward_fn: Callable[[Module], Any] = None,\n wrap_inputs_fn: Callable[[Tuple, Dict], Tuple[Tuple, Dict]] = None,\n wrap_outputs_fn: Callable[[Tuple, Dict], Tuple[Tupl... | [
"0.6805067",
"0.64645237",
"0.63963777",
"0.629331",
"0.6279846",
"0.62571394",
"0.62136424",
"0.6200934",
"0.6185239",
"0.61641246",
"0.6146346",
"0.61316377",
"0.6119912",
"0.60950714",
"0.60887957",
"0.60552573",
"0.60521084",
"0.6035455",
"0.60300136",
"0.60277575",
"0.60... | 0.0 | -1 |
Create compression algorithm builders by a given list of algorithm names. | def create_compression_algorithm_builder(config: NNCFConfig, should_init=True) -> PTCompressionAlgorithmBuilder:
algo_names = extract_algorithm_names(config)
return create_compression_algorithm_builder_from_algo_names(algo_names, config, should_init) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_compression_algorithm_builder_from_algo_names(\n algo_names: List[str], config: NNCFConfig, should_init: bool\n) -> PTCompressionAlgorithmBuilder:\n if not algo_names:\n algo_builder_classes = [NoCompressionAlgorithmBuilder]\n else:\n algo_builder_classes = [PT_COMPRESSION_ALGORIT... | [
"0.8176492",
"0.58072144",
"0.56273663",
"0.5360396",
"0.5243267",
"0.5199738",
"0.5186726",
"0.5184808",
"0.5183975",
"0.5176187",
"0.5148908",
"0.51379925",
"0.51363987",
"0.5109506",
"0.5105624",
"0.51028985",
"0.5090822",
"0.50801146",
"0.50719637",
"0.5062348",
"0.505617... | 0.62809783 | 1 |
Create compression algorithm builders by a given list of algorithm names. | def create_compression_algorithm_builder_from_algo_names(
algo_names: List[str], config: NNCFConfig, should_init: bool
) -> PTCompressionAlgorithmBuilder:
if not algo_names:
algo_builder_classes = [NoCompressionAlgorithmBuilder]
else:
algo_builder_classes = [PT_COMPRESSION_ALGORITHMS.get(alg... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_compression_algorithm_builder(config: NNCFConfig, should_init=True) -> PTCompressionAlgorithmBuilder:\n algo_names = extract_algorithm_names(config)\n return create_compression_algorithm_builder_from_algo_names(algo_names, config, should_init)",
"def algorithms_factory():\n all_algorithms = [... | [
"0.62809783",
"0.58072144",
"0.56273663",
"0.5360396",
"0.5243267",
"0.5199738",
"0.5186726",
"0.5184808",
"0.5183975",
"0.5176187",
"0.5148908",
"0.51379925",
"0.51363987",
"0.5109506",
"0.5105624",
"0.51028985",
"0.5090822",
"0.50801146",
"0.50719637",
"0.5062348",
"0.50561... | 0.8176492 | 0 |
Helper to call ``ir.actions.report.xml.render_report()``. | def render_report(cr, uid, ids, name, data, context=None):
registry = yuancloud.modules.registry.RegistryManager.get(cr.dbname)
return registry['ir.actions.report.xml'].render_report(cr, uid, ids, name, data, context) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate_xml_report(self, parser, data, objects,context=None):\n raise NotImplementedError()",
"def display_reports(self, layout): # pylint: disable=arguments-differ",
"def _generate_report(self):\n raise NotImplementedError",
"def render_report(self, res_ids, name, data):\n report =... | [
"0.6349062",
"0.63135356",
"0.60601676",
"0.60378766",
"0.59094083",
"0.59055895",
"0.58374727",
"0.57946086",
"0.57253027",
"0.56983304",
"0.5696021",
"0.5689943",
"0.56596303",
"0.5653516",
"0.56528664",
"0.56278044",
"0.5620049",
"0.56102747",
"0.55857426",
"0.55817837",
"... | 0.7703999 | 0 |
Returns timings for parts, where the video should be kept | def getSectionsOfNewVideo (silences, duration):
return [0.0] + silences + [duration] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def video_time():\r\n # The full time has the form \"0:32 / 3:14\"\r\n full_time = world.css_text('div.vidtime')\r\n\r\n # Split the time at the \" / \", to get [\"0:32\", \"3:14\"]\r\n elapsed_str, duration_str = full_time.split(' / ')\r\n\r\n # Convert each string to seconds\r\n return (parse_t... | [
"0.6023563",
"0.59063125",
"0.56466246",
"0.5624058",
"0.56141603",
"0.560595",
"0.5565088",
"0.5434631",
"0.5414467",
"0.538054",
"0.538054",
"0.538054",
"0.5377151",
"0.5325003",
"0.5311827",
"0.5305418",
"0.5302824",
"0.5241184",
"0.5222898",
"0.5206784",
"0.5206103",
"0... | 0.5964529 | 1 |
Remove implicit resolvers for a particular tag Takes care not to modify resolvers in super classes. We want to load datetimes as strings, not dates, because we go on to serialise as json which doesn't have the advanced types of yaml, and leads to incompatibilities down the track. | def remove_implicit_resolver(cls, tag_to_remove):
if 'yaml_implicit_resolvers' not in cls.__dict__:
cls.yaml_implicit_resolvers = cls.yaml_implicit_resolvers.copy()
for first_letter, mappings in cls.yaml_implicit_resolvers.items():
cls.yaml_implicit_resolvers[first_letter] = [
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_implicit_resolver(cls, tag_to_remove):\n if 'yaml_implicit_resolvers' not in cls.__dict__:\n cls.yaml_implicit_resolvers = cls.yaml_implicit_resolvers.copy()\n\n for first_letter, mappings in cls.yaml_implicit_resolvers.items():\n cls.yaml_implicit_resolvers[first_let... | [
"0.72351325",
"0.4945799",
"0.48895606",
"0.4787134",
"0.47753465",
"0.4747511",
"0.47394142",
"0.47089127",
"0.46628618",
"0.46158558",
"0.46017975",
"0.45002973",
"0.44972196",
"0.4452387",
"0.44437444",
"0.44306776",
"0.43933737",
"0.43895388",
"0.43718284",
"0.43617448",
... | 0.72606164 | 0 |
Try to get consent status for a single email address | def test_get_one(self, requests_mock, accepts_marketing):
matcher = requests_mock.post(
f'{settings.CONSENT_SERVICE_BASE_URL}'
f'{consent.CONSENT_SERVICE_PERSON_PATH_LOOKUP}',
json={
'results': [
{
'email': 'foo@bar.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Get_applicant_status(self, email):\n status = None\n if email in self.Attendees:\n status = ApplicantStatus.Accepted\n elif email in self.Waitlist:\n status = ApplicantStatus.Waitlisted\n else:\n raise MissingAddressException(email)\n return s... | [
"0.66149795",
"0.58070236",
"0.57529634",
"0.57529634",
"0.5640133",
"0.5606867",
"0.55703324",
"0.5566895",
"0.55520564",
"0.5475473",
"0.54483193",
"0.54387474",
"0.541679",
"0.53587914",
"0.5347143",
"0.53188384",
"0.5258927",
"0.525825",
"0.52227",
"0.5204715",
"0.5188246... | 0.60032743 | 1 |
Try to get consent status for a list of email addresses | def test_get_many(self, requests_mock, accepts_marketing, emails):
matcher = requests_mock.post(
f'{settings.CONSENT_SERVICE_BASE_URL}'
f'{consent.CONSENT_SERVICE_PERSON_PATH_LOOKUP}',
json={
'results': [
{
'email': ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def Get_applicant_status(self, email):\n status = None\n if email in self.Attendees:\n status = ApplicantStatus.Accepted\n elif email in self.Waitlist:\n status = ApplicantStatus.Waitlisted\n else:\n raise MissingAddressException(email)\n return s... | [
"0.61944306",
"0.6052294",
"0.57121867",
"0.5533557",
"0.54612494",
"0.54612494",
"0.5418097",
"0.52885437",
"0.5284941",
"0.5283386",
"0.5248901",
"0.5231524",
"0.522646",
"0.5220439",
"0.5209896",
"0.520168",
"0.51967436",
"0.5160234",
"0.5155434",
"0.5138707",
"0.51335585"... | 0.6141746 | 1 |
Try to update consent status | def test_update(self, requests_mock, accepts_marketing):
matcher = requests_mock.post(
f'{settings.CONSENT_SERVICE_BASE_URL}'
f'{consent.CONSENT_SERVICE_PERSON_PATH}',
json={
'consents': [
CONSENT_SERVICE_EMAIL_CONSENT_TYPE,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_confirm_add_flow_request_wrong_consent_status(self):\n self.client.login(username='duck', password='duck')\n res = self.client.get(\n '/v1/flow_requests/consents_confirmed/?success=true&consent_confirm_id={}'.format(WRONG_CONFIRM_ID))\n self.assertEqual(res.status_code, 302... | [
