100000:\\n continue\\n kwargs = {'vocab':self.vocab}\\n words = tokenizer.tokenize_wrapper(code, **kwargs)\\n tokens += len(words)\\n for word in words:\\n self.dictionary.add_word(word)\\n except:\\n pass\\n # Tokenize file content\\n with open(path, 'r') as f:\\n ids = torch.LongTensor(tokens)\\n token = 0\\n for line in f:\\n filename = line.strip()\\n code_path = RAW_DATA_PATH + filename\\n assert os.path.exists(code_path)\\n try:\\n with open(code_path, 'r') as code_f:\\n code = code_f.read()\\n if len(code) > 100000:\\n continue\\n kwargs = {'vocab':self.vocab}\\n words = tokenizer.tokenize_wrapper(code, **kwargs)\\n for word in words:\\n ids[token] = self.dictionary.word2idx[word]\\n token += 1\\n self.tic_marks.append(len(words))\\n except Exception as e:\\n #raise e\\n pass\\n return ids\",\n \"def tokenize(self, path):\\n assert os.path.exists(path)\\n # Add words to the dictionary\\n with open(path, 'r') as f:\\n tokens = 0\\n for line in f:\\n words = line.split()\\n tokens += len(words)\\n for word in words:\\n if word in self.vocabulary:\\n self.dictionary.add_word(word)\\n\\n # Tokenize file content\\n with open(path, 'r') as f:\\n ids = torch.LongTensor(tokens)\\n token = 0\\n for line in f:\\n words = line.split()\\n for word in words:\\n if word in self.vocabulary:\\n ids[token] = self.dictionary.word2idx[word]\\n else:\\n ids[token] = self.dictionary.word2idx['<unk>']\\n token += 1\\n return ids\",\n \"def tokenize(self, path, build_dict=False, thd=0):\\n\\n assert os.path.exists(path)\\n\\n if build_dict:\\n # Add words to the dictionary\\n with open(path, 'r') as f:\\n for line in f:\\n words = line.split()\\n for word in words:\\n self.dictionary.add_word(word)\\n if thd > 1:\\n self.dictionary.rebuild_by_freq(thd)\\n\\n # Tokenize file content\\n ids_list = []\\n with open(path, 'r') as f:\\n for line in f:\\n words = line.split()\\n ids = []\\n for word in words:\\n ids.append(self.dictionary[word])\\n ids_list.append(ids)\\n\\n return ids_list\",\n \"def tokenize(self, path):\\n dropped = 0\\n with open(path, 'r') as f:\\n linecount = 0\\n lines = []\\n for line in f:\\n linecount += 1\\n if self.lowercase:\\n words = line[:-1].lower().strip().split(\\\" \\\")\\n else:\\n words = line[:-1].strip().split(\\\" \\\")\\n if len(words) > self.maxlen:\\n dropped += 1\\n continue\\n words = ['<sos>'] + words\\n words += ['<eos>']\\n # vectorize\\n vocab = self.dictionary.word2idx\\n unk_idx = vocab['<oov>']\\n indices = [vocab[w] if w in vocab else unk_idx for w in words]\\n lines.append(indices)\\n\\n print(\\\"Number of sentences dropped from {}: {} out of {} total\\\".\\n format(path, dropped, linecount))\\n return lines\",\n \"def read_tokens(file_path):\\n\\n text = open(file_path).read().lower()\\n\\n tokens = []\\n for token in text.split():\\n if token.isalpha():\\n tokens.append(token)\\n\\n return tokens\",\n \"def tokenize(self, path):\\n assert os.path.exists(path)\\n # Add symbol to the dictionary\\n with open(path, 'r') as f:\\n tokens = 0\\n for line in f:\\n tokens += len(line)\\n for s in line:\\n self.dictionary.add_symbol(s)\\n\\n # Tokenize file content\\n with open(path, 'r') as f:\\n ids = torch.LongTensor(tokens)\\n token = 0\\n for line in f:\\n for s in line:\\n ids[token] = self.dictionary.symbol2idx[s]\\n token += 1\\n\\n return ids\",\n \"def _tokenize(self, fileName):\\n debug.show(\\\"Tokenizing...\\\")\\n try:\\n f = open(fileName, 'r')\\n code = f.read()\\n except IOError:\\n sys.exit('Error: Bad input file')\\n f.close()\\n pattern = '/?[a-zA-Z][a-zA-Z0-9_]*|[-]?[0-9]+|[}{]|%.*|[^\\\\t\\\\n ]'\\n return re.findall(pattern, code)\",\n \"def _load_file(file_path: str) -> List[str]:\\n with open(file_path, 'r') as f:\\n file_content = f.read()\\n\\n # Removes all comments\\n str_without_comments = re.sub('\\\\/\\\\*[\\\\s\\\\S]*?\\\\*\\\\/|([^\\\\\\\\:]|^)\\\\/\\\\/.*$', '', file_content, flags=re.MULTILINE)\\n str_without_new_lines = str_without_comments.lstrip('\\\\n').replace('\\\\n', ' ').replace('\\\\t', ' ') # Remove new lines and tabs\\n return JackTokenizer._split_keep_seperators(str_without_new_lines) # Splits the string by symbols and spaces\",\n \"def tokenizer(text):\\n for entry in text.split('$$$$\\\\n'):\\n if entry.rstrip():\\n lines_stream = deque(entry.split('\\\\n'))\\n else:\\n continue\\n\\n # yield from _molfile(stream=lines_stream)\\n for token in _molfile(stream=lines_stream):\\n yield token\\n\\n if len(lines_stream):\\n # yield from _sdfile(stream=lines_stream)\\n for token in _sdfile(stream=lines_stream):\\n yield token\\n\\n yield EndOfFile()\",\n \"def preprocess_file_to_tokens(self, dirPath, fnx):\\n fnxPath = os.path.join(dirPath, fnx)\\n with open(fnxPath) as f:\\n fnx_data = f.read()\\n fnx_data_nnl = re.sub(r'\\\\n', ' ', fnx_data)\\n fnx_data_sb = re.sub(r\\\"( )+\\\", ' ', fnx_data_nnl)\\n\\n fnx_tokens = nltk.word_tokenize(fnx_data_sb)\\n fnx_unk, fnx_tokens_prepped = self.infreq_to_UNK(fnx_tokens)\\n \\n return fnx_tokens_prepped\",\n \"def _split_to_tokens(self, file_content: str):\\n cur_token = ''\\n string_started = False\\n for line in file_content.split('\\\\n'):\\n cur_token = ''\\n line = line.strip()\\n if line.startswith('#'): # skip comments\\n continue\\n for char in line:\\n if string_started:\\n if char == '\\\"': # string ended\\n self._add_non_empty_token(cur_token)\\n cur_token = '' # start of a new string\\n string_started = False\\n else:\\n cur_token += char\\n elif char == '\\\"':\\n self._add_non_empty_token(cur_token)\\n cur_token = '' # start of a new string\\n string_started = True\\n elif (char == \\\" \\\" and not string_started) or char == '\\\\n':\\n self._add_non_empty_token(cur_token)\\n cur_token = ''\\n elif char in [':', '{', '}', '[', ']', ',']:\\n self._add_non_empty_token(cur_token)\\n self._tokens.append(char)\\n cur_token = ''\\n else:\\n cur_token += char\\n self._add_non_empty_token(cur_token)\\n self._add_non_empty_token(cur_token)\",\n \"def _process(self, file: bytes) -> List[Tuple[str]]:\\n decoded_text = file.decode('utf-8')\\n # Replace end of line tokens\\n if self.eol is not None and not self.split_by_sentence:\\n decoded_text = decoded_text.replace('\\\\n', self.eol)\\n\\n # Split by sentence or unroll\\n if self.split_by_sentence:\\n nltk.download('punkt', quiet=True)\\n text = [(sent.strip(),) for sent in nltk.tokenize.sent_tokenize(decoded_text)]\\n else:\\n text = [(decoded_text,)]\\n\\n return text\",\n \"def tokenize(fp):\\n\\n tokenizer = Tokenizer()\\n\\n for (lineno, line) in enumerate(fp):\\n try:\\n line = line.decode('utf8')\\n except UnicodeDecodeError as detail:\\n print >>sys.stderr, \\\"failed to decode line %i: %s\\\" % (lineno+1,\\n detail)\\n line = line.decode('utf8', 'replace')\\n\\n # This should use \\\"yield from ...\\\" (new in Python 3.3)\\n for t in tokenizer.feed_data(line):\\n yield t\\n last = tokenizer.finalize()\\n if last:\\n yield last\",\n \"def tokenize(src):\\n\\n pass\",\n \"def file_to_tokens(ifiles: list) -> list:\\n if not ifiles:\\n return []\\n #\\n # derived from https://stackoverflow.com/questions/16710076\\n # regex to split a string preserving quoted fields\\n #\\n rpat = re.compile(\\n r\\\"\\\"\\\" #\\n (?:[^\\\\s\\\"]+)| # match non-delimiter\\n (?<=\\\\W)\\\"(?:\\\\\\\\.|[^\\\"]*)\\\"(?=\\\\W)| # match double quoted\\n (?<=\\\\W)'(?:\\\\\\\\.|.*?)'(?=\\\\W) # match single quoted\\n \\\"\\\"\\\",\\n re.X,\\n )\\n #\\n if not isinstance(ifiles, list):\\n ifiles = [ifiles]\\n #\\n for ffile in ifiles:\\n try:\\n ff = open(ffile, mode=\\\"r\\\")\\n except OSError:\\n logging.debug(\\\"open(%s) failed on %s\\\", ffile)\\n else:\\n for line in ff:\\n if re.match(r\\\"^\\\\s*#\\\", line): # skip block comment\\n continue\\n if re.match(r\\\"^\\\\s*$\\\", line): # skip white space line\\n continue\\n # strip inline cmnt '<space(s)># to end of line'\\n sline = re.sub(r\\\"\\\"\\\"(\\\\s*#[^\\\\'\\\"]*$)\\\"\\\"\\\", \\\"\\\", line)\\n # tokenize what remains\\n yield [\\\"\\\".join(t) for t in rpat.findall(sline)]\\n ff.close()\\n else:\\n return []\",\n \"def ReadAndTokenize(filename):\\n global CACHE\\n global VOCABULARY\\n if filename in CACHE:\\n return CACHE[filename]\\n comment = open(filename).read()\\n words = Tokenize(comment)\\n\\n terms = collections.Counter()\\n for w in words:\\n VOCABULARY[w] += 1\\n terms[w] += 1\\n\\n CACHE[filename] = terms\\n return terms\",\n \"def tokenize(fp):\\n for line in fp:\\n line = line.strip()\\n if line[0] == '#':\\n continue\\n for tok in line.split():\\n yield tok\",\n \"def tokenizeFile(path: Path) -> Tokens:\\n tokens: Tokens = []\\n commentActive = False\\n\\n with path.open() as file:\\n logging.debug(f\\\"Tokenizing file '{file.name}'\\\")\\n\\n lineNumber = 0\\n for line in file:\\n line = line.rstrip()\\n lineNumber += 1\\n lineIndex = 0\\n while len(line) > 0:\\n # Is a multi-line comment active?\\n if commentActive:\\n # Check if a comment end exists on the line\\n commentEnd = line.find(\\\"*/\\\")\\n\\n # If no comment end, go to next line\\n if commentEnd == -1:\\n break\\n\\n # Otherwise split the remainder of the comment from the line\\n line = line[commentEnd + 2 :]\\n commentActive = False\\n continue\\n\\n # If no characters left, go to next line\\n line = line.lstrip()\\n if len(line) == 0:\\n break\\n\\n # Skip line if line comment\\n if line.startswith(\\\"//\\\"):\\n break\\n\\n # Check for block comment start\\n if line.startswith(\\\"/*\\\") or line.startswith(\\\"/**\\\"):\\n # Check if a comment end exists on same line\\n commentEnd = line.find(\\\"*/\\\")\\n\\n # If no comment end, go to next line\\n if commentEnd == -1:\\n commentActive = True\\n break\\n\\n # Otherwise split the remainder of the comment from the line\\n line = line[commentEnd + 2 :]\\n commentActive = False\\n continue\\n\\n # Check for symbol\\n if line[0] in SYMBOLS:\\n tokens.append(\\n Token(TokenType.SYMBOL, line[0], lineNumber, lineIndex)\\n )\\n line = line[1:]\\n lineIndex += 1\\n continue\\n\\n # Check for integer constant\\n if line[0].isdigit():\\n i = 0\\n for i in range(0, len(line)):\\n if line[i].isalpha():\\n raise TokenizerException(\\n f\\\"Invalid character found in integer constant on line {lineNumber}\\\"\\n )\\n elif not line[i].isdigit():\\n break\\n\\n tokens.append(\\n Token(TokenType.INTEGER_CONST, line[:i], lineNumber, lineIndex)\\n )\\n line = line[i:]\\n lineIndex += 1\\n continue\\n\\n # Check for string constant\\n if line[0] == '\\\"':\\n stringEnd = line.find('\\\"', 1)\\n if stringEnd == -1:\\n raise TokenizerException(\\n f\\\"Cannot find closing symbol for string constant on line {lineNumber}\\\"\\n )\\n tokens.append(\\n Token(\\n TokenType.STRING_CONST,\\n line[1:stringEnd],\\n lineNumber,\\n lineIndex,\\n )\\n )\\n line = line[stringEnd + 1 :]\\n lineIndex += 1\\n continue\\n\\n # Check for keyword\\n found = False\\n for keyword in KEYWORDS:\\n if line.startswith(keyword):\\n found = True\\n tokens.append(\\n Token(TokenType.KEYWORD, keyword, lineNumber, lineIndex)\\n )\\n line = line[len(keyword) :]\\n lineIndex += 1\\n break\\n if found:\\n continue\\n\\n # Check for identifier\\n if line[0].isalpha() or line[0] == \\\"_\\\":\\n i = 0\\n for i in range(0, len(line)):\\n if not line[i].isalnum() and not line[i] == \\\"_\\\":\\n break\\n\\n tokens.append(\\n Token(TokenType.IDENTIFIER, line[:i], lineNumber, lineIndex)\\n )\\n line = line[i:]\\n lineIndex += 1\\n continue\\n\\n raise TokenizerException(\\\"Invalid character\\\", line[0], line)\\n\\n return tokens\",\n \"def tokenize_file(\\n infile, outfile, language, check_language=False, punctuation=False, ftfy=False\\n):\\n for line in infile:\\n if ftfy:\\n # Run all ftfy fixes, but don't let it introduce line breaks\\n line = fix_text(line.rstrip()).replace('\\\\n', ' ')\\n else:\\n # Run only specific quick fixes from ftfy\\n line = fix_surrogates(unescape_html(line.rstrip()))\\n tokens = tokenize(\\n line, language, include_punctuation=punctuation, external_wordlist=True\\n )\\n checked_lang = None\\n if check_language:\\n checked_lang, _confidence = detect_language_checked(line.rstrip())\\n if (not check_language):\\n print(' '.join(tokens), file=outfile)\\n else: \\n if langcodes.tag_distance(checked_lang, language) < 10:\\n print(' '.join(tokens), file=outfile)\",\n \"def tokenize(self):\\n\\n x = [] # input documents\\n for file_path in glob.glob(self.train_dir + '*.txt'):\\n file_as_string = open(file_path).read()\\n x.append(file_as_string)\\n\\n self.tokenizer.fit_on_texts(x)\\n print('input vocabulary size:', len(self.tokenizer.word_index))\\n pickle_file = open('Model/tokenizer.p', 'wb')\\n pickle.dump(self.tokenizer, pickle_file)\",\n \"def tokenize(self, path, training_set=False):\\n assert os.path.exists(path)\\n with open(path, encoding='utf8') as fin:\\n num_lines = sum(1 for _ in fin.readlines())\\n with open(path, 'r', encoding=\\\"utf8\\\") as f:\\n words = []\\n for i, line in enumerate(tqdm(f, total=num_lines)):\\n if self.max_lines > 0 and i > self.max_lines:\\n break\\n line = line.strip()\\n if not line:\\n continue # Skip empty lines.\\n elif line.startswith('='):\\n continue # Skip headers.\\n else:\\n sentence = (self.order - 1) * [SOS] + \\\\\\n [process(word, self.lower) for word in line.split()] + [EOS]\\n if training_set:\\n words.extend(sentence)\\n self.vocab.update(sentence)\\n else:\\n sentence = [word if word in self.vocab else UNK for word in sentence]\\n words.extend(sentence)\\n return words\",\n \"def sent_tokenize_with_unks(self, path, fname):\\n assert os.path.exists(path)\\n all_ids = []\\n sents = []\\n fpath = os.path.join(path, fname)\\n with open(fpath, 'r+', encoding=\\\"utf-8\\\") as f:\\n lines = f.read().split('\\\\n')\\n for line in lines:\\n if line.strip() == \\\"\\\":\\n continue\\n sents.append(line.strip())\\n words = line.strip().split()\\n # tokenize file content\\n ids = torch.LongTensor(len(words)+2)\\n ids[0] = self.word2idx['<sos>']\\n for j, word in enumerate(words):\\n if word not in self.word2idx:\\n ids[j+1] = self.word2idx[\\\"<unk>\\\"]\\n else:\\n ids[j+1] = self.word2idx[word]\\n ids[j+2] = self.word2idx['<eos>']\\n all_ids.append(ids) \\n return (sents, all_ids)\",\n \"def WcTokenizer(file_num, files, path):\\n\\n # Data Loading\\n # loads the input text off the input file, the scheduler or the invoking pipeline decides which file to\\n # load using the supplied file number.\\n text = textLoader(path+files[file_num])\\n\\n # is a fast conversion of uppercase to lowercase and removal of the following `'-_\\n cleaned_text = text.translate(table)\\n\\n # matching words/tokens\\n words = re.findall(tokenPattern, cleaned_text)\\n\\n # emits a tuple (word,1) for every occurrence of the word, ata a later stage the combiner/reducer will sum these\\n # occurrences into a wordcount\\n wordcount = [ (word, 1) for word in words ]\\n\\n # return the wordcount list. list of tuples [(word1, 1), (word2, 1), ..., (word1, 1)]\\n return wordcount\",\n \"def read_file(path, tok=False):\\n with open_file(path) as f:\\n for line in f.readlines():\\n words = split_sentence(line.strip(), tok)\\n yield words\",\n \"def pre_process(filename, check=True):\\n ntokens = 0\\n if check:\\n lines = []\\n for line in codecs.open(filename, \\\"r\\\", \\\"utf-8\\\"):\\n # lines += line.replace(\\\"\\\\n\\\",\\\"\\\").split() + [\\\"<eos>\\\"]\\n lines += line.split() + [\\\"<eos>\\\"]\\n ntokens = len(lines)\\n\\n lines = []\\n for line in codecs.open(filename, \\\"r\\\", \\\"utf-8\\\"):\\n lines.append(line.replace(\\\"@.@\\\", \\\"&numb\\\") + \\\"<eos>\\\")\\n\\n split_lines = [nltk.sent_tokenize(line.strip())\\n for line in lines]\\n\\n new = []\\n for line in split_lines:\\n if len(line) > 1:\\n new_lines = line[:-2]\\n new_line = line[-2] + \\\" \\\" + line[-1]\\n new_lines += [new_line]\\n else:\\n new_lines = line\\n new += new_lines\\n\\n split_lines = [line.replace(\\\"&numb\\\", \\\"@.@\\\")\\n for line in new]\\n\\n if check:\\n len_new = [len(l.split()) for l in split_lines]\\n assert ntokens == sum(len_new)\\n print(\\\"# of tokens in the original file = {0}\\\\n\\\"\\n \\\"# of tokens after pre-processing = {1}\\\\n\\\".format(\\n ntokens, sum(len_new)))\\n\\n output = \\\"{0}.sents\\\".format(filename)\\n print(\\\"Saving pre-processed file to {0}\\\\n\\\".format(output))\\n with codecs.open(output, \\\"w\\\", \\\"utf-8\\\") as f:\\n for line in split_lines:\\n f.write(\\\"{0}\\\\n\\\".format(line.replace(\\\"\\\\n\\\", \\\"\\\")))\",\n \"def read_file(filename, tokenizer, is_cased):\\n sents = []\\n with open(filename) as f:\\n for line in f:\\n sents.append(tokenizer(line, is_cased))\\n return sents\",\n \"def tokenize(self, text):\\n # Ignore non-ASCII characters.\\n text = remove_non_ascii(text)\\n text = text.translate(Tokenizer.trans)\\n tokens = [t for t in text.split() \\n if len(t) >= self._config[u'min_len']\\n and t not in self._config[u'stopwords']]\\n self._counter.update(tokens)\",\n \"def tokenize_with_unks(self, path, fname):\\n assert os.path.exists(path)\\n fpath = os.path.join(path, fname)\\n with open(fpath, 'r+', encoding=\\\"utf-8\\\") as f:\\n lines = f.read().split('\\\\n')\\n total_num = len(lines)\\n \\n # Tokenize file content\\n ids = torch.zeros((total_num, self.seq_len), dtype=torch.long)\\n ids_masking = torch.zeros((total_num, self.seq_len))\\n for i, line in enumerate(lines):\\n if line.strip() == \\\"\\\":\\n continue\\n words = line.strip().split()\\n ids[i, 0] = self.word2idx['<sos>']\\n ids_masking[i, 0] = 1\\n for j, word in enumerate(words[:self.seq_len-1]):\\n ids_masking[i, j+1] = 1\\n if word not in self.word2idx:\\n ids[i, j+1] = self.word2idx[\\\"<unk>\\\"]\\n else:\\n ids[i, j+1] = self.word2idx[word]\\n return ids, ids_masking\",\n \"def file_to_tokenized_docs_generator(file_path, encoder, args):\\n reader = Reader(file_path)\\n string_iterable = reader.stream_data(threaded=False)\\n string_iterable = eot_splitting_generator(string_iterable, encoder)\\n\\n token_list_gen = prep_and_tokenize_generator(string_iterable,\\n encoder,\\n normalize_with_ftfy=args.normalize_with_ftfy,\\n normalize_with_wikitext_detokenize=args.normalize_with_wikitext_detokenize\\n )\\n return token_list_gen\",\n \"def extract_tokenized_in_place(dirname, in_fname):\\n\\n in_path = dp.path_iteration.get_gigaword_path(dirname, in_fname)\\n\\n # Make the out_path.\\n out_fname = in_fname[:-4]\\n out_path = dp.path_iteration.get_tokenized_path(dirname, out_fname)\\n\\n # Ensure the output directory exists\\n out_dir = os.path.dirname(out_path)\\n if not os.path.exists(out_dir):\\n os.makedirs(out_dir)\\n\\n # Extract the tokens.\\n extract_tokenized_from_file(in_path, out_path)\",\n \"def tokenize(self, fileid):\\n for paragraph in self.corpus.paras(fileids=fileid):\\n yield [\\n nltk.pos_tag(nltk.wordpunct_tokenize(sent))\\n for sent in nltk.sent_tokenize(paragraph)\\n ]\",\n \"def tokenization(text):\\n\\n global tokenizer_tree\\n tokenised_document = tokenizer_tree.tokenize(text)\\n return tokenised_document\",\n \"def tokenize(text):\\n source = list(text.rstrip().replace('\\\\n', ' '))\\n return source\",\n \"def tokenize(self, fileid):\\n for paragraph in self.corpus.paras(fileids=fileid):\\n yield [\\n pos_tag(wordpunct_tokenize(sent))\\n for sent in sent_tokenize(paragraph)\\n ]\",\n \"def tokenize(self, file_name):\\n main_body = self.cast.nodes[0].body[-1]\\n token_string = self.visit(main_body)\\n\\n variable_map = self.dump_var_map()\\n value_map = self.dump_val_map()\\n\\n out_file = open(file_name, \\\"w\\\")\\n out_file.write(f\\\"{token_string}\\\\n\\\")\\n\\n for var in variable_map:\\n out_file.write(f\\\"{var}\\\\n\\\")\\n\\n for val in value_map:\\n out_file.write(f\\\"{val}\\\\n\\\")\",\n \"def get_word_tokens(path):\\n\\n logger.debug(\\\"loading texts\\\")\\n text = \\\"\\\"\\n\\n if not os.path.exists(path):\\n raise OSError(f\\\"Given path '{path}' does not exist\\\")\\n\\n # read files from directory\\n for file in os.listdir(path):\\n if file.endswith(\\\".txt\\\"):\\n with open(os.path.join(path, file), \\\"r\\\") as f:\\n text = \\\" \\\".join([text, f.read()])\\n\\n # tokenize text\\n logger.debug(\\\"tokenizing texts\\\")\\n sentences = nltk.sent_tokenize(text)\\n words = [nltk.word_tokenize(sentence) for sentence in sentences]\\n\\n # flatten list\\n words = list(chain.from_iterable(words))\\n\\n return words\",\n \"def tokenize(self, text: str) -> ['token']:\\n # project2: takes a str to tokenize instead of a text file\\n tokens = []\\n word = ''\\n for char in text:\\n # read one char at a time to save memory\\n if char in string.ascii_uppercase:\\n word += char.lower() # tokens are lower case only\\n elif (char in string.ascii_lowercase) or (char in string.digits): # accepted char for token\\n word += char\\n elif word != '':\\n # this branch will only be triggered at the end of a token,\\n # avoid adding empty string to list of tokens when there's multiple non-alphanumeric chars in a row\\n tokens.append(word)\\n word = ''\\n return tokens\",\n \"def tokenize(text):\\n return text.split(' ')\",\n \"def _tokenize(self, text: str) -> List[str]:\\n return self.bert_model.tokenize(text.strip())\",\n \"def tokenize(self, raw_text):\\n # TODO implement\\n raw_tokens = word_tokenize(raw_text.decode('utf8'))\\n return self.filter_tokens(raw_tokens)\\n # return self.split_by(raw_tokens, '-')\",\n \"def _tokenize(self, text):\\n split_tokens = []\\n for token in self.basic_tokenizer.tokenize(text):\\n for sub_token in self.wordpiece_tokenizer.tokenize(token):\\n split_tokens.append(sub_token)\\n return split_tokens\",\n \"def _tokenize(self, text: str) -> List[str]:\\n return self.bpe.tokenize(text)\",\n \"def __init__(self, file):\\n self.__in_file = file\\n self.__token = \\\"\\\" # will contain the current token\\n self.__tokens = [] # will contain all tokens\\n self.__lines = file.readlines()\\n self.__lines = \\\"\\\".join(self.__lines) # all lines as one string, in order to make the iteration easier.\\n self.__i = 0\\n self.build_tokens()\",\n \"def tokenize(self, path):\\n # Convert class 1,2 to 0,1\\n # print(\\\"Convert class 1,2 to 0,1\\\")\\n # Convert class 1,2 to 0,1\\n dropped = cropped = 0\\n oov_count = 0.\\n word_count = 0.\\n with open(path, 'r') as f:\\n linecount = 0\\n lines = []\\n tags = []\\n for line in f:\\n linecount += 1\\n if self.max_lines > 1 and linecount >= self.max_lines:\\n break\\n if self.lowercase:\\n words = line.lower().strip().split()\\n else:\\n words = line.strip().split()\\n tag, words = int(words[0]), words[1:]\\n\\n # if applying BPE\\n if self.apply_bpe:\\n words = [bp for word in words\\n for bp in self.bpemb_en.encode(word)]\\n\\n if len(words) > self.maxlen:\\n cropped += 1\\n words = words[:self.maxlen]\\n# try:\\n# crop_words = words[:maxlen]\\n# last_period = max(rindex(crop_words, '.'), rindex(crop_words, '!'), rindex(crop_words, ','))\\n# except:\\n# last_period = self.maxlen\\n# if last_period < 10:\\n# print(\\\"Sentence too short! {}\\\".format(words))\\n# words = words[:last_period]\\n if len(words) < 3:\\n dropped += 1\\n# print(words)\\n continue\\n words = ['<sos>'] + words\\n words += ['<eos>']\\n\\n # vectorize\\n vocab = self.dictionary.word2idx\\n unk_idx = vocab['<oov>']\\n indices = [vocab[w] if w in vocab else unk_idx for w in words]\\n word_count += len(indices)\\n oov_count += sum([1 if ii==unk_idx else 0 for ii in indices])\\n # add to output list\\n lines.append(indices)\\n # Convert class 1,2 to 0,1\\n # tag = tag - 1\\n tags.append(tag)\\n # tags = to_class_id(tags)\\n print(\\\"Number of sentences cropped from {}: {} out of {} total, dropped {}. OOV rate {:.3f}\\\".\\n format(path, cropped, linecount, dropped, oov_count/word_count))\\n\\n return list(zip(tags, lines))\",\n \"def _tokenize(self, text: str) -> List[str]:\\n text = text.lower().strip()\\n return self.bpe.tokenize(text)\",\n \"def tokenize(self, fileids=None, categories=None):\\n morph = pymorphy2.MorphAnalyzer()\\n for paragraph in self.paras(fileids=fileids):\\n yield [\\n (word, morph.parse(word)[0].tag.POS)\\n # pos_tag(wordpunct_tokenize(sent), lang='rus')\\n for sent in sent_tokenize(paragraph)\\n for word in wordpunct_tokenize(sent)\\n ]\",\n \"def tokenize(self, text):\\n text = utils.convert_to_unicode(text)\\n text = self._clean_text(text)\\n\\n # This was added on November 1st, 2018 for the multilingual and Chinese\\n # models. This is also applied to the English models now, but it doesn't\\n # matter since the English models were not trained on any Chinese data\\n # and generally don't have any Chinese data in them (there are Chinese\\n # characters in the vocabulary because Wikipedia does have some Chinese\\n # words in the English Wikipedia.).\\n text = self._tokenize_chinese_chars(text)\\n\\n orig_tokens = whitespace_tokenize(text)\\n split_tokens = []\\n for token in orig_tokens:\\n if self.do_lower_case:\\n token = token.lower()\\n token = self._run_strip_accents(token)\\n split_tokens.extend(self._run_split_on_punc(token))\\n\\n output_tokens = whitespace_tokenize(\\\" \\\".join(split_tokens))\\n return output_tokens\",\n \"def tokenize(text):\\n sentence = Sentence(text)\\n return sentence.tokens()\",\n \"def tokenize_words(line):\\n return\",\n \"def tokenize(self, text):\\n text = convert_to_unicode(text)\\n text = self._clean_text(text)\\n\\n # This was added on November 1st, 2018 for the multilingual and Chinese\\n # models. This is also applied to the English models now, but it doesn't\\n # matter since the English models were not trained on any Chinese data\\n # and generally don't have any Chinese data in them (there are Chinese\\n # characters in the vocabulary because Wikipedia does have some Chinese\\n # words in the English Wikipedia.).\\n text = self._tokenize_chinese_chars(text)\\n\\n orig_tokens = whitespace_tokenize(text)\\n split_tokens = []\\n for token in orig_tokens:\\n if self.do_lower_case:\\n token = token.lower()\\n token = self._run_strip_accents(token)\\n if self.split_on_punc:\\n split_tokens.extend(self._run_split_on_punc(token))\\n else:\\n split_tokens.append(token) # pragma: no cover\\n\\n output_tokens = whitespace_tokenize(\\\" \\\".join(split_tokens))\\n return output_tokens\",\n \"def process_corpus(self):\\n sentences = []\\n sentence = []\\n with open(str(self.file), encoding=self.encoding) as f:\\n\\n line = f.readline()\\n\\n while line:\\n\\n if line.startswith(\\\"#\\\"):\\n line = f.readline()\\n continue\\n\\n if line.strip().replace(\\\"\\\", \\\"\\\") == \\\"\\\":\\n if len(sentence) > 0:\\n self.infer_space_after(sentence)\\n if self.tagging_scheme is not None:\\n self.convert_tag_scheme(\\n sentence, target_scheme=\\\"iobes\\\"\\n )\\n\\n sentences.append(sentence)\\n sentence = []\\n\\n else:\\n fields = re.split(r\\\"\\\\s+\\\", line)\\n token = fields[0] # text column\\n token_tags = {\\n v: fields[k]\\n for k, v in self.columns.items()\\n if v != \\\"text\\\"\\n }\\n sentence.append({\\\"name\\\": token, \\\"tags\\\": token_tags})\\n\\n line = f.readline()\\n\\n return sentences\",\n \"def train(self, file_path):\\n print(\\\"Training CharacterTokenizer ...\\\")\\n rx = re.compile(r\\\"\\\\B(.)\\\")\\n\\n text = open(file_path, \\\"r\\\").read()\\n text = rx.sub(r\\\" ##\\\\1\\\", text)\\n\\n tokens_frequency = defaultdict(int)\\n for word in text.split(\\\" \\\"):\\n tokens_frequency[word] += 1\\n\\n self.vocab = self._truncate_dict(dict(tokens_frequency))\\n self.vocab_size = len(self.vocab)\",\n \"def load_lexicons(self, folder: str, file_name: str):\\n file_path = os.path.join(folder, file_name)\\n with open(file_path, \\\"r\\\", encoding=\\\"utf-8\\\") as f_in:\\n for line in f_in.readlines():\\n line = line.replace(\\\"\\\\n\\\", \\\"\\\")\\n tokens = list(line)\\n self.insert(tokens)\",\n \"def _tokenize(source):\\n lines = source.split(\\\"\\\\n\\\")\\n print(\\n \\\"{type:<10}{string:<25} {start:^12} {end:^12}\\\".format(\\n type=\\\"Type\\\", string=\\\"String\\\", start=\\\"Start\\\", end=\\\"End\\\"\\n )\\n )\\n print(\\\"-\\\" * 60)\\n for line in lines:\\n tokens = collect_tokens(line)\\n for token in tokens:\\n print(token)\",\n \"def tokenize(self, lines):\\n tokens = []\\n\\n for line_number, line in enumerate(lines):\\n self._line_number = line_number + 1\\n self._line = line.rstrip('\\\\r\\\\n')\\n\\n last_end = 0\\n for token in _TOKEN_RE.finditer(self._line):\\n if token.start() != last_end:\\n self.error(\\n 'invalid token',\\n token=Token('INVALID',\\n self._line[last_end:token.start()],\\n self.filename, self._line,\\n self._line_number, last_end))\\n last_end = token.end()\\n\\n # Omit whitespace and comments now to avoid sprinkling this logic\\n # elsewhere.\\n if token.lastgroup in ('WHITESPACE', 'COMMENT',\\n 'CONTINUATION'):\\n continue\\n tokens.append(\\n Token(token.lastgroup, token.group(), self.filename,\\n self._line, self._line_number, token.start()))\\n if last_end != len(self._line):\\n self.error(\\n 'invalid token',\\n token=Token('INVALID', self._line[last_end:],\\n self.filename, self._line, self._line_number,\\n last_end))\\n\\n if self._line.endswith('\\\\\\\\'):\\n # This line is not finished yet.\\n continue\\n\\n if tokens:\\n # Return a copy of the token list so that the caller can be free\\n # to modify it.\\n yield tokens[::]\\n tokens.clear()\",\n \"def tokenize(self, text):\\n text = convert_to_unicode(text)\\n text = self.clean_text(text)\\n\\n # This was added on November 1st, 2018 for the multilingual and Chinese\\n # models. This is also applied to the English models now, but it doesn't\\n # matter since the English models were not trained on any Chinese data\\n # and generally don't have any Chinese data in them (there are Chinese\\n # characters in the vocabulary because Wikipedia does have some Chinese\\n # words in the English Wikipedia.).\\n text = self._add_space_around_cjk_chars(text)\\n\\n orig_tokens = split_by_whitespace(text)\\n split_tokens = []\\n for token in orig_tokens:\\n if self.do_lower_case:\\n token = token.lower()\\n token = remove_accents(token)\\n split_tokens.extend(split_by_punctuation(token))\\n\\n output_tokens = split_by_whitespace(\\\" \\\".join(split_tokens))\\n return output_tokens\",\n \"def tokenize(self, fileid):\\n for paragraph in self.corpus.paras(fileids=fileid):\\n sents = []\\n for sent in sent_tokenize(paragraph, language='russian'):\\n words = []\\n for word in wordpunct_tokenize(sent):\\n token = self.lemmatize(word)\\n if not self.is_punct(token) and not self.is_stopword(token):\\n\\n words.append((token, str(self.morph.parse(word)[0].tag.POS)))\\n\\n sents.append(words)\\n yield sents\\n # yield [\\n # (word, morph.parse(word)[0].tag.POS)\\n # # pos_tag(wordpunct_tokenize(sent), lang='rus')\\n # for sent in sent_tokenize(paragraph, language='russian')\\n # for word in wordpunct_tokenize(sent)\\n # ]\\n # yield [\\n # pos_tag(wordpunct_tokenize(sent), lang='rus')\\n # for sent in sent_tokenize(paragraph, language='russian')\\n # ]\",\n \"def read_txt(filename):\\n file_object = open(filename, 'r')\\n file_as_string = file_object.read()\\n return create_word_list(file_as_string)\",\n \"def tokenize(self):\\n self.__create_tokens()\\n copy_obj = rtf2xml.copy.Copy(bug_handler = self.__bug_handler)\\n if self.__copy:\\n copy_obj.copy_file(self.__write_to, \\\"tokenize.data\\\")\\n copy_obj.rename(self.__write_to, self.__file)\\n os.remove(self.__write_to)\",\n \"def parse_text(text=None, file=None):\\n if not text:\\n text = open(file).readlines()\\n parsed_text = re.split(ARTICLE_TOKEN, text)\\n return parsed_text\",\n \"def tokenize(self, text):\\n # text = convert_to_unicode(text)\\n\\n output_tokens = []\\n for token in split_by_whitespace(text):\\n chars = list(token)\\n if len(chars) > self.max_input_chars_per_word:\\n output_tokens.append(self.unk_token)\\n continue\\n\\n is_bad = False\\n start = 0\\n sub_tokens = []\\n while start < len(chars):\\n end = len(chars)\\n cur_substr = None\\n while start < end:\\n substr = \\\"\\\".join(chars[start:end])\\n if start > 0:\\n substr = \\\"##\\\" + substr\\n if substr in self.vocab:\\n cur_substr = substr\\n break\\n end -= 1\\n if cur_substr is None:\\n is_bad = True\\n break\\n sub_tokens.append(cur_substr)\\n start = end\\n\\n if is_bad:\\n output_tokens.append(self.unk_token)\\n else:\\n output_tokens.extend(sub_tokens)\\n return output_tokens\",\n \"def tokenize(txt):\\n Depunc = depunctuate(txt).lower()\\n Tokens = word_tokenize(Depunc)\\n \\n return Tokens\",\n \"def tokenize(text):\\n tokens = TreebankWordTokenizer().tokenize(text)\\n tokens = lemmatize(tokens)\\n tokens = filter(lambda s: len(s) > 2, tokens) # remove tokens with < 3 chars\\n return tokens\",\n \"def tokenize_file_keep_comments(infilename):\\n# reg expss\\n\\timport re\\n\\timport sys\\n\\timport fileinput\\n\\timport math\\n\\n#\\n# open and parse input \\n#\\n\\n\\ttry:\\n\\t\\tfp = open (infilename, 'r')\\n\\texcept IOError:\\n\\t\\tprint \\\"Error opening file\\\"\\n\\t\\traise\\n\\n\\tlines = fp.readlines ()\\n\\n#\\n# put all tokens into tokens and remove comments\\n#\\n\\ttokens = []\\n\\tfor line in lines:\\n\\t\\ttmp = re.split ('[ \\\\t\\\\n]*',line)\\n#\\t\\tprint \\\"tmp = \\\", tmp\\n\\t\\tfor tok in tmp:\\n\\t\\t\\tif (tok != ''):\\n\\t\\t\\t\\ttokens.append(tok)\\n#\\tprint \\\"tokens = \\\", tokens\\n\\n\\tfp.close()\\n\\n\\treturn tokens\",\n \"def parse(self, file_name):\\n try:\\n fp = open(file_name, encoding='utf-8')\\n trigrams = []\\n\\n for line in fp.readlines():\\n for i in range(len(line) - 2):\\n trigrams.append(line[i : i + 3].lower())\\n\\n fp.close()\\n return trigrams\\n except UnicodeDecodeError:\\n print(\\\"Skipping file: \\\", file_name)\\n return []\",\n \"def _load_tokens(self, filename):\\n self.tokens = dict()\\n with open(filename, 'r') as f:\\n for line in f.readlines():\\n raw = line.strip().split('\\\\t')\\n self.tokens[raw[0]] = int(raw[1])\",\n \"def tokenize(lines, token='word'):\\n if token == 'word':\\n return [line.split() for line in lines]\\n elif token == 'char':\\n return [list(line) for line in lines]\\n else:\\n print('ERROR: unknown token type: ' + token)\",\n \"def tokenize(self, text):\\n text = self._clean_text(text)\\n text = self._tokenize_chinese_chars(text)\\n orig_tokens = whitespace_tokenize(text)\\n split_tokens = []\\n for token in orig_tokens:\\n if self.do_lower_case and token not in self.never_split:\\n token = token.lower()\\n token = self._run_strip_accents(token)\\n split_tokens.extend(self._run_split_on_punc(token))\\n output_tokens = whitespace_tokenize(' '.join(split_tokens))\\n return output_tokens\",\n \"def tokenize(self, text):\\n\\n text = convert_to_unicode(text)\\n\\n output_tokens = []\\n for token in whitespace_tokenize(text):\\n chars = list(token)\\n if len(chars) > self.max_input_chars_per_word:\\n output_tokens.append(self.unk_token) # pragma: no cover\\n continue # pragma: no cover\\n\\n is_bad = False\\n start = 0\\n sub_tokens = []\\n while start < len(chars):\\n end = len(chars)\\n cur_substr = None\\n while start < end:\\n substr = \\\"\\\".join(chars[start:end])\\n if start > 0:\\n substr = \\\"##\\\" + substr # pragma: no cover\\n if substr in self.vocab:\\n cur_substr = substr\\n break\\n end -= 1 # pragma: no cover\\n if cur_substr is None:\\n is_bad = True # pragma: no cover\\n break # pragma: no cover\\n sub_tokens.append(cur_substr)\\n start = end\\n\\n if is_bad:\\n output_tokens.append(self.unk_token) # pragma: no cover\\n else:\\n output_tokens.extend(sub_tokens)\\n return output_tokens\",\n \"def tokenize(text):\\n return tokens_re.findall(text)\",\n \"def tokenize(self, texts: List[str]) -> List[Token]:\\n raise NotImplementedError\",\n \"def _read_words(filename):\\n with tf.gfile.GFile(filename, \\\"r\\\") as f:\\n return f.read().replace(\\\"\\\\n\\\", \\\"<eos>\\\").split()\",\n \"def tokenize(self, text):\\n\\n text = convert_to_unicode(text)\\n\\n output_tokens = []\\n for token in whitespace_tokenize(text):\\n chars = list(token)\\n if len(chars) > self.max_input_chars_per_word:\\n output_tokens.append(self.unk_token)\\n continue\\n\\n is_bad = False\\n start = 0\\n sub_tokens = []\\n while start < len(chars):\\n end = len(chars)\\n cur_substr = None\\n while start < end:\\n substr = \\\"\\\".join(chars[start:end])\\n ### joonho.lim @ 2019-03-15\\n # if start > 0:\\n # substr = \\\"##\\\" + substr\\n # print ( '[substr]\\\\t%s\\\\t%s\\\\t%d\\\\t%d' % ( substr, substr in self.vocab, start, end))\\n if substr in self.vocab:\\n cur_substr = substr\\n break\\n end -= 1\\n if cur_substr is None:\\n is_bad = True\\n break\\n sub_tokens.append(cur_substr)\\n start = end\\n\\n if is_bad:\\n output_tokens.append(self.unk_token)\\n else:\\n output_tokens.extend(sub_tokens)\\n output_tokens.insert(0, '[CLS]')\\n output_tokens.append('[SEP]')\\n return output_tokens\",\n \"def load(fp, encoding=None, lexer=None, tokenizer=None, detokenize=True):\\n lexer = lexer() if lexer else Lexer()\\n stream = TokenStream(fp, tokenizer=tokenizer)\\n parse = lexer.parse(stream)\\n\\n if detokenize:\\n return list(lexer.detokenize(parse))\\n return parse\",\n \"def tokenize(document):\\n words = nltk.word_tokenize(document)\\n # tokenize files into a list of words\\n return [\\n word.lower() for word in words\\n # filter out all punctuation (import string) and all stopwords\\n if not all(char in string.punctuation for char in word) \\n and word not in nltk.corpus.stopwords.words(\\\"english\\\")\\n ]\",\n \"def tokenize(text):\\n wt = nltk.RegexpTokenizer(pattern=r'\\\\s+', gaps=True)\\n tokens = wt.tokenize(text)\\n return tokens\",\n \"def tokenize(raw_text):\\n tokenized_text = nltk.tokenize.word_tokenize(raw_text)\\n return tokenized_text\",\n \"def _tokenize(self, text: str) -> List[str]:\\n tokens = []\\n for token in re.findall(self._pat, text):\\n tokens.extend(bpe_token for bpe_token in self._bpe(token).split(\\\" \\\"))\\n return tokens\",\n \"def _tokenize(ustr1):\\n\\n return list(tk.generate_tokens(io.StringIO(ustr1).readline))\",\n \"def read_file(filename):\\n\\n sentences = open(filename).read().strip().split(\\\"\\\\n\\\\n\\\") #separate tweets\\n ret = []\\n for sent in sentences:\\n lines = sent.split(\\\"\\\\n\\\") #each word in the tweet\\n pairs = [L.split(\\\"\\\\t\\\") for L in lines] #Funniest O\\n tokens = [tok for tok,tag in pairs]\\n tags = [tag for tok,tag in pairs]\\n ret.append( (tokens,tags) )\\n return ret\",\n \"def text2tokens(raw_text):\\n clean_text = raw_text.lower().translate(translate_tab)\\n tokens = [token.strip() for token in tokenizer.tokenize(clean_text)]\\n tokens = [token for token in tokens if token not in eng_stopwords]\\n stemmed_tokens = [stemmer.stem(token) for token in tokens]\\n return [token for token in stemmed_tokens if len(token) > 2] # skip short tokens\",\n \"def data_to_token_ids(data_path, target_path, vocabulary_path,\\n tokenizer=None, normalize_digits=True,\\n UNK_ID=3, _DIGIT_RE=re.compile(br\\\"\\\\d\\\")):\\n if not gfile.Exists(target_path):\\n print(\\\"Tokenizing data in %s\\\" % data_path)\\n vocab, _ = initialize_vocabulary(vocabulary_path)\\n with gfile.GFile(data_path, mode=\\\"rb\\\") as data_file:\\n with gfile.GFile(target_path, mode=\\\"w\\\") as tokens_file:\\n counter = 0\\n for line in data_file:\\n counter += 1\\n if counter % 100000 == 0:\\n print(\\\" tokenizing line %d\\\" % counter)\\n token_ids = sentence_to_token_ids(line, vocab, tokenizer,\\n normalize_digits, UNK_ID=UNK_ID,\\n _DIGIT_RE=_DIGIT_RE)\\n tokens_file.write(\\\" \\\".join([str(tok) for tok in token_ids]) + \\\"\\\\n\\\")\\n else:\\n print(\\\"Target path %s exists\\\" % target_path)\",\n \"def data_to_token_ids(data_path, target_path, vocabulary_path):\\n if not gfile.Exists(target_path):\\n print(\\\"Tokenizing data in %s\\\" % data_path)\\n vocab, _ = initialize_vocabulary(vocabulary_path)\\n with gfile.GFile(data_path, mode=\\\"rb\\\") as data_file:\\n with gfile.GFile(target_path, mode=\\\"w\\\") as tokens_file:\\n counter = 0\\n for line in data_file:\\n counter += 1\\n if counter % 100000 == 0:\\n print(\\\" tokenizing line %d\\\" % counter)\\n token_ids = sentence_to_token_ids(tf.compat.as_bytes(line), vocab)\\n tokens_file.write(\\\" \\\".join([str(tok) for tok in token_ids]) + \\\"\\\\n\\\")\",\n \"def tokenize(self, text):\\n return self._tokenize(text)\",\n \"def tokenize(self, start_pos=0, text=None):\\n pass\",\n \"def __gen_tokenization_file(self):\\n paula_id = '{}.{}.tok'.format(self.corpus_name, self.name)\\n E, tree = gen_paula_etree(paula_id)\\n self.paulamap['tokenization'] = paula_id\\n\\n base_paula_id = '{}.{}.text'.format(self.corpus_name, self.name)\\n mlist = E('markList',\\n {'type': 'tok',\\n XMLBASE: base_paula_id+'.xml'})\\n tok_tuples = self.dg.get_tokens()\\n for (tid, onset, tlen) in get_onsets(tok_tuples):\\n # even SaltNPepper still uses xpointers for string-ranges!\\n xp = \\\"#xpointer(string-range(//body,'',{},{}))\\\".format(onset, tlen)\\n mlist.append(E('mark', {'id': tid,\\n XLINKHREF: xp}))\\n tree.append(mlist)\\n self.files[paula_id] = tree\\n self.file2dtd[paula_id] = PaulaDTDs.mark\\n return paula_id\",\n \"def tokenize(raw_text):\\n def _xop(tokens):\\n def _(x):\\n return xop(x, \\\"op\\\", tokens)\\n return _\\n \\n raw_tokens=xversa_split(raw_text, tokens=Op_Tokens+Group_Tokens)\\n tokens=map(xtotype, raw_tokens) \\n tokens=map(_xop(Op_Tokens+Group_Tokens), tokens)\\n return tokens\",\n \"def data_to_token_ids(data_path, target_path, vocabulary_path,\\n tokenizer=None, normalize_digits=True):\\n if not gfile.Exists(target_path):\\n print(\\\"Tokenizing data in %s\\\" % data_path)\\n vocab, _ = initialize_vocabulary(vocabulary_path)\\n with gfile.GFile(data_path, mode=\\\"rb\\\") as data_file:\\n with gfile.GFile(target_path, mode=\\\"w\\\") as tokens_file:\\n counter = 0\\n for line in data_file:\\n counter += 1\\n if counter % 100000 == 0:\\n print(\\\" tokenizing line %d\\\" % counter)\\n token_ids = sentence_to_token_ids(line, vocab, tokenizer,\\n normalize_digits)\\n tokens_file.write(\\\" \\\".join([str(tok) for tok in token_ids]) + \\\"\\\\n\\\")\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7719976","0.75545526","0.7165831","0.7099891","0.69710827","0.6951215","0.6950511","0.69196737","0.6892084","0.6843397","0.68261045","0.68261045","0.68049693","0.6728787","0.6702082","0.66849124","0.66235036","0.6609853","0.6596192","0.6591185","0.65899533","0.6581077","0.6578101","0.65728354","0.6544455","0.65262365","0.65197444","0.65137154","0.65062606","0.64858973","0.6481356","0.6409836","0.63830066","0.63806766","0.6370707","0.6370641","0.63650084","0.6358733","0.63545513","0.633624","0.632175","0.6301747","0.6298326","0.6279479","0.626376","0.6242814","0.6186997","0.61699736","0.6147876","0.61147404","0.6097937","0.6096315","0.6076616","0.60753864","0.606675","0.60427815","0.6038976","0.60331196","0.60316706","0.60266787","0.6020328","0.6019712","0.60064375","0.5987945","0.59878707","0.59836215","0.59821326","0.59793663","0.5969494","0.59546655","0.5945298","0.5925544","0.5911465","0.5902323","0.58999395","0.5899622","0.5898311","0.5891806","0.5847067","0.58356947","0.58181113","0.5814906","0.58116597","0.58086634","0.5799464","0.5783102","0.5779493","0.5776255","0.5774413","0.5767116","0.5767005","0.5762658","0.5761854","0.5756641","0.57549053","0.5754225","0.574806","0.57473475","0.57453483","0.5742024"],"string":"[\n \"0.7719976\",\n \"0.75545526\",\n \"0.7165831\",\n \"0.7099891\",\n \"0.69710827\",\n \"0.6951215\",\n \"0.6950511\",\n \"0.69196737\",\n \"0.6892084\",\n \"0.6843397\",\n \"0.68261045\",\n \"0.68261045\",\n \"0.68049693\",\n \"0.6728787\",\n \"0.6702082\",\n \"0.66849124\",\n \"0.66235036\",\n \"0.6609853\",\n \"0.6596192\",\n \"0.6591185\",\n \"0.65899533\",\n \"0.6581077\",\n \"0.6578101\",\n \"0.65728354\",\n \"0.6544455\",\n \"0.65262365\",\n \"0.65197444\",\n \"0.65137154\",\n \"0.65062606\",\n \"0.64858973\",\n \"0.6481356\",\n \"0.6409836\",\n \"0.63830066\",\n \"0.63806766\",\n \"0.6370707\",\n \"0.6370641\",\n \"0.63650084\",\n \"0.6358733\",\n \"0.63545513\",\n \"0.633624\",\n \"0.632175\",\n \"0.6301747\",\n \"0.6298326\",\n \"0.6279479\",\n \"0.626376\",\n \"0.6242814\",\n \"0.6186997\",\n \"0.61699736\",\n \"0.6147876\",\n \"0.61147404\",\n \"0.6097937\",\n \"0.6096315\",\n \"0.6076616\",\n \"0.60753864\",\n \"0.606675\",\n \"0.60427815\",\n \"0.6038976\",\n \"0.60331196\",\n \"0.60316706\",\n \"0.60266787\",\n \"0.6020328\",\n \"0.6019712\",\n \"0.60064375\",\n \"0.5987945\",\n \"0.59878707\",\n \"0.59836215\",\n \"0.59821326\",\n \"0.59793663\",\n \"0.5969494\",\n \"0.59546655\",\n \"0.5945298\",\n \"0.5925544\",\n \"0.5911465\",\n \"0.5902323\",\n \"0.58999395\",\n \"0.5899622\",\n \"0.5898311\",\n \"0.5891806\",\n \"0.5847067\",\n \"0.58356947\",\n \"0.58181113\",\n \"0.5814906\",\n \"0.58116597\",\n \"0.58086634\",\n \"0.5799464\",\n \"0.5783102\",\n \"0.5779493\",\n \"0.5776255\",\n \"0.5774413\",\n \"0.5767116\",\n \"0.5767005\",\n \"0.5762658\",\n \"0.5761854\",\n \"0.5756641\",\n \"0.57549053\",\n \"0.5754225\",\n \"0.574806\",\n \"0.57473475\",\n \"0.57453483\",\n \"0.5742024\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6815363"},"document_rank":{"kind":"string","value":"12"}}},{"rowIdx":21883,"cells":{"query":{"kind":"string","value":"Return the expected keys for the log entry."},"document":{"kind":"string","value":"def expected_log_keys(learner: adaptive.BaseLearner) -> list[str]:\n # Check if the result contains the expected keys\n expected_keys = [\n \"elapsed_time\",\n \"overhead\",\n \"npoints\",\n \"cpu_usage\",\n \"mem_usage\",\n ]\n if not _at_least_adaptive_version(\"0.16.0\", raises=False) and not isinstance(\n learner,\n adaptive.SequenceLearner,\n ):\n # The loss cache for SequenceLearner was introduced in adaptive 0.16.0\n # see https://github.com/python-adaptive/adaptive/pull/411\n expected_keys.append(\"latest_loss\")\n return expected_keys"},"metadata":{"kind":"string","value":"{\n \"objective\": {\n \"self\": [],\n \"paired\": [],\n \"triplet\": [\n [\n \"query\",\n \"document\",\n \"negatives\"\n ]\n ]\n }\n}"},"negatives":{"kind":"list like","value":["def check(self, entry_type:str, x:str):\n keys = set()\n x = self._decode(x)\n\n for log in self.logs:\n for datum in log[entry_type]:\n res = self._follow(datum, x)\n\n if type(res) == dict:\n for key in res.keys():\n keys.add(key)\n elif type(res) == list:\n keys.add('< %d' % len(res))\n \n return list(keys)","def provideExpectedMetaKeys(self):\n return self.metadataKeys, self.metadataParams","def keys():","def keyValues(self): # real signature unknown; restored from __doc__\n return []","def assertKeys(self, data, expected):\r\n self.assertEqual(sorted(data.keys()), sorted(expected))","def get_keys(self):\n self._logger.info(ME + '.get_keys()')\n\n tmp_primary_keys = []\n tmp_data_keys = []\n try:\n tmp_primary_keys = config.get(ME, 'primary_keys').split(',')\n tmp_data_keys = config.get(ME, 'data_keys').split(',')\n self.index_key = config.get(ME, 'index_key') #FIXME: this is bad\n except KeyError as error_msg:\n self._logger.error(\n 'EXCEPTION: Keys missing' +\n '\\r\\tprimary_keys={0}'.format(','.join(tmp_primary_keys)) +\n '\\r\\tdata_keys={0}'.format(','.join(tmp_data_keys)) +\n '\\r\\tindex_key={0}'.format(self.index_key),\n exc_info=True\n )\n raise Connection.TableKeysMissing(error_msg, ME)\n\n self._logger.debug(\n 'keys validated:' + \\\n '\\r\\tprimary_keys={0}'.format(','.join(tmp_primary_keys)) +\n '\\r\\tdata_keys={0}'.format(','.join(tmp_data_keys)) +\n '\\r\\tindex_key={0}'.format(self.index_key)\n )\n return tmp_primary_keys, tmp_data_keys","def check_keys(self):","def AllKeys(self) -> _n_0_t_1[str]:","def keys(self):\n return","def Keys(self) -> _n_1_t_4:","def keys(self) -> Sequence[str]:\n raise NotImplementedError","def validate_instruction_keys(instruction: TransactionInstruction, expected: int) -> None:\n if len(instruction.keys) < expected:\n raise ValueError(f\"invalid instruction: found {len(instruction.keys)} keys, expected at least {expected}\")","def keys(self) -> List[str]:\n raise NotImplementedError","def keys(self):\n raise NotImplementedError","def valid_config_keys():\n click.echo(', '.join(get_class_properties(GenConfig)))","def test_log_key(self):\n kwargs = {\n \"follow_up\": None,\n \"notes\": \"This is a test.\",\n \"gdoc_link\": \"\",\n \"state_id\": \"KS\",\n \"contact\": None,\n \"user_id\": 9,\n \"formal_request\": False,\n \"date\": \"2013-03-28\",\n \"org_id\": 15,\n \"subject\": \"Test subject line\"\n }\n log = Log(**kwargs)\n log.save()\n self.assertEqual(log.log_key(), ('KS', '2013-03-28', 'Test subject line'))\n self.assertEqual(log.log_key(as_string=True), 'KS - 2013-03-28 - Test subject line')\n\n # Test with contact\n contact = Contact.objects.all()[0]\n log.contact = contact\n log.save()\n expected = (\n 'KS',\n '2013-03-28',\n u'Williams (Kansas Secretary of State elections division)',\n 'Test subject line',\n )\n self.assertEqual(expected, log.log_key())","def _extra_keys(self):\r\n return []","def keys(self) -> t.Tuple[str, ...]:\n return self._keys","def get_group_keys(self):\r\n if len(self.conflicting_exclusives) == 0:\r\n return [\"<none>\"]\r\n else:\r\n return self.key_to_targets.keys()","def _get_expected_sections(self):\n\n with open(self.filename, \"r\") as fh:\n yaml = ruamel.yaml.YAML(typ=\"unsafe\", pure=True)\n data = yaml.load(fh.read())\n keys = list(data.keys())\n return keys","def transform_info_keys():\n return (TraceKeys.CLASS_NAME, TraceKeys.ID, TraceKeys.TRACING, TraceKeys.DO_TRANSFORM)","def getDiscoveredMetricsKeys(self):\n return tuple(self.__foundMetrcsKeySet)","def keys(self) -> List:\n pass","def keys(self):\n\t\treturn tuple(self.dist.keys())","def keys(self) -> tuple(Any, ...): # type: ignore\n return tuple(self.contents.keys())","def state_info_keys(self):\n return [k for k, _ in self.state_info_specs]","def _get_missing_keys(self):\n REQUIRED_KEYS = [\n 'date_purchased', 'cost', 'supply_type_id'\n ]\n\n return [key for key in REQUIRED_KEYS if not key in self.request.data]","def key_attributes(self):\n\n return [level.key for level in self.levels]","def test_toomanykeys(self):\n self.assertRaises(recordparser.KeyListMismatchError,\n recordparser.getfields, \"1234567890\", \"10s\", (\"key1\", \"key2\"))","def valid_config_keys():\n click.echo(', '.join(get_class_properties(OffshoreInputsConfig)))","def get_evaluated_keys(self) -> List[str]:\n return force_list(self.evaluated_keys)","def keys(self):\n query = \"\"\"SELECT column_name, data_type, character_maximum_length\n FROM INFORMATION_SCHEMA.COLUMNS WHERE table_name = 'ngc2236';\"\"\"\n result = self.wifsip.query(query)\n keys = [r[0] for r in result]\n return keys","def test_all_keys(self):\n\t\tdocumentation: dict = self.handlerObject.build_swagger_documentation()\n\t\t# print(\"Test documentation was generated. The corresponding object is:\\n{0}\".format(documentation))\n\t\tself.assertTrue(compare_keys(documentation, TEST_ALL_KEYS_EXPECTED_OUTPUT))","async def keys(self) -> Iterable[str]:","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_sample_ids)","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_sample_ids)","def get_keys(self) -> Dict[str, str]:\n return self.http.get(self.config.paths.keys)","def test_toofewkeys(self):\n self.assertRaises(recordparser.KeyListMismatchError,\n recordparser.getfields, \"1234567890\", \"10s\", ())","def test_overall_report_keys():\n keys = overall_data.keys()\n assert('banner_report' in keys)\n assert('rewarded_report' in keys)\n assert('interstitial_report' in keys)\n assert('overall_report' in keys)","def key_attributes(self):\n\n return [level.key for level in self._levels.values()]","def keys(self, installer_context):\n return self.spec.keys(self.data, installer_context)","def get_registered_input_keys(self):\n return tuple(self._input_keys)","def test_section_keys(self):\n ars = self.ar[2009][11]['general']\n self.assertEqual(ars.keys(), self.__class__.wanted_lines)","def get_keys(self):\r\n return self._keys","def mandatory_info(self):\n return [info.key for info in self.entry_info if not info.optional]","def _get_report_keys(self, text, key_format=r'\\:\\w+\\:'):\n return [i.replace(\":\", \"\") for i in re.findall(r'\\:\\w+\\:', text)]","def test_get_keys(self):\n response = self.client.get_keys()\n assert isinstance(response, dict)\n assert 'public' in response\n assert 'private' in response\n assert response['public'] is not None\n assert response['private'] is not None","def get_extra_metadata_keys() -> list[str]:\n keys = [\n \"srow_x\",\n \"srow_y\",\n \"srow_z\",\n \"quatern_b\",\n \"quatern_c\",\n \"quatern_d\",\n \"qoffset_x\",\n \"qoffset_y\",\n \"qoffset_z\",\n \"dim\",\n \"pixdim\",\n *[f\"dim[{i}]\" for i in range(8)],\n *[f\"pixdim[{i}]\" for i in range(8)],\n ]\n\n # TODO: it would be good to remove these, but they are currently being used in the\n # codebase.\n # keys += [\n # \"original_affine\",\n # \"spatial_shape\",\n # \"spacing\",\n # ]\n\n return keys","def keys(self):\n if self.widget:\n w_keys = self.widget.keys()\n else:\n w_keys = []\n return sorted(w_keys + list(ttfont_dict_keys) + [case_s])","def keys(self):\n return _osgAnimation.mapVertexInfluence_keys(self)","def special_token_keys(self) -> List[str]:\n return list(self._special_token_kv.keys())","async def get_keys(self):\n return self.dict.keys()","def get_computed_stats_keys():\n\n # order not important\n result = [NUM_KITS_KEY, NUM_SAMPLES_KEY,\n NUM_SAMPLES_RECEIVED_KEY, NUM_UNIQUE_SOURCES_KEY,\n NUM_PARTIALLY_RETURNED_KITS_KEY,\n NUM_FULLY_RETURNED_KITS_KEY, NUM_KITS_W_PROBLEMS_KEY]\n\n num_status_keys = get_status_num_keys()\n result.extend(num_status_keys)\n return result","def test_get_transaction_types_key(self):\n pass","def required_event_keys(cls):\n return {'app_type', 'destination_function_name', 'schedule_expression'}","def test_keys_eq(self):\n self.assertListEqual(self.result, self.expected)","def report_keyset(self):\n for i, matchset in enumerate(self.matches):\n if len(matchset) == 1:\n print \"[%02d]\" % i, fmt(sorted([k for k, data in matchset.items()]), BLUE)\n elif len(matchset) != 0:\n print \"[%02d]\" % i, fmt(sorted([k for k, data in matchset.items()]), WHITE)\n else:\n print \"[%02d]\" % i, fmt(\"[X]\", RED)","def keys(targets):","def _additional_keys(self):\n addkeys = []\n if self.data.has_key('name'):\n addkeys += ['long imdb name']\n if self.data.has_key('headshot'):\n addkeys += ['full-size headshot']\n return addkeys","def keysAll():","def keys(self) -> List[str]:\n return self.__stash.keys()","def key_usages(self) -> pulumi.Output[Sequence[str]]:\n return pulumi.get(self, \"key_usages\")","def keys(self):\n return self.keys","def test_load_from_file_to_dict_key_names(self):\n self.assertEqual(list(self.loaded_json_dict), self.expected_dict_keys)","def keys(self):\n return [key for key, value in self.items()]","def keys(self):\n return self.params.keys()","def assert_keys_match(keys, expected, allow_missing=True):\n if not allow_missing:\n missing = expected - keys\n assert not missing, 'missing keys: %s' % missing\n extra = keys - expected\n assert not extra, 'extraneous keys: %s' % extra","def test_serialization_keys(self):\n serialized_inspection = serializers.InspectionSerializer(\n self.inspection\n )\n self.assertSetEqual(\n set(serialized_inspection.fields.keys()),\n INSPECTION_INSPECTED_KEYS\n )","def keys(self):\n return _keys(self)","def keys(self):\n return _keys(self)","def list_configuration_keys() -> Tuple[List[str], Dict[int, str]]:\n lookup = {i: v for (i, v) in enumerate(PipetteConfigurations.__fields__)}\n return [\n f\"{i}: {v}\" for (i, v) in enumerate(PipetteConfigurations.__fields__)\n ], lookup","def keys(self):\n list_all_dict = self.list_all()\n return list_all_dict[\"nodes\"] + list_all_dict[\"groups\"]","def test_4():\n input, output = get_input()\n with open(\n os.path.join(os.path.dirname(__file__), \"..\", \"test_data\",\n \"main_test4_keys.txt\")) as f:\n returnkeys = f.read().splitlines()\n os.system(f\" python -m vcflat -i {input} -o {output}\")\n\n with open(output, \"r\") as f:\n reader = csv.DictReader(f)\n assert [\n i for i in returnkeys if i in set(reader.fieldnames[0].split(\"\\t\"))\n ]\n os.remove(output)","def test_badly_formatted_entry(self):\n with pytest.raises(AssertionError) as exc_info:\n list(parser.generate_commands(yaml.load(\"\"\"\n - key1: 1\n key2: 2\n \"\"\")))\n assert \"Command has multiple top-level keys: ['key1', 'key2']\" in str(exc_info.value)","def keys(self):\n keys = set()\n with pd.HDFStore(self.rootpath, mode=\"r\") as hdf:\n hdf5_keys = hdf.keys()\n\n for key in hdf5_keys:\n kp = key.split(\"/\")\n if len(kp) == 5:\n print(kp, len(kp))\n keys.add(kp[4])\n return list(keys)","def test_postive_get_auth_horizon_check_keys(self):\n r = self.res.get('/auth/config/'+utils.partner, headers=utils.headers)\n logging.info(\"Return response is '%s'\" % r)\n # convert string to dictionary\n rd = ast.literal_eval(r)\n logging.info(\"Return response in dictionary format is '%s'\" % rd)\n self.assertEqual(self.res.response.status, 200)\n keys = ['type', 'web_endpoint', 'client_endpoint', 'org_name']\n self.assertTrue(utils.is_same_array(keys, rd.keys()), \"Keys are not correct!\")","def keys(self):\n keys = [key for key in self.__dict__.keys() if self[key] is not None]\n keys = [key for key in keys if key[:2] != '__' and key[-2:] != '__']\n return keys","def keys(self):\n keys = [key for key in self.__dict__.keys() if self[key] is not None]\n keys = [key for key in keys if key[:2] != '__' and key[-2:] != '__']\n return keys","def keys(self):\n keys = [key for key in self.__dict__.keys() if self[key] is not None]\n keys = [key for key in keys if key[:2] != '__' and key[-2:] != '__']\n return keys","def get_keys(self):\r\n\t\tlogger.debug(\"Getting the keys\")\r\n\t\t\r\n\t\treturn db.get_items('keys')","def keys(self):\n return ['title', 'keywords', 'description', 'url', 'content_file',\n 'language', 'phone', 'email']","def get_key_list(self) -> list:\n return self.key_functs.keys()","def keys(self):\r\n return [key for key, value in self.iteritems()]","def changed_keys(self):\n return self._changed_keys","def keys(self):\n return self.__keys","def keys(self):\n return [entry.key for entry in self.table if entry.value is not None]","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_categories)","def test_keys(self):\n obs = self.tester.keys()\n self.assertTrue(isinstance(obs, Iterable))\n self.assertEqual(set(obs), self.exp_categories)","def array_keys(item):\n return item.keys()","def keys(self):\n return sorted(\n super().keys() + list(ttfont_dict_keys) + [text_s, case_s]\n )","def get_keys(self) -> list:\r\n keys = []\r\n for key, value in self._items:\r\n keys.append(key)\r\n return keys","def get_implementation_specific_keys(cls):\n if cls.implementation_specific_keys is None:\n cls.implementation_specific_keys = cls.to_py_string_list(\n cls.get_drmaa2_library().drmaa2_version_impl_spec())\n return cls.implementation_specific_keys","def get_event_attributes_from_log(log):\n all_attributes = set()\n for trace in log:\n for event in trace:\n all_attributes = all_attributes.union(set(event.keys()))\n if xes.DEFAULT_TRANSITION_KEY in all_attributes:\n all_attributes.remove(xes.DEFAULT_TRANSITION_KEY)\n return all_attributes","def teststep_config(self):\n teststep_key_names = self.config['reader_settings']['test_steps']\n return teststep_key_names","def keys(cls):\n if cls._keys is None:\n cls._keys = tuple(attr for attr in dir(cls) if all((not callable(getattr(cls, attr)),\n not attr.startswith('_'),\n not attr == 'ALL')))\n return cls._keys","def _check_entry_keys(self, entry, n, key, required, xor_sets=None, optional=None):\n xor_sets = set(tuple()) if xor_sets is None else xor_sets\n optional = set() if optional is None else optional\n nth = ordinal(n)\n if not isinstance(entry, dict):\n raise TypeError(f\"Each entry must be a dictionary, error on {nth} {key}\")\n if len(required - entry.keys()) > 0:\n missing_keys = required - entry.keys()\n raise ValueError(\n f\"Each entry of {key} requires keys of: {', '.join(sorted(required))}. \"\n f\"Missing {sorted(missing_keys)} on {nth} entry, possibly others.\"\n )\n allowable_keys = required | optional | set().union(*xor_sets)\n if not set(entry.keys()) <= allowable_keys:\n unknown_keys = set(entry.keys()) - allowable_keys\n err_msg = f\"Got unknown keys in {nth} {key}: {', '.join(unknown_keys)}\"\n raise ValueError(err_msg)\n for xor_set in sorted(xor_sets):\n if len(xor_set & entry.keys()) != 1:\n err_msg = f\"For {key}, must specify one of {xor_set} but not both\"\n err_msg += f\". Error on {nth} entry, possibly others\"\n raise ValueError(err_msg)","def test_config_keys() -> None:\n with pytest.raises(ConfigFileError):\n f = StringIO(\"\"\"\n configs:\n has.periods:\n urls:\n - json://localhost\n \"\"\")\n load_config(_logger, f)\n with pytest.raises(ConfigFileError):\n f = StringIO(\"\"\"\n configs:\n bademail@:\n urls:\n - json://localhost\n \"\"\")\n load_config(_logger, f)\n f = StringIO(\"\"\"\n configs:\n user@example.com:\n urls:\n - json://localhost\n \"\"\")\n mrise = load_config(_logger, f)\n assert len(mrise.senders) == 1\n key = Key(user='user', domain='example.com')\n assert key in mrise.senders","def key_usages(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"key_usages\")","def key_usages(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\n return pulumi.get(self, \"key_usages\")","def test_allow_extra_keys(self):\n from natcap.invest import validation\n\n args = {'a': 'a', 'b': 'b'}\n spec = {\n 'a': {\n 'type': 'freestyle_string',\n 'name': 'a',\n 'about': 'a freestyle string',\n 'required': True\n }\n }\n message = 'DEBUG:natcap.invest.validation:Provided key b does not exist in MODEL_SPEC'\n\n with self.assertLogs('natcap.invest.validation', level='DEBUG') as cm:\n validation.validate(args, spec)\n self.assertTrue(message in cm.output)"],"string":"[\n \"def check(self, entry_type:str, x:str):\\n keys = set()\\n x = self._decode(x)\\n\\n for log in self.logs:\\n for datum in log[entry_type]:\\n res = self._follow(datum, x)\\n\\n if type(res) == dict:\\n for key in res.keys():\\n keys.add(key)\\n elif type(res) == list:\\n keys.add('< %d' % len(res))\\n \\n return list(keys)\",\n \"def provideExpectedMetaKeys(self):\\n return self.metadataKeys, self.metadataParams\",\n \"def keys():\",\n \"def keyValues(self): # real signature unknown; restored from __doc__\\n return []\",\n \"def assertKeys(self, data, expected):\\r\\n self.assertEqual(sorted(data.keys()), sorted(expected))\",\n \"def get_keys(self):\\n self._logger.info(ME + '.get_keys()')\\n\\n tmp_primary_keys = []\\n tmp_data_keys = []\\n try:\\n tmp_primary_keys = config.get(ME, 'primary_keys').split(',')\\n tmp_data_keys = config.get(ME, 'data_keys').split(',')\\n self.index_key = config.get(ME, 'index_key') #FIXME: this is bad\\n except KeyError as error_msg:\\n self._logger.error(\\n 'EXCEPTION: Keys missing' +\\n '\\\\r\\\\tprimary_keys={0}'.format(','.join(tmp_primary_keys)) +\\n '\\\\r\\\\tdata_keys={0}'.format(','.join(tmp_data_keys)) +\\n '\\\\r\\\\tindex_key={0}'.format(self.index_key),\\n exc_info=True\\n )\\n raise Connection.TableKeysMissing(error_msg, ME)\\n\\n self._logger.debug(\\n 'keys validated:' + \\\\\\n '\\\\r\\\\tprimary_keys={0}'.format(','.join(tmp_primary_keys)) +\\n '\\\\r\\\\tdata_keys={0}'.format(','.join(tmp_data_keys)) +\\n '\\\\r\\\\tindex_key={0}'.format(self.index_key)\\n )\\n return tmp_primary_keys, tmp_data_keys\",\n \"def check_keys(self):\",\n \"def AllKeys(self) -> _n_0_t_1[str]:\",\n \"def keys(self):\\n return\",\n \"def Keys(self) -> _n_1_t_4:\",\n \"def keys(self) -> Sequence[str]:\\n raise NotImplementedError\",\n \"def validate_instruction_keys(instruction: TransactionInstruction, expected: int) -> None:\\n if len(instruction.keys) < expected:\\n raise ValueError(f\\\"invalid instruction: found {len(instruction.keys)} keys, expected at least {expected}\\\")\",\n \"def keys(self) -> List[str]:\\n raise NotImplementedError\",\n \"def keys(self):\\n raise NotImplementedError\",\n \"def valid_config_keys():\\n click.echo(', '.join(get_class_properties(GenConfig)))\",\n \"def test_log_key(self):\\n kwargs = {\\n \\\"follow_up\\\": None,\\n \\\"notes\\\": \\\"This is a test.\\\",\\n \\\"gdoc_link\\\": \\\"\\\",\\n \\\"state_id\\\": \\\"KS\\\",\\n \\\"contact\\\": None,\\n \\\"user_id\\\": 9,\\n \\\"formal_request\\\": False,\\n \\\"date\\\": \\\"2013-03-28\\\",\\n \\\"org_id\\\": 15,\\n \\\"subject\\\": \\\"Test subject line\\\"\\n }\\n log = Log(**kwargs)\\n log.save()\\n self.assertEqual(log.log_key(), ('KS', '2013-03-28', 'Test subject line'))\\n self.assertEqual(log.log_key(as_string=True), 'KS - 2013-03-28 - Test subject line')\\n\\n # Test with contact\\n contact = Contact.objects.all()[0]\\n log.contact = contact\\n log.save()\\n expected = (\\n 'KS',\\n '2013-03-28',\\n u'Williams (Kansas Secretary of State elections division)',\\n 'Test subject line',\\n )\\n self.assertEqual(expected, log.log_key())\",\n \"def _extra_keys(self):\\r\\n return []\",\n \"def keys(self) -> t.Tuple[str, ...]:\\n return self._keys\",\n \"def get_group_keys(self):\\r\\n if len(self.conflicting_exclusives) == 0:\\r\\n return [\\\"<none>\\\"]\\r\\n else:\\r\\n return self.key_to_targets.keys()\",\n \"def _get_expected_sections(self):\\n\\n with open(self.filename, \\\"r\\\") as fh:\\n yaml = ruamel.yaml.YAML(typ=\\\"unsafe\\\", pure=True)\\n data = yaml.load(fh.read())\\n keys = list(data.keys())\\n return keys\",\n \"def transform_info_keys():\\n return (TraceKeys.CLASS_NAME, TraceKeys.ID, TraceKeys.TRACING, TraceKeys.DO_TRANSFORM)\",\n \"def getDiscoveredMetricsKeys(self):\\n return tuple(self.__foundMetrcsKeySet)\",\n \"def keys(self) -> List:\\n pass\",\n \"def keys(self):\\n\\t\\treturn tuple(self.dist.keys())\",\n \"def keys(self) -> tuple(Any, ...): # type: ignore\\n return tuple(self.contents.keys())\",\n \"def state_info_keys(self):\\n return [k for k, _ in self.state_info_specs]\",\n \"def _get_missing_keys(self):\\n REQUIRED_KEYS = [\\n 'date_purchased', 'cost', 'supply_type_id'\\n ]\\n\\n return [key for key in REQUIRED_KEYS if not key in self.request.data]\",\n \"def key_attributes(self):\\n\\n return [level.key for level in self.levels]\",\n \"def test_toomanykeys(self):\\n self.assertRaises(recordparser.KeyListMismatchError,\\n recordparser.getfields, \\\"1234567890\\\", \\\"10s\\\", (\\\"key1\\\", \\\"key2\\\"))\",\n \"def valid_config_keys():\\n click.echo(', '.join(get_class_properties(OffshoreInputsConfig)))\",\n \"def get_evaluated_keys(self) -> List[str]:\\n return force_list(self.evaluated_keys)\",\n \"def keys(self):\\n query = \\\"\\\"\\\"SELECT column_name, data_type, character_maximum_length\\n FROM INFORMATION_SCHEMA.COLUMNS WHERE table_name = 'ngc2236';\\\"\\\"\\\"\\n result = self.wifsip.query(query)\\n keys = [r[0] for r in result]\\n return keys\",\n \"def test_all_keys(self):\\n\\t\\tdocumentation: dict = self.handlerObject.build_swagger_documentation()\\n\\t\\t# print(\\\"Test documentation was generated. The corresponding object is:\\\\n{0}\\\".format(documentation))\\n\\t\\tself.assertTrue(compare_keys(documentation, TEST_ALL_KEYS_EXPECTED_OUTPUT))\",\n \"async def keys(self) -> Iterable[str]:\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_sample_ids)\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_sample_ids)\",\n \"def get_keys(self) -> Dict[str, str]:\\n return self.http.get(self.config.paths.keys)\",\n \"def test_toofewkeys(self):\\n self.assertRaises(recordparser.KeyListMismatchError,\\n recordparser.getfields, \\\"1234567890\\\", \\\"10s\\\", ())\",\n \"def test_overall_report_keys():\\n keys = overall_data.keys()\\n assert('banner_report' in keys)\\n assert('rewarded_report' in keys)\\n assert('interstitial_report' in keys)\\n assert('overall_report' in keys)\",\n \"def key_attributes(self):\\n\\n return [level.key for level in self._levels.values()]\",\n \"def keys(self, installer_context):\\n return self.spec.keys(self.data, installer_context)\",\n \"def get_registered_input_keys(self):\\n return tuple(self._input_keys)\",\n \"def test_section_keys(self):\\n ars = self.ar[2009][11]['general']\\n self.assertEqual(ars.keys(), self.__class__.wanted_lines)\",\n \"def get_keys(self):\\r\\n return self._keys\",\n \"def mandatory_info(self):\\n return [info.key for info in self.entry_info if not info.optional]\",\n \"def _get_report_keys(self, text, key_format=r'\\\\:\\\\w+\\\\:'):\\n return [i.replace(\\\":\\\", \\\"\\\") for i in re.findall(r'\\\\:\\\\w+\\\\:', text)]\",\n \"def test_get_keys(self):\\n response = self.client.get_keys()\\n assert isinstance(response, dict)\\n assert 'public' in response\\n assert 'private' in response\\n assert response['public'] is not None\\n assert response['private'] is not None\",\n \"def get_extra_metadata_keys() -> list[str]:\\n keys = [\\n \\\"srow_x\\\",\\n \\\"srow_y\\\",\\n \\\"srow_z\\\",\\n \\\"quatern_b\\\",\\n \\\"quatern_c\\\",\\n \\\"quatern_d\\\",\\n \\\"qoffset_x\\\",\\n \\\"qoffset_y\\\",\\n \\\"qoffset_z\\\",\\n \\\"dim\\\",\\n \\\"pixdim\\\",\\n *[f\\\"dim[{i}]\\\" for i in range(8)],\\n *[f\\\"pixdim[{i}]\\\" for i in range(8)],\\n ]\\n\\n # TODO: it would be good to remove these, but they are currently being used in the\\n # codebase.\\n # keys += [\\n # \\\"original_affine\\\",\\n # \\\"spatial_shape\\\",\\n # \\\"spacing\\\",\\n # ]\\n\\n return keys\",\n \"def keys(self):\\n if self.widget:\\n w_keys = self.widget.keys()\\n else:\\n w_keys = []\\n return sorted(w_keys + list(ttfont_dict_keys) + [case_s])\",\n \"def keys(self):\\n return _osgAnimation.mapVertexInfluence_keys(self)\",\n \"def special_token_keys(self) -> List[str]:\\n return list(self._special_token_kv.keys())\",\n \"async def get_keys(self):\\n return self.dict.keys()\",\n \"def get_computed_stats_keys():\\n\\n # order not important\\n result = [NUM_KITS_KEY, NUM_SAMPLES_KEY,\\n NUM_SAMPLES_RECEIVED_KEY, NUM_UNIQUE_SOURCES_KEY,\\n NUM_PARTIALLY_RETURNED_KITS_KEY,\\n NUM_FULLY_RETURNED_KITS_KEY, NUM_KITS_W_PROBLEMS_KEY]\\n\\n num_status_keys = get_status_num_keys()\\n result.extend(num_status_keys)\\n return result\",\n \"def test_get_transaction_types_key(self):\\n pass\",\n \"def required_event_keys(cls):\\n return {'app_type', 'destination_function_name', 'schedule_expression'}\",\n \"def test_keys_eq(self):\\n self.assertListEqual(self.result, self.expected)\",\n \"def report_keyset(self):\\n for i, matchset in enumerate(self.matches):\\n if len(matchset) == 1:\\n print \\\"[%02d]\\\" % i, fmt(sorted([k for k, data in matchset.items()]), BLUE)\\n elif len(matchset) != 0:\\n print \\\"[%02d]\\\" % i, fmt(sorted([k for k, data in matchset.items()]), WHITE)\\n else:\\n print \\\"[%02d]\\\" % i, fmt(\\\"[X]\\\", RED)\",\n \"def keys(targets):\",\n \"def _additional_keys(self):\\n addkeys = []\\n if self.data.has_key('name'):\\n addkeys += ['long imdb name']\\n if self.data.has_key('headshot'):\\n addkeys += ['full-size headshot']\\n return addkeys\",\n \"def keysAll():\",\n \"def keys(self) -> List[str]:\\n return self.__stash.keys()\",\n \"def key_usages(self) -> pulumi.Output[Sequence[str]]:\\n return pulumi.get(self, \\\"key_usages\\\")\",\n \"def keys(self):\\n return self.keys\",\n \"def test_load_from_file_to_dict_key_names(self):\\n self.assertEqual(list(self.loaded_json_dict), self.expected_dict_keys)\",\n \"def keys(self):\\n return [key for key, value in self.items()]\",\n \"def keys(self):\\n return self.params.keys()\",\n \"def assert_keys_match(keys, expected, allow_missing=True):\\n if not allow_missing:\\n missing = expected - keys\\n assert not missing, 'missing keys: %s' % missing\\n extra = keys - expected\\n assert not extra, 'extraneous keys: %s' % extra\",\n \"def test_serialization_keys(self):\\n serialized_inspection = serializers.InspectionSerializer(\\n self.inspection\\n )\\n self.assertSetEqual(\\n set(serialized_inspection.fields.keys()),\\n INSPECTION_INSPECTED_KEYS\\n )\",\n \"def keys(self):\\n return _keys(self)\",\n \"def keys(self):\\n return _keys(self)\",\n \"def list_configuration_keys() -> Tuple[List[str], Dict[int, str]]:\\n lookup = {i: v for (i, v) in enumerate(PipetteConfigurations.__fields__)}\\n return [\\n f\\\"{i}: {v}\\\" for (i, v) in enumerate(PipetteConfigurations.__fields__)\\n ], lookup\",\n \"def keys(self):\\n list_all_dict = self.list_all()\\n return list_all_dict[\\\"nodes\\\"] + list_all_dict[\\\"groups\\\"]\",\n \"def test_4():\\n input, output = get_input()\\n with open(\\n os.path.join(os.path.dirname(__file__), \\\"..\\\", \\\"test_data\\\",\\n \\\"main_test4_keys.txt\\\")) as f:\\n returnkeys = f.read().splitlines()\\n os.system(f\\\" python -m vcflat -i {input} -o {output}\\\")\\n\\n with open(output, \\\"r\\\") as f:\\n reader = csv.DictReader(f)\\n assert [\\n i for i in returnkeys if i in set(reader.fieldnames[0].split(\\\"\\\\t\\\"))\\n ]\\n os.remove(output)\",\n \"def test_badly_formatted_entry(self):\\n with pytest.raises(AssertionError) as exc_info:\\n list(parser.generate_commands(yaml.load(\\\"\\\"\\\"\\n - key1: 1\\n key2: 2\\n \\\"\\\"\\\")))\\n assert \\\"Command has multiple top-level keys: ['key1', 'key2']\\\" in str(exc_info.value)\",\n \"def keys(self):\\n keys = set()\\n with pd.HDFStore(self.rootpath, mode=\\\"r\\\") as hdf:\\n hdf5_keys = hdf.keys()\\n\\n for key in hdf5_keys:\\n kp = key.split(\\\"/\\\")\\n if len(kp) == 5:\\n print(kp, len(kp))\\n keys.add(kp[4])\\n return list(keys)\",\n \"def test_postive_get_auth_horizon_check_keys(self):\\n r = self.res.get('/auth/config/'+utils.partner, headers=utils.headers)\\n logging.info(\\\"Return response is '%s'\\\" % r)\\n # convert string to dictionary\\n rd = ast.literal_eval(r)\\n logging.info(\\\"Return response in dictionary format is '%s'\\\" % rd)\\n self.assertEqual(self.res.response.status, 200)\\n keys = ['type', 'web_endpoint', 'client_endpoint', 'org_name']\\n self.assertTrue(utils.is_same_array(keys, rd.keys()), \\\"Keys are not correct!\\\")\",\n \"def keys(self):\\n keys = [key for key in self.__dict__.keys() if self[key] is not None]\\n keys = [key for key in keys if key[:2] != '__' and key[-2:] != '__']\\n return keys\",\n \"def keys(self):\\n keys = [key for key in self.__dict__.keys() if self[key] is not None]\\n keys = [key for key in keys if key[:2] != '__' and key[-2:] != '__']\\n return keys\",\n \"def keys(self):\\n keys = [key for key in self.__dict__.keys() if self[key] is not None]\\n keys = [key for key in keys if key[:2] != '__' and key[-2:] != '__']\\n return keys\",\n \"def get_keys(self):\\r\\n\\t\\tlogger.debug(\\\"Getting the keys\\\")\\r\\n\\t\\t\\r\\n\\t\\treturn db.get_items('keys')\",\n \"def keys(self):\\n return ['title', 'keywords', 'description', 'url', 'content_file',\\n 'language', 'phone', 'email']\",\n \"def get_key_list(self) -> list:\\n return self.key_functs.keys()\",\n \"def keys(self):\\r\\n return [key for key, value in self.iteritems()]\",\n \"def changed_keys(self):\\n return self._changed_keys\",\n \"def keys(self):\\n return self.__keys\",\n \"def keys(self):\\n return [entry.key for entry in self.table if entry.value is not None]\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_categories)\",\n \"def test_keys(self):\\n obs = self.tester.keys()\\n self.assertTrue(isinstance(obs, Iterable))\\n self.assertEqual(set(obs), self.exp_categories)\",\n \"def array_keys(item):\\n return item.keys()\",\n \"def keys(self):\\n return sorted(\\n super().keys() + list(ttfont_dict_keys) + [text_s, case_s]\\n )\",\n \"def get_keys(self) -> list:\\r\\n keys = []\\r\\n for key, value in self._items:\\r\\n keys.append(key)\\r\\n return keys\",\n \"def get_implementation_specific_keys(cls):\\n if cls.implementation_specific_keys is None:\\n cls.implementation_specific_keys = cls.to_py_string_list(\\n cls.get_drmaa2_library().drmaa2_version_impl_spec())\\n return cls.implementation_specific_keys\",\n \"def get_event_attributes_from_log(log):\\n all_attributes = set()\\n for trace in log:\\n for event in trace:\\n all_attributes = all_attributes.union(set(event.keys()))\\n if xes.DEFAULT_TRANSITION_KEY in all_attributes:\\n all_attributes.remove(xes.DEFAULT_TRANSITION_KEY)\\n return all_attributes\",\n \"def teststep_config(self):\\n teststep_key_names = self.config['reader_settings']['test_steps']\\n return teststep_key_names\",\n \"def keys(cls):\\n if cls._keys is None:\\n cls._keys = tuple(attr for attr in dir(cls) if all((not callable(getattr(cls, attr)),\\n not attr.startswith('_'),\\n not attr == 'ALL')))\\n return cls._keys\",\n \"def _check_entry_keys(self, entry, n, key, required, xor_sets=None, optional=None):\\n xor_sets = set(tuple()) if xor_sets is None else xor_sets\\n optional = set() if optional is None else optional\\n nth = ordinal(n)\\n if not isinstance(entry, dict):\\n raise TypeError(f\\\"Each entry must be a dictionary, error on {nth} {key}\\\")\\n if len(required - entry.keys()) > 0:\\n missing_keys = required - entry.keys()\\n raise ValueError(\\n f\\\"Each entry of {key} requires keys of: {', '.join(sorted(required))}. \\\"\\n f\\\"Missing {sorted(missing_keys)} on {nth} entry, possibly others.\\\"\\n )\\n allowable_keys = required | optional | set().union(*xor_sets)\\n if not set(entry.keys()) <= allowable_keys:\\n unknown_keys = set(entry.keys()) - allowable_keys\\n err_msg = f\\\"Got unknown keys in {nth} {key}: {', '.join(unknown_keys)}\\\"\\n raise ValueError(err_msg)\\n for xor_set in sorted(xor_sets):\\n if len(xor_set & entry.keys()) != 1:\\n err_msg = f\\\"For {key}, must specify one of {xor_set} but not both\\\"\\n err_msg += f\\\". Error on {nth} entry, possibly others\\\"\\n raise ValueError(err_msg)\",\n \"def test_config_keys() -> None:\\n with pytest.raises(ConfigFileError):\\n f = StringIO(\\\"\\\"\\\"\\n configs:\\n has.periods:\\n urls:\\n - json://localhost\\n \\\"\\\"\\\")\\n load_config(_logger, f)\\n with pytest.raises(ConfigFileError):\\n f = StringIO(\\\"\\\"\\\"\\n configs:\\n bademail@:\\n urls:\\n - json://localhost\\n \\\"\\\"\\\")\\n load_config(_logger, f)\\n f = StringIO(\\\"\\\"\\\"\\n configs:\\n user@example.com:\\n urls:\\n - json://localhost\\n \\\"\\\"\\\")\\n mrise = load_config(_logger, f)\\n assert len(mrise.senders) == 1\\n key = Key(user='user', domain='example.com')\\n assert key in mrise.senders\",\n \"def key_usages(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"key_usages\\\")\",\n \"def key_usages(self) -> Optional[pulumi.Input[Sequence[pulumi.Input[str]]]]:\\n return pulumi.get(self, \\\"key_usages\\\")\",\n \"def test_allow_extra_keys(self):\\n from natcap.invest import validation\\n\\n args = {'a': 'a', 'b': 'b'}\\n spec = {\\n 'a': {\\n 'type': 'freestyle_string',\\n 'name': 'a',\\n 'about': 'a freestyle string',\\n 'required': True\\n }\\n }\\n message = 'DEBUG:natcap.invest.validation:Provided key b does not exist in MODEL_SPEC'\\n\\n with self.assertLogs('natcap.invest.validation', level='DEBUG') as cm:\\n validation.validate(args, spec)\\n self.assertTrue(message in cm.output)\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6501111","0.62000287","0.61273104","0.61169225","0.6086626","0.60766816","0.60219604","0.6018274","0.5984301","0.5967556","0.5930063","0.5905795","0.5868908","0.586158","0.5827901","0.57728463","0.5768729","0.5760981","0.5749968","0.5745742","0.5743827","0.5736735","0.5705893","0.5696833","0.56902754","0.568159","0.56736904","0.56550694","0.5620599","0.56125647","0.5595046","0.55856234","0.5584458","0.5574261","0.5566152","0.5566152","0.55658495","0.55595326","0.55562645","0.55513346","0.5510009","0.5498997","0.5478422","0.54766524","0.5476572","0.54717594","0.5455532","0.5454598","0.5452023","0.545079","0.5448151","0.54481125","0.54408634","0.542827","0.54272765","0.5420579","0.54131275","0.5405548","0.5402098","0.5393665","0.5392897","0.53911424","0.5389822","0.5387698","0.5385704","0.53836405","0.5376034","0.5367895","0.53647816","0.53647816","0.53613675","0.53525996","0.53497833","0.53474","0.5345937","0.5338983","0.5332101","0.5332101","0.5332101","0.5329501","0.5329105","0.53289145","0.53251404","0.53221154","0.5318229","0.5317557","0.53164476","0.53164476","0.5315385","0.5313758","0.5305391","0.53041834","0.53002095","0.52964246","0.52892256","0.52883834","0.5286786","0.52773964","0.52773964","0.52755034"],"string":"[\n \"0.6501111\",\n \"0.62000287\",\n \"0.61273104\",\n \"0.61169225\",\n \"0.6086626\",\n \"0.60766816\",\n \"0.60219604\",\n \"0.6018274\",\n \"0.5984301\",\n \"0.5967556\",\n \"0.5930063\",\n \"0.5905795\",\n \"0.5868908\",\n \"0.586158\",\n \"0.5827901\",\n \"0.57728463\",\n \"0.5768729\",\n \"0.5760981\",\n \"0.5749968\",\n \"0.5745742\",\n \"0.5743827\",\n \"0.5736735\",\n \"0.5705893\",\n \"0.5696833\",\n \"0.56902754\",\n \"0.568159\",\n \"0.56736904\",\n \"0.56550694\",\n \"0.5620599\",\n \"0.56125647\",\n \"0.5595046\",\n \"0.55856234\",\n \"0.5584458\",\n \"0.5574261\",\n \"0.5566152\",\n \"0.5566152\",\n \"0.55658495\",\n \"0.55595326\",\n \"0.55562645\",\n \"0.55513346\",\n \"0.5510009\",\n \"0.5498997\",\n \"0.5478422\",\n \"0.54766524\",\n \"0.5476572\",\n \"0.54717594\",\n \"0.5455532\",\n \"0.5454598\",\n \"0.5452023\",\n \"0.545079\",\n \"0.5448151\",\n \"0.54481125\",\n \"0.54408634\",\n \"0.542827\",\n \"0.54272765\",\n \"0.5420579\",\n \"0.54131275\",\n \"0.5405548\",\n \"0.5402098\",\n \"0.5393665\",\n \"0.5392897\",\n \"0.53911424\",\n \"0.5389822\",\n \"0.5387698\",\n \"0.5385704\",\n \"0.53836405\",\n \"0.5376034\",\n \"0.5367895\",\n \"0.53647816\",\n \"0.53647816\",\n \"0.53613675\",\n \"0.53525996\",\n \"0.53497833\",\n \"0.53474\",\n \"0.5345937\",\n \"0.5338983\",\n \"0.5332101\",\n \"0.5332101\",\n \"0.5332101\",\n \"0.5329501\",\n \"0.5329105\",\n \"0.53289145\",\n \"0.53251404\",\n \"0.53221154\",\n \"0.5318229\",\n \"0.5317557\",\n \"0.53164476\",\n \"0.53164476\",\n \"0.5315385\",\n \"0.5313758\",\n \"0.5305391\",\n \"0.53041834\",\n \"0.53002095\",\n \"0.52964246\",\n \"0.52892256\",\n \"0.52883834\",\n \"0.5286786\",\n \"0.52773964\",\n \"0.52773964\",\n \"0.52755034\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.71414775"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":21884,"cells":{"query":{"kind":"string","value":"Make a representation of object that is easy to find in corpus."},"document":{"kind":"string","value":"def friendly_id(self):\n id = f\"{self.annotator_id}_{self.document_title.split('_')[0]}\"\n\n try: # try making an sentence identifier if there is an in_sentence attrib\n sen_id = \",\".join(str(se.element_id + 1) for se in self.in_sentence)\n id += f\"_s{sen_id}\"\n except Exception as e:\n print(e)\n pass\n\n if isinstance(self, Event):\n id += f\"_{self.event_fulltype}\"\n elif isinstance(self, Participant) or isinstance(self, Filler):\n id += f\"_{self.role}\"\n\n text_ellips = (\n (self.text[:15] + \"..\" + self.text[-15:])\n if len(self.text) > 32\n else self.text\n )\n id += f\"-{text_ellips}\"\n return 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 __repr__(self):\n return ''.joint(\"A corpus made of \", len(self._corpus),\n \" with types: \", [type(item) for item in self._corpus])","def build(self, obj):\n if isinstance(obj, self.art_type):\n return obj\n elif isinstance(obj, (tuple, list, dict, set)):\n if obj.__class__ is tuple:\n return self.build_tuple(obj)\n elif obj.__class__ is dict:\n return self.build_dict(obj)\n elif obj.__class__ is list:\n return self.build_list(obj)\n else:\n return self.build_set(obj)\n elif isinstance(obj, SageObject):\n return self.build_from_magic_method(obj)\n else:\n return self.build_from_string(obj)","def serialize(self):\n return {\n 'word': self.word,\n 'pos': self.pos,\n 'label': self.label,\n 'dependency': self.dependency\n }","def __init__(self, corpus_name, corpus, data_date=''):\n\n # Test Patterns and sets for use later in our topic model\n # '^[a-z0-9 _-]{2,}$' --> Match letters, numbers, spaces, underscores, dashes. Ignore case. Length of 2 or more.\n self.phrase_pattern = re.compile('^[a-z0-9 _-]{2,}$', re.IGNORECASE)\n self.date_pattern = re.compile('20[0-9]{2}-[0-1][0-9]-[0-3][0-9]$')\n self.punct = {p for p in string.punctuation}\n self.empty_words = {'a', 'an', 'that', 'the', 'this'}\n self.nouns = {ss.NOUN, ss.PROPN}\n self.entities = {ss.PERSON, ss.NORP, ss.FACILITY, ss.ORG, ss.GPE, ss.LOC, ss.PRODUCT, ss.EVENT, ss.WORK_OF_ART,\n ss.LANGUAGE} # spaCy entities that indicate a proper noun.\n\n # Check the user arguments\n # TODO: What data to I expect in the dictionary passed from the \"get_\" class?\n assert 2 < len(corpus_name) < 21 and re.match(self.phrase_pattern, corpus_name), \\\n 'A corpus_name (between 3 and 20 characters; made of letters, numbers, underscores, or dashes) is required.'\n assert data_date == '' or re.match(self.date_pattern, data_date), \\\n 'If you include a data_date, it must match the form 20YY-MM-DD.'\n assert type(corpus) is dict, 'The corpus must be a dictionary.'\n assert len(corpus) > 0, 'The corpus of texts has no data.'\n\n # Topic metadata & settings\n self.corpus_name = corpus_name.replace(' ', '') # (str) The name of the set (or corpus) of texts.\n self.data_date = data_date\n\n # Primary Data Structures\n self.texts = corpus # The dict of dicts that contains all of our texts for analysis: {text_id: {}}\n self.topics = {} # A dict of dicts for primary topics: {topic: {}}\n self.ngrams = {} # A dict of dicts for ngrams that will help us understand primary topics: {ngram_lemma: {}}\n self.model_output = {'name': corpus_name,\n 'dataDate': data_date,\n 'runDate': datetime.now().strftime(\"%Y-%m-%d %H:%M\"),\n 'textCount': len(corpus)} # For results as json\n\n # Get known entities\n try:\n with open(config.INPUT_DIR + 'known_entities.txt', 'r') as file:\n self.known_entities = set(file.read().split(' '))\n except IOError:\n print('Add a text file named \"known_entities.txt\" that lists named entities (space-delimited) that we '\n 'want to be sure are treated as proper nouns.')\n self.known_entities = set()\n\n # Stop Words\n try:\n with open(config.INPUT_DIR + 'stop_words.txt', 'r') as file:\n self.stop_words = set(file.read().split(' '))\n except IOError:\n print('Add a text file named \"stop_words.txt\" that lists common words (space-delimited) that we should '\n 'ignore in most text processing.')\n self.stop_words = set()\n\n # Loop through texts and tokenize: 'doc' and 'titleDoc' are lists of spaCy tokens, with named entities called\n # recognized and joined. 'textClean' is a string of lemmatized words, excluding stopwords and punctuation.\n # It's used by Doc2Vec.\n for text_id, text in self.texts.items():\n text['doc'] = self._tokenize(text['text'])\n text['titleDoc'] = self._tokenize(text['title'])\n text['textClean'] = ' '.join([token.text.lower() if token.lemma_ == '-PRON-' else token.lemma_ for\n token in text['doc'] if token.lemma_ not in\n self.stop_words and token.text not in self.punct])","def __repr__(self):\n return '%s.from_text(%r)' % (self.__class__.__name__, self.to_text())","def fromObject(cls, obj, decode=None):\n if obj.__doc__ is None:\n return cls(u'' if decode else '')\n r = cls.fromString(obj.__doc__, decode=decode)\n return r","def serialize(self, obj):\n return obj","def to_doc(self):\n doc = {}\n for _key, _value in vars(self).items():\n if isinstance(_value, TargetingCriterion):\n doc[_key] = _value.to_doc()\n else:\n doc[_key] = _value\n\n # Save metadata for use when re-cerealizing data\n doc['_metadata'] = {\n 'cls': self.__class__.__name__,\n }\n return doc","def create_corpus_for_artist(artist):\n raise NotImplementedError","def _format_obj(cls, **kwargs):\n def doc_rebuilder(obj):\n if kwargs.pop('_VOID_',False):\n return ''\n try:\n doc = getattr(obj,'__doc__')\n assert doc\n except:\n return ''\n else:\n return doc.format(**kwargs) # str(doc).format(**kwargs)\n return doc_rebuilder","def build_document(self, labels_from_json):\n if not len(self.raw_labels):\n self.get_labels(labels_from_json)\n raw_text = self.instance_input_file.read()\n document = self.DOCUMENT_CLASS(self.identifier, title=self.identifier)\n document.build_from_text(raw_text, start_index=0)\n for start_index, end_index in self.raw_labels:\n document.add_label_for_position(\n 'claim', int(start_index), int(end_index))\n return document","def construct(self):\n return self.as_search().construct()","def __getitem__(self, given):\n return corpus(\n {\n \"location\": self.location,\n \"exemplars\": [self.exemplars[given]]\n if not isinstance(given, slice)\n else self.exemplars[given],\n }\n )","def serialize(self) -> Dict:\n return dict(\n author=self.doc.author if self.doc else None,\n name=self.name,\n functions=[x.serialize() for x in self.functions],\n classes=[x.serialize() for x in self.classes],\n doc=self.doc.serialize() if self.doc else None,\n )","def __init__(self,typing,reflection,year):\n self.name = str(self)\n self.typing = typing\n self.reflection = reflection\n self.year = year","def __init__(self, obj):\n from lxml import objectify\n try:\n self.root = objectify.fromstring(obj.data)\n except:\n # try something else\n self.root = objectify.fromstring(obj)\n self.obj = obj","def __repr__(self) -> str:\n return f\"<Doc[{self.desc}]>\"","def __init__(self):\n self.children = {}\n self.is_a_word = (False,None)\n self.index = []","def jsonizable_object(self):\n obj = {\n 'title': self.title,\n 'url': self.url,\n 'abstract': self.abstract\n }\n if self.metadata:\n obj['metadata'] = self.metadata\n return obj","def __init__(self, documents):\n Classifier.__init__(self, documents)\n documents = set(documents)\n term_document_matrix = TermDocumentMatrix(documents, compute_word_vectors=False, compute_document_vectors=False)\n self.vocabulary = set(term_document_matrix.vocabulary)\n self.tree = self.get_tree(documents, self.vocabulary)","def __str__(self) -> str:\n obj_dict: Dict[str, Any] = {}\n obj_dict[\"doc\"] = self.doc\n obj_dict[\"type\"] = self.type\n obj_dict[\"name\"] = self.name\n\n line_range = self.line_range()\n obj_dict[\"start_line\"] = line_range[0]\n obj_dict[\"end_line\"] = line_range[1]\n\n obj_dict[\"children\"] = []\n\n for child in self.children.values():\n obj_dict[\"children\"].append(json.loads(str(child)))\n\n return json.dumps(obj_dict)","def bundle_instance(obj):\n\n content, contents = osl_encode(obj, True)\n # should be a bunch of documents, not just one.\n bundle = [json.dumps(c) for c in contents]\n return bundle","def get_corpus_object(corpus_name):\n\tif corpus_name.startswith(\"yelp\"):\n\t\treturn CorpusYelp(make_storagedir(corpus_name), GLOVEPATH, *JSONS)\n\t\t\n\telif corpus_name == \"newsgroup\":\n\t\treturn CorpusNewsgroup(make_storagedir(corpus_name), GLOVEPATH)\n\n\traise Exception(\"Unknown corpus\", corpus_name)","def build_from_string(self, obj):\n if self.string_type is unicode and not isinstance(obj, unicode):\n obj = str(obj).decode('utf-8')\n if self.string_type is str and not isinstance(obj, str):\n obj = unicode(obj).encode('utf-8')\n return self.art_type(obj.splitlines())","def _mkObject(self):\n return ImmutableObject(\n store=self.store,\n hash=u'somehash',\n contentDigest=u'quux',\n content=self.store.newFilePath('foo'),\n contentType=u'application/octet-stream')","def __buildDocumentClassDocString():\n\n # build a dictionary of tags and their descriptions, seems a little over\n # the top, but keeps all the information in one place\n tagsStrings = {\n \"comment\" : \"Define the comment string\",\n \"define\" : \"Define the symbol name for #define's\",\n \"info\" : \"Information string, to end up in the 'info' output\",\n \"instance\" : \"Instance name\",\n \"matlabRoot\" : \"Name of variable used by the matlab output\",\n \"members\" : \"List of symbols, which are going to be children of this symbol\",\n \"name\" : \"Name of this symbol\",\n \"size\" : \"Size of this symbol, i.e. indicate it is an array\",\n \"subtype\" : \"Define the actual type of general symbol\",\n \"symbol\" : \"Define a symbol, either a top level entity a child in a members\",\n \"test\" : \"Define the preprocessor test\",\n \"text\" : \"Text to put into a banner symbol\",\n \"title\" : \"Set the overall document title\",\n \"value\" : \"Define a value for this symbol\",\n \"valuesRequired\" : \"Does the enumeration allow automatic value assignment in entries\",\n }\n # build the list of classes\n classes = dict(filter(lambda (k,v): type(v) == types.ClassType, globals().iteritems()))\n (tagsUsed, optionsUsed) = buildKeys(classes)\n\n # build the string we are going to add to the document class\n s = \"Document class that represents the XML document and contains the data.\\n\\n\"\n s += \"Available tags:\\n\"\n\n for tag in tagsStrings:\n try:\n used = \" Required by : %s\\n\" % (\", \".join(tagsUsed[tag]))\n except KeyError:\n used = \"\"\n try:\n opts = \" Optional for: %s\\n\" % (\", \".join(optionsUsed[tag]))\n except KeyError:\n opts = \"\"\n s += \" %s\\n %s\\n %s\\n\\n%s%s\\n\" % (tag, \"-\"*len(tag), tagsStrings[tag], used, opts)\n\n return s","def __init__(self):\n self._pronunciations = nltk.corpus.cmudict.dict()\n \"\"\"\n API Documentation for CMU dictionary corpus\n http://www.nltk.org/api/nltk.corpus.reader.html#module-nltk.corpus.reader.cmudict\n \"\"\"","def __init__(self):\n self.kids = [{}]\n self.root = 0\n self.vocabular = set([])","def __init__(self, corpus):\n self.train(corpus)","def __init__(self):\n self.children = dict()\n self.isWord = False","def __init__(self, inputs, labels, storage_method=\"raw\", force_shorten=True,\n data_dir=\"./data\", tokenizer_path=\"./\"):\n Doc.__init__(self, storage_method, force_shorten, data_dir, tokenizer_path)\n self.all_docs = {}\n\n for text, label in zip(inputs, labels):\n self.add_to_section(text, label)\n self.compute_lens()\n\n self.all_labels = list(self.all_docs.keys())","def make_objects(self):\n pass","def __init__(self, tokenizer, stemmer=None, stopwords=None):\n self.tokenizer = tokenizer\n self.stemmer = stemmer\n self.index = defaultdict(list)\n self.documents = {}\n self.__unique_id = 0 #can be used as docid\n if not stopwords:\n self.stopwords = list()\n else:\n self.stopwords = list(stopwords)","def xml2obj(self, src):\n\n\t\tclass DataNode(object):\n\t\t\tdef __init__(self):\n\t\t\t\tself._attrs = {} # XML attributes and child elements\n\t\t\t\tself.data = None # child text data\n\n\t\t\tdef __len__(self):\n\t\t\t\t# treat single element as a list of 1\n\t\t\t\treturn 1\n\n\t\t\tdef __getitem__(self, key):\n\t\t\t\tif isinstance(key, basestring):\n\t\t\t\t\treturn self._attrs.get(key,None)\n\t\t\t\telse:\n\t\t\t\t\treturn [self][key]\n\n\t\t\tdef __contains__(self, name):\n\t\t\t\treturn self._attrs.has_key(name)\n\n\t\t\tdef __nonzero__(self):\n\t\t\t\treturn bool(self._attrs or self.data)\n\n\t\t\tdef __getattr__(self, name):\n\t\t\t\tif name.startswith('__'):\n\t\t\t\t\t# need to do this for Python special methods???\n\t\t\t\t\traise AttributeError(name)\n\t\t\t\treturn self._attrs.get(name,None)\n\n\t\t\tdef _add_xml_attr(self, name, value):\n\t\t\t\tif name in self._attrs:\n\t\t\t\t\t\t# multiple attribute of the same name are represented by a list\n\t\t\t\t\t\tchildren = self._attrs[name]\n\t\t\t\t\t\tif not isinstance(children, list):\n\t\t\t\t\t\t\tchildren = [children]\n\t\t\t\t\t\t\tself._attrs[name] = children\n\t\t\t\t\t\tchildren.append(value)\n\t\t\t\telse:\n\t\t\t\t\tself._attrs[name] = value\n\n\t\t\tdef __str__(self):\n\t\t\t\treturn self.data or ''\n\n\t\t\tdef __repr__(self):\n\t\t\t\titems = sorted(self._attrs.items())\n\t\t\t\tif self.data:\n\t\t\t\t\titems.append(('data', self.data))\n\t\t\t\treturn u'{%s}' % ', '.join([u'%s:%s' % (k,repr(v)) for k,v in items])\n\n\t\tclass TreeBuilder(xml.sax.handler.ContentHandler):\n\t\t\tdef __init__(self):\n\t\t\t\tself.stack = []\n\t\t\t\tself.root = DataNode()\n\t\t\t\tself.current = self.root\n\t\t\t\tself.text_parts = []\n\t\t\t\tself.publicObjects = {}\n\n\t\t\tdef startElement(self, name, attrs):\n\t\t\t\tself.stack.append((self.current, self.text_parts))\n\t\t\t\tself.current = DataNode()\n\t\t\t\tself.text_parts = []\n\t\t\t\t# xml attributes --> python attributes\n\t\t\t\tfor k, v in attrs.items():\n\t\t\t\t\t# Register PublicObject in lookup map\n\t\t\t\t\tif k == \"publicID\":\n\t\t\t\t\t\tself.publicObjects[v] = self.current\n\t\t\t\t\tself.current._add_xml_attr(k, v)\n\n\t\t\tdef endElement(self, name):\n\t\t\t\ttext = ''.join(self.text_parts).strip()\n\t\t\t\tif text:\n\t\t\t\t\tself.current.data = text\n\t\t\t\tif self.current._attrs:\n\t\t\t\t\tobj = self.current\n\t\t\t\telse:\n\t\t\t\t\t# a text only node is simply represented by the string\n\t\t\t\t\tobj = text or ''\n\t\t\t\t\t# try to store the object as float if possible\n\t\t\t\t\ttry: obj = float(obj)\n\t\t\t\t\texcept: pass\n\t\t\t\tself.current, self.text_parts = self.stack.pop()\n\t\t\t\tself.current._add_xml_attr(name, obj)\n\n\t\t\tdef characters(self, content):\n\t\t\t\tself.text_parts.append(content)\n\n\t\tbuilder = TreeBuilder()\n\t\tif isinstance(src,basestring):\n\t\t\txml.sax.parseString(src, builder)\n\t\telse:\n\t\t\txml.sax.parse(src, builder)\n\t\treturn builder","def doc(obj):\n return Documentation.fromObject(obj).first","def __init__(self, num_docs, upper, lower, stemmer=True):\n\n # load data from json file\n self.num_docs = num_docs\n self.data = pd.read_json('data/arxiv-metadata-oai-snapshot.json', lines=True,\n nrows=self.num_docs)\n\n # create a list of list of documents from the abstracts, lowercase words, remove non-alphanumerical words\n self.stemmer = PorterStemmer() if stemmer == True else None\n self.docs = [self.preprocess(doc) for doc in self.data['abstract'].tolist()]\n\n # count occurrences per word and prune words that occur above some threshold\n self.counts = self.get_counts()\n self.prune(upper=upper, lower=lower)\n\n # obtain 'final' vocabulary, convert to integer ids.\n self.vocab = self.get_vocab()\n self.docs2ids()","def make_object(self, data, **kwargs):\n if not data:\n return None\n return Note(\n title=data['title'],\n content=data['content'],\n )","def __init__(self, vocab: Sequence[Any], start=1):\n self.obj_to_idx = dict()\n self.idx_to_obj = dict()\n self.start = start\n for obj in vocab:\n if obj not in self.obj_to_idx:\n self.idx_to_obj[len(self.obj_to_idx)+start] = obj\n self.obj_to_idx[obj] = len(self.obj_to_idx)+start","def make_data(docs):\n vocab=make_vocab_from_docs(docs)\n word_dicts=list(map(lambda x: parse_doc(x,vocab),docs))\n Nd=list(map(len,docs))\n M,V=len(docs),len(vocab)\n return (vocab,word_dicts,Nd,M,V)","def obj(self) -> object:\n pass","def build_from_mongo(cls, data, use_cls=True):\n # If a _cls is specified, we have to use this document class\n if use_cls and '_cls' in data:\n cls = cls.opts.instance.retrieve_embedded_document(data['_cls'])\n doc = cls()\n doc.from_mongo(data)\n return doc","def _builder(o_name,_nodes,_tagged_reals):\n obj = _tagged_reals[o_name]\n \n if isinstance(obj,ElementaryReal):\n un = UncertainReal._archived_elementary(\n uid = obj.uid,\n x = obj.x\n )\n _tagged_reals[o_name] = un \n \n elif isinstance(obj,IntermediateReal): \n \n _node = _nodes[obj.uid] \n \n un = UncertainReal(\n obj.value,\n _vector_index_to_node( obj.u_components ),\n _vector_index_to_node( obj.d_components ),\n _ivector_index_to_node( obj.i_components, _nodes ),\n _node,\n )\n \n _tagged_reals[o_name] = un\n\n else:\n assert False, \"unexpected: {!r}\".format(obj)\n\n return un","def serialize(self):","def _convert_construction_info_to_string(obj):\n if not hasattr(obj, '_constructor_args'):\n raise AttributeError('obj has no attribute _constructor_args.')\n import StringIO\n output = StringIO.StringIO()\n info = {}\n info['_module_name'] = obj.__class__.__module__\n info['_class_name'] = obj.__class__.__name__\n encoded_constructor_args = {}\n for k, v in obj._constructor_args.items():\n if isinstance(v, chainer.Link):\n encoded_constructor_args[k] \\\n = _convert_construction_info_to_string(v)\n elif isinstance(v, six.string_types):\n encoded_constructor_args[k] = 'STR' + v\n else:\n encoded_constructor_args[k] = v\n info['_encoded_constructor_args'] = encoded_constructor_args\n numpy.save(output, arr=info)\n encoded_construction_info = 'OBJ' + output.getvalue()\n output.close()\n return encoded_construction_info","def __init__(self):\n self._pronunciations = nltk.corpus.cmudict.dict()","def __init__(self):\n self._pronunciations = nltk.corpus.cmudict.dict()","def __init__(self, author, title, text=\"\", haiku=\"False\"):\n self.author = author\n self.title = title\n self.text = text\n self.haiku = haiku\n self.name = self\n Poem.obj_created += 1","def __str__(self):\n\n return '__str__ for Object'","def serialize(self, obj):\n pass","def __str__(self):\n table = 'objects'.join(self.galcat.__str__().split('objects')[1:])\n return self.__repr__()+'\\n'+table","def serialize(self):\n return {\n 'name': self.name,\n 'description': self.description,\n 'category': self.category\n }","def __repr__(self):\n return self.textual_representation().encode(\"utf-8\")","def test_01_BuildObjects(self):\n # print(PrettyFormatAny.form(self.m_pyhouse_obj, 'A1-01-A - Main', 190))\n # print(PrettyFormatAny.form(self.m_pyhouse_obj.House, 'A1-02-B - House', 190))\n # print(PrettyFormatAny.form(self.m_pyhouse_obj.House.Location, 'A1-01-C - Location', 190))\n self.assertIsInstance(self.m_pyhouse_obj, PyHouseInformation)\n self.assertIsInstance(self.m_pyhouse_obj.House, HouseInformation)\n self.assertIsInstance(self.m_pyhouse_obj.House.Location, LocationInformationPrivate)","def __repr__(self):\n return f\"PhraseModel({json.dumps(self.json, indent=2)})\"","def _cls_repr(self):\n return [] # TODO: return I guess samples/labels/chunks","def __str__(self):\n return self.make_flat()","def make_lexicon_txt(self):\n raise NotImplementedError","def __str__(self):\n return str(self.__dict__['_obj'])","def __repr__(self):\n\n return \"<Terms term=%s>\" % (self.word)","def to_representation(self) -> str:\n raise NotImplementedError()","def __init__(self, scene: 'Scene', word: str, container=None, attrs=None) -> None:\n if container:\n super(Object, self).__init__(scene, container=container)\n else:\n super(Object, self).__init__(scene)\n self.word = word\n if attrs:\n self.attributes = attrs\n else:\n self.attributes = list()","def to_model(cls, obj):\n\n new_model = cls()\n\n for key, value in obj.iteritems():\n if value:\n if key == 'transcripts':\n setattr(new_model, key, [ModelConverter.to_model(Transcript, t) for t in value])\n elif key == 'acts' and cls == Transcript:\n setattr(new_model, key, [ModelConverter.to_model(Act, a) for a in value])\n elif key == 'subtitles':\n setattr(new_model, key, [ModelConverter.to_model(Subtitle, s) for s in value])\n else:\n setattr(new_model, key, value)\n\n return new_model","def custom_encode(obj):\n if isinstance(obj, DictionaryMethods):\n key = '__Dictionary__'\n return {key: [list(obj), obj.alpha, obj.pat, obj.pat_args,\n obj.auto_fields]}\n elif isinstance(obj, Entry):\n return obj.data\n else:\n raise TypeError(\"obj {!r} of type {}\".format(obj, type(obj)))","def construct(self, thing):\n ret = None\n if isinstance(thing, str):\n ret = thing\n elif isinstance(thing, QuerySet):\n ret = self._qs(thing)\n elif isinstance(thing, (tuple, list)):\n ret = self._list(thing)\n elif isinstance(thing, dict):\n ret = self._dict(thing)\n elif isinstance(thing, decimal.Decimal):\n ret = str(thing)\n elif isinstance(thing, Model):\n ret = self._model(thing)\n elif isinstance(thing, datetime.datetime):\n o2 = thing.replace(tzinfo=dateutil.tz.tzutc())\n return datetime.datetime.strftime(o2, self.DATETIME_FORMAT)\n elif isinstance(thing, datetime.date):\n d = datetime_safe.new_date(thing)\n return d.strftime(self.DATE_FORMAT)\n elif isinstance(thing, datetime.time):\n return thing.strftime(self.TIME_FORMAT)\n else:\n ret = smart_unicode(thing, strings_only=True)\n\n return ret","def serialize(self, obj):\n return dill.dumps(obj, 0).decode('latin-1')","def __str__(self):\n ret_str = \"\"\n for element_type in ('nodes', 'edges', 'layers'):\n elements = getattr(self, element_type)\n subtype_counts = defaultdict(int)\n ret_str += \"{0} {1}:\\n\".format(len(elements), element_type)\n for element in elements:\n subtype_counts[type(element).__name__] += 1\n for subtype in subtype_counts:\n ret_str += \"\\t{0}: {1}\\n\".format(subtype,\n subtype_counts[subtype])\n ret_str += \"\\n\"\n ret_str += \"primary text:\\n{0}\".format(self.text.encode('utf-8'))\n return ret_str","def __init__(self, debug=False):\n self.words = {}\n self.debug = debug","def mof(self):\n\n mof_str = \"instance of \" + self.__class__.__name__ + \" {\\n\"\n for key, value in self.__dict__.items():\n value_type = type(value)\n if value_type not in self.__supported_types:\n continue\n\n mof_str += \"%s = \" % key\n if value_type == int:\n mof_str += \"%d\" % value\n elif value_type == bool:\n mof_str += str(value).lower()\n else:\n mof_str += '\"%s\"' % value\n mof_str += \";\\n\"\n\n mof_str += \"};\"\n return mof_str","def nonliteral(object, *args, **kw):\n\n almost_a_representation = ' '.join(itertools.chain((object.__class__.__name__,),\n _representation_args_and_kw(*args, **kw)))\n\n return '<{}>'.format(almost_a_representation)","def _get_raw_doc(self):\n self.logger.debug(\"In _get_raw_doc.\")\n\n doc = {\n 'acl': {\n 'read': ['all'],\n 'write': [SubjectAttribute.namespace]\n },\n 'linkage': self._links,\n 'ns': SubjectAttribute.namespace,\n 'node_type': 'subject_attr',\n 'meta': {\n 'study': self._study,\n 'subtype': self._study,\n 'tags': self._tags\n }\n }\n\n if self._id is not None:\n self.logger.debug(__name__ + \" object has the OSDF id set.\")\n doc['id'] = self._id\n\n if self._version is not None:\n self.logger.debug(__name__ + \" object has the OSDF version set.\")\n doc['ver'] = self._version\n\n # Handle optional properties\n if self._aerobics is not None:\n self.logger.debug(\"%s object has aerobics set.\", __name__)\n doc['meta']['aerobics'] = self._aerobics\n\n if self._alcohol is not None:\n self.logger.debug(\"%s object has alcohol set.\", __name__)\n doc['meta']['alcohol'] = self._alcohol\n\n if self._allergies is not None:\n self.logger.debug(\"%s object has allergies set.\", __name__)\n doc['meta']['allergies'] = self._allergies\n\n if self._asthma is not None:\n self.logger.debug(\"%s object has asthma set.\", __name__)\n doc['meta']['asthma'] = self._asthma\n\n if self._cad is not None:\n self.logger.debug(\"%s object has cad set.\", __name__)\n doc['meta']['cad'] = self._cad\n\n if self._chf is not None:\n self.logger.debug(\"%s object has chf set.\", __name__)\n doc['meta']['chf'] = self._chf\n\n if self._comment is not None:\n self.logger.debug(\"%s object has comment set.\", __name__)\n doc['meta']['comment'] = self._comment\n\n if self._contact is not None:\n self.logger.debug(\"%s object has contact set.\", __name__)\n doc['meta']['contact'] = self._contact\n\n if self._diabetes is not None:\n self.logger.debug(\"%s object has diabetes set.\", __name__)\n doc['meta']['diabetes'] = self._diabetes\n\n if self._education is not None:\n self.logger.debug(\"%s object has education set.\", __name__)\n doc['meta']['education'] = self._education\n\n if self._family_history is not None:\n self.logger.debug(\"%s object has family_history set.\", __name__)\n doc['meta']['family_history'] = self._family_history\n\n if self._father is not None:\n self.logger.debug(\"%s object has father set.\", __name__)\n doc['meta']['father'] = self._father\n\n if self._ga_at_delivery is not None:\n self.logger.debug(\"%s object has ga_at_delivery set.\", __name__)\n doc['meta']['ga_at_delivery'] = self._ga_at_delivery\n\n if self._gallbladder is not None:\n self.logger.debug(\"%s object has gallbladder set.\", __name__)\n doc['meta']['gallbladder'] = self._gallbladder\n\n if self._hyperlipidemia is not None:\n self.logger.debug(\"%s object has hyperlipidemia set.\", __name__)\n doc['meta']['hyperlipidemia'] = self._hyperlipidemia\n\n if self._hypertension is not None:\n self.logger.debug(\"%s object has hypertension set.\", __name__)\n doc['meta']['hypertension'] = self._hypertension\n\n if self._illicit_drug is not None:\n self.logger.debug(\"%s object has illicit_drug set.\", __name__)\n doc['meta']['illicit_drug'] = self._illicit_drug\n\n if self._kidney is not None:\n self.logger.debug(\"%s object has kidney set.\", __name__)\n doc['meta']['kidney'] = self._kidney\n\n if self._liver is not None:\n self.logger.debug(\"%s object has liver set.\", __name__)\n doc['meta']['liver'] = self._liver\n\n if self._lmp is not None:\n self.logger.debug(\"%s object has lmp set.\", __name__)\n doc['meta']['lmp'] = self._lmp\n\n if self._mother is not None:\n self.logger.debug(\"%s object has mother set.\", __name__)\n doc['meta']['mother'] = self._mother\n\n if self._occupation is not None:\n self.logger.debug(\"%s object has occupation set.\", __name__)\n doc['meta']['occupation'] = self._occupation\n\n if self._osa is not None:\n self.logger.debug(\"%s object has osa set.\", __name__)\n doc['meta']['osa'] = self._osa\n\n if self._pancreatitis is not None:\n self.logger.debug(\"%s object has pancreatitis set.\", __name__)\n doc['meta']['pancreatitis'] = self._pancreatitis\n\n if self._postmenopausal is not None:\n self.logger.debug(\"%s object has postmenopausal set.\", __name__)\n doc['meta']['postmenopausal'] = self._postmenopausal\n\n if self._preg_term is not None:\n self.logger.debug(\"%s object has preg_term set.\", __name__)\n doc['meta']['preg_term'] = self._preg_term\n\n if self._pvd is not None:\n self.logger.debug(\"%s object has pvd set.\", __name__)\n doc['meta']['pvd'] = self._pvd\n\n if self._rx is not None:\n self.logger.debug(\"%s object has rx set.\", __name__)\n doc['meta']['rx'] = self._rx\n\n if self._siblings is not None:\n self.logger.debug(\"%s object has siblings set.\", __name__)\n doc['meta']['siblings'] = self._siblings\n\n if self._subproject is not None:\n self.logger.debug(\"%s object has subproject set.\", __name__)\n doc['meta']['subproject'] = self._subproject\n\n if self._survey_id is not None:\n self.logger.debug(\"%s object has survey_id set.\", __name__)\n doc['meta']['survey_id'] = self._survey_id\n\n if self._tobacco is not None:\n self.logger.debug(\"%s object has tobacco set.\", __name__)\n doc['meta']['tobacco'] = self._tobacco\n\n return doc","def from_dict(cls, dikt) -> 'Reviewer':\n return util.deserialize_model(dikt, cls)","def __init__(self):\n if self.__class__ == IdentifierTransformer:\n _self = None\n else:\n _self = self\n this = _libsbml.new_IdentifierTransformer(_self, )\n try: self.this.append(this)\n except: self.this = this","def __repr__(self):\n\n return \"<StudyTerm word=%s pmid=%d frequency=%f>\" % (\n self.word, self.pmid, self.frequency)","def serialize_forstorage(cls, obj):\n return misc.serialize_forstorage(obj)","def parse(self):\n self.builder.add_abstract(self.abstract)\n self.builder.add_title(self.title, subtitle=self.subtitle)\n self.builder.add_copyright(**self.copyright)\n self.builder.add_document_type(self.document_type)\n self.builder.add_license(**self.license)\n for author in self.authors:\n self.builder.add_author(author)\n self.builder.add_publication_info(**self.publication_info)\n for collab in self.collaborations:\n self.builder.add_collaboration(collab)\n for doi in self.dois:\n self.builder.add_doi(**doi)\n for keyword in self.keywords:\n self.builder.add_keyword(keyword)\n if self.imprints_date:\n self.builder.add_imprint_date(self.imprints_date)\n elif self.publication_date:\n self.builder.add_imprint_date(\n self.publication_date.dumps()\n )\n for reference in self.references:\n self.builder.add_reference(reference)\n\n return self.builder.record","def from_serializable(self, _):\n\n assert False, \"Not implemented\"","def GetDataAsObject(self):","def __add__(self, other):\n return corpus({\"location\": None, \"exemplars\": self.exemplars + other.exemplars})","def construct(obj):\n if isinstance(obj, OrderedDict):\n new_obj = OrderedDict()\n for key, value in obj.items():\n new_obj[key] = construct(value)\n elif not isinstance(obj, OrderedDict) and isinstance(obj, dict):\n new_obj = dict()\n for key, value in obj.items():\n new_obj[key] = construct(value)\n elif isinstance(obj, list):\n new_obj = list()\n for value in obj:\n new_obj.append(construct(value))\n elif isinstance(obj, tuple):\n base = list()\n for value in obj:\n base.append(construct(value))\n new_obj = tuple(base)\n elif isinstance(obj, str):\n new_obj = str(obj)\n elif isinstance(obj, (int, float, complex, type(None))) or inspect.isclass(obj):\n new_obj = obj\n else:\n raise TypeError(\"Object of unsupported type was passed to construct function: %s\" % type(obj))\n return new_obj","def __repr__(self):\t\n\t\treturn arabicRepr.repr(self.__dict__);","def to_doc(self):\n doc = {}\n if not self._data:\n return doc\n\n for operator, _values in self._data.items():\n doc[operator] = [d.to_doc() for d in _values]\n\n # Save metadata for use when re-cerealizing data\n doc['_metadata'] = {\n 'cls': self.__class__.__name__,\n }\n return doc","def __init__(self):\n # Initialise class attributes (visibility ease)\n self.__corpus__ = None\n self.__pron_det_pos_words__ = None\n self.__triples_corpus__ = None\n self.__entities_in_doc__ = None\n self.__wvmodel__ = None\n \n # For purpose of parsing relation triplets later\n # Load pretrained embedding model\n #plog('Loading pretrained word embeddings. This will take some time to load...')\n #self.__wvmodel__ = api.load('fasttext-wiki-news-subwords-300')\n #plog('Pretrained word embeddings loaded!')","def __init__(self, corpus, n, ignore_unigram):\n self.corpus = corpus\n self.n = n\n self.ignore_unigram = ignore_unigram","def __repr__(self):\n st = 'FOclass(\\\"'+self.name+'\\\", syntax=\\\"'+self.syntax+\\\n '\\\"'+', axioms=[ \\n\\\"'+'\\\",\\n\\\"'.join(self.axioms) + '\\\"]'\n if self.options!=[]:\n st += ',\\noptions=[ \\n\\\"'+'\\\",\\n\\\"'.join(self.options) + '\\\"]'\n if self.results!=[]:\n st += ',\\nresults=[ \\n\\\"'+'\\\",\\n\\\"'.join(self.results) + '\\\"]'\n return st + ')'","def __repr__(self):\n\n # Intro text.\n text = \"The data pipe storage object.\\n\"\n\n # Special objects/methods (to avoid the getattr() function call on).\n spec_obj = ['exp_info', 'mol', 'interatomic', 'diff_tensor', 'structure']\n\n # Objects.\n text = text + \"\\n\"\n text = text + \"Objects:\\n\"\n for name in dir(self):\n # Molecular list.\n if name == 'mol':\n text = text + \" mol: The molecule list (for the storage of the spin system specific data)\\n\"\n\n # Interatomic data list.\n if name == 'interatomic':\n text = text + \" interatomic: The interatomic data list (for the storage of the inter-spin system data)\\n\"\n\n # Diffusion tensor.\n if name == 'diff_tensor':\n text = text + \" diff_tensor: The Brownian rotational diffusion tensor data object\\n\"\n\n # Molecular structure.\n if name == 'structure':\n text = text + \" structure: The 3D molecular data object\\n\"\n\n # The experimental info data container.\n if name == 'exp_info':\n text = text + \" exp_info: The data container for experimental information\\n\"\n\n # Skip the PipeContainer methods.\n if name in self.__class__.__dict__:\n continue\n\n # Skip certain objects.\n if match(\"^_\", name) or name in spec_obj:\n continue\n\n # Add the object's attribute to the text string.\n text = text + \" \" + name + \": \" + repr(getattr(self, name)) + \"\\n\"\n\n # Return the text representation.\n return text","def build_document(self):\n pass","def _get_and_build_text_structure(self):\n return Text_structure(self.filename, self)","def __init__(self, **kwargs):\n self.swagger_types = {\n 'key': 'str',\n 'display_name': 'str',\n 'description': 'str',\n 'glossary_key': 'str',\n 'parent_term_key': 'str',\n 'is_allowed_to_have_child_terms': 'bool',\n 'path': 'str',\n 'lifecycle_state': 'str',\n 'time_created': 'datetime',\n 'time_updated': 'datetime',\n 'created_by_id': 'str',\n 'updated_by_id': 'str',\n 'owner': 'str',\n 'workflow_status': 'str',\n 'uri': 'str',\n 'associated_object_count': 'int',\n 'associated_objects': 'list[TermAssociatedObject]'\n }\n\n self.attribute_map = {\n 'key': 'key',\n 'display_name': 'displayName',\n 'description': 'description',\n 'glossary_key': 'glossaryKey',\n 'parent_term_key': 'parentTermKey',\n 'is_allowed_to_have_child_terms': 'isAllowedToHaveChildTerms',\n 'path': 'path',\n 'lifecycle_state': 'lifecycleState',\n 'time_created': 'timeCreated',\n 'time_updated': 'timeUpdated',\n 'created_by_id': 'createdById',\n 'updated_by_id': 'updatedById',\n 'owner': 'owner',\n 'workflow_status': 'workflowStatus',\n 'uri': 'uri',\n 'associated_object_count': 'associatedObjectCount',\n 'associated_objects': 'associatedObjects'\n }\n\n self._key = None\n self._display_name = None\n self._description = None\n self._glossary_key = None\n self._parent_term_key = None\n self._is_allowed_to_have_child_terms = None\n self._path = None\n self._lifecycle_state = None\n self._time_created = None\n self._time_updated = None\n self._created_by_id = None\n self._updated_by_id = None\n self._owner = None\n self._workflow_status = None\n self._uri = None\n self._associated_object_count = None\n self._associated_objects = None","def __init__(self, corpus_features, no):\n # We need corpus_features since it contains corpus wide info\n # about the frequencies of features\n self.corpus_features = corpus_features\n # value of the class, if known. Null string if unknown.\n self.cat = \"\"\n self.binary_features = []\n self.d_nominal_features = {}\n # meta_data allows for storing a non-feature string with a features record\n self.meta_data = \"\"\n # unique identifier of the annotation record\n self.no = no","def copy(self):\n newobj = Corpus(docs=self.docs.copy())\n newobj.doc_paths = self.doc_paths.copy()\n\n return newobj","def __str__(self):\n sio = StringIO()\n # name:\n sio.write(self.name + \":\\n\")\n # Tags\n # - name: value\n for t, v in self.tags:\n sio.write('- ' + t + ': ' + v + '\\n')\n # Sets\n # a,b,d-f) w x r, w x r, [rest] work x rep\n sio.write(','.join(str(s.order) for s in self.sets) + ') ')\n sets = [str(s) for s in self.sets]\n sio.write(', '.join(sets) + '\\n')\n return sio.getvalue()","def doc(self):\n return {'_id': self._id,\n 'text': self.text}","def object(self):","def __init__(self, corpus):\n self.corpus = corpus\n self.corpus_seg = None\n self.bm25_instance = None\n self.tokenizer = Tokenizer()","def __sub__(self, other):\n return corpus(\n {\n \"location\": None,\n \"exemplars\": [_ for _ in self.exemplars if _ not in other.exemplars],\n }\n )","def literal(object, *args, **kw):\n\n args_and_kw = ', '.join(_representation_args_and_kw(*args, **kw))\n\n return '{class_name}({args_and_kw})'.format(class_name=object.__class__.__name__, args_and_kw=args_and_kw)","def from_serializable(self, _):\n\n assert False, \"not implemented\"","def __str__(self):\n return str(self.obj)","def __generate_delimited_objects__(self, output, **kwargs):\n triple_map = kwargs.get(\"triple_map\")\n subject = kwargs.get('subject')\n # Subject is blank-node, try to retrieve subject IRI\n predicate = kwargs.get('predicate')\n element = kwargs.get('element')\n datatype = kwargs.get('datatype', None)\n delimiters = kwargs.get('delimiters')\n subjects = []\n for delimiter in delimiters:\n values = element.text.split(delimiter)\n for row in values:\n if datatype is not None:\n obj_ = rdflib.Literal(row.strip(), datatype=datatype)\n else:\n obj_ = rdflib.Literal(row.strip())\n if isinstance(subject, rdflib.BNode):\n new_subject = rdflib.BNode()\n class_ = triple_map.subjectMap.class_\n output.add((new_subject, NS_MGR.rdf.type.rdflib, class_))\n for parent_subject, parent_predicate in \\\n output.subject_predicates(object=subject):\n output.add((parent_subject,\n parent_predicate,\n new_subject))\n else:\n new_subject = subject\n subjects.append(new_subject)\n output.add((new_subject, predicate, obj_))\n return subjects","def get_model(self):\n return Doc()","def make_object():\n return object()"],"string":"[\n \"def __repr__(self):\\n return ''.joint(\\\"A corpus made of \\\", len(self._corpus),\\n \\\" with types: \\\", [type(item) for item in self._corpus])\",\n \"def build(self, obj):\\n if isinstance(obj, self.art_type):\\n return obj\\n elif isinstance(obj, (tuple, list, dict, set)):\\n if obj.__class__ is tuple:\\n return self.build_tuple(obj)\\n elif obj.__class__ is dict:\\n return self.build_dict(obj)\\n elif obj.__class__ is list:\\n return self.build_list(obj)\\n else:\\n return self.build_set(obj)\\n elif isinstance(obj, SageObject):\\n return self.build_from_magic_method(obj)\\n else:\\n return self.build_from_string(obj)\",\n \"def serialize(self):\\n return {\\n 'word': self.word,\\n 'pos': self.pos,\\n 'label': self.label,\\n 'dependency': self.dependency\\n }\",\n \"def __init__(self, corpus_name, corpus, data_date=''):\\n\\n # Test Patterns and sets for use later in our topic model\\n # '^[a-z0-9 _-]{2,}$' --> Match letters, numbers, spaces, underscores, dashes. Ignore case. Length of 2 or more.\\n self.phrase_pattern = re.compile('^[a-z0-9 _-]{2,}$', re.IGNORECASE)\\n self.date_pattern = re.compile('20[0-9]{2}-[0-1][0-9]-[0-3][0-9]$')\\n self.punct = {p for p in string.punctuation}\\n self.empty_words = {'a', 'an', 'that', 'the', 'this'}\\n self.nouns = {ss.NOUN, ss.PROPN}\\n self.entities = {ss.PERSON, ss.NORP, ss.FACILITY, ss.ORG, ss.GPE, ss.LOC, ss.PRODUCT, ss.EVENT, ss.WORK_OF_ART,\\n ss.LANGUAGE} # spaCy entities that indicate a proper noun.