"0.6403219",
"0.6326517",
"0.6045587",
"0.5909922",
"0.5812921",
"0.57628024",
"0.5692828",
"0.5603599",
"0.55674136",
"0.5560386",
"0.549486",
"0.5482111",
"0.5482111",
"0.5476812",
"0.544154",
"0.54324704",
"0.5428267",
"0.5428241",
"0.54031616",
"0.5402912",
"0.540165",
... | 0.64573574 | 0 |
This function retrieves all ACISAs | def RetrieveACISA():
db = DBConnector()
cur = db.cursor()
SQLcmd = "SELECT * FROM snaps.SNAPsLocation"
cur.execute(SQLcmd)
returnList = []
count = 0
for item in cur.fetchall():
count += 1
tmplist = [item[1], item[2], count, str(item[0])]
returnList.append(tmplist)
return returnList | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_acls():\n return config.get_cfg_storage(ID_ACL)",
"def getAcdcs(url, requests):\n acdcs = []\n for request in requests:\n name=request['id']\n #if a wrong or weird name\n if len(request['key'])<3:\n print request\n continue\n if 'ACDC' not in nam... | [
"0.6468968",
"0.62329483",
"0.6149895",
"0.6125198",
"0.6006901",
"0.59983075",
"0.59268194",
"0.58295614",
"0.58222836",
"0.58132493",
"0.5776532",
"0.57738966",
"0.5739669",
"0.57129836",
"0.57093567",
"0.5707091",
"0.5696865",
"0.5693562",
"0.5683094",
"0.5662152",
"0.5629... | 0.63769305 | 1 |
Method to calculate a stat over all time steps | def time_stat(self, stat="mean"):
# create cdo command and run it
cdo_command = f"cdo -tim{stat}"
run_this(cdo_command, self, output="ensemble") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_stat_values(self):",
"def compute_stats(self):\n if self.stats is not None:\n return\n self.stats = np.zeros(STEPS_MAX + 1)\n for m in self.missions:\n m.compute_stats()\n self.stats += 100 * m.stats\n self.stats /= len(self.missions)",
"def... | [
"0.7152318",
"0.6610999",
"0.64921373",
"0.6394629",
"0.63097644",
"0.62772894",
"0.6209305",
"0.6110621",
"0.6110621",
"0.6110621",
"0.61086136",
"0.6045909",
"0.596642",
"0.5933937",
"0.5890365",
"0.58721894",
"0.58537525",
"0.5809977",
"0.58057666",
"0.57885444",
"0.573436... | 0.68386406 | 1 |
Calculate the sum of all values. | def sum(self):
return time_stat(self, stat="sum") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sum(self):\n return sum(self.values)",
"def sum(self) -> float:\n return sum(self.values)",
"def sum (self):\n return self.values.sum ()",
"def sum (self):\n return self.values.sum ()",
"def _sum(self):\n s = 0\n for element, value in self.items():\n ... | [
"0.829055",
"0.81755704",
"0.8065702",
"0.8065702",
"0.7918524",
"0.7903193",
"0.7888516",
"0.7864948",
"0.7796982",
"0.77682406",
"0.77394885",
"0.76733375",
"0.76402175",
"0.76350355",
"0.7526063",
"0.74657136",
"0.7424097",
"0.73245305",
"0.7189328",
"0.71821636",
"0.71768... | 0.6479448 | 62 |
Calculate the mean of all values. | def mean(self):
return time_stat(self, stat="mean") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def mean(values):\r\n return sum(values) / float(len(values))",
"def mean(vals):",
"def mean(self):\n return self.aggregate(np.mean)",
"def mean(self, values):\n return self.aggregate(values, \"mean\")",
"def get_mean(self):\n self.meanval = np.mean(self.adulist)",
"def mean(value... | [
"0.80817807",
"0.80095905",
"0.79848087",
"0.79562736",
"0.7932791",
"0.7910605",
"0.7860225",
"0.78547275",
"0.7822969",
"0.78094804",
"0.76588786",
"0.7649044",
"0.75542116",
"0.75120634",
"0.75096875",
"0.7501223",
"0.74895203",
"0.7484985",
"0.7479248",
"0.74709976",
"0.7... | 0.0 | -1 |
Calculate the minimums of all values. | def min(self):
return time_stat(self, stat="min") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def min(self, values):\n return self.aggregate(values, \"min\")",
"def argmin(self, values):\n return self.aggregate(values, \"argmin\")",
"def get_minimum():\n return [\n convert_variables([0.78547, 0.78547, 0.78547]),\n ]",
"def pmin(\n *x: NumericType,\n ... | [
"0.74669844",
"0.7113077",
"0.70211583",
"0.6998979",
"0.6964122",
"0.68508005",
"0.6805708",
"0.6718231",
"0.6707366",
"0.6705791",
"0.6616426",
"0.6613132",
"0.6566346",
"0.65005034",
"0.64451647",
"0.64139503",
"0.6405389",
"0.6403661",
"0.63760275",
"0.63603747",
"0.63527... | 0.0 | -1 |
Calculate the maximums of all values. | def max(self):
return time_stat(self, stat="max") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def max(self, values):\n return self.aggregate(values, \"max\")",
"def argmax(self, values):\n return self.aggregate(values, \"argmax\")",
"def maxs(self):\n return self._maxs",
"def max(self):\n return self._reduce_for_stat_function(F.max, only_numeric=False)",
"def _multiple_v... | [
"0.75460076",
"0.7304106",
"0.7165441",
"0.70699817",
"0.6978723",
"0.6956079",
"0.68157953",
"0.68139267",
"0.6805858",
"0.67493033",
"0.67341715",
"0.6709677",
"0.6672466",
"0.66649884",
"0.66636693",
"0.6657917",
"0.6644122",
"0.6643254",
"0.6637891",
"0.6631197",
"0.66247... | 0.6113221 | 68 |
Calculate the ranges of all values. | def range(self):
return time_stat(self, stat="range") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_ranges(self) -> typing.List[typing.Tuple[float, float]]:\n return self.ranges[:]",
"def ranges(self):\n return self._ranges",
"def _calc_range(self) -> np.ndarray:\n if self._is_ct25k():\n range_resolution = 30\n n_gates = 256\n else:\n n_gat... | [
"0.7304999",
"0.7190477",
"0.70206416",
"0.6923109",
"0.671551",
"0.6701029",
"0.66305655",
"0.66285425",
"0.6583244",
"0.65568984",
"0.6547148",
"0.6417848",
"0.6378743",
"0.6366492",
"0.6347865",
"0.63414705",
"0.6340766",
"0.63363886",
"0.6291575",
"0.62688875",
"0.6259675... | 0.5900169 | 59 |
Calculate the variances of all values. | def var(self):
return time_stat(self, stat="var") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def variance(self):\n return 1 / self.count() * sum((number-self.average())**2 for number in self.numbers)",
"def variance(dataset):\n avg = sum(dataset)/len(dataset)\n v = 0.0\n for data in dataset:\n v += (data - avg) * (data - avg)\n v = v / len(dataset)\n return v",
"def varian... | [
"0.80125666",
"0.77931684",
"0.7649437",
"0.7533885",
"0.75283134",
"0.74715513",
"0.7426311",
"0.7419505",
"0.7395441",
"0.73077893",
"0.73077893",
"0.7302322",
"0.7250643",
"0.724442",
"0.724442",
"0.724442",
"0.72422016",
"0.72269577",
"0.7226093",
"0.7064695",
"0.7051725"... | 0.0 | -1 |
Calculate the cumulative sums of all values. | def cum_sum(self):
# create cdo command and runit
cdo_command = "cdo -timcumsum"
run_this(cdo_command, self, output="ensemble") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _cumulative_sum(xs):\r\n cumsum = 0\r\n for x in xs:\r\n cumsum += x\r\n yield cumsum",
"def cumsum(self):\n return self._lift(lambda c: c.cumsum)",
"def cumulative_sum(x):\n total = 0\n sum = []\n for num in x:\n total += num\n sum.append(total)\n retur... | [
"0.76194537",
"0.75675356",
"0.7451289",
"0.7389407",
"0.7123383",
"0.70414937",
"0.7039762",
"0.69669527",
"0.69507825",
"0.69205093",
"0.69205093",
"0.69205093",
"0.6823596",
"0.68017983",
"0.67675036",
"0.672222",
"0.6675939",
"0.6646776",
"0.6646776",
"0.6646776",
"0.6630... | 0.66462934 | 20 |
Calculate the percentile of all values | def percentile(self, p=None):
if p is None:
raise ValueError("Please supply p")
if type(p) not in [int, float]:
raise TypeError("p is a " + str(type(p)) + ", not int or float")
if (p < 0) or (p > 100):
raise ValueError("p: " + str(p) + " is not between 0 and 100!")