\\n\\n # Check the user arguments\\n # TODO: What data to I expect in the dictionary passed from the \\\"get_\\\" class?\\n assert 2 < len(corpus_name) < 21 and re.match(self.phrase_pattern, corpus_name), \\\\\\n 'A corpus_name (between 3 and 20 characters; made of letters, numbers, underscores, or dashes) is required.'\\n assert data_date == '' or re.match(self.date_pattern, data_date), \\\\\\n 'If you include a data_date, it must match the form 20YY-MM-DD.'\\n assert type(corpus) is dict, 'The corpus must be a dictionary.'\\n assert len(corpus) > 0, 'The corpus of texts has no data.'\\n\\n # Topic metadata & settings\\n self.corpus_name = corpus_name.replace(' ', '') # (str) The name of the set (or corpus) of texts.\\n self.data_date = data_date\\n\\n # Primary Data Structures\\n self.texts = corpus # The dict of dicts that contains all of our texts for analysis: {text_id: {}}\\n self.topics = {} # A dict of dicts for primary topics: {topic: {}}\\n self.ngrams = {} # A dict of dicts for ngrams that will help us understand primary topics: {ngram_lemma: {}}\\n self.model_output = {'name': corpus_name,\\n 'dataDate': data_date,\\n 'runDate': datetime.now().strftime(\\\"%Y-%m-%d %H:%M\\\"),\\n 'textCount': len(corpus)} # For results as json\\n\\n # Get known entities\\n try:\\n with open(config.INPUT_DIR + 'known_entities.txt', 'r') as file:\\n self.known_entities = set(file.read().split(' '))\\n except IOError:\\n print('Add a text file named \\\"known_entities.txt\\\" that lists named entities (space-delimited) that we '\\n 'want to be sure are treated as proper nouns.')\\n self.known_entities = set()\\n\\n # Stop Words\\n try:\\n with open(config.INPUT_DIR + 'stop_words.txt', 'r') as file:\\n self.stop_words = set(file.read().split(' '))\\n except IOError:\\n print('Add a text file named \\\"stop_words.txt\\\" that lists common words (space-delimited) that we should '\\n 'ignore in most text processing.')\\n self.stop_words = set()\\n\\n # Loop through texts and tokenize: 'doc' and 'titleDoc' are lists of spaCy tokens, with named entities called\\n # recognized and joined. 'textClean' is a string of lemmatized words, excluding stopwords and punctuation.\\n # It's used by Doc2Vec.\\n for text_id, text in self.texts.items():\\n text['doc'] = self._tokenize(text['text'])\\n text['titleDoc'] = self._tokenize(text['title'])\\n text['textClean'] = ' '.join([token.text.lower() if token.lemma_ == '-PRON-' else token.lemma_ for\\n token in text['doc'] if token.lemma_ not in\\n self.stop_words and token.text not in self.punct])\",\n \"def __repr__(self):\\n return '%s.from_text(%r)' % (self.__class__.__name__, self.to_text())\",\n \"def fromObject(cls, obj, decode=None):\\n if obj.__doc__ is None:\\n return cls(u'' if decode else '')\\n r = cls.fromString(obj.__doc__, decode=decode)\\n return r\",\n \"def serialize(self, obj):\\n return obj\",\n \"def to_doc(self):\\n doc = {}\\n for _key, _value in vars(self).items():\\n if isinstance(_value, TargetingCriterion):\\n doc[_key] = _value.to_doc()\\n else:\\n doc[_key] = _value\\n\\n # Save metadata for use when re-cerealizing data\\n doc['_metadata'] = {\\n 'cls': self.__class__.__name__,\\n }\\n return doc\",\n \"def create_corpus_for_artist(artist):\\n raise NotImplementedError\",\n \"def _format_obj(cls, **kwargs):\\n def doc_rebuilder(obj):\\n if kwargs.pop('_VOID_',False):\\n return ''\\n try:\\n doc = getattr(obj,'__doc__')\\n assert doc\\n except:\\n return ''\\n else:\\n return doc.format(**kwargs) # str(doc).format(**kwargs)\\n return doc_rebuilder\",\n \"def build_document(self, labels_from_json):\\n if not len(self.raw_labels):\\n self.get_labels(labels_from_json)\\n raw_text = self.instance_input_file.read()\\n document = self.DOCUMENT_CLASS(self.identifier, title=self.identifier)\\n document.build_from_text(raw_text, start_index=0)\\n for start_index, end_index in self.raw_labels:\\n document.add_label_for_position(\\n 'claim', int(start_index), int(end_index))\\n return document\",\n \"def construct(self):\\n return self.as_search().construct()\",\n \"def __getitem__(self, given):\\n return corpus(\\n {\\n \\\"location\\\": self.location,\\n \\\"exemplars\\\": [self.exemplars[given]]\\n if not isinstance(given, slice)\\n else self.exemplars[given],\\n }\\n )\",\n \"def serialize(self) -> Dict:\\n return dict(\\n author=self.doc.author if self.doc else None,\\n name=self.name,\\n functions=[x.serialize() for x in self.functions],\\n classes=[x.serialize() for x in self.classes],\\n doc=self.doc.serialize() if self.doc else None,\\n )\",\n \"def __init__(self,typing,reflection,year):\\n self.name = str(self)\\n self.typing = typing\\n self.reflection = reflection\\n self.year = year\",\n \"def __init__(self, obj):\\n from lxml import objectify\\n try:\\n self.root = objectify.fromstring(obj.data)\\n except:\\n # try something else\\n self.root = objectify.fromstring(obj)\\n self.obj = obj\",\n \"def __repr__(self) -> str:\\n return f\\\"<Doc[{self.desc}]>\\\"\",\n \"def __init__(self):\\n self.children = {}\\n self.is_a_word = (False,None)\\n self.index = []\",\n \"def jsonizable_object(self):\\n obj = {\\n 'title': self.title,\\n 'url': self.url,\\n 'abstract': self.abstract\\n }\\n if self.metadata:\\n obj['metadata'] = self.metadata\\n return obj\",\n \"def __init__(self, documents):\\n Classifier.__init__(self, documents)\\n documents = set(documents)\\n term_document_matrix = TermDocumentMatrix(documents, compute_word_vectors=False, compute_document_vectors=False)\\n self.vocabulary = set(term_document_matrix.vocabulary)\\n self.tree = self.get_tree(documents, self.vocabulary)\",\n \"def __str__(self) -> str:\\n obj_dict: Dict[str, Any] = {}\\n obj_dict[\\\"doc\\\"] = self.doc\\n obj_dict[\\\"type\\\"] = self.type\\n obj_dict[\\\"name\\\"] = self.name\\n\\n line_range = self.line_range()\\n obj_dict[\\\"start_line\\\"] = line_range[0]\\n obj_dict[\\\"end_line\\\"] = line_range[1]\\n\\n obj_dict[\\\"children\\\"] = []\\n\\n for child in self.children.values():\\n obj_dict[\\\"children\\\"].append(json.loads(str(child)))\\n\\n return json.dumps(obj_dict)\",\n \"def bundle_instance(obj):\\n\\n content, contents = osl_encode(obj, True)\\n # should be a bunch of documents, not just one.\\n bundle = [json.dumps(c) for c in contents]\\n return bundle\",\n \"def get_corpus_object(corpus_name):\\n\\tif corpus_name.startswith(\\\"yelp\\\"):\\n\\t\\treturn CorpusYelp(make_storagedir(corpus_name), GLOVEPATH, *JSONS)\\n\\t\\t\\n\\telif corpus_name == \\\"newsgroup\\\":\\n\\t\\treturn CorpusNewsgroup(make_storagedir(corpus_name), GLOVEPATH)\\n\\n\\traise Exception(\\\"Unknown corpus\\\", corpus_name)\",\n \"def build_from_string(self, obj):\\n if self.string_type is unicode and not isinstance(obj, unicode):\\n obj = str(obj).decode('utf-8')\\n if self.string_type is str and not isinstance(obj, str):\\n obj = unicode(obj).encode('utf-8')\\n return self.art_type(obj.splitlines())\",\n \"def _mkObject(self):\\n return ImmutableObject(\\n store=self.store,\\n hash=u'somehash',\\n contentDigest=u'quux',\\n content=self.store.newFilePath('foo'),\\n contentType=u'application/octet-stream')\",\n \"def __buildDocumentClassDocString():\\n\\n # build a dictionary of tags and their descriptions, seems a little over\\n # the top, but keeps all the information in one place\\n tagsStrings = {\\n \\\"comment\\\" : \\\"Define the comment string\\\",\\n \\\"define\\\" : \\\"Define the symbol name for #define's\\\",\\n \\\"info\\\" : \\\"Information string, to end up in the 'info' output\\\",\\n \\\"instance\\\" : \\\"Instance name\\\",\\n \\\"matlabRoot\\\" : \\\"Name of variable used by the matlab output\\\",\\n \\\"members\\\" : \\\"List of symbols, which are going to be children of this symbol\\\",\\n \\\"name\\\" : \\\"Name of this symbol\\\",\\n \\\"size\\\" : \\\"Size of this symbol, i.e. indicate it is an array\\\",\\n \\\"subtype\\\" : \\\"Define the actual type of general symbol\\\",\\n \\\"symbol\\\" : \\\"Define a symbol, either a top level entity a child in a members\\\",\\n \\\"test\\\" : \\\"Define the preprocessor test\\\",\\n \\\"text\\\" : \\\"Text to put into a banner symbol\\\",\\n \\\"title\\\" : \\\"Set the overall document title\\\",\\n \\\"value\\\" : \\\"Define a value for this symbol\\\",\\n \\\"valuesRequired\\\" : \\\"Does the enumeration allow automatic value assignment in entries\\\",\\n }\\n # build the list of classes\\n classes = dict(filter(lambda (k,v): type(v) == types.ClassType, globals().iteritems()))\\n (tagsUsed, optionsUsed) = buildKeys(classes)\\n\\n # build the string we are going to add to the document class\\n s = \\\"Document class that represents the XML document and contains the data.\\\\n\\\\n\\\"\\n s += \\\"Available tags:\\\\n\\\"\\n\\n for tag in tagsStrings:\\n try:\\n used = \\\" Required by : %s\\\\n\\\" % (\\\", \\\".join(tagsUsed[tag]))\\n except KeyError:\\n used = \\\"\\\"\\n try:\\n opts = \\\" Optional for: %s\\\\n\\\" % (\\\", \\\".join(optionsUsed[tag]))\\n except KeyError:\\n opts = \\\"\\\"\\n s += \\\" %s\\\\n %s\\\\n %s\\\\n\\\\n%s%s\\\\n\\\" % (tag, \\\"-\\\"*len(tag), tagsStrings[tag], used, opts)\\n\\n return s\",\n \"def __init__(self):\\n self._pronunciations = nltk.corpus.cmudict.dict()\\n \\\"\\\"\\\"\\n API Documentation for CMU dictionary corpus\\n http://www.nltk.org/api/nltk.corpus.reader.html#module-nltk.corpus.reader.cmudict\\n \\\"\\\"\\\"\",\n \"def __init__(self):\\n self.kids = [{}]\\n self.root = 0\\n self.vocabular = set([])\",\n \"def __init__(self, corpus):\\n self.train(corpus)\",\n \"def __init__(self):\\n self.children = dict()\\n self.isWord = False\",\n \"def __init__(self, inputs, labels, storage_method=\\\"raw\\\", force_shorten=True,\\n data_dir=\\\"./data\\\", tokenizer_path=\\\"./\\\"):\\n Doc.__init__(self, storage_method, force_shorten, data_dir, tokenizer_path)\\n self.all_docs = {}\\n\\n for text, label in zip(inputs, labels):\\n self.add_to_section(text, label)\\n self.compute_lens()\\n\\n self.all_labels = list(self.all_docs.keys())\",\n \"def make_objects(self):\\n pass\",\n \"def __init__(self, tokenizer, stemmer=None, stopwords=None):\\n self.tokenizer = tokenizer\\n self.stemmer = stemmer\\n self.index = defaultdict(list)\\n self.documents = {}\\n self.__unique_id = 0 #can be used as docid\\n if not stopwords:\\n self.stopwords = list()\\n else:\\n self.stopwords = list(stopwords)\",\n \"def xml2obj(self, src):\\n\\n\\t\\tclass DataNode(object):\\n\\t\\t\\tdef __init__(self):\\n\\t\\t\\t\\tself._attrs = {} # XML attributes and child elements\\n\\t\\t\\t\\tself.data = None # child text data\\n\\n\\t\\t\\tdef __len__(self):\\n\\t\\t\\t\\t# treat single element as a list of 1\\n\\t\\t\\t\\treturn 1\\n\\n\\t\\t\\tdef __getitem__(self, key):\\n\\t\\t\\t\\tif isinstance(key, basestring):\\n\\t\\t\\t\\t\\treturn self._attrs.get(key,None)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\treturn [self][key]\\n\\n\\t\\t\\tdef __contains__(self, name):\\n\\t\\t\\t\\treturn self._attrs.has_key(name)\\n\\n\\t\\t\\tdef __nonzero__(self):\\n\\t\\t\\t\\treturn bool(self._attrs or self.data)\\n\\n\\t\\t\\tdef __getattr__(self, name):\\n\\t\\t\\t\\tif name.startswith('__'):\\n\\t\\t\\t\\t\\t# need to do this for Python special methods???\\n\\t\\t\\t\\t\\traise AttributeError(name)\\n\\t\\t\\t\\treturn self._attrs.get(name,None)\\n\\n\\t\\t\\tdef _add_xml_attr(self, name, value):\\n\\t\\t\\t\\tif name in self._attrs:\\n\\t\\t\\t\\t\\t\\t# multiple attribute of the same name are represented by a list\\n\\t\\t\\t\\t\\t\\tchildren = self._attrs[name]\\n\\t\\t\\t\\t\\t\\tif not isinstance(children, list):\\n\\t\\t\\t\\t\\t\\t\\tchildren = [children]\\n\\t\\t\\t\\t\\t\\t\\tself._attrs[name] = children\\n\\t\\t\\t\\t\\t\\tchildren.append(value)\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tself._attrs[name] = value\\n\\n\\t\\t\\tdef __str__(self):\\n\\t\\t\\t\\treturn self.data or ''\\n\\n\\t\\t\\tdef __repr__(self):\\n\\t\\t\\t\\titems = sorted(self._attrs.items())\\n\\t\\t\\t\\tif self.data:\\n\\t\\t\\t\\t\\titems.append(('data', self.data))\\n\\t\\t\\t\\treturn u'{%s}' % ', '.join([u'%s:%s' % (k,repr(v)) for k,v in items])\\n\\n\\t\\tclass TreeBuilder(xml.sax.handler.ContentHandler):\\n\\t\\t\\tdef __init__(self):\\n\\t\\t\\t\\tself.stack = []\\n\\t\\t\\t\\tself.root = DataNode()\\n\\t\\t\\t\\tself.current = self.root\\n\\t\\t\\t\\tself.text_parts = []\\n\\t\\t\\t\\tself.publicObjects = {}\\n\\n\\t\\t\\tdef startElement(self, name, attrs):\\n\\t\\t\\t\\tself.stack.append((self.current, self.text_parts))\\n\\t\\t\\t\\tself.current = DataNode()\\n\\t\\t\\t\\tself.text_parts = []\\n\\t\\t\\t\\t# xml attributes --> python attributes\\n\\t\\t\\t\\tfor k, v in attrs.items():\\n\\t\\t\\t\\t\\t# Register PublicObject in lookup map\\n\\t\\t\\t\\t\\tif k == \\\"publicID\\\":\\n\\t\\t\\t\\t\\t\\tself.publicObjects[v] = self.current\\n\\t\\t\\t\\t\\tself.current._add_xml_attr(k, v)\\n\\n\\t\\t\\tdef endElement(self, name):\\n\\t\\t\\t\\ttext = ''.join(self.text_parts).strip()\\n\\t\\t\\t\\tif text:\\n\\t\\t\\t\\t\\tself.current.data = text\\n\\t\\t\\t\\tif self.current._attrs:\\n\\t\\t\\t\\t\\tobj = self.current\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\t# a text only node is simply represented by the string\\n\\t\\t\\t\\t\\tobj = text or ''\\n\\t\\t\\t\\t\\t# try to store the object as float if possible\\n\\t\\t\\t\\t\\ttry: obj = float(obj)\\n\\t\\t\\t\\t\\texcept: pass\\n\\t\\t\\t\\tself.current, self.text_parts = self.stack.pop()\\n\\t\\t\\t\\tself.current._add_xml_attr(name, obj)\\n\\n\\t\\t\\tdef characters(self, content):\\n\\t\\t\\t\\tself.text_parts.append(content)\\n\\n\\t\\tbuilder = TreeBuilder()\\n\\t\\tif isinstance(src,basestring):\\n\\t\\t\\txml.sax.parseString(src, builder)\\n\\t\\telse:\\n\\t\\t\\txml.sax.parse(src, builder)\\n\\t\\treturn builder\",\n \"def doc(obj):\\n return Documentation.fromObject(obj).first\",\n \"def __init__(self, num_docs, upper, lower, stemmer=True):\\n\\n # load data from json file\\n self.num_docs = num_docs\\n self.data = pd.read_json('data/arxiv-metadata-oai-snapshot.json', lines=True,\\n nrows=self.num_docs)\\n\\n # create a list of list of documents from the abstracts, lowercase words, remove non-alphanumerical words\\n self.stemmer = PorterStemmer() if stemmer == True else None\\n self.docs = [self.preprocess(doc) for doc in self.data['abstract'].tolist()]\\n\\n # count occurrences per word and prune words that occur above some threshold\\n self.counts = self.get_counts()\\n self.prune(upper=upper, lower=lower)\\n\\n # obtain 'final' vocabulary, convert to integer ids.\\n self.vocab = self.get_vocab()\\n self.docs2ids()\",\n \"def make_object(self, data, **kwargs):\\n if not data:\\n return None\\n return Note(\\n title=data['title'],\\n content=data['content'],\\n )\",\n \"def __init__(self, vocab: Sequence[Any], start=1):\\n self.obj_to_idx = dict()\\n self.idx_to_obj = dict()\\n self.start = start\\n for obj in vocab:\\n if obj not in self.obj_to_idx:\\n self.idx_to_obj[len(self.obj_to_idx)+start] = obj\\n self.obj_to_idx[obj] = len(self.obj_to_idx)+start\",\n \"def make_data(docs):\\n vocab=make_vocab_from_docs(docs)\\n word_dicts=list(map(lambda x: parse_doc(x,vocab),docs))\\n Nd=list(map(len,docs))\\n M,V=len(docs),len(vocab)\\n return (vocab,word_dicts,Nd,M,V)\",\n \"def obj(self) -> object:\\n pass\",\n \"def build_from_mongo(cls, data, use_cls=True):\\n # If a _cls is specified, we have to use this document class\\n if use_cls and '_cls' in data:\\n cls = cls.opts.instance.retrieve_embedded_document(data['_cls'])\\n doc = cls()\\n doc.from_mongo(data)\\n return doc\",\n \"def _builder(o_name,_nodes,_tagged_reals):\\n obj = _tagged_reals[o_name]\\n \\n if isinstance(obj,ElementaryReal):\\n un = UncertainReal._archived_elementary(\\n uid = obj.uid,\\n x = obj.x\\n )\\n _tagged_reals[o_name] = un \\n \\n elif isinstance(obj,IntermediateReal): \\n \\n _node = _nodes[obj.uid] \\n \\n un = UncertainReal(\\n obj.value,\\n _vector_index_to_node( obj.u_components ),\\n _vector_index_to_node( obj.d_components ),\\n _ivector_index_to_node( obj.i_components, _nodes ),\\n _node,\\n )\\n \\n _tagged_reals[o_name] = un\\n\\n else:\\n assert False, \\\"unexpected: {!r}\\\".format(obj)\\n\\n return un\",\n \"def serialize(self):\",\n \"def _convert_construction_info_to_string(obj):\\n if not hasattr(obj, '_constructor_args'):\\n raise AttributeError('obj has no attribute _constructor_args.')\\n import StringIO\\n output = StringIO.StringIO()\\n info = {}\\n info['_module_name'] = obj.__class__.__module__\\n info['_class_name'] = obj.__class__.__name__\\n encoded_constructor_args = {}\\n for k, v in obj._constructor_args.items():\\n if isinstance(v, chainer.Link):\\n encoded_constructor_args[k] \\\\\\n = _convert_construction_info_to_string(v)\\n elif isinstance(v, six.string_types):\\n encoded_constructor_args[k] = 'STR' + v\\n else:\\n encoded_constructor_args[k] = v\\n info['_encoded_constructor_args'] = encoded_constructor_args\\n numpy.save(output, arr=info)\\n encoded_construction_info = 'OBJ' + output.getvalue()\\n output.close()\\n return encoded_construction_info\",\n \"def __init__(self):\\n self._pronunciations = nltk.corpus.cmudict.dict()\",\n \"def __init__(self):\\n self._pronunciations = nltk.corpus.cmudict.dict()\",\n \"def __init__(self, author, title, text=\\\"\\\", haiku=\\\"False\\\"):\\n self.author = author\\n self.title = title\\n self.text = text\\n self.haiku = haiku\\n self.name = self\\n Poem.obj_created += 1\",\n \"def __str__(self):\\n\\n return '__str__ for Object'\",\n \"def serialize(self, obj):\\n pass\",\n \"def __str__(self):\\n table = 'objects'.join(self.galcat.__str__().split('objects')[1:])\\n return self.__repr__()+'\\\\n'+table\",\n \"def serialize(self):\\n return {\\n 'name': self.name,\\n 'description': self.description,\\n 'category': self.category\\n }\",\n \"def __repr__(self):\\n return self.textual_representation().encode(\\\"utf-8\\\")\",\n \"def test_01_BuildObjects(self):\\n # print(PrettyFormatAny.form(self.m_pyhouse_obj, 'A1-01-A - Main', 190))\\n # print(PrettyFormatAny.form(self.m_pyhouse_obj.House, 'A1-02-B - House', 190))\\n # print(PrettyFormatAny.form(self.m_pyhouse_obj.House.Location, 'A1-01-C - Location', 190))\\n self.assertIsInstance(self.m_pyhouse_obj, PyHouseInformation)\\n self.assertIsInstance(self.m_pyhouse_obj.House, HouseInformation)\\n self.assertIsInstance(self.m_pyhouse_obj.House.Location, LocationInformationPrivate)\",\n \"def __repr__(self):\\n return f\\\"PhraseModel({json.dumps(self.json, indent=2)})\\\"\",\n \"def _cls_repr(self):\\n return [] # TODO: return I guess samples/labels/chunks\",\n \"def __str__(self):\\n return self.make_flat()\",\n \"def make_lexicon_txt(self):\\n raise NotImplementedError\",\n \"def __str__(self):\\n return str(self.__dict__['_obj'])\",\n \"def __repr__(self):\\n\\n return \\\"<Terms term=%s>\\\" % (self.word)\",\n \"def to_representation(self) -> str:\\n raise NotImplementedError()\",\n \"def __init__(self, scene: 'Scene', word: str, container=None, attrs=None) -> None:\\n if container:\\n super(Object, self).__init__(scene, container=container)\\n else:\\n super(Object, self).__init__(scene)\\n self.word = word\\n if attrs:\\n self.attributes = attrs\\n else:\\n self.attributes = list()\",\n \"def to_model(cls, obj):\\n\\n new_model = cls()\\n\\n for key, value in obj.iteritems():\\n if value:\\n if key == 'transcripts':\\n setattr(new_model, key, [ModelConverter.to_model(Transcript, t) for t in value])\\n elif key == 'acts' and cls == Transcript:\\n setattr(new_model, key, [ModelConverter.to_model(Act, a) for a in value])\\n elif key == 'subtitles':\\n setattr(new_model, key, [ModelConverter.to_model(Subtitle, s) for s in value])\\n else:\\n setattr(new_model, key, value)\\n\\n return new_model\",\n \"def custom_encode(obj):\\n if isinstance(obj, DictionaryMethods):\\n key = '__Dictionary__'\\n return {key: [list(obj), obj.alpha, obj.pat, obj.pat_args,\\n obj.auto_fields]}\\n elif isinstance(obj, Entry):\\n return obj.data\\n else:\\n raise TypeError(\\\"obj {!r} of type {}\\\".format(obj, type(obj)))\",\n \"def construct(self, thing):\\n ret = None\\n if isinstance(thing, str):\\n ret = thing\\n elif isinstance(thing, QuerySet):\\n ret = self._qs(thing)\\n elif isinstance(thing, (tuple, list)):\\n ret = self._list(thing)\\n elif isinstance(thing, dict):\\n ret = self._dict(thing)\\n elif isinstance(thing, decimal.Decimal):\\n ret = str(thing)\\n elif isinstance(thing, Model):\\n ret = self._model(thing)\\n elif isinstance(thing, datetime.datetime):\\n o2 = thing.replace(tzinfo=dateutil.tz.tzutc())\\n return datetime.datetime.strftime(o2, self.DATETIME_FORMAT)\\n elif isinstance(thing, datetime.date):\\n d = datetime_safe.new_date(thing)\\n return d.strftime(self.DATE_FORMAT)\\n elif isinstance(thing, datetime.time):\\n return thing.strftime(self.TIME_FORMAT)\\n else:\\n ret = smart_unicode(thing, strings_only=True)\\n\\n return ret\",\n \"def serialize(self, obj):\\n return dill.dumps(obj, 0).decode('latin-1')\",\n \"def __str__(self):\\n ret_str = \\\"\\\"\\n for element_type in ('nodes', 'edges', 'layers'):\\n elements = getattr(self, element_type)\\n subtype_counts = defaultdict(int)\\n ret_str += \\\"{0} {1}:\\\\n\\\".format(len(elements), element_type)\\n for element in elements:\\n subtype_counts[type(element).__name__] += 1\\n for subtype in subtype_counts:\\n ret_str += \\\"\\\\t{0}: {1}\\\\n\\\".format(subtype,\\n subtype_counts[subtype])\\n ret_str += \\\"\\\\n\\\"\\n ret_str += \\\"primary text:\\\\n{0}\\\".format(self.text.encode('utf-8'))\\n return ret_str\",\n \"def __init__(self, debug=False):\\n self.words = {}\\n self.debug = debug\",\n \"def mof(self):\\n\\n mof_str = \\\"instance of \\\" + self.__class__.__name__ + \\\" {\\\\n\\\"\\n for key, value in self.__dict__.items():\\n value_type = type(value)\\n if value_type not in self.__supported_types:\\n continue\\n\\n mof_str += \\\"%s = \\\" % key\\n if value_type == int:\\n mof_str += \\\"%d\\\" % value\\n elif value_type == bool:\\n mof_str += str(value).lower()\\n else:\\n mof_str += '\\\"%s\\\"' % value\\n mof_str += \\\";\\\\n\\\"\\n\\n mof_str += \\\"};\\\"\\n return mof_str\",\n \"def nonliteral(object, *args, **kw):\\n\\n almost_a_representation = ' '.join(itertools.chain((object.__class__.__name__,),\\n _representation_args_and_kw(*args, **kw)))\\n\\n return '<{}>'.format(almost_a_representation)\",\n \"def _get_raw_doc(self):\\n self.logger.debug(\\\"In _get_raw_doc.\\\")\\n\\n doc = {\\n 'acl': {\\n 'read': ['all'],\\n 'write': [SubjectAttribute.namespace]\\n },\\n 'linkage': self._links,\\n 'ns': SubjectAttribute.namespace,\\n 'node_type': 'subject_attr',\\n 'meta': {\\n 'study': self._study,\\n 'subtype': self._study,\\n 'tags': self._tags\\n }\\n }\\n\\n if self._id is not None:\\n self.logger.debug(__name__ + \\\" object has the OSDF id set.\\\")\\n doc['id'] = self._id\\n\\n if self._version is not None:\\n self.logger.debug(__name__ + \\\" object has the OSDF version set.\\\")\\n doc['ver'] = self._version\\n\\n # Handle optional properties\\n if self._aerobics is not None:\\n self.logger.debug(\\\"%s object has aerobics set.\\\", __name__)\\n doc['meta']['aerobics'] = self._aerobics\\n\\n if self._alcohol is not None:\\n self.logger.debug(\\\"%s object has alcohol set.\\\", __name__)\\n doc['meta']['alcohol'] = self._alcohol\\n\\n if self._allergies is not None:\\n self.logger.debug(\\\"%s object has allergies set.\\\", __name__)\\n doc['meta']['allergies'] = self._allergies\\n\\n if self._asthma is not None:\\n self.logger.debug(\\\"%s object has asthma set.\\\", __name__)\\n doc['meta']['asthma'] = self._asthma\\n\\n if self._cad is not None:\\n self.logger.debug(\\\"%s object has cad set.\\\", __name__)\\n doc['meta']['cad'] = self._cad\\n\\n if self._chf is not None:\\n self.logger.debug(\\\"%s object has chf set.\\\", __name__)\\n doc['meta']['chf'] = self._chf\\n\\n if self._comment is not None:\\n self.logger.debug(\\\"%s object has comment set.\\\", __name__)\\n doc['meta']['comment'] = self._comment\\n\\n if self._contact is not None:\\n self.logger.debug(\\\"%s object has contact set.\\\", __name__)\\n doc['meta']['contact'] = self._contact\\n\\n if self._diabetes is not None:\\n self.logger.debug(\\\"%s object has diabetes set.\\\", __name__)\\n doc['meta']['diabetes'] = self._diabetes\\n\\n if self._education is not None:\\n self.logger.debug(\\\"%s object has education set.\\\", __name__)\\n doc['meta']['education'] = self._education\\n\\n if self._family_history is not None:\\n self.logger.debug(\\\"%s object has family_history set.\\\", __name__)\\n doc['meta']['family_history'] = self._family_history\\n\\n if self._father is not None:\\n self.logger.debug(\\\"%s object has father set.\\\", __name__)\\n doc['meta']['father'] = self._father\\n\\n if self._ga_at_delivery is not None:\\n self.logger.debug(\\\"%s object has ga_at_delivery set.\\\", __name__)\\n doc['meta']['ga_at_delivery'] = self._ga_at_delivery\\n\\n if self._gallbladder is not None:\\n self.logger.debug(\\\"%s object has gallbladder set.\\\", __name__)\\n doc['meta']['gallbladder'] = self._gallbladder\\n\\n if self._hyperlipidemia is not None:\\n self.logger.debug(\\\"%s object has hyperlipidemia set.\\\", __name__)\\n doc['meta']['hyperlipidemia'] = self._hyperlipidemia\\n\\n if self._hypertension is not None:\\n self.logger.debug(\\\"%s object has hypertension set.\\\", __name__)\\n doc['meta']['hypertension'] = self._hypertension\\n\\n if self._illicit_drug is not None:\\n self.logger.debug(\\\"%s object has illicit_drug set.\\\", __name__)\\n doc['meta']['illicit_drug'] = self._illicit_drug\\n\\n if self._kidney is not None:\\n self.logger.debug(\\\"%s object has kidney set.\\\", __name__)\\n doc['meta']['kidney'] = self._kidney\\n\\n if self._liver is not None:\\n self.logger.debug(\\\"%s object has liver set.\\\", __name__)\\n doc['meta']['liver'] = self._liver\\n\\n if self._lmp is not None:\\n self.logger.debug(\\\"%s object has lmp set.\\\", __name__)\\n doc['meta']['lmp'] = self._lmp\\n\\n if self._mother is not None:\\n self.logger.debug(\\\"%s object has mother set.\\\", __name__)\\n doc['meta']['mother'] = self._mother\\n\\n if self._occupation is not None:\\n self.logger.debug(\\\"%s object has occupation set.\\\", __name__)\\n doc['meta']['occupation'] = self._occupation\\n\\n if self._osa is not None:\\n self.logger.debug(\\\"%s object has osa set.\\\", __name__)\\n doc['meta']['osa'] = self._osa\\n\\n if self._pancreatitis is not None:\\n self.logger.debug(\\\"%s object has pancreatitis set.\\\", __name__)\\n doc['meta']['pancreatitis'] = self._pancreatitis\\n\\n if self._postmenopausal is not None:\\n self.logger.debug(\\\"%s object has postmenopausal set.\\\", __name__)\\n doc['meta']['postmenopausal'] = self._postmenopausal\\n\\n if self._preg_term is not None:\\n self.logger.debug(\\\"%s object has preg_term set.\\\", __name__)\\n doc['meta']['preg_term'] = self._preg_term\\n\\n if self._pvd is not None:\\n self.logger.debug(\\\"%s object has pvd set.\\\", __name__)\\n doc['meta']['pvd'] = self._pvd\\n\\n if self._rx is not None:\\n self.logger.debug(\\\"%s object has rx set.\\\", __name__)\\n doc['meta']['rx'] = self._rx\\n\\n if self._siblings is not None:\\n self.logger.debug(\\\"%s object has siblings set.\\\", __name__)\\n doc['meta']['siblings'] = self._siblings\\n\\n if self._subproject is not None:\\n self.logger.debug(\\\"%s object has subproject set.\\\", __name__)\\n doc['meta']['subproject'] = self._subproject\\n\\n if self._survey_id is not None:\\n self.logger.debug(\\\"%s object has survey_id set.\\\", __name__)\\n doc['meta']['survey_id'] = self._survey_id\\n\\n if self._tobacco is not None:\\n self.logger.debug(\\\"%s object has tobacco set.\\\", __name__)\\n doc['meta']['tobacco'] = self._tobacco\\n\\n return doc\",\n \"def from_dict(cls, dikt) -> 'Reviewer':\\n return util.deserialize_model(dikt, cls)\",\n \"def __init__(self):\\n if self.__class__ == IdentifierTransformer:\\n _self = None\\n else:\\n _self = self\\n this = _libsbml.new_IdentifierTransformer(_self, )\\n try: self.this.append(this)\\n except: self.this = this\",\n \"def __repr__(self):\\n\\n return \\\"<StudyTerm word=%s pmid=%d frequency=%f>\\\" % (\\n self.word, self.pmid, self.frequency)\",\n \"def serialize_forstorage(cls, obj):\\n return misc.serialize_forstorage(obj)\",\n \"def parse(self):\\n self.builder.add_abstract(self.abstract)\\n self.builder.add_title(self.title, subtitle=self.subtitle)\\n self.builder.add_copyright(**self.copyright)\\n self.builder.add_document_type(self.document_type)\\n self.builder.add_license(**self.license)\\n for author in self.authors:\\n self.builder.add_author(author)\\n self.builder.add_publication_info(**self.publication_info)\\n for collab in self.collaborations:\\n self.builder.add_collaboration(collab)\\n for doi in self.dois:\\n self.builder.add_doi(**doi)\\n for keyword in self.keywords:\\n self.builder.add_keyword(keyword)\\n if self.imprints_date:\\n self.builder.add_imprint_date(self.imprints_date)\\n elif self.publication_date:\\n self.builder.add_imprint_date(\\n self.publication_date.dumps()\\n )\\n for reference in self.references:\\n self.builder.add_reference(reference)\\n\\n return self.builder.record\",\n \"def from_serializable(self, _):\\n\\n assert False, \\\"Not implemented\\\"\",\n \"def GetDataAsObject(self):\",\n \"def __add__(self, other):\\n return corpus({\\\"location\\\": None, \\\"exemplars\\\": self.exemplars + other.exemplars})\",\n \"def construct(obj):\\n if isinstance(obj, OrderedDict):\\n new_obj = OrderedDict()\\n for key, value in obj.items():\\n new_obj[key] = construct(value)\\n elif not isinstance(obj, OrderedDict) and isinstance(obj, dict):\\n new_obj = dict()\\n for key, value in obj.items():\\n new_obj[key] = construct(value)\\n elif isinstance(obj, list):\\n new_obj = list()\\n for value in obj:\\n new_obj.append(construct(value))\\n elif isinstance(obj, tuple):\\n base = list()\\n for value in obj:\\n base.append(construct(value))\\n new_obj = tuple(base)\\n elif isinstance(obj, str):\\n new_obj = str(obj)\\n elif isinstance(obj, (int, float, complex, type(None))) or inspect.isclass(obj):\\n new_obj = obj\\n else:\\n raise TypeError(\\\"Object of unsupported type was passed to construct function: %s\\\" % type(obj))\\n return new_obj\",\n \"def __repr__(self):\\t\\n\\t\\treturn arabicRepr.repr(self.__dict__);\",\n \"def to_doc(self):\\n doc = {}\\n if not self._data:\\n return doc\\n\\n for operator, _values in self._data.items():\\n doc[operator] = [d.to_doc() for d in _values]\\n\\n # Save metadata for use when re-cerealizing data\\n doc['_metadata'] = {\\n 'cls': self.__class__.__name__,\\n }\\n return doc\",\n \"def __init__(self):\\n # Initialise class attributes (visibility ease)\\n self.__corpus__ = None\\n self.__pron_det_pos_words__ = None\\n self.__triples_corpus__ = None\\n self.__entities_in_doc__ = None\\n self.__wvmodel__ = None\\n \\n # For purpose of parsing relation triplets later\\n # Load pretrained embedding model\\n #plog('Loading pretrained word embeddings. This will take some time to load...')\\n #self.__wvmodel__ = api.load('fasttext-wiki-news-subwords-300')\\n #plog('Pretrained word embeddings loaded!')\",\n \"def __init__(self, corpus, n, ignore_unigram):\\n self.corpus = corpus\\n self.n = n\\n self.ignore_unigram = ignore_unigram\",\n \"def __repr__(self):\\n st = 'FOclass(\\\\\\\"'+self.name+'\\\\\\\", syntax=\\\\\\\"'+self.syntax+\\\\\\n '\\\\\\\"'+', axioms=[ \\\\n\\\\\\\"'+'\\\\\\\",\\\\n\\\\\\\"'.join(self.axioms) + '\\\\\\\"]'\\n if self.options!=[]:\\n st += ',\\\\noptions=[ \\\\n\\\\\\\"'+'\\\\\\\",\\\\n\\\\\\\"'.join(self.options) + '\\\\\\\"]'\\n if self.results!=[]:\\n st += ',\\\\nresults=[ \\\\n\\\\\\\"'+'\\\\\\\",\\\\n\\\\\\\"'.join(self.results) + '\\\\\\\"]'\\n return st + ')'\",\n \"def __repr__(self):\\n\\n # Intro text.\\n text = \\\"The data pipe storage object.\\\\n\\\"\\n\\n # Special objects/methods (to avoid the getattr() function call on).\\n spec_obj = ['exp_info', 'mol', 'interatomic', 'diff_tensor', 'structure']\\n\\n # Objects.\\n text = text + \\\"\\\\n\\\"\\n text = text + \\\"Objects:\\\\n\\\"\\n for name in dir(self):\\n # Molecular list.\\n if name == 'mol':\\n text = text + \\\" mol: The molecule list (for the storage of the spin system specific data)\\\\n\\\"\\n\\n # Interatomic data list.\\n if name == 'interatomic':\\n text = text + \\\" interatomic: The interatomic data list (for the storage of the inter-spin system data)\\\\n\\\"\\n\\n # Diffusion tensor.\\n if name == 'diff_tensor':\\n text = text + \\\" diff_tensor: The Brownian rotational diffusion tensor data object\\\\n\\\"\\n\\n # Molecular structure.\\n if name == 'structure':\\n text = text + \\\" structure: The 3D molecular data object\\\\n\\\"\\n\\n # The experimental info data container.\\n if name == 'exp_info':\\n text = text + \\\" exp_info: The data container for experimental information\\\\n\\\"\\n\\n # Skip the PipeContainer methods.\\n if name in self.__class__.__dict__:\\n continue\\n\\n # Skip certain objects.\\n if match(\\\"^_\\\", name) or name in spec_obj:\\n continue\\n\\n # Add the object's attribute to the text string.\\n text = text + \\\" \\\" + name + \\\": \\\" + repr(getattr(self, name)) + \\\"\\\\n\\\"\\n\\n # Return the text representation.\\n return text\",\n \"def build_document(self):\\n pass\",\n \"def _get_and_build_text_structure(self):\\n return Text_structure(self.filename, self)\",\n \"def __init__(self, **kwargs):\\n self.swagger_types = {\\n 'key': 'str',\\n 'display_name': 'str',\\n 'description': 'str',\\n 'glossary_key': 'str',\\n 'parent_term_key': 'str',\\n 'is_allowed_to_have_child_terms': 'bool',\\n 'path': 'str',\\n 'lifecycle_state': 'str',\\n 'time_created': 'datetime',\\n 'time_updated': 'datetime',\\n 'created_by_id': 'str',\\n 'updated_by_id': 'str',\\n 'owner': 'str',\\n 'workflow_status': 'str',\\n 'uri': 'str',\\n 'associated_object_count': 'int',\\n 'associated_objects': 'list[TermAssociatedObject]'\\n }\\n\\n self.attribute_map = {\\n 'key': 'key',\\n 'display_name': 'displayName',\\n 'description': 'description',\\n 'glossary_key': 'glossaryKey',\\n 'parent_term_key': 'parentTermKey',\\n 'is_allowed_to_have_child_terms': 'isAllowedToHaveChildTerms',\\n 'path': 'path',\\n 'lifecycle_state': 'lifecycleState',\\n 'time_created': 'timeCreated',\\n 'time_updated': 'timeUpdated',\\n 'created_by_id': 'createdById',\\n 'updated_by_id': 'updatedById',\\n 'owner': 'owner',\\n 'workflow_status': 'workflowStatus',\\n 'uri': 'uri',\\n 'associated_object_count': 'associatedObjectCount',\\n 'associated_objects': 'associatedObjects'\\n }\\n\\n self._key = None\\n self._display_name = None\\n self._description = None\\n self._glossary_key = None\\n self._parent_term_key = None\\n self._is_allowed_to_have_child_terms = None\\n self._path = None\\n self._lifecycle_state = None\\n self._time_created = None\\n self._time_updated = None\\n self._created_by_id = None\\n self._updated_by_id = None\\n self._owner = None\\n self._workflow_status = None\\n self._uri = None\\n self._associated_object_count = None\\n self._associated_objects = None\",\n \"def __init__(self, corpus_features, no):\\n # We need corpus_features since it contains corpus wide info\\n # about the frequencies of features\\n self.corpus_features = corpus_features\\n # value of the class, if known. Null string if unknown.\\n self.cat = \\\"\\\"\\n self.binary_features = []\\n self.d_nominal_features = {}\\n # meta_data allows for storing a non-feature string with a features record\\n self.meta_data = \\\"\\\"\\n # unique identifier of the annotation record\\n self.no = no\",\n \"def copy(self):\\n newobj = Corpus(docs=self.docs.copy())\\n newobj.doc_paths = self.doc_paths.copy()\\n\\n return newobj\",\n \"def __str__(self):\\n sio = StringIO()\\n # name:\\n sio.write(self.name + \\\":\\\\n\\\")\\n # Tags\\n # - name: value\\n for t, v in self.tags:\\n sio.write('- ' + t + ': ' + v + '\\\\n')\\n # Sets\\n # a,b,d-f) w x r, w x r, [rest] work x rep\\n sio.write(','.join(str(s.order) for s in self.sets) + ') ')\\n sets = [str(s) for s in self.sets]\\n sio.write(', '.join(sets) + '\\\\n')\\n return sio.getvalue()\",\n \"def doc(self):\\n return {'_id': self._id,\\n 'text': self.text}\",\n \"def object(self):\",\n \"def __init__(self, corpus):\\n self.corpus = corpus\\n self.corpus_seg = None\\n self.bm25_instance = None\\n self.tokenizer = Tokenizer()\",\n \"def __sub__(self, other):\\n return corpus(\\n {\\n \\\"location\\\": None,\\n \\\"exemplars\\\": [_ for _ in self.exemplars if _ not in other.exemplars],\\n }\\n )\",\n \"def literal(object, *args, **kw):\\n\\n args_and_kw = ', '.join(_representation_args_and_kw(*args, **kw))\\n\\n return '{class_name}({args_and_kw})'.format(class_name=object.__class__.__name__, args_and_kw=args_and_kw)\",\n \"def from_serializable(self, _):\\n\\n assert False, \\\"not implemented\\\"\",\n \"def __str__(self):\\n return str(self.obj)\",\n \"def __generate_delimited_objects__(self, output, **kwargs):\\n triple_map = kwargs.get(\\\"triple_map\\\")\\n subject = kwargs.get('subject')\\n # Subject is blank-node, try to retrieve subject IRI\\n predicate = kwargs.get('predicate')\\n element = kwargs.get('element')\\n datatype = kwargs.get('datatype', None)\\n delimiters = kwargs.get('delimiters')\\n subjects = []\\n for delimiter in delimiters:\\n values = element.text.split(delimiter)\\n for row in values:\\n if datatype is not None:\\n obj_ = rdflib.Literal(row.strip(), datatype=datatype)\\n else:\\n obj_ = rdflib.Literal(row.strip())\\n if isinstance(subject, rdflib.BNode):\\n new_subject = rdflib.BNode()\\n class_ = triple_map.subjectMap.class_\\n output.add((new_subject, NS_MGR.rdf.type.rdflib, class_))\\n for parent_subject, parent_predicate in \\\\\\n output.subject_predicates(object=subject):\\n output.add((parent_subject,\\n parent_predicate,\\n new_subject))\\n else:\\n new_subject = subject\\n subjects.append(new_subject)\\n output.add((new_subject, predicate, obj_))\\n return subjects\",\n \"def get_model(self):\\n return Doc()\",\n \"def make_object():\\n return object()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6311109","0.6053932","0.5645673","0.56127447","0.5482682","0.54713523","0.5400021","0.53901446","0.5380758","0.5339382","0.5327804","0.5323678","0.53051144","0.53021866","0.5297798","0.52929544","0.52819115","0.52755284","0.5261796","0.5260795","0.524931","0.5241852","0.5240803","0.5214266","0.5212438","0.52115065","0.52035886","0.5187476","0.518653","0.518498","0.5183618","0.5163691","0.51635486","0.5160387","0.5156688","0.51559603","0.5149706","0.5148397","0.5136265","0.5125136","0.51245433","0.51188004","0.51153755","0.5114599","0.5106537","0.5106537","0.510577","0.51049286","0.51011425","0.5093674","0.50857383","0.50841385","0.5051153","0.5048382","0.50465953","0.50348365","0.50325084","0.50286436","0.5019294","0.50172776","0.50124454","0.50092304","0.5008246","0.50015885","0.4999168","0.49969232","0.49967277","0.49936748","0.4993597","0.4984456","0.49799478","0.497223","0.4971645","0.49668378","0.49660695","0.4964341","0.49603632","0.49597317","0.49575296","0.49492195","0.4946414","0.49435523","0.49407804","0.4937931","0.49327064","0.49319342","0.49317744","0.49287322","0.49235684","0.4918636","0.49172652","0.49145976","0.49136847","0.49136528","0.4908849","0.49087682","0.49079588","0.49060866","0.4904399","0.4899349","0.48987404"],"string":"[\n \"0.6311109\",\n \"0.6053932\",\n \"0.5645673\",\n \"0.56127447\",\n \"0.5482682\",\n \"0.54713523\",\n \"0.5400021\",\n \"0.53901446\",\n \"0.5380758\",\n \"0.5339382\",\n \"0.5327804\",\n \"0.5323678\",\n \"0.53051144\",\n \"0.53021866\",\n \"0.5297798\",\n \"0.52929544\",\n \"0.52819115\",\n \"0.52755284\",\n \"0.5261796\",\n \"0.5260795\",\n \"0.524931\",\n \"0.5241852\",\n \"0.5240803\",\n \"0.5214266\",\n \"0.5212438\",\n \"0.52115065\",\n \"0.52035886\",\n \"0.5187476\",\n \"0.518653\",\n \"0.518498\",\n \"0.5183618\",\n \"0.5163691\",\n \"0.51635486\",\n \"0.5160387\",\n \"0.5156688\",\n \"0.51559603\",\n \"0.5149706\",\n \"0.5148397\",\n \"0.5136265\",\n \"0.5125136\",\n \"0.51245433\",\n \"0.51188004\",\n \"0.51153755\",\n \"0.5114599\",\n \"0.5106537\",\n \"0.5106537\",\n \"0.510577\",\n \"0.51049286\",\n \"0.51011425\",\n \"0.5093674\",\n \"0.50857383\",\n \"0.50841385\",\n \"0.5051153\",\n \"0.5048382\",\n \"0.50465953\",\n \"0.50348365\",\n \"0.50325084\",\n \"0.50286436\",\n \"0.5019294\",\n \"0.50172776\",\n \"0.50124454\",\n \"0.50092304\",\n \"0.5008246\",\n \"0.50015885\",\n \"0.4999168\",\n \"0.49969232\",\n \"0.49967277\",\n \"0.49936748\",\n \"0.4993597\",\n \"0.4984456\",\n \"0.49799478\",\n \"0.497223\",\n \"0.4971645\",\n \"0.49668378\",\n \"0.49660695\",\n \"0.4964341\",\n \"0.49603632\",\n \"0.49597317\",\n \"0.49575296\",\n \"0.49492195\",\n \"0.4946414\",\n \"0.49435523\",\n \"0.49407804\",\n \"0.4937931\",\n \"0.49327064\",\n \"0.49319342\",\n \"0.49317744\",\n \"0.49287322\",\n \"0.49235684\",\n \"0.4918636\",\n \"0.49172652\",\n \"0.49145976\",\n \"0.49136847\",\n \"0.49136528\",\n \"0.4908849\",\n \"0.49087682\",\n \"0.49079588\",\n \"0.49060866\",\n \"0.4904399\",\n \"0.4899349\",\n \"0.48987404\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":21885,"cells":{"query":{"kind":"string","value":"Return the Spacy processed sentences in which the element is positioned."},"document":{"kind":"string","value":"def get_processed_sentence(self):\n\n if not hasattr(self, \"in_sentence\"):\n raise AttributeError(f\"{self} does not have attribute 'in_sentence'.\")\n\n try:\n sen_ixs = [sen.element_id for sen in self.in_sentence]\n except TypeError as e:\n sen_ixs = [self.in_sentence.element_id]\n\n sen_procs = [\n s\n for i, s in enumerate(self.in_document.sentences_processed)\n if i in sen_ixs\n ]\n return sen_procs"},"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 sentences(self) -> List[str]:\n\t\treturn [self.text[start:end] for start, end in self.tokenizations]","def sentences(self) -> List[str]:\n\t\treturn [self.text[start:end] for start, end in self.tokenizations]","def get_sentences(self):\n return [s for s in self.text.split('\\n')]","def get_processed_sentences(self):\n\n self.__resolve_coreference()\n self.__tokenize_sentences()\n\n return self.tokenized_sentences","def sentences(self, tag=False, tag_method=None):\n self.__set_text_node(self.root_)\n sentence_nodes = filter(lambda n: n.nodeType == n.ELEMENT_NODE and n.tagName == 's',\n list(self.text_node.childNodes))\n sentences = []\n for s in sentence_nodes:\n current = []\n TimeMLDoc.__get_text(s, current, False)\n #print(current)\n if not tag:\n sentences.append(''.join([ c[0] for c in current]))\n else:\n sentences.append(tag_method(current))\n return sentences","def get_sentences(self):\n return self._sentences","def ie_preprocess(document):\n sentences = nltk.sent_tokenize(document) #NLTK default sentence segmenter\n #print sentences # sentences are segmented\n sentences = [nltk.word_tokenize(sent) for sent in sentences] # NLTK word tokenizer \n #print sentences # sentences are tokenized\n sentences = [nltk.pos_tag(sent) for sent in sentences] # NLTK POS tagger \n #print sentences # sentences are POS tagged\n return sentences","def pos(self):\n if 'pos' not in self.annotators:\n return None\n return [t[self.POS] for t in self.data]","def pos(self):\n if 'pos' not in self.annotators:\n return None\n return [t[self.POS] for t in self.data]","def sentences(self) -> List[str]:\n\t\treturn [sentence for sentence in re.split('(?<=[.!?])', self.text)]","def get_sentence(self):","def sentence_segment(self, doc, candidate_pos, lower):\n sentences = []\n for sent in doc.sents:\n selected_words = []\n for token in sent:\n # Store words only with cadidate POS tag\n if token.pos_ in candidate_pos and token.is_stop is False:\n if lower is True:\n selected_words.append(token.text.lower())\n else:\n selected_words.append(token.text)\n sentences.append(selected_words)\n return sentences","def gather_sentences(self):\n sentences = Sentence.objects.all()\n return sentences","def sentencing(any_text, nlp):\n nlp.add_pipe(nlp.create_pipe('sentencizer'))\n doc = nlp(any_text)\n sentences = [sent.string.strip() for sent in doc.sents]\n return sentences","def collect_sentences(self):\n sentences = []\n for document in self.documents:\n for sentence_token in document.sentences:\n sentences.append(sentence_token)\n return sentences","def get_sentences(text):\n \n return text.split('.')","def getTrueSentences(self, form):","def return_augmented_sentences(self) -> list:\n return self.augmented_sentence_list","def gather_sentences(self):\n if self.company_name:\n companies = Company.objects.filter(name__contains=self.company_name)\n dpefs = DPEF.objects.filter(company__in=companies)\n sentences = Sentence.objects.filter(dpef__in=dpefs).all()\n else:\n sentences = Sentence.objects.none() # TODO: can be set to none later when all works\n return sentences","def split_sentences(self, text):\n assert isinstance(text, str)\n text = text.replace('\\n', '')\n\n if text.strip() == '':\n return []\n\n output = self._annotate(text, properties={\n \"annotators\": \"tokenize,ssplit\",\n \"coref.md.type\": \"dep\",\n \"coref.mode\": \"statistical\"\n })\n\n sentences = []\n for sentence in output['sentences']:\n num_token = len(sentence['tokens'])\n start_index = sentence['tokens'][0]['characterOffsetBegin']\n end_index = sentence['tokens'][num_token - 1]['characterOffsetEnd']\n sentences.append(text[start_index:end_index])\n return sentences","def calculate_texts(self) -> None:\n texts = []\n for text in self.texts:\n paragraphs = list(filter(lambda x: x != \"\", text.split(\"\\n\\n\")))\n for paragraph in paragraphs:\n text = paragraph.replace(\"\\n\", \" \").strip()\n if len(text) > self.split_threshold_min:\n text_sentences = nlp(text)\n sentences = []\n for sentence in text_sentences.sents:\n current = sentence.text\n sentences.append(current.strip())\n texts.extend(sentences)\n else:\n texts.append(text)\n self.texts = list(set(texts))","def sentences(summary, nlp):\n text = remove_spurious_words(text_of(summary))\n all_sentence = [sentence for sentence in re.split(\"[。，？！\\n]\", text) if sentence]\n all_sentence = [re.sub('[ ]+', ' ', sentence.encode('gb2312', 'ignore').decode('gb2312')).strip() for sentence in\n all_sentence]\n return [nlp.ner(sentence) for sentence in all_sentence if sentence]","def get_sentences(self, text_to_parse):\r\n simple_sentence, *rest = text_to_parse.split('(', 1)\r\n kana_sentence, *rest = rest[0].split(')', 1)\r\n return simple_sentence.strip(), kana_sentence.strip().replace(' ', '')","def get_sentences(text, nlp):\n\n # get sentences from text\n sentences = [sentence for sentence in\n text.replace('!', '.').replace('?', '.').split('.')]\n\n processed_sentences = [convert_to_string(remove_junk(tokenize_text(sentence, nlp))) for sentence in\n text.replace('!', '.').replace('?', '.').split('.')]\n\n # convert the sentences into a list of document vectors\n sentence_vector_list = [nlp(sentence).vector for sentence in processed_sentences]\n\n return sentences, sentence_vector_list","def getPosition(self):\n\t\txxx1 = self.stokes()\n\t\txxx2 = self.thp()\n\t\txxx3 = self.tthp()\n\t\treturn [xxx1, xxx2, xxx3]","def get_sentence_sentiments(comment):\n sentence_score_list = []\n\n split_comment = make_sentences(comment)\n for sentence in split_comment:\n if sentence == ' ' or sentence == '' or sentence == ' ':\n continue\n text = flair.data.Sentence(sentence)\n flair_sentiment.predict(text)\n\n value = text.labels[0].to_dict()['value']\n if value == 'POSITIVE':\n result = text.to_dict()['labels'][0]['confidence']\n else:\n result = -(text.to_dict()['labels'][0]['confidence'])\n\n sentence_score = round(result, 6)\n sentence_score_list.append(sentence_score)\n\n return sentence_score_list","def extract_sentences_from_text(self, text_data):\n pass","def getConstantSentenceForms(self):","def split(text):\n doc = nlp(text)\n sentences = [x.text_with_ws for x in doc.sents]\n return sentences","def get_sentences(self):\n for tree in self.tree_generator():\n yield tree[\"title\"] + \" \" + tree[\"selftext\"]\n for _, comment in tree[\"comments\"].items():\n yield comment[\"body\"]","def get_pos(sent):\n tokens = get_tokens(sent)\n return [tag for (token, tag) in nltk.pos_tag(tokens)]","def current_document_text(self):\n return self.current_document.lines","def pos_tag_sents(\n sentences: List[List[str]], engine: str = \"perceptron\", corpus: str = \"orchid\"\n) -> List[List[Tuple[str, str]]]:\n if not sentences:\n return []\n\n return [pos_tag(sent, engine=engine, corpus=corpus) for sent in sentences]","def sentence_segment(self, doc, candidate_pos, lower,bigrams,trigrams):\n sentences = []\n for sent in doc.sents:\n selected_words = []\n bigram_words=[]\n for token in sent:\n bigram_words.append(token.text)\n # Store words only with cadidate POS tag\n if token.pos_ in candidate_pos and token.is_stop is False:\n if lower is True:\n selected_words.append(token.text.lower())\n else:\n selected_words.append(token.text)\n if bigrams==True:\n for i in range(len(sent)-1):\n if sent[i].pos_ in candidate_pos and sent[i].is_stop is False and sent[i+1].pos_ in candidate_pos and sent[i+1].is_stop is False:\n if lower is True:\n selected_words.append(sent[i].text.lower())\n else:\n selected_words.append(str(sent[i].text+\" \"+sent[i+1].text))\n if trigrams==True:\n for i in range(len(sent)-2):\n if sent[i].pos_ in candidate_pos and sent[i].is_stop is False and sent[i+1].pos_ in candidate_pos and sent[i+1].is_stop is False and sent[i+2].pos_ in candidate_pos and sent[i+2].is_stop is False:\n if lower is True:\n selected_words.append(sent[i].text.lower())\n else:\n selected_words.append(str(sent[i].text+\" \"+sent[i+1].text+\" \"+sent[i+2].text))\n sentences.append(selected_words)\n return sentences","def sentences(a, b):\n\n # TODO\n return []","def full_pos_tag(self, sentence):\n tags = self.pos_tag(sentence)\n for i in range(len( tags)):\n tags[i] = self.get_complete_tag(tags[i])\n return tags","def _partsofspeech(self):\n \n l = len(self._words)\n for i in range(l):\n self._words[i]['pos'] = pos_tag( [ self._words[i]['word'] ])[0][1]","def get_sentences_list(text: str, model_type: str) -> t.List[str]:\n sentences = []\n sent_offsets = []\n stok = SentenceTokenizer.from_type(model_type)\n if isinstance(text, list):\n sentences, sent_offsets = list(zip(*map(stok.tokenize, text)))\n elif isinstance(text, str):\n sentences, sent_offsets = stok.tokenize(text)\n return sentences, sent_offsets","def test_get_sentence_sentiments():\n long_comment = [\"This was a really sucky movie. I will probably never go see this movie ever again. I am going to \"\n \"tell my whole family never to watch this movie. I very much enjoyed the special cameo in it \"\n \"though. I loved the plot line.\"]\n\n sentence_score_list = get_sentence_sentiments(long_comment[0])\n print(long_comment[0])\n print('per sentence sentiment:', sentence_score_list)\n print()","def get_sentences(paragraph):\n punctuation = re.compile(r'[\\.!?]')\n sentences = [sentence.strip() for sentence in punctuation.split(paragraph)]\n sentences = filter(lambda x: x, sentences)\n return sentences","def _sentence(self, node, offset_mngr):\n text = self._text(node)\n offset = offset_mngr.update(node, text)\n return text, offset","def getSentences(self, ser):\n\n tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n sentencesSer = ser.apply(tokenizer.tokenize)\n return sentencesSer","def parts_of_speech_tags(self, tokenized_doc):\n return [(token.text, token.pos_) for token in self.parser(\n tokenized_doc)]","def sentences(self):\n return re.compile(r'[.!?;:,\\t\\(\\)\\\"\\']|\\s-\\s').split(self.text)","def get(self, pos):\n return self.sentence[pos]","def fetch_sentences(self, content):\n score_increments, sentences = ProximityScorePlugin.run(self.sanitised_question_split,\n self.sanitised_answers,\n content)\n for key in sentences:\n self.sentences[key].append(sentences[key])\n # print(self.sentences)\n # get key sentences of this round\n key_sentences = ProximityScorePlugin.get_key_sentences(self.sentences, self.sanitised_question_split)\n # append them to self.key_sentences\n for key in key_sentences:\n if key_sentences[key] not in self.key_sentences[key]:\n self.key_sentences[key].append(key_sentences[key])\n self.finalise_key_sentences() # added to reduce to one sentence per answer\n # print(f\"Best Sentences: {self.key_sentences}\")","def process_spacy(self):\n\n def prevent_sentence_boundary_detection(doc):\n for token in doc:\n # This will entirely disable spaCy's sentence detection\n token.is_sent_start = False\n return doc\n\n def process_sentence(sen_tokens):\n doc = spacy.tokens.Doc(nlp.vocab, words=sen_tokens)\n tagger(doc)\n prevent_sbd(doc)\n ner(doc)\n parser(doc)\n return doc\n\n # setup spacy nlp pipeline\n nlp = spacy.load(\"en_core_web_lg\")\n parser = nlp.get_pipe(\"parser\")\n nlp.add_pipe(\n prevent_sentence_boundary_detection, name=\"prevent-sbd\", before=\"parser\"\n )\n\n tagger = nlp.get_pipe(\"tagger\")\n prevent_sbd = nlp.get_pipe(\"prevent-sbd\")\n parser = nlp.get_pipe(\"parser\")\n ner = nlp.get_pipe(\"ner\")\n\n for doc in self.annotation_documents:\n doc.sentences_processed = []\n for sen in doc.sentences:\n sen_tokens = [t.text for t in sen.tokens]\n sen_proc = process_sentence(sen_tokens)\n # add processed sentence to doc\n doc.sentences_processed.append(sen_proc)\n\n print(f\"Processed with Spacy: {doc.document_id}\")","def gather_sentences(self):\n companies = filter_company_from_form(self)\n dpefs = DPEF.objects\n if self.cleaned_data[\"start_period\"] is not None:\n dpefs = dpefs.filter(year__gte=self.cleaned_data[\"start_period\"])\n if self.cleaned_data[\"end_period\"] is not None:\n dpefs = dpefs.filter(year__lte=self.cleaned_data[\"end_period\"])\n dpefs = dpefs.filter(company__in=companies)\n sentences = Sentence.objects.filter(dpef__in=dpefs).all()\n return sentences","def sentences(self, text):\n if not self.__isValidInput(text):\n return [Sentence(text, Sentence.NONE)]\n\n uniText = unicode_str(text)\n result = []\n textLen = len(uniText)\n sentenceLen = c_size_t()\n position = 0\n while textLen > 0:\n sentenceType = self.__lib.voikkoNextSentenceStartUcs4(\n self.__handle,\n uniText[position:],\n textLen,\n byref(sentenceLen),\n )\n sentenceText = uniText[position:position + sentenceLen.value]\n result.append(Sentence(sentenceText, sentenceType))\n if sentenceType == Sentence.NONE:\n break\n position = position + sentenceLen.value\n textLen = textLen - sentenceLen.value\n return result","def sentences(self, text):\n return re.findall(r'([A-Z][^\\.!?]*[\\.!?])', text)","def get_enron_sentences(self):\n helper._print_subheader('Reading ' + directories.ENRON_TRAIN_SENTENCES_TXT_PATH + '...')\n with open(directories.ENRON_TRAIN_SENTENCES_TXT_PATH, 'r', encoding='utf-8') as txt_file:\n for index, line in enumerate(txt_file):\n if index % 1000000 == 0 and index != 0:\n helper._print(f'{index} sentences read')\n break\n preproccesed_line = simple_preprocess(line)\n if preproccesed_line != []:\n yield preproccesed_line\n helper._print(f'{index} sentences read')\n helper._print_subheader('Done reading Enron email data!')","def sentence_parse(list_of_posts): \n for parsedPosts in nlp.pipe(line_review(list_of_posts)):\n for sent in parsedPosts.sents:\n yield str(sent)","def sentences_from_string(this_class, text):\n # Note that the native method below leaks. We work around this\n # by acquiring its pointer in __init__\n sentReps = parser.sentRepsFromString(text)\n return list(map(this_class, sentReps))","def getSentence(self):\n return self.tokens[0].sentence","def get_context(words, position, window_size=2):\n start = max(0, position - window_size)\n stop = min(position + window_size, len(words))\n context_words = words[start:position] + words[position + 1:stop + 1]\n\n return list(context_words)","def process_token_sentence(text):\n\n sentences = nltk.sent_tokenize(text)\n tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]\n tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]\n sentences = nltk.ne_chunk_sents(tagged_sentences, binary=True)\n\n return sentences","def extract_sentences(paper_path, para_yes):\n\n f = open(paper_path, 'rb')\n doc = Document.from_file(f, readers=[HtmlReader()])\n\n sen_yes_arr = list()\n sen_no_arr = list()\n\n elem_all = np.arange(0,len(doc))\n para_no = np.delete(elem_all, para_yes)\n\n for i in para_no:\n if type(doc.elements[i]) == chemdataextractor.doc.text.Paragraph:\n for sentence in doc.elements[i]:\n sen_no_arr.append(sentence)\n\n for i in para_yes:\n if type(doc.elements[i]) == chemdataextractor.doc.text.Paragraph:\n for sentence in doc.elements[i]:\n sen_yes_arr.append(sentence)\n\n\n return sen_yes_arr, sen_no_arr","def process_text(text):\n return [token.text for token in nlp(text) if not token.is_stop]","def positions(self):\n return self.preorder()","def getTokens(self):\n list = []\n for i in range(self.startIdx, self.endIdx + 1):\n token = self.sentence[i]\n list.append(token)\n return list","def process(self, doc):\n # don't try to process null notes\n if not doc[1]:\n if self.verbose:\n print(\"Error segmenting doc\",doc[0])\n return []\n # odd notes may throw an error. Just continue rather than stopping the entire process\n try:\n sentences = self.sentence_tokenizer.segToSentenceSpans(doc[1])\n except KeyError:\n if self.verbose:\n print(\"Error segmenting doc\",doc[0])\n return []\n\n #context_doc = pyConTextGraph.ConTextDocument() # ConTextDoc not needed for simple usage\n\n doc_annots = list()\n\n for sentence in sentences:\n # run sentence tokenizer on input text, return the spans\n sentence_text = doc[1][sentence.begin:sentence.end]\n # process every sentence by adding markup\n markup = pyConTextGraph.ConTextMarkup()\n markup.setRawText(sentence_text)\n markup.cleanText()\n # apply targets and modifiers\n markup.markItems(self.targets, mode=\"target\")\n markup.markItems(self.modifiers, mode=\"modifier\")\n # address scope of modifiers to targets, remove inactive modifiers and self-modifying relationships\n markup.pruneMarks()\n markup.applyModifiers()\n markup.pruneSelfModifyingRelationships()\n markup.dropInactiveModifiers()\n\n marked_targets = markup.getMarkedTargets()\n for marked_target in marked_targets:\n modifiers = markup.getModifiers(marked_target)\n if not modifiers:\n span = (sentence.begin+marked_target.getSpan()[0],sentence.begin+marked_target.getSpan()[1])\n if self.mode == 'combined':\n annot = (doc[0], marked_target.getPhrase(), span[0], span[1], marked_target.getCategory()[0]+'_unspecified', marked_target.getCode())\n elif self.mode == 'separate':\n annot = (doc[0], marked_target.getPhrase(), span[0], span[1], marked_target.getCategory()[0], 'unspecified', marked_target.getCode())\n if annot not in doc_annots:\n doc_annots.append(annot)\n else:\n for modifier in modifiers:\n if marked_target.getSpan()[0] < modifier.getSpan()[0]:\n span = (sentence.begin+marked_target.getSpan()[0],sentence.begin+modifier.getSpan()[1])\n else:\n span = (sentence.begin+modifier.getSpan()[0],sentence.begin+marked_target.getSpan()[1])\n if self.mode == 'combined':\n annot = (doc[0], doc[1][span[0]:span[1]], span[0], span[1], marked_target.getCategory()[0]+'_'+modifier.getCategory()[0], marked_target.getCode())\n elif self.mode == 'separate':\n annot = (doc[0], doc[1][span[0]:span[1]], span[0], span[1], marked_target.getCategory()[0], modifier.getCategory()[0], marked_target.getCode())\n if annot not in doc_annots:\n doc_annots.append(annot)\n\n #context_doc.addMarkup(markup)\n\n return doc_annots","def get_sentence_window(self, pattern, sentences, constraints):\n split_pattern = pattern.split()\n\n if len(split_pattern) > 1:\n textsnippets = self.__get_sentence_window_more_words(split_pattern, sentences, constraints)\n else:\n textsnippets = self.__get_sentence_window_one_word(pattern, sentences, constraints)\n print(textsnippets)\n return textsnippets","def prepocess_pos_tagged_texts(tweet_tokens):\n return [TextPreprocessor.additional_text_preprocessing_with_pos(json.loads(t)) for t in tweet_tokens]","def sentence(self) -> str:\n id_word = {nodeattr['position']-1: nodeattr['form']\n for nodeid, nodeattr in self.syntax_nodes.items()}\n return ' '.join([id_word[i] for i in range(max(list(id_word.keys()))+1)])","def abstract2sents(abstract):\n\tcur = 0\n\tsents = []\n\twhile True:\n\t\ttry:\n\t\t\tstart_p = abstract.index(SENTENCE_START, cur)\n\t\t\tend_p = abstract.index(SENTENCE_END, start_p + 1)\n\t\t\tcur = end_p + len(SENTENCE_END)\n\t\t\tsents.append(abstract[start_p + len(SENTENCE_START):end_p].strip())\n\t\texcept ValueError as e: # no more sentences\n\t\t\treturn sents","def parser(sent_list): #input: list of sentences","def process_doc(nlp, matcher, text, split_sents=False):\n doc = nlp(text)\n sent_ids = [(sent.start, sent.end) for sent in doc.sents]\n matches = matcher(doc)\n # identify matches over the same span (MWE matches)\n\n outputs = []\n insertion_idxs = []\n if split_sents:\n segment_ids = sent_ids\n else:\n segment_ids = [(sent_ids[0][0], sent_ids[-1][1])]\n\n for start, end in segment_ids:\n sent_outputs = []\n sent_insertion_idxs = []\n # get sentence clues\n sent_cues = set([elm._.clue for elm in doc[start:end] if elm._.clue!