self.run()
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def percentile(self, values, percent):\r\n if not values:\r\n return None\r\n k = (len(values)-1) * percent\r\n f = math.floor(k)\r\n c = math.ceil(k)\r\n if f == c:\r\n return values[int(k)]\r\n d0 = values[int(f)] * (c-k)\r\n d1 = values[int(... | [
"0.7536633",
"0.73546964",
"0.7115302",
"0.7113865",
"0.7047356",
"0.6971398",
"0.69039315",
"0.6857011",
"0.68506926",
"0.6806298",
"0.6801701",
"0.6801701",
"0.67855835",
"0.67740655",
"0.67420775",
"0.6736659",
"0.6730492",
"0.67275125",
"0.67147666",
"0.67132276",
"0.6688... | 0.6432344 | 48 |
Member function finding the connected elements of the Operator. Starting from a given visible state v, it finds all other visible states v' such that the Operator matrix element H(v,v') is different from zero. | def find_conn(self, v):
pass | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def connected((e,r)):\n \n # Deal with the middle case so we don't divide by zero\n if r==0: return [(1,1),(2,1),(3,1),(4,1),(5,1),(0,1)]\n # If the input is impossible, return nothing to prune the branch (shouldn't\n # happen)\n if e>=6*r: return []\n connected=[]\n mult=e//r\n rem=e % r... | [
"0.5978792",
"0.5884315",
"0.58464736",
"0.5796087",
"0.57762533",
"0.57726276",
"0.5764417",
"0.5762539",
"0.56606954",
"0.5632598",
"0.56248784",
"0.5591906",
"0.55837506",
"0.55837506",
"0.5572793",
"0.5548584",
"0.5541398",
"0.5533818",
"0.5529619",
"0.54843754",
"0.54757... | 0.0 | -1 |
Member function return random state of the Operator. | def random_states(self, num_of_states):
return numpy.random.choice([-1, 1], size=(num_of_states, ) + self.hilbert_state_shape) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rand(self):\n self.state = (self.a * self.state + self.c)\n return self.state",
"def _random_function(self, random_state):\n return random_state.rand",
"def rand(self):\n return self.State.rand()",
"def _get_random_state(self):\n self._validate_random_state()\n r... | [
"0.77615845",
"0.7757945",
"0.76763046",
"0.73098624",
"0.7200661",
"0.7015469",
"0.6799375",
"0.67601836",
"0.67269987",
"0.6699716",
"0.6632682",
"0.66309154",
"0.6621688",
"0.6599762",
"0.6579501",
"0.6552287",
"0.6517373",
"0.65139276",
"0.64921236",
"0.6456837",
"0.64295... | 0.0 | -1 |
Calculate the aggregated mean and stds. | def _get_aggregated_mean_std(self, means, stds, n):
mean = means.view(n, -1).sum(0) / n
std = (
stds.view(n, -1).sum(0) / n
+ ((means.view(n, -1) - mean) ** 2).view(n, -1).sum(0) / n
)
return mean.detach(), std.detach() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def aggregate_stats(self):\n if self.split_bn.track_running_stats:\n (\n self.bn.running_mean.data,\n self.bn.running_var.data,\n ) = self._get_aggregated_mean_std(\n self.split_bn.running_mean,\n self.split_bn.running_var,\n ... | [
"0.75994617",
"0.7462015",
"0.73687404",
"0.72231203",
"0.7077105",
"0.6966083",
"0.69622374",
"0.69211644",
"0.691378",
"0.6900489",
"0.6860511",
"0.6856823",
"0.6847475",
"0.6832801",
"0.68259156",
"0.6784394",
"0.67798734",
"0.6750028",
"0.67471087",
"0.6724783",
"0.668773... | 0.75622755 | 1 |
Synchronize running_mean, and running_var. Call this before eval. | def aggregate_stats(self):
if self.split_bn.track_running_stats:
(
self.bn.running_mean.data,
self.bn.running_var.data,
) = self._get_aggregated_mean_std(
self.split_bn.running_mean,
self.split_bn.running_var,
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _synchronize_vars_using_mean(new_var: NestedMap,\n old_var: NestedMap) -> NestedMap:\n delta = new_var - old_var\n delta_mean = jax.lax.pmean(delta, axis_name=data_parallel_axis_name)\n updated_var = old_var + delta_mean\n return updated_var",
"def _for... | [
"0.61866266",
"0.59162766",
"0.5869464",
"0.5706126",
"0.5659805",
"0.55348825",
"0.54772735",
"0.54338247",
"0.5433502",
"0.5417435",
"0.5414351",
"0.53781146",
"0.5374504",
"0.53155285",
"0.53055024",
"0.5271861",
"0.5214751",
"0.52096206",
"0.520088",
"0.5198845",
"0.51728... | 0.5927717 | 1 |
Return duration in years | def duration(self):
if self.is_valid:
return relativedelta(self.expiry, datetime.date.today()).years
else:
return -1 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def days_to_years(datum):\n return datum/DAYS_PER_YEAR",
"def year(self):\n return self._years",
"def unit_yr(self):\n return ((self.time_base * 60.0) * 24.0) * 365.0",
"def periods_in_a_year(self) -> float:\n return self.length / self.yearfrac",
"def _unit_yr(self):\n return ((se... | [
"0.72326726",
"0.7115902",
"0.70439005",
"0.70209014",
"0.6918941",
"0.68189144",
"0.6782731",
"0.67128307",
"0.66573805",
"0.6547447",
"0.6528867",
"0.6528867",
"0.6477337",
"0.64250714",
"0.6410062",
"0.64020795",
"0.63959104",
"0.6354012",
"0.6353335",
"0.6349158",
"0.6343... | 0.7217856 | 1 |
Transition from iceboot to domapp by uploading 'domappFile', uncompressing it and executing from iceboot. Load domapp FPGA first. | def uploadDomapp2(self, domappFile):
if not os.path.exists(domappFile): raise DomappFileNotFoundException(domappFile)
size = os.stat(domappFile)[ST_SIZE]
if size <= 0: return (False, "size error: %s %d bytes" % (domappFile, size))
# Load domapp FPGA
ok, txt = self.se("s\" domapp.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def load(app, verbose, replay, exp_config=None):\n if replay:\n exp_config = exp_config or {}\n exp_config[\"replay\"] = True\n log(header, chevrons=False)\n loader = LoaderDeployment(app, Output(), verbose, exp_config)\n loader.run()",
"def software_load(self, filename: str) -> None:\n... | [
"0.55233026",
"0.5206039",
"0.51236546",
"0.50307345",
"0.4985857",
"0.49178445",
"0.48859143",
"0.48607603",
"0.48495775",
"0.48479488",
"0.4829573",
"0.4828603",
"0.48110473",
"0.47907218",
"0.47523627",
"0.4730287",
"0.46991777",
"0.46957707",
"0.46943602",
"0.4688376",
"0... | 0.7968039 | 0 |
Function decorator for unittest test cases to specify test case timeout. | def timeout(time_limit):
class TimeoutException(Exception):
""" Subclass Exception to catch timer expiration during search """
pass
def handler(*args, **kwargs):
""" Generic handler to raise an exception when a timer expires """
raise TimeoutException("Test aborted due to timeo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_timeout(timeout):\n def decor(f):\n @functools.wraps(f)\n def inner(self, *args, **kwargs):\n self.useFixture(fixtures.Timeout(timeout, gentle=True))\n return f(self, *args, **kwargs)\n return inner\n return decor",
"def pytest_timeout_set_timer(item, sett... | [
"0.79690397",
"0.75481164",
"0.7531505",
"0.74053466",
"0.7395922",
"0.7079493",
"0.70790184",
"0.69973075",
"0.69770676",
"0.69770676",
"0.69739807",
"0.6917172",
"0.67718583",
"0.6764333",
"0.67116076",
"0.6696234",
"0.668591",
"0.6672928",
"0.6670006",
"0.6657833",
"0.6647... | 0.7551795 | 1 |
Generic handler to raise an exception when a timer expires | def handler(*args, **kwargs):
raise TimeoutException("Test aborted due to timeout. Test was " +
"expected to finish in less than {} second(s).".format(time_limit)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _api_timer_expiration_handler(self):\n\n try:\n self._api_lock.release()\n except:\n pass",
"def timer_object_timeout(seconds=5):\n def raise_timeout_exception():\n raise TimeoutReachedException(seconds=seconds)\n\n return Timer(seconds, raise_timeout_exceptio... | [
"0.7070175",
"0.6462937",
"0.64580625",
"0.6379626",
"0.63559324",
"0.62736166",
"0.62136364",
"0.6183778",
"0.6169782",
"0.61290145",
"0.60942507",
"0.60581577",
"0.5950327",
"0.5919582",
"0.58957946",
"0.58702505",
"0.58493656",
"0.5843904",
"0.58418983",
"0.58407634",
"0.5... | 0.5578642 | 38 |
Return counts of (total, unique) nodes visited | def counts(self):
return sum(self.counter.values()), len(self.visited) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def count_nodes(self):\n\t\treturn self.__count_nodes(self)",
"def count_unvisited(data):\n count = sum(n.count(\"n\") for n in data)\n return count",
"def count(self):\n\t\treturn len(list(self.nodes))",
"def node_count(self) -> int:\n return int(self.graph_tuple_stats.node_count or 0)",
"def get... | [
"0.7426861",
"0.736728",
"0.7362337",
"0.71854764",
"0.7132183",
"0.7039101",
"0.70088387",
"0.6983478",
"0.67954326",
"0.678729",
"0.6775083",
"0.6736858",
"0.6726595",
"0.67247325",
"0.6679473",
"0.66466904",
"0.66429496",
"0.6642772",
"0.6615163",
"0.66069305",
"0.6603299"... | 0.78785557 | 1 |
Test pruning in CustomPlayer.alphabeta | def test_alphabeta_pruning(self):
h, w = 15, 15
depth = 6
method = "alphabeta"
value_table = [[0] * w for _ in range(h)]
value_table[3][14] = 1
eval_fn = EvalTable(value_table)
blocked_cells = [(0, 9), (0, 13), (0, 14), (1, 8), (1, 9), (1, 14),
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_punish():\n game = Game()\n game.punish()\n assert game.mistake_count == 1",