= None])\n for sent_cue in sent_cues:\n per_match_outputs = []\n per_match_insertion_idxs = []\n for i, elm in enumerate(doc[start:end]):\n if elm._.clue == sent_cue:\n per_match_outputs.append('[CUE]')\n per_match_insertion_idxs.append(i)\n per_match_outputs.append(elm.text)\n sent_outputs.append(per_match_outputs)\n sent_insertion_idxs.append(per_match_insertion_idxs)\n outputs.append(sent_outputs)\n insertion_idxs.append(sent_insertion_idxs)\n return outputs, insertion_idxs","def translate_sentence_and_get_lexicon_sentiment(self, sentence):\n #Translate word\n translated_sentence = self.translater.translate(sentence)\n translated_words = tokenizer(translated_sentence)\n sentiments = []\n for word in translated_words:\n sentiment = self.sentiment_lexicon.get_values(word)\n if sentiment!=None:\n sentiments.append(sentiment)\n return sentiments","def make_sentences(self):\n\n if self.document == None:\n return\n\n sent = sent_tokenize(self.document) # contains raw sentences\n\n\n # Create parameters for NER and Dependency Parsing a\n # and pass it to the sentence objcet\n\n # set config file\n config = CP.RawConfigParser()\n config = config\n config.read('config.py')\n\n # Server for dependency parsing\n\n server = ServerProxy(JsonRpc20(),TransportTcpIp(addr=(\"127.0.0.1\", 8080), timeout=200.0))\n\n # Parameters for Named entitye recognition\n\n # get the classifier and tagger location from config file\n tagger = config.get('NER','tagger') # gets the path of the stanford tagger\n classifier = config.get('NER','classifier') # gets the path of the stanford classifier\n st = StanfordNERTagger(classifier,tagger)\n for i in range(len(sent)):\n s = Sentence(sent[i],i,server, st, 'test')\n self.sentences.append(s)","def get_positions(self):\n return self.positions","def make_sentences(comment):\n sentences = [sent for sent in split_single(comment)]\n return sentences","def pos_tags(self):\n \n msg(\"Getting POS tag list...\")\n tags = []\n \n # loop through sentences\n for sent in self.tagged_sents:\n \n # loop through tagged words\n for (word, pos) in sent:\n \n # add tag if it's not already in list\n if pos not in tags:\n tags.append(pos)\n\n msg(\"done\\n\")\n \n return tags","def abstract2sents(abstract):\n cur = 0\n sents = []\n while True:\n try:\n start_p = abstract.index(SENTENCE_START, cur)\n end_p = abstract.index(SENTENCE_END, start_p + 1)\n cur = end_p + len(SENTENCE_END)\n sents.append(abstract[start_p+len(SENTENCE_START):end_p])\n except ValueError as e: # no more sentences\n return sents","def getSentenceInfo(sentence):\n\tpass","def _mark_entities(self, c, markers):\n sent = c.get_parent().words\n positions = [\n [c[i].get_word_start(), c[i].get_word_end()]\n for i in range(self.cardinality)\n ]\n seq = mark_entities(sent, positions, markers=markers, style=\"insert\")\n return [w for w in seq if w.strip()]","def extractTokensToPredict(data_path):\n \n sentences = []\n ids = []\n \n for event, element in etree.iterparse(data_path, tag=\"sentence\"):\n current_sentence = []\n current_ids = []\n if event == 'end':\n #For every child of the sentence tag\n for child in element:\n #Get the lemma of the token\n lemma = child.attrib['lemma']\n if ''' in lemma:\n #If it is present, substitute it\n lemma = re.sub(r'(')', '\\'', lemma)\n #Check also for '&apos = \"\"\n if '\\'\\'' in word:\n lemma = re.sub(r'(\\'\\')', '\\'', lemma)\n if child.tag == 'instance':\n current_ids.append(child.attrib['id'])\n else:\n current_ids.append('0')\n current_sentence.append(lemma)\n if current_sentence and current_ids:\n sentences.append(current_sentence)\n ids.append(current_ids)\n #Clear to save memory\n element.clear()\n \n print(\"File completely parsed. Total number of sentences %i \\n\" %len(sentences))\n print()\n return sentences, ids","def normalized_pos_tags(self):\n pos_list = []\n for pos in self.pos_tags:\n pos_list.extend([i for i in re.split('[:;]', pos) if i != ''])\n return pos_list","def additional_text_preprocessing_with_pos(pos_dict):\n\n tags_to_lemmatize = ['a', 'n', 'v', 'r']\n\n pos_dict = TextPreprocessor.find_named_entities(pos_dict)\n if pos_dict is None:\n return None, None\n prepro = list()\n contains_spelling_mistake = False\n for t in pos_dict:\n token = t['token']\n tag = t['tag'].lower()\n if token not in TextPreprocessor.PUNCTUATION and tag != \",\":\n\n token = TextPreprocessor.replace_user_mentions(token)\n token = TextPreprocessor.replace_urls(token)\n replaced = [token]\n for i in replaced:\n\n i = TextPreprocessor.replace_all_punctuation(i)\n if i.lower() not in TextPreprocessor.STOPWORDS and i != 'URL' and i!= 'USERMENTION':\n if i != \"\" and not re.match('\\B#\\w*[a-zA-Z]+\\w*', i):\n before = i\n i = TextPreprocessor.SPELL_CHECKER.correct(i, tag)\n if i != before:\n contains_spelling_mistake = True\n if tag in tags_to_lemmatize:\n i = TextPreprocessor.lemmatize(i, tag)\n i = TextPreprocessor.stem(i, tag)\n # check again, since stemming, lemmatization or spelling correction can produce stopwords\n # if i.lower() not in TextPreprocessor.STOPWORDS:\n if i != 'URL' and i!= 'USERMENTION' and i!='':\n i = i.lower()\n if re.match(\".*[a-zA-Z]'\", i):\n i = i[:-1]\n prepro.append(i)\n return prepro, contains_spelling_mistake","def named_entity_list_spacy(self) -> List:\n #choose language\n if self.lang == 'en':\n lang_for_spacy = 'en_core_web_sm'\n elif self.lang == 'de':\n lang_for_spacy = 'de_core_news_sm'\n elif self.lang == 'fr':\n lang_for_spacy = 'fr_core_news_md'\n #load file and convert input string\n nlp = spacy.load(lang_for_spacy)\n doc = nlp(self.sent)\n #build the output list\n for ent in doc.ents:\n #avoid that 'Abstract', 'Title' and 'Author' are counted between NEs\n if ent.text != 'Abstract' and ent.text != 'Title' and ent.text != 'Author':\n self.named_entity_list.append((ent.text, ent.start_char, \n ent.end_char, ent.label_))\n return self.named_entity_list","def sentence_split(self, text):\n return split_into_sentences(text)","def get_text_positions(self, node, padded):\n # type: (AstNode, bool) -> Tuple[Tuple[int, int], Tuple[int, int]]\n if getattr(node, \"_broken_positions\", None):\n # This node was marked in util.annotate_fstring_nodes as having untrustworthy lineno/col_offset.\n return (1, 0), (1, 0)\n\n if supports_tokenless(node):\n return self._get_text_positions_tokenless(node, padded)\n\n return self.asttokens.get_text_positions(node, padded)","def splitInSentence(self,text):\n return self._support.splitInPhrase(text)","def split_words(self, position=None):\r\n if position is None:\r\n position = self.offset\r\n text = self.source_code[:position]\r\n return re.findall(self.id_regex, text)","def transform_sentence(sequence, vocab_processor):\n return next(vocab_processor.transform([sequence])).tolist()","def get_sentiments(self, d):\n\t\twords = tuple(d.split())\n\t\ttagged = nltk.pos_tag(words)\n\n\t\tnouns = 0\n\t\tadjectives = 0\n\t\tverbs = 0\n\t\tadverbs = 0\n\n\t\tfor word, pos_tag in tagged:\n\n\t\t\tif pos_tag.startswith(\"NN\"):\n\t\t\t\tnouns += 1\n\n\t\t\tif pos_tag.startswith(\"JJ\"):\n\t\t\t\tadjectives += 1\n\n\t\t\tif pos_tag.startswith(\"VB\"):\n\t\t\t\tverbs += 1\n\n\t\t\tif pos_tag.startswith(\"RB\"):\n\t\t\t\tadverbs += 1\n\t\t# adding one to consider empty document\n\t\tl = len(words) + 1\n\t\treturn [nouns / l, adjectives / l, verbs / l, adverbs / l]","def __call__(self, text):\r\n if self.use_pos_tagging:\r\n return [self.wnl.lemmatize(t, self.pos(t)) for t in word_tokenize(self.clean(text))]\r\n else:\r\n return [self.wnl.lemmatize(t) for t in word_tokenize(self.clean(text))]","def getpos(self):\n return self.pos.cartesianas()","def sent_tokenize(line):\n sentences = []\n cur_pos = 0\n if not re.search(r\"\\.\", line):\n return [line]\n\n for match in re.finditer(r\"\\.\", line):\n _pos = match.start()\n end_pos = match.end()\n if is_end_of_sentence(_pos, line):\n tmpsent = line[cur_pos:end_pos]\n tmpsent = tmpsent.strip()\n if is_valid_xml(tmpsent):\n cur_pos = end_pos\n sentences.append(tmpsent)\n\n if len(sentences) == 0:\n sentences.append(line)\n elif cur_pos < len(line)-1:\n sentences.append(line[cur_pos+1:])\n return sentences","def batch_sentences(self, sentences, bos, eos, indices=None):\n batch_size = len(sentences)\n slen = max([len(s) for s in sentences])\n if bos:\n slen += 1\n if eos:\n slen += 1\n \n def pad_sent(s, max_len, bos, eos):\n ret = s\n if bos:\n ret = [self.bos_index] + ret\n if eos:\n ret = ret + [self.eos_index]\n ret = ret + [self.pad_index for _ in range(max_len - len(ret))]\n return ret\n\n sent_tensor = [pad_sent(s, slen, bos, eos) for s in sentences]\n sent_tensor = torch.from_numpy(np.array(sent_tensor)).long()\n \n if indices is None:\n return sent_tensor\n else:\n return sent_tensor, indices","def position(self):\n return self._pos.to_list()","def paragraph(self, text):\n return [text]","def current_buffer_texts(self):\n return self.current_buffer.text","def get_closest_sentence(self, query_emb_init, paper_id, text, topk=10):\n csv_filepath = os.path.join(\"selected\", paper_id + '.csv')\n df_article = pd.read_csv(csv_filepath, index_col=0)\n \n normalize = lambda x: x/np.linalg.norm(x, axis = 1, keepdims = True)\n query_emb = normalize(query_emb_init)\n val_norm = normalize(df_article.values)\n score_arr = safe_sparse_dot(val_norm, query_emb.T)\n\n # sort in decreasing order\n similar_paragr = sorted(zip(range(len(score_arr)), score_arr), \n key = lambda x: x[1], reverse=True)[:min(topk, len(score_arr))] \n\n # obtain the text of this closest paragraphs\n field_text = text.split(\"\\n\")\n similar_paragr = [(field_text[idx], score) for (idx, score) in similar_paragr]\n\n return similar_paragr","def get_nerspos(tokens, ners):\n pos_list = list()\n for ner in ners:\n pos = get_nerpos(tokens, ner)\n pos_list.append(pos)\n\n return pos_list","def pos_tag(self, sentence):\n tags = []\n tokens = sentence.split(\" \")\n for i in range(len(tokens)):\n tags.append('')\n for i in range (len(tokens)):\n feat = []\n feat.append(self.features(tokens,tags,i))\n tag_predicted = self.postagger.predict(feat)[0]\n tags[i] = tag_predicted\n return tags","def simple(self, sentence):\r\n output_list = [\r\n self.position_list[\r\n np.argmax(\r\n [\r\n (math.log(self.emission_probability[pos][sentence[i]]))\r\n + (math.log(self.posterior_probability[pos]))\r\n if sentence[i] in self.emission_probability[pos]\r\n else (math.log(1 / float(10 ** 10)))\r\n + (math.log(self.posterior_probability[pos]))\r\n for pos in self.position_list\r\n ]\r\n )\r\n ]\r\n for i in range(len(sentence))\r\n ]\r\n return output_list","def get_texts(self) -> List[str]:\n return self.texts","def parse(self, text):\n assert isinstance(text, str)\n if text.strip() == '':\n return [], []\n\n output = self._annotate(text, properties={\n \"annotators\": \"tokenize,ssplit,pos\",\n \"coref.md.type\": \"dep\",\n \"coref.mode\": \"statistical\"\n })\n\n words = []\n postags = []\n\n for sentence in output['sentences']:\n for token in sentence['tokens']:\n word = token['word']\n pos = token['pos']\n word = re.sub(r'\\s', '', word)\n words.append(word)\n postags.append(pos)\n return words, postags","def __get_sentence_window_one_word(self, pattern, sentences, constraints):\n textsnippets = []\n for ind, sent in enumerate(sentences):\n tokens = self.tokenizer.tokenize(sent)\n for i, token in enumerate(tokens):\n if check_pattern(pattern, token):\n if constraints is not None:\n self.__check_constraints(constraints, (i, i), ind, pattern, sent, sentences, textsnippets, tokens)\n else:\n self.__get_sentence_window_help(i, ind, sentences, textsnippets)\n return textsnippets","def get_positions(self):\n\n return np.array([p.position for p in self.particles])"],"string":"[\n \"def sentences(self) -> List[str]:\\n\\t\\treturn [self.text[start:end] for start, end in self.tokenizations]\",\n \"def sentences(self) -> List[str]:\\n\\t\\treturn [self.text[start:end] for start, end in self.tokenizations]\",\n \"def get_sentences(self):\\n return [s for s in self.text.split('\\\\n')]\",\n \"def get_processed_sentences(self):\\n\\n self.__resolve_coreference()\\n self.__tokenize_sentences()\\n\\n return self.tokenized_sentences\",\n \"def sentences(self, tag=False, tag_method=None):\\n self.__set_text_node(self.root_)\\n sentence_nodes = filter(lambda n: n.nodeType == n.ELEMENT_NODE and n.tagName == 's',\\n list(self.text_node.childNodes))\\n sentences = []\\n for s in sentence_nodes:\\n current = []\\n TimeMLDoc.__get_text(s, current, False)\\n #print(current)\\n if not tag:\\n sentences.append(''.join([ c[0] for c in current]))\\n else:\\n sentences.append(tag_method(current))\\n return sentences\",\n \"def get_sentences(self):\\n return self._sentences\",\n \"def ie_preprocess(document):\\n sentences = nltk.sent_tokenize(document) #NLTK default sentence segmenter\\n #print sentences # sentences are segmented\\n sentences = [nltk.word_tokenize(sent) for sent in sentences] # NLTK word tokenizer \\n #print sentences # sentences are tokenized\\n sentences = [nltk.pos_tag(sent) for sent in sentences] # NLTK POS tagger \\n #print sentences # sentences are POS tagged\\n return sentences\",\n \"def pos(self):\\n if 'pos' not in self.annotators:\\n return None\\n return [t[self.POS] for t in self.data]\",\n \"def pos(self):\\n if 'pos' not in self.annotators:\\n return None\\n return [t[self.POS] for t in self.data]\",\n \"def sentences(self) -> List[str]:\\n\\t\\treturn [sentence for sentence in re.split('(?<=[.!?])', self.text)]\",\n \"def get_sentence(self):\",\n \"def sentence_segment(self, doc, candidate_pos, lower):\\n sentences = []\\n for sent in doc.sents:\\n selected_words = []\\n for token in sent:\\n # Store words only with cadidate POS tag\\n if token.pos_ in candidate_pos and token.is_stop is False:\\n if lower is True:\\n selected_words.append(token.text.lower())\\n else:\\n selected_words.append(token.text)\\n sentences.append(selected_words)\\n return sentences\",\n \"def gather_sentences(self):\\n sentences = Sentence.objects.all()\\n return sentences\",\n \"def sentencing(any_text, nlp):\\n nlp.add_pipe(nlp.create_pipe('sentencizer'))\\n doc = nlp(any_text)\\n sentences = [sent.string.strip() for sent in doc.sents]\\n return sentences\",\n \"def collect_sentences(self):\\n sentences = []\\n for document in self.documents:\\n for sentence_token in document.sentences:\\n sentences.append(sentence_token)\\n return sentences\",\n \"def get_sentences(text):\\n \\n return text.split('.')\",\n \"def getTrueSentences(self, form):\",\n \"def return_augmented_sentences(self) -> list:\\n return self.augmented_sentence_list\",\n \"def gather_sentences(self):\\n if self.company_name:\\n companies = Company.objects.filter(name__contains=self.company_name)\\n dpefs = DPEF.objects.filter(company__in=companies)\\n sentences = Sentence.objects.filter(dpef__in=dpefs).all()\\n else:\\n sentences = Sentence.objects.none() # TODO: can be set to none later when all works\\n return sentences\",\n \"def split_sentences(self, text):\\n assert isinstance(text, str)\\n text = text.replace('\\\\n', '')\\n\\n if text.strip() == '':\\n return []\\n\\n output = self._annotate(text, properties={\\n \\\"annotators\\\": \\\"tokenize,ssplit\\\",\\n \\\"coref.md.type\\\": \\\"dep\\\",\\n \\\"coref.mode\\\": \\\"statistical\\\"\\n })\\n\\n sentences = []\\n for sentence in output['sentences']:\\n num_token = len(sentence['tokens'])\\n start_index = sentence['tokens'][0]['characterOffsetBegin']\\n end_index = sentence['tokens'][num_token - 1]['characterOffsetEnd']\\n sentences.append(text[start_index:end_index])\\n return sentences\",\n \"def calculate_texts(self) -> None:\\n texts = []\\n for text in self.texts:\\n paragraphs = list(filter(lambda x: x != \\\"\\\", text.split(\\\"\\\\n\\\\n\\\")))\\n for paragraph in paragraphs:\\n text = paragraph.replace(\\\"\\\\n\\\", \\\" \\\").strip()\\n if len(text) > self.split_threshold_min:\\n text_sentences = nlp(text)\\n sentences = []\\n for sentence in text_sentences.sents:\\n current = sentence.text\\n sentences.append(current.strip())\\n texts.extend(sentences)\\n else:\\n texts.append(text)\\n self.texts = list(set(texts))\",\n \"def sentences(summary, nlp):\\n text = remove_spurious_words(text_of(summary))\\n all_sentence = [sentence for sentence in re.split(\\\"[。，？！\\\\n]\\\", text) if sentence]\\n all_sentence = [re.sub('[ ]+', ' ', sentence.encode('gb2312', 'ignore').decode('gb2312')).strip() for sentence in\\n all_sentence]\\n return [nlp.ner(sentence) for sentence in all_sentence if sentence]\",\n \"def get_sentences(self, text_to_parse):\\r\\n simple_sentence, *rest = text_to_parse.split('(', 1)\\r\\n kana_sentence, *rest = rest[0].split(')', 1)\\r\\n return simple_sentence.strip(), kana_sentence.strip().replace(' ', '')\",\n \"def get_sentences(text, nlp):\\n\\n # get sentences from text\\n sentences = [sentence for sentence in\\n text.replace('!', '.').replace('?', '.').split('.')]\\n\\n processed_sentences = [convert_to_string(remove_junk(tokenize_text(sentence, nlp))) for sentence in\\n text.replace('!', '.').replace('?', '.').split('.')]\\n\\n # convert the sentences into a list of document vectors\\n sentence_vector_list = [nlp(sentence).vector for sentence in processed_sentences]\\n\\n return sentences, sentence_vector_list\",\n \"def getPosition(self):\\n\\t\\txxx1 = self.stokes()\\n\\t\\txxx2 = self.thp()\\n\\t\\txxx3 = self.tthp()\\n\\t\\treturn [xxx1, xxx2, xxx3]\",\n \"def get_sentence_sentiments(comment):\\n sentence_score_list = []\\n\\n split_comment = make_sentences(comment)\\n for sentence in split_comment:\\n if sentence == ' ' or sentence == '' or sentence == ' ':\\n continue\\n text = flair.data.Sentence(sentence)\\n flair_sentiment.predict(text)\\n\\n value = text.labels[0].to_dict()['value']\\n if value == 'POSITIVE':\\n result = text.to_dict()['labels'][0]['confidence']\\n else:\\n result = -(text.to_dict()['labels'][0]['confidence'])\\n\\n sentence_score = round(result, 6)\\n sentence_score_list.append(sentence_score)\\n\\n return sentence_score_list\",\n \"def extract_sentences_from_text(self, text_data):\\n pass\",\n \"def getConstantSentenceForms(self):\",\n \"def split(text):\\n doc = nlp(text)\\n sentences = [x.text_with_ws for x in doc.sents]\\n return sentences\",\n \"def get_sentences(self):\\n for tree in self.tree_generator():\\n yield tree[\\\"title\\\"] + \\\" \\\" + tree[\\\"selftext\\\"]\\n for _, comment in tree[\\\"comments\\\"].items():\\n yield comment[\\\"body\\\"]\",\n \"def get_pos(sent):\\n tokens = get_tokens(sent)\\n return [tag for (token, tag) in nltk.pos_tag(tokens)]\",\n \"def current_document_text(self):\\n return self.current_document.lines\",\n \"def pos_tag_sents(\\n sentences: List[List[str]], engine: str = \\\"perceptron\\\", corpus: str = \\\"orchid\\\"\\n) -> List[List[Tuple[str, str]]]:\\n if not sentences:\\n return []\\n\\n return [pos_tag(sent, engine=engine, corpus=corpus) for sent in sentences]\",\n \"def sentence_segment(self, doc, candidate_pos, lower,bigrams,trigrams):\\n sentences = []\\n for sent in doc.sents:\\n selected_words = []\\n bigram_words=[]\\n for token in sent:\\n bigram_words.append(token.text)\\n # Store words only with cadidate POS tag\\n if token.pos_ in candidate_pos and token.is_stop is False:\\n if lower is True:\\n selected_words.append(token.text.lower())\\n else:\\n selected_words.append(token.text)\\n if bigrams==True:\\n for i in range(len(sent)-1):\\n if sent[i].pos_ in candidate_pos and sent[i].is_stop is False and sent[i+1].pos_ in candidate_pos and sent[i+1].is_stop is False:\\n if lower is True:\\n selected_words.append(sent[i].text.lower())\\n else:\\n selected_words.append(str(sent[i].text+\\\" \\\"+sent[i+1].text))\\n if trigrams==True:\\n for i in range(len(sent)-2):\\n if sent[i].pos_ in candidate_pos and sent[i].is_stop is False and sent[i+1].pos_ in candidate_pos and sent[i+1].is_stop is False and sent[i+2].pos_ in candidate_pos and sent[i+2].is_stop is False:\\n if lower is True:\\n selected_words.append(sent[i].text.lower())\\n else:\\n selected_words.append(str(sent[i].text+\\\" \\\"+sent[i+1].text+\\\" \\\"+sent[i+2].text))\\n sentences.append(selected_words)\\n return sentences\",\n \"def sentences(a, b):\\n\\n # TODO\\n return []\",\n \"def full_pos_tag(self, sentence):\\n tags = self.pos_tag(sentence)\\n for i in range(len( tags)):\\n tags[i] = self.get_complete_tag(tags[i])\\n return tags\",\n \"def _partsofspeech(self):\\n \\n l = len(self._words)\\n for i in range(l):\\n self._words[i]['pos'] = pos_tag( [ self._words[i]['word'] ])[0][1]\",\n \"def get_sentences_list(text: str, model_type: str) -> t.List[str]:\\n sentences = []\\n sent_offsets = []\\n stok = SentenceTokenizer.from_type(model_type)\\n if isinstance(text, list):\\n sentences, sent_offsets = list(zip(*map(stok.tokenize, text)))\\n elif isinstance(text, str):\\n sentences, sent_offsets = stok.tokenize(text)\\n return sentences, sent_offsets\",\n \"def test_get_sentence_sentiments():\\n long_comment = [\\\"This was a really sucky movie. I will probably never go see this movie ever again. I am going to \\\"\\n \\\"tell my whole family never to watch this movie. I very much enjoyed the special cameo in it \\\"\\n \\\"though. I loved the plot line.\\\"]\\n\\n sentence_score_list = get_sentence_sentiments(long_comment[0])\\n print(long_comment[0])\\n print('per sentence sentiment:', sentence_score_list)\\n print()\",\n \"def get_sentences(paragraph):\\n punctuation = re.compile(r'[\\\\.!?]')\\n sentences = [sentence.strip() for sentence in punctuation.split(paragraph)]\\n sentences = filter(lambda x: x, sentences)\\n return sentences\",\n \"def _sentence(self, node, offset_mngr):\\n text = self._text(node)\\n offset = offset_mngr.update(node, text)\\n return text, offset\",\n \"def getSentences(self, ser):\\n\\n tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\\n sentencesSer = ser.apply(tokenizer.tokenize)\\n return sentencesSer\",\n \"def parts_of_speech_tags(self, tokenized_doc):\\n return [(token.text, token.pos_) for token in self.parser(\\n tokenized_doc)]\",\n \"def sentences(self):\\n return re.compile(r'[.!?;:,\\\\t\\\\(\\\\)\\\\\\\"\\\\']|\\\\s-\\\\s').split(self.text)\",\n \"def get(self, pos):\\n return self.sentence[pos]\",\n \"def fetch_sentences(self, content):\\n score_increments, sentences = ProximityScorePlugin.run(self.sanitised_question_split,\\n self.sanitised_answers,\\n content)\\n for key in sentences:\\n self.sentences[key].append(sentences[key])\\n # print(self.sentences)\\n # get key sentences of this round\\n key_sentences = ProximityScorePlugin.get_key_sentences(self.sentences, self.sanitised_question_split)\\n # append them to self.key_sentences\\n for key in key_sentences:\\n if key_sentences[key] not in self.key_sentences[key]:\\n self.key_sentences[key].append(key_sentences[key])\\n self.finalise_key_sentences() # added to reduce to one sentence per answer\\n # print(f\\\"Best Sentences: {self.key_sentences}\\\")\",\n \"def process_spacy(self):\\n\\n def prevent_sentence_boundary_detection(doc):\\n for token in doc:\\n # This will entirely disable spaCy's sentence detection\\n token.is_sent_start = False\\n return doc\\n\\n def process_sentence(sen_tokens):\\n doc = spacy.tokens.Doc(nlp.vocab, words=sen_tokens)\\n tagger(doc)\\n prevent_sbd(doc)\\n ner(doc)\\n parser(doc)\\n return doc\\n\\n # setup spacy nlp pipeline\\n nlp = spacy.load(\\\"en_core_web_lg\\\")\\n parser = nlp.get_pipe(\\\"parser\\\")\\n nlp.add_pipe(\\n prevent_sentence_boundary_detection, name=\\\"prevent-sbd\\\", before=\\\"parser\\\"\\n )\\n\\n tagger = nlp.get_pipe(\\\"tagger\\\")\\n prevent_sbd = nlp.get_pipe(\\\"prevent-sbd\\\")\\n parser = nlp.get_pipe(\\\"parser\\\")\\n ner = nlp.get_pipe(\\\"ner\\\")\\n\\n for doc in self.annotation_documents:\\n doc.sentences_processed = []\\n for sen in doc.sentences:\\n sen_tokens = [t.text for t in sen.tokens]\\n sen_proc = process_sentence(sen_tokens)\\n # add processed sentence to doc\\n doc.sentences_processed.append(sen_proc)\\n\\n print(f\\\"Processed with Spacy: {doc.document_id}\\\")\",\n \"def gather_sentences(self):\\n companies = filter_company_from_form(self)\\n dpefs = DPEF.objects\\n if self.cleaned_data[\\\"start_period\\\"] is not None:\\n dpefs = dpefs.filter(year__gte=self.cleaned_data[\\\"start_period\\\"])\\n if self.cleaned_data[\\\"end_period\\\"] is not None:\\n dpefs = dpefs.filter(year__lte=self.cleaned_data[\\\"end_period\\\"])\\n dpefs = dpefs.filter(company__in=companies)\\n sentences = Sentence.objects.filter(dpef__in=dpefs).all()\\n return sentences\",\n \"def sentences(self, text):\\n if not self.__isValidInput(text):\\n return [Sentence(text, Sentence.NONE)]\\n\\n uniText = unicode_str(text)\\n result = []\\n textLen = len(uniText)\\n sentenceLen = c_size_t()\\n position = 0\\n while textLen > 0:\\n sentenceType = self.__lib.voikkoNextSentenceStartUcs4(\\n self.__handle,\\n uniText[position:],\\n textLen,\\n byref(sentenceLen),\\n )\\n sentenceText = uniText[position:position + sentenceLen.value]\\n result.append(Sentence(sentenceText, sentenceType))\\n if sentenceType == Sentence.NONE:\\n break\\n position = position + sentenceLen.value\\n textLen = textLen - sentenceLen.value\\n return result\",\n \"def sentences(self, text):\\n return re.findall(r'([A-Z][^\\\\.!?]*[\\\\.!?])', text)\",\n \"def get_enron_sentences(self):\\n helper._print_subheader('Reading ' + directories.ENRON_TRAIN_SENTENCES_TXT_PATH + '...')\\n with open(directories.ENRON_TRAIN_SENTENCES_TXT_PATH, 'r', encoding='utf-8') as txt_file:\\n for index, line in enumerate(txt_file):\\n if index % 1000000 == 0 and index != 0:\\n helper._print(f'{index} sentences read')\\n break\\n preproccesed_line = simple_preprocess(line)\\n if preproccesed_line != []:\\n yield preproccesed_line\\n helper._print(f'{index} sentences read')\\n helper._print_subheader('Done reading Enron email data!')\",\n \"def sentence_parse(list_of_posts): \\n for parsedPosts in nlp.pipe(line_review(list_of_posts)):\\n for sent in parsedPosts.sents:\\n yield str(sent)\",\n \"def sentences_from_string(this_class, text):\\n # Note that the native method below leaks. We work around this\\n # by acquiring its pointer in __init__\\n sentReps = parser.sentRepsFromString(text)\\n return list(map(this_class, sentReps))\",\n \"def getSentence(self):\\n return self.tokens[0].sentence\",\n \"def get_context(words, position, window_size=2):\\n start = max(0, position - window_size)\\n stop = min(position + window_size, len(words))\\n context_words = words[start:position] + words[position + 1:stop + 1]\\n\\n return list(context_words)\",\n \"def process_token_sentence(text):\\n\\n sentences = nltk.sent_tokenize(text)\\n tokenized_sentences = [nltk.word_tokenize(sentence) for sentence in sentences]\\n tagged_sentences = [nltk.pos_tag(sentence) for sentence in tokenized_sentences]\\n sentences = nltk.ne_chunk_sents(tagged_sentences, binary=True)\\n\\n return sentences\",\n \"def extract_sentences(paper_path, para_yes):\\n\\n f = open(paper_path, 'rb')\\n doc = Document.from_file(f, readers=[HtmlReader()])\\n\\n sen_yes_arr = list()\\n sen_no_arr = list()\\n\\n elem_all = np.arange(0,len(doc))\\n para_no = np.delete(elem_all, para_yes)\\n\\n for i in para_no:\\n if type(doc.elements[i]) == chemdataextractor.doc.text.Paragraph:\\n for sentence in doc.elements[i]:\\n sen_no_arr.append(sentence)\\n\\n for i in para_yes:\\n if type(doc.elements[i]) == chemdataextractor.doc.text.Paragraph:\\n for sentence in doc.elements[i]:\\n sen_yes_arr.append(sentence)\\n\\n\\n return sen_yes_arr, sen_no_arr\",\n \"def process_text(text):\\n return [token.text for token in nlp(text) if not token.is_stop]\",\n \"def positions(self):\\n return self.preorder()\",\n \"def getTokens(self):\\n list = []\\n for i in range(self.startIdx, self.endIdx + 1):\\n token = self.sentence[i]\\n list.append(token)\\n return list\",\n \"def process(self, doc):\\n # don't try to process null notes\\n if not doc[1]:\\n if self.verbose:\\n print(\\\"Error segmenting doc\\\",doc[0])\\n return []\\n # odd notes may throw an error. Just continue rather than stopping the entire process\\n try:\\n sentences = self.sentence_tokenizer.segToSentenceSpans(doc[1])\\n except KeyError:\\n if self.verbose:\\n print(\\\"Error segmenting doc\\\",doc[0])\\n return []\\n\\n #context_doc = pyConTextGraph.ConTextDocument() # ConTextDoc not needed for simple usage\\n\\n doc_annots = list()\\n\\n for sentence in sentences:\\n # run sentence tokenizer on input text, return the spans\\n sentence_text = doc[1][sentence.begin:sentence.end]\\n # process every sentence by adding markup\\n markup = pyConTextGraph.ConTextMarkup()\\n markup.setRawText(sentence_text)\\n markup.cleanText()\\n # apply targets and modifiers\\n markup.markItems(self.targets, mode=\\\"target\\\")\\n markup.markItems(self.modifiers, mode=\\\"modifier\\\")\\n # address scope of modifiers to targets, remove inactive modifiers and self-modifying relationships\\n markup.pruneMarks()\\n markup.applyModifiers()\\n markup.pruneSelfModifyingRelationships()\\n markup.dropInactiveModifiers()\\n\\n marked_targets = markup.getMarkedTargets()\\n for marked_target in marked_targets:\\n modifiers = markup.getModifiers(marked_target)\\n if not modifiers:\\n span = (sentence.begin+marked_target.getSpan()[0],sentence.begin+marked_target.getSpan()[1])\\n if self.mode == 'combined':\\n annot = (doc[0], marked_target.getPhrase(), span[0], span[1], marked_target.getCategory()[0]+'_unspecified', marked_target.getCode())\\n elif self.mode == 'separate':\\n annot = (doc[0], marked_target.getPhrase(), span[0], span[1], marked_target.getCategory()[0], 'unspecified', marked_target.getCode())\\n if annot not in doc_annots:\\n doc_annots.append(annot)\\n else:\\n for modifier in modifiers:\\n if marked_target.getSpan()[0] < modifier.getSpan()[0]:\\n span = (sentence.begin+marked_target.getSpan()[0],sentence.begin+modifier.getSpan()[1])\\n else:\\n span = (sentence.begin+modifier.getSpan()[0],sentence.begin+marked_target.getSpan()[1])\\n if self.mode == 'combined':\\n annot = (doc[0], doc[1][span[0]:span[1]], span[0], span[1], marked_target.getCategory()[0]+'_'+modifier.getCategory()[0], marked_target.getCode())\\n elif self.mode == 'separate':\\n annot = (doc[0], doc[1][span[0]:span[1]], span[0], span[1], marked_target.getCategory()[0], modifier.getCategory()[0], marked_target.getCode())\\n if annot not in doc_annots:\\n doc_annots.append(annot)\\n\\n #context_doc.addMarkup(markup)\\n\\n return doc_annots\",\n \"def get_sentence_window(self, pattern, sentences, constraints):\\n split_pattern = pattern.split()\\n\\n if len(split_pattern) > 1:\\n textsnippets = self.__get_sentence_window_more_words(split_pattern, sentences, constraints)\\n else:\\n textsnippets = self.__get_sentence_window_one_word(pattern, sentences, constraints)\\n print(textsnippets)\\n return textsnippets\",\n \"def prepocess_pos_tagged_texts(tweet_tokens):\\n return [TextPreprocessor.additional_text_preprocessing_with_pos(json.loads(t)) for t in tweet_tokens]\",\n \"def sentence(self) -> str:\\n id_word = {nodeattr['position']-1: nodeattr['form']\\n for nodeid, nodeattr in self.syntax_nodes.items()}\\n return ' '.join([id_word[i] for i in range(max(list(id_word.keys()))+1)])\",\n \"def abstract2sents(abstract):\\n\\tcur = 0\\n\\tsents = []\\n\\twhile True:\\n\\t\\ttry:\\n\\t\\t\\tstart_p = abstract.index(SENTENCE_START, cur)\\n\\t\\t\\tend_p = abstract.index(SENTENCE_END, start_p + 1)\\n\\t\\t\\tcur = end_p + len(SENTENCE_END)\\n\\t\\t\\tsents.append(abstract[start_p + len(SENTENCE_START):end_p].strip())\\n\\t\\texcept ValueError as e: # no more sentences\\n\\t\\t\\treturn sents\",\n \"def parser(sent_list): #input: list of sentences\",\n \"def process_doc(nlp, matcher, text, split_sents=False):\\n doc = nlp(text)\\n sent_ids = [(sent.start, sent.end) for sent in doc.sents]\\n matches = matcher(doc)\\n # identify matches over the same span (MWE matches)\\n\\n outputs = []\\n insertion_idxs = []\\n if split_sents:\\n segment_ids = sent_ids\\n else:\\n segment_ids = [(sent_ids[0][0], sent_ids[-1][1])]\\n\\n for start, end in segment_ids:\\n sent_outputs = []\\n sent_insertion_idxs = []\\n # get sentence clues\\n sent_cues = set([elm._.clue for elm in doc[start:end] if elm._.clue!= None])\\n for sent_cue in sent_cues:\\n per_match_outputs = []\\n per_match_insertion_idxs = []\\n for i, elm in enumerate(doc[start:end]):\\n if elm._.clue == sent_cue:\\n per_match_outputs.append('[CUE]')\\n per_match_insertion_idxs.append(i)\\n per_match_outputs.append(elm.text)\\n sent_outputs.append(per_match_outputs)\\n sent_insertion_idxs.append(per_match_insertion_idxs)\\n outputs.append(sent_outputs)\\n insertion_idxs.append(sent_insertion_idxs)\\n return outputs, insertion_idxs\",\n \"def translate_sentence_and_get_lexicon_sentiment(self, sentence):\\n #Translate word\\n translated_sentence = self.translater.translate(sentence)\\n translated_words = tokenizer(translated_sentence)\\n sentiments = []\\n for word in translated_words:\\n sentiment = self.sentiment_lexicon.get_values(word)\\n if sentiment!=None:\\n sentiments.append(sentiment)\\n return sentiments\",\n \"def make_sentences(self):\\n\\n if self.document == None:\\n return\\n\\n sent = sent_tokenize(self.document) # contains raw sentences\\n\\n\\n # Create parameters for NER and Dependency Parsing a\\n # and pass it to the sentence objcet\\n\\n # set config file\\n config = CP.RawConfigParser()\\n config = config\\n config.read('config.py')\\n\\n # Server for dependency parsing\\n\\n server = ServerProxy(JsonRpc20(),TransportTcpIp(addr=(\\\"127.0.0.1\\\", 8080), timeout=200.0))\\n\\n # Parameters for Named entitye recognition\\n\\n # get the classifier and tagger location from config file\\n tagger = config.get('NER','tagger') # gets the path of the stanford tagger\\n classifier = config.get('NER','classifier') # gets the path of the stanford classifier\\n st = StanfordNERTagger(classifier,tagger)\\n for i in range(len(sent)):\\n s = Sentence(sent[i],i,server, st, 'test')\\n self.sentences.append(s)\",\n \"def get_positions(self):\\n return self.positions\",\n \"def make_sentences(comment):\\n sentences = [sent for sent in split_single(comment)]\\n return sentences\",\n \"def pos_tags(self):\\n \\n msg(\\\"Getting POS tag list...\\\")\\n tags = []\\n \\n # loop through sentences\\n for sent in self.tagged_sents:\\n \\n # loop through tagged words\\n for (word, pos) in sent:\\n \\n # add tag if it's not already in list\\n if pos not in tags:\\n tags.append(pos)\\n\\n msg(\\\"done\\\\n\\\")\\n \\n return tags\",\n \"def abstract2sents(abstract):\\n cur = 0\\n sents = []\\n while True:\\n try:\\n start_p = abstract.index(SENTENCE_START, cur)\\n end_p = abstract.index(SENTENCE_END, start_p + 1)\\n cur = end_p + len(SENTENCE_END)\\n sents.append(abstract[start_p+len(SENTENCE_START):end_p])\\n except ValueError as e: # no more sentences\\n return sents\",\n \"def getSentenceInfo(sentence):\\n\\tpass\",\n \"def _mark_entities(self, c, markers):\\n sent = c.get_parent().words\\n positions = [\\n [c[i].get_word_start(), c[i].get_word_end()]\\n for i in range(self.cardinality)\\n ]\\n seq = mark_entities(sent, positions, markers=markers, style=\\\"insert\\\")\\n return [w for w in seq if w.strip()]\",\n \"def extractTokensToPredict(data_path):\\n \\n sentences = []\\n ids = []\\n \\n for event, element in etree.iterparse(data_path, tag=\\\"sentence\\\"):\\n current_sentence = []\\n current_ids = []\\n if event == 'end':\\n #For every child of the sentence tag\\n for child in element:\\n #Get the lemma of the token\\n lemma = child.attrib['lemma']\\n if ''' in lemma:\\n #If it is present, substitute it\\n lemma = re.sub(r'(')', '\\\\'', lemma)\\n #Check also for '&apos = \\\"\\\"\\n if '\\\\'\\\\'' in word:\\n lemma = re.sub(r'(\\\\'\\\\')', '\\\\'', lemma)\\n if child.tag == 'instance':\\n current_ids.append(child.attrib['id'])\\n else:\\n current_ids.append('0')\\n current_sentence.append(lemma)\\n if current_sentence and current_ids:\\n sentences.append(current_sentence)\\n ids.append(current_ids)\\n #Clear to save memory\\n element.clear()\\n \\n print(\\\"File completely parsed. Total number of sentences %i \\\\n\\\" %len(sentences))\\n print()\\n return sentences, ids\",\n \"def normalized_pos_tags(self):\\n pos_list = []\\n for pos in self.pos_tags:\\n pos_list.extend([i for i in re.split('[:;]', pos) if i != ''])\\n return pos_list\",\n \"def additional_text_preprocessing_with_pos(pos_dict):\\n\\n tags_to_lemmatize = ['a', 'n', 'v', 'r']\\n\\n pos_dict = TextPreprocessor.find_named_entities(pos_dict)\\n if pos_dict is None:\\n return None, None\\n prepro = list()\\n contains_spelling_mistake = False\\n for t in pos_dict:\\n token = t['token']\\n tag = t['tag'].lower()\\n if token not in TextPreprocessor.PUNCTUATION and tag != \\\",\\\":\\n\\n token = TextPreprocessor.replace_user_mentions(token)\\n token = TextPreprocessor.replace_urls(token)\\n replaced = [token]\\n for i in replaced:\\n\\n i = TextPreprocessor.replace_all_punctuation(i)\\n if i.lower() not in TextPreprocessor.STOPWORDS and i != 'URL' and i!= 'USERMENTION':\\n if i != \\\"\\\" and not re.match('\\\\B#\\\\w*[a-zA-Z]+\\\\w*', i):\\n before = i\\n i = TextPreprocessor.SPELL_CHECKER.correct(i, tag)\\n if i != before:\\n contains_spelling_mistake = True\\n if tag in tags_to_lemmatize:\\n i = TextPreprocessor.lemmatize(i, tag)\\n i = TextPreprocessor.stem(i, tag)\\n # check again, since stemming, lemmatization or spelling correction can produce stopwords\\n # if i.lower() not in TextPreprocessor.STOPWORDS:\\n if i != 'URL' and i!= 'USERMENTION' and i!='':\\n i = i.lower()\\n if re.match(\\\".*[a-zA-Z]'\\\", i):\\n i = i[:-1]\\n prepro.append(i)\\n return prepro, contains_spelling_mistake\",\n \"def named_entity_list_spacy(self) -> List:\\n #choose language\\n if self.lang == 'en':\\n lang_for_spacy = 'en_core_web_sm'\\n elif self.lang == 'de':\\n lang_for_spacy = 'de_core_news_sm'\\n elif self.lang == 'fr':\\n lang_for_spacy = 'fr_core_news_md'\\n #load file and convert input string\\n nlp = spacy.load(lang_for_spacy)\\n doc = nlp(self.sent)\\n #build the output list\\n for ent in doc.ents:\\n #avoid that 'Abstract', 'Title' and 'Author' are counted between NEs\\n if ent.text != 'Abstract' and ent.text != 'Title' and ent.text != 'Author':\\n self.named_entity_list.append((ent.text, ent.start_char, \\n ent.end_char, ent.label_))\\n return self.named_entity_list\",\n \"def sentence_split(self, text):\\n return split_into_sentences(text)\",\n \"def get_text_positions(self, node, padded):\\n # type: (AstNode, bool) -> Tuple[Tuple[int, int], Tuple[int, int]]\\n if getattr(node, \\\"_broken_positions\\\", None):\\n # This node was marked in util.annotate_fstring_nodes as having untrustworthy lineno/col_offset.\\n return (1, 0), (1, 0)\\n\\n if supports_tokenless(node):\\n return self._get_text_positions_tokenless(node, padded)\\n\\n return self.asttokens.get_text_positions(node, padded)\",\n \"def splitInSentence(self,text):\\n return self._support.splitInPhrase(text)\",\n \"def split_words(self, position=None):\\r\\n if position is None:\\r\\n position = self.offset\\r\\n text = self.source_code[:position]\\r\\n return re.findall(self.id_regex, text)\",\n \"def transform_sentence(sequence, vocab_processor):\\n return next(vocab_processor.transform([sequence])).tolist()\",\n \"def get_sentiments(self, d):\\n\\t\\twords = tuple(d.split())\\n\\t\\ttagged = nltk.pos_tag(words)\\n\\n\\t\\tnouns = 0\\n\\t\\tadjectives = 0\\n\\t\\tverbs = 0\\n\\t\\tadverbs = 0\\n\\n\\t\\tfor word, pos_tag in tagged:\\n\\n\\t\\t\\tif pos_tag.startswith(\\\"NN\\\"):\\n\\t\\t\\t\\tnouns += 1\\n\\n\\t\\t\\tif pos_tag.startswith(\\\"JJ\\\"):\\n\\t\\t\\t\\tadjectives += 1\\n\\n\\t\\t\\tif pos_tag.startswith(\\\"VB\\\"):\\n\\t\\t\\t\\tverbs += 1\\n\\n\\t\\t\\tif pos_tag.startswith(\\\"RB\\\"):\\n\\t\\t\\t\\tadverbs += 1\\n\\t\\t# adding one to consider empty document\\n\\t\\tl = len(words) + 1\\n\\t\\treturn [nouns / l, adjectives / l, verbs / l, adverbs / l]\",\n \"def __call__(self, text):\\r\\n if self.use_pos_tagging:\\r\\n return [self.wnl.lemmatize(t, self.pos(t)) for t in word_tokenize(self.clean(text))]\\r\\n else:\\r\\n return [self.wnl.lemmatize(t) for t in word_tokenize(self.clean(text))]\",\n \"def getpos(self):\\n return self.pos.cartesianas()\",\n \"def sent_tokenize(line):\\n sentences = []\\n cur_pos = 0\\n if not re.search(r\\\"\\\\.\\\", line):\\n return [line]\\n\\n for match in re.finditer(r\\\"\\\\.\\\", line):\\n _pos = match.start()\\n end_pos = match.end()\\n if is_end_of_sentence(_pos, line):\\n tmpsent = line[cur_pos:end_pos]\\n tmpsent = tmpsent.strip()\\n if is_valid_xml(tmpsent):\\n cur_pos = end_pos\\n sentences.append(tmpsent)\\n\\n if len(sentences) == 0:\\n sentences.append(line)\\n elif cur_pos < len(line)-1:\\n sentences.append(line[cur_pos+1:])\\n return sentences\",\n \"def batch_sentences(self, sentences, bos, eos, indices=None):\\n batch_size = len(sentences)\\n slen = max([len(s) for s in sentences])\\n if bos:\\n slen += 1\\n if eos:\\n slen += 1\\n \\n def pad_sent(s, max_len, bos, eos):\\n ret = s\\n if bos:\\n ret = [self.bos_index] + ret\\n if eos:\\n ret = ret + [self.eos_index]\\n ret = ret + [self.pad_index for _ in range(max_len - len(ret))]\\n return ret\\n\\n sent_tensor = [pad_sent(s, slen, bos, eos) for s in sentences]\\n sent_tensor = torch.from_numpy(np.array(sent_tensor)).long()\\n \\n if indices is None:\\n return sent_tensor\\n else:\\n return sent_tensor, indices\",\n \"def position(self):\\n return self._pos.to_list()\",\n \"def paragraph(self, text):\\n return [text]\",\n \"def current_buffer_texts(self):\\n return self.current_buffer.text\",\n \"def get_closest_sentence(self, query_emb_init, paper_id, text, topk=10):\\n csv_filepath = os.path.join(\\\"selected\\\", paper_id + '.csv')\\n df_article = pd.read_csv(csv_filepath, index_col=0)\\n \\n normalize = lambda x: x/np.linalg.norm(x, axis = 1, keepdims = True)\\n query_emb = normalize(query_emb_init)\\n val_norm = normalize(df_article.values)\\n score_arr = safe_sparse_dot(val_norm, query_emb.T)\\n\\n # sort in decreasing order\\n similar_paragr = sorted(zip(range(len(score_arr)), score_arr), \\n key = lambda x: x[1], reverse=True)[:min(topk, len(score_arr))] \\n\\n # obtain the text of this closest paragraphs\\n field_text = text.split(\\\"\\\\n\\\")\\n similar_paragr = [(field_text[idx], score) for (idx, score) in similar_paragr]\\n\\n return similar_paragr\",\n \"def get_nerspos(tokens, ners):\\n pos_list = list()\\n for ner in ners:\\n pos = get_nerpos(tokens, ner)\\n pos_list.append(pos)\\n\\n return pos_list\",\n \"def pos_tag(self, sentence):\\n tags = []\\n tokens = sentence.split(\\\" \\\")\\n for i in range(len(tokens)):\\n tags.append('')\\n for i in range (len(tokens)):\\n feat = []\\n feat.append(self.features(tokens,tags,i))\\n tag_predicted = self.postagger.predict(feat)[0]\\n tags[i] = tag_predicted\\n return tags\",\n \"def simple(self, sentence):\\r\\n output_list = [\\r\\n self.position_list[\\r\\n np.argmax(\\r\\n [\\r\\n (math.log(self.emission_probability[pos][sentence[i]]))\\r\\n + (math.log(self.posterior_probability[pos]))\\r\\n if sentence[i] in self.emission_probability[pos]\\r\\n else (math.log(1 / float(10 ** 10)))\\r\\n + (math.log(self.posterior_probability[pos]))\\r\\n for pos in self.position_list\\r\\n ]\\r\\n )\\r\\n ]\\r\\n for i in range(len(sentence))\\r\\n ]\\r\\n return output_list\",\n \"def get_texts(self) -> List[str]:\\n return self.texts\",\n \"def parse(self, text):\\n assert isinstance(text, str)\\n if text.strip() == '':\\n return [], []\\n\\n output = self._annotate(text, properties={\\n \\\"annotators\\\": \\\"tokenize,ssplit,pos\\\",\\n \\\"coref.md.type\\\": \\\"dep\\\",\\n \\\"coref.mode\\\": \\\"statistical\\\"\\n })\\n\\n words = []\\n postags = []\\n\\n for sentence in output['sentences']:\\n for token in sentence['tokens']:\\n word = token['word']\\n pos = token['pos']\\n word = re.sub(r'\\\\s', '', word)\\n words.append(word)\\n postags.append(pos)\\n return words, postags\",\n \"def __get_sentence_window_one_word(self, pattern, sentences, constraints):\\n textsnippets = []\\n for ind, sent in enumerate(sentences):\\n tokens = self.tokenizer.tokenize(sent)\\n for i, token in enumerate(tokens):\\n if check_pattern(pattern, token):\\n if constraints is not None:\\n self.__check_constraints(constraints, (i, i), ind, pattern, sent, sentences, textsnippets, tokens)\\n else:\\n self.__get_sentence_window_help(i, ind, sentences, textsnippets)\\n return textsnippets\",\n \"def get_positions(self):\\n\\n return np.array([p.position for p in self.particles])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.69350046","0.69350046","0.6880929","0.68049884","0.6687255","0.6577726","0.64769137","0.6395686","0.6395686","0.6374306","0.63575345","0.6353244","0.6340651","0.62586695","0.6229776","0.622054","0.6184957","0.61654425","0.6101179","0.6096108","0.60736525","0.5989865","0.5985511","0.5948307","0.59462005","0.59279954","0.5926191","0.58984786","0.5885713","0.5880664","0.5872358","0.58134043","0.5812778","0.5811228","0.57912725","0.5770664","0.575098","0.57441026","0.57321995","0.5723932","0.5721907","0.57153","0.570725","0.570286","0.56894165","0.5682507","0.56802255","0.5666206","0.56609786","0.56557727","0.56523603","0.56161255","0.55983","0.5534352","0.55295855","0.55169094","0.5513739","0.55122614","0.5512179","0.5505076","0.54992455","0.5498394","0.54771334","0.54601294","0.54518634","0.5451176","0.54343104","0.54315746","0.54291505","0.5428368","0.54209566","0.5418178","0.53952193","0.539262","0.5366733","0.53620887","0.53558254","0.5350869","0.5347474","0.534501","0.53378135","0.53316045","0.5323813","0.53122216","0.530996","0.5305703","0.5305694","0.53009284","0.5300847","0.53005254","0.52994764","0.5294223","0.52758265","0.5274529","0.5270982","0.5270736","0.5266283","0.5244336","0.5243178","0.52406496"],"string":"[\n \"0.69350046\",\n \"0.69350046\",\n \"0.6880929\",\n \"0.68049884\",\n \"0.6687255\",\n \"0.6577726\",\n \"0.64769137\",\n \"0.6395686\",\n \"0.6395686\",\n \"0.6374306\",\n \"0.63575345\",\n \"0.6353244\",\n \"0.6340651\",\n \"0.62586695\",\n \"0.6229776\",\n \"0.622054\",\n \"0.6184957\",\n \"0.61654425\",\n \"0.6101179\",\n \"0.6096108\",\n \"0.60736525\",\n \"0.5989865\",\n \"0.5985511\",\n \"0.5948307\",\n \"0.59462005\",\n \"0.59279954\",\n \"0.5926191\",\n \"0.58984786\",\n \"0.5885713\",\n \"0.5880664\",\n \"0.5872358\",\n \"0.58134043\",\n \"0.5812778\",\n \"0.5811228\",\n \"0.57912725\",\n \"0.5770664\",\n \"0.575098\",\n \"0.57441026\",\n \"0.57321995\",\n \"0.5723932\",\n \"0.5721907\",\n \"0.57153\",\n \"0.570725\",\n \"0.570286\",\n \"0.56894165\",\n \"0.5682507\",\n \"0.56802255\",\n \"0.5666206\",\n \"0.56609786\",\n \"0.56557727\",\n \"0.56523603\",\n \"0.56161255\",\n \"0.55983\",\n \"0.5534352\",\n \"0.55295855\",\n \"0.55169094\",\n \"0.5513739\",\n \"0.55122614\",\n \"0.5512179\",\n \"0.5505076\",\n \"0.54992455\",\n \"0.5498394\",\n \"0.54771334\",\n \"0.54601294\",\n \"0.54518634\",\n \"0.5451176\",\n \"0.54343104\",\n \"0.54315746\",\n \"0.54291505\",\n \"0.5428368\",\n \"0.54209566\",\n \"0.5418178\",\n \"0.53952193\",\n \"0.539262\",\n \"0.5366733\",\n \"0.53620887\",\n \"0.53558254\",\n \"0.5350869\",\n \"0.5347474\",\n \"0.534501\",\n \"0.53378135\",\n \"0.53316045\",\n \"0.5323813\",\n \"0.53122216\",\n \"0.530996\",\n \"0.5305703\",\n \"0.5305694\",\n \"0.53009284\",\n \"0.5300847\",\n \"0.53005254\",\n \"0.52994764\",\n \"0.5294223\",\n \"0.52758265\",\n \"0.5274529\",\n \"0.5270982\",\n \"0.5270736\",\n \"0.5266283\",\n \"0.5244336\",\n \"0.5243178\",\n \"0.52406496\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7071078"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":21886,"cells":{"query":{"kind":"string","value":"Check if the annotation is pronominal by full parsed PoS tag. All tokens should be pronominal (works best for anaphoric pronominal mentions as intended)."},"document":{"kind":"string","value":"def check_pronominal(self):\n pronom_tags = [\"PRP\", \"PRP$\", \"WDT\", \"WP\", \"WP$\"]\n token_procs = self.get_processed_tokens()\n all_pronom = all(\n t.tag_ in pronom_tags for t in token_procs\n ) # True if all tokens are pronom_tags\n # print(f\"{' '.join(t.text + '.' + t.tag_ for t in token_procs)}: Pronominal = {all_pronom}\")\n return all_pronom"},"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 contains_pronoun(cluster):\n for mention in cluster:\n if any([w.tag_.startswith(\"PRP\") for w in mention]):\n # Found a mention with a pronoun\n return True\n return False","def find_pronoun(sent):\n pronoun = None\n\n for word, part_of_speech in sent.pos_tags:\n # Disambiguate pronouns\n if part_of_speech == 'PRP' and word.lower() == 'you':\n # pronoun = 'I' +++++++++ORIGINAL++++++++++++\n pronoun = 'I'\n elif part_of_speech == 'PRP' and word == 'I':\n # If the user mentioned themselves, then they will definitely be the pronoun\n # pronoun = 'You' +++++++++ORIGINAL++++++++++++\n pronoun = 'You'\n return pronoun","def is_noun(tag):\r\n return tag in ['NN', 'NNS', 'NNP', 'NNPS']","def is_pronounced(word):\n pronounced = [\"pronounced\", \"pronunciation\", \"pronounsed\", \"pronouced\", \"pronouned\", \\\n \"pronounciated\", \"prenounced\", \"prounouced\", \"pernounced\", \"purnounced\", \\\n \"pronoused\", \"pronuced\", \"pronunced\", \"pronnounced\", \"pronanced\", \\\n \"prononced\", \"prounounced\", \"prononsed\", \"prononuced\", \"pernunciation\", \\\n \"prononciation\", \"prounciation\", \"pronouciation\", \"pronounciated\", \\\n \"pronounciation\", \"pronanciation\", \"prononcation\", \"pernounciation\", \\\n \"prononceation\", \"prenunciation\", \"prononseation\", \"prounouciation\", \\\n \"pronuniation\", \"pronunication\", \"prenounciation\", \"pronuntiation\", \\\n \"pronuncition\", \"pronociation\", \"prenunsiation\", \"pronounsation\", \\\n \"pronounceation\", \"pronounication\", \"pronauciation\", \"pronounciacion\", \\\n \"pronounsiation\"]\n for p in pronounced:\n if word.lower() == p:\n return True\n return False","def is_sonnet(poem):\n return len([line for line in poem.split(\"\\n\") if line]) == 14","def verifyProperNounAtSentenceStart(idx, tagged_term, tagged_terms, lexicon):\n term, tag, norm = tagged_term\n if (tag in ('NNP', 'NNPS') and\n (idx == 0 or tagged_terms[idx-1][1] == '.')):\n lower_term = term.lower()\n lower_tag = lexicon.get(lower_term)\n if lower_tag in ('NN', 'NNS'):\n tagged_term[0] = tagged_term[2] = lower_term\n tagged_term[1] = lower_tag","def _entry_morphophonemics_annotation_is_valid(entry: _LexiconEntry) -> None:\n compound = _is_compound_of(entry)\n morphophonemics = _morphophonemics_of(entry)\n\n if compound == \"true\" and morphophonemics == \"~\":\n raise InvalidLexiconEntryError(\n \"Entry is marked as ending with compounding marker but it is missing\"\n \" morphophonemics annotation.\")","def is_noun(tag_string):\n result = True if tag_string in POS.POS_tags.noun_tags else False\n return result","def is_adjective(tag):\r\n return tag in ['JJ', 'JJR', 'JJS']","def can_generate_ransom_note(self):\n if self.ransom_text == '' or self.ransom_text == ' ':\n return True\n ransom_text_words = self.ransom_text.split(' ')\n magazine_text_words = self.magazine_text.split(' ')\n # counting the occurrences of words in the ransom and magazine texts.\n ransom_count = self._count_words_in_string(ransom_text_words)\n magazine_count = self._count_words_in_string(magazine_text_words)\n result = False\n for i in ransom_text_words:\n # if magazine_count hashmap doesn't have word\n if magazine_count.get(i) is None:\n result = False\n break\n # if ransom_count hashmap have less word occurances than magazine count.\n if ransom_count.get(i) <= magazine_count.get(i):\n result = True\n else:\n result = False\n break\n return result","def is_annotated(page_id, line_n, token, annotations):\n positions = [p for a in annotations for p in a[\"positions\"]]\n for position in positions:\n if(position[\"page_id\"]==page_id):\n if(position[\"line_n\"]==line_n):\n if(token[\"offset_start\"] >= position[\"start\"]):\n if(token[\"offset_end\"] <= position[\"end\"]):\n return True\n return False","def _is_annotated(nodes: List[Node]):\n annotated = False\n for node in nodes:\n annotated = annotated or (\n \"quantization_annotation\" in node.meta\n and node.meta[\"quantization_annotation\"]._annotated\n )\n return annotated","def _check_personal_pronouns(pronoun: str, last_nouns: list) -> list:\n pronoun_details = []\n pronoun_lower = pronoun.lower()\n if pronoun == 'I' or pronoun_lower in ('me', 'myself', 'my'):\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\n elif pronoun_lower in ('we', 'us', 'ourselves', 'our'):\n # Find singular or plural person nouns (any gender)\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, True))\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\n elif pronoun_lower in ('they', 'them', 'themselves', 'their'):\n # Give preference to persons (any gender or number)\n noun_list = _check_criteria(pronoun_lower, last_nouns, None, None, True)\n if noun_list:\n pronoun_details.extend(noun_list)\n else:\n # Check for non-persons\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, False))\n elif pronoun_lower in ('she', 'herself', 'her'):\n # Find singular, feminine, person nouns\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, True, True))\n elif pronoun_lower in ('he', 'himself', 'him'):\n # Find singular, masculine, person nouns\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, False, True))\n elif pronoun_lower in ('it', 'itself', 'its'):\n # Find singular, non-person nouns (no gender)\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, None, False))\n final_details = [] # May be duplicates in the list due to duplicates in last_nouns\n for pronoun_detail in pronoun_details:\n if pronoun_detail in final_details:\n continue\n final_details.append(pronoun_detail)\n return final_details","def nao_tem_passageiros(self):\n return self.counter.ja_viajaram == self.counter.num_passageiros","def _is_opinion_mod(token: tokens.Token) -> bool:\n is_mod = token.dep_ in {\"amod\", \"advmod\"}\n is_op = token.text.lower() in _OPINION_WORDS\n return is_mod and is_op","def is_lyrics_approved():","def is_psionic(self) -> bool:\n return ATTRIBUTE.Psionic.value in self.type_data.attributes","def rule_vb_prp_nn(words):\n if index_tag_seq(words, ['VB', 'PRP$', 'NNS', 'RB']) > -1:\n return move_tag_seq(words, ['VB', 'PRP$', 'NNS'], 'end')\n return None","def check_pra_symbol(symbol):\n # Platts\n if len(symbol) == 7 and symbol[:2] in [\n 'PC', 'PA', 'AA', 'PU', 'F1', 'PH', 'PJ', 'PG', 'PO', 'PP', ]:\n return True\n\n # Argus\n if '.' in symbol:\n sm = symbol.split('.')[0]\n if len(sm) == 9 and sm.startswith('PA'):\n return True\n\n return False","def isOk(annots):\n if annots == []:\n return True\n for a in annots:\n for label in a.labels:\n if (label != 'hasOkCopies' and\n label != 'hasBadCopies' and\n not label.startswith('count_')):\n return False\n return True","def check_if_extra_pronoun(pronoun_dict):\n for key, value in sorted(pronoun_dict.iteritems(), key=lambda (k,v): (v,k), reverse = True):\n skip = False\n for pronoun_list in all_pronouns:\n if key in pronoun_list:\n skip = True\n break\n if not skip:\n print \"%s: %s\" % (key, value)","def __isNoun__(self, word):\n self.nouns = ('door', 'bear', 'princess', 'cabinet')\n for noun in self.nouns:\n if noun == word:\n return ('noun', word), True\n return None, False","def isDisambiguatedByNextNoun(self, word):\n\t\treturn 'noun' in disambig_const.DISAMBIGUATATION_TABLE.get(word, {});","def token_filter(tok):\n return tok is token or \\\n tok.dep_.endswith(\"mod\") or \\\n tok.dep_ == \"compound\"","def are_present_quiz(verb, pronoun):\n\n return functions.conjugate_present_are_verb(verb, pronoun, \"presente\")","def _does_token_overlap_with_annotation(\n token: Token, annot_start: int, annot_end: int\n) -> bool:\n\n return (\n annot_start <= token.idx <= annot_end\n or token.idx <= annot_start <= token.idx + len(token)\n )","def check_if_replacable(self, word):\n word_tag = pos_tag([word])\n if 'NN' in word_tag[0][1] or 'JJ' in word_tag[0][1] or 'VB' in word_tag[0][1]:\n return True\n else:\n return False","def additional_text_preprocessing_with_pos(pos_dict):\n\n tags_to_lemmatize = ['a', 'n', 'v', 'r']\n\n pos_dict = TextPreprocessor.find_named_entities(pos_dict)\n if pos_dict is None:\n return None, None\n prepro = list()\n contains_spelling_mistake = False\n for t in pos_dict:\n token = t['token']\n tag = t['tag'].lower()\n if token not in TextPreprocessor.PUNCTUATION and tag != \",\":\n\n token = TextPreprocessor.replace_user_mentions(token)\n token = TextPreprocessor.replace_urls(token)\n replaced = [token]\n for i in replaced:\n\n i = TextPreprocessor.replace_all_punctuation(i)\n if i.lower() not in TextPreprocessor.STOPWORDS and i != 'URL' and i!= 'USERMENTION':\n if i != \"\" and not re.match('\\B#\\w*[a-zA-Z]+\\w*', i):\n before = i\n i = TextPreprocessor.SPELL_CHECKER.correct(i, tag)\n if i != before:\n contains_spelling_mistake = True\n if tag in tags_to_lemmatize:\n i = TextPreprocessor.lemmatize(i, tag)\n i = TextPreprocessor.stem(i, tag)\n # check again, since stemming, lemmatization or spelling correction can produce stopwords\n # if i.lower() not in TextPreprocessor.STOPWORDS:\n if i != 'URL' and i!= 'USERMENTION' and i!='':\n i = i.lower()\n if re.match(\".*[a-zA-Z]'\", i):\n i = i[:-1]\n prepro.append(i)\n return prepro, contains_spelling_mistake","def _is_valid_pt(content_type: str) -> bool:\n content_type = content_type.strip()\n return content_type in SPECIFICATION_PRIMITIVE_TYPES","def test_spires_syntax_detected_naked_author_leading_spaces(self):\n converter = search_engine_query_parser.SpiresToInvenioSyntaxConverter()\n spi_search = converter.is_applicable(\" author ellis\")\n self.assertEqual(spi_search, True)","def commence_par(mot, prefixe)->bool:\n return mot[:len(prefixe)] == prefixe","def _canProcessTags(self, grammar, pos_tags):\n badTags = []\n for tag in pos_tags:\n if tag not in grammar.tags:\n badTags.append(tag)\n logger.debug(\"Grammar can't handle tag:\" + tag)\n if badTags:\n return False\n else:\n return True","def is_projective(sent):\n spans = []\n for token in sent:\n s = tuple(sorted([token[\"id\"],token[\"parent\"]]))\n spans.append(s)\n spans = sorted(spans)\n for l,h in spans:\n for l1, h1 in spans:\n if (l,h) == (l1,h1): continue\n if l< l1 < h and h1 > h:\n return False\n return True","def are_subjunctivepr_quiz(verb, pronoun):\n return functions.conjugate_subjunctivepr_are_verb(verb, pronoun, \"congiuntivo\")","def startsParagraph(s: str) -> bool:\n if not s.strip():\n val = False\n elif s.strip() in ('\"\"\"', \"'''\"):\n val = True\n elif s[0].isdigit():\n i = 0\n while i < len(s) and s[i].isdigit():\n i += 1\n val = g.match(s, i, ')') or g.match(s, i, '.')\n elif s[0].isalpha():\n # Careful: single characters only.\n # This could cause problems in some situations.\n val = (\n (g.match(s, 1, ')') or g.match(s, 1, '.')) and\n (len(s) < 2 or s[2] in ' \\t\\n'))\n else:\n val = s.startswith('@') or s.startswith('-')\n return val","def pob(self):\n if self.index >= self.length:\n return False\n \n self._pob, n = self.parse_pob()\n if self._pob is not None:\n self.idx_pob = self.index\n self.index += n\n self.isaddr = True\n if self.index < self.length and self.words[self.index]['word'] == ',':\n self.index += 1\n if self._debug: print(\"POB\", self._pob, self.idx_pob)\n return True\n return False","def filter_tree(tree):\n return find_pos(tree)\n\n # if tree.parent() is None:\n # return False\n\n # noun_phrase = re.match(\"NP|VP\", tree.parent().label())\n # pos = re.match(\"NN.