"def test_fav_6(self):\n\t\tplayer_list = [Player(\"Blake Base\", 1, 300000, 10), Player(\"Corey Catcher\", 2, 500000, 20), Player(\"Dexter Dugout\", 3, 200000, 50)]\n\t\tself.assertEqual( free_agent_vorp(player_list, ... | [
"0.63363713",
"0.6252242",
"0.5996028",
"0.59319055",
"0.58755577",
"0.58621126",
"0.584469",
"0.58357257",
"0.5830485",
"0.57789344",
"0.5775311",
"0.5746706",
"0.5746534",
"0.572389",
"0.5722571",
"0.5664175",
"0.56187457",
"0.56148756",
"0.5613109",
"0.55841666",
"0.557837... | 0.54096156 | 38 |
Test iterative deepening for CustomPlayer.minimax | def test_id(self):
w, h = 11, 11
method = "minimax"
value_table = [[0] * w for _ in range(h)]
value_table[3][0] = 1
value_table[2][3] = 1
value_table[4][4] = 2
value_table[7][2] = 3
eval_fn = EvalTable(value_table)
depths = ["7+", "6", "5", "4", ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_minimax(self):\n h, w = 7, 7 # board size\n starting_location = (2, 3)\n adversary_location = (0, 0) # top left corner\n iterative_search = False\n method = \"minimax\"\n\n # The agent under test starts at position (2, 3) on the board, which\n # gives eig... | [
"0.6731821",
"0.6642983",
"0.66008",
"0.6585139",
"0.65825796",
"0.6374647",
"0.636703",
"0.62933755",
"0.6269931",
"0.6251355",
"0.6185939",
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"0.6150096",
"0.6106896",
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"0.6084524",
"0.6046474",
"0.60085076",
"0.60018104",
"0.5991797",
"0.5991797",
... | 0.65098166 | 5 |
Test output interface of CustomEval | def test_custom_eval(self):
player1 = "Player1"
player2 = "Player2"
game = isolation.Board(player1, player2)
heuristic = game_agent.CustomEval()
self.assertIsInstance(heuristic.score(game, player1), float,
"The heuristic function should return a floating point") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(self):\n self.eval()",
"def evaluate_output(self, output: int) -> Callable[[str], bool]:\n raise NotImplementedError",
"def evaluate_outputs(self):\n raise NotImplementedError(\n 'evaluate_outputs called but not implemented in the derived class.'\n )",
"def eva... | [
"0.73954976",
"0.724084",
"0.68502486",
"0.67774224",
"0.67774224",
"0.6679344",
"0.6601674",
"0.65922654",
"0.6552735",
"0.65413964",
"0.65413964",
"0.65413964",
"0.64627516",
"0.64274406",
"0.63952595",
"0.6380676",
"0.63543075",
"0.6329702",
"0.62897515",
"0.62686455",
"0.... | 0.6096009 | 28 |
Get data from an Amarok database. We fetch rating and score as well as Amarok's unique id for the track to have more reliable syncing after the initial import. | def get_amarok_data(item, db):
if hasattr(item, 'amarok_uid') and item.amarok_uid:
condition = "REPLACE(uniqueid, 'amarok-sqltrackuid://', '') = '%s'" % MySQLdb.escape_string(item.amarok_uid)
else:
condition = "REPLACE(CONCAT_WS('/',lastmountpoint, rpath), '/./', '/') = '%s'" % MySQLdb.escape_s... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def retrieve_from_db(self):\n pass",
"def fetch_from_db(self):\n self._potential_deals = DBApi.get_instance().potential_records\n self._filters = DBApi.get_instance().filters\n # Add markdown for url\n for data in self._potential_deals:\n data[\"url\"] = f\"[Link]({d... | [
"0.5660886",
"0.54901147",
"0.53773314",
"0.53103805",
"0.5294919",
"0.5287652",
"0.5280436",
"0.5218586",
"0.5123491",
"0.51149195",
"0.50887793",
"0.50697964",
"0.50557685",
"0.50500786",
"0.5047312",
"0.5042681",
"0.50382304",
"0.5036712",
"0.50281346",
"0.5021935",
"0.501... | 0.719275 | 0 |
get word2idx and word_embed | def read_old_glove(filepath):
print('reading glove files:', filepath)
word2idx = {}
word_embed = [['0'] * 300] # word_embed[0] = [0] * 300, represent the <PAD>
with open(filepath, 'r') as f:
for idx, line in enumerate(f):
line_list = line.split()
word = ' '.join(line... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_idx2word():\n idx2word = {}\n d = train_data.shared['word2idx']\n for word, idx in d.items():\n print(word)\n idx2word[idx] = word\n if config.use_glove_for_unk:\n d2 = train_data.shared['new_word2idx']\n for word, idx in d2.items():\... | [
"0.7195846",
"0.7079771",
"0.6538253",
"0.64752114",
"0.6429589",
"0.63948965",
"0.62947494",
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"0.60700834",
"0.60502183",
"0.5999034",
"0.5995582",
"0.59707487",
"0.59692556",
"0.59548... | 0.5724637 | 53 |
This is the method to call and analyze text with the supplied features | def analyze(self, features, text=None, url=None, html=None,
clean=True, xpath=None, fallback_to_raw=True,
return_analyzed_text=False, language=None):
body = {
'clean': clean,
'fallback_to_raw': fallback_to_raw,
'return_analyzed_text': return_an... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test__extract_features(self):\n text_sample = \"I really really love this movie\"\n feature_sample = ['really','love','good']\n feature_score_type = \"presence\"\n model_sample = Model(feature_sample,feature_score_type)\n result_features = model_sample.extract_features(text_s... | [
"0.65689343",
"0.6477436",
"0.64146715",
"0.6368037",
"0.6330938",
"0.6260085",
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"0.60735404",
"0.60727555",
"0.6036561",
"0.6018226",
"0.6005281",
"0.6003854... | 0.72335 | 0 |
Wrapper to customize reward of energym environments. | def __init__(self, env: gym.Env, info_obs_weights: dict):
super().__init__(env)
self.info_obs_weights = info_obs_weights | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, env):\n gym.RewardWrapper.__init__(self, env)",
"def reward(self, env):\n del env\n return 1",
"def add_env_args(parser):\n # sawyer\n parser.add_argument(\n \"--reward_type\",\n type=str,\n default=\"dense\",\n choices=[\"dense\", \"sparse\"],\... | [
"0.64872587",
"0.6484783",
"0.64239675",
"0.62843674",
"0.6223589",
"0.6176895",
"0.6125481",
"0.59527415",
"0.59006923",
"0.58524704",
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"0.57492626",
"0.5742453",
"0.5741872",
"0.57114106",
"0.56750464",
"0.5674529",
"0.5666... | 0.0 | -1 |
Check if all files are available before going deeper | def _check_before_run(self):
if not osp.exists(self.dataset_dir):
raise RuntimeError("'{}' is not available".format(self.dataset_dir))
if not osp.exists(self.train_dir):
raise RuntimeError("'{}' is not available".format(self.train_dir))
if not osp.exists(self.query_dir):
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def init_check(self):\n for required_file in self._required_files:\n # Check if required files are there\n # FIXME Sometimes it doesn't work :?\n if required_file not in self.files:\n self.valid = False",
"def check_for_missing_files(self, path):\n re... | [
"0.6751535",
"0.67471784",
"0.67164296",
"0.6647079",
"0.6547783",
"0.6459677",
"0.639774",
"0.63332593",
"0.6278475",
"0.6277023",
"0.62649715",
"0.62649715",
"0.62649715",
"0.62176186",
"0.61934626",
"0.61883324",
"0.6181167",
"0.6169776",
"0.6145234",
"0.6128254",
"0.60981... | 0.6022399 | 38 |
Called if no route is entered | def main():
return redirect('chat') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def pre_runroute_callable(self, route, request):\n return None",
"def route(self):\n pass",
"def post_runroute_callable(self, request):\n return None",
"def test_abort_route_when_id_route_not_exist(self):\n\n pass",
"def route(self, env):\n return None",
"def route(... | [
"0.713702",
"0.6929891",
"0.6734935",
"0.632262",
"0.62484354",
"0.62403715",
"0.6215604",
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"0.6100147",
"0.60336834",
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"0.5653751",
"0.56365013",
"0.5586848",
"0.5540685",
"0.5540685",
"0.5502926",
... | 0.0 | -1 |
Main page, for chatting | def chat():
username = request.cookies.get('username')
if username != None and username != "":
return r.renderContent('chat.html', name=username)
return redirect('/login') | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def chat():\n kwargs = {\"title\": u\"chat channel\", \"entries\": log.getLogEntries()}\n return render_template(\"chat.html\", **kwargs)",
"def view_contact_chat(self):\n if self._user.chats == {}:\n print(\"No chats to be viewed yet\")\n self.homepage()\n \n ... | [
"0.73176163",
"0.71010524",
"0.6999558",
"0.69722027",
"0.69469863",
"0.6837348",
"0.682718",
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"0.6714431",
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"0.6454156",
"0.6447436",
"0.641861",
"0.6282684",
"0.62469214",
"0.62395823",
"0.6222036... | 0.63437665 | 17 |
Route for handling logging in | def login():
error = None
if request.method == 'POST':
if not db.login(request.form['username'], request.form['password']):
error = 'Invalid username or password. Please try again!'
else:
resp = make_response(redirect(url_for('main')))
resp.set_cookie('username', request... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def log_login(sender, request, user, **kwargs):\n stracks.user(user).log(\"? has logged in\", action=stracks.login())",
"def handle_login(self, request):\n self._verify_headers(request)\n self._verify_auth_parameters(request, check_session=False)\n data = self.responses['login']\n ... | [
"0.6088461",
"0.60372317",
"0.6023485",
"0.58360314",
"0.5830466",
"0.5750337",
"0.57417244",
"0.5734808",
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"0.5713437",
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"0.5670487",
"0.5635086",
"0.56344676",
"0.56225646",
"0.56205153",
"0.56148446",
"0.5611... | 0.0 | -1 |
Route for posting messages or getting the prerendered html | def message():
if request.method == 'POST':
db.log_msg(request.form['text'], request.cookies.get('username'))
return db.get_all_messages() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def render_POST(self, request):\n\t\tprint request.args[\"message\"][0]\n\t\tself.jabberClient.sendMessage(request.args[\"message\"][0])\n\t\treturn ''",
"def post(self, request, *args, **kwargs):\n return self.render_to_response(self.get_context_data())",
"def input_post(): #TODO, error handling for pr... | [
"0.6790318",
"0.67297566",
"0.66144854",
"0.6388285",
"0.63673466",
"0.6282351",
"0.6156348",
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"0.5997232",
"0.5917214",
"0.5899693",
"0.5897377",
"0.58870757",
"0.5816963... | 0.59398806 | 16 |
Method for showing kanban board | def kanban():
if request.method == 'GET':
(todo, doing, done) = db.get_all_kanban()
return r.renderContent('kanban.html',
todo=Markup(todo), doing=Markup(doing), done=Markup(done))
else:
db.log_kanban(request.form['status'], request.form['value'])
return r.renderContent('kanban.h... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def show_board(self):\n self._game_board.display()",
"def show_board(self):\n print(self.game_board)",
"def show_board(self):\n board_vis = f\"\\n{'*' * 22}Board state{'*' * 23}\\n\"\n str_p2_store=\" \"+str(self.p2_store()) if self.p2_store()<10 else str(self.p2_store())\n b... | [
"0.72244227",
"0.6975949",
"0.68790525",
"0.67226493",
"0.6711845",
"0.66894794",
"0.66305685",
"0.6616085",
"0.6584913",
"0.6556513",
"0.652724",
"0.6510904",
"0.64619637",
"0.6442191",
"0.6401692",
"0.6401686",
"0.6382453",
"0.6379178",
"0.63448644",
"0.63213986",
"0.629003... | 0.6246042 | 29 |
Remove ``>`` from beginning of a line. | def clean(self, line):
m = self.RE.match(line)
if line.strip() == ">":
return ""
elif m:
return m.group(2)
else:
return line | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean(self, line):\r\n m = self.RE.match(line)\r\n if line.strip() == \">\":\r\n return \"\"\r\n elif m:\r\n return m.group(2)\r\n else:\r\n return line",
"def dealFirstLine(line):\n\n print \"%s\" % (line.strip('\\n'))",
"def _remove_beginnin... | [
"0.7322892",
"0.65217817",
"0.6428228",
"0.64100033",
"0.6187956",
"0.61572856",
"0.6113667",
"0.6068053",
"0.6057279",
"0.6052678",
"0.5984401",
"0.59601843",
"0.5782217",
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"0.5701244",
"0.5687602",
"0.56819564",
"0.5668284",
"0.5666465",
"0.5642651",
"0.5623701"... | 0.7390938 | 0 |
Convert ttyrec files to videos | def main(ctx, ttyrec, encoding, ibm, outfile, size, fps, font_size, font_file,
bold_font_file, info, info_all):
if ibm:
encoding = 'cp437'
fp, def_outfile = open_or_get(ttyrec)
try:
with fp:
updates = list(read_ttyrec(fp, encoding=encoding, errors='replace'))
except ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def seqIo_toVid(fName, ext='avi'):\n\n assert fName[-3:]=='seq', 'Not a seq file'\n sr = seqIo_reader(fName)\n N = sr.header['numFrames']\n h = sr.header['height']\n w = sr.header['width']\n fps = sr.header['fps']\n\n out = fName[:-3]+ext\n sw = skvideo.io.FFmpegWriter(out)\n # sw = cv2... | [
"0.67228967",
"0.6123915",
"0.6026224",
"0.6026062",
"0.6018919",
"0.60161096",
"0.5921599",
"0.58913285",
"0.58782333",
"0.58761436",
"0.5870621",
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"0.5802016",
"0.57724094",
"0.57724094",
"0.574259",
"0.5720971",
"0.5720353",
"0.570617... | 0.70133644 | 0 |
Set the SQLAlchemy query | def set_sa_query(self, query):
self.sa_query = query | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_query(self, query):\n return self.set_param(\"query\", query)",
"def query(self, query):\n self._query = query",
"def set_query(self, query):\n query = pylastica.query.Query.create(query)\n data = query.to_dict()\n return self.set_param('query', data['query'])",
"de... | [
"0.7269278",
"0.71905774",
"0.70059764",
"0.6978373",
"0.69707596",
"0.69707596",
"0.69707596",
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"0.668698",
"0.6570881",
"0.64990544",
"0.6484255",
"0.64412546",
"0.6415398",
"0.63802713",
"0.6367533",
"0.62840843",
"0.6221531",
"0.6157206",
"0.6138676",
"0.6108... | 0.7026373 | 2 |
Set the "entity_class_registry" field | def set_entity_class_registry(self, entity_class_registry):
self.entity_class_registry = entity_class_registry | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def register_class(self, entity_class):\n key = entity_class.__collection_name__\n\n if key not in self._registered_types:\n self._registered_types[key] = entity_class",
"def _extract_entity_class_registry(self):\n for description in self.sa_query.column_descriptions:\n ... | [
"0.6296474",
"0.6145517",
"0.5784777",
"0.57402545",
"0.5640025",
"0.5552346",
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"0.5203314",
"0.52020997",
"0.52020997",
"0.51808226",
"0.5144154",
"0.51290417",
"0.51211... | 0.8463285 | 0 |
Extract an entity class registry from one of the models of the inner SQLAlchemy query. This result of this function is used by several SQLAlchemy components during the extraction of the SQL query from a SQLAlchemy query. | def _extract_entity_class_registry(self):
for description in self.sa_query.column_descriptions:
if "entity" in description:
declarative_meta = description["entity"]
_class_registry = getattr(
declarative_meta, "_decl_class_registry", None)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_real_object(self):\n query_string = dedent(f\"\"\"\\\n import app.config.models_importer as models_importer\n\n class_ = models_importer.all_models['{self.ref_class}']\n \n class_.query.get({self.ref_id})\"\"\")\n\n return exec(query_string)",
"def _n... | [
"0.5555714",
"0.5259711",
"0.5109347",
"0.50943285",
"0.50772786",
"0.49984002",
"0.4953237",
"0.49206263",
"0.47721955",
"0.47709483",
"0.47572222",
"0.47535753",
"0.47392863",
"0.47341767",
"0.47276932",
"0.46964145",
"0.46887028",
"0.46723166",
"0.4664276",
"0.4661921",
"0... | 0.75623906 | 0 |
Execute the query, and return its result as rows | def matching_objects(self, filter_deleted):
from rome.core.orm.utils import get_literal_query
from rome.lang.sql_parser import QueryParser
from rome.core.rows.rows import construct_rows
read_deleted = self.read_deleted
if filter_deleted:
read_deleted = "no"
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute(self, query):\n with self.conn.cursor() as cur:\n # Execute the query\n try:\n cur.execute(query)\n except Exception as exc:\n print(\"Unable to execute query. Error was {0}\".format(str(exc)))\n exit()\n ro... | [
"0.8222279",
"0.7665415",
"0.76420516",
"0.7597238",
"0.7581581",
"0.75336605",
"0.75020784",
"0.75014627",
"0.7499659",
"0.7481231",
"0.7469864",
"0.7459134",
"0.74526256",
"0.7436286",
"0.7409394",
"0.7389792",
"0.73760194",
"0.7298189",
"0.72854406",
"0.7282933",
"0.725281... | 0.0 | -1 |
Execute the query, and return its result as rows | def all(self, filter_deleted=False):
objects = self.matching_objects(filter_deleted=filter_deleted)
return objects | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute(self, query):\n with self.conn.cursor() as cur:\n # Execute the query\n try:\n cur.execute(query)\n except Exception as exc:\n print(\"Unable to execute query. Error was {0}\".format(str(exc)))\n exit()\n ro... | [
"0.8222279",
"0.7665415",
"0.76420516",
"0.7597238",
"0.7581581",
"0.75336605",
"0.75020784",
"0.75014627",
"0.7499659",
"0.7481231",
"0.7469864",