*|JJ|CD|VBN\", tree.label())\n # return noun_phrase and pos","def _is_compound_of(entry: _LexiconEntry) -> str:\n return entry[\"is_compound\"].lower()","def get_nouns(root):\n nouns = []\n for child in root.findall(\"./xdrs/taggedtokens/tagtoken/tags\"):\n noun = False\n for grandchildren in child.findall(\"./tag[@type='pos']\"):\n if grandchildren.text == 'NN' or grandchildren.text == 'NNS':\n noun = True\n if noun == True:\n for grandchildren in child.findall(\"./tag[@type='lemma']\"):\n nouns.append(grandchildren.text)\n return nouns","def are_subjunctiveimp_quiz(verb, pronoun):\n return functions.conjugate_subjunctiveimp_are_verb(verb, pronoun, \"congiuntivo\")","def check_for_comment_about_bot(pronoun, noun, adjective):\n resp = None\n if pronoun == 'I' and (noun or adjective):\n pprint(\"WORRRRRRRRRRKING\")\n if noun:\n if random.choice((True, False)):\n resp = random.choice(SELF_VERBS_WITH_NOUN_CAPS_PLURAL).format(**{'noun': noun.pluralize().capitalize()})\n else:\n resp = random.choice(SELF_VERBS_WITH_NOUN_LOWER).format(**{'noun': noun})\n else:\n resp = random.choice(SELF_VERBS_WITH_ADJECTIVE).format(**{'adjective': adjective})\n return resp","def get_extras(self, text=None):\n if text is None:\n text = self.nltk_text(self.text)\n # Tag parts of speech\n tagged = nltk.pos_tag(text)\n # Try for composed NNP / NNPS\n is_proper_noun = False\n text = []\n proper_noun = \"\"\n for (word, tag) in tagged:\n if not is_proper_noun and (tag == 'NNP' or tag == 'NNPS'):\n # Start building a proper noun\n proper_noun = word\n # Set it true\n is_proper_noun = True\n # Add it to annotations anyway\n text.append(word)\n elif tag == 'NNP' or tag == 'NNPS':\n # Previous was proper noun. So it may be combined\n proper_noun += \" \" + word\n # Add the single word to annotations anyway\n text.append(word)\n elif is_proper_noun and tag == 'IN':\n # Add what we have by now to the text\n text.append(proper_noun)\n # Previous was proper noun. So it may be composed\n proper_noun += \" \" + word\n elif is_proper_noun:\n # Add what we have by now to the text\n text.append(proper_noun)\n # Finished with proper noun, so set it false\n is_proper_noun = False\n # Remove duplicates\n seen = {}\n result = []\n for w in text:\n if w in seen:\n continue\n seen[w] = 1\n result.append(w)\n # Eliminate common\n result = [w for w in result if w.lower() not in self.common_words and\n w.lower() not in stopwords.words('english')]\n return result","def check_for_metatitle(self, interest_name: str):\n if interest_name.endswith(\"/streaming/p*\"):\n return True\n else:\n return False","def is_complete(self):\n return all([\n len(strip_tags(score.notes)) > 0 for score in self.scores.all()\n ])","def parse_pob(self):\n \n index = self.index\n start = self.index \n \n if self.words[index]['word'] == 'pob' or self.words[index]['word'] == 'pmb':\n index += 1\n if index == self.length:\n return None, 0\n if self.words[index]['word'] == '.':\n return self.words[index+1]['word'], 3\n else:\n return self.words[index]['word'], 2\n elif self.words[index]['word'] == 'p':\n index += 1\n if index == self.length:\n return None, 0\n if self.words[index]['word'] == '.':\n index += 1\n if index == self.length:\n return None, 0\n if self.words[index]['word'] not in ['o', 'm']:\n return None, 0\n index += 1\n if index == self.length:\n return None, 0\n if self.words[index]['word'] == '.':\n index += 1\n if index == self.length:\n return None, 0\n if self.words[index]['word'] in ['b', 'box']:\n index += 1\n if index == self.length:\n return None, 0\n elif not self.words[index]['word'].isdigit():\n return None,0\n if self.words[index]['word'] == '.':\n index += 1\n if index == self.length:\n return None, 0\n return self.words[index]['word'], index - start + 1\n \n if self.words[index]['word'] == 'po':\n index += 1\n if index == self.length:\n return None, 0\n if self.words[index]['word'] == 'box':\n index += 1\n if index == self.length:\n return None, 0\n return self.words[index]['word'], index - start + 1\n \n return None, 0","def mountable(self):\n return True","def convert(token):\n trait = Trait(\n value=\"pregnant\" if token.group.get(\"pos\") else \"not pregnant\",\n start=token.start,\n end=token.end,\n )\n return trait","def replace_last_pronoun(schema, pronoun):\n pronouns = [\n \"eles\",\n \"ela\",\n \"ele\",\n \"os\",\n \"dela\",\n \"it\",\n \"o\",\n \"la\",\n \"lo\",\n \"a\",\n \"la\",\n \"las\",\n \"deles\",\n \"delas\",\n \"dele\",\n \"sua\",\n \"seu\",\n \"suas\",\n ]\n\n replaced = []\n\n # Aqui substituo todas as marcações no texto por ela mesmo com espaço antes, pois senão o split faz com que a palavra contenha esse sinal nela\n # Com o espaço antes, no split() o sinal de pontuação vira uma palavra. Ex: Olá. -> [\"Olá\", \".\"]\n reversed_split_sentence = schema.replace(\"'\", \" '\")\n reversed_split_sentence = reversed_split_sentence.replace('\"', ' \"')\n reversed_split_sentence = reversed_split_sentence.replace(\".\", \" .\")\n reversed_split_sentence = reversed_split_sentence.replace(\",\", \" ,\")\n reversed_split_sentence = reversed_split_sentence.replace(\"-\", \" - \")\n\n reversed_split_sentence = reversed_split_sentence.split()\n reversed_split_sentence.reverse()\n\n already_replaced = False\n for word in reversed_split_sentence:\n if word.lower() == pronoun.lower() and not already_replaced:\n word_to_replace = \"[MASK]\"\n already_replaced = True\n elif word.lower() == \"lo\" and pronoun.lower() == \"-lo\" and not already_replaced:\n word_to_replace = \"[MASK]\"\n already_replaced = True\n else:\n word_to_replace = word\n replaced.append(word_to_replace)\n\n # Caso o pronome do snippet não seja encontrado na frase.\n # Em português, não é para isso ocorrer, pois sempre o pronome do campo 'pronoun' vai ser encontrado\n # na frase, mas é importante manter, caso tenhamos algum problema\n if already_replaced == False:\n print(\"SEM PRONOME\")\n replaced = []\n for word in reversed_split_sentence:\n # Neste caso, pegamos o primeiro pronome, de trás para frente, que está na lista de pronomes válidos do WSC\n if word.lower() in pronouns and not already_replaced:\n word_to_replace = \"[MASK]\"\n already_replaced = True\n else:\n word_to_replace = word\n replaced.append(word_to_replace)\n\n replaced.reverse()\n print(replaced)\n\n output = \" \".join(replaced)\n\n # Aqui desfazemos aquela artimanha de adicionar espaço antes de pontuações, para que fique correto na frase final\n output = output.replace(\" '\", \"'\")\n output = output.replace(' \"', '\"')\n output = output.replace(\" .\", \".\")\n output = output.replace(\" ,\", \",\")\n output = output.replace(\" - \", \"-\")\n\n return output","def is_punctuation_automata(final_word: str) -> list:\n\n punct_words = ({'+': 'symb_add', '-': 'symb_diff', '*': 'symb_mult', '/': 'symb_div', '>': 'symb_gt',\n '<': 'symb_lt', '(': 'symb_oparentesis', ')': 'symb_cparentesis', '.':'symb_dot',\n ':': 'symb_col',';':'symb_semicol',',':'symb_coma', '=': 'symb_eq',':=':'symb_assign',\n '>=':'symb_gte', '<=':'symb_lte', '<>':'symb_neq'})\n\n if final_word in punct_words:\n token = punct_words[final_word]\n return [True, token]\n else :\n return [False, \"\"]","def annotated_var(prs):\n def trt(acc):\n v,ann = acc \n if len(ann) > 0:\n return c.copy_token(v,{'annotation':ann[0]})\n return v\n return c.paren(var() + colon_annotation(prs)).treat(trt)","def are_ere_conditionalpr_quiz(verb, pronoun):\n return functions.conjugate_conditionalpr_are_ere_verb(verb, pronoun, \"condizionale\")","def tokenize_annotations(annotation, binarize=True, \n nostop=False, lower=True, stem=False, \n special_features=False,\n class_feature=False,\n bigrams=False):\n\n if not type(annotation) == str:\n annotation = \" \".join(annotation)\n \n\n # different possibilities to tokenize\n tokens = re.split(\"[\\\\s\\.\\,]+\", annotation)\n \n # lowercasing\n if lower:\n tokens = [t.lower() for t in tokens]\n\n\n # stemming\n if stem:\n # adding stems to the tokens\n tokens.extend([stemmer.stem(t) for t in tokens])\n \n\n if nostop:\n tokens = [t for t in tokens if t not in stopset]\n\n if bigrams:\n n = 2\n tokens.extend([\" \".join(tokens[i:i+n]) for i in xrange(len(tokens)-n+1)]) \n\n if special_features:\n # numbers\n #numbers = [\"SPECIAL:num\" for t in tokens if t.isdigit()]\n #tokens.extend(set(numbers))\n \n # files\n #files = [\"SPECIAL:file\" for t in tokens if \"file\" in t]\n #files = [t for t in tokens if \"file\" not in t]\n if any(\"file\" in t for t in tokens):\n tokens.append(\"SPECIAL:file\")\n\n # squares\n if any(square_pattern.match(t) != None for t in tokens):\n tokens.append(\"SPECIAL:square\")\n\n # moves\n if any(move_pattern.match(t) != None for t in tokens):\n tokens.append(\"SPECIAL:move\")\n\n #tokens.extend(files)\n \n pass\n \n if class_feature:\n tokens.append(\"<CLASS>\")\n \n if binarize:\n tokens = set(tokens)\n\n\n return tokens","def __decide_pos(pos: str) -> str:\n to_return = None\n if pos == 'NOUN':\n to_return = \"n\"\n if pos == 'VERB':\n to_return = 'v'\n if pos == 'ADJ':\n to_return = 'a'\n if pos == 'ADV':\n to_return = 'r'\n return to_return","def _process_prosody(sonority):\n assert 9 not in sonority[1:-1]\n assert sonority[0] == sonority[-1] == 9\n\n # create the output values\n psequence = []\n first = True # stores whether first syllable is currently being processed\n\n for i in range(1, len(sonority) - 1):\n # get a segment with context\n a, b, c = sonority[i - 1], sonority[i], sonority[i + 1]\n\n if b == 7: # a vowel\n if first:\n psequence.append('X')\n first = False\n elif c == 9: # last\n psequence.append('Z')\n else:\n psequence.append('Y')\n elif b == 8: # a tone\n psequence.append('T')\n elif a >= b >= c or c == 8: # descending\n if c == 9: # word final position\n psequence.append('Z' if b == 7 else 'N') # vowel or consonant\n else:\n if first:\n first = False\n psequence.append('A')\n else:\n psequence.append('L')\n elif b < c or a > b <= c or a < b <= c: # ascending\n # check for syllable first\n if a == 9:\n psequence.append('A')\n elif a >= b:\n if c == 9:\n psequence.append('N')\n else:\n if psequence[-1] != 'A':\n psequence = psequence[:-1] + [psequence[-1].replace('L', 'M')] + ['B']\n else:\n psequence.append('C')\n else:\n psequence.append('C')\n elif a < b > c: # consonant peak\n if first:\n psequence.append('X')\n first = False\n else:\n psequence.append('Y')\n else:\n raise ValueError(\n \"Conversion to prosodic string failed due to a condition which was not \"\n \"defined in the convertion, for details compare the numerical string \"\n \"{0} with the profile string {1}\".format(sonority, psequence))\n return psequence","def test_spires_syntax_detected_naked_title(self):\n converter = search_engine_query_parser.SpiresToInvenioSyntaxConverter()\n spi_search = converter.is_applicable(\"t muon\")\n self.assertEqual(spi_search, True)","def _is_title(self):\n ph = _child(self.__nvXxPr.nvPr, 'p:ph')\n if ph is None:\n return False\n # idx defaults to 0 when idx attr is absent\n ph_idx = ph.get('idx', '0')\n # title placeholder is identified by idx of 0\n return ph_idx == '0'","def start_tag_or_none(self, token):\n if self.patterns['start_tag'].match(token):\n return token[2:-6].upper()","def single_line_paragraph(s: str) -> bool:\n return s.startswith('@') or s.strip() in ('\"\"\"', \"'''\")","def is_apt(self):\r\n return self.has_label('apt')","def is_punct(self, word, language):","def validaAnno(anno):\n valid = ((len(anno) == 4) and (int(anno)) > 1970)\n if (valid and((len(anno) == 4) and (int(anno)) > 1970)):\n valid=True\n if(valid == False):\n print(\"anno invalido.\")\n labelinformativo('anno invalido')\n return valid","def _is_real_word(self, token):\n return not (token in self._non_words)","def token_key(token):\n morphotagged = analysis(token).get('raw')\n lemma_pos = (analysis(token).get('lemma'), analysis(token).get('partOfSpeech'))\n return morphotagged or lemma_pos","def nltk_condition(stem):\n return len(stem) > 1 and self._is_consonant(stem, len(stem) - 1)","def isImportantToken(self, token, ignoreSemanticTagList=[]):\n if len(ignoreSemanticTagList) > 0: \n tags = token.getSemanticTagMatches(ignoreSemanticTagList)\n else:\n tags = []\n return token.isSymbol() == False \\\n and token.text not in self.ignoreWords and len(tags) == 0","def filter_pos(self):\n all_tokens = []\n for zettel in self.lemma_tokens:\n tokens = []\n for word in zettel:\n if word[1] in ['NN', 'NNS', 'NNP', 'NNPS', 'NG']: # NG = n_gram\n tokens.append(word)\n all_tokens.append(tokens)\n self.lemma_tokens = all_tokens","def _is_simple_type(cls):\n return all([\n AnnotationWrapper(anno).is_simple_in_opt_and_not_opt\n for anno in cls._used_annotations()\n ])","def is_neutral(self, P):\n\t\treturn P.x is None","def _is_positron(argument):\n\n if not isinstance(argument, str):\n return False\n\n if argument == 'e+' or argument.lower() == 'positron':\n return True\n else:\n return False","def parse_token(self, token, tags=[WORD, POS, CHUNK, PNP, REL, ANCHOR, LEMMA]):\n p = { WORD: \"\", \n POS: None, \n IOB: None,\n CHUNK: None,\n PNP: None,\n REL: None,\n ROLE: None,\n ANCHOR: None,\n LEMMA: None }\n # Split the slash-formatted token into separate tags in the given order.\n # Decode &slash; characters (usually in words and lemmata).\n # Assume None for missing tags (except the word itself, which defaults to an empty string).\n custom = {}\n for k, v in izip(tags, token.split(\"/\")):\n if SLASH0 in v:\n v = v.replace(SLASH, \"/\")\n if k not in p:\n custom[k] = None\n if v != OUTSIDE or k == WORD or k == LEMMA: # \"type O negative\" => \"O\" != OUTSIDE.\n (p if k not in custom else custom)[k] = v\n # Split IOB-prefix from the chunk tag:\n # B- marks the start of a new chunk, \n # I- marks inside of a chunk.\n ch = p[CHUNK]\n if ch is not None and ch.startswith((\"B-\", \"I-\")):\n p[IOB], p[CHUNK] = ch[:1], ch[2:] # B-NP\n # Split the role from the relation:\n # NP-SBJ-1 => relation id is 1 and role is SBJ, \n # VP-1 => relation id is 1 with no role.\n # Tokens may be tagged with multiple relations (e.g., NP-OBJ-1*NP-OBJ-3).\n if p[REL] is not None:\n ch, p[REL], p[ROLE] = self._parse_relation(p[REL])\n # Infer a missing chunk tag from the relation tag (e.g., NP-SBJ-1 => NP).\n # For PP relation tags (e.g., PP-CLR-1), the first chunk is PP, the following chunks NP.\n if ch == \"PP\" \\\n and self._previous \\\n and self._previous[REL] == p[REL] \\\n and self._previous[ROLE] == p[ROLE]: \n ch = \"NP\"\n if p[CHUNK] is None and ch != OUTSIDE:\n p[CHUNK] = ch\n self._previous = p\n # Return the tags in the right order for Sentence.append().\n return p[WORD], p[LEMMA], p[POS], p[CHUNK], p[ROLE], p[REL], p[PNP], p[ANCHOR], p[IOB], custom","def findPOS(word):\r\n\t\r\n lisPOS = list(wordtags[word])\r\n if \"ADJ\" in lisPOS:\r\n return \"ADJECTIVE\"\r\n if \"ADV\" in lisPOS:\r\n return \"ADVERB\"\r\n if \"NOUN\" in lisPOS:\r\n return \"NOUN\"","def sobrou_pessoas(self):\n return self.counter.ja_viajaram + self.counter.capacidade_max_carro > self.counter.num_passageiros","def is_token_marker(self):\n return self.id in TOKEN","def annotated_vars(prs):\n def trt(acc):\n vs,ann = acc\n if len(ann)==0:\n return vs\n assert(len(ann)==1)\n return [c.copy_token(v,ann[0]) for v in vs]\n return c.paren(var().plus() + colon_annotation_or_meta()).treat(trt)","def has_osm_data(self):\n return settings.MSPRAY_OSM_PRESENCE_FIELD in self.data","def check_format_of_annotation_in_file(self):\n if not self.is_span_valid():\n sys.exit()","def atPodium(self, sparseOK=False, one_mOK=False):\n pos = self.getPos()\n ok = (pos == self.GANG_ON_PODIUM) or (pos == self.GANG_AT_DENSE)\n if sparseOK:\n ok = ok or (pos == self.GANG_AT_SPARSE)\n if one_mOK:\n ok = ok or (pos == self.GANG_AT_1M)\n\n return ok","def specific_pos_score(self, word):\n if word[0][1] == 'NNP':\n return 5\n if word[0][1] == 'NN':\n return 2\n else:\n return 1","def test_3():\n results = base_tests(long_anno=1)\n assert results[0][\"Consequence\"] == \"To Long Annotation\"","def get_nps(self):\n\n # determine all leaf ids in the parse tree which refer to a noun\n nouns = []\n for node_id in self.parsetree.nodes():\n node = self.parsetree.node[node_id]\n if not node['is_leaf']:\n continue\n leaf_idx = node['left_leaf_idx']\n if leaf_idx >= len(self.tokens):\n continue\n self.words[leaf_idx] == node['label']\n is_noun = self.tokens[leaf_idx].POS.cdata[0] == 'N'\n if is_noun:\n nouns.append(node_id)\n\n NPs = set()\n for noun in nouns:\n NPs.add(self.get_np_for_idx(noun))\n return NPs","def tag2predictions(ote_tag_sequence, ts_tag_sequence):\n n_tag = len(ote_tag_sequence)\n # opinion target sequence and targeted sentiment sequence\n ot_sequence, ts_sequence = [], []\n beg, end = -1, -1\n for i in range(n_tag):\n tag = ote_tag_sequence[i]\n if tag == 'S':\n ot_sequence.append((i, i))\n elif tag == 'B':\n beg = i\n elif tag == 'E':\n end = i\n if end > beg and beg != -1:\n ot_sequence.append((beg, end))\n beg, end = -1, -1\n sentiments = []\n beg, end = -1, -1\n for i in range(n_tag):\n ts_tag = ts_tag_sequence[i]\n # current position and sentiment\n eles = ts_tag.split('-')\n if len(eles) == 2:\n pos, sentiment = eles\n else:\n pos, sentiment = 'O', 'O'\n if sentiment != 'O':\n # current word is a subjective word\n sentiments.append(sentiment)\n if pos == 'S':\n # singleton\n ts_sequence.append((i, i, sentiments[0]))\n sentiments = []\n elif pos == 'B':\n beg = i\n elif pos == 'E':\n end = i\n # schema1: only the consistent sentiment tags are accepted\n # that is, all of the sentiment tags are the same\n if end > beg > -1 and len(set(sentiments)) == 1:\n ts_sequence.append((beg, end, sentiment))\n sentiments = []\n beg, end = -1, -1\n\n # schema2: only consider the sentiment at the beginning of the aspect span\n # if end > beg > -1:\n # ts_sequence.append((beg, end, sentiments[0]))\n # sentiments = []\n # beg, end = -1, -1\n return ot_sequence, ts_sequence","def pron_lemma(word):\n if re.match(r\"(their|theirs|them|themselves)\", word):\n return (\"they\")\n elif re.match(r\"(his|him|himself)\", word):\n return (\"he\")\n elif re.match(r\"(her|hers|herself)\", word):\n return (\"she\")\n elif re.match(r\"(its|itself)\", word):\n return (\"it\")\n elif re.match(r\"(your|yours|yourself)\", word):\n return (\"you\")\n elif re.match(r\"(our|us|ours)\", word):\n return (\"we\")\n elif re.match(r\"(me|mine|my|myself)\", word):\n return (\"I\")\n elif word == (\"I\"):\n return word\n else:\n return word.lower()","def _is_auxilary(token, chunk_token_ids):\n if (token.head.i in chunk_token_ids and # if dep-tree current token's parent (head) is somewhere inside the main part of PC represented as chunk_token_ids (sequence of numeric token identifiers)\n token.dep_ == 'aux' and # if the dep-tree label on the edge between some word from main part of PC and current token is AUX (auxilary)\n token.i not in chunk_token_ids): # if token is outside of detected main part of PC\n return True # yep, it's auxilary\n else:\n return False","def ifValidSERP(soupObject):\n return soupObject.find(\"p\", class_=\"noresult_tit\") is None and soupObject.title is not None","def _parse_unground_proposition(self, array):\n negative = False\n if array[1] == 'not':\n negative = True\n array = array[2:-1]\n return Predicate(array[1], array[2:-1], False, negative)","def is_token_instance(self, t: Token) -> bool:\n return t.token_type == TokenType.AMPLIFICATION","def is_pos(self, term):\n return term in self.pos","def morph_pronoun(lex, pronoun_type, case, gender, number, distance, animate,\n person, restrictive):\n if lex and isinstance(lex, basestring) and not (lex in ['none', 'nil']):\n return lex\n if pronoun_type == 'personal':\n # start with the 'he' then augmen by person, then, by number, \n # gender and finally by case\n # this is a port of the hack in the morphology.scm code\n if (not animate) or (gender == 'feminine'):\n anime = 'yes'\n if animate == 'no':\n gender = 'neuter'\n \n lex = 'he'\n if person and not (person == 'third'):\n lex = lexicon.PRONOUNS_PERSON[person]\n if number and not (number == 'masculine'):\n lex = lexicon.PROUNOUNS_NUMBER[number]\n if gender and not (gender == 'masculine'):\n lex = lexicon.PRONOUNS_GENDER[gender]\n if case and not (case == 'subjective'):\n lex = lexicon.PRONOUNS_CASE[case]\n else:\n return lex\n\n if pronoun-type and pronoun-type == 'demonstrative':\n if not (number in ['first', 'second']):\n if distance == 'far':\n return 'those'\n else:\n return 'these'\n elif distance == 'far':\n return 'that'\n else: \n return 'this'\n\n if pronoun-type == 'relative':\n if not animate:\n animate = 'no'\n if animate == 'no':\n gender = 'neuter'\n if restrictive == 'yes':\n return 'that'\n elif case == 'possessive':\n return 'whose'\n elif case == 'objective':\n if gender == 'neuter':\n return 'which'\n else:\n return 'whom'\n elif gender == 'neuter':\n return 'which'\n elif animate == 'no':\n return 'which'\n else:\n return 'who'\n \n if pronoun-type == 'question':\n # this is the conversion of another hack in FUF morphology\n # start at line 76 in morphology.scm\n if not animate:\n animate == 'no'\n if animate == 'no':\n gender == 'neuter'\n if restrictive == 'yes':\n return 'which'\n elif case == 'possessive':\n return 'whose'\n elif case == 'objective':\n if gender == 'neuter':\n return 'what'\n else:\n return 'whom'\n elif gender == 'neuter':\n return 'what'\n elif animate == 'no':\n return 'what'\n else:\n return 'who'\n \n if pronoun-type == 'quantified':\n return lex","def test_noun_chunks_is_parsed(fi_tokenizer):\n doc = fi_tokenizer(\"Tämä on testi\")\n with pytest.raises(ValueError):\n list(doc.noun_chunks)","def has_annotations(filepath):\n return filepath.endswith('.ll') and '[#uses=' in open(filepath).read()","def ParseYesNo(src):\n return src.strip().lower() == u'yes'","def get_nouns(lemmas_tags):\r\n nouns = []\r\n for lemma in lemmas_tags:\r\n \"\"\"si la etiqueta es None porque no tiene lemma o es un sustantivo\"\"\"\r\n if lemma[1] == None or lemma[1][0] == 'n':\r\n \"\"\"se agrega solamente el lemma\"\"\"\r\n nouns.append(lemma[0])\r\n return nouns","def strip_proper_pos(text: Union[List[str], str]) -> List[str]:\n\n text = __join_if_list(text)\n try:\n tagged = pos_tag(text.split())\n except LookupError:\n nltk.download('averaged_perceptron_tagger')\n tagged = pos_tag(text.split())\n\n without_propernouns = [word for word, pos in tagged if pos is not 'NPP' and pos is not 'NNPS']\n return without_propernouns","def check_marked_paragraph(paragraph, number):\n\n\tq = 0 # счетчик найденных маркеров\n\tchars = '<> ' # возможные символы в каретке\n\n\tfor i in range(len(paragraph.runs)):\n\t\tif \"<>\" in paragraph.runs[i].text: # если в тексте каретки встречается маркер\n\t\t\tfor c in paragraph.runs[i].text: # проверяем каждый символ в каретке\n\t\t\t\tif c not in chars: # если он не входит в список разрешенных символов\n\t\t\t\t\treturn False\n\t\t\tq += 1 # если проверка пройдена, увеличиваем счетчик\n\t\telif \"<\" in paragraph.runs[i].text and \">\" in paragraph.runs[i+1].text: # если маркер разделен на две соседние каретки\n\t\t\tfor c in paragraph.runs[i].text: # проверяем каждую из кареток\n\t\t\t\tif c not in chars:\n\t\t\t\t\treturn False\n\t\t\tfor c in paragraph.runs[i+1].text:\n\t\t\t\tif c not in chars:\n\t\t\t\t\treturn False\n\t\t\tq += 1\n\n\tif q != number: # если количество маркеров не совпало с указанным в выводе\n\t\treturn False\n\telse:\n\t\treturn True","def is_pod(self):\r\n return conf.lib.clang_isPODType(self)","def _get_prep_with_word(token: spacy.tokens.Token) -> (str, spacy.tokens.Token):\n if token is None:\n return \"\", None\n\n prep = None\n # search of prepositions\n for child in token.rights:\n if child.dep_ == \"prep\":\n prep = child\n break\n if prep is None:\n return \"\", None\n\n for word in prep.children:\n # if preposition has child of type 'object of preposition' or 'complement of a preposition'\n # then add it to the result\n if word.dep_ in [\"pobj\", \"pcomp\"]:\n chunk_str = SpacyEventExtractor._get_chunk(word)\n return str(prep) + \" \" + chunk_str, word\n\n return \"\", None","def penn2morphy(self,penntag):\r\n morphy_tag = {'NN':'n', 'JJ':'a',\r\n 'VB':'v', 'RB':'r'}\r\n try:\r\n return morphy_tag[penntag[:2]]\r\n except:\r\n return 'n'","def isOkToBoost(self, node):\n para = \"p\"\n stepsAway = 0\n minimumStopWordCount = 5\n maxStepsAwayFromNode = 3\n \n nodes = self.walkSiblings(node)\n for currentNode in nodes:\n # p\n if currentNode.tag == para:\n if stepsAway >= maxStepsAwayFromNode:\n return False\n paraText = Parser.getText(currentNode)\n wordStats = StopWords().getStopWordCount(paraText)\n if wordStats.getStopWordCount > minimumStopWordCount:\n return True\n stepsAway += 1\n return False","def is_arxiv_post_2007(val):\n return arxiv_post_2007_regexp.match(val) or arxiv_post_2007_with_class_regexp.match(\n val\n )","def penn2morphy(penntag):\n morphy_tag = {'NN':'n', 'JJ':'a',\n 'VB':'v', 'RB':'r'}\n try:\n return morphy_tag[penntag[:2]]\n except:\n return 'n'"],"string":"[\n \"def contains_pronoun(cluster):\\n for mention in cluster:\\n if any([w.tag_.startswith(\\\"PRP\\\") for w in mention]):\\n # Found a mention with a pronoun\\n return True\\n return False\",\n \"def find_pronoun(sent):\\n pronoun = None\\n\\n for word, part_of_speech in sent.pos_tags:\\n # Disambiguate pronouns\\n if part_of_speech == 'PRP' and word.lower() == 'you':\\n # pronoun = 'I' +++++++++ORIGINAL++++++++++++\\n pronoun = 'I'\\n elif part_of_speech == 'PRP' and word == 'I':\\n # If the user mentioned themselves, then they will definitely be the pronoun\\n # pronoun = 'You' +++++++++ORIGINAL++++++++++++\\n pronoun = 'You'\\n return pronoun\",\n \"def is_noun(tag):\\r\\n return tag in ['NN', 'NNS', 'NNP', 'NNPS']\",\n \"def is_pronounced(word):\\n pronounced = [\\\"pronounced\\\", \\\"pronunciation\\\", \\\"pronounsed\\\", \\\"pronouced\\\", \\\"pronouned\\\", \\\\\\n \\\"pronounciated\\\", \\\"prenounced\\\", \\\"prounouced\\\", \\\"pernounced\\\", \\\"purnounced\\\", \\\\\\n \\\"pronoused\\\", \\\"pronuced\\\", \\\"pronunced\\\", \\\"pronnounced\\\", \\\"pronanced\\\", \\\\\\n \\\"prononced\\\", \\\"prounounced\\\", \\\"prononsed\\\", \\\"prononuced\\\", \\\"pernunciation\\\", \\\\\\n \\\"prononciation\\\", \\\"prounciation\\\", \\\"pronouciation\\\", \\\"pronounciated\\\", \\\\\\n \\\"pronounciation\\\", \\\"pronanciation\\\", \\\"prononcation\\\", \\\"pernounciation\\\", \\\\\\n \\\"prononceation\\\", \\\"prenunciation\\\", \\\"prononseation\\\", \\\"prounouciation\\\", \\\\\\n \\\"pronuniation\\\", \\\"pronunication\\\", \\\"prenounciation\\\", \\\"pronuntiation\\\", \\\\\\n \\\"pronuncition\\\", \\\"pronociation\\\", \\\"prenunsiation\\\", \\\"pronounsation\\\", \\\\\\n \\\"pronounceation\\\", \\\"pronounication\\\", \\\"pronauciation\\\", \\\"pronounciacion\\\", \\\\\\n \\\"pronounsiation\\\"]\\n for p in pronounced:\\n if word.lower() == p:\\n return True\\n return False\",\n \"def is_sonnet(poem):\\n return len([line for line in poem.split(\\\"\\\\n\\\") if line]) == 14\",\n \"def verifyProperNounAtSentenceStart(idx, tagged_term, tagged_terms, lexicon):\\n term, tag, norm = tagged_term\\n if (tag in ('NNP', 'NNPS') and\\n (idx == 0 or tagged_terms[idx-1][1] == '.')):\\n lower_term = term.lower()\\n lower_tag = lexicon.get(lower_term)\\n if lower_tag in ('NN', 'NNS'):\\n tagged_term[0] = tagged_term[2] = lower_term\\n tagged_term[1] = lower_tag\",\n \"def _entry_morphophonemics_annotation_is_valid(entry: _LexiconEntry) -> None:\\n compound = _is_compound_of(entry)\\n morphophonemics = _morphophonemics_of(entry)\\n\\n if compound == \\\"true\\\" and morphophonemics == \\\"~\\\":\\n raise InvalidLexiconEntryError(\\n \\\"Entry is marked as ending with compounding marker but it is missing\\\"\\n \\\" morphophonemics annotation.\\\")\",\n \"def is_noun(tag_string):\\n result = True if tag_string in POS.POS_tags.noun_tags else False\\n return result\",\n \"def is_adjective(tag):\\r\\n return tag in ['JJ', 'JJR', 'JJS']\",\n \"def can_generate_ransom_note(self):\\n if self.ransom_text == '' or self.ransom_text == ' ':\\n return True\\n ransom_text_words = self.ransom_text.split(' ')\\n magazine_text_words = self.magazine_text.split(' ')\\n # counting the occurrences of words in the ransom and magazine texts.\\n ransom_count = self._count_words_in_string(ransom_text_words)\\n magazine_count = self._count_words_in_string(magazine_text_words)\\n result = False\\n for i in ransom_text_words:\\n # if magazine_count hashmap doesn't have word\\n if magazine_count.get(i) is None:\\n result = False\\n break\\n # if ransom_count hashmap have less word occurances than magazine count.\\n if ransom_count.get(i) <= magazine_count.get(i):\\n result = True\\n else:\\n result = False\\n break\\n return result\",\n \"def is_annotated(page_id, line_n, token, annotations):\\n positions = [p for a in annotations for p in a[\\\"positions\\\"]]\\n for position in positions:\\n if(position[\\\"page_id\\\"]==page_id):\\n if(position[\\\"line_n\\\"]==line_n):\\n if(token[\\\"offset_start\\\"] >= position[\\\"start\\\"]):\\n if(token[\\\"offset_end\\\"] <= position[\\\"end\\\"]):\\n return True\\n return False\",\n \"def _is_annotated(nodes: List[Node]):\\n annotated = False\\n for node in nodes:\\n annotated = annotated or (\\n \\\"quantization_annotation\\\" in node.meta\\n and node.meta[\\\"quantization_annotation\\\"]._annotated\\n )\\n return annotated\",\n \"def _check_personal_pronouns(pronoun: str, last_nouns: list) -> list:\\n pronoun_details = []\\n pronoun_lower = pronoun.lower()\\n if pronoun == 'I' or pronoun_lower in ('me', 'myself', 'my'):\\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\\n elif pronoun_lower in ('we', 'us', 'ourselves', 'our'):\\n # Find singular or plural person nouns (any gender)\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, True))\\n pronoun_details.append(('Narrator', 'SINGPERSON', ':Person', ':Narrator'))\\n elif pronoun_lower in ('they', 'them', 'themselves', 'their'):\\n # Give preference to persons (any gender or number)\\n noun_list = _check_criteria(pronoun_lower, last_nouns, None, None, True)\\n if noun_list:\\n pronoun_details.extend(noun_list)\\n else:\\n # Check for non-persons\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, None, None, False))\\n elif pronoun_lower in ('she', 'herself', 'her'):\\n # Find singular, feminine, person nouns\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, True, True))\\n elif pronoun_lower in ('he', 'himself', 'him'):\\n # Find singular, masculine, person nouns\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, False, True))\\n elif pronoun_lower in ('it', 'itself', 'its'):\\n # Find singular, non-person nouns (no gender)\\n pronoun_details.extend(_check_criteria(pronoun_lower, last_nouns, True, None, False))\\n final_details = [] # May be duplicates in the list due to duplicates in last_nouns\\n for pronoun_detail in pronoun_details:\\n if pronoun_detail in final_details:\\n continue\\n final_details.append(pronoun_detail)\\n return final_details\",\n \"def nao_tem_passageiros(self):\\n return self.counter.ja_viajaram == self.counter.num_passageiros\",\n \"def _is_opinion_mod(token: tokens.Token) -> bool:\\n is_mod = token.dep_ in {\\\"amod\\\", \\\"advmod\\\"}\\n is_op = token.text.lower() in _OPINION_WORDS\\n return is_mod and is_op\",\n \"def is_lyrics_approved():\",\n \"def is_psionic(self) -> bool:\\n return ATTRIBUTE.Psionic.value in self.type_data.attributes\",\n \"def rule_vb_prp_nn(words):\\n if index_tag_seq(words, ['VB', 'PRP$', 'NNS', 'RB']) > -1:\\n return move_tag_seq(words, ['VB', 'PRP$', 'NNS'], 'end')\\n return None\",\n \"def check_pra_symbol(symbol):\\n # Platts\\n if len(symbol) == 7 and symbol[:2] in [\\n 'PC', 'PA', 'AA', 'PU', 'F1', 'PH', 'PJ', 'PG', 'PO', 'PP', ]:\\n return True\\n\\n # Argus\\n if '.' in symbol:\\n sm = symbol.split('.')[0]\\n if len(sm) == 9 and sm.startswith('PA'):\\n return True\\n\\n return False\",\n \"def isOk(annots):\\n if annots == []:\\n return True\\n for a in annots:\\n for label in a.labels:\\n if (label != 'hasOkCopies' and\\n label != 'hasBadCopies' and\\n not label.startswith('count_')):\\n return False\\n return True\",\n \"def check_if_extra_pronoun(pronoun_dict):\\n for key, value in sorted(pronoun_dict.iteritems(), key=lambda (k,v): (v,k), reverse = True):\\n skip = False\\n for pronoun_list in all_pronouns:\\n if key in pronoun_list:\\n skip = True\\n break\\n if not skip:\\n print \\\"%s: %s\\\" % (key, value)\",\n \"def __isNoun__(self, word):\\n self.nouns = ('door', 'bear', 'princess', 'cabinet')\\n for noun in self.nouns:\\n if noun == word:\\n return ('noun', word), True\\n return None, False\",\n \"def isDisambiguatedByNextNoun(self, word):\\n\\t\\treturn 'noun' in disambig_const.DISAMBIGUATATION_TABLE.get(word, {});\",\n \"def token_filter(tok):\\n return tok is token or \\\\\\n tok.dep_.endswith(\\\"mod\\\") or \\\\\\n tok.dep_ == \\\"compound\\\"\",\n \"def are_present_quiz(verb, pronoun):\\n\\n return functions.conjugate_present_are_verb(verb, pronoun, \\\"presente\\\")\",\n \"def _does_token_overlap_with_annotation(\\n token: Token, annot_start: int, annot_end: int\\n) -> bool:\\n\\n return (\\n annot_start <= token.idx <= annot_end\\n or token.idx <= annot_start <= token.idx + len(token)\\n )\",\n \"def check_if_replacable(self, word):\\n word_tag = pos_tag([word])\\n if 'NN' in word_tag[0][1] or 'JJ' in word_tag[0][1] or 'VB' in word_tag[0][1]:\\n return True\\n else:\\n return False\",\n \"def additional_text_preprocessing_with_pos(pos_dict):\\n\\n tags_to_lemmatize = ['a', 'n', 'v', 'r']\\n\\n pos_dict = TextPreprocessor.find_named_entities(pos_dict)\\n if pos_dict is None:\\n return None, None\\n prepro = list()\\n contains_spelling_mistake = False\\n for t in pos_dict:\\n token = t['token']\\n tag = t['tag'].lower()\\n if token not in TextPreprocessor.PUNCTUATION and tag != \\\",\\\":\\n\\n token = TextPreprocessor.replace_user_mentions(token)\\n token = TextPreprocessor.replace_urls(token)\\n replaced = [token]\\n for i in replaced:\\n\\n i = TextPreprocessor.replace_all_punctuation(i)\\n if i.lower() not in TextPreprocessor.STOPWORDS and i != 'URL' and i!= 'USERMENTION':\\n if i != \\\"\\\" and not re.match('\\\\B#\\\\w*[a-zA-Z]+\\\\w*', i):\\n before = i\\n i = TextPreprocessor.SPELL_CHECKER.correct(i, tag)\\n if i != before:\\n contains_spelling_mistake = True\\n if tag in tags_to_lemmatize:\\n i = TextPreprocessor.lemmatize(i, tag)\\n i = TextPreprocessor.stem(i, tag)\\n # check again, since stemming, lemmatization or spelling correction can produce stopwords\\n # if i.lower() not in TextPreprocessor.STOPWORDS:\\n if i != 'URL' and i!= 'USERMENTION' and i!='':\\n i = i.lower()\\n if re.match(\\\".*[a-zA-Z]'\\\", i):\\n i = i[:-1]\\n prepro.append(i)\\n return prepro, contains_spelling_mistake\",\n \"def _is_valid_pt(content_type: str) -> bool:\\n content_type = content_type.strip()\\n return content_type in SPECIFICATION_PRIMITIVE_TYPES\",\n \"def test_spires_syntax_detected_naked_author_leading_spaces(self):\\n converter = search_engine_query_parser.SpiresToInvenioSyntaxConverter()\\n spi_search = converter.is_applicable(\\\" author ellis\\\")\\n self.assertEqual(spi_search, True)\",\n \"def commence_par(mot, prefixe)->bool:\\n return mot[:len(prefixe)] == prefixe\",\n \"def _canProcessTags(self, grammar, pos_tags):\\n badTags = []\\n for tag in pos_tags:\\n if tag not in grammar.tags:\\n badTags.append(tag)\\n logger.debug(\\\"Grammar can't handle tag:\\\" + tag)\\n if badTags:\\n return False\\n else:\\n return True\",\n \"def is_projective(sent):\\n spans = []\\n for token in sent:\\n s = tuple(sorted([token[\\\"id\\\"],token[\\\"parent\\\"]]))\\n spans.append(s)\\n spans = sorted(spans)\\n for l,h in spans:\\n for l1, h1 in spans:\\n if (l,h) == (l1,h1): continue\\n if l< l1 < h and h1 > h:\\n return False\\n return True\",\n \"def are_subjunctivepr_quiz(verb, pronoun):\\n return functions.conjugate_subjunctivepr_are_verb(verb, pronoun, \\\"congiuntivo\\\")\",\n \"def startsParagraph(s: str) -> bool:\\n if not s.strip():\\n val = False\\n elif s.strip() in ('\\\"\\\"\\\"', \\\"'''\\\"):\\n val = True\\n elif s[0].isdigit():\\n i = 0\\n while i < len(s) and s[i].isdigit():\\n i += 1\\n val = g.match(s, i, ')') or g.match(s, i, '.')\\n elif s[0].isalpha():\\n # Careful: single characters only.\\n # This could cause problems in some situations.\\n val = (\\n (g.match(s, 1, ')') or g.match(s, 1, '.')) and\\n (len(s) < 2 or s[2] in ' \\\\t\\\\n'))\\n else:\\n val = s.startswith('@') or s.startswith('-')\\n return val\",\n \"def pob(self):\\n if self.index >= self.length:\\n return False\\n \\n self._pob, n = self.parse_pob()\\n if self._pob is not None:\\n self.idx_pob = self.index\\n self.index += n\\n self.isaddr = True\\n if self.index < self.length and self.words[self.index]['word'] == ',':\\n self.index += 1\\n if self._debug: print(\\\"POB\\\", self._pob, self.idx_pob)\\n return True\\n return False\",\n \"def filter_tree(tree):\\n return find_pos(tree)\\n\\n # if tree.parent() is None:\\n # return False\\n\\n # noun_phrase = re.match(\\\"NP|VP\\\", tree.parent().label())\\n # pos = re.match(\\\"NN.*|JJ|CD|VBN\\\", tree.label())\\n # return noun_phrase and pos\",\n \"def _is_compound_of(entry: _LexiconEntry) -> str:\\n return entry[\\\"is_compound\\\"].lower()\",\n \"def get_nouns(root):\\n nouns = []\\n for child in root.findall(\\\"./xdrs/taggedtokens/tagtoken/tags\\\"):\\n noun = False\\n for grandchildren in child.findall(\\\"./tag[@type='pos']\\\"):\\n if grandchildren.text == 'NN' or grandchildren.text == 'NNS':\\n noun = True\\n if noun == True:\\n for grandchildren in child.findall(\\\"./tag[@type='lemma']\\\"):\\n nouns.append(grandchildren.text)\\n return nouns\",\n \"def are_subjunctiveimp_quiz(verb, pronoun):\\n return functions.conjugate_subjunctiveimp_are_verb(verb, pronoun, \\\"congiuntivo\\\")\",\n \"def check_for_comment_about_bot(pronoun, noun, adjective):\\n resp = None\\n if pronoun == 'I' and (noun or adjective):\\n pprint(\\\"WORRRRRRRRRRKING\\\")\\n if noun:\\n if random.choice((True, False)):\\n resp = random.choice(SELF_VERBS_WITH_NOUN_CAPS_PLURAL).format(**{'noun': noun.pluralize().capitalize()})\\n else:\\n resp = random.choice(SELF_VERBS_WITH_NOUN_LOWER).format(**{'noun': noun})\\n else:\\n resp = random.choice(SELF_VERBS_WITH_ADJECTIVE).format(**{'adjective': adjective})\\n return resp\",\n \"def get_extras(self, text=None):\\n if text is None:\\n text = self.nltk_text(self.text)\\n # Tag parts of speech\\n tagged = nltk.pos_tag(text)\\n # Try for composed NNP / NNPS\\n is_proper_noun = False\\n text = []\\n proper_noun = \\\"\\\"\\n for (word, tag) in tagged:\\n if not is_proper_noun and (tag == 'NNP' or tag == 'NNPS'):\\n # Start building a proper noun\\n proper_noun = word\\n # Set it true\\n is_proper_noun = True\\n # Add it to annotations anyway\\n text.append(word)\\n elif tag == 'NNP' or tag == 'NNPS':\\n # Previous was proper noun. So it may be combined\\n proper_noun += \\\" \\\" + word\\n # Add the single word to annotations anyway\\n text.append(word)\\n elif is_proper_noun and tag == 'IN':\\n # Add what we have by now to the text\\n text.append(proper_noun)\\n # Previous was proper noun. So it may be composed\\n proper_noun += \\\" \\\" + word\\n elif is_proper_noun:\\n # Add what we have by now to the text\\n text.append(proper_noun)\\n # Finished with proper noun, so set it false\\n is_proper_noun = False\\n # Remove duplicates\\n seen = {}\\n result = []\\n for w in text:\\n if w in seen:\\n continue\\n seen[w] = 1\\n result.append(w)\\n # Eliminate common\\n result = [w for w in result if w.lower() not in self.common_words and\\n w.lower() not in stopwords.words('english')]\\n return result\",\n \"def check_for_metatitle(self, interest_name: str):\\n if interest_name.endswith(\\\"/streaming/p*\\\"):\\n return True\\n else:\\n return False\",\n \"def is_complete(self):\\n return all([\\n len(strip_tags(score.notes)) > 0 for score in self.scores.all()\\n ])\",\n \"def parse_pob(self):\\n \\n index = self.index\\n start = self.index \\n \\n if self.words[index]['word'] == 'pob' or self.words[index]['word'] == 'pmb':\\n index += 1\\n if index == self.length:\\n return None, 0\\n if self.words[index]['word'] == '.':\\n return self.words[index+1]['word'], 3\\n else:\\n return self.words[index]['word'], 2\\n elif self.words[index]['word'] == 'p':\\n index += 1\\n if index == self.length:\\n return None, 0\\n if self.words[index]['word'] == '.':\\n index += 1\\n if index == self.length:\\n return None, 0\\n if self.words[index]['word'] not in ['o', 'm']:\\n return None, 0\\n index += 1\\n if index == self.length:\\n return None, 0\\n if self.words[index]['word'] == '.':\\n index += 1\\n if index == self.length:\\n return None, 0\\n if self.words[index]['word'] in ['b', 'box']:\\n index += 1\\n if index == self.length:\\n return None, 0\\n elif not self.words[index]['word'].isdigit():\\n return None,0\\n if self.words[index]['word'] == '.':\\n index += 1\\n if index == self.length:\\n return None, 0\\n return self.words[index]['word'], index - start + 1\\n \\n if self.words[index]['word'] == 'po':\\n index += 1\\n if index == self.length:\\n return None, 0\\n if self.words[index]['word'] == 'box':\\n index += 1\\n if index == self.length:\\n return None, 0\\n return self.words[index]['word'], index - start + 1\\n \\n return None, 0\",\n \"def mountable(self):\\n return True\",\n \"def convert(token):\\n trait = Trait(\\n value=\\\"pregnant\\\" if token.group.get(\\\"pos\\\") else \\\"not pregnant\\\",\\n start=token.start,\\n end=token.end,\\n )\\n return trait\",\n \"def replace_last_pronoun(schema, pronoun):\\n pronouns = [\\n \\\"eles\\\",\\n \\\"ela\\\",\\n \\\"ele\\\",\\n \\\"os\\\",\\n \\\"dela\\\",\\n \\\"it\\\",\\n \\\"o\\\",\\n \\\"la\\\",\\n \\\"lo\\\",\\n \\\"a\\\",\\n \\\"la\\\",\\n \\\"las\\\",\\n \\\"deles\\\",\\n \\\"delas\\\",\\n \\\"dele\\\",\\n \\\"sua\\\",\\n \\\"seu\\\",\\n \\\"suas\\\",\\n ]\\n\\n replaced = []\\n\\n # Aqui substituo todas as marcações no texto por ela mesmo com espaço antes, pois senão o split faz com que a palavra contenha esse sinal nela\\n # Com o espaço antes, no split() o sinal de pontuação vira uma palavra. Ex: Olá. -> [\\\"Olá\\\", \\\".\\\"]\\n reversed_split_sentence = schema.replace(\\\"'\\\", \\\" '\\\")\\n reversed_split_sentence = reversed_split_sentence.replace('\\\"', ' \\\"')\\n reversed_split_sentence = reversed_split_sentence.replace(\\\".\\\", \\\" .\\\")\\n reversed_split_sentence = reversed_split_sentence.replace(\\\",\\\", \\\" ,\\\")\\n reversed_split_sentence = reversed_split_sentence.replace(\\\"-\\\", \\\" - \\\")\\n\\n reversed_split_sentence = reversed_split_sentence.split()\\n reversed_split_sentence.reverse()\\n\\n already_replaced = False\\n for word in reversed_split_sentence:\\n if word.lower() == pronoun.lower() and not already_replaced:\\n word_to_replace = \\\"[MASK]\\\"\\n already_replaced = True\\n elif word.lower() == \\\"lo\\\" and pronoun.lower() == \\\"-lo\\\" and not already_replaced:\\n word_to_replace = \\\"[MASK]\\\"\\n already_replaced = True\\n else:\\n word_to_replace = word\\n replaced.append(word_to_replace)\\n\\n # Caso o pronome do snippet não seja encontrado na frase.\\n # Em português, não é para isso ocorrer, pois sempre o pronome do campo 'pronoun' vai ser encontrado\\n # na frase, mas é importante manter, caso tenhamos algum problema\\n if already_replaced == False:\\n print(\\\"SEM PRONOME\\\")\\n replaced = []\\n for word in reversed_split_sentence:\\n # Neste caso, pegamos o primeiro pronome, de trás para frente, que está na lista de pronomes válidos do WSC\\n if word.lower() in pronouns and not already_replaced:\\n word_to_replace = \\\"[MASK]\\\"\\n already_replaced = True\\n else:\\n word_to_replace = word\\n replaced.append(word_to_replace)\\n\\n replaced.reverse()\\n print(replaced)\\n\\n output = \\\" \\\".join(replaced)\\n\\n # Aqui desfazemos aquela artimanha de adicionar espaço antes de pontuações, para que fique correto na frase final\\n output = output.replace(\\\" '\\\", \\\"'\\\")\\n output = output.replace(' \\\"', '\\\"')\\n output = output.replace(\\\" .\\\", \\\".\\\")\\n output = output.replace(\\\" ,\\\", \\\",\\\")\\n output = output.replace(\\\" - \\\", \\\"-\\\")\\n\\n return output\",\n \"def is_punctuation_automata(final_word: str) -> list:\\n\\n punct_words = ({'+': 'symb_add', '-': 'symb_diff', '*': 'symb_mult', '/': 'symb_div', '>': 'symb_gt',\\n '<': 'symb_lt', '(': 'symb_oparentesis', ')': 'symb_cparentesis', '.':'symb_dot',\\n ':': 'symb_col',';':'symb_semicol',',':'symb_coma', '=': 'symb_eq',':=':'symb_assign',\\n '>=':'symb_gte', '<=':'symb_lte', '<>':'symb_neq'})\\n\\n if final_word in punct_words:\\n token = punct_words[final_word]\\n return [True, token]\\n else :\\n return [False, \\\"\\\"]\",\n \"def annotated_var(prs):\\n def trt(acc):\\n v,ann = acc \\n if len(ann) > 0:\\n return c.copy_token(v,{'annotation':ann[0]})\\n return v\\n return c.paren(var() + colon_annotation(prs)).treat(trt)\",\n \"def are_ere_conditionalpr_quiz(verb, pronoun):\\n return functions.conjugate_conditionalpr_are_ere_verb(verb, pronoun, \\\"condizionale\\\")\",\n \"def tokenize_annotations(annotation, binarize=True, \\n nostop=False, lower=True, stem=False, \\n special_features=False,\\n class_feature=False,\\n bigrams=False):\\n\\n if not type(annotation) == str:\\n annotation = \\\" \\\".join(annotation)\\n \\n\\n # different possibilities to tokenize\\n tokens = re.split(\\\"[\\\\\\\\s\\\\.\\\\,]+\\\", annotation)\\n \\n # lowercasing\\n if lower:\\n tokens = [t.lower() for t in tokens]\\n\\n\\n # stemming\\n if stem:\\n # adding stems to the tokens\\n tokens.extend([stemmer.stem(t) for t in tokens])\\n \\n\\n if nostop:\\n tokens = [t for t in tokens if t not in stopset]\\n\\n if bigrams:\\n n = 2\\n tokens.extend([\\\" \\\".join(tokens[i:i+n]) for i in xrange(len(tokens)-n+1)]) \\n\\n if special_features:\\n # numbers\\n #numbers = [\\\"SPECIAL:num\\\" for t in tokens if t.isdigit()]\\n #tokens.extend(set(numbers))\\n \\n # files\\n #files = [\\\"SPECIAL:file\\\" for t in tokens if \\\"file\\\" in t]\\n #files = [t for t in tokens if \\\"file\\\" not in t]\\n if any(\\\"file\\\" in t for t in tokens):\\n tokens.append(\\\"SPECIAL:file\\\")\\n\\n # squares\\n if any(square_pattern.match(t) != None for t in tokens):\\n tokens.append(\\\"SPECIAL:square\\\")\\n\\n # moves\\n if any(move_pattern.match(t) != None for t in tokens):\\n tokens.append(\\\"SPECIAL:move\\\")\\n\\n #tokens.extend(files)\\n \\n pass\\n \\n if class_feature:\\n tokens.append(\\\"<CLASS>\\\")\\n \\n if binarize:\\n tokens = set(tokens)\\n\\n\\n return tokens\",\n \"def __decide_pos(pos: str) -> str:\\n to_return = None\\n if pos == 'NOUN':\\n to_return = \\\"n\\\"\\n if pos == 'VERB':\\n to_return = 'v'\\n if pos == 'ADJ':\\n to_return = 'a'\\n if pos == 'ADV':\\n to_return = 'r'\\n return to_return\",\n \"def _process_prosody(sonority):\\n assert 9 not in sonority[1:-1]\\n assert sonority[0] == sonority[-1] == 9\\n\\n # create the output values\\n psequence = []\\n first = True # stores whether first syllable is currently being processed\\n\\n for i in range(1, len(sonority) - 1):\\n # get a segment with context\\n a, b, c = sonority[i - 1], sonority[i], sonority[i + 1]\\n\\n if b == 7: # a vowel\\n if first:\\n psequence.append('X')\\n first = False\\n elif c == 9: # last\\n psequence.append('Z')\\n else:\\n psequence.append('Y')\\n elif b == 8: # a tone\\n psequence.append('T')\\n elif a >= b >= c or c == 8: # descending\\n if c == 9: # word final position\\n psequence.append('Z' if b == 7 else 'N') # vowel or consonant\\n else:\\n if first:\\n first = False\\n psequence.append('A')\\n else:\\n psequence.append('L')\\n elif b < c or a > b <= c or a < b <= c: # ascending\\n # check for syllable first\\n if a == 9:\\n psequence.append('A')\\n elif a >= b:\\n if c == 9:\\n psequence.append('N')\\n else:\\n if psequence[-1] != 'A':\\n psequence = psequence[:-1] + [psequence[-1].replace('L', 'M')] + ['B']\\n else:\\n psequence.append('C')\\n else:\\n psequence.append('C')\\n elif a < b > c: # consonant peak\\n if first:\\n psequence.append('X')\\n first = False\\n else:\\n psequence.append('Y')\\n else:\\n raise ValueError(\\n \\\"Conversion to prosodic string failed due to a condition which was not \\\"\\n \\\"defined in the convertion, for details compare the numerical string \\\"\\n \\\"{0} with the profile string {1}\\\".format(sonority, psequence))\\n return psequence\",\n \"def test_spires_syntax_detected_naked_title(self):\\n converter = search_engine_query_parser.SpiresToInvenioSyntaxConverter()\\n spi_search = converter.is_applicable(\\\"t muon\\\")\\n self.assertEqual(spi_search, True)\",\n \"def _is_title(self):\\n ph = _child(self.__nvXxPr.nvPr, 'p:ph')\\n if ph is None:\\n return False\\n # idx defaults to 0 when idx attr is absent\\n ph_idx = ph.get('idx', '0')\\n # title placeholder is identified by idx of 0\\n return ph_idx == '0'\",\n \"def start_tag_or_none(self, token):\\n if self.patterns['start_tag'].match(token):\\n return token[2:-6].upper()\",\n \"def single_line_paragraph(s: str) -> bool:\\n return s.startswith('@') or s.strip() in ('\\\"\\\"\\\"', \\\"'''\\\")\",\n \"def is_apt(self):\\r\\n return self.has_label('apt')\",\n \"def is_punct(self, word, language):\",\n \"def validaAnno(anno):\\n valid = ((len(anno) == 4) and (int(anno)) > 1970)\\n if (valid and((len(anno) == 4) and (int(anno)) > 1970)):\\n valid=True\\n if(valid == False):\\n print(\\\"anno invalido.\\\")\\n labelinformativo('anno invalido')\\n return valid\",\n \"def _is_real_word(self, token):\\n return not (token in self._non_words)\",\n \"def token_key(token):\\n morphotagged = analysis(token).get('raw')\\n lemma_pos = (analysis(token).get('lemma'), analysis(token).get('partOfSpeech'))\\n return morphotagged or lemma_pos\",\n \"def nltk_condition(stem):\\n return len(stem) > 1 and self._is_consonant(stem, len(stem) - 1)\",\n \"def isImportantToken(self, token, ignoreSemanticTagList=[]):\\n if len(ignoreSemanticTagList) > 0: \\n tags = token.getSemanticTagMatches(ignoreSemanticTagList)\\n else:\\n tags = []\\n return token.isSymbol() == False \\\\\\n and token.text not in self.ignoreWords and len(tags) == 0\",\n \"def filter_pos(self):\\n all_tokens = []\\n for zettel in self.lemma_tokens:\\n tokens = []\\n for word in zettel:\\n if word[1] in ['NN', 'NNS', 'NNP', 'NNPS', 'NG']: # NG = n_gram\\n tokens.append(word)\\n all_tokens.append(tokens)\\n self.lemma_tokens = all_tokens\",\n \"def _is_simple_type(cls):\\n return all([\\n AnnotationWrapper(anno).is_simple_in_opt_and_not_opt\\n for anno in cls._used_annotations()\\n ])\",\n \"def is_neutral(self, P):\\n\\t\\treturn P.x is None\",\n \"def _is_positron(argument):\\n\\n if not isinstance(argument, str):\\n return False\\n\\n if argument == 'e+' or argument.lower() == 'positron':\\n return True\\n else:\\n return False\",\n \"def parse_token(self, token, tags=[WORD, POS, CHUNK, PNP, REL, ANCHOR, LEMMA]):\\n p = { WORD: \\\"\\\", \\n POS: None, \\n IOB: None,\\n CHUNK: None,\\n PNP: None,\\n REL: None,\\n ROLE: None,\\n ANCHOR: None,\\n LEMMA: None }\\n # Split the slash-formatted token into separate tags in the given order.\\n # Decode &slash; characters (usually in words and lemmata).\\n # Assume None for missing tags (except the word itself, which defaults to an empty string).\\n custom = {}\\n for k, v in izip(tags, token.split(\\\"/\\\")):\\n if SLASH0 in v:\\n v = v.replace(SLASH, \\\"/\\\")\\n if k not in p:\\n custom[k] = None\\n if v != OUTSIDE or k == WORD or k == LEMMA: # \\\"type O negative\\\" => \\\"O\\\" != OUTSIDE.\\n (p if k not in custom else custom)[k] = v\\n # Split IOB-prefix from the chunk tag:\\n # B- marks the start of a new chunk, \\n # I- marks inside of a chunk.\\n ch = p[CHUNK]\\n if ch is not None and ch.startswith((\\\"B-\\\", \\\"I-\\\")):\\n p[IOB], p[CHUNK] = ch[:1], ch[2:] # B-NP\\n # Split the role from the relation:\\n # NP-SBJ-1 => relation id is 1 and role is SBJ, \\n # VP-1 => relation id is 1 with no role.\\n # Tokens may be tagged with multiple relations (e.g., NP-OBJ-1*NP-OBJ-3).\\n if p[REL] is not None:\\n ch, p[REL], p[ROLE] = self._parse_relation(p[REL])\\n # Infer a missing chunk tag from the relation tag (e.g., NP-SBJ-1 => NP).\\n # For PP relation tags (e.g., PP-CLR-1), the first chunk is PP, the following chunks NP.\\n if ch == \\\"PP\\\" \\\\\\n and self._previous \\\\\\n and self._previous[REL] == p[REL] \\\\\\n and self._previous[ROLE] == p[ROLE]: \\n ch = \\\"NP\\\"\\n if p[CHUNK] is None and ch != OUTSIDE:\\n p[CHUNK] = ch\\n self._previous = p\\n # Return the tags in the right order for Sentence.append().\\n return p[WORD], p[LEMMA], p[POS], p[CHUNK], p[ROLE], p[REL], p[PNP], p[ANCHOR], p[IOB], custom\",\n \"def findPOS(word):\\r\\n\\t\\r\\n lisPOS = list(wordtags[word])\\r\\n if \\\"ADJ\\\" in lisPOS:\\r\\n return \\\"ADJECTIVE\\\"\\r\\n if \\\"ADV\\\" in lisPOS:\\r\\n return \\\"ADVERB\\\"\\r\\n if \\\"NOUN\\\" in lisPOS:\\r\\n return \\\"NOUN\\\"\",\n \"def sobrou_pessoas(self):\\n return self.counter.ja_viajaram + self.counter.capacidade_max_carro > self.counter.num_passageiros\",\n \"def is_token_marker(self):\\n return self.id in TOKEN\",\n \"def annotated_vars(prs):\\n def trt(acc):\\n vs,ann = acc\\n if len(ann)==0:\\n return vs\\n assert(len(ann)==1)\\n return [c.copy_token(v,ann[0]) for v in vs]\\n return c.paren(var().plus() + colon_annotation_or_meta()).treat(trt)\",\n \"def has_osm_data(self):\\n return settings.MSPRAY_OSM_PRESENCE_FIELD in self.data\",\n \"def check_format_of_annotation_in_file(self):\\n if not self.is_span_valid():\\n sys.exit()\",\n \"def atPodium(self, sparseOK=False, one_mOK=False):\\n pos = self.getPos()\\n ok = (pos == self.GANG_ON_PODIUM) or (pos == self.GANG_AT_DENSE)\\n if sparseOK:\\n ok = ok or (pos == self.GANG_AT_SPARSE)\\n if one_mOK:\\n ok = ok or (pos == self.GANG_AT_1M)\\n\\n return ok\",\n \"def specific_pos_score(self, word):\\n if word[0][1] == 'NNP':\\n return 5\\n if word[0][1] == 'NN':\\n return 2\\n else:\\n return 1\",\n \"def test_3():\\n results = base_tests(long_anno=1)\\n assert results[0][\\\"Consequence\\\"] == \\\"To Long Annotation\\\"\",\n \"def get_nps(self):\\n\\n # determine all leaf ids in the parse tree which refer to a noun\\n nouns = []\\n for node_id in self.parsetree.nodes():\\n node = self.parsetree.node[node_id]\\n if not node['is_leaf']:\\n continue\\n leaf_idx = node['left_leaf_idx']\\n if leaf_idx >= len(self.tokens):\\n continue\\n self.words[leaf_idx] == node['label']\\n is_noun = self.tokens[leaf_idx].POS.cdata[0] == 'N'\\n if is_noun:\\n nouns.append(node_id)\\n\\n NPs = set()\\n for noun in nouns:\\n NPs.add(self.get_np_for_idx(noun))\\n return NPs\",\n \"def tag2predictions(ote_tag_sequence, ts_tag_sequence):\\n n_tag = len(ote_tag_sequence)\\n # opinion target sequence and targeted sentiment sequence\\n ot_sequence, ts_sequence = [], []\\n beg, end = -1, -1\\n for i in range(n_tag):\\n tag = ote_tag_sequence[i]\\n if tag == 'S':\\n ot_sequence.append((i, i))\\n elif tag == 'B':\\n beg = i\\n elif tag == 'E':\\n end = i\\n if end > beg and beg != -1:\\n ot_sequence.append((beg, end))\\n beg, end = -1, -1\\n sentiments = []\\n beg, end = -1, -1\\n for i in range(n_tag):\\n ts_tag = ts_tag_sequence[i]\\n # current position and sentiment\\n eles = ts_tag.split('-')\\n if len(eles) == 2:\\n pos, sentiment = eles\\n else:\\n pos, sentiment = 'O', 'O'\\n if sentiment != 'O':\\n # current word is a subjective word\\n sentiments.append(sentiment)\\n if pos == 'S':\\n # singleton\\n ts_sequence.append((i, i, sentiments[0]))\\n sentiments = []\\n elif pos == 'B':\\n beg = i\\n elif pos == 'E':\\n end = i\\n # schema1: only the consistent sentiment tags are accepted\\n # that is, all of the sentiment tags are the same\\n if end > beg > -1 and len(set(sentiments)) == 1:\\n ts_sequence.append((beg, end, sentiment))\\n sentiments = []\\n beg, end = -1, -1\\n\\n # schema2: only consider the sentiment at the beginning of the aspect span\\n # if end > beg > -1:\\n # ts_sequence.append((beg, end, sentiments[0]))\\n # sentiments = []\\n # beg, end = -1, -1\\n return ot_sequence, ts_sequence\",\n \"def pron_lemma(word):\\n if re.match(r\\\"(their|theirs|them|themselves)\\\", word):\\n return (\\\"they\\\")\\n elif re.match(r\\\"(his|him|himself)\\\", word):\\n return (\\\"he\\\")\\n elif re.match(r\\\"(her|hers|herself)\\\", word):\\n return (\\\"she\\\")\\n elif re.match(r\\\"(its|itself)\\\", word):\\n return (\\\"it\\\")\\n elif re.match(r\\\"(your|yours|yourself)\\\", word):\\n return (\\\"you\\\")\\n elif re.match(r\\\"(our|us|ours)\\\", word):\\n return (\\\"we\\\")\\n elif re.match(r\\\"(me|mine|my|myself)\\\", word):\\n return (\\\"I\\\")\\n elif word == (\\\"I\\\"):\\n return word\\n else:\\n return word.lower()\",\n \"def _is_auxilary(token, chunk_token_ids):\\n if (token.head.i in chunk_token_ids and # if dep-tree current token's parent (head) is somewhere inside the main part of PC represented as chunk_token_ids (sequence of numeric token identifiers)\\n token.dep_ == 'aux' and # if the dep-tree label on the edge between some word from main part of PC and current token is AUX (auxilary)\\n token.i not in chunk_token_ids): # if token is outside of detected main part of PC\\n return True # yep, it's auxilary\\n else:\\n return False\",\n \"def ifValidSERP(soupObject):\\n return soupObject.find(\\\"p\\\", class_=\\\"noresult_tit\\\") is None and soupObject.title is not None\",\n \"def _parse_unground_proposition(self, array):\\n negative = False\\n if array[1] == 'not':\\n negative = True\\n array = array[2:-1]\\n return Predicate(array[1], array[2:-1], False, negative)\",\n \"def is_token_instance(self, t: Token) -> bool:\\n return t.token_type == TokenType.AMPLIFICATION\",\n \"def is_pos(self, term):\\n return term in self.pos\",\n \"def morph_pronoun(lex, pronoun_type, case, gender, number, distance, animate,\\n person, restrictive):\\n if lex and isinstance(lex, basestring) and not (lex in ['none', 'nil']):\\n return lex\\n if pronoun_type == 'personal':\\n # start with the 'he' then augmen by person, then, by number, \\n # gender and finally by case\\n # this is a port of the hack in the morphology.scm code\\n if (not animate) or (gender == 'feminine'):\\n anime = 'yes'\\n if animate == 'no':\\n gender = 'neuter'\\n \\n lex = 'he'\\n if person and not (person == 'third'):\\n lex = lexicon.PRONOUNS_PERSON[person]\\n if number and not (number == 'masculine'):\\n lex = lexicon.PROUNOUNS_NUMBER[number]\\n if gender and not (gender == 'masculine'):\\n lex = lexicon.PRONOUNS_GENDER[gender]\\n if case and not (case == 'subjective'):\\n lex = lexicon.PRONOUNS_CASE[case]\\n else:\\n return lex\\n\\n if pronoun-type and pronoun-type == 'demonstrative':\\n if not (number in ['first', 'second']):\\n if distance == 'far':\\n return 'those'\\n else:\\n return 'these'\\n elif distance == 'far':\\n return 'that'\\n else: \\n return 'this'\\n\\n if pronoun-type == 'relative':\\n if not animate:\\n animate = 'no'\\n if animate == 'no':\\n gender = 'neuter'\\n if restrictive == 'yes':\\n return 'that'\\n elif case == 'possessive':\\n return 'whose'\\n elif case == 'objective':\\n if gender == 'neuter':\\n return 'which'\\n else:\\n return 'whom'\\n elif gender == 'neuter':\\n return 'which'\\n elif animate == 'no':\\n return 'which'\\n else:\\n return 'who'\\n \\n if pronoun-type == 'question':\\n # this is the conversion of another hack in FUF morphology\\n # start at line 76 in morphology.scm\\n if not animate:\\n animate == 'no'\\n if animate == 'no':\\n gender == 'neuter'\\n if restrictive == 'yes':\\n return 'which'\\n elif case == 'possessive':\\n return 'whose'\\n elif case == 'objective':\\n if gender == 'neuter':\\n return 'what'\\n else:\\n return 'whom'\\n elif gender == 'neuter':\\n return 'what'\\n elif animate == 'no':\\n return 'what'\\n else:\\n return 'who'\\n \\n if pronoun-type == 'quantified':\\n return lex\",\n \"def test_noun_chunks_is_parsed(fi_tokenizer):\\n doc = fi_tokenizer(\\\"Tämä on testi\\\")\\n with pytest.raises(ValueError):\\n list(doc.noun_chunks)\",\n \"def has_annotations(filepath):\\n return filepath.endswith('.ll') and '[#uses=' in open(filepath).read()\",\n \"def ParseYesNo(src):\\n return src.strip().lower() == u'yes'\",\n \"def get_nouns(lemmas_tags):\\r\\n nouns = []\\r\\n for lemma in lemmas_tags:\\r\\n \\\"\\\"\\\"si la etiqueta es None porque no tiene lemma o es un sustantivo\\\"\\\"\\\"\\r\\n if lemma[1] == None or lemma[1][0] == 'n':\\r\\n \\\"\\\"\\\"se agrega solamente el lemma\\\"\\\"\\\"\\r\\n nouns.append(lemma[0])\\r\\n return nouns\",\n \"def strip_proper_pos(text: Union[List[str], str]) -> List[str]:\\n\\n text = __join_if_list(text)\\n try:\\n tagged = pos_tag(text.split())\\n except LookupError:\\n nltk.download('averaged_perceptron_tagger')\\n tagged = pos_tag(text.split())\\n\\n without_propernouns = [word for word, pos in tagged if pos is not 'NPP' and pos is not 'NNPS']\\n return without_propernouns\",\n \"def check_marked_paragraph(paragraph, number):\\n\\n\\tq = 0 # счетчик найденных маркеров\\n\\tchars = '<> ' # возможные символы в каретке\\n\\n\\tfor i in range(len(paragraph.runs)):\\n\\t\\tif \\\"<>\\\" in paragraph.runs[i].text: # если в тексте каретки встречается маркер\\n\\t\\t\\tfor c in paragraph.runs[i].text: # проверяем каждый символ в каретке\\n\\t\\t\\t\\tif c not in chars: # если он не входит в список разрешенных символов\\n\\t\\t\\t\\t\\treturn False\\n\\t\\t\\tq += 1 # если проверка пройдена, увеличиваем счетчик\\n\\t\\telif \\\"<\\\" in paragraph.runs[i].text and \\\">\\\" in paragraph.runs[i+1].text: # если маркер разделен на две соседние каретки\\n\\t\\t\\tfor c in paragraph.runs[i].text: # проверяем каждую из кареток\\n\\t\\t\\t\\tif c not in chars:\\n\\t\\t\\t\\t\\treturn False\\n\\t\\t\\tfor c in paragraph.runs[i+1].text:\\n\\t\\t\\t\\tif c not in chars:\\n\\t\\t\\t\\t\\treturn False\\n\\t\\t\\tq += 1\\n\\n\\tif q != number: # если количество маркеров не совпало с указанным в выводе\\n\\t\\treturn False\\n\\telse:\\n\\t\\treturn True\",\n \"def is_pod(self):\\r\\n return conf.lib.clang_isPODType(self)\",\n \"def _get_prep_with_word(token: spacy.tokens.Token) -> (str, spacy.tokens.Token):\\n if token is None:\\n return \\\"\\\", None\\n\\n prep = None\\n # search of prepositions\\n for child in token.rights:\\n if child.dep_ == \\\"prep\\\":\\n prep = child\\n break\\n if prep is None:\\n return \\\"\\\", None\\n\\n for word in prep.children:\\n # if preposition has child of type 'object of preposition' or 'complement of a preposition'\\n # then add it to the result\\n if word.dep_ in [\\\"pobj\\\", \\\"pcomp\\\"]:\\n chunk_str = SpacyEventExtractor._get_chunk(word)\\n return str(prep) + \\\" \\\" + chunk_str, word\\n\\n return \\\"\\\", None\",\n \"def penn2morphy(self,penntag):\\r\\n morphy_tag = {'NN':'n', 'JJ':'a',\\r\\n 'VB':'v', 'RB':'r'}\\r\\n try:\\r\\n return morphy_tag[penntag[:2]]\\r\\n except:\\r\\n return 'n'\",\n \"def isOkToBoost(self, node):\\n para = \\\"p\\\"\\n stepsAway = 0\\n minimumStopWordCount = 5\\n maxStepsAwayFromNode = 3\\n \\n nodes = self.walkSiblings(node)\\n for currentNode in nodes:\\n # p\\n if currentNode.tag == para:\\n if stepsAway >= maxStepsAwayFromNode:\\n return False\\n paraText = Parser.getText(currentNode)\\n wordStats = StopWords().getStopWordCount(paraText)\\n if wordStats.getStopWordCount > minimumStopWordCount:\\n return True\\n stepsAway += 1\\n return False\",\n \"def is_arxiv_post_2007(val):\\n return arxiv_post_2007_regexp.match(val) or arxiv_post_2007_with_class_regexp.match(\\n val\\n )\",\n \"def penn2morphy(penntag):\\n morphy_tag = {'NN':'n', 'JJ':'a',\\n 'VB':'v', 'RB':'r'}\\n try:\\n return morphy_tag[penntag[:2]]\\n except:\\n return 'n'\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61451167","0.58428335","0.5761659","0.55204415","0.542974","0.53121424","0.52965194","0.5273935","0.5249292","0.5238592","0.52133167","0.5151928","0.5150011","0.5145963","0.5134433","0.5119308","0.5116591","0.50993526","0.5094933","0.5078213","0.50759107","0.5022919","0.5019613","0.49810898","0.4968922","0.49677217","0.49573064","0.49554756","0.4926266","0.49099398","0.49017236","0.4888883","0.4886646","0.48852685","0.48696905","0.48671812","0.48644546","0.48637676","0.48544344","0.48456115","0.4824656","0.48186582","0.47894156","0.47889125","0.47767934","0.47707456","0.47700757","0.476839","0.47657898","0.4761772","0.4760155","0.475647","0.47542423","0.47538942","0.47527146","0.47518393","0.47475863","0.47449732","0.47409856","0.47378293","0.47171256","0.47125193","0.47061154","0.47049028","0.46995634","0.4681355","0.46800795","0.46793774","0.46764675","0.467636","0.46678343","0.46604228","0.46561447","0.46526212","0.46506464","0.46491462","0.4641493","0.46412522","0.46396884","0.46326733","0.4632603","0.46304113","0.4618852","0.46155387","0.4609114","0.46055976","0.4603326","0.4602424","0.45910242","0.4589068","0.45878243","0.4586312","0.45837158","0.4578652","0.45779693","0.457591","0.4574846","0.45708063","0.45707706","0.4559936"],"string":"[\n \"0.61451167\",\n \"0.58428335\",\n \"0.5761659\",\n \"0.55204415\",\n \"0.542974\",\n \"0.53121424\",\n \"0.52965194\",\n \"0.5273935\",\n \"0.5249292\",\n \"0.5238592\",\n \"0.52133167\",\n \"0.5151928\",\n \"0.5150011\",\n \"0.5145963\",\n \"0.5134433\",\n \"0.5119308\",\n \"0.5116591\",\n \"0.50993526\",\n \"0.5094933\",\n \"0.5078213\",\n \"0.50759107\",\n \"0.5022919\",\n \"0.5019613\",\n \"0.49810898\",\n \"0.4968922\",\n \"0.49677217\",\n \"0.49573064\",\n \"0.49554756\",\n \"0.4926266\",\n \"0.49099398\",\n \"0.49017236\",\n \"0.4888883\",\n \"0.4886646\",\n \"0.48852685\",\n \"0.48696905\",\n \"0.48671812\",\n \"0.48644546\",\n \"0.48637676\",\n \"0.48544344\",\n \"0.48456115\",\n \"0.4824656\",\n \"0.48186582\",\n \"0.47894156\",\n \"0.47889125\",\n \"0.47767934\",\n \"0.47707456\",\n \"0.47700757\",\n \"0.476839\",\n \"0.47657898\",\n \"0.4761772\",\n \"0.4760155\",\n \"0.475647\",\n \"0.47542423\",\n \"0.47538942\",\n \"0.47527146\",\n \"0.47518393\",\n \"0.47475863\",\n \"0.47449732\",\n \"0.47409856\",\n \"0.47378293\",\n \"0.47171256\",\n \"0.47125193\",\n \"0.47061154\",\n \"0.47049028\",\n \"0.46995634\",\n \"0.4681355\",\n \"0.46800795\",\n \"0.46793774\",\n \"0.46764675\",\n \"0.467636\",\n \"0.46678343\",\n \"0.46604228\",\n \"0.46561447\",\n \"0.46526212\",\n \"0.46506464\",\n \"0.46491462\",\n \"0.4641493\",\n \"0.46412522\",\n \"0.46396884\",\n \"0.46326733\",\n \"0.4632603\",\n \"0.46304113\",\n \"0.4618852\",\n \"0.46155387\",\n \"0.4609114\",\n \"0.46055976\",\n \"0.4603326\",\n \"0.4602424\",\n \"0.45910242\",\n \"0.4589068\",\n \"0.45878243\",\n \"0.4586312\",\n \"0.45837158\",\n \"0.4578652\",\n \"0.45779693\",\n \"0.457591\",\n \"0.4574846\",\n \"0.45708063\",\n \"0.45707706\",\n \"0.4559936\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.72758985"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":21887,"cells":{"query":{"kind":"string","value":"Replace annotation unit by its Canonical referent."