"0.7459134",
"0.74526256",
"0.7436286",
"0.7409394",
"0.7389792",
"0.73760194",
"0.7298189",
"0.72854406",
"0.7282933",
"0.725281... | 0.0 | -1 |
Executes the query and returns the first matching row. | def first(self, filter_deleted=False):
objects = self.matching_objects(filter_deleted=filter_deleted)
if len(objects) > 0:
value = objects[0]
if self.session is not None:
if hasattr(value, "id"):
self.session.watch(value)
return va... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fetchone(self):\n\n self._check_executed()\n\n if self._query_id == -1:\n msg = \"query didn't result in a resultset\"\n self._exception_handler(ProgrammingError, msg)\n\n if self.rownumber >= self.rowcount:\n return None\n\n if self.rownumber >= (se... | [
"0.8280298",
"0.80509466",
"0.79736346",
"0.7938769",
"0.79376507",
"0.78329694",
"0.7832246",
"0.76202947",
"0.7573742",
"0.75649405",
"0.7521718",
"0.75176406",
"0.7498476",
"0.74828917",
"0.7433074",
"0.7370562",
"0.72961265",
"0.72804594",
"0.7238881",
"0.72119147",
"0.72... | 0.0 | -1 |
Executes the query and returns the number of matching rows. | def count(self):
objects = self.all()
return len(objects) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _execute_rowcount(self, query, *parameters, **kwparameters):\n result = -1\n cursor = self._cursor()\n ret = self._execute(cursor, query, parameters, kwparameters)\n if ret != -1:\n result = cursor.rowcount\n cursor.close()\n return result",
"def execute_r... | [
"0.8263714",
"0.75019467",
"0.74157685",
"0.74035823",
"0.73712915",
"0.73479736",
"0.7320812",
"0.7266392",
"0.7265946",
"0.72563446",
"0.72047913",
"0.7165406",
"0.71650726",
"0.71557415",
"0.71451116",
"0.71380395",
"0.7132593",
"0.701933",
"0.6974487",
"0.69694316",
"0.69... | 0.0 | -1 |
Handle only one batch, updating the internal state Params ====== | def train_one_batch(self, imgs: Tensor) -> None:
with torch.no_grad():
# b * c * w * h
embeddings = self._embed_batch(imgs.to(self.device))
if self.embedding_list is None:
self.embedding_list = reshape_embedding(embeddings.cpu().detach().numpy())
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_batch(self, *args, **kwargs):\n pass",
"def process_state_batch(self, batch):\n return batch",
"def process_state_batch(self, batch):\n return batch",
"def handle_batch(self, batch: Mapping[str, Any]) -> None:\n self.batch = {**batch, **self.forward(batch)}",
"def on_... | [
"0.7484445",
"0.70731485",
"0.70731485",
"0.6753256",
"0.63523346",
"0.6343672",
"0.6236089",
"0.6083565",
"0.60835004",
"0.59652734",
"0.5964963",
"0.59062594",
"0.58258647",
"0.58248407",
"0.58213854",
"0.58161604",
"0.5768676",
"0.56665844",
"0.56641805",
"0.5637965",
"0.5... | 0.0 | -1 |
Computes the distance matrix for each image patch Params ====== | def predict(self,
new_imgs: Tensor,
compare_all: bool = False) -> Tensor:
embeddings = self._embed_batch(new_imgs)
embeddings = reshape_embedding(embeddings.cpu().detach().numpy())
#print(self.embedding_coreset.shape)
#print(embeddings.shape)
knn =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def im_dist_mat(self):\n mat = np.zeros([self.I, self.M])\n for i in range(self.I):\n for m in range(self.M):\n mat[i, m] = distance(self.I_coords[i], self.M_coords[m])\n return mat",
"def _calculate_distances(boxes, homography):\n pos_markers = []\n p... | [
"0.62255037",
"0.6161878",
"0.61389506",
"0.59978783",
"0.59745884",
"0.593692",
"0.59346217",
"0.5861669",
"0.5828466",
"0.57970315",
"0.5744683",
"0.5730956",
"0.5708846",
"0.5702996",
"0.56961113",
"0.56669694",
"0.5647084",
"0.56355965",
"0.5611184",
"0.5610667",
"0.55949... | 0.0 | -1 |
Call f on each item in seq, calling inter() in between. | def interleave(inter, f, seq):
seq = iter(seq)
try:
f(next(seq))
except StopIteration:
pass
else:
for x in seq:
inter()
f(x) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sequence(f, lst: list) -> list:\n ret = []\n for ele in lst:\n ret.append(f(ele))\n return ret",
"def intersperse(value, seq):\n seq = iter(seq)\n\n try:\n yield next(seq)\n except StopIteration:\n return\n\n for item in seq:\n yield value\n yield item"... | [
"0.61680573",
"0.6034688",
"0.6013112",
"0.5890083",
"0.5763176",
"0.5744706",
"0.5691687",
"0.5686545",
"0.56322396",
"0.5617056",
"0.5564194",
"0.55386853",
"0.55105126",
"0.5491585",
"0.5479231",
"0.5478667",
"0.5475974",
"0.54754245",
"0.5453396",
"0.5437749",
"0.5408432"... | 0.77389354 | 0 |
CodeGenerator(tree, file=sys.stdout) > None. Print the source for tree to file. | def __init__(self, tree, file = sys.stdout):
self.f = file
self.future_imports = []
self._indent = 0
# dict of locals used to determine if variable already exists in assignments
self._locals = ["pyflamegpu"]
self._device_functions = []
self._message_iterator_var =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def generate(self):\n tree = self._generator.build_tree()\n\n for entry in tree:\n print(entry)",
"def log_tree(self, description, tree):\n if self.log:\n print(description)\n print_content(astor.to_source(tree), '.py')\n print()\n print... | [
"0.64935875",
"0.61715996",
"0.6161561",
"0.6096719",
"0.5994618",
"0.5992754",
"0.5926395",
"0.5877472",
"0.5846541",
"0.5706362",
"0.56789345",
"0.5666667",
"0.56597686",
"0.56378835",
"0.5628827",
"0.56088674",
"0.558582",
"0.5557028",
"0.5552948",
"0.55473834",
"0.5543297... | 0.0 | -1 |
Gets the device function name by translating a typed Python version to a templated cpp version. Python functions looks like getVariableFloatArray6 and translate to getVariable This function will detect and test against a set of known types and also extract the Array length This function returns None if the string is in... | def _deviceVariableFunctionName(self, tree, permitted_prefixes, allow_lengths = True):
cpp_func_name = ""
py_func = tree.attr
# extract function name start
for prefix in permitted_prefixes:
if py_func.startswith(prefix):
cpp_func_name = prefix
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def function_name_to_string(func):\n if func == statistical_parity_difference:\n return \"Statistical Parity Difference\"\n if func == theil_index:\n return \"Theil Index\"\n if func == equal_opportunity_difference:\n return \"Equal Opportunity Difference\"\n if func == disparate_i... | [
"0.5464446",
"0.5418039",
"0.53736573",
"0.5311592",
"0.53082407",
"0.52509713",
"0.52308387",
"0.52233297",
"0.5139834",
"0.5137596",
"0.5136655",
"0.51061374",
"0.50472605",
"0.50168824",
"0.5014381",
"0.49955937",
"0.4979121",
"0.49771407",
"0.49526176",
"0.4947558",
"0.49... | 0.69010454 | 0 |
Function will handle a getMacroEnvironment function (assuming it is correctly formatted (by checking with _deviceVariableFunctionName first)) | def dispatchMacroEnvFunction(self, tree, tree_parent):
cpp_func_name = "getMacroProperty"
py_func = tree.attr
# extract type from function name
py_type = py_func[len(cpp_func_name):]
if py_type not in self._fgpu_types:
self.RaiseError(tree, f"'{py_type}' is not a vali... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_get_environment_string(self):\n pass",
"def __MakeEnvironment(self):\n environment= os.environ.copy()\n\n for key, value in self.__context.items():\n if type(value) is str:\n name = \"QMV_\" + key.replace(\".\", \"__\")\n environment[name]= value\n\n return environment... | [
"0.58264863",
"0.5568216",
"0.53334945",
"0.5327967",
"0.51543343",
"0.51488876",
"0.51430243",
"0.5069374",
"0.50373465",
"0.5026348",
"0.50191504",
"0.5002579",
"0.49759382",
"0.49720997",
"0.49513227",
"0.49360543",
"0.4913783",
"0.49026003",
"0.48999316",
"0.4882077",
"0.... | 0.61382043 | 0 |
There is a limited set of types and formats of type description supported. Types can be either; 1) A python built in type of int or float, or 2) A subset of numpy types prefixed with either numpy or np. e.g. np.int16 This function translates and a catches unsupported types but does not translate a function call (i.e. c... | def dispatchType(self, tree):
if isinstance(tree, ast.Name):
if tree.id not in self.basic_arg_types:
self.RaiseError(tree, "Not a supported type")
self.write(tree.id)
elif isinstance(tree, ast.Attribute):
if not isinstance(tree.value, ast.Name) :
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _type_translate(p_type, default_v=None):\n translate_dict = {'float': 'float',\n 'double': 'real',\n 'int': 'int',\n 'gr_complex': 'complex',\n 'char': 'byte',\n ... | [
"0.65132457",
"0.6380109",
"0.63286304",
"0.6217381",
"0.6156143",
"0.6123783",
"0.6113949",
"0.6086107",
"0.6069828",
"0.60447073",
"0.602957",
"0.600259",
"0.5983749",
"0.597207",
"0.5966418",
"0.5907714",
"0.58719164",
"0.58526105",
"0.5849916",
"0.5831822",
"0.5815611",
... | 0.5697489 | 36 |
Handles arguments for a FLAME GPU device function. Arguments must use type hinting to be translated to cpp. | def dispatchFGPUDeviceFunctionArgs(self, tree):
# reset the locals variable stack
self._locals = ["pyflamegpu"]
# input message
first = True
annotation = None
for arg in tree.args.args:
# ensure that there is a type annotation
if not arg.annotation... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _deviceVariableFunctionName(self, tree, permitted_prefixes, allow_lengths = True):\n cpp_func_name = \"\"\n py_func = tree.attr\n # extract function name start\n for prefix in permitted_prefixes:\n if py_func.startswith(prefix):\n cpp_func_name = prefix\n ... | [
"0.6057609",
"0.5804018",
"0.5786905",
"0.5687931",
"0.5632874",
"0.55926454",
"0.55586314",
"0.54775053",
"0.5402661",
"0.53772503",
"0.5370256",
"0.5299433",
"0.5298682",
"0.5296351",
"0.52958226",
"0.52583855",
"0.52490014",
"0.5243063",
"0.52260655",
"0.51923186",
"0.5190... | 0.77259445 | 0 |
Message iterator call maybe a simple one (e.g. message_in(x, y, z)) or a call to a member (e.g. message_in.wrap()) Using this function avoid using the global call one which may accept member function calls to things that are not iterators. | def dispatchMessageIteratorCall(self, tree):
# simple case not a member function just an iterator with arguments
if isinstance(tree.func, ast.Name):
self.write(f"FLAMEGPU->{tree.func.id}")
if isinstance(tree.func, ast.Attribute) :
if isinstance(tree.func.value, ast.Name):... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def call(self, message: Message) -> None:\n self.fn(message)",
"def _call(self, x):\n return x.inner(x)",
"def test_dispatch_inbound(self):\n msg_helper = MessageHelper()\n worker_helper = WorkerHelper()\n broker = self.setup_broker(worker_helper)\n self.assertEqual(br... | [
"0.5965189",
"0.5781531",
"0.5567268",
"0.5560412",
"0.5555918",
"0.5474647",
"0.53749025",
"0.5369496",
"0.5356947",
"0.532416",
"0.53201944",
"0.52734697",
"0.5214312",
"0.51924616",
"0.51724297",
"0.51710767",
"0.51597893",
"0.51278067",
"0.5124339",
"0.5106569",
"0.509719... | 0.73008895 | 0 |
This is a special case of a range based for loop in which iterator item returns a const referecne to the message. Any user specified message value can be used. | def dispatchMessageLoop(self, tree):
self.fill("for (const auto& ")
self.dispatch(tree.target)
self.write(" : ")
# if simple message iterator
if isinstance(tree.iter, ast.Name):
if not tree.iter.id == self._input_message_var:
self.RaiseError(t, f"Messa... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_message(self, i):\n pass",
"def __iter__(self):\n return iter([self.format_message(record) for record in self._messages])",
"def _For(self, t):\n # if message loop then process differently\n if isinstance(t.iter, ast.Name):\n if t.iter.id == self._input_message_va... | [
"0.6812082",
"0.6144076",
"0.59823215",
"0.57963836",
"0.5497247",
"0.5477851",
"0.5458338",
"0.5450091",
"0.5326207",
"0.5284618",
"0.52789956",
"0.51939684",
"0.5179175",
"0.51783484",
"0.51677006",
"0.5141689",
"0.510057",
"0.5071497",
"0.5068647",
"0.5062799",
"0.5049284"... | 0.5924833 | 3 |
A very limited set of function calls to members are supported so these are fully evaluated here. t_parent is the Call ast object required if the argument need to be modified (i.e. in the case of macro environment properties) Function calls permitted are; pyflamegpu.function a supported function call. e.g. pyflamegpu.ge... | def dispatchMemberFunction(self, t, t_parent):
# it could be possible that the Call object has no value property e.g. a()()
if not hasattr(t, "value"):
self.RaiseError(t, f"Function call is in an unsupported format.")
# Nested member functions (e.g. x.y.z())
if isinstance(t.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _Call(self, t):\n # check calls but let attributes check in their own dispatcher\n funcs = self._device_functions + self.pythonbuiltins + [self._input_message_var] # message_input variable is a valid function name as certain message types have arguments on iterator\n if isinstance(t.func, ... | [
"0.6362595",
"0.58942914",
"0.57930183",
"0.5696235",
"0.56648827",
"0.5555311",
"0.5387862",
"0.52408487",
"0.5235522",
"0.52275836",
"0.51776576",
"0.5120119",
"0.51014704",
"0.509886",
"0.506699",
"0.50667155",
"0.5059268",
"0.50211924",
"0.49914703",
"0.4978251",
"0.49437... | 0.790886 | 0 |
Same as a standard python expression but ends with semicolon | def _Expr(self, tree):
# Catch odd case of multi line strings and doc strings which are Expr with a Constant string type value
if isinstance(tree.value, ast.Constant):
if isinstance(tree.value.value, str):
return
# catch special case of Python 3.7 Where doc string is ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def expr():\n def p(tok):\n # commas can appear in quantified variables\n return not(tok.value in [';','.'])\n return reparse('expr').process(c.balanced_condition(p))",
"def compile(expression):",
"def evaluate(compiled_expression):",
"def result(self) -> global___Expression:",
"def exp... | [
"0.65578294",
"0.5910947",
"0.5799009",
"0.5718435",
"0.562569",
"0.5539511",
"0.5498102",
"0.5459333",
"0.5452541",
"0.54354775",
"0.54278094",
"0.5413188",
"0.5406052",
"0.54030687",
"0.53684604",
"0.52868867",
"0.52864975",
"0.5281365",
"0.5280854",
"0.5279634",
"0.5274355... | 0.53314483 | 15 |
No such concept in C++. Standard assignment can be used in any location. | def _NamedExpr(self, tree):
self.write("(")
self.dispatch(tree.target)
self.write(" = ")
self.dispatch(tree.value)
self.write(")") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_50_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 2]of real;\n\t\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\"\"\"\n\t\texp... | [
"0.5709383",
"0.56389916",
"0.56078136",
"0.5524775",
"0.55129427",
"0.5444868",
"0.54026616",
"0.53342205",
"0.53342205",
"0.53342205",
"0.53342205",
"0.5263527",
"0.52464676",
"0.52318645",
"0.5230673",
"0.52164626",
"0.5199737",
"0.51738065",
"0.5153252",
"0.51271296",
"0.... | 0.0 | -1 |
Assignment will use the auto type to define a variable at first use else will perform standard assignment. | def _Assign(self, t):
if len(t.targets) > 1:
self.RaiseError(t, "Assignment to multiple targets not supported")
if not isinstance(t.targets[0], ast.Name):
self.RaiseError(t, "Assignment to complex expressions not supported")
self.fill()
# check if target exists in... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_47_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tfunction f(): array[1 .. 3] of real;\n\t\tvar a: array[1 .. 3] of real;\n\t\tbegin a[2]:=1.1; return a; end\n\t\tprocedure main(); var x:array[1 .. 3]of real;\n\t\tbegin f()[1]:=x[1]:=1; with y:real;y:real; do begin end end\"\"\"\n\t\texp... | [
"0.65518016",
"0.63790643",
"0.6358774",
"0.6354869",
"0.61757886",
"0.61371475",
"0.6126114",
"0.6119964",
"0.60451835",
"0.5970678",
"0.5915198",
"0.58865404",
"0.5870152",
"0.5870152",
"0.5870152",
"0.5870152",
"0.5810308",
"0.5782519",
"0.57597",
"0.57563984",
"0.5729333"... | 0.6059261 | 8 |
Similar to assignment in terms of restrictions. E.g. Allow only single named variable assignments. Also requires the named variable to already exist in scope. | def _AugAssign(self, t):
if not isinstance(t.target, ast.Name):
self.RaiseError(t, "Augmented assignment to complex expressions not supported")
# check if target exists in locals
if t.target.id not in self._locals :
self.RaiseError(t, "Augmented assignment not permitted o... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_46_assign_statement(self):\n\t\tinput = \"\"\"var x,y:integer;\n\t\tprocedure main(); var x:array[1 .. 3]of real; begin x[1]:=1;\n\t\twith y:integer;y:real; do begin end end\"\"\"\n\t\texpect = \"Redeclared Variable: y\"\n\t\tself.assertTrue(TestChecker.test(input,expect,446))",