},"document":{"kind":"string","value":"def replace_by_canonical_referent(self, cross_sentence=True):\n replaced_count = 0\n\n # replace the participant in their containers\n def replace_in_containers(myobj, replacing):\n containers = {\n \"event\": \"participants\",\n \"sentiment_expression\": \"targets\",\n \"sentence\": \"participants\",\n }\n for c_name, attrib_n in containers.items():\n c_name = f\"in_{c_name}\"\n if hasattr(myobj, c_name):\n for c in getattr(myobj, c_name):\n to_replace_in = getattr(c, attrib_n)\n to_replace_in.append(replacing)\n if myobj in to_replace_in:\n to_replace_in.remove(myobj)\n to_replace_in.sort(key=lambda x: x.begin)\n\n if self.canonical_referents and self.canonical_referents != \"from_canonref\":\n # sometimes there are multiple canonrefs tagged\n # this can be a) annotation mistake or\n # b) multiple reference to a group, e.g. \"all\" refers to three companies.\n for canonref in self.canonical_referents:\n # check whether canonical referent is in same sentence\n same_sentence = [s.element_id for s in self.in_sentence] == [\n s.element_id for s in canonref.in_sentence\n ]\n # always replace when cross_sentence is true\n # if cross_sentence is False (disallowed) only replace when canonref is in same sentence\n if cross_sentence or same_sentence:\n # replace element\n replacing_participant = Participant(\n canonref.text,\n canonref.begin,\n canonref.end,\n canonref.element_id,\n canonref.annotator_id,\n canonref.document_title,\n canonref.in_document,\n self.role,\n \"from_canonref\",\n self.link_id,\n canonref.tokens,\n )\n replacing_participant.in_sentence = self.in_sentence\n replacing_participant.in_document = self.in_document\n if hasattr(self, \"in_sentiment_expression\"):\n replacing_participant.in_sentiment_expression = (\n self.in_sentiment_expression\n )\n replacing_participant.from_original_participant = copy(self)\n print(\n f\"Replaced {self} with {canonref}. (in same sentence:{same_sentence})\"\n )\n\n replace_in_containers(self, replacing_participant)\n\n # replace on document\n self.in_document.participants.append(replacing_participant)\n if self in self.in_document.participants:\n self.in_document.participants.remove(self)\n\n replaced_count += 1\n return replaced_count"},"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 sanitized(self) -> AnnotationWrapper:\n res = self.replace('typing.', ''). \\\n replace('telegrambotapiwrapper.typelib.', ''). \\\n replace(\"<class '\", \"\"). \\\n replace(\"'>\", \"\")\n return AnnotationWrapper(res)","def copy_annotations(source: str, target: str) -> str:\n if isinstance(source, AnnotatedStr):\n if not isinstance(target, AnnotatedStr):\n target = AnnotatedStr(target)\n target.optional = source.optional\n target.exists = source.exists\n target.phony = source.phony\n target.precious = source.precious\n return target","def fixing_annotation(key, n):\n if key + '.' + n not in self.propbank:\n key = key.replace('-', '_')\n return key + '.' + n","def shorten_name(region_name, atlas):\n sub_list = dict(ctx=harvard_oxford_ctx_subs,\n sub=harvard_oxford_sub_subs)\n for pat, rep in sub_list[atlas]:\n region_name = re.sub(pat, rep, region_name).strip()\n return region_name","def mask_annotations(document: Document,\n replacement_formatter: Callable[[Annotation], str] = _uppercase_formatter\n ) -> Document:\n # Amount of characters by which start point of annotation is adjusted\n # Positive shift if replacement is longer than original annotation\n # Negative shift if replacement is shorter\n shift = 0\n\n original_text_pointer = 0\n text_rewritten = ''\n annotations_rewritten = []\n\n for annotation in document.annotations:\n replacement = replacement_formatter(annotation)\n part = document.text[original_text_pointer:annotation.start]\n\n start = annotation.start + shift\n end = start + len(replacement)\n shift += len(replacement) - len(annotation.text)\n\n text_rewritten += part + replacement\n original_text_pointer = annotation.end\n annotations_rewritten.append(annotation._replace(\n start=start,\n end=end,\n text=replacement\n ))\n\n text_rewritten += document.text[original_text_pointer:]\n return Document(name=document.name, text=text_rewritten, annotations=annotations_rewritten)","def to_rna(self):\n thymidine = 'T'\n uracil = 'U'\n return self.dna.replace(thymidine, uracil)","def targ_ent(self, ent: 'Entity') -> None:\n if ent['origin']:\n self.target = Vec.from_str(ent['origin'])","def replaceTopLevelAnnotationElement(self, *args):\n return _libsbml.SBase_replaceTopLevelAnnotationElement(self, *args)","def resolveAlias(self, alias):","def test_replace_identity(self):\n pass","def changeAnnotations(folderPathToReannotate, annotationTochange=[u'0.3', u'1.1']):\n # transform the annotation into a list if need be\n if type(annotationTochange) is str:\n annotationTochange = [annotationTochange]\n # get the annotation data\n sentEnList, sentFrList, sentRefList, sentAnnotList = getAnnotationData(folderPathToReannotate)\n # print the annotator cheat sheet\n printCheatSheet()\n # annotate only when we find the problematic old annotation\n for indexAnnot, oldAnnot in enumerate(list(sentAnnotList)):\n if oldAnnot in annotationTochange:\n src = sentEnList[indexAnnot] if u'en-fr' in sentRefList[indexAnnot] else sentFrList[indexAnnot]\n trgt = sentFrList[indexAnnot] if u'en-fr' in sentRefList[indexAnnot] else sentEnList[indexAnnot]\n print(u'{0} - {1}'.format(indexAnnot+1, src))\n print(u'{0} - {1}'.format(indexAnnot+1, trgt))\n # get the first part of the annotation (aligned or not)\n annotatorGeneralInput = input(u'Old annotation is {0}, what is the new one: '.format(oldAnnot))\n # make sure to have the right general annotation\n while True:\n if annotatorGeneralInput in [u'0', u'1', u'0.0', u'0.1', u'0.2',\n u'1.0', u'1.1', u'1.2', u'1.3', u'1.4', u'c', u'correction']:\n break\n else:\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n annotatorGeneralInput = input(u'Repeat annotation: ')\n if annotatorGeneralInput in [u'c', u'correct']:\n annotatorGeneralInput, sentAnnotList = correctionToAnnotation(sentAnnotList)\n # if we still need to specify what type of alignment or misalignment\n if annotatorGeneralInput in [u'0', u'1']:\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n # get the second part of the annotation (aligned or not)\n annotatorSpecificInput = input(u'Specific type annotation: ')\n typeAnswers = [u'0', u'1', u'2'] if annotatorGeneralInput == 0 else [u'0', u'1', u'2', u'3', u'4']\n # make sure to have the right specific annotation\n while True:\n if annotatorSpecificInput in typeAnswers:\n break\n else:\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n annotatorSpecificInput = input(u'Repeat type annotation: ')\n # save to the list of annotations\n sentAnnotList[indexAnnot] = u'{0}.{1}'.format(annotatorGeneralInput, annotatorSpecificInput)\n # if the right answer was given in the right format right away\n else:\n # save to the list of annotations\n sentAnnotList[indexAnnot] = str(annotatorGeneralInput)\n # remove the lines from the terminal before getting to the next pair\n utilsOs.moveUpAndLeftNLines(3, slowly=False)\n # erase all remainder of the previous sentences and go back up again\n for e in range(2):\n print(u' ' * (max([len(src), len(trgt)]) + 6))\n utilsOs.moveUpAndLeftNLines(2, slowly=False)\n # remove format problematic annotations\n sentAnnotList = [annot if annot != u'1.1.0' else u'1.1' for annot in sentAnnotList]\n sentAnnotList = [annot if annot != u'0.1.0' else u'0.1' for annot in sentAnnotList ]\n # dump new annotation\n sentAnnotPath = u'{0}sampleAnnotation.tsv'.format(folderPathToReannotate)\n utilsOs.dumpRawLines(sentAnnotList, sentAnnotPath, addNewline=True, rewrite=True)","def fix_name_nga(artist):\n if \"sculptor\" in artist:\n return artist[:artist.find(\"sculptor\")].strip()\n else:\n return artist.strip()","def replace(self, input_anno: Annotation) -> Tuple[bool, str]:\n raise NotImplementedError","def pronoun_fix(self, section):\n return re.sub(reg_abbrev, repl, section)","def merge_nonjunk_into_new_name(self, event=None):\n # Delete all original names\n aid_list = self.all_aid_list\n aid_list_filtered = ut.filterfalse_items(\n aid_list, self.ibs.get_annot_isjunk(aid_list)\n )\n # Rename annotations\n self.ibs.set_annot_names_to_same_new_name(aid_list_filtered)\n self.update_callback()\n self.backend_callback()\n self.show_page()","def replace_canonical_referents(self, **kwargs):\n replaced_count = 0\n for doc in self.annotation_documents:\n for sen in doc.sentences:\n for part in sen.participants:\n replaced_count += part.replace_by_canonical_referent(**kwargs) #this will replace in-place in project, but returns a count too.\n\n return replaced_count","def _revert_encoded_reg_name(self, vdef):\n if vdef.find(\"%\") != -1:\n for (o_reg, re_reg) in self.arch.reg_rename_tbl.items():\n vdef = vdef.replace(re_reg, o_reg)\n return vdef","def go_annot_prefix_lookup(dataprovider):\n if dataprovider in [\"MGI\", \"Human\"]:\n return \"\"\n return dataprovider + \":\"","def unstring_annotation(node: ast.expr, ctx:'model.Documentable', section:str='annotation') -> ast.expr:\n try:\n expr = _AnnotationStringParser().visit(node)\n except SyntaxError as ex:\n module = ctx.module\n assert module is not None\n module.report(f'syntax error in {section}: {ex}', lineno_offset=node.lineno, section=section)\n return node\n else:\n assert isinstance(expr, ast.expr), expr\n return expr","def transcribe(dna_string):\n from utils import thymine, uracil\n return dna_string.replace(thymine, uracil)","def _fix_up(self, cls, code_name):","def test_replace_namespaced_build_details(self):\n pass","def _convert_annotations(self, ast):\n self.annotations = IDLAnnotations(ast)","def replaceMetadata(self, obj, dom):\n self.update_semantics = 'replace'\n # create a metadata dict that has all the defaults, overridden by the\n # current dom values. This way we will 'clear' the properties not in\n # the dom.\n metadata = copy(METADATA_DEFAULTS)\n metadata.update(self.getMetadata(dom, METADATA_MAPPING))\n if metadata:\n self.validate_metadata(metadata)\n metadata = self.fixEntities(metadata, ATTRIBUTES_TO_FIX)\n if ICollection.providedBy(obj):\n obj.collection_metadata(**metadata)\n elif IModule.providedBy(obj):\n obj.update_metadata(**metadata)\n # we set GoogleAnalyticsTrackingCode explicitly, since the script\n # 'update_metadata' ignores empty strings.\n obj.GoogleAnalyticsTrackingCode = metadata.get('GoogleAnalyticsTrackingCode')\n self.updateRoles(obj, dom)\n obj.reindexObject(idxs=metadata.keys())","def replace(match_obj):\n return match_obj.group(0)[1:]","def updateResidueAnnotation(residue):\n\n for atom in residue.atoms:\n if atom.atomSet:\n for resonanceSet in atom.atomSet.resonanceSets:\n updateResonanceSetAnnotation(resonanceSet)\n\n resonances = set()\n for residueProb in residue.residueProbs:\n resonances.update(residueProb.resonanceGroup.resonances)\n\n for spinSystem in residue.resonanceGroups:\n resonances.update(spinSystem.resonances)\n\n for resonance in resonances:\n updateResonanceAnnotation(resonance)","def test_replace_namespaced_role(self):\n pass","def restoreMountoffset(ant, subarray=DEFAULT):\n mpPrefix = \"Control.Antenna%d.\"%ant\n azoff = queryDouble( mpPrefix + \"azimuthMountOffset\", 24 )\n eloff= queryDouble( mpPrefix + \"elevationMountOffset\", 24 )\n mountoffset(azoff, eloff, ant, tmo=1, waiton=NONE, subarray=subarray)","def test_paidcourseregistrationannotation_unicode(self):\r\n self.assertEqual(unicode(self.annotation), u'{} : {}'.format(self.course_key.to_deprecated_string(), self.TEST_ANNOTATION))","def cleanup(segment):\n cnt = ''.join(segment.file_content)\n index = cnt.find('\\\\annotate')\n if index < 0:\n return\n while index >= 0:\n cnt, new_ind = parse_annotation(cnt, index)\n index = cnt.find('\\\\annotate', new_ind)\n f = codecs.open(segment.filename, 'w', 'utf-8')\n f.write(cnt)\n f.close()\n info('Updated: {} {}'.format(segment.voice_name, segment.name))","def _replace_units(original_units, values_by_name):\n q = 1\n for arg_name, exponent in original_units.items():\n q = q * values_by_name[arg_name] ** exponent\n\n return getattr(q, \"_units\", UnitsContainer({}))","def replace(name, newobject):","def retranslate(self):\r\n pass","def retranslate(self):\r\n pass","def prefix_replace(original, old, new):\n ...","def telescope_to_nominal(tel_coord,norm_frame):\n alt_tel,az_tel = tel_coord.pointing_direction\n alt_norm,az_norm = norm_frame.array_direction\n\n alt_trans,az_trans = offset_to_altaz(tel_coord.x,tel_coord.y,az_tel,alt_tel)\n x,y = altaz_to_offset(az_trans,alt_trans,az_norm,alt_norm)\n x = x*u.rad\n y = y*u.rad\n\n representation = CartesianRepresentation(x.to(tel_coord.x.unit),y.to(tel_coord.x.unit),0*tel_coord.x.unit)\n\n return norm_frame.realize_frame(representation)","def fix_rotation(self):\n self.rotate(self.rotation)\n self.annotations.rotate(self.rotation)\n self.rotation = 0","def normalize(self) -> \"CharacterizationPixel\":\n return replace(\n self,\n data=self.data/self.norm,\n mean=self.mean/self.norm,\n norm=np.ones_like(self.norm),\n )","def token_to_alias(raw_text, vocab):\n pass","def replace_contractions(sample):\n sample[\"full_text\"] = contractions.fix(sample[\"full_text\"])\n return sample","def annotate(self, annotation):\n self._data = self._data.annotate(**annotation)","def removeAnnotation(self,i=0):\n #print \"REMOVE %s\" % i\n map(undraw,self._annotations[i])\n del self._annotations[i]","def replace_attribute(text, tag, obj_group, eliminate=False):\n\n group = get_tag_group(tag)\n if mapping(tag) == 'relation':\n # Actual relation tag, else position tag.\n if tag == '<R>':\n relation_list = gvars.METAINFO['relation_phrases'][obj_group['relation']]\n relation_cand = random.choice(relation_list)\n else:\n relation_cand = obj_group['relation']\n\n return text.replace(tag, relation_cand)\n\n if mapping(tag) == 'shape':\n if eliminate:\n replacer = 'thing'\n else:\n replacer = str(obj_group['objects'][group][mapping(tag)])\n\n # Plural forms for groups.\n if obj_group.get('count', 1) > 1 or obj_group.get('use_plural', False):\n replacer += 's'\n elif mapping(tag) == 'count':\n if eliminate:\n replacer = ''\n else:\n replacer = str(obj_group['count'])\n else:\n if eliminate:\n replacer = ''\n else:\n replacer = str(obj_group['objects'][group][mapping(tag)])\n return text.replace(tag, replacer)","def unsetAnnotation(self):\n return _libsbml.SBase_unsetAnnotation(self)","def test_04_remove_annotations(self):\n self.addAnnotation(\"annotation1\", self.host.id, \"HOST\")\n self.removeAnnotation(self.added_annotations[-1].annotation.id)\n del self.added_annotations[-1]","def SetRingSubstituentPosition(mol, ring, ring_substituent, alpha, beta):\n coordinate = []\n molconformer = mol.GetConformer()\n bondlength = rdMolTransforms.GetBondLength(molconformer, *ring_substituent)\n ring_coord = [list(molconformer.GetAtomPosition(node)) for node in ring]\n substituent_coord = [list(molconformer.GetAtomPosition(node)) for node in ring_substituent]\n ringcenter = np.mean(ring_coord, axis=0)\n ring_coord_ = np.array(ring_coord) - ringcenter\n substituent_coord_ = np.array(substituent_coord) - ringcenter\n S = np.diff(substituent_coord) # vector from ring atom to substituent\n s = S/np.linalg.norm(S) \n n = GetNormal(ring_coord_) # Normal vector \n R = np.array(substituent_coord[0]) # ring atom \n U = R - np.dot(R,n)*n\n u = U/np.linalg.norm(U)\n v = np.cross(n,u)\n x = fixzero(bondlength*np.sin(alpha)*np.cos(-beta))\n y = fixzero(bondlength*np.sin(alpha)*np.sin(-beta))\n z = fixzero(bondlength*np.cos(alpha))\n b = np.array([x,y,z])\n T = np.array([u,v,n]).T\n ring_substituent_pos = np.matmul(T,b) + np.array(substituent_coord)[0]","def get_disambiguator(self):","def get_disambiguator(self):","def updateResonanceAnnotation(resonance):\n \n if hasattr(resonance,'guiName'):\n del resonance.guiName\n\n if hasattr(resonance,'label'):\n del resonance.label\n \n makeResonanceGuiName(resonance)\n \n for contrib in resonance.peakDimContribs:\n contrib.peakDim.setAnnotation( makePeakDimAnnotation(contrib.peakDim) )\n\n if hasattr(resonance, 'onebond'):\n updateResonanceAnnotation(resonance.onebond)","def test_replace_namespaced_scale_scale(self):\n pass","def test_replace_namespaced_policy(self):\n pass","def get_origin(cls, annotation: Any) -> Any:\n # Resolve custom NewTypes, recursively.\n actual = checks.resolve_supertype(annotation)\n # Extract the origin of the annotation, recursively.\n actual = getattr(actual, \"__origin__\", actual)\n if checks.isoptionaltype(annotation) or checks.isclassvartype(annotation):\n args = cls.get_args(annotation)\n return cls.get_origin(args[0]) if args else actual\n\n # provide defaults for generics\n if not checks.isbuiltintype(actual):\n actual = cls._check_generics(actual)\n\n return actual","def to_rna(dna):\r\n return dna.translate(str.maketrans('ATGC','UACG'))","def test_replace_net_namespace(self):\n pass","def deal_attitude(self, deal_attitude):\n\n self._deal_attitude = deal_attitude","def changeAlias(self, alias, node):","def nominal_to_telescope(norm_coord,tel_frame):\n alt_tel,az_tel = tel_frame.pointing_direction\n alt_norm,az_norm = norm_coord.array_direction\n\n alt_trans,az_trans = offset_to_altaz(norm_coord.x,norm_coord.y,az_norm,alt_norm)\n x,y = altaz_to_offset(az_trans,alt_trans,az_tel,alt_tel)\n x = x*u.rad\n y = y*u.rad\n\n representation = CartesianRepresentation(x.to(norm_coord.x.unit),y.to(norm_coord.x.unit),0*norm_coord.x.unit)\n\n return tel_frame.realize_frame(representation)","def fix_coord_units(cat, RA, DEC):\n cat[RA].info.unit = u.deg\n cat[DEC].info.unit = u.deg\n return cat","def __repr__(self):\n return f\"Annotation '{self.text}' at {super().__repr__()}\"","def _normalize_element_name(some_str, element_aliases_dict=None):\n some_str = _lowercase_despace_depunctuate(some_str)\n if element_aliases_dict:\n for key in element_aliases_dict.keys():\n if key == some_str:\n some_str = element_aliases_dict[key]\n return some_str","def get_alias(a_fm, description, quark_mass):\n quark = conventions.quark_masses\n mask = utils.bundle_mask(quark, a_fm=a_fm, description=description, mq=quark_mass)\n return utils.extract_unique(quark[mask], 'alias')","def unmangle_measurement_name(measurement_name):\n measurement_name = measurement_name.replace('_sp_', ' ')\n measurement_name = measurement_name.replace('_dsh_', '-')\n return measurement_name","def replace_typeval(self, combined, replacement):\n raise NotImplementedError(\"This is an abstract method.\")","def update_short_name(name):\n # First verify that the common errors have been fixed\n name = update_street_name(name)\n\n # Find the abbreviation to replace\n m = over_abbr_re.search(name)\n if m:\n if m.group() in abbreviations:\n name = over_abbr_re.sub(abbreviations[m.group()], name)\n\n return name","def uncanonicalize(self, url):\n pass","def test_replace_namespaced_route(self):\n pass","def reflejar(self, una_senal): \n self.blancos.reflejar(una_senal)\n\n return una_senal","def fixAuthor(self, pageText):\n informationRegex = re.compile(\n r'\\|Author=Original uploader was '\n r'(?P<author>\\[\\[:\\w+:\\w+:\\w+\\|\\w+\\]\\] at \\[.+\\])')\n selfRegex = re.compile(\n r'{{self\\|author='\n r'(?P<author>\\[\\[:\\w+:\\w+:\\w+\\|\\w+\\]\\] at \\[.+\\])\\|')\n\n # Find the |Author=Original uploader was ....\n informationMatch = informationRegex.search(pageText)\n\n # Find the {{self|author=\n selfMatch = selfRegex.search(pageText)\n\n # Check if both are found and are equal\n if (informationMatch and selfMatch):\n if informationMatch.group('author') == selfMatch.group('author'):\n # Replace |Author=Original uploader was ... with |Author= ...\n pageText = informationRegex.sub(r'|Author=\\g<author>',\n pageText)\n return pageText","def nominal_to_altaz(altaz_coord,norm_coord):\n alt_norm,az_norm = norm_coord.array_direction\n az = altaz_coord.az\n alt = altaz_coord.alt\n x,y = altaz_to_offset(az,alt,az_norm,alt_norm)\n x=x*u.rad\n y=y*u.rad\n representation = CartesianRepresentation(x.to(u.deg),y.to(u.deg),0*u.deg)\n\n return norm_coord.realize_frame(representation)","def rna(self):\n return self.seq.replace('T', 'U').replace('t', 'u')","def updateResidueProbAnnotation(residueProb):\n\n for resonance in residueProb.resonanceGroup.resonances:\n updateResonanceAnnotation(resonance)","def unit2internal(src_unit: Union[str, float]):\n return _parse_unit(src_unit, conversion_factor=_conversion_factor_internal)","def _convert_reference_fields_to_new_namespace(reference):\r\n assert isinstance(reference, UsageKey)\r\n if source_course_id == reference.course_key:\r\n return reference.map_into_course(dest_course_id)\r\n else:\r\n return reference","def update_name(name, mapping):\n m = street_type_re.search(name)\n if m:\n street_type = m.group()\n for key, value in mapping.iteritems():\n if street_type == key:\n name = name.replace(key,value)\n\n return name","def test_replace_namespaced_template(self):\n pass","def convert_to_namespace(file, output, keyword, author):\n resource = parse_bel_resource(file)\n\n write_namespace(\n namespace_keyword=(keyword or resource['AnnotationDefinition']['Keyword']),\n namespace_name=resource['AnnotationDefinition']['Keyword'],\n namespace_description=resource['AnnotationDefinition']['DescriptionString'],\n author_name=author,\n namespace_domain=NAMESPACE_DOMAIN_OTHER,\n values=resource['Values'],\n citation_name=resource['Citation']['NameString'],\n file=output,\n )","def unmangle_gcov_filename(gcov_filename: str):\n return gcov_filename.replace('^', '..').replace('#', '/').replace('~', ':')","def _canonify(self, rut):\n rut = smart_unicode(rut).replace(' ', '').replace('.', '').replace('-', '')\n return rut[:-1], rut[-1]","def normalize_identifier(prefix: str, identifier: str) -> str:\n # A \"banana\" is an embedded prefix that isn't actually part of the identifier.\n # Usually this corresponds to the prefix itself, with some specific stylization\n # such as in the case of FBbt. The banana does NOT include a colon \":\" at the end\n banana = get_banana(prefix)\n if banana:\n banana = f\"{banana}:\"\n if not identifier.startswith(banana):\n return f\"{banana}{identifier}\"\n # Handle when the namespace is in the LUI, but no specific banana\n # has been given. This is most common for OBO Foundry ontologies'\n # identifiers, like CHEBI:XXXX\n elif namespace_in_lui(prefix):\n banana = f\"{prefix.upper()}:\"\n if not identifier.startswith(banana):\n return f\"{banana}{identifier}\"\n\n # TODO Unnecessary redundant prefix?\n # elif identifier.lower().startswith(f'{prefix}:'):\n #\n\n return identifier","def to_rna(seq):\n seq=seq.replace('A','U')\n seq=seq.replace('T','A')\n seq=seq.replace('C',\"P\")\n seq=seq.replace('G','C')\n seq=seq.replace('P','G')\n return seq","def _move_quant_attributes_into_annotations(model):\n if onnx is None:\n raise ModuleNotFoundError(\"Installation of ONNX is required.\")\n\n model = copy.deepcopy(model)\n qaname = \"finn_datatype\"\n for n in model.graph.node:\n for a in n.attribute:\n mark_for_removal = False\n if a.name == \"weight_qnt\":\n # assume second input is weight, make sure it has an initializer\n w_tensor_name = n.input[1]\n assert w_tensor_name in [x.name for x in model.graph.initializer]\n tq = onnx.StringStringEntryProto(key = qaname, value = a.s)\n ta = onnx.TensorAnnotation(\n tensor_name = w_tensor_name,\n quant_parameter_tensor_names = [tq]\n )\n model.graph.quantization_annotation.append(ta)\n mark_for_removal = True\n elif a.name == \"activation_qnt\":\n a_tensor_name = n.output[0]\n tq = onnx.StringStringEntryProto(key = qaname, value = a.s)\n ta = onnx.TensorAnnotation(\n tensor_name = a_tensor_name,\n quant_parameter_tensor_names = [tq]\n )\n model.graph.quantization_annotation.append(ta)\n mark_for_removal = True\n if mark_for_removal:\n n.attribute.remove(a)\n return model","def __str__(self):\n return f\"Annotation '{self.text}' at {super().__repr__()}\"","def setAnnotation(self, *args):\n return _libsbml.SpeciesReference_setAnnotation(self, *args)","def convert_atlas(document, current_unit, new_unit):\n root = document.getroot()\n\n for subtexture in root:\n x, y = subtexture.attrib['x'], subtexture.attrib['y']\n subtexture.attrib['x'] = str((int(x) / current_unit) * new_unit)\n subtexture.attrib['y'] = str((int(y) / current_unit) * new_unit)\n subtexture.attrib['width'] = str(new_unit)\n subtexture.attrib['height'] = str(new_unit)\n\n return document","def unpatch(cls):\n xml.dom.minidom.Element = cls._original_element","def _setintermediary_institution_56A(self, val):\n self.swift_obj.IntermediaryInstitution_A = val\n self.swift_obj.IntermediaryInstitution_A.swiftTag = '56A'","def test_models_organization_fields_code_normalize(self):\n organization = factories.OrganizationFactory()\n\n organization.code = \"Là&ça boô\"\n organization.save()\n self.assertEqual(organization.code, \"LACA-BOO\")","def _namespace_mangle(self, namespace):\n return namespace.replace(\".\", \"__\")","def adjust_for_location(header_guard):\n # Remove public prefixes\n header_guard = public_prefix.sub('', header_guard, 1)\n # Replace sysroot prefixes\n header_guard = sysroot_prefix.sub('SYSROOT_', header_guard, 1)\n\n return header_guard","def copy_annotations_from_unaligned(aligned_seqrec: SeqRecord, unaligned_seqrec: SeqRecord):\n # NCBI Blast id includes description, whereas alignment does not\n assert aligned_seqrec.id in unaligned_seqrec.id, f\"{aligned_seqrec.id} <> {unaligned_seqrec.id}\"\n # copy annotations from previous\n newrec = deepcopy(aligned_seqrec)\n newrec.annotations = unaligned_seqrec.annotations\n # clear any letter annotations added during deepcopy\n newrec.letter_annotations = dict()\n # original sequence and letter annotations\n seq = unaligned_seqrec.seq\n letter_annotations = unaligned_seqrec.letter_annotations\n # index to track position in original sequence\n i = 0\n for j, letter in enumerate(aligned_seqrec.seq):\n if letter in [gap_letter, stop_letter]:\n for key, values in letter_annotations.items():\n # convert strings into lists of characters,\n # then combine into string at end of loop\n if key == \"seqnums\":\n letter_annotation = None\n elif all(isinstance(value, str) for value in values):\n letter_annotation = gap_letter\n else:\n letter_annotation = None\n newrec.letter_annotations.setdefault(key, list()).append(letter_annotation)\n else:\n while seq[i] in [gap_letter, stop_letter]:\n i += 1\n assert letter == seq[i], f\"letter {letter} at {j} <> seq {seq[i]} at {i}\"\n for key in letter_annotations.keys():\n newrec.letter_annotations.setdefault(key, list()).append(letter_annotations[key][i])\n i += 1\n # convert list of chars into string\n for key, values in letter_annotations.items():\n if isinstance(values, str):\n newrec.letter_annotations[key] = \"\".join(newrec.letter_annotations[key])\n return newrec","def adjust_references_of_iat(self, start, end, gap_size):\n self.pe_manager.adjust_data_in_range(start, end, gap_size)","def _setordering_institution_52A(self, val):\n self.swift_obj.OrderingInstitution_A = val\n self.swift_obj.OrderingInstitution_A.swiftTag = '52A'","def project(self, other, normalized=...):\n ...","def _normalize(self, metric_name, submit_method, prefix):\n metric_prefix = \"mongodb.\" if not prefix else \"mongodb.{0}.\".format(prefix)\n metric_suffix = \"ps\" if submit_method == RATE else \"\"\n\n # Replace case-sensitive metric name characters\n for pattern, repl in self.CASE_SENSITIVE_METRIC_NAME_SUFFIXES.iteritems():\n metric_name = re.compile(pattern).sub(repl, metric_name)\n\n # Normalize, and wrap\n return u\"{metric_prefix}{normalized_metric_name}{metric_suffix}\".format(\n normalized_metric_name=self.normalize(metric_name.lower()),\n metric_prefix=metric_prefix, metric_suffix=metric_suffix\n )","def dna_to_rna(dna: str, start=0):\n return dna.replace(\"T\", \"U\")","def _derive_origin_(self, doc):\n return self.fam.c_derive_origin(self, doc)","def setAngularUnits(self,unit):\n\n\t\t#Sanity check\n\t\tassert unit.physical_type==\"angle\"\n\t\tself.side_angle = self.side_angle.to(unit)","def replace_x_y_nominal_lat_lon(ds):\n ds = ds.copy()\n if 'x' in ds.dims and 'y' in ds.dims:\n \n nominal_y = ds.lat.mean('x')\n # extract the equatorial lat and take those lon values as nominal lon\n eq_ind = abs(ds.lat.mean('x')).load().argmin().data\n nominal_x = ds.lon.isel(y=eq_ind)\n ds.coords['x'].data = nominal_x.data\n ds.coords['y'].data = nominal_y.data\n\n ds = ds.sortby('x')\n ds = ds.sortby('y')\n \n else:\n warnings.warn('No x and y found in dimensions for source_id:%s. This likely means that you forgot to rename the dataset or this is the German unstructured model' %ds.attrs['source_id'])\n return ds","def ocr_correction(token):","def fix_label(label):\n\n replace_dict = {'_': ' ',\n 'degE': '$^{\\circ}$E',\n 'ms-1': '$m s^{-1}$',\n 'm.s-1': '$m s^{-1}$',\n 'Wm-2': '$W m^{-2}$',\n '1000000 m2.s-1': '$10^6$m$^2$s$^{-1}$'\n } \n\n for value, replacement in list(replace_dict.items()):\n label = label.replace(value, replacement)\n\n return label"],"string":"[\n \"def sanitized(self) -> AnnotationWrapper:\\n res = self.replace('typing.', ''). \\\\\\n replace('telegrambotapiwrapper.typelib.', ''). \\\\\\n replace(\\\"<class '\\\", \\\"\\\"). \\\\\\n replace(\\\"'>\\\", \\\"\\\")\\n return AnnotationWrapper(res)\",\n \"def copy_annotations(source: str, target: str) -> str:\\n if isinstance(source, AnnotatedStr):\\n if not isinstance(target, AnnotatedStr):\\n target = AnnotatedStr(target)\\n target.optional = source.optional\\n target.exists = source.exists\\n target.phony = source.phony\\n target.precious = source.precious\\n return target\",\n \"def fixing_annotation(key, n):\\n if key + '.' + n not in self.propbank:\\n key = key.replace('-', '_')\\n return key + '.' + n\",\n \"def shorten_name(region_name, atlas):\\n sub_list = dict(ctx=harvard_oxford_ctx_subs,\\n sub=harvard_oxford_sub_subs)\\n for pat, rep in sub_list[atlas]:\\n region_name = re.sub(pat, rep, region_name).strip()\\n return region_name\",\n \"def mask_annotations(document: Document,\\n replacement_formatter: Callable[[Annotation], str] = _uppercase_formatter\\n ) -> Document:\\n # Amount of characters by which start point of annotation is adjusted\\n # Positive shift if replacement is longer than original annotation\\n # Negative shift if replacement is shorter\\n shift = 0\\n\\n original_text_pointer = 0\\n text_rewritten = ''\\n annotations_rewritten = []\\n\\n for annotation in document.annotations:\\n replacement = replacement_formatter(annotation)\\n part = document.text[original_text_pointer:annotation.start]\\n\\n start = annotation.start + shift\\n end = start + len(replacement)\\n shift += len(replacement) - len(annotation.text)\\n\\n text_rewritten += part + replacement\\n original_text_pointer = annotation.end\\n annotations_rewritten.append(annotation._replace(\\n start=start,\\n end=end,\\n text=replacement\\n ))\\n\\n text_rewritten += document.text[original_text_pointer:]\\n return Document(name=document.name, text=text_rewritten, annotations=annotations_rewritten)\",\n \"def to_rna(self):\\n thymidine = 'T'\\n uracil = 'U'\\n return self.dna.replace(thymidine, uracil)\",\n \"def targ_ent(self, ent: 'Entity') -> None:\\n if ent['origin']:\\n self.target = Vec.from_str(ent['origin'])\",\n \"def replaceTopLevelAnnotationElement(self, *args):\\n return _libsbml.SBase_replaceTopLevelAnnotationElement(self, *args)\",\n \"def resolveAlias(self, alias):\",\n \"def test_replace_identity(self):\\n pass\",\n \"def changeAnnotations(folderPathToReannotate, annotationTochange=[u'0.3', u'1.1']):\\n # transform the annotation into a list if need be\\n if type(annotationTochange) is str:\\n annotationTochange = [annotationTochange]\\n # get the annotation data\\n sentEnList, sentFrList, sentRefList, sentAnnotList = getAnnotationData(folderPathToReannotate)\\n # print the annotator cheat sheet\\n printCheatSheet()\\n # annotate only when we find the problematic old annotation\\n for indexAnnot, oldAnnot in enumerate(list(sentAnnotList)):\\n if oldAnnot in annotationTochange:\\n src = sentEnList[indexAnnot] if u'en-fr' in sentRefList[indexAnnot] else sentFrList[indexAnnot]\\n trgt = sentFrList[indexAnnot] if u'en-fr' in sentRefList[indexAnnot] else sentEnList[indexAnnot]\\n print(u'{0} - {1}'.format(indexAnnot+1, src))\\n print(u'{0} - {1}'.format(indexAnnot+1, trgt))\\n # get the first part of the annotation (aligned or not)\\n annotatorGeneralInput = input(u'Old annotation is {0}, what is the new one: '.format(oldAnnot))\\n # make sure to have the right general annotation\\n while True:\\n if annotatorGeneralInput in [u'0', u'1', u'0.0', u'0.1', u'0.2',\\n u'1.0', u'1.1', u'1.2', u'1.3', u'1.4', u'c', u'correction']:\\n break\\n else:\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n annotatorGeneralInput = input(u'Repeat annotation: ')\\n if annotatorGeneralInput in [u'c', u'correct']:\\n annotatorGeneralInput, sentAnnotList = correctionToAnnotation(sentAnnotList)\\n # if we still need to specify what type of alignment or misalignment\\n if annotatorGeneralInput in [u'0', u'1']:\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n # get the second part of the annotation (aligned or not)\\n annotatorSpecificInput = input(u'Specific type annotation: ')\\n typeAnswers = [u'0', u'1', u'2'] if annotatorGeneralInput == 0 else [u'0', u'1', u'2', u'3', u'4']\\n # make sure to have the right specific annotation\\n while True:\\n if annotatorSpecificInput in typeAnswers:\\n break\\n else:\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n annotatorSpecificInput = input(u'Repeat type annotation: ')\\n # save to the list of annotations\\n sentAnnotList[indexAnnot] = u'{0}.{1}'.format(annotatorGeneralInput, annotatorSpecificInput)\\n # if the right answer was given in the right format right away\\n else:\\n # save to the list of annotations\\n sentAnnotList[indexAnnot] = str(annotatorGeneralInput)\\n # remove the lines from the terminal before getting to the next pair\\n utilsOs.moveUpAndLeftNLines(3, slowly=False)\\n # erase all remainder of the previous sentences and go back up again\\n for e in range(2):\\n print(u' ' * (max([len(src), len(trgt)]) + 6))\\n utilsOs.moveUpAndLeftNLines(2, slowly=False)\\n # remove format problematic annotations\\n sentAnnotList = [annot if annot != u'1.1.0' else u'1.1' for annot in sentAnnotList]\\n sentAnnotList = [annot if annot != u'0.1.0' else u'0.1' for annot in sentAnnotList ]\\n # dump new annotation\\n sentAnnotPath = u'{0}sampleAnnotation.tsv'.format(folderPathToReannotate)\\n utilsOs.dumpRawLines(sentAnnotList, sentAnnotPath, addNewline=True, rewrite=True)\",\n \"def fix_name_nga(artist):\\n if \\\"sculptor\\\" in artist:\\n return artist[:artist.find(\\\"sculptor\\\")].strip()\\n else:\\n return artist.strip()\",\n \"def replace(self, input_anno: Annotation) -> Tuple[bool, str]:\\n raise NotImplementedError\",\n \"def pronoun_fix(self, section):\\n return re.sub(reg_abbrev, repl, section)\",\n \"def merge_nonjunk_into_new_name(self, event=None):\\n # Delete all original names\\n aid_list = self.all_aid_list\\n aid_list_filtered = ut.filterfalse_items(\\n aid_list, self.ibs.get_annot_isjunk(aid_list)\\n )\\n # Rename annotations\\n self.ibs.set_annot_names_to_same_new_name(aid_list_filtered)\\n self.update_callback()\\n self.backend_callback()\\n self.show_page()\",\n \"def replace_canonical_referents(self, **kwargs):\\n replaced_count = 0\\n for doc in self.annotation_documents:\\n for sen in doc.sentences:\\n for part in sen.participants:\\n replaced_count += part.replace_by_canonical_referent(**kwargs) #this will replace in-place in project, but returns a count too.\\n\\n return replaced_count\",\n \"def _revert_encoded_reg_name(self, vdef):\\n if vdef.find(\\\"%\\\") != -1:\\n for (o_reg, re_reg) in self.arch.reg_rename_tbl.items():\\n vdef = vdef.replace(re_reg, o_reg)\\n return vdef\",\n \"def go_annot_prefix_lookup(dataprovider):\\n if dataprovider in [\\\"MGI\\\", \\\"Human\\\"]:\\n return \\\"\\\"\\n return dataprovider + \\\":\\\"\",\n \"def unstring_annotation(node: ast.expr, ctx:'model.Documentable', section:str='annotation') -> ast.expr:\\n try:\\n expr = _AnnotationStringParser().visit(node)\\n except SyntaxError as ex:\\n module = ctx.module\\n assert module is not None\\n module.report(f'syntax error in {section}: {ex}', lineno_offset=node.lineno, section=section)\\n return node\\n else:\\n assert isinstance(expr, ast.expr), expr\\n return expr\",\n \"def transcribe(dna_string):\\n from utils import thymine, uracil\\n return dna_string.replace(thymine, uracil)\",\n \"def _fix_up(self, cls, code_name):\",\n \"def test_replace_namespaced_build_details(self):\\n pass\",\n \"def _convert_annotations(self, ast):\\n self.annotations = IDLAnnotations(ast)\",\n \"def replaceMetadata(self, obj, dom):\\n self.update_semantics = 'replace'\\n # create a metadata dict that has all the defaults, overridden by the\\n # current dom values. This way we will 'clear' the properties not in\\n # the dom.\\n metadata = copy(METADATA_DEFAULTS)\\n metadata.update(self.getMetadata(dom, METADATA_MAPPING))\\n if metadata:\\n self.validate_metadata(metadata)\\n metadata = self.fixEntities(metadata, ATTRIBUTES_TO_FIX)\\n if ICollection.providedBy(obj):\\n obj.collection_metadata(**metadata)\\n elif IModule.providedBy(obj):\\n obj.update_metadata(**metadata)\\n # we set GoogleAnalyticsTrackingCode explicitly, since the script\\n # 'update_metadata' ignores empty strings.\\n obj.GoogleAnalyticsTrackingCode = metadata.get('GoogleAnalyticsTrackingCode')\\n self.updateRoles(obj, dom)\\n obj.reindexObject(idxs=metadata.keys())\",\n \"def replace(match_obj):\\n return match_obj.group(0)[1:]\",\n \"def updateResidueAnnotation(residue):\\n\\n for atom in residue.atoms:\\n if atom.atomSet:\\n for resonanceSet in atom.atomSet.resonanceSets:\\n updateResonanceSetAnnotation(resonanceSet)\\n\\n resonances = set()\\n for residueProb in residue.residueProbs:\\n resonances.update(residueProb.resonanceGroup.resonances)\\n\\n for spinSystem in residue.resonanceGroups:\\n resonances.update(spinSystem.resonances)\\n\\n for resonance in resonances:\\n updateResonanceAnnotation(resonance)\",\n \"def test_replace_namespaced_role(self):\\n pass\",\n \"def restoreMountoffset(ant, subarray=DEFAULT):\\n mpPrefix = \\\"Control.Antenna%d.\\\"%ant\\n azoff = queryDouble( mpPrefix + \\\"azimuthMountOffset\\\", 24 )\\n eloff= queryDouble( mpPrefix + \\\"elevationMountOffset\\\", 24 )\\n mountoffset(azoff, eloff, ant, tmo=1, waiton=NONE, subarray=subarray)\",\n \"def test_paidcourseregistrationannotation_unicode(self):\\r\\n self.assertEqual(unicode(self.annotation), u'{} : {}'.format(self.course_key.to_deprecated_string(), self.TEST_ANNOTATION))\",\n \"def cleanup(segment):\\n cnt = ''.join(segment.file_content)\\n index = cnt.find('\\\\\\\\annotate')\\n if index < 0:\\n return\\n while index >= 0:\\n cnt, new_ind = parse_annotation(cnt, index)\\n index = cnt.find('\\\\\\\\annotate', new_ind)\\n f = codecs.open(segment.filename, 'w', 'utf-8')\\n f.write(cnt)\\n f.close()\\n info('Updated: {} {}'.format(segment.voice_name, segment.name))\",\n \"def _replace_units(original_units, values_by_name):\\n q = 1\\n for arg_name, exponent in original_units.items():\\n q = q * values_by_name[arg_name] ** exponent\\n\\n return getattr(q, \\\"_units\\\", UnitsContainer({}))\",\n \"def replace(name, newobject):\",\n \"def retranslate(self):\\r\\n pass\",\n \"def retranslate(self):\\r\\n pass\",\n \"def prefix_replace(original, old, new):\\n ...\",\n \"def telescope_to_nominal(tel_coord,norm_frame):\\n alt_tel,az_tel = tel_coord.pointing_direction\\n alt_norm,az_norm = norm_frame.array_direction\\n\\n alt_trans,az_trans = offset_to_altaz(tel_coord.x,tel_coord.y,az_tel,alt_tel)\\n x,y = altaz_to_offset(az_trans,alt_trans,az_norm,alt_norm)\\n x = x*u.rad\\n y = y*u.rad\\n\\n representation = CartesianRepresentation(x.to(tel_coord.x.unit),y.to(tel_coord.x.unit),0*tel_coord.x.unit)\\n\\n return norm_frame.realize_frame(representation)\",\n \"def fix_rotation(self):\\n self.rotate(self.rotation)\\n self.annotations.rotate(self.rotation)\\n self.rotation = 0\",\n \"def normalize(self) -> \\\"CharacterizationPixel\\\":\\n return replace(\\n self,\\n data=self.data/self.norm,\\n mean=self.mean/self.norm,\\n norm=np.ones_like(self.norm),\\n )\",\n \"def token_to_alias(raw_text, vocab):\\n pass\",\n \"def replace_contractions(sample):\\n sample[\\\"full_text\\\"] = contractions.fix(sample[\\\"full_text\\\"])\\n return sample\",\n \"def annotate(self, annotation):\\n self._data = self._data.annotate(**annotation)\",\n \"def removeAnnotation(self,i=0):\\n #print \\\"REMOVE %s\\\" % i\\n map(undraw,self._annotations[i])\\n del self._annotations[i]\",\n \"def replace_attribute(text, tag, obj_group, eliminate=False):\\n\\n group = get_tag_group(tag)\\n if mapping(tag) == 'relation':\\n # Actual relation tag, else position tag.\\n if tag == '<R>':\\n relation_list = gvars.METAINFO['relation_phrases'][obj_group['relation']]\\n relation_cand = random.choice(relation_list)\\n else:\\n relation_cand = obj_group['relation']\\n\\n return text.replace(tag, relation_cand)\\n\\n if mapping(tag) == 'shape':\\n if eliminate:\\n replacer = 'thing'\\n else:\\n replacer = str(obj_group['objects'][group][mapping(tag)])\\n\\n # Plural forms for groups.\\n if obj_group.get('count', 1) > 1 or obj_group.get('use_plural', False):\\n replacer += 's'\\n elif mapping(tag) == 'count':\\n if eliminate:\\n replacer = ''\\n else:\\n replacer = str(obj_group['count'])\\n else:\\n if eliminate:\\n replacer = ''\\n else:\\n replacer = str(obj_group['objects'][group][mapping(tag)])\\n return text.replace(tag, replacer)\",\n \"def unsetAnnotation(self):\\n return _libsbml.SBase_unsetAnnotation(self)\",\n \"def test_04_remove_annotations(self):\\n self.addAnnotation(\\\"annotation1\\\", self.host.id, \\\"HOST\\\")\\n self.removeAnnotation(self.added_annotations[-1].annotation.id)\\n del self.added_annotations[-1]\",\n \"def SetRingSubstituentPosition(mol, ring, ring_substituent, alpha, beta):\\n coordinate = []\\n molconformer = mol.GetConformer()\\n bondlength = rdMolTransforms.GetBondLength(molconformer, *ring_substituent)\\n ring_coord = [list(molconformer.GetAtomPosition(node)) for node in ring]\\n substituent_coord = [list(molconformer.GetAtomPosition(node)) for node in ring_substituent]\\n ringcenter = np.mean(ring_coord, axis=0)\\n ring_coord_ = np.array(ring_coord) - ringcenter\\n substituent_coord_ = np.array(substituent_coord) - ringcenter\\n S = np.diff(substituent_coord) # vector from ring atom to substituent\\n s = S/np.linalg.norm(S) \\n n = GetNormal(ring_coord_) # Normal vector \\n R = np.array(substituent_coord[0]) # ring atom \\n U = R - np.dot(R,n)*n\\n u = U/np.linalg.norm(U)\\n v = np.cross(n,u)\\n x = fixzero(bondlength*np.sin(alpha)*np.cos(-beta))\\n y = fixzero(bondlength*np.sin(alpha)*np.sin(-beta))\\n z = fixzero(bondlength*np.cos(alpha))\\n b = np.array([x,y,z])\\n T = np.array([u,v,n]).T\\n ring_substituent_pos = np.matmul(T,b) + np.array(substituent_coord)[0]\",\n \"def get_disambiguator(self):\",\n \"def get_disambiguator(self):\",\n \"def updateResonanceAnnotation(resonance):\\n \\n if hasattr(resonance,'guiName'):\\n del resonance.guiName\\n\\n if hasattr(resonance,'label'):\\n del resonance.label\\n \\n makeResonanceGuiName(resonance)\\n \\n for contrib in resonance.peakDimContribs:\\n contrib.peakDim.setAnnotation( makePeakDimAnnotation(contrib.peakDim) )\\n\\n if hasattr(resonance, 'onebond'):\\n updateResonanceAnnotation(resonance.onebond)\",\n \"def test_replace_namespaced_scale_scale(self):\\n pass\",\n \"def test_replace_namespaced_policy(self):\\n pass\",\n \"def get_origin(cls, annotation: Any) -> Any:\\n # Resolve custom NewTypes, recursively.\\n actual = checks.resolve_supertype(annotation)\\n # Extract the origin of the annotation, recursively.\\n actual = getattr(actual, \\\"__origin__\\\", actual)\\n if checks.isoptionaltype(annotation) or checks.isclassvartype(annotation):\\n args = cls.get_args(annotation)\\n return cls.get_origin(args[0]) if args else actual\\n\\n # provide defaults for generics\\n if not checks.isbuiltintype(actual):\\n actual = cls._check_generics(actual)\\n\\n return actual\",\n \"def to_rna(dna):\\r\\n return dna.translate(str.maketrans('ATGC','UACG'))\",\n \"def test_replace_net_namespace(self):\\n pass\",\n \"def deal_attitude(self, deal_attitude):\\n\\n self._deal_attitude = deal_attitude\",\n \"def changeAlias(self, alias, node):\",\n \"def nominal_to_telescope(norm_coord,tel_frame):\\n alt_tel,az_tel = tel_frame.pointing_direction\\n alt_norm,az_norm = norm_coord.array_direction\\n\\n alt_trans,az_trans = offset_to_altaz(norm_coord.x,norm_coord.y,az_norm,alt_norm)\\n x,y = altaz_to_offset(az_trans,alt_trans,az_tel,alt_tel)\\n x = x*u.rad\\n y = y*u.rad\\n\\n representation = CartesianRepresentation(x.to(norm_coord.x.unit),y.to(norm_coord.x.unit),0*norm_coord.x.unit)\\n\\n return tel_frame.realize_frame(representation)\",\n \"def fix_coord_units(cat, RA, DEC):\\n cat[RA].info.unit = u.deg\\n cat[DEC].info.unit = u.deg\\n return cat\",\n \"def __repr__(self):\\n return f\\\"Annotation '{self.text}' at {super().__repr__()}\\\"\",\n \"def _normalize_element_name(some_str, element_aliases_dict=None):\\n some_str = _lowercase_despace_depunctuate(some_str)\\n if element_aliases_dict:\\n for key in element_aliases_dict.keys():\\n if key == some_str:\\n some_str = element_aliases_dict[key]\\n return some_str\",\n \"def get_alias(a_fm, description, quark_mass):\\n quark = conventions.quark_masses\\n mask = utils.bundle_mask(quark, a_fm=a_fm, description=description, mq=quark_mass)\\n return utils.extract_unique(quark[mask], 'alias')\",\n \"def unmangle_measurement_name(measurement_name):\\n measurement_name = measurement_name.replace('_sp_', ' ')\\n measurement_name = measurement_name.replace('_dsh_', '-')\\n return measurement_name\",\n \"def replace_typeval(self, combined, replacement):\\n raise NotImplementedError(\\\"This is an abstract method.\\\")\",\n \"def update_short_name(name):\\n # First verify that the common errors have been fixed\\n name = update_street_name(name)\\n\\n # Find the abbreviation to replace\\n m = over_abbr_re.search(name)\\n if m:\\n if m.group() in abbreviations:\\n name = over_abbr_re.sub(abbreviations[m.group()], name)\\n\\n return name\",\n \"def uncanonicalize(self, url):\\n pass\",\n \"def test_replace_namespaced_route(self):\\n pass\",\n \"def reflejar(self, una_senal): \\n self.blancos.reflejar(una_senal)\\n\\n return una_senal\",\n \"def fixAuthor(self, pageText):\\n informationRegex = re.compile(\\n r'\\\\|Author=Original uploader was '\\n r'(?P<author>\\\\[\\\\[:\\\\w+:\\\\w+:\\\\w+\\\\|\\\\w+\\\\]\\\\] at \\\\[.+\\\\])')\\n selfRegex = re.compile(\\n r'{{self\\\\|author='\\n r'(?P<author>\\\\[\\\\[:\\\\w+:\\\\w+:\\\\w+\\\\|\\\\w+\\\\]\\\\] at \\\\[.+\\\\])\\\\|')\\n\\n # Find the |Author=Original uploader was ....\\n informationMatch = informationRegex.search(pageText)\\n\\n # Find the {{self|author=\\n selfMatch = selfRegex.search(pageText)\\n\\n # Check if both are found and are equal\\n if (informationMatch and selfMatch):\\n if informationMatch.group('author') == selfMatch.group('author'):\\n # Replace |Author=Original uploader was ... with |Author= ...\\n pageText = informationRegex.sub(r'|Author=\\\\g<author>',\\n pageText)\\n return pageText\",\n \"def nominal_to_altaz(altaz_coord,norm_coord):\\n alt_norm,az_norm = norm_coord.array_direction\\n az = altaz_coord.az\\n alt = altaz_coord.alt\\n x,y = altaz_to_offset(az,alt,az_norm,alt_norm)\\n x=x*u.rad\\n y=y*u.rad\\n representation = CartesianRepresentation(x.to(u.deg),y.to(u.deg),0*u.deg)\\n\\n return norm_coord.realize_frame(representation)\",\n \"def rna(self):\\n return self.seq.replace('T', 'U').replace('t', 'u')\",\n \"def updateResidueProbAnnotation(residueProb):\\n\\n for resonance in residueProb.resonanceGroup.resonances:\\n updateResonanceAnnotation(resonance)\",\n \"def unit2internal(src_unit: Union[str, float]):\\n return _parse_unit(src_unit, conversion_factor=_conversion_factor_internal)\",\n \"def _convert_reference_fields_to_new_namespace(reference):\\r\\n assert isinstance(reference, UsageKey)\\r\\n if source_course_id == reference.course_key:\\r\\n return reference.map_into_course(dest_course_id)\\r\\n else:\\r\\n return reference\",\n \"def update_name(name, mapping):\\n m = street_type_re.search(name)\\n if m:\\n street_type = m.group()\\n for key, value in mapping.iteritems():\\n if street_type == key:\\n name = name.replace(key,value)\\n\\n return name\",\n \"def test_replace_namespaced_template(self):\\n pass\",\n \"def convert_to_namespace(file, output, keyword, author):\\n resource = parse_bel_resource(file)\\n\\n write_namespace(\\n namespace_keyword=(keyword or resource['AnnotationDefinition']['Keyword']),\\n namespace_name=resource['AnnotationDefinition']['Keyword'],\\n namespace_description=resource['AnnotationDefinition']['DescriptionString'],\\n author_name=author,\\n namespace_domain=NAMESPACE_DOMAIN_OTHER,\\n values=resource['Values'],\\n citation_name=resource['Citation']['NameString'],\\n file=output,\\n )\",\n \"def unmangle_gcov_filename(gcov_filename: str):\\n return gcov_filename.replace('^', '..').replace('#', '/').replace('~', ':')\",\n \"def _canonify(self, rut):\\n rut = smart_unicode(rut).replace(' ', '').replace('.', '').replace('-', '')\\n return rut[:-1], rut[-1]\",\n \"def normalize_identifier(prefix: str, identifier: str) -> str:\\n # A \\\"banana\\\" is an embedded prefix that isn't actually part of the identifier.\\n # Usually this corresponds to the prefix itself, with some specific stylization\\n # such as in the case of FBbt. The banana does NOT include a colon \\\":\\\" at the end\\n banana = get_banana(prefix)\\n if banana:\\n banana = f\\\"{banana}:\\\"\\n if not identifier.startswith(banana):\\n return f\\\"{banana}{identifier}\\\"\\n # Handle when the namespace is in the LUI, but no specific banana\\n # has been given. This is most common for OBO Foundry ontologies'\\n # identifiers, like CHEBI:XXXX\\n elif namespace_in_lui(prefix):\\n banana = f\\\"{prefix.upper()}:\\\"\\n if not identifier.startswith(banana):\\n return f\\\"{banana}{identifier}\\\"\\n\\n # TODO Unnecessary redundant prefix?\\n # elif identifier.lower().startswith(f'{prefix}:'):\\n #\\n\\n return identifier\",\n \"def to_rna(seq):\\n seq=seq.replace('A','U')\\n seq=seq.replace('T','A')\\n seq=seq.replace('C',\\\"P\\\")\\n seq=seq.replace('G','C')\\n seq=seq.replace('P','G')\\n return seq\",\n \"def _move_quant_attributes_into_annotations(model):\\n if onnx is None:\\n raise ModuleNotFoundError(\\\"Installation of ONNX is required.\\\")\\n\\n model = copy.deepcopy(model)\\n qaname = \\\"finn_datatype\\\"\\n for n in model.graph.node:\\n for a in n.attribute:\\n mark_for_removal = False\\n if a.name == \\\"weight_qnt\\\":\\n # assume second input is weight, make sure it has an initializer\\n w_tensor_name = n.input[1]\\n assert w_tensor_name in [x.name for x in model.graph.initializer]\\n tq = onnx.StringStringEntryProto(key = qaname, value = a.s)\\n ta = onnx.TensorAnnotation(\\n tensor_name = w_tensor_name,\\n quant_parameter_tensor_names = [tq]\\n )\\n model.graph.quantization_annotation.append(ta)\\n mark_for_removal = True\\n elif a.name == \\\"activation_qnt\\\":\\n a_tensor_name = n.output[0]\\n tq = onnx.StringStringEntryProto(key = qaname, value = a.s)\\n ta = onnx.TensorAnnotation(\\n tensor_name = a_tensor_name,\\n quant_parameter_tensor_names = [tq]\\n )\\n model.graph.quantization_annotation.append(ta)\\n mark_for_removal = True\\n if mark_for_removal:\\n n.attribute.remove(a)\\n return model\",\n \"def __str__(self):\\n return f\\\"Annotation '{self.text}' at {super().__repr__()}\\\"\",\n \"def setAnnotation(self, *args):\\n return _libsbml.SpeciesReference_setAnnotation(self, *args)\",\n \"def convert_atlas(document, current_unit, new_unit):\\n root = document.getroot()\\n\\n for subtexture in root:\\n x, y = subtexture.attrib['x'], subtexture.attrib['y']\\n subtexture.attrib['x'] = str((int(x) / current_unit) * new_unit)\\n subtexture.attrib['y'] = str((int(y) / current_unit) * new_unit)\\n subtexture.attrib['width'] = str(new_unit)\\n subtexture.attrib['height'] = str(new_unit)\\n\\n return document\",\n \"def unpatch(cls):\\n xml.dom.minidom.Element = cls._original_element\",\n \"def _setintermediary_institution_56A(self, val):\\n self.swift_obj.IntermediaryInstitution_A = val\\n self.swift_obj.IntermediaryInstitution_A.swiftTag = '56A'\",\n \"def test_models_organization_fields_code_normalize(self):\\n organization = factories.OrganizationFactory()\\n\\n organization.code = \\\"Là&ça boô\\\"\\n organization.save()\\n self.assertEqual(organization.code, \\\"LACA-BOO\\\")\",\n \"def _namespace_mangle(self, namespace):\\n return namespace.replace(\\\".\\\", \\\"__\\\")\",\n \"def adjust_for_location(header_guard):\\n # Remove public prefixes\\n header_guard = public_prefix.sub('', header_guard, 1)\\n # Replace sysroot prefixes\\n header_guard = sysroot_prefix.sub('SYSROOT_', header_guard, 1)\\n\\n return header_guard\",\n \"def copy_annotations_from_unaligned(aligned_seqrec: SeqRecord, unaligned_seqrec: SeqRecord):\\n # NCBI Blast id includes description, whereas alignment does not\\n assert aligned_seqrec.id in unaligned_seqrec.id, f\\\"{aligned_seqrec.id} <> {unaligned_seqrec.id}\\\"\\n # copy annotations from previous\\n newrec = deepcopy(aligned_seqrec)\\n newrec.annotations = unaligned_seqrec.annotations\\n # clear any letter annotations added during deepcopy\\n newrec.letter_annotations = dict()\\n # original sequence and letter annotations\\n seq = unaligned_seqrec.seq\\n letter_annotations = unaligned_seqrec.letter_annotations\\n # index to track position in original sequence\\n i = 0\\n for j, letter in enumerate(aligned_seqrec.seq):\\n if letter in [gap_letter, stop_letter]:\\n for key, values in letter_annotations.items():\\n # convert strings into lists of characters,\\n # then combine into string at end of loop\\n if key == \\\"seqnums\\\":\\n letter_annotation = None\\n elif all(isinstance(value, str) for value in values):\\n letter_annotation = gap_letter\\n else:\\n letter_annotation = None\\n newrec.letter_annotations.setdefault(key, list()).append(letter_annotation)\\n else:\\n while seq[i] in [gap_letter, stop_letter]:\\n i += 1\\n assert letter == seq[i], f\\\"letter {letter} at {j} <> seq {seq[i]} at {i}\\\"\\n for key in letter_annotations.keys():\\n newrec.letter_annotations.setdefault(key, list()).append(letter_annotations[key][i])\\n i += 1\\n # convert list of chars into string\\n for key, values in letter_annotations.items():\\n if isinstance(values, str):\\n newrec.letter_annotations[key] = \\\"\\\".join(newrec.letter_annotations[key])\\n return newrec\",\n \"def adjust_references_of_iat(self, start, end, gap_size):\\n self.pe_manager.adjust_data_in_range(start, end, gap_size)\",\n \"def _setordering_institution_52A(self, val):\\n self.swift_obj.OrderingInstitution_A = val\\n self.swift_obj.OrderingInstitution_A.swiftTag = '52A'\",\n \"def project(self, other, normalized=...):\\n ...