"def _variable(self, nam... | [
"0.62667334",
"0.6235484",
"0.62117046",
"0.6118622",
"0.60492593",
"0.59797275",
"0.59298253",
"0.5684028",
"0.5679982",
"0.5677362",
"0.5655891",
"0.5630487",
"0.5610054",
"0.5558762",
"0.54264975",
"0.5404717",
"0.5368703",
"0.5344528",
"0.5336999",
"0.5330405",
"0.5314537... | 0.0 | -1 |
Standard cpp like return with semicolon. | def _Return(self, t):
self.fill("return")
if t.value:
self.write(" ")
self.dispatch(t.value)
self.write(";") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compile_return(self) -> None:\n self._consume('return')\n if self._get_current_token() != ';':\n self.compile_expression()\n else:\n self.writer.write_push('CONST', 0)\n self.writer.write_return()\n self._consume(';')",
"def compile_return(self):\r\n ... | [
"0.7318816",
"0.69216275",
"0.6916768",
"0.6747743",
"0.6609503",
"0.6026339",
"0.60176194",
"0.59240377",
"0.58299434",
"0.57947284",
"0.56949216",
"0.5653559",
"0.56448203",
"0.56211615",
"0.5538628",
"0.55126405",
"0.549617",
"0.54757154",
"0.5461631",
"0.54565257",
"0.537... | 0.61995023 | 5 |
cassert does exist but probably not required in FGPU functions and unclear if supported by jitfy | def _Assert(self, t):
self.RaiseError(t, "Assert not supported") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_perspective_transform():\n # TODO: write this\n assert(True)",
"def boolean():\n\n bool(Assert(1))\n\n with Assert.raises(AssertionError):\n bool(Assert(0))",
"def boolean():\r\n\r\n bool(Assert(1))\r\n\r\n with Assert.raises(AssertionError):\r\n bool(Assert(0))",
"de... | [
"0.5553334",
"0.53317463",
"0.5246943",
"0.52441424",
"0.5225567",
"0.50993943",
"0.50408936",
"0.49993053",
"0.49924356",
"0.49918866",
"0.49887422",
"0.49359533",
"0.4932513",
"0.4932513",
"0.49268204",
"0.49176776",
"0.49025792",
"0.4897762",
"0.48943457",
"0.48824537",
"0... | 0.4522558 | 64 |
This is old school python printing so no need to support | def _Print(self, t):
self.RaiseError(t, "Print not supported") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _printable(self):\n pass",
"def print_out():\n pass",
"def my_print(self):\n if self.__size == 0:\n print(\"\")\n return\n [print(\"\") for x in range(0, self.__position[1])]\n for i in range(0, self.__size):\n [print(\" \", end=\"\") for ... | [
"0.73725426",
"0.72929084",
"0.7111852",
"0.70446706",
"0.7013182",
"0.70031095",
"0.69107485",
"0.69100827",
"0.6871159",
"0.68606776",
"0.6803045",
"0.680181",
"0.6794203",
"0.67602473",
"0.6700353",
"0.6650724",
"0.66261536",
"0.66122717",
"0.6503248",
"0.64987963",
"0.649... | 0.6141068 | 66 |
Exceptions are obviously supported in cpp but not in CUDA device code | def _Raise(self, t):
self.RaiseError(t, "Exception raising not supported") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def handle_device_error(self, device: SimplePeriphDev):\r\n pass",
"def test_nonpositive_nu_raises_exception(nu):\n with pytest.raises(ValueError):\n kernels.Matern(input_dim=1, nu=nu)",
"def unexpected_error(self, exception):",
"def test_gcc_crash(self):\r\n self.validate((1, 10, 213... | [
"0.6091167",
"0.5937788",
"0.5826724",
"0.5811849",
"0.56694853",
"0.5567256",
"0.5509975",
"0.55026484",
"0.54509616",
"0.54496866",
"0.54445916",
"0.5436744",
"0.5431065",
"0.5427501",
"0.5389274",
"0.538114",
"0.5358859",
"0.5358747",
"0.5344634",
"0.5293775",
"0.52785766"... | 0.0 | -1 |
Checks the decorators of the function definition much must be either 'pyflamegpu.agent_function', 'pyflamegpu.agent_function_condition' or 'pyflamegpu.device_function'. Each is then processed in a different way using a specific dispatcher. Function calls are actually checked and only permitted (or user defined) functio... | def _FunctionDef(self, t):
self.write("\n")
# check decorators
if len(t.decorator_list) != 1 or not isinstance(t.decorator_list[0], ast.Attribute):
self.RaiseError(t, "Function definitions require a single pyflamegpu decorator of either 'pyflamegpu.agent_function', 'pyflamegpu.agent_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def visit_FunctionDef(self, node):\n self.functions[node.name] = self._generate_pytest_decorators(node.decorator_list)\n self.generic_visit(node)",
"def isValidFunction(self):\n for token in self.value:\n if token.type == 'defFunction' or token.type == 'callFunction':\n ... | [
"0.6134093",
"0.5936042",
"0.57259756",
"0.56994855",
"0.5666439",
"0.5570822",
"0.5568335",
"0.55557805",
"0.5536746",
"0.55267",
"0.5503715",
"0.55015457",
"0.54883873",
"0.5477766",
"0.5459286",
"0.54585314",
"0.5451225",
"0.543271",
"0.54145896",
"0.5368069",
"0.53297734"... | 0.7230727 | 0 |
Two type for for loop are supported. Either; 1) Message for loop in which case the format requires a iterator using the named pyflamegpu function argument of 'message_in' 2) A range based for loop with 1 to 3 arguments which is converted into a c style loop | def _For(self, t):
# if message loop then process differently
if isinstance(t.iter, ast.Name):
if t.iter.id == self._input_message_var:
self.dispatchMessageLoop(t)
else:
self.RaiseError(t, "Range based for loops only support message iteration using... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def dispatchMessageLoop(self, tree):\n self.fill(\"for (const auto& \")\n self.dispatch(tree.target)\n self.write(\" : \")\n # if simple message iterator\n if isinstance(tree.iter, ast.Name):\n if not tree.iter.id == self._input_message_var:\n self.Raise... | [
"0.6300082",
"0.5951897",
"0.5818866",
"0.57553744",
"0.55254656",
"0.55113477",
"0.5369612",
"0.5264706",
"0.51863104",
"0.5155934",
"0.5096092",
"0.50791895",
"0.5068876",
"0.5014519",
"0.49778667",
"0.49353746",
"0.48620737",
"0.48602274",
"0.48535544",
"0.48398575",
"0.48... | 0.6590012 | 0 |
Fairly straightforward translation to if, else if, else format | def _If(self, t):
self.fill("if (")
self.dispatch(t.test)
self.write(")")
self.enter()
self.dispatch(t.body)
self.leave()
# collapse nested ifs into equivalent elifs.
while (t.orelse and len(t.orelse) == 1 and
isinstance(t.orelse[0], ast.If)... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def ifelse(test, if_true, if_false):\n if test:\n return if_true\n else:\n return if_false",
"def switch(cond, ift, iff):",
"def _ifelse(self):\n debug.show(\"ifelse:Stack = \" + str(self.opStack))\n if self.opStack.size() >= 3:\n falseCode = check.isCode(self.opSta... | [
"0.6925929",
"0.6345551",
"0.6319299",
"0.6283738",
"0.62381434",
"0.62381434",
"0.62381434",
"0.62381434",
"0.6104107",
"0.60394055",
"0.5961958",
"0.59224445",
"0.58788913",
"0.5835179",
"0.57912",
"0.57817644",
"0.5748567",
"0.57284987",
"0.5728384",
"0.5704196",
"0.568847... | 0.5588862 | 26 |
Straightforward translation to c style while loop | def _While(self, t):
self.fill("while (")
self.dispatch(t.test)
self.write(")")
self.enter()
self.dispatch(t.body)
self.leave()
if t.orelse:
self.RaiseError(t, "While else not supported") | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def syntax_while():\n i = 0\n while i < 5:\n print(i)\n i += 1\n\n ## Output\n # 0\n # 1\n # 2\n # 3\n # 4",
"def while_loop(n):\n\tprint(\"\\n\\nlet's see what a while loop is\\n\")\n\twhile n > 0:\n\t\tprint(f\"n = {n}\")\n\t\tn -= 1",
"def whileLoop(count):\n result=... | [
"0.6793967",
"0.66965246",
"0.65839756",
"0.6332659",
"0.6076374",
"0.6045058",
"0.60284704",
"0.5919505",
"0.5916802",
"0.5843467",
"0.5810192",
"0.57871115",
"0.577109",
"0.5753707",
"0.571716",
"0.5670822",
"0.56154424",
"0.55902815",
"0.5575924",
"0.55405825",
"0.54837346... | 0.6501587 | 3 |
Everything ends up as a Name once it is an identifier | def _Name(self, t):
self.write(t.id) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def identifier(self):",
"def get_identifier(self):",
"def name(self, name):\n pass",
"def name(self):",
"def name(self):",
"def name(self):",
"def name(self):",
"def get_name():",
"def name(self):\n ...",
"def name():\n pass",
"def name():\n pass",
"def getName():"... | [
"0.792435",
"0.74011093",
"0.73938805",
"0.7328443",
"0.7328443",
"0.7328443",
"0.7328443",
"0.7276922",
"0.72753966",
"0.72493345",
"0.72493345",
"0.72163546",
"0.72163546",
"0.72163546",
"0.72163546",
"0.72163546",
"0.72163546",
"0.7212629",
"0.7212629",
"0.7212629",
"0.721... | 0.68990785 | 37 |
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