\",\n \"def _normalize(self, metric_name, submit_method, prefix):\\n metric_prefix = \\\"mongodb.\\\" if not prefix else \\\"mongodb.{0}.\\\".format(prefix)\\n metric_suffix = \\\"ps\\\" if submit_method == RATE else \\\"\\\"\\n\\n # Replace case-sensitive metric name characters\\n for pattern, repl in self.CASE_SENSITIVE_METRIC_NAME_SUFFIXES.iteritems():\\n metric_name = re.compile(pattern).sub(repl, metric_name)\\n\\n # Normalize, and wrap\\n return u\\\"{metric_prefix}{normalized_metric_name}{metric_suffix}\\\".format(\\n normalized_metric_name=self.normalize(metric_name.lower()),\\n metric_prefix=metric_prefix, metric_suffix=metric_suffix\\n )\",\n \"def dna_to_rna(dna: str, start=0):\\n return dna.replace(\\\"T\\\", \\\"U\\\")\",\n \"def _derive_origin_(self, doc):\\n return self.fam.c_derive_origin(self, doc)\",\n \"def setAngularUnits(self,unit):\\n\\n\\t\\t#Sanity check\\n\\t\\tassert unit.physical_type==\\\"angle\\\"\\n\\t\\tself.side_angle = self.side_angle.to(unit)\",\n \"def replace_x_y_nominal_lat_lon(ds):\\n ds = ds.copy()\\n if 'x' in ds.dims and 'y' in ds.dims:\\n \\n nominal_y = ds.lat.mean('x')\\n # extract the equatorial lat and take those lon values as nominal lon\\n eq_ind = abs(ds.lat.mean('x')).load().argmin().data\\n nominal_x = ds.lon.isel(y=eq_ind)\\n ds.coords['x'].data = nominal_x.data\\n ds.coords['y'].data = nominal_y.data\\n\\n ds = ds.sortby('x')\\n ds = ds.sortby('y')\\n \\n else:\\n warnings.warn('No x and y found in dimensions for source_id:%s. This likely means that you forgot to rename the dataset or this is the German unstructured model' %ds.attrs['source_id'])\\n return ds\",\n \"def ocr_correction(token):\",\n \"def fix_label(label):\\n\\n replace_dict = {'_': ' ',\\n 'degE': '$^{\\\\circ}$E',\\n 'ms-1': '$m s^{-1}$',\\n 'm.s-1': '$m s^{-1}$',\\n 'Wm-2': '$W m^{-2}$',\\n '1000000 m2.s-1': '$10^6$m$^2$s$^{-1}$'\\n } \\n\\n for value, replacement in list(replace_dict.items()):\\n label = label.replace(value, replacement)\\n\\n return label\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.5260051","0.5162772","0.5024999","0.50209576","0.50172675","0.49961436","0.49337363","0.49098545","0.4908495","0.48858345","0.48811057","0.48475933","0.48307392","0.48301882","0.4786552","0.47759408","0.4762112","0.4751336","0.4724744","0.47108185","0.47001886","0.4692547","0.4669248","0.46510604","0.46473688","0.4635142","0.46332777","0.46213362","0.46187866","0.46137246","0.46089604","0.45951915","0.4576209","0.4576209","0.4573867","0.45736095","0.45649493","0.4552771","0.45516145","0.45447856","0.45402923","0.45201132","0.45138058","0.4509768","0.45055592","0.4488145","0.44820568","0.44820568","0.4465209","0.4452726","0.4442076","0.44419634","0.44369835","0.44353408","0.443378","0.44337705","0.44332036","0.44290978","0.44251558","0.44233382","0.44223034","0.4414394","0.44098005","0.44090468","0.4403804","0.4393741","0.43935847","0.43922293","0.43902752","0.43885702","0.43839237","0.43794754","0.4378023","0.4375717","0.4373299","0.43691778","0.43583754","0.43566987","0.43552363","0.43516973","0.43495995","0.43480635","0.4347018","0.434677","0.4341758","0.43414184","0.4338923","0.43382937","0.4335741","0.43347633","0.4331031","0.4326844","0.43248942","0.4317568","0.43166983","0.43130267","0.4309582","0.43041766","0.4302501","0.42983806"],"string":"[\n \"0.5260051\",\n \"0.5162772\",\n \"0.5024999\",\n \"0.50209576\",\n \"0.50172675\",\n \"0.49961436\",\n \"0.49337363\",\n \"0.49098545\",\n \"0.4908495\",\n \"0.48858345\",\n \"0.48811057\",\n \"0.48475933\",\n \"0.48307392\",\n \"0.48301882\",\n \"0.4786552\",\n \"0.47759408\",\n \"0.4762112\",\n \"0.4751336\",\n \"0.4724744\",\n \"0.47108185\",\n \"0.47001886\",\n \"0.4692547\",\n \"0.4669248\",\n \"0.46510604\",\n \"0.46473688\",\n \"0.4635142\",\n \"0.46332777\",\n \"0.46213362\",\n \"0.46187866\",\n \"0.46137246\",\n \"0.46089604\",\n \"0.45951915\",\n \"0.4576209\",\n \"0.4576209\",\n \"0.4573867\",\n \"0.45736095\",\n \"0.45649493\",\n \"0.4552771\",\n \"0.45516145\",\n \"0.45447856\",\n \"0.45402923\",\n \"0.45201132\",\n \"0.45138058\",\n \"0.4509768\",\n \"0.45055592\",\n \"0.4488145\",\n \"0.44820568\",\n \"0.44820568\",\n \"0.4465209\",\n \"0.4452726\",\n \"0.4442076\",\n \"0.44419634\",\n \"0.44369835\",\n \"0.44353408\",\n \"0.443378\",\n \"0.44337705\",\n \"0.44332036\",\n \"0.44290978\",\n \"0.44251558\",\n \"0.44233382\",\n \"0.44223034\",\n \"0.4414394\",\n \"0.44098005\",\n \"0.44090468\",\n \"0.4403804\",\n \"0.4393741\",\n \"0.43935847\",\n \"0.43922293\",\n \"0.43902752\",\n \"0.43885702\",\n \"0.43839237\",\n \"0.43794754\",\n \"0.4378023\",\n \"0.4375717\",\n \"0.4373299\",\n \"0.43691778\",\n \"0.43583754\",\n \"0.43566987\",\n \"0.43552363\",\n \"0.43516973\",\n \"0.43495995\",\n \"0.43480635\",\n \"0.4347018\",\n \"0.434677\",\n \"0.4341758\",\n \"0.43414184\",\n \"0.4338923\",\n \"0.43382937\",\n \"0.4335741\",\n \"0.43347633\",\n \"0.4331031\",\n \"0.4326844\",\n \"0.43248942\",\n \"0.4317568\",\n \"0.43166983\",\n \"0.43130267\",\n \"0.4309582\",\n \"0.43041766\",\n \"0.4302501\",\n \"0.42983806\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.4454109"},"document_rank":{"kind":"string","value":"49"}}},{"rowIdx":21888,"cells":{"query":{"kind":"string","value":"Custom repr for easier debugging."},"document":{"kind":"string","value":"def __repr__(self):\n text = str(self)\n text_ellips = (text[:31] + \"..\" + text[-31:]) if len(text) > 64 else text\n return f\"{self.element_id}. {text_ellips}...\""},"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 __str__(self):\r\n return repr(self)","def __repr__(self) -> str:\n items = (\"{}={}\".format(k, repr(v)) for k, v in self.__dict__.items())\n return \"<{}({})>\".format(self.__class__.__name__, \", \".join(items))","def __str__(self):\n return repr(self)","def __repr__(self):\n # this is a good default __repr__\n # Q: using the docs, can you figure out what this is doing?\n return f\"<{type(self).__name__} {self.__dict__}>\"","def __repr__(self):\n string_representation = self.__str__().replace(\"{\", \"(\").replace(\n \"}\", \")\").replace(\":\", \"=\")\n return f\"{type(self).__name__}{string_representation}\"","def __repr__(self) -> str:\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())","def __repr__(self) -> str:\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())","def __repr__(self) -> str:\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())","def _extra_repr(self):\n return ''","def repr(self):\n raise InterpreterError(\"Unimplemented repr()\")","def __repr__(self) -> str:\n return self.__str__()","def __repr__(self) -> str:\n return self.__str__()","def __repr__(self) -> str:\n return self.__str__()","def __repr__(self) -> str:\n return self.__str__()","def __repr__(self):\n return self.pretty_print(self.__dict__)","def _repr_(self):\n return repr(self.element())","def __repr__(self):\r\n return self.__str__()","def __repr__(self):\n return u\"(\" + str(self) + \", \" + str(dict(self)) + u')'","def __repr__(self) -> str:\n skip = ()\n items = (\n \"{}={}\".format(k, repr(v))\n for k, v in self.__dict__.items()\n if k not in skip\n )\n return \"<{}({})>\".format(self.__class__.__name__, \", \".join(items))","def repr_(object_):\n return repr(object_)","def repr(x) -> String:\n pass","def __repr__(self):\n\n rep = \"\"\n rep += str(self.literal)+\"\\n\"\n rep += str(self.bindings)+\"\\n\"\n rep += str(self.facts)+\"\\n\"\n return (rep)","def __repr__(self):\n repr_parts = ['<', self.__class__.__name__]\n self._add_type_specific_repr_fields(repr_parts)\n repr_parts.append('>')\n return ''.join(repr_parts)","def __repr__(self):\n\n return '%s(%s)' % (self, self._format_entries(),)","def __repr__(self) -> str:\n ...","def __repr__(self) -> str:\n ...","def __repr__(self) -> str:\n ...","def __repr__(self) -> str:\n ...","def __repr__(self) -> str:\n ...","def __repr__(self):\n extras = self._extra_repr()\n if isinstance(extras, list):\n extras = ' '.join([str(e) for e in extras])\n try:\n if self.id:\n return '<%s %s 0x%x>' % (type(self).__name__,\n ('%s %s' % (self.id, extras)).strip(),\n hash(self))\n return '<%s %s 0x%x>' % (type(self).__name__, extras, hash(self))\n except Exception as e:\n return \"can't convert to string: %s\" % e","def __repr__(self):\n args = [self.name]\n if self.propertiesstr:\n args.append(self.propertiesstr)\n args = map(repr, args)\n return \"%s(%s)\" % (type(self).__name__, \", \".join(args))","def __repr__(self):\n # type: () -> str\n return self.to_str()","def __repr__(self):\n # type: () -> str\n return self.to_str()","def __repr__(self):\n # type: () -> str\n return self.to_str()","def __repr__(self):\n args = []\n if self.name != \"alpha\":\n args.append(repr(self.name))\n if self.propertiesstr:\n args.append(repr(self.propertiesstr))\n elif self.propertiesstr:\n args.append(\"attr=%r\" % self.propertiesstr)\n return \"%s(%s)\" % (type(self).__name__, \", \".join(args))","def __repr__(self) -> str:\n return str(self)","def __repr__(self):\n args = [self.name, self.flags]\n if self.propertiesstr:\n args.append(self.propertiesstr)\n args = map(repr, args)\n return \"%s(%s)\" % (type(self).__name__, \", \".join(args))","def __repr__(self):\n cls = self.__class__.__name__\n return '%s(%s)' % (cls, repr(self.d))","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self) -> str:\n\t\treturn \"\"","def __repr__(self):\n str(self)","def __repr__(self):\n return \"\\'{}\\'\".format(self.__str__())","def __repr__(self):\n s = \"\"\n for v in self.V():\n s += f\"{v.__repr__()}\\n\"\n \n return s","def __repr__ (self):\n\t\tStr = \"\"\n\t\tfor i in self.structref:\n\t\t\tStr = Str + \"%-15s = \"%(i[self.NAME])\n\t\t\tvalue = self.value [i[self.NAME]]\n\t\t\tif isInteger(value):\n\t\t\t\tStr = Str + \"%d, 0x%X\"%(value,value)\n\t\t\t\tif value >= 0x20 and value <= 0xFF:\n\t\t\t\t\tStr = Str + \" '\" + chr (value) + \"'\"\n\t\t\telse:\n\t\t\t\tif type(value) == type(bytes(0)):\n\t\t\t\t\tStr = Str + value.decode(\"utf8\",\"ignore\")\n\t\t\t\telse:\n\t\t\t\t\tStr = Str + str(value) \n\t\t\t\t\t\n\t\t\tStr = Str + \"\\n\"\n\t\treturn Str","def __repr__(self):\r\n return self.to_str()","def __repr__(self):\r\n return self.to_str()","def __repr__(self):\n return pformat(vars(self))","def __repr__(self):\n\n return self._repr__base(rich_output=False)","def __repr__(self):\n\n return self._repr__base(rich_output=False)","def __repr__(self) -> str:\n return '{:s}({!s})'.format(self.__class__.__name__,\n super().__repr__())","def __repr__(self):\r\n return str(self)","def __repr__(self):\n return super().__repr__()","def __repr__(self):\n return '{}<{}, line {} char {}>'.format(\n self.__class__.__name__, repr(self._data),\n self._lineno, self._charno)","def __repr__(self):\n poargs = []\n kwargs = {}\n self._collect_repr_args(poargs, kwargs)\n arg_strs = []\n if poargs:\n arg_strs.append(', '.join('{!r}'.format(arg) for arg in poargs))\n if kwargs:\n arg_strs.append(', '.join('{}={!r}'.format(k, v)\n for k, v in kwargs.items()))\n arg_str = ', '.join(arg_strs)\n return '{}({})'.format(self.__class__.__name__, arg_str)","def __repr__(self):\r\n\t\treturn str(self)","def __repr__(self):\n return repr(self.__dict__['_obj'])","def __repr__(self):\n \n return \"%s(%s)\" % (self.__class__.__name__, self.__str__())","def __repr__(self) ->str:\n # Our __repr__ is recursive, because it can also be called\n # via repr...!\n return ('{}({}, {})'.format(self.__class__.__name__,\n repr(self.value),\n repr(self.children))\n if self.children\n else 'Tree({})'.format(repr(self.value)))","def _repr_(self):\n return repr(self.label())","def __repr__(self):\n return '%s(%r)' % (self.__class__.__name__, str(self))","def __repr__(self) -> str:\n\t\treturn \"- {}\\n{}\\n\".format(self.name, self.__str__())","def __repr__(self):\n\t\treturn str(self)","def __repr__(self):\n\t\treturn str(self)","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()"],"string":"[\n \"def __str__(self):\\r\\n return repr(self)\",\n \"def __repr__(self) -> str:\\n items = (\\\"{}={}\\\".format(k, repr(v)) for k, v in self.__dict__.items())\\n return \\\"<{}({})>\\\".format(self.__class__.__name__, \\\", \\\".join(items))\",\n \"def __str__(self):\\n return repr(self)\",\n \"def __repr__(self):\\n # this is a good default __repr__\\n # Q: using the docs, can you figure out what this is doing?\\n return f\\\"<{type(self).__name__} {self.__dict__}>\\\"\",\n \"def __repr__(self):\\n string_representation = self.__str__().replace(\\\"{\\\", \\\"(\\\").replace(\\n \\\"}\\\", \\\")\\\").replace(\\\":\\\", \\\"=\\\")\\n return f\\\"{type(self).__name__}{string_representation}\\\"\",\n \"def __repr__(self) -> str:\\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())\",\n \"def __repr__(self) -> str:\\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())\",\n \"def __repr__(self) -> str:\\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())\",\n \"def _extra_repr(self):\\n return ''\",\n \"def repr(self):\\n raise InterpreterError(\\\"Unimplemented repr()\\\")\",\n \"def __repr__(self) -> str:\\n return self.__str__()\",\n \"def __repr__(self) -> str:\\n return self.__str__()\",\n \"def __repr__(self) -> str:\\n return self.__str__()\",\n \"def __repr__(self) -> str:\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.pretty_print(self.__dict__)\",\n \"def _repr_(self):\\n return repr(self.element())\",\n \"def __repr__(self):\\r\\n return self.__str__()\",\n \"def __repr__(self):\\n return u\\\"(\\\" + str(self) + \\\", \\\" + str(dict(self)) + u')'\",\n \"def __repr__(self) -> str:\\n skip = ()\\n items = (\\n \\\"{}={}\\\".format(k, repr(v))\\n for k, v in self.__dict__.items()\\n if k not in skip\\n )\\n return \\\"<{}({})>\\\".format(self.__class__.__name__, \\\", \\\".join(items))\",\n \"def repr_(object_):\\n return repr(object_)\",\n \"def repr(x) -> String:\\n pass\",\n \"def __repr__(self):\\n\\n rep = \\\"\\\"\\n rep += str(self.literal)+\\\"\\\\n\\\"\\n rep += str(self.bindings)+\\\"\\\\n\\\"\\n rep += str(self.facts)+\\\"\\\\n\\\"\\n return (rep)\",\n \"def __repr__(self):\\n repr_parts = ['<', self.__class__.__name__]\\n self._add_type_specific_repr_fields(repr_parts)\\n repr_parts.append('>')\\n return ''.join(repr_parts)\",\n \"def __repr__(self):\\n\\n return '%s(%s)' % (self, self._format_entries(),)\",\n \"def __repr__(self) -> str:\\n ...\",\n \"def __repr__(self) -> str:\\n ...\",\n \"def __repr__(self) -> str:\\n ...\",\n \"def __repr__(self) -> str:\\n ...\",\n \"def __repr__(self) -> str:\\n ...\",\n \"def __repr__(self):\\n extras = self._extra_repr()\\n if isinstance(extras, list):\\n extras = ' '.join([str(e) for e in extras])\\n try:\\n if self.id:\\n return '<%s %s 0x%x>' % (type(self).__name__,\\n ('%s %s' % (self.id, extras)).strip(),\\n hash(self))\\n return '<%s %s 0x%x>' % (type(self).__name__, extras, hash(self))\\n except Exception as e:\\n return \\\"can't convert to string: %s\\\" % e\",\n \"def __repr__(self):\\n args = [self.name]\\n if self.propertiesstr:\\n args.append(self.propertiesstr)\\n args = map(repr, args)\\n return \\\"%s(%s)\\\" % (type(self).__name__, \\\", \\\".join(args))\",\n \"def __repr__(self):\\n # type: () -> str\\n return self.to_str()\",\n \"def __repr__(self):\\n # type: () -> str\\n return self.to_str()\",\n \"def __repr__(self):\\n # type: () -> str\\n return self.to_str()\",\n \"def __repr__(self):\\n args = []\\n if self.name != \\\"alpha\\\":\\n args.append(repr(self.name))\\n if self.propertiesstr:\\n args.append(repr(self.propertiesstr))\\n elif self.propertiesstr:\\n args.append(\\\"attr=%r\\\" % self.propertiesstr)\\n return \\\"%s(%s)\\\" % (type(self).__name__, \\\", \\\".join(args))\",\n \"def __repr__(self) -> str:\\n return str(self)\",\n \"def __repr__(self):\\n args = [self.name, self.flags]\\n if self.propertiesstr:\\n args.append(self.propertiesstr)\\n args = map(repr, args)\\n return \\\"%s(%s)\\\" % (type(self).__name__, \\\", \\\".join(args))\",\n \"def __repr__(self):\\n cls = self.__class__.__name__\\n return '%s(%s)' % (cls, repr(self.d))\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self) -> str:\\n\\t\\treturn \\\"\\\"\",\n \"def __repr__(self):\\n str(self)\",\n \"def __repr__(self):\\n return \\\"\\\\'{}\\\\'\\\".format(self.__str__())\",\n \"def __repr__(self):\\n s = \\\"\\\"\\n for v in self.V():\\n s += f\\\"{v.__repr__()}\\\\n\\\"\\n \\n return s\",\n \"def __repr__ (self):\\n\\t\\tStr = \\\"\\\"\\n\\t\\tfor i in self.structref:\\n\\t\\t\\tStr = Str + \\\"%-15s = \\\"%(i[self.NAME])\\n\\t\\t\\tvalue = self.value [i[self.NAME]]\\n\\t\\t\\tif isInteger(value):\\n\\t\\t\\t\\tStr = Str + \\\"%d, 0x%X\\\"%(value,value)\\n\\t\\t\\t\\tif value >= 0x20 and value <= 0xFF:\\n\\t\\t\\t\\t\\tStr = Str + \\\" '\\\" + chr (value) + \\\"'\\\"\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif type(value) == type(bytes(0)):\\n\\t\\t\\t\\t\\tStr = Str + value.decode(\\\"utf8\\\",\\\"ignore\\\")\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tStr = Str + str(value) \\n\\t\\t\\t\\t\\t\\n\\t\\t\\tStr = Str + \\\"\\\\n\\\"\\n\\t\\treturn Str\",\n \"def __repr__(self):\\r\\n return self.to_str()\",\n \"def __repr__(self):\\r\\n return self.to_str()\",\n \"def __repr__(self):\\n return pformat(vars(self))\",\n \"def __repr__(self):\\n\\n return self._repr__base(rich_output=False)\",\n \"def __repr__(self):\\n\\n return self._repr__base(rich_output=False)\",\n \"def __repr__(self) -> str:\\n return '{:s}({!s})'.format(self.__class__.__name__,\\n super().__repr__())\",\n \"def __repr__(self):\\r\\n return str(self)\",\n \"def __repr__(self):\\n return super().__repr__()\",\n \"def __repr__(self):\\n return '{}<{}, line {} char {}>'.format(\\n self.__class__.__name__, repr(self._data),\\n self._lineno, self._charno)\",\n \"def __repr__(self):\\n poargs = []\\n kwargs = {}\\n self._collect_repr_args(poargs, kwargs)\\n arg_strs = []\\n if poargs:\\n arg_strs.append(', '.join('{!r}'.format(arg) for arg in poargs))\\n if kwargs:\\n arg_strs.append(', '.join('{}={!r}'.format(k, v)\\n for k, v in kwargs.items()))\\n arg_str = ', '.join(arg_strs)\\n return '{}({})'.format(self.__class__.__name__, arg_str)\",\n \"def __repr__(self):\\r\\n\\t\\treturn str(self)\",\n \"def __repr__(self):\\n return repr(self.__dict__['_obj'])\",\n \"def __repr__(self):\\n \\n return \\\"%s(%s)\\\" % (self.__class__.__name__, self.__str__())\",\n \"def __repr__(self) ->str:\\n # Our __repr__ is recursive, because it can also be called\\n # via repr...!\\n return ('{}({}, {})'.format(self.__class__.__name__,\\n repr(self.value),\\n repr(self.children))\\n if self.children\\n else 'Tree({})'.format(repr(self.value)))\",\n \"def _repr_(self):\\n return repr(self.label())\",\n \"def __repr__(self):\\n return '%s(%r)' % (self.__class__.__name__, str(self))\",\n \"def __repr__(self) -> str:\\n\\t\\treturn \\\"- {}\\\\n{}\\\\n\\\".format(self.name, self.__str__())\",\n \"def __repr__(self):\\n\\t\\treturn str(self)\",\n \"def __repr__(self):\\n\\t\\treturn str(self)\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.82584816","0.8253082","0.82329077","0.8191106","0.81365615","0.81327766","0.81327766","0.81327766","0.8118914","0.8113123","0.80449194","0.80449194","0.80449194","0.80449194","0.80425906","0.801727","0.7994082","0.79937655","0.798417","0.79790634","0.7972586","0.79625803","0.79574156","0.79463106","0.7942413","0.7942413","0.7942413","0.7942413","0.7942413","0.7942101","0.7941265","0.793972","0.793972","0.793972","0.79091245","0.79010326","0.79008275","0.7889171","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.7884694","0.78777313","0.786947","0.7868581","0.78610224","0.78464353","0.78437954","0.78437954","0.78425395","0.7832232","0.7832232","0.78283024","0.782547","0.7814436","0.7812117","0.7801883","0.7793761","0.7792899","0.7780289","0.7779985","0.77766407","0.77705324","0.7768399","0.776455","0.776455","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307","0.77631307"],"string":"[\n \"0.82584816\",\n \"0.8253082\",\n \"0.82329077\",\n \"0.8191106\",\n \"0.81365615\",\n \"0.81327766\",\n \"0.81327766\",\n \"0.81327766\",\n \"0.8118914\",\n \"0.8113123\",\n \"0.80449194\",\n \"0.80449194\",\n \"0.80449194\",\n \"0.80449194\",\n \"0.80425906\",\n \"0.801727\",\n \"0.7994082\",\n \"0.79937655\",\n \"0.798417\",\n \"0.79790634\",\n \"0.7972586\",\n \"0.79625803\",\n \"0.79574156\",\n \"0.79463106\",\n \"0.7942413\",\n \"0.7942413\",\n \"0.7942413\",\n \"0.7942413\",\n \"0.7942413\",\n \"0.7942101\",\n \"0.7941265\",\n \"0.793972\",\n \"0.793972\",\n \"0.793972\",\n \"0.79091245\",\n \"0.79010326\",\n \"0.79008275\",\n \"0.7889171\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.7884694\",\n \"0.78777313\",\n \"0.786947\",\n \"0.7868581\",\n \"0.78610224\",\n \"0.78464353\",\n \"0.78437954\",\n \"0.78437954\",\n \"0.78425395\",\n \"0.7832232\",\n \"0.7832232\",\n \"0.78283024\",\n \"0.782547\",\n \"0.7814436\",\n \"0.7812117\",\n \"0.7801883\",\n \"0.7793761\",\n \"0.7792899\",\n \"0.7780289\",\n \"0.7779985\",\n \"0.77766407\",\n \"0.77705324\",\n \"0.7768399\",\n \"0.776455\",\n \"0.776455\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\",\n \"0.77631307\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":21889,"cells":{"query":{"kind":"string","value":"Get a list of token objects for the event extent. The extent can be set to include discontiguous_triggers, participants, and/or fillers. Setting this to an empty list will only return the original In the definition of an event nugget we include all of these."},"document":{"kind":"string","value":"def get_extent_tokens(self, extent=[\"discontiguous_triggers\"], source_order=True):\n all_tokens = self.tokens.copy()\n core_sen_idx = all_tokens[0].in_sentence.element_id # to ensure discont is in same sentence\n\n for ext in extent:\n if getattr(self, ext):\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens if t.in_sentence.element_id == core_sen_idx)\n\n all_tokens = list(\n set(all_tokens)\n ) # this is necessary because trigger and participant spans can overlap\n if source_order: # return tokens in the order they appear in source text\n all_tokens.sort(key=lambda x: x.begin)\n\n return all_tokens"},"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_extent_tokens(self, extent=[], source_order=True):\n all_tokens = self.tokens.copy()\n\n for ext in extent:\n if getattr(self, ext):\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens)\n\n all_tokens = list(\n set(all_tokens)\n ) # this is necessary because trigger and participant spans can overlap\n if source_order: # return tokens in the order they appear in source text\n all_tokens.sort(key=lambda x: x.begin)\n\n return all_tokens","def get_extent_token_ids(self, **kwargs):\n token_span = self.get_extent_tokens(**kwargs)\n return [t.index for t in token_span]","def get_tokens(self):\r\n return TokenGroup.get_tokens(self._tu, self.extent)","def getTokens(self):\n # NOTE: seems to be used by the evitaNominalTrainer only\n tokenList = []\n for chunkOrToken in self.dtrs:\n if chunkOrToken.isToken():\n tokenList += [chunkOrToken]\n elif chunkOrToken.isChunk():\n tokenList += chunkOrToken.dtrs\n else:\n logger.warn(\"Sentence element that is not a chunk or token\")\n return tokenList","def get_tokens(tu, extent):\r\n tokens_memory = POINTER(Token)()\r\n tokens_count = c_uint()\r\n\r\n conf.lib.clang_tokenize(tu, extent, byref(tokens_memory),\r\n byref(tokens_count))\r\n\r\n count = int(tokens_count.value)\r\n\r\n # If we get no tokens, no memory was allocated. Be sure not to return\r\n # anything and potentially call a destructor on nothing.\r\n if count < 1:\r\n return\r\n\r\n tokens_array = cast(tokens_memory, POINTER(Token * count)).contents\r\n\r\n token_group = TokenGroup(tu, tokens_memory, tokens_count)\r\n\r\n for i in xrange(0, count):\r\n token = Token()\r\n token.int_data = tokens_array[i].int_data\r\n token.ptr_data = tokens_array[i].ptr_data\r\n token._tu = tu\r\n token._group = token_group\r\n\r\n yield token","def get_tokens(self):\n def _traverse_preorder(cursor, token_list): # There is a method called \"walk_preorder\" in Cursor class. Here we need to ignore some subtrees so we implement on our own.\n if cursor.location.file and cursor.location.file.name != self.filepath: # exclude \"#include <...>\"\n return\n if (cursor.kind, cursor.spelling) in exclude_types: # exclude node in 'exclude_types'\n return\n \n token_list.append(cursor)\n for child in cursor.get_children():\n _traverse_preorder(child, token_list)\n\n tokens = []\n _traverse_preorder(self.cursor, tokens)\n return tokens","def getTokens(self):\n list = []\n for i in range(self.startIdx, self.endIdx + 1):\n token = self.sentence[i]\n list.append(token)\n return list","def tokens(self):\n # type: () -> List[Token]\n return self._tokens","def get_tokens(self) -> List[str]:\n return self.tokens","def tokens(self):\n return self.rpc.call(MsfRpcMethod.AuthTokenList)['tokens']","def get_tokens(self, locations=None, extent=None):\r\n if locations is not None:\r\n extent = SourceRange(start=locations[0], end=locations[1])\r\n\r\n return TokenGroup.get_tokens(self, extent)","def get_gene_tokens(self, classification: Classification) -> List[GeneToken]:\n return self.get_protein_gene_symbol_tokens(classification)","def get_tokens(self):\r\n return self.token_set","def tokens(self):\n return self._tokens","def tokens(self):\n return self._tokens","def tokens(self):\n return self._tokens","def tokens(self):\n return self.__tokens","def tokens(self):\r\n return self.iter_tokens(self._blob)","def special_tokens(self) -> List[Hashable]:\n return list(self._special_token_kv.values())","def get_tokens(self, node, include_extra=False):\n # type: (AstNode, bool) -> Iterator[Token]\n return self.token_range(node.first_token, node.last_token, include_extra=include_extra)","def extent(self):\r\n return conf.lib.clang_getTokenExtent(self._tu, self)","def extent(self) -> typing.Tuple[str, ...]:\n return self._extent.members()","def tokens(self):\n tokens = [k for k in self.tok2ind.keys()\n if k not in {'<NULL>', '<UNK>'}]\n return tokens","def tokens(self):\n return self._sentrep.tokens()","def all_tokens(self) -> List[Hashable]:\n return self._all_tokens","def get_token_names(self) -> List[str]:\n return list(self._tokens.keys())","def tokens(self) -> list:\n if self._tokens is None:\n tokens_ = sorted(list(self.elements()))\n self._tokens = tokens_\n return self._tokens","def get_tokens(self):\n\t\treturn self.get_starttokens() + self.get_endtokens()","def get_token_types() -> List[type]:\n return ExtensionTokenTypes.__TOKEN_TYPES","def tokens(self):\n tokens = []\n for index in range(len(self.sentrep)):\n tokens.append(self.sentrep.getWord(index).lexeme())\n return tokens","def tokens(self):\n\t\tlabels_and_synonyms = list(itertools.chain.from_iterable(list(self.term_to_tokens.values())))\n\t\ttokens = set(list(itertools.chain.from_iterable([word_tokenize(x) for x in labels_and_synonyms])))\n\t\treturn(list(tokens))","def get_tokens(self, document):\n raise NotImplementedError()","def importantTokenList(self, ignoreSemanticTagList=[]):\n list = tokenlist.TokenList()\n for token in self.tokens:\n if token.isAcronym():\n expansion = token.getAcronymExpansion()\n \n# print token.text, '=', \n# for t in expansion:\n# print t.text,\n# print\n\n# list.append(token)\n# for t in token.getAcronymExpansion():\n# if self.isImportantToken(t):\n# list.append(t)\n\n if expansion == None or len(expansion) == 0:\n list.append(token)\n else:\n for t in token.getAcronymExpansion():\n if self.isImportantToken(t, ignoreSemanticTagList=ignoreSemanticTagList):\n list.append(t)\n elif self.isImportantToken(token, ignoreSemanticTagList=ignoreSemanticTagList):\n list.append(token)\n \n return list","def entities(self):\n if 'ner' not in self.annotators:\n return None\n return [t[self.NER] for t in self.data]","def entities(self):\n if 'ner' not in self.annotators:\n return None\n return [t[self.NER] for t in self.data]","def getTokens(self):\n return self.__token","def get_etfs_list(self):\n return list(self.etfs.keys())","def special_tokens_map_extended(self):\n return self._special_tokens","def getExons(self):\n rtrn = []\n for i in range(0,len(self.exonStarts)):\n rtrn.append(Interval(self.chr,self.exonStarts[i],self.exonEnds[i],self.strand,name = self.name+\"_exon_\"+str(i+1)))\n return rtrn","def get_Tokens(self):\n return self._output.get('Tokens', None)","def list_tokens(self) -> str:\n\n return self._format_symbol_table_content(\"Tokens\", self._symbols.keys())","def get_token_list():\n token_list = []\n tokens_dir_path = os.path.join(BASE_DIR, TOKENS_DIR)\n for dir, dirs, files in os.walk(tokens_dir_path):\n for file_name in files:\n file = open(os.path.join(tokens_dir_path, file_name), 'r')\n token_list.append(file.read().strip())\n file.close()\n return token_list","def get_nft_list(self):\n tokens = self.api.find_all(\"nft\", \"nfts\", query={})\n return tokens","def tokens():\n return ['access token', 'refresh token']","def get_event_list(self):\n event_list = []\n eventLocation = -1\n for element in self:\n eventLocation += 1\n if element.isChunk():\n event = element.embedded_event()\n if event:\n event_list.append((eventLocation, event.eid))\n return event_list","def entity_groups(self):\n entities = self.entities()\n if not entities:\n return None\n non_ent = self.opts.get('non_ent', 'O')\n groups = []\n idx = 0\n while idx < len(entities):\n ner_tag = entities[idx]\n # Check for entity tag\n if ner_tag != non_ent:\n # Chomp the sequence\n start = idx\n while (idx < len(entities) and entities[idx] == ner_tag):\n idx += 1\n groups.append((self.slice(start, idx).untokenize(), ner_tag))\n else:\n idx += 1\n return groups","def entity_groups(self):\n entities = self.entities()\n if not entities:\n return None\n non_ent = self.opts.get('non_ent', 'O')\n groups = []\n idx = 0\n while idx < len(entities):\n ner_tag = entities[idx]\n # Check for entity tag\n if ner_tag != non_ent:\n # Chomp the sequence\n start = idx\n while (idx < len(entities) and entities[idx] == ner_tag):\n idx += 1\n groups.append((self.slice(start, idx).untokenize(), ner_tag))\n else:\n idx += 1\n return groups","def non_special_tokens(self) -> List[Hashable]:\n return self._non_special_tokens","def list_extents(extent_entries, node_id):\n extents = extent_entries[node_id]\n result = []\n for extent_entry in extents:\n result.append({\n \"offset\": extent_entry.key.content.offset,\n \"size\": extent_entry.record.size,\n \"block\": extent_entry.record.block\n })\n return result","def list_revoked_tokens(self):\n raise exception.NotImplemented() # pragma: no cover","def get_entities(self):\n return list(self._entities.values())","def extract_entities(event):\n # TODO The text should probably already be tagged and tokenized before this step\n tree = ne_chunk(event.pos_tagged)\n entities = set([])\n\n people = tree.subtrees(lambda x: x.node == \"PERSON\")\n for person in people:\n entities.add(\" \".join([leaf[0] for leaf in person.leaves()]))\n\n places = tree.subtrees(lambda x: x.node == \"GPE\")\n for place in places:\n entities.add(\" \".join([leaf[0] for leaf in place.leaves()]))\n\n organizations = tree.subtrees(lambda x: x.node == \"ORGANIZATION\")\n for org in organizations:\n entities.add(\" \".join([leaf[0] for leaf in org.leaves()]))\n \n return entities","def tokenize(self):\n tknzr = TweetTokenizer()\n tkn = []\n for tweet in self.tweets:\n for word in tknzr.tokenize(tweet):\n tkn.append(word)\n return tkn","def _get_global_entities(nest):\n entities = nest.get('global_entities', None)\n if not entities:\n return []\n return list(entities.keys())","def synth_tokens(self):\n if self.lliagraph:\n return self.lliagraph.synth_tokens.items()\n else:\n return []","def getTokenAnnotatedType(self, token, sentenceGraph):\n if len(sentenceGraph.tokenIsEntityHead[token]) > 0 and not self.noAnnType:\n annTypes = set()\n for entity in sentenceGraph.tokenIsEntityHead[token]:\n eType = self.getEntityType(entity)\n if eType != None and not eType in annTypes and not eType in self.filterAnnTypes:\n if self.entity1 == None and self.entity2 == None:\n annTypes.add(eType)\n else:\n if self.maximum:\n annTypes.add(eType)\n if self.entity1 == entity:\n if not self.maximum:\n return [eType]\n else:\n annTypes.add(\"e1_\"+eType)\n elif self.entity2 == entity:\n if not self.maximum:\n return [eType]\n else:\n annTypes.add(\"e2_\"+eType)\n else:\n annTypes.add(eType)\n annTypes = list(annTypes)\n annTypes.sort()\n if self.maximum:\n return annTypes[0:2]\n else:\n return annTypes[0:1] #annTypes[0:2]\n else:\n return [\"noAnnType\"]","def sentences(self) -> List[str]:\n\t\treturn [self.text[start:end] for start, end in self.tokenizations]","def sentences(self) -> List[str]:\n\t\treturn [self.text[start:end] for start, end in self.tokenizations]","def get_read_tokens(mastertoken, config):\n mt_path = mastertoken['paths']['self']\n url = \"{}{}/read_tokens.json\".\\\n format(config['domain_base'], mt_path)\n\n try:\n resp = (api_call(url, 'get', config['debug']))\n tokens = resp.json()\n except ValueError as ex:\n abort(\"Unexpected response from packagecloud API: \"\n \"{}\".format(ex.message))\n\n return tokens['read_tokens']","def comment_tokens(self):\n return self._comment_tokens","def special_token_keys(self) -> List[str]:\n return list(self._special_token_kv.keys())","def tokens_and_tags(self):\n return list(zip(self.tokens(), self.tags()))","def token_values(self):\n return self._token_values","def extent(self):\n return np.array(self._extent)","def get_components(self):\r\n return [Token.from_multiword(word, index, self) for index, word in enumerate(self.wordform.split('_'))]","def special_tokens(self, ):\n\n if self.tokenizer.bos_token is None or self.tokenizer.eos_token is None:\n special_tokens = self.tokenizer.build_inputs_with_special_tokens([])\n special_tokens_ids = self.tokenizer.convert_ids_to_tokens(special_tokens)\n self.tokenizer.bos_token, self.tokenizer.eos_token = special_tokens_ids\n\n special_tokens = self.tokenizer.eos_token, self.tokenizer.bos_token\n return special_tokens","def extent(self):\n\n x = np.array([0, self.nx]) * self.dx + self.corner_grid.x0\n ypoint = [0, self.ny] if self.origin == 'lower-left' else [self.ny, 0]\n y = np.array(ypoint) * self.dy + self.corner_grid.y0\n\n return [x[0], x[1], y[0], y[1]]","def get_event_list(self):\n pass","def tokens(self):\n data, end = \\\n self.pat.traverse(lambda obj, *args: obj.tokens(*args),\n self.begin, self.data)\n return data","def get_entities(self, clean=False):\n return list(self.iter_entities(clean=clean))","def emotions(self):\n return self._emotions","def extract_entities(self) :\n entities = []\n googleEntityList = self.googleLanguageModel.analyze_entities() \n watsonEntityList = self.watsonLanguageModel['entities']\n\n for entity in googleEntityList.entities[:self.entitySizeLimit]:\n if len(entity.metadata) > 0:\n entities.append({ 'name' : entity.name, 'metadata' : entity.metadata})\n \n for entity in watsonEntityList[:self.entitySizeLimit]: \n entities.append({ 'name': entity['text'], 'metadata': entity.get('disambiguation', {})}) \n\n return entities","def collect_ners(self, ents):\r\n collected_ners = []\r\n for token in ents:\r\n if token.label_ in self.ners_label:\r\n collected_ners.append(token.text + '/' + token.label_)\r\n return collected_ners","def tokens(self):\r\n return Tokens(self)","def get_predict_windows(self, extent: Box) -> List[Box]:\n chip_size = self.config.chip_size\n return extent.get_windows(chip_size, chip_size)","def get_license_tokens():\n query = {\n \"type\": \"op\",\n \"cmd\": \"<show><license-token-files></license-token-files></show>\",\n }\n\n return __proxy__[\"panos.call\"](query)","def getIntrons(self):\n rtrn = []\n for i in range(0,len(self.exonStarts)-1):\n rtrn.append(Interval(self.chr,self.exonEnds[i]+1,self.exonStarts[i+1]-1))\n return rtrn","def getTokens(self):\n self.__require_privilaged_access()\n with DBSession(self.__config_db) as session:\n user = self.getLoggedInUser()\n sessionTokens = session.query(Session) \\\n .filter(Session.user_name == user) \\\n .filter(Session.can_expire.is_(False)) \\\n .all()\n\n result = []\n for t in sessionTokens:\n result.append(SessionTokenData(\n t.token,\n t.description,\n str(t.last_access)))\n\n return result","def event_list(self):\n return self._event_list","def _get_all_possible_tokens_ahead(self, context):\n tokens = []\n if context.position < len(context.input_str):\n for terminal in self.grammar.terminals.values():\n try:\n tok = terminal.recognizer(context.input_str,\n context.position)\n except TypeError:\n tok = terminal.recognizer(context, context.input_str,\n context.position)\n additional_data = ()\n if type(tok) is tuple:\n tok, *additional_data = tok\n if tok:\n tokens.append(Token(terminal, tok, context.position,\n additional_data))\n return tokens","def entity_tags(self):\n return self._entity_tags","def named_entities(self) -> List[str]:","def _get_all_possible_tokens_ahead(self, context):\n tokens = []\n if context.position < len(context.input_str):\n for terminal in self.grammar.terminals.values():\n try:\n tok = terminal.recognizer(context.input_str,\n context.position)\n except TypeError:\n tok = terminal.recognizer(context, context.input_str,\n context.position)\n additional_data = ()\n if type(tok) is tuple:\n tok, *additional_data = tok\n if tok:\n tokens.append(Token(terminal, tok, additional_data))\n return tokens","def token_exp(token, exptype=False):\n line_ids = set()\n for gate in token['GATE']:\n if ((not exptype or exptype == 'ese') and re_ese.match(gate['ann_type'])):\n line_ids.add(gate['line_id'])\n if ((not exptype or exptype == 'ose') and re_ose.match(gate['ann_type'])):\n line_ids.add(gate['line_id'])\n if ((not exptype or exptype == 'dse') and re_dse.match(gate['ann_type'])):\n line_ids.add(gate['line_id'])\n return line_ids","def _list_tokens(self, user_id, tenant_id=None, trust_id=None,\n consumer_id=None):\n raise exception.NotImplemented() # pragma: no cover","def get_token_list(text):\n return text.split()","def eventList(self):\n return self._eventList","def multiword_tokens(self):\n return self._mwts","def __repr__(self) -> str:\n return \"<Twilio.Oauth.V1.TokenList>\"","def additional_special_tokens_ids(self):\n return [self.token_to_id(token) for token in self.additional_special_tokens]","def bearer_tokens(self):\n return self._bearer_tokens","def named_entity_list_stanford_nlp(self) -> List:\n stanza.download(self.lang, processors = 'tokenize,mwt,ner')\n #load file and convert input string\n nlp = stanza.Pipeline(self.lang, processors = 'tokenize,mwt,ner')\n doc = nlp(self.sent)\n #build the output list\n for sentence in doc.ents:\n #avoid that 'Abstract', 'Title' and 'Author' are counted between NEs\n if sentence.text != 'Abstract' and sentence.text != 'Title' and sentence.text != 'Author':\n self.named_entity_list.append((sentence.text, \n sentence.start_char, \n sentence.end_char, sentence.type))\n return self.named_entity_list","def get_events(self):\n events = []\n for device in self:\n events.extend(self[device].get_events())\n return events","def keys(self):\n return list(self.token2id.values())","def get_tokens():\n return (HDFS_SITE_KEY, NAMESERVICE_KEY, NN_HTTP_ADDRESS_KEY, DFS_POLICY_KEY,\n EXECUTABLE_SEARCH_PATHS, NN_HTTPS_ADDRESS_KEY, SMOKEUSER_KEY,\n KERBEROS_KEYTAB, KERBEROS_PRINCIPAL, SECURITY_ENABLED_KEY,\n METRICS_COLLECTOR_VIP_HOST_KEY, METRICS_COLLECTOR_VIP_PORT_KEY,\n METRICS_COLLECTOR_WEBAPP_ADDRESS_KEY, AMS_HTTP_POLICY)","def entities(self):\n return self._entities","def explicit_list(self):\n exts = []\n for ext in self.extensions.values():\n if ext.implicit:\n continue\n exts.append(ext)\n return exts","def chunk(tokens):\n\n # Uses NLTK function to pair each token with its Part Of Speech\n entity_list = []\n pos = nltk.pos_tag(tokens)\n named_entities_chunk = nltk.ne_chunk(pos, binary=True)\n\n # Finds named entities in tokens, stores in list of strings\n for i in range(0, len(named_entities_chunk)):\n ents = named_entities_chunk.pop()\n if getattr(ents, 'label', None) is not None and ents.label() == \"NE\":\n entity_list.append([ne for ne in ents])\n\n # Combines named entity components, pulls off the POF labels\n return [' '.join(next(zip(*l))) for l in entity_list]","def tokenize(expression):\n return [t.strip() for t in TOKEN_SEPARATOR.split(expression.strip()) if t]","def getFirstTorsionInds(self, extended):\n offset = 6 if extended else 0\n torsionInds = np.array(range(offset + 5, self.natoms * 3, 3))\n primaryTorsions = sorted(list(set(self._firstTorsionTInd)))\n return list(torsionInds[primaryTorsions])"],"string":"[\n \"def get_extent_tokens(self, extent=[], source_order=True):\\n all_tokens = self.tokens.copy()\\n\\n for ext in extent:\\n if getattr(self, ext):\\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens)\\n\\n all_tokens = list(\\n set(all_tokens)\\n ) # this is necessary because trigger and participant spans can overlap\\n if source_order: # return tokens in the order they appear in source text\\n all_tokens.sort(key=lambda x: x.begin)\\n\\n return all_tokens\",\n \"def get_extent_token_ids(self, **kwargs):\\n token_span = self.get_extent_tokens(**kwargs)\\n return [t.index for t in token_span]\",\n \"def get_tokens(self):\\r\\n return TokenGroup.get_tokens(self._tu, self.extent)\",\n \"def getTokens(self):\\n # NOTE: seems to be used by the evitaNominalTrainer only\\n tokenList = []\\n for chunkOrToken in self.dtrs:\\n if chunkOrToken.isToken():\\n tokenList += [chunkOrToken]\\n elif chunkOrToken.isChunk():\\n tokenList += chunkOrToken.dtrs\\n else:\\n logger.warn(\\\"Sentence element that is not a chunk or token\\\")\\n return tokenList\",\n \"def get_tokens(tu, extent):\\r\\n tokens_memory = POINTER(Token)()\\r\\n tokens_count = c_uint()\\r\\n\\r\\n conf.lib.clang_tokenize(tu, extent, byref(tokens_memory),\\r\\n byref(tokens_count))\\r\\n\\r\\n count = int(tokens_count.value)\\r\\n\\r\\n # If we get no tokens, no memory was allocated. Be sure not to return\\r\\n # anything and potentially call a destructor on nothing.\\r\\n if count < 1:\\r\\n return\\r\\n\\r\\n tokens_array = cast(tokens_memory, POINTER(Token * count)).contents\\r\\n\\r\\n token_group = TokenGroup(tu, tokens_memory, tokens_count)\\r\\n\\r\\n for i in xrange(0, count):\\r\\n token = Token()\\r\\n token.int_data = tokens_array[i].int_data\\r\\n token.ptr_data = tokens_array[i].ptr_data\\r\\n token._tu = tu\\r\\n token._group = token_group\\r\\n\\r\\n yield token\",\n \"def get_tokens(self):\\n def _traverse_preorder(cursor, token_list): # There is a method called \\\"walk_preorder\\\" in Cursor class. Here we need to ignore some subtrees so we implement on our own.\\n if cursor.location.file and cursor.location.file.name != self.filepath: # exclude \\\"#include <...>\\\"\\n return\\n if (cursor.kind, cursor.spelling) in exclude_types: # exclude node in 'exclude_types'\\n return\\n \\n token_list.append(cursor)\\n for child in cursor.get_children():\\n _traverse_preorder(child, token_list)\\n\\n tokens = []\\n _traverse_preorder(self.cursor, tokens)\\n return tokens\",\n \"def getTokens(self):\\n list = []\\n for i in range(self.startIdx, self.endIdx + 1):\\n token = self.sentence[i]\\n list.append(token)\\n return list\",\n \"def tokens(self):\\n # type: () -> List[Token]\\n return self._tokens\",\n \"def get_tokens(self) -> List[str]:\\n return self.tokens\",\n \"def tokens(self):\\n return self.rpc.call(MsfRpcMethod.AuthTokenList)['tokens']\",\n \"def get_tokens(self, locations=None, extent=None):\\r\\n if locations is not None:\\r\\n extent = SourceRange(start=locations[0], end=locations[1])\\r\\n\\r\\n return TokenGroup.get_tokens(self, extent)\",\n \"def get_gene_tokens(self, classification: Classification) -> List[GeneToken]:\\n return self.get_protein_gene_symbol_tokens(classification)\",\n \"def get_tokens(self):\\r\\n return self.token_set\",\n \"def tokens(self):\\n return self._tokens\",\n \"def tokens(self):\\n return self._tokens\",\n \"def tokens(self):\\n return self._tokens\",\n \"def tokens(self):\\n return self.__tokens\",\n \"def tokens(self):\\r\\n return self.iter_tokens(self._blob)\",\n \"def special_tokens(self) -> List[Hashable]:\\n return list(self._special_token_kv.values())\",\n \"def get_tokens(self, node, include_extra=False):\\n # type: (AstNode, bool) -> Iterator[Token]\\n return self.token_range(node.first_token, node.last_token, include_extra=include_extra)\",\n \"def extent(self):\\r\\n return conf.lib.clang_getTokenExtent(self._tu, self)\",\n \"def extent(self) -> typing.Tuple[str, ...]:\\n return self._extent.members()\",\n \"def tokens(self):\\n tokens = [k for k in self.tok2ind.keys()\\n if k not in {'<NULL>', '<UNK>'}]\\n return tokens\",\n \"def tokens(self):\\n return self._sentrep.tokens()\",\n \"def all_tokens(self) -> List[Hashable]:\\n return self._all_tokens\",\n \"def get_token_names(self) -> List[str]:\\n return list(self._tokens.keys())\",\n \"def tokens(self) -> list:\\n if self._tokens is None:\\n tokens_ = sorted(list(self.elements()))\\n self._tokens = tokens_\\n return self._tokens\",\n \"def get_tokens(self):\\n\\t\\treturn self.get_starttokens() + self.get_endtokens()\",\n \"def get_token_types() -> List[type]:\\n return ExtensionTokenTypes.__TOKEN_TYPES\",\n \"def tokens(self):\\n tokens = []\\n for index in range(len(self.sentrep)):\\n tokens.append(self.sentrep.getWord(index).lexeme())\\n return tokens\",\n \"def tokens(self):\\n\\t\\tlabels_and_synonyms = list(itertools.chain.from_iterable(list(self.term_to_tokens.values())))\\n\\t\\ttokens = set(list(itertools.chain.from_iterable([word_tokenize(x) for x in labels_and_synonyms])))\\n\\t\\treturn(list(tokens))\",\n \"def get_tokens(self, document):\\n raise NotImplementedError()\",\n \"def importantTokenList(self, ignoreSemanticTagList=[]):\\n list = tokenlist.TokenList()\\n for token in self.tokens:\\n if token.isAcronym():\\n expansion = token.getAcronymExpansion()\\n \\n# print token.text, '=', \\n# for t in expansion:\\n# print t.text,\\n# print\\n\\n# list.append(token)\\n# for t in token.getAcronymExpansion():\\n# if self.isImportantToken(t):\\n# list.append(t)\\n\\n if expansion == None or len(expansion) == 0:\\n list.append(token)\\n else:\\n for t in token.getAcronymExpansion():\\n if self.isImportantToken(t, ignoreSemanticTagList=ignoreSemanticTagList):\\n list.append(t)\\n elif self.isImportantToken(token, ignoreSemanticTagList=ignoreSemanticTagList):\\n list.append(token)\\n \\n return list\",\n \"def entities(self):\\n if 'ner' not in self.annotators:\\n return None\\n return [t[self.NER] for t in self.data]\",\n \"def entities(self):\\n if 'ner' not in self.annotators:\\n return None\\n return [t[self.NER] for t in self.data]\",\n \"def getTokens(self):\\n return self.__token\",\n \"def get_etfs_list(self):\\n return list(self.etfs.keys())\",\n \"def special_tokens_map_extended(self):\\n return self._special_tokens\",\n \"def getExons(self):\\n rtrn = []\\n for i in range(0,len(self.exonStarts)):\\n rtrn.append(Interval(self.chr,self.exonStarts[i],self.exonEnds[i],self.strand,name = self.name+\\\"_exon_\\\"+str(i+1)))\\n return rtrn\",\n \"def get_Tokens(self):\\n return self._output.get('Tokens', None)\",\n \"def list_tokens(self) -> str:\\n\\n return self._format_symbol_table_content(\\\"Tokens\\\", self._symbols.keys())\",\n \"def get_token_list():\\n token_list = []\\n tokens_dir_path = os.path.join(BASE_DIR, TOKENS_DIR)\\n for dir, dirs, files in os.walk(tokens_dir_path):\\n for file_name in files:\\n file = open(os.path.join(tokens_dir_path, file_name), 'r')\\n token_list.append(file.read().strip())\\n file.close()\\n return token_list\",\n \"def get_nft_list(self):\\n tokens = self.api.find_all(\\\"nft\\\", \\\"nfts\\\", query={})\\n return tokens\",\n \"def tokens():\\n return ['access token', 'refresh token']\",\n \"def get_event_list(self):\\n event_list = []\\n eventLocation = -1\\n for element in self:\\n eventLocation += 1\\n if element.isChunk():\\n event = element.embedded_event()\\n if event:\\n event_list.append((eventLocation, event.eid))\\n return event_list\",\n \"def entity_groups(self):\\n entities = self.entities()\\n if not entities:\\n return None\\n non_ent = self.opts.get('non_ent', 'O')\\n groups = []\\n idx = 0\\n while idx < len(entities):\\n ner_tag = entities[idx]\\n # Check for entity tag\\n if ner_tag != non_ent:\\n # Chomp the sequence\\n start = idx\\n while (idx < len(entities) and entities[idx] == ner_tag):\\n idx += 1\\n groups.append((self.slice(start, idx).untokenize(), ner_tag))\\n else:\\n idx += 1\\n return groups\",\n \"def entity_groups(self):\\n entities = self.entities()\\n if not entities:\\n return None\\n non_ent = self.opts.get('non_ent', 'O')\\n groups = []\\n idx = 0\\n while idx < len(entities):\\n ner_tag = entities[idx]\\n # Check for entity tag\\n if ner_tag != non_ent:\\n # Chomp the sequence\\n start = idx\\n while (idx < len(entities) and entities[idx] == ner_tag):\\n idx += 1\\n groups.append((self.slice(start, idx).untokenize(), ner_tag))\\n else:\\n idx += 1\\n return groups\",\n \"def non_special_tokens(self) -> List[Hashable]:\\n return self._non_special_tokens\",\n \"def list_extents(extent_entries, node_id):\\n extents = extent_entries[node_id]\\n result = []\\n for extent_entry in extents:\\n result.append({\\n \\\"offset\\\": extent_entry.key.content.offset,\\n \\\"size\\\": extent_entry.record.size,\\n \\\"block\\\": extent_entry.record.block\\n })\\n return result\",\n \"def list_revoked_tokens(self):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"def get_entities(self):\\n return list(self._entities.values())\",\n \"def extract_entities(event):\\n # TODO The text should probably already be tagged and tokenized before this step\\n tree = ne_chunk(event.pos_tagged)\\n entities = set([])\\n\\n people = tree.subtrees(lambda x: x.node == \\\"PERSON\\\")\\n for person in people:\\n entities.add(\\\" \\\".join([leaf[0] for leaf in person.leaves()]))\\n\\n places = tree.subtrees(lambda x: x.node == \\\"GPE\\\")\\n for place in places:\\n entities.add(\\\" \\\".join([leaf[0] for leaf in place.leaves()]))\\n\\n organizations = tree.subtrees(lambda x: x.node == \\\"ORGANIZATION\\\")\\n for org in organizations:\\n entities.add(\\\" \\\".join([leaf[0] for leaf in org.leaves()]))\\n \\n return entities\",\n \"def tokenize(self):\\n tknzr = TweetTokenizer()\\n tkn = []\\n for tweet in self.tweets:\\n for word in tknzr.tokenize(tweet):\\n tkn.append(word)\\n return tkn\",\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 synth_tokens(self):\\n if self.lliagraph:\\n return self.lliagraph.synth_tokens.items()\\n else:\\n return []\",\n \"def getTokenAnnotatedType(self, token, sentenceGraph):\\n if len(sentenceGraph.tokenIsEntityHead[token]) > 0 and not self.noAnnType:\\n annTypes = set()\\n for entity in sentenceGraph.tokenIsEntityHead[token]:\\n eType = self.getEntityType(entity)\\n if eType != None and not eType in annTypes and not eType in self.filterAnnTypes:\\n if self.entity1 == None and self.entity2 == None:\\n annTypes.add(eType)\\n else:\\n if self.maximum:\\n annTypes.add(eType)\\n if self.entity1 == entity:\\n if not self.maximum:\\n return [eType]\\n else:\\n annTypes.add(\\\"e1_\\\"+eType)\\n elif self.entity2 == entity:\\n if not self.maximum:\\n return [eType]\\n else:\\n annTypes.add(\\\"e2_\\\"+eType)\\n else:\\n annTypes.add(eType)\\n annTypes = list(annTypes)\\n annTypes.sort()\\n if self.maximum:\\n return annTypes[0:2]\\n else:\\n return annTypes[0:1] #annTypes[0:2]\\n else:\\n return [\\\"noAnnType\\\"]\",\n \"def sentences(self) -> List[str]:\\n\\t\\treturn [self.text[start:end] for start, end in self.tokenizations]\",\n \"def sentences(self) -> List[str]:\\n\\t\\treturn [self.text[start:end] for start, end in self.tokenizations]\",\n \"def get_read_tokens(mastertoken, config):\\n mt_path = mastertoken['paths']['self']\\n url = \\\"{}{}/read_tokens.json\\\".\\\\\\n format(config['domain_base'], mt_path)\\n\\n try:\\n resp = (api_call(url, 'get', config['debug']))\\n tokens = resp.json()\\n except ValueError as ex:\\n abort(\\\"Unexpected response from packagecloud API: \\\"\\n \\\"{}\\\".format(ex.message))\\n\\n return tokens['read_tokens']\",\n \"def comment_tokens(self):\\n return self._comment_tokens\",\n \"def special_token_keys(self) -> List[str]:\\n return list(self._special_token_kv.keys())\",\n \"def tokens_and_tags(self):\\n return list(zip(self.tokens(), self.tags()))\",\n \"def token_values(self):\\n return self._token_values\",\n \"def extent(self):\\n return np.array(self._extent)\",\n \"def get_components(self):\\r\\n return [Token.from_multiword(word, index, self) for index, word in enumerate(self.wordform.split('_'))]\",\n \"def special_tokens(self, ):\\n\\n if self.tokenizer.bos_token is None or self.tokenizer.eos_token is None:\\n special_tokens = self.tokenizer.build_inputs_with_special_tokens([])\\n special_tokens_ids = self.tokenizer.convert_ids_to_tokens(special_tokens)\\n self.tokenizer.bos_token, self.tokenizer.eos_token = special_tokens_ids\\n\\n special_tokens = self.tokenizer.eos_token, self.tokenizer.bos_token\\n return special_tokens\",\n \"def extent(self):\\n\\n x = np.array([0, self.nx]) * self.dx + self.corner_grid.x0\\n ypoint = [0, self.ny] if self.origin == 'lower-left' else [self.ny, 0]\\n y = np.array(ypoint) * self.dy + self.corner_grid.y0\\n\\n return [x[0], x[1], y[0], y[1]]\",\n \"def get_event_list(self):\\n pass\",\n \"def tokens(self):\\n data, end = \\\\\\n self.pat.traverse(lambda obj, *args: obj.tokens(*args),\\n self.begin, self.data)\\n return data\",\n \"def get_entities(self, clean=False):\\n return list(self.iter_entities(clean=clean))\",\n \"def emotions(self):\\n return self._emotions\",\n \"def extract_entities(self) :\\n entities = []\\n googleEntityList = self.googleLanguageModel.analyze_entities() \\n watsonEntityList = self.watsonLanguageModel['entities']\\n\\n for entity in googleEntityList.entities[:self.entitySizeLimit]:\\n if len(entity.metadata) > 0:\\n entities.append({ 'name' : entity.name, 'metadata' : entity.metadata})\\n \\n for entity in watsonEntityList[:self.entitySizeLimit]: \\n entities.append({ 'name': entity['text'], 'metadata': entity.get('disambiguation', {})}) \\n\\n return entities\",\n \"def collect_ners(self, ents):\\r\\n collected_ners = []\\r\\n for token in ents:\\r\\n if token.label_ in self.ners_label:\\r\\n collected_ners.append(token.text + '/' + token.label_)\\r\\n return collected_ners\",\n \"def tokens(self):\\r\\n return Tokens(self)\",\n \"def get_predict_windows(self, extent: Box) -> List[Box]:\\n chip_size = self.config.chip_size\\n return extent.get_windows(chip_size, chip_size)\",\n \"def get_license_tokens():\\n query = {\\n \\\"type\\\": \\\"op\\\",\\n \\\"cmd\\\": \\\"<show><license-token-files></license-token-files></show>\\\",\\n }\\n\\n return __proxy__[\\\"panos.call\\\"](query)\",\n \"def getIntrons(self):\\n rtrn = []\\n for i in range(0,len(self.exonStarts)-1):\\n rtrn.append(Interval(self.chr,self.exonEnds[i]+1,self.exonStarts[i+1]-1))\\n return rtrn\",\n \"def getTokens(self):\\n self.__require_privilaged_access()\\n with DBSession(self.__config_db) as session:\\n user = self.getLoggedInUser()\\n sessionTokens = session.query(Session) \\\\\\n .filter(Session.user_name == user) \\\\\\n .filter(Session.can_expire.is_(False)) \\\\\\n .all()\\n\\n result = []\\n for t in sessionTokens:\\n result.append(SessionTokenData(\\n t.token,\\n t.description,\\n str(t.last_access)))\\n\\n return result\",\n \"def event_list(self):\\n return self._event_list\",\n \"def _get_all_possible_tokens_ahead(self, context):\\n tokens = []\\n if context.position < len(context.input_str):\\n for terminal in self.grammar.terminals.values():\\n try:\\n tok = terminal.recognizer(context.input_str,\\n context.position)\\n except TypeError:\\n tok = terminal.recognizer(context, context.input_str,\\n context.position)\\n additional_data = ()\\n if type(tok) is tuple:\\n tok, *additional_data = tok\\n if tok:\\n tokens.append(Token(terminal, tok, context.position,\\n additional_data))\\n return tokens\",\n \"def entity_tags(self):\\n return self._entity_tags\",\n \"def named_entities(self) -> List[str]:\",\n \"def _get_all_possible_tokens_ahead(self, context):\\n tokens = []\\n if context.position < len(context.input_str):\\n for terminal in self.grammar.terminals.values():\\n try:\\n tok = terminal.recognizer(context.input_str,\\n context.position)\\n except TypeError:\\n tok = terminal.recognizer(context, context.input_str,\\n context.position)\\n additional_data = ()\\n if type(tok) is tuple:\\n tok, *additional_data = tok\\n if tok:\\n tokens.append(Token(terminal, tok, additional_data))\\n return tokens\",\n \"def token_exp(token, exptype=False):\\n line_ids = set()\\n for gate in token['GATE']:\\n if ((not exptype or exptype == 'ese') and re_ese.match(gate['ann_type'])):\\n line_ids.add(gate['line_id'])\\n if ((not exptype or exptype == 'ose') and re_ose.match(gate['ann_type'])):\\n line_ids.add(gate['line_id'])\\n if ((not exptype or exptype == 'dse') and re_dse.match(gate['ann_type'])):\\n line_ids.add(gate['line_id'])\\n return line_ids\",\n \"def _list_tokens(self, user_id, tenant_id=None, trust_id=None,\\n consumer_id=None):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"def get_token_list(text):\\n return text.split()\",\n \"def eventList(self):\\n return self._eventList\",\n \"def multiword_tokens(self):\\n return self._mwts\",\n \"def __repr__(self) -> str:\\n return \\\"<Twilio.Oauth.V1.TokenList>\\\"\",\n \"def additional_special_tokens_ids(self):\\n return [self.token_to_id(token) for token in self.additional_special_tokens]\",\n \"def bearer_tokens(self):\\n return self._bearer_tokens\",\n \"def named_entity_list_stanford_nlp(self) -> List:\\n stanza.download(self.lang, processors = 'tokenize,mwt,ner')\\n #load file and convert input string\\n nlp = stanza.Pipeline(self.lang, processors = 'tokenize,mwt,ner')\\n doc = nlp(self.sent)\\n #build the output list\\n for sentence in doc.ents:\\n #avoid that 'Abstract', 'Title' and 'Author' are counted between NEs\\n if sentence.text != 'Abstract' and sentence.text != 'Title' and sentence.text != 'Author':\\n self.named_entity_list.append((sentence.text, \\n sentence.start_char, \\n sentence.end_char, sentence.type))\\n return self.named_entity_list\",\n \"def get_events(self):\\n events = []\\n for device in self:\\n events.extend(self[device].get_events())\\n return events\",\n \"def keys(self):\\n return list(self.token2id.values())\",\n \"def get_tokens():\\n return (HDFS_SITE_KEY, NAMESERVICE_KEY, NN_HTTP_ADDRESS_KEY, DFS_POLICY_KEY,\\n EXECUTABLE_SEARCH_PATHS, NN_HTTPS_ADDRESS_KEY, SMOKEUSER_KEY,\\n KERBEROS_KEYTAB, KERBEROS_PRINCIPAL, SECURITY_ENABLED_KEY,\\n METRICS_COLLECTOR_VIP_HOST_KEY, METRICS_COLLECTOR_VIP_PORT_KEY,\\n METRICS_COLLECTOR_WEBAPP_ADDRESS_KEY, AMS_HTTP_POLICY)\",\n \"def entities(self):\\n return self._entities\",\n \"def explicit_list(self):\\n exts = []\\n for ext in self.extensions.values():\\n if ext.implicit:\\n continue\\n exts.append(ext)\\n return exts\",\n \"def chunk(tokens):\\n\\n # Uses NLTK function to pair each token with its Part Of Speech\\n entity_list = []\\n pos = nltk.pos_tag(tokens)\\n named_entities_chunk = nltk.ne_chunk(pos, binary=True)\\n\\n # Finds named entities in tokens, stores in list of strings\\n for i in range(0, len(named_entities_chunk)):\\n ents = named_entities_chunk.pop()\\n if getattr(ents, 'label', None) is not None and ents.label() == \\\"NE\\\":\\n entity_list.append([ne for ne in ents])\\n\\n # Combines named entity components, pulls off the POF labels\\n return [' '.join(next(zip(*l))) for l in entity_list]\",\n \"def tokenize(expression):\\n return [t.strip() for t in TOKEN_SEPARATOR.split(expression.strip()) if t]\",\n \"def getFirstTorsionInds(self, extended):\\n offset = 6 if extended else 0\\n torsionInds = np.array(range(offset + 5, self.natoms * 3, 3))\\n primaryTorsions = sorted(list(set(self._firstTorsionTInd)))\\n return list(torsionInds[primaryTorsions])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.77589923","0.6758597","0.64047307","0.6147929","0.60916317","0.6056437","0.5989617","0.58163434","0.58126074","0.56831574","0.567847","0.56537056","0.55833375","0.54851276","0.54851276","0.54851276","0.5463045","0.5441857","0.54370433","0.53718114","0.5361964","0.5328001","0.53169554","0.5305214","0.5298392","0.5282702","0.528012","0.5275274","0.5257753","0.52499425","0.52375925","0.52288973","0.5218681","0.5185573","0.5185573","0.51590127","0.5158808","0.51361555","0.5133018","0.51025987","0.50994503","0.50947374","0.50648284","0.504797","0.50312483","0.5030964","0.5030964","0.502448","0.4999916","0.49985343","0.49864906","0.49680784","0.49587616","0.4939033","0.49345016","0.49331477","0.49230412","0.49230412","0.49062213","0.4901643","0.4890375","0.48849264","0.4877348","0.48767018","0.48745957","0.48599908","0.48532602","0.4839078","0.4828316","0.4810688","0.48030564","0.4795888","0.47803208","0.47656223","0.47017455","0.4692755","0.46847996","0.46614483","0.46532708","0.46515703","0.4646947","0.464424","0.46387285","0.4634693","0.4617117","0.4610221","0.4594123","0.4592494","0.45825434","0.4568016","0.45632327","0.4563137","0.45564646","0.45547336","0.4547132","0.45468897","0.4528767","0.45286885","0.4527328","0.4527325"],"string":"[\n \"0.77589923\",\n \"0.6758597\",\n \"0.64047307\",\n \"0.6147929\",\n \"0.60916317\",\n \"0.6056437\",\n \"0.5989617\",\n \"0.58163434\",\n \"0.58126074\",\n \"0.56831574\",\n \"0.567847\",\n \"0.56537056\",\n \"0.55833375\",\n \"0.54851276\",\n \"0.54851276\",\n \"0.54851276\",\n \"0.5463045\",\n \"0.5441857\",\n \"0.54370433\",\n \"0.53718114\",\n \"0.5361964\",\n \"0.5328001\",\n \"0.53169554\",\n \"0.5305214\",\n \"0.5298392\",\n \"0.5282702\",\n \"0.528012\",\n \"0.5275274\",\n \"0.5257753\",\n \"0.52499425\",\n \"0.52375925\",\n \"0.52288973\",\n \"0.5218681\",\n \"0.5185573\",\n \"0.5185573\",\n \"0.51590127\",\n \"0.5158808\",\n \"0.51361555\",\n \"0.5133018\",\n \"0.51025987\",\n \"0.50994503\",\n \"0.50947374\",\n \"0.50648284\",\n \"0.504797\",\n \"0.50312483\",\n \"0.5030964\",\n \"0.5030964\",\n \"0.502448\",\n \"0.4999916\",\n \"0.49985343\",\n \"0.49864906\",\n \"0.49680784\",\n \"0.49587616\",\n \"0.4939033\",\n \"0.49345016\",\n \"0.49331477\",\n \"0.49230412\",\n \"0.49230412\",\n \"0.49062213\",\n \"0.4901643\",\n \"0.4890375\",\n \"0.48849264\",\n \"0.4877348\",\n \"0.48767018\",\n \"0.48745957\",\n \"0.48599908\",\n \"0.48532602\",\n \"0.4839078\",\n \"0.4828316\",\n \"0.4810688\",\n \"0.48030564\",\n \"0.4795888\",\n \"0.47803208\",\n \"0.47656223\",\n \"0.47017455\",\n \"0.4692755\",\n \"0.46847996\",\n \"0.46614483\",\n \"0.46532708\",\n \"0.46515703\",\n \"0.4646947\",\n \"0.464424\",\n \"0.46387285\",\n \"0.4634693\",\n \"0.4617117\",\n \"0.4610221\",\n \"0.4594123\",\n \"0.4592494\",\n \"0.45825434\",\n \"0.4568016\",\n \"0.45632327\",\n \"0.4563137\",\n \"0.45564646\",\n \"0.45547336\",\n \"0.4547132\",\n \"0.45468897\",\n \"0.4528767\",\n \"0.45286885\",\n \"0.4527328\",\n \"0.4527325\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7712019"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":21890,"cells":{"query":{"kind":"string","value":"Get a list of token ids for the event extent. Relies on Event.get_extent_tokens() for fetching the tokens."},"document":{"kind":"string","value":"def get_extent_token_ids(self, **kwargs):\n token_span = self.get_extent_tokens(**kwargs)\n return [t.index for t in token_span]"},"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_extent_tokens(self, extent=[], source_order=True):\n all_tokens = self.tokens.copy()\n\n for ext in extent:\n if getattr(self, ext):\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens)\n\n all_tokens = list(\n set(all_tokens)\n ) # this is necessary because trigger and participant spans can overlap\n if source_order: # return tokens in the order they appear in source text\n all_tokens.sort(key=lambda x: x.begin)\n\n return all_tokens","def get_extent_tokens(self, extent=[\"discontiguous_triggers\"], source_order=True):\n all_tokens = self.tokens.copy()\n core_sen_idx = all_tokens[0].in_sentence.element_id # to ensure discont is in same sentence\n\n for ext in extent:\n if getattr(self, ext):\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens if t.in_sentence.element_id == core_sen_idx)\n\n all_tokens = list(\n set(all_tokens)\n ) # this is necessary because trigger and participant spans can overlap\n if source_order: # return tokens in the order they appear in source text\n all_tokens.sort(key=lambda x: x.begin)\n\n return all_tokens","def get_tokens(self):\r\n return TokenGroup.get_tokens(self._tu, self.extent)","def tokens(self):\n return self.rpc.call(MsfRpcMethod.AuthTokenList)['tokens']","def getTokens(self):\n list = []\n for i in range(self.startIdx, self.endIdx + 1):\n token = self.sentence[i]\n list.append(token)\n return list","def getEventIds(self):\n eventIdsLst = []\n for event in self.eventsLst:\n eventIdsLst.append(event['id'])\n return eventIdsLst","def getTokens(self):\n # NOTE: seems to be used by the evitaNominalTrainer only\n tokenList = []\n for chunkOrToken in self.dtrs:\n if chunkOrToken.isToken():\n tokenList += [chunkOrToken]\n elif chunkOrToken.isChunk():\n tokenList += chunkOrToken.dtrs\n else:\n logger.warn(\"Sentence element that is not a chunk or token\")\n return tokenList","def get_tokens(self) -> List[str]:\n return self.tokens","def get_ids(self,tokens, tokenizer, max_seq_length):\n token_ids = tokenizer.convert_tokens_to_ids(tokens,)\n input_ids = token_ids + [0] * (max_seq_length-len(token_ids))\n return input_ids","def keys(self):\n return list(self.token2id.values())","def get_gene_tokens(self, classification: Classification) -> List[GeneToken]:\n return self.get_protein_gene_symbol_tokens(classification)","def tokens(self):\n return self._tokens","def tokens(self):\n return self._tokens","def tokens(self):\n return self._tokens","def get_tokens(tu, extent):\r\n tokens_memory = POINTER(Token)()\r\n tokens_count = c_uint()\r\n\r\n conf.lib.clang_tokenize(tu, extent, byref(tokens_memory),\r\n byref(tokens_count))\r\n\r\n count = int(tokens_count.value)\r\n\r\n # If we get no tokens, no memory was allocated. Be sure not to return\r\n # anything and potentially call a destructor on nothing.\r\n if count < 1:\r\n return\r\n\r\n tokens_array = cast(tokens_memory, POINTER(Token * count)).contents\r\n\r\n token_group = TokenGroup(tu, tokens_memory, tokens_count)\r\n\r\n for i in xrange(0, count):\r\n token = Token()\r\n token.int_data = tokens_array[i].int_data\r\n token.ptr_data = tokens_array[i].ptr_data\r\n token._tu = tu\r\n token._group = token_group\r\n\r\n yield token","def convert_tokens_to_ids(self, tokens):\n ids = []\n if isinstance(tokens, str):\n if tokens in self.special_tokens:\n return self.special_tokens[tokens]\n else:\n return self.encoder.get(tokens, self.unk_id)\n for token in tokens:\n if token in self.special_tokens:\n ids.append(self.special_tokens[token])\n else:\n ids.append(self.encoder.get(token, self.unk_id))\n return ids","def get_tokens(self):\r\n return self.token_set","def GetCharacterIds(self):\n rv = []\n for charEntry in ClientAPI.GetCharacterEntries():\n rv.append(charEntry.CharacterId)\n return rv","def additional_special_tokens_ids(self):\n return [self.token_to_id(token) for token in self.additional_special_tokens]","def get_token_names(self) -> List[str]:\n return list(self._tokens.keys())","def tokens(self):\n # type: () -> List[Token]\n return self._tokens","def tokens(self):\n return self.__tokens","def get_tokens(self, locations=None, extent=None):\r\n if locations is not None:\r\n extent = SourceRange(start=locations[0], end=locations[1])\r\n\r\n return TokenGroup.get_tokens(self, extent)","def get_ids(self) -> List[str]:","def convert_tokens_to_ids(self, tokens):\n ids = []\n for token in tokens:\n ids.append(self.vocab[token])\n if len(ids) > self.max_len:\n logger.warning('Token indices sequence length is longer than the specified maximum sequence length for this BERT model ({} > {}). Running this sequence through BERT will result in indexing errors'.format(len(ids), self.max_len))\n return ids","def get_tokens(self):\n def _traverse_preorder(cursor, token_list): # There is a method called \"walk_preorder\" in Cursor class. Here we need to ignore some subtrees so we implement on our own.\n if cursor.location.file and cursor.location.file.name != self.filepath: # exclude \"#include <...>\"\n return\n if (cursor.kind, cursor.spelling) in exclude_types: # exclude node in 'exclude_types'\n return\n \n token_list.append(cursor)\n for child in cursor.get_children():\n _traverse_preorder(child, token_list)\n\n tokens = []\n _traverse_preorder(self.cursor, tokens)\n return tokens","def extent(self):\r\n return conf.lib.clang_getTokenExtent(self._tu, self)","def tokens(self):\r\n return self.iter_tokens(self._blob)","def get_ids(cls, text):\n tokens = TokenizerContainer.TOKENIZER.tokenize(text)\n token_ids = TokenizerContainer.TOKENIZER.convert_tokens_to_ids(tokens)\n input_ids = token_ids + [0] * (cls.MAX_LEN-len(token_ids))\n return tokens, input_ids","def get_identifiers(self) -> list[str]:\n output: list[str] = self._template.get_identifiers()\n return output","def get_Tokens(self):\n return self._output.get('Tokens', None)","def get_eids(self):\n return [d['eid'] for d in self._json]","def get_token_types() -> List[type]:\n return ExtensionTokenTypes.__TOKEN_TYPES","def get_ids(self, text):\n\n tokens = [token.orth for token in self.tokenizer(text)]\n ids = []\n for token in tokens:\n try:\n id = self.vocab.vectors.key2row[token]\n except KeyError:\n id = self.oov_id\n\n ids.append(id)\n\n return ids","def get_ids(self, text):\n\n tokens = [token.orth for token in self.tokenizer(text)]\n ids = []\n for token in tokens:\n try:\n id = self._vocab.vectors.key2row[token]\n except KeyError:\n id = self.oov_id\n\n ids.append(id)\n\n return ids","def tokens(self):\n\t\tlabels_and_synonyms = list(itertools.chain.from_iterable(list(self.term_to_tokens.values())))\n\t\ttokens = set(list(itertools.chain.from_iterable([word_tokenize(x) for x in labels_and_synonyms])))\n\t\treturn(list(tokens))","def getTokens(self):\n return self.__token","def get_incident_ids() -> list:\n incidents = demisto.get(demisto.context(), \"EmailCampaign.incidents\")\n return [incident['id'] for incident in incidents]","def tokens(self):\n return self._sentrep.tokens()","def get_tokens(self):\n\t\treturn self.get_starttokens() + self.get_endtokens()","def list_ids(token):\n\n init_tenant_context(token, db)\n\n data = []\n LOGGER.debug(f\" Fetching list with known devices\")\n for id in db.session.query(Device.id).all():\n data.append(id[0])\n return data","def tokens():\n return ['access token', 'refresh token']","def getIDs(self):\n return self.multiengine.getIDs()","def get_token_list():\n token_list = []\n tokens_dir_path = os.path.join(BASE_DIR, TOKENS_DIR)\n for dir, dirs, files in os.walk(tokens_dir_path):\n for file_name in files:\n file = open(os.path.join(tokens_dir_path, file_name), 'r')\n token_list.append(file.read().strip())\n file.close()\n return token_list","def token_values(self):\n return self._token_values","def tokens(self):\n tokens = []\n for index in range(len(self.sentrep)):\n tokens.append(self.sentrep.getWord(index).lexeme())\n return tokens","def tokens(self) -> list:\n if self._tokens is None:\n tokens_ = sorted(list(self.elements()))\n self._tokens = tokens_\n return self._tokens","def getIds(self) -> List[int]:\n return list(self.users.keys())","def geneIds(self):\n\t\treturn self._dataframe.index.tolist()","def _get_token_ids(self, tokens):\n token_ids = self.tokenizer.convert_tokens_to_ids(tokens)\n pad_amount = self.max_seq_length - len(tokens)\n input_ids = token_ids + [0] * pad_amount\n return np.array(input_ids)","def get_observation_ids(self) -> list:\n return list(self.time_to_obs.keys())","def token_exp(token, exptype=False):\n line_ids = set()\n for gate in token['GATE']:\n if ((not exptype or exptype == 'ese') and re_ese.match(gate['ann_type'])):\n line_ids.add(gate['line_id'])\n if ((not exptype or exptype == 'ose') and re_ose.match(gate['ann_type'])):\n line_ids.add(gate['line_id'])\n if ((not exptype or exptype == 'dse') and re_dse.match(gate['ann_type'])):\n line_ids.add(gate['line_id'])\n return line_ids","def ids(self):\n\n return self._d.keys()","def list_extents(extent_entries, node_id):\n extents = extent_entries[node_id]\n result = []\n for extent_entry in extents:\n result.append({\n \"offset\": extent_entry.key.content.offset,\n \"size\": extent_entry.record.size,\n \"block\": extent_entry.record.block\n })\n return result","def access_token(self):\n social_auth = self.social_auth.get()\n return social_auth.tokens","def map_exp_ids(self, exp):\n\n ret = []\n for x in exp:\n ret.append(\n (\"{}_{}_{}\".format(\n self.model.tokenizer_._convert_id_to_token(int(self.example['input_ids'][x[0]])),\n TT_NAMES[int(self.example['token_type_ids'][x[0]])],\n int(self.example['position_ids'][x[0]])\n ), x[1])\n )\n\n return ret","def get_ids(self):\n return self._ids","def get_tokens(self, node, include_extra=False):\n # type: (AstNode, bool) -> Iterator[Token]\n return self.token_range(node.first_token, node.last_token, include_extra=include_extra)","def get_all_group_ids(token) -> list:\n ids=list()\n _dict = perform_request(app_config.ENDPOINT, token)\n while True:\n for obj in _dict[\"value\"]:\n ids.append(obj[\"id\"])\n if \"@odata.nextLink\" not in _dict:\n return ids\n _dict = perform_request(_dict[\"@odata.nextLink\"], token)","def ids(self):\n return list(self._id_generator())","def ids(self):\n return list(self._id_generator())","def convert_ids_to_tokens(self, ids):\n tokens = []\n for i in ids:\n tokens.append(self.ids_to_tokens[i])\n return tokens","def tokens(self):\n tokens = [k for k in self.tok2ind.keys()\n if k not in {'<NULL>', '<UNK>'}]\n return tokens","def get_es_ids(self):\n search = self.search.source(['uri']).sort(['uri'])\n es_ids = [item.meta.id for item in search.scan()]\n return es_ids","def get_token_list(text):\n return text.split()","def identity_ids(self) -> Optional[Sequence[str]]:\n return pulumi.get(self, \"identity_ids\")","def get_tokens(self, document):\n raise NotImplementedError()","def get_email_ids(self):\n if self.emails is None or self.emails == '':\n return []\n email_ids = self.emails.replace(' ', '')\n return email_ids.split(',')","def identifiers(self) -> List[str]:\n raise NotImplementedError","def list_tokens(self) -> str:\n\n return self._format_symbol_table_content(\"Tokens\", self._symbols.keys())","def list_emails_ids(self, *criteria, mailbox=None) -> List[str]:\n ids = self.get_raw_emails_ids(*criteria, mailbox=mailbox)\n if ids is None:\n raise Exception(f'Cannot get the list of email in the mailbox {self._selected_mailbox}! No list of IDs is returned!')\n return self._search(ids)\n # ListEmailIds.reset()\n # ListEmailIds.parse(ids[0].decode())\n # return ListEmailIds.get_tokens_values()","def map_tokens_to_ids(self, tokens: List[str], max_length: int = None):\n # truncate extra tokens and pad to `max_length`\n if max_length:\n tokens = tokens[:max_length]\n tokens = tokens + [self._pad_token]*(max_length-len(tokens))\n return [self.map_token_to_id(token) for token in tokens]","def getTokens(self):\n self.__require_privilaged_access()\n with DBSession(self.__config_db) as session:\n user = self.getLoggedInUser()\n sessionTokens = session.query(Session) \\\n .filter(Session.user_name == user) \\\n .filter(Session.can_expire.is_(False)) \\\n .all()\n\n result = []\n for t in sessionTokens:\n result.append(SessionTokenData(\n t.token,\n t.description,\n str(t.last_access)))\n\n return result","def list_ids(self, start: int = None, end: int = None) -> List:\n return [i.unique_id for i in self.data[start:end]]","def convert_tokens_to_ids(self, tokens, max_len=None):\n if max_len is not None:\n token_length = len(tokens)\n if max_len < token_length:\n tokens = tokens[:max_len]\n else:\n for _ in range(max_len - token_length):\n tokens.append(self.pad_token())\n return [self.stoi(tok) for tok in tokens]","def ids(self):\n\n if len(self._id_ranges) % 2 != 0:\n raise AutoIDException(message=ExceptionsMessage.AutoIDIllegalRanges)\n\n ids = []\n for i in range(int(len(self._id_ranges) / 2)):\n begin = self._id_ranges[i * 2]\n end = self._id_ranges[i * 2 + 1]\n for j in range(begin, end):\n ids.append(j)\n\n return ids","def get_event_list(self):\n event_list = []\n eventLocation = -1\n for element in self:\n eventLocation += 1\n if element.isChunk():\n event = element.embedded_event()\n if event:\n event_list.append((eventLocation, event.eid))\n return event_list","def get_entities(self):\n return list(self._entities.values())","def obs_ids(self) -> list:\n return list(self._instruments.keys())","def ids(self) -> Sequence[str]:\n return pulumi.get(self, \"ids\")","def ids(self) -> Sequence[str]:\n return pulumi.get(self, \"ids\")","def getIDs():","def get_tokens():\n return (HDFS_SITE_KEY, NAMESERVICE_KEY, NN_HTTP_ADDRESS_KEY, DFS_POLICY_KEY,\n EXECUTABLE_SEARCH_PATHS, NN_HTTPS_ADDRESS_KEY, SMOKEUSER_KEY,\n KERBEROS_KEYTAB, KERBEROS_PRINCIPAL, SECURITY_ENABLED_KEY,\n METRICS_COLLECTOR_VIP_HOST_KEY, METRICS_COLLECTOR_VIP_PORT_KEY,\n METRICS_COLLECTOR_WEBAPP_ADDRESS_KEY, AMS_HTTP_POLICY)","def _list_tokens(self, user_id, tenant_id=None, trust_id=None,\n consumer_id=None):\n raise exception.NotImplemented() # pragma: no cover","def get_ids(self, sentence):\n return [self.get_id(word) for word in sentence.strip().split(' ')]","def get_all_concept_ids(self) -> Set[str]:\n last_evaluated_key = None\n concept_ids = []\n params = {\n \"ProjectionExpression\": \"concept_id\",\n }\n while True:\n if last_evaluated_key:\n response = self.genes.scan(\n ExclusiveStartKey=last_evaluated_key, **params\n )\n else:\n response = self.genes.scan(**params)\n records = response[\"Items\"]\n for record in records:\n concept_ids.append(record[\"concept_id\"])\n last_evaluated_key = response.get(\"LastEvaluatedKey\")\n if not last_evaluated_key:\n break\n return set(concept_ids)","def ids(self):\n return self._ids","def __getScanIds(self, msfile, scansel):\n scanlist = []\n if scansel.isspace() or scansel.__len__() == 0:\n # Get all the scan ids\n mymsmd = msmdtool()\n mymsmd.open(msfile)\n scans = mymsmd.scannumbers()\n mymsmd.close()\n scanlist = map(str,scans)\n else:\n try: \n myms.open(msfile)\n myms.msselect({'scan':scansel})\n scans = myms.msselectedindices()['scan']\n scanlist = map(str,scans)\n myms.close()\n except:\n myms.close()\n scanlist = []\n \n return scanlist","def list_revoked_tokens(self):\n raise exception.NotImplemented() # pragma: no cover","def getLocationsIds():\n with driver.session() as s:\n ids = s.write_transaction(getLocationsId)\n\n lIds = []\n for idEl in ids:\n lIds.append(idEl[\"ID(l)\"])\n\n return lIds","def tokenize(self):\n tknzr = TweetTokenizer()\n tkn = []\n for tweet in self.tweets:\n for word in tknzr.tokenize(tweet):\n tkn.append(word)\n return tkn","def identer(self) -> List[str]:\n self._populer_identer()\n if self._identer:\n return [str(ident) for ident in self._identer if ident]\n return []","def special_tokens(self) -> List[Hashable]:\n return list(self._special_token_kv.values())","def get_read_tokens(mastertoken, config):\n mt_path = mastertoken['paths']['self']\n url = \"{}{}/read_tokens.json\".\\\n format(config['domain_base'], mt_path)\n\n try:\n resp = (api_call(url, 'get', config['debug']))\n tokens = resp.json()\n except ValueError as ex:\n abort(\"Unexpected response from packagecloud API: \"\n \"{}\".format(ex.message))\n\n return tokens['read_tokens']","def object_ids(self):\n return self._extract_set('id')","def getTokenAnnotatedType(self, token, sentenceGraph):\n if len(sentenceGraph.tokenIsEntityHead[token]) > 0 and not self.noAnnType:\n annTypes = set()\n for entity in sentenceGraph.tokenIsEntityHead[token]:\n eType = self.getEntityType(entity)\n if eType != None and not eType in annTypes and not eType in self.filterAnnTypes:\n if self.entity1 == None and self.entity2 == None:\n annTypes.add(eType)\n else:\n if self.maximum:\n annTypes.add(eType)\n if self.entity1 == entity:\n if not self.maximum:\n return [eType]\n else:\n annTypes.add(\"e1_\"+eType)\n elif self.entity2 == entity:\n if not self.maximum:\n return [eType]\n else:\n annTypes.add(\"e2_\"+eType)\n else:\n annTypes.add(eType)\n annTypes = list(annTypes)\n annTypes.sort()\n if self.maximum:\n return annTypes[0:2]\n else:\n return annTypes[0:1] #annTypes[0:2]\n else:\n return [\"noAnnType\"]","def all_tokens(self) -> List[Hashable]:\n return self._all_tokens","def get_doc_ids(self):\n cursor = self.connection.cursor()\n cursor.execute(\"SELECT id FROM documents\")\n results = [r[0] for r in cursor.fetchall()]\n cursor.close()\n return results","def get_doc_ids(self):\n cursor = self.connection.cursor()\n cursor.execute(\"SELECT id FROM documents\")\n results = [r[0] for r in cursor.fetchall()]\n cursor.close()\n return results","def get_ids(self):\n return [item.id for item in self.items]"],"string":"[\n \"def get_extent_tokens(self, extent=[], source_order=True):\\n all_tokens = self.tokens.copy()\\n\\n for ext in extent:\\n if getattr(self, ext):\\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens)\\n\\n all_tokens = list(\\n set(all_tokens)\\n ) # this is necessary because trigger and participant spans can overlap\\n if source_order: # return tokens in the order they appear in source text\\n all_tokens.sort(key=lambda x: x.begin)\\n\\n return all_tokens\",\n \"def get_extent_tokens(self, extent=[\\\"discontiguous_triggers\\\"], source_order=True):\\n all_tokens = self.tokens.copy()\\n core_sen_idx = all_tokens[0].in_sentence.element_id # to ensure discont is in same sentence\\n\\n for ext in extent:\\n if getattr(self, ext):\\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens if t.in_sentence.element_id == core_sen_idx)\\n\\n all_tokens = list(\\n set(all_tokens)\\n ) # this is necessary because trigger and participant spans can overlap\\n if source_order: # return tokens in the order they appear in source text\\n all_tokens.sort(key=lambda x: x.begin)\\n\\n return all_tokens\",\n \"def get_tokens(self):\\r\\n return TokenGroup.get_tokens(self._tu, self.extent)\",\n \"def tokens(self):\\n return self.rpc.call(MsfRpcMethod.AuthTokenList)['tokens']\",\n \"def getTokens(self):\\n list = []\\n for i in range(self.startIdx, self.endIdx + 1):\\n token = self.sentence[i]\\n list.append(token)\\n return list\",\n \"def getEventIds(self):\\n eventIdsLst = []\\n for event in self.eventsLst:\\n eventIdsLst.append(event['id'])\\n return eventIdsLst\",\n \"def getTokens(self):\\n # NOTE: seems to be used by the evitaNominalTrainer only\\n tokenList = []\\n for chunkOrToken in self.dtrs:\\n if chunkOrToken.isToken():\\n tokenList += [chunkOrToken]\\n elif chunkOrToken.isChunk():\\n tokenList += chunkOrToken.dtrs\\n else:\\n logger.warn(\\\"Sentence element that is not a chunk or token\\\")\\n return tokenList\",\n \"def get_tokens(self) -> List[str]:\\n return self.tokens\",\n \"def get_ids(self,tokens, tokenizer, max_seq_length):\\n token_ids = tokenizer.convert_tokens_to_ids(tokens,)\\n input_ids = token_ids + [0] * (max_seq_length-len(token_ids))\\n return input_ids\",\n \"def keys(self):\\n return list(self.token2id.values())\",\n \"def get_gene_tokens(self, classification: Classification) -> List[GeneToken]:\\n return self.get_protein_gene_symbol_tokens(classification)\",\n \"def tokens(self):\\n return self._tokens\",\n \"def tokens(self):\\n return self._tokens\",\n \"def tokens(self):\\n return self._tokens\",\n \"def get_tokens(tu, extent):\\r\\n tokens_memory = POINTER(Token)()\\r\\n tokens_count = c_uint()\\r\\n\\r\\n conf.lib.clang_tokenize(tu, extent, byref(tokens_memory),\\r\\n byref(tokens_count))\\r\\n\\r\\n count = int(tokens_count.value)\\r\\n\\r\\n # If we get no tokens, no memory was allocated. Be sure not to return\\r\\n # anything and potentially call a destructor on nothing.\\r\\n if count < 1:\\r\\n return\\r\\n\\r\\n tokens_array = cast(tokens_memory, POINTER(Token * count)).contents\\r\\n\\r\\n token_group = TokenGroup(tu, tokens_memory, tokens_count)\\r\\n\\r\\n for i in xrange(0, count):\\r\\n token = Token()\\r\\n token.int_data = tokens_array[i].int_data\\r\\n token.ptr_data = tokens_array[i].ptr_data\\r\\n token._tu = tu\\r\\n token._group = token_group\\r\\n\\r\\n yield token\",\n \"def convert_tokens_to_ids(self, tokens):\\n ids = []\\n if isinstance(tokens, str):\\n if tokens in self.special_tokens:\\n return self.special_tokens[tokens]\\n else:\\n return self.encoder.get(tokens, self.unk_id)\\n for token in tokens:\\n if token in self.special_tokens:\\n ids.append(self.special_tokens[token])\\n else:\\n ids.append(self.encoder.get(token, self.unk_id))\\n return ids\",\n \"def get_tokens(self):\\r\\n return self.token_set\",\n \"def GetCharacterIds(self):\\n rv = []\\n for charEntry in ClientAPI.GetCharacterEntries():\\n rv.append(charEntry.CharacterId)\\n return rv\",\n \"def additional_special_tokens_ids(self):\\n return [self.token_to_id(token) for token in self.additional_special_tokens]\",\n \"def get_token_names(self) -> List[str]:\\n return list(self._tokens.keys())\",\n \"def tokens(self):\\n # type: () -> List[Token]\\n return self._tokens\",\n \"def tokens(self):\\n return self.__tokens\",\n \"def get_tokens(self, locations=None, extent=None):\\r\\n if locations is not None:\\r\\n extent = SourceRange(start=locations[0], end=locations[1])\\r\\n\\r\\n return TokenGroup.get_tokens(self, extent)\",\n \"def get_ids(self) -> List[str]:\",\n \"def convert_tokens_to_ids(self, tokens):\\n ids = []\\n for token in tokens:\\n ids.append(self.vocab[token])\\n if len(ids) > self.max_len:\\n logger.warning('Token indices sequence length is longer than the specified maximum sequence length for this BERT model ({} > {}). Running this sequence through BERT will result in indexing errors'.format(len(ids), self.max_len))\\n return ids\",\n \"def get_tokens(self):\\n def _traverse_preorder(cursor, token_list): # There is a method called \\\"walk_preorder\\\" in Cursor class. Here we need to ignore some subtrees so we implement on our own.\\n if cursor.location.file and cursor.location.file.name != self.filepath: # exclude \\\"#include <...>\\\"\\n return\\n if (cursor.kind, cursor.spelling) in exclude_types: # exclude node in 'exclude_types'\\n return\\n \\n token_list.append(cursor)\\n for child in cursor.get_children():\\n _traverse_preorder(child, token_list)\\n\\n tokens = []\\n _traverse_preorder(self.cursor, tokens)\\n return tokens\",\n \"def extent(self):\\r\\n return conf.lib.clang_getTokenExtent(self._tu, self)\",\n \"def tokens(self):\\r\\n return self.iter_tokens(self._blob)\",\n \"def get_ids(cls, text):\\n tokens = TokenizerContainer.TOKENIZER.tokenize(text)\\n token_ids = TokenizerContainer.TOKENIZER.convert_tokens_to_ids(tokens)\\n input_ids = token_ids + [0] * (cls.MAX_LEN-len(token_ids))\\n return tokens, input_ids\",\n \"def get_identifiers(self) -> list[str]:\\n output: list[str] = self._template.get_identifiers()\\n return output\",\n \"def get_Tokens(self):\\n return self._output.get('Tokens', None)\",\n \"def get_eids(self):\\n return [d['eid'] for d in self._json]\",\n \"def get_token_types() -> List[type]:\\n return ExtensionTokenTypes.__TOKEN_TYPES\",\n \"def get_ids(self, text):\\n\\n tokens = [token.orth for token in self.tokenizer(text)]\\n ids = []\\n for token in tokens:\\n try:\\n id = self.vocab.vectors.key2row[token]\\n except KeyError:\\n id = self.oov_id\\n\\n ids.append(id)\\n\\n return ids\",\n \"def get_ids(self, text):\\n\\n tokens = [token.orth for token in self.tokenizer(text)]\\n ids = []\\n for token in tokens:\\n try:\\n id = self._vocab.vectors.key2row[token]\\n except KeyError:\\n id = self.oov_id\\n\\n ids.append(id)\\n\\n return ids\",\n \"def tokens(self):\\n\\t\\tlabels_and_synonyms = list(itertools.chain.from_iterable(list(self.term_to_tokens.values())))\\n\\t\\ttokens = set(list(itertools.chain.from_iterable([word_tokenize(x) for x in labels_and_synonyms])))\\n\\t\\treturn(list(tokens))\",\n \"def getTokens(self):\\n return self.__token\",\n \"def get_incident_ids() -> list:\\n incidents = demisto.get(demisto.context(), \\\"EmailCampaign.incidents\\\")\\n return [incident['id'] for incident in incidents]\",\n \"def tokens(self):\\n return self._sentrep.tokens()\",\n \"def get_tokens(self):\\n\\t\\treturn self.get_starttokens() + self.get_endtokens()\",\n \"def list_ids(token):\\n\\n init_tenant_context(token, db)\\n\\n data = []\\n LOGGER.debug(f\\\" Fetching list with known devices\\\")\\n for id in db.session.query(Device.id).all():\\n data.append(id[0])\\n return data\",\n \"def tokens():\\n return ['access token', 'refresh token']\",\n \"def getIDs(self):\\n return self.multiengine.getIDs()\",\n \"def get_token_list():\\n token_list = []\\n tokens_dir_path = os.path.join(BASE_DIR, TOKENS_DIR)\\n for dir, dirs, files in os.walk(tokens_dir_path):\\n for file_name in files:\\n file = open(os.path.join(tokens_dir_path, file_name), 'r')\\n token_list.append(file.read().strip())\\n file.close()\\n return token_list\",\n \"def token_values(self):\\n return self._token_values\",\n \"def tokens(self):\\n tokens = []\\n for index in range(len(self.sentrep)):\\n tokens.append(self.sentrep.getWord(index).lexeme())\\n return tokens\",\n \"def tokens(self) -> list:\\n if self._tokens is None:\\n tokens_ = sorted(list(self.elements()))\\n self._tokens = tokens_\\n return self._tokens\",\n \"def getIds(self) -> List[int]:\\n return list(self.users.keys())\",\n \"def geneIds(self):\\n\\t\\treturn self._dataframe.index.tolist()\",\n \"def _get_token_ids(self, tokens):\\n token_ids = self.tokenizer.convert_tokens_to_ids(tokens)\\n pad_amount = self.max_seq_length - len(tokens)\\n input_ids = token_ids + [0] * pad_amount\\n return np.array(input_ids)\",\n \"def get_observation_ids(self) -> list:\\n return list(self.time_to_obs.keys())\",\n \"def token_exp(token, exptype=False):\\n line_ids = set()\\n for gate in token['GATE']:\\n if ((not exptype or exptype == 'ese') and re_ese.match(gate['ann_type'])):\\n line_ids.add(gate['line_id'])\\n if ((not exptype or exptype == 'ose') and re_ose.match(gate['ann_type'])):\\n line_ids.add(gate['line_id'])\\n if ((not exptype or exptype == 'dse') and re_dse.match(gate['ann_type'])):\\n line_ids.add(gate['line_id'])\\n return line_ids\",\n \"def ids(self):\\n\\n return self._d.keys()\",\n \"def list_extents(extent_entries, node_id):\\n extents = extent_entries[node_id]\\n result = []\\n for extent_entry in extents:\\n result.append({\\n \\\"offset\\\": extent_entry.key.content.offset,\\n \\\"size\\\": extent_entry.record.size,\\n \\\"block\\\": extent_entry.record.block\\n })\\n return result\",\n \"def access_token(self):\\n social_auth = self.social_auth.get()\\n return social_auth.tokens\",\n \"def map_exp_ids(self, exp):\\n\\n ret = []\\n for x in exp:\\n ret.append(\\n (\\\"{}_{}_{}\\\".format(\\n self.model.tokenizer_._convert_id_to_token(int(self.example['input_ids'][x[0]])),\\n TT_NAMES[int(self.example['token_type_ids'][x[0]])],\\n int(self.example['position_ids'][x[0]])\\n ), x[1])\\n )\\n\\n return ret\",\n \"def get_ids(self):\\n return self._ids\",\n \"def get_tokens(self, node, include_extra=False):\\n # type: (AstNode, bool) -> Iterator[Token]\\n return self.token_range(node.first_token, node.last_token, include_extra=include_extra)\",\n \"def get_all_group_ids(token) -> list:\\n ids=list()\\n _dict = perform_request(app_config.ENDPOINT, token)\\n while True:\\n for obj in _dict[\\\"value\\\"]:\\n ids.append(obj[\\\"id\\\"])\\n if \\\"@odata.nextLink\\\" not in _dict:\\n return ids\\n _dict = perform_request(_dict[\\\"@odata.nextLink\\\"], token)\",\n \"def ids(self):\\n return list(self._id_generator())\",\n \"def ids(self):\\n return list(self._id_generator())\",\n \"def convert_ids_to_tokens(self, ids):\\n tokens = []\\n for i in ids:\\n tokens.append(self.ids_to_tokens[i])\\n return tokens\",\n \"def tokens(self):\\n tokens = [k for k in self.tok2ind.keys()\\n if k not in {'<NULL>', '<UNK>'}]\\n return tokens\",\n \"def get_es_ids(self):\\n search = self.search.source(['uri']).sort(['uri'])\\n es_ids = [item.meta.id for item in search.scan()]\\n return es_ids\",\n \"def get_token_list(text):\\n return text.split()\",\n \"def identity_ids(self) -> Optional[Sequence[str]]:\\n return pulumi.get(self, \\\"identity_ids\\\")\",\n \"def get_tokens(self, document):\\n raise NotImplementedError()\",\n \"def get_email_ids(self):\\n if self.emails is None or self.emails == '':\\n return []\\n email_ids = self.emails.replace(' ', '')\\n return email_ids.split(',')\",\n \"def identifiers(self) -> List[str]:\\n raise NotImplementedError\",\n \"def list_tokens(self) -> str:\\n\\n return self._format_symbol_table_content(\\\"Tokens\\\", self._symbols.keys())\",\n \"def list_emails_ids(self, *criteria, mailbox=None) -> List[str]:\\n ids = self.get_raw_emails_ids(*criteria, mailbox=mailbox)\\n if ids is None:\\n raise Exception(f'Cannot get the list of email in the mailbox {self._selected_mailbox}! No list of IDs is returned!')\\n return self._search(ids)\\n # ListEmailIds.reset()\\n # ListEmailIds.parse(ids[0].decode())\\n # return ListEmailIds.get_tokens_values()\",\n \"def map_tokens_to_ids(self, tokens: List[str], max_length: int = None):\\n # truncate extra tokens and pad to `max_length`\\n if max_length:\\n tokens = tokens[:max_length]\\n tokens = tokens + [self._pad_token]*(max_length-len(tokens))\\n return [self.map_token_to_id(token) for token in tokens]\",\n \"def getTokens(self):\\n self.__require_privilaged_access()\\n with DBSession(self.__config_db) as session:\\n user = self.getLoggedInUser()\\n sessionTokens = session.query(Session) \\\\\\n .filter(Session.user_name == user) \\\\\\n .filter(Session.can_expire.is_(False)) \\\\\\n .all()\\n\\n result = []\\n for t in sessionTokens:\\n result.append(SessionTokenData(\\n t.token,\\n t.description,\\n str(t.last_access)))\\n\\n return result\",\n \"def list_ids(self, start: int = None, end: int = None) -> List:\\n return [i.unique_id for i in self.data[start:end]]\",\n \"def convert_tokens_to_ids(self, tokens, max_len=None):\\n if max_len is not None:\\n token_length = len(tokens)\\n if max_len < token_length:\\n tokens = tokens[:max_len]\\n else:\\n for _ in range(max_len - token_length):\\n tokens.append(self.pad_token())\\n return [self.stoi(tok) for tok in tokens]\",\n \"def ids(self):\\n\\n if len(self._id_ranges) % 2 != 0:\\n raise AutoIDException(message=ExceptionsMessage.AutoIDIllegalRanges)\\n\\n ids = []\\n for i in range(int(len(self._id_ranges) / 2)):\\n begin = self._id_ranges[i * 2]\\n end = self._id_ranges[i * 2 + 1]\\n for j in range(begin, end):\\n ids.append(j)\\n\\n return ids\",\n \"def get_event_list(self):\\n event_list = []\\n eventLocation = -1\\n for element in self:\\n eventLocation += 1\\n if element.isChunk():\\n event = element.embedded_event()\\n if event:\\n event_list.append((eventLocation, event.eid))\\n return event_list\",\n \"def get_entities(self):\\n return list(self._entities.values())\",\n \"def obs_ids(self) -> list:\\n return list(self._instruments.keys())\",\n \"def ids(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"ids\\\")\",\n \"def ids(self) -> Sequence[str]:\\n return pulumi.get(self, \\\"ids\\\")\",\n \"def getIDs():\",\n \"def get_tokens():\\n return (HDFS_SITE_KEY, NAMESERVICE_KEY, NN_HTTP_ADDRESS_KEY, DFS_POLICY_KEY,\\n EXECUTABLE_SEARCH_PATHS, NN_HTTPS_ADDRESS_KEY, SMOKEUSER_KEY,\\n KERBEROS_KEYTAB, KERBEROS_PRINCIPAL, SECURITY_ENABLED_KEY,\\n METRICS_COLLECTOR_VIP_HOST_KEY, METRICS_COLLECTOR_VIP_PORT_KEY,\\n METRICS_COLLECTOR_WEBAPP_ADDRESS_KEY, AMS_HTTP_POLICY)\",\n \"def _list_tokens(self, user_id, tenant_id=None, trust_id=None,\\n consumer_id=None):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"def get_ids(self, sentence):\\n return [self.get_id(word) for word in sentence.strip().split(' ')]\",\n \"def get_all_concept_ids(self) -> Set[str]:\\n last_evaluated_key = None\\n concept_ids = []\\n params = {\\n \\\"ProjectionExpression\\\": \\\"concept_id\\\",\\n }\\n while True:\\n if last_evaluated_key:\\n response = self.genes.scan(\\n ExclusiveStartKey=last_evaluated_key, **params\\n )\\n else:\\n response = self.genes.scan(**params)\\n records = response[\\\"Items\\\"]\\n for record in records:\\n concept_ids.append(record[\\\"concept_id\\\"])\\n last_evaluated_key = response.get(\\\"LastEvaluatedKey\\\")\\n if not last_evaluated_key:\\n break\\n return set(concept_ids)\",\n \"def ids(self):\\n return self._ids\",\n \"def __getScanIds(self, msfile, scansel):\\n scanlist = []\\n if scansel.isspace() or scansel.__len__() == 0:\\n # Get all the scan ids\\n mymsmd = msmdtool()\\n mymsmd.open(msfile)\\n scans = mymsmd.scannumbers()\\n mymsmd.close()\\n scanlist = map(str,scans)\\n else:\\n try: \\n myms.open(msfile)\\n myms.msselect({'scan':scansel})\\n scans = myms.msselectedindices()['scan']\\n scanlist = map(str,scans)\\n myms.close()\\n except:\\n myms.close()\\n scanlist = []\\n \\n return scanlist\",\n \"def list_revoked_tokens(self):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"def getLocationsIds():\\n with driver.session() as s:\\n ids = s.write_transaction(getLocationsId)\\n\\n lIds = []\\n for idEl in ids:\\n lIds.append(idEl[\\\"ID(l)\\\"])\\n\\n return lIds\",\n \"def tokenize(self):\\n tknzr = TweetTokenizer()\\n tkn = []\\n for tweet in self.tweets:\\n for word in tknzr.tokenize(tweet):\\n tkn.append(word)\\n return tkn\",\n \"def identer(self) -> List[str]:\\n self._populer_identer()\\n if self._identer:\\n return [str(ident) for ident in self._identer if ident]\\n return []\",\n \"def special_tokens(self) -> List[Hashable]:\\n return list(self._special_token_kv.values())\",\n \"def get_read_tokens(mastertoken, config):\\n mt_path = mastertoken['paths']['self']\\n url = \\\"{}{}/read_tokens.json\\\".\\\\\\n format(config['domain_base'], mt_path)\\n\\n try:\\n resp = (api_call(url, 'get', config['debug']))\\n tokens = resp.json()\\n except ValueError as ex:\\n abort(\\\"Unexpected response from packagecloud API: \\\"\\n \\\"{}\\\".format(ex.message))\\n\\n return tokens['read_tokens']\",\n \"def object_ids(self):\\n return self._extract_set('id')\",\n \"def getTokenAnnotatedType(self, token, sentenceGraph):\\n if len(sentenceGraph.tokenIsEntityHead[token]) > 0 and not self.noAnnType:\\n annTypes = set()\\n for entity in sentenceGraph.tokenIsEntityHead[token]:\\n eType = self.getEntityType(entity)\\n if eType != None and not eType in annTypes and not eType in self.filterAnnTypes:\\n if self.entity1 == None and self.entity2 == None:\\n annTypes.add(eType)\\n else:\\n if self.maximum:\\n annTypes.add(eType)\\n if self.entity1 == entity:\\n if not self.maximum:\\n return [eType]\\n else:\\n annTypes.add(\\\"e1_\\\"+eType)\\n elif self.entity2 == entity:\\n if not self.maximum:\\n return [eType]\\n else:\\n annTypes.add(\\\"e2_\\\"+eType)\\n else:\\n annTypes.add(eType)\\n annTypes = list(annTypes)\\n annTypes.sort()\\n if self.maximum:\\n return annTypes[0:2]\\n else:\\n return annTypes[0:1] #annTypes[0:2]\\n else:\\n return [\\\"noAnnType\\\"]\",\n \"def all_tokens(self) -> List[Hashable]:\\n return self._all_tokens\",\n \"def get_doc_ids(self):\\n cursor = self.connection.cursor()\\n cursor.execute(\\\"SELECT id FROM documents\\\")\\n results = [r[0] for r in cursor.fetchall()]\\n cursor.close()\\n return results\",\n \"def get_doc_ids(self):\\n cursor = self.connection.cursor()\\n cursor.execute(\\\"SELECT id FROM documents\\\")\\n results = [r[0] for r in cursor.fetchall()]\\n cursor.close()\\n return results\",\n \"def get_ids(self):\\n return [item.id for item in self.items]\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7306065","0.7179015","0.67308676","0.66366893","0.6510626","0.63861024","0.6332538","0.6213533","0.6002818","0.59769213","0.59459466","0.58939326","0.58939326","0.58939326","0.5877605","0.58751804","0.58647084","0.5804137","0.5802045","0.5801702","0.57865965","0.57757777","0.57729423","0.5757898","0.575747","0.57363987","0.57216275","0.5713857","0.5707053","0.5706606","0.56973195","0.56908673","0.5688514","0.5673832","0.5669974","0.5657842","0.56442785","0.56348133","0.5634805","0.56179816","0.5617652","0.56037605","0.5564543","0.55424863","0.5493151","0.5460408","0.5447675","0.5446028","0.54435855","0.5436847","0.5436217","0.5431861","0.5409233","0.5397105","0.5396471","0.5392504","0.53868026","0.5386118","0.5372522","0.5371919","0.5371919","0.53569293","0.5341372","0.53400016","0.5311223","0.5309982","0.5307096","0.5306082","0.52994555","0.5298745","0.52984554","0.52937573","0.5288995","0.52812177","0.5274881","0.5274472","0.52720094","0.52506316","0.52039963","0.51966023","0.51966023","0.51804674","0.51799244","0.51735204","0.5167593","0.5164823","0.5161496","0.515279","0.5152195","0.5148917","0.5139994","0.5139848","0.5129683","0.5128938","0.5121208","0.5108717","0.5101254","0.50992674","0.50992674","0.5097195"],"string":"[\n \"0.7306065\",\n \"0.7179015\",\n \"0.67308676\",\n \"0.66366893\",\n \"0.6510626\",\n \"0.63861024\",\n \"0.6332538\",\n \"0.6213533\",\n \"0.6002818\",\n \"0.59769213\",\n \"0.59459466\",\n \"0.58939326\",\n \"0.58939326\",\n \"0.58939326\",\n \"0.5877605\",\n \"0.58751804\",\n \"0.58647084\",\n \"0.5804137\",\n \"0.5802045\",\n \"0.5801702\",\n \"0.57865965\",\n \"0.57757777\",\n \"0.57729423\",\n \"0.5757898\",\n \"0.575747\",\n \"0.57363987\",\n \"0.57216275\",\n \"0.5713857\",\n \"0.5707053\",\n \"0.5706606\",\n \"0.56973195\",\n \"0.56908673\",\n \"0.5688514\",\n \"0.5673832\",\n \"0.5669974\",\n \"0.5657842\",\n \"0.56442785\",\n \"0.56348133\",\n \"0.5634805\",\n \"0.56179816\",\n \"0.5617652\",\n \"0.56037605\",\n \"0.5564543\",\n \"0.55424863\",\n \"0.5493151\",\n \"0.5460408\",\n \"0.5447675\",\n \"0.5446028\",\n \"0.54435855\",\n \"0.5436847\",\n \"0.5436217\",\n \"0.5431861\",\n \"0.5409233\",\n \"0.5397105\",\n \"0.5396471\",\n \"0.5392504\",\n \"0.53868026\",\n \"0.5386118\",\n \"0.5372522\",\n \"0.5371919\",\n \"0.5371919\",\n \"0.53569293\",\n \"0.5341372\",\n \"0.53400016\",\n \"0.5311223\",\n \"0.5309982\",\n \"0.5307096\",\n \"0.5306082\",\n \"0.52994555\",\n \"0.5298745\",\n \"0.52984554\",\n \"0.52937573\",\n \"0.5288995\",\n \"0.52812177\",\n \"0.5274881\",\n \"0.5274472\",\n \"0.52720094\",\n \"0.52506316\",\n \"0.52039963\",\n \"0.51966023\",\n \"0.51966023\",\n \"0.51804674\",\n \"0.51799244\",\n \"0.51735204\",\n \"0.5167593\",\n \"0.5164823\",\n \"0.5161496\",\n \"0.515279\",\n \"0.5152195\",\n \"0.5148917\",\n \"0.5139994\",\n \"0.5139848\",\n \"0.5129683\",\n \"0.5128938\",\n \"0.5121208\",\n \"0.5108717\",\n \"0.5101254\",\n \"0.50992674\",\n \"0.50992674\",\n \"0.5097195\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.85507065"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":21891,"cells":{"query":{"kind":"string","value":"Get a list of token objects for the sentiment expression extent. The extent can be set to include targets. Setting this to an empty list will only return the original tokens."},"document":{"kind":"string","value":"def get_extent_tokens(self, extent=[], source_order=True):\n all_tokens = self.tokens.copy()\n\n for ext in extent:\n if getattr(self, ext):\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens)\n\n all_tokens = list(\n set(all_tokens)\n ) # this is necessary because trigger and participant spans can overlap\n if source_order: # return tokens in the order they appear in source text\n all_tokens.sort(key=lambda x: x.begin)\n\n return all_tokens"},"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_extent_tokens(self, extent=[\"discontiguous_triggers\"], source_order=True):\n all_tokens = self.tokens.copy()\n core_sen_idx = all_tokens[0].in_sentence.element_id # to ensure discont is in same sentence\n\n for ext in extent:\n if getattr(self, ext):\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens if t.in_sentence.element_id == core_sen_idx)\n\n all_tokens = list(\n set(all_tokens)\n ) # this is necessary because trigger and participant spans can overlap\n if source_order: # return tokens in the order they appear in source text\n all_tokens.sort(key=lambda x: x.begin)\n\n return all_tokens","def get_extent_token_ids(self, **kwargs):\n token_span = self.get_extent_tokens(**kwargs)\n return [t.index for t in token_span]","def get_tokens(tu, extent):\r\n tokens_memory = POINTER(Token)()\r\n tokens_count = c_uint()\r\n\r\n conf.lib.clang_tokenize(tu, extent, byref(tokens_memory),\r\n byref(tokens_count))\r\n\r\n count = int(tokens_count.value)\r\n\r\n # If we get no tokens, no memory was allocated. Be sure not to return\r\n # anything and potentially call a destructor on nothing.\r\n if count < 1:\r\n return\r\n\r\n tokens_array = cast(tokens_memory, POINTER(Token * count)).contents\r\n\r\n token_group = TokenGroup(tu, tokens_memory, tokens_count)\r\n\r\n for i in xrange(0, count):\r\n token = Token()\r\n token.int_data = tokens_array[i].int_data\r\n token.ptr_data = tokens_array[i].ptr_data\r\n token._tu = tu\r\n token._group = token_group\r\n\r\n yield token","def get_tokens(self, locations=None, extent=None):\r\n if locations is not None:\r\n extent = SourceRange(start=locations[0], end=locations[1])\r\n\r\n return TokenGroup.get_tokens(self, extent)","def getTokens(self):\n list = []\n for i in range(self.startIdx, self.endIdx + 1):\n token = self.sentence[i]\n list.append(token)\n return list","def get_tokens(self):\r\n return TokenGroup.get_tokens(self._tu, self.extent)","def tokens(self):\n tokens = []\n for index in range(len(self.sentrep)):\n tokens.append(self.sentrep.getWord(index).lexeme())\n return tokens","def tokens(self):\n return self._sentrep.tokens()","def getTokens(self):\n # NOTE: seems to be used by the evitaNominalTrainer only\n tokenList = []\n for chunkOrToken in self.dtrs:\n if chunkOrToken.isToken():\n tokenList += [chunkOrToken]\n elif chunkOrToken.isChunk():\n tokenList += chunkOrToken.dtrs\n else:\n logger.warn(\"Sentence element that is not a chunk or token\")\n return tokenList","def sentences(self) -> List[str]:\n\t\treturn [self.text[start:end] for start, end in self.tokenizations]","def sentences(self) -> List[str]:\n\t\treturn [self.text[start:end] for start, end in self.tokenizations]","def tokenize(self):\n tknzr = TweetTokenizer()\n tkn = []\n for tweet in self.tweets:\n for word in tknzr.tokenize(tweet):\n tkn.append(word)\n return tkn","def get_tokens(self) -> List[str]:\n return self.tokens","def get_tokens(self):\r\n return self.token_set","def tokens(self):\n # type: () -> List[Token]\n return self._tokens","def tokens(self):\n\t\tlabels_and_synonyms = list(itertools.chain.from_iterable(list(self.term_to_tokens.values())))\n\t\ttokens = set(list(itertools.chain.from_iterable([word_tokenize(x) for x in labels_and_synonyms])))\n\t\treturn(list(tokens))","def tokens(self):\n return self._tokens","def tokens(self):\n return self._tokens","def tokens(self):\n return self._tokens","def extent(self):\r\n return conf.lib.clang_getTokenExtent(self._tu, self)","def tokenize_sentences(self, sents):\n token_sentence_list = []\n for sentence in sents:\n token_sentence_list.append(self.tokenizer(sentence))\n return token_sentence_list","def get_tokens(self):\n def _traverse_preorder(cursor, token_list): # There is a method called \"walk_preorder\" in Cursor class. Here we need to ignore some subtrees so we implement on our own.\n if cursor.location.file and cursor.location.file.name != self.filepath: # exclude \"#include <...>\"\n return\n if (cursor.kind, cursor.spelling) in exclude_types: # exclude node in 'exclude_types'\n return\n \n token_list.append(cursor)\n for child in cursor.get_children():\n _traverse_preorder(child, token_list)\n\n tokens = []\n _traverse_preorder(self.cursor, tokens)\n return tokens","def tokens(self):\n return self.__tokens","def tokenize(self, texts: List[str]) -> List[Token]:\n raise NotImplementedError","def get_gene_tokens(self, classification: Classification) -> List[GeneToken]:\n return self.get_protein_gene_symbol_tokens(classification)","def multiword_tokens(self):\n return self._mwts","def get_sentences_list(text: str, model_type: str) -> t.List[str]:\n sentences = []\n sent_offsets = []\n stok = SentenceTokenizer.from_type(model_type)\n if isinstance(text, list):\n sentences, sent_offsets = list(zip(*map(stok.tokenize, text)))\n elif isinstance(text, str):\n sentences, sent_offsets = stok.tokenize(text)\n return sentences, sent_offsets","def tokens(self):\r\n return self.iter_tokens(self._blob)","def sents(self):\n\n text = str()\n for file in os.listdir(self.path):\n # checks if the given path contains a text file and opens it\n if file.endswith(\".txt\"):\n with open(self.path + \"/\" + file) as connection:\n text += connection.read()\n\n # tokenizes the text to sentences and tokenizes the tokenized sentences to words\n sentences_list = nltk.sent_tokenize(text)\n word_list = [nltk.word_tokenize(sent) for sent in sentences_list]\n\n return word_list","def return_tag_tokens(self, tags_indexes, observations):\n tag_pred = []\n for tag_index in tags_indexes:\n tag_pred.append(observations.T.index[tag_index])\n return tag_pred","def math_tokens(cls, content):\r\n\r\n tokens = cls.math_pattern.findall(content)\r\n math = []\r\n\r\n for token in tokens:\r\n # print(\"Token = \"+token,flush=True)\r\n\r\n if token.endswith(\"math>\"): # MathML token\r\n ## # Does not handle the case where one math_tan expression is nested inside another\r\n ## # (likely with different namespaces)\r\n ## # N.B. Removing this check speeds up processing significantly (FWT)\r\n ## token = cls.inner_math.sub(r\"\\0\",token) # find innermost <*:math_tan\r\n token = cls.close_tag.sub(\"</\", token) # drop namespaces (FWT)\r\n token = cls.open_tag.sub(\"<\", token)\r\n math.append(token)\r\n\r\n else: # LaTeX math_tan expression\r\n tex = token.strip(\"$\") # TODO: handle other latex delimiters\r\n math.append(LatexToMathML.convert_to_mathml(tex))\r\n\r\n return math","def entities(self):\n if 'ner' not in self.annotators:\n return None\n return [t[self.NER] for t in self.data]","def entities(self):\n if 'ner' not in self.annotators:\n return None\n return [t[self.NER] for t in self.data]","def importantTokenList(self, ignoreSemanticTagList=[]):\n list = tokenlist.TokenList()\n for token in self.tokens:\n if token.isAcronym():\n expansion = token.getAcronymExpansion()\n \n# print token.text, '=', \n# for t in expansion:\n# print t.text,\n# print\n\n# list.append(token)\n# for t in token.getAcronymExpansion():\n# if self.isImportantToken(t):\n# list.append(t)\n\n if expansion == None or len(expansion) == 0:\n list.append(token)\n else:\n for t in token.getAcronymExpansion():\n if self.isImportantToken(t, ignoreSemanticTagList=ignoreSemanticTagList):\n list.append(t)\n elif self.isImportantToken(token, ignoreSemanticTagList=ignoreSemanticTagList):\n list.append(token)\n \n return list","def get_myst_extensions(tokens: List[Token]) -> Set[str]:\n extensions = set()\n for token in tokens:\n if token.type == \"substitution_inline\" or token.type == \"substitution_block\":\n extensions.add(\"substitution\")\n elif token.type == \"front_matter_key_open\":\n key_path = token.meta.get(\"key_path\")\n if key_path and key_path[0] == \"substitutions\":\n extensions.add(\"substitution\")\n elif token.type == \"directive_open\" and \":\" in token.markup:\n extensions.add(\"colon_fence\")\n elif (\n token.type == \"math_inline\"\n or token.type == \"math_block\"\n or token.type == \"math_block_eqno\"\n ):\n extensions.add(\"dollarmath\")\n elif token.type == \"dl_open\":\n extensions.add(\"deflist\")\n return extensions","def get_tokens(self, numbered_lines):\n tokens = []\n tokens_append = tokens.append\n\n # simple tokenization: spaces and some punctuation\n splitter = re.compile('[\\\\t =;]+').split\n\n for _line_number, line in numbered_lines:\n line = line.strip()\n if line:\n line = prepare_text_line(line)\n if line :\n line = strip_markup(line)\n if line and line.strip():\n for tok in splitter(line):\n # strip trailing quotes and ignore empties\n tok = tok.strip(\"' \")\n if not tok:\n continue\n # strip trailing colons: why?\n tok = tok.rstrip(':').strip()\n # strip leading @: : why?\n tok = tok.lstrip('@').strip()\n if tok and tok not in (':',):\n tokens_append(tok)\n logger.debug('CopyrightDetector:tokens: ' + repr(tokens))\n return tokens","def tokens(self) -> list:\n if self._tokens is None:\n tokens_ = sorted(list(self.elements()))\n self._tokens = tokens_\n return self._tokens","def tokenize(self, sentences):\n tokenized = []\n en = []\n prog = re.compile(r'\\s+')\n\n for sentence in tqdm(sentences):\n sentence = unicodedata.normalize(\"NFKD\", sentence.strip())\n\n sentence = \"\".join([\n \" \"+character+\" \"\n if re.match(r\"[^a-zA-Z]\", character)\n else character\n for character in sentence\n ])\n tokenized.append(sentence.split())\n\n self.build_vocabulary(tokenized)\n\n return tokenized","def get_Tokens(self):\n return self._output.get('Tokens', None)","def collect_sentences(self):\n sentences = []\n for document in self.documents:\n for sentence_token in document.sentences:\n sentences.append(sentence_token)\n return sentences","def get_tokens(self, document):\n raise NotImplementedError()","def extract_content(tweets):\n result = []\n for t in tweets:\n text = t.text\n result.append(tokenize(text))\n return result","def synth_tokens(self):\n if self.lliagraph:\n return self.lliagraph.synth_tokens.items()\n else:\n return []","def tokenize_sents(sents, tokenizer):\n return [tokenizer(sent) for sent in sents]","def getTokens(self):\n return self.__token","def tokenize_helper(self, corpus_sentences):\n tokenized_corpus = []\n for sentence in corpus_sentences:\n tokens = sentence.split()\n tokenized_sentence = []\n for token in tokens:\n if token.isspace() is False:\n tokenized_sentence.append(token)\n tokenized_corpus.append(tokenized_sentence)\n\n return tokenized_corpus","def get_token_types() -> List[type]:\n return ExtensionTokenTypes.__TOKEN_TYPES","def get_tokens(self, node, include_extra=False):\n # type: (AstNode, bool) -> Iterator[Token]\n return self.token_range(node.first_token, node.last_token, include_extra=include_extra)","def tokens(self):\n return self.rpc.call(MsfRpcMethod.AuthTokenList)['tokens']","def pos_tag_sents(\n sentences: List[List[str]], engine: str = \"perceptron\", corpus: str = \"orchid\"\n) -> List[List[Tuple[str, str]]]:\n if not sentences:\n return []\n\n return [pos_tag(sent, engine=engine, corpus=corpus) for sent in sentences]","def tokenize_sentences(sentences):\r\n \r\n # Initialize the list of lists of tokenized sentences\r\n tokenized_sentences = []\r\n \r\n for sentence in sentences:\r\n \r\n # Convert to lowercase letters\r\n sentence = sentence.lower()\r\n \r\n # Convert into a list of words\r\n \r\n tokenized = nltk.word_tokenize(sentence)\r\n \r\n # append the list of words to the list of lists\r\n tokenized_sentences.append(tokenized)\r\n \r\n return tokenized_sentences","def get_tokens(self):\n\t\treturn self.get_starttokens() + self.get_endtokens()","def get_tokenized_sentences(snippets):\n sentences = []\n id2sentences = []\n for snippet in snippets:\n snippet_sent = split_single(snippet[1])\n id2sentences.extend([snippet[0]] * len(snippet_sent))\n sentences.extend(snippet_sent)\n\n return [Sentence(sentence, use_tokenizer=True) for sentence in sentences], id2sentences","def tokens(self):\n data, end = \\\n self.pat.traverse(lambda obj, *args: obj.tokens(*args),\n self.begin, self.data)\n return data","def _tokenize(self, sentence: str) -> List[str]: \n tokens = word_tokenize(sentence)\n tokens = [t.lower() for t in tokens if len(t) < 25 and self.pattern.match(t)]\n \n if self.include_stopword == True:\n return tokens\n stop_words = set(stopwords.words('english'))\n res = []\n for t in tokens:\n if t in stop_words:\n continue\n res.append(t)\n return res","def sentences(self) -> List[str]:\n\t\treturn [sentence for sentence in re.split('(?<=[.!?])', self.text)]","def get_list_of_tokens(self, to_lower=True, remove_newlines=True):\n if not self.tokens:\n assert self.url, \"Wrong url provided!\"\n self.text = self._get_text(remove_newlines=remove_newlines)\n self.log.debug(u\"First 100 characters of text from {url}: {text}\"\n \"\".format(url=self.url,\n text=self.text[:100]))\n self.tokens = self._get_tokens()\n self.log.debug(u\"First 100 tokens of text from {url}: {tokens}\"\n \"\".format(url=self.url,\n tokens=self.tokens))\n if to_lower:\n # all words to lowercase\n self.tokens = list(map(lambda s: s.lower(), self.tokens))\n return self.tokens","def get_tokens(self, *args, **kwargs) -> List[Token]:\n for text in args:\n for char in text:\n self.set_tokens(char)\n self.flush_temp()\n\n return self.tokens","def tokens_and_tags(self):\n return list(zip(self.tokens(), self.tags()))","def get_sentences(self):\n return [s for s in self.text.split('\\n')]","def get_target_tags(self):\n return ['en:' + self.tag_manager.normalize_tag_wtokenization(t, self.tries['en'], prefixed=False) for t in self.tag_manager.unprefixed_target_tags]","def get_tokens(sent):\n return word_tokenize(sent)","def tokens(self):\n tokens = [k for k in self.tok2ind.keys()\n if k not in {'<NULL>', '<UNK>'}]\n return tokens","def get_nft_list(self):\n tokens = self.api.find_all(\"nft\", \"nfts\", query={})\n return tokens","def tokenize(t):\n tweet_tok = TweetTokenizer(strip_handles=True, reduce_len=True)\n tokens = tweet_tok.tokenize(t)\n wnl = WordNetLemmatizer()\n stems = []\n for item in tokens:\n stems.append(wnl.lemmatize(item))\n return stems","def map_exp_ids(self, exp):\n\n ret = []\n for x in exp:\n ret.append(\n (\"{}_{}_{}\".format(\n self.model.tokenizer_._convert_id_to_token(int(self.example['input_ids'][x[0]])),\n TT_NAMES[int(self.example['token_type_ids'][x[0]])],\n int(self.example['position_ids'][x[0]])\n ), x[1])\n )\n\n return ret","def _batch_tokenize(self, text: List[str]) -> List[List[str]]:\n return self.bert_model.batch_tokenize([t.strip() for t in text])","def get_token_list():\n token_list = []\n tokens_dir_path = os.path.join(BASE_DIR, TOKENS_DIR)\n for dir, dirs, files in os.walk(tokens_dir_path):\n for file_name in files:\n file = open(os.path.join(tokens_dir_path, file_name), 'r')\n token_list.append(file.read().strip())\n file.close()\n return token_list","def get_token_names(self) -> List[str]:\n return list(self._tokens.keys())","def tokenize_en(text):\n return [tok.text for tok in spacy_en.tokenizer(text)]","def prepocess_pos_tagged_texts(tweet_tokens):\n return [TextPreprocessor.additional_text_preprocessing_with_pos(json.loads(t)) for t in tweet_tokens]","def tokenize(text: str):\n # Create list of word tokens\n token_list = []\n # nlp = init_nlp(TaggingMethod.SPACY, Lang.ES, size=DictionarySize.MEDIUM)\n doc = nlp(text)\n token_list = [token.text for token in doc]\n # for token in doc:\n # token_list.append(token.text)\n return token_list","def tokenize(expression):\n return [t.strip() for t in TOKEN_SEPARATOR.split(expression.strip()) if t]","def get_tokens(self, text):\n if text is not None:\n text = text.strip()\n words = self.safe_split(text)\n return words\n return []","def get_token_list(text):\n return text.split()","def tokenize_tweets_by_word(self):\n sent_tok_list = []\n all_tok_list = []\n\n for t in range(len(self.all_tweets) - 1):\n sent_list = sent_tokenize(self.all_tweets[t][0][1]) # all_tweets[tweet][look at tuple][look at text]\n all_tok_list.append([])\n for s in sent_list: # for each sentence\n word_tok_list = word_tokenize(s)\n pos_tag_list = pos_tag(word_tok_list)\n all_tok_list[t].append(pos_tag_list)\n print(pos_tag_list)\n\n return all_tok_list","def tokens(self):\r\n return Tokens(self)","def get_emotions(tweet):\n classifier = train_model('NRC-Emotion-Intensity-Lexicon-v1.txt')\n\n emotions = []\n probabilities = classifier.prob_classify(tweets_features(tweet))\n for sample in probabilities.samples():\n emotions.append((sample, probabilities.prob(sample)))\n\n label = classifier.classify(tweets_features(tweet))\n\n return emotions, label","def extract_entities_with_indices(self, options = {}, transform = lambda x: x):\r\n if not self.text:\r\n return []\r\n\r\n # extract all entities\r\n entities = self.extract_urls_with_indices(options) + \\\r\n self.extract_hashtags_with_indices({'check_url_overlap': False}) + \\\r\n self.extract_mentions_or_lists_with_indices() + \\\r\n self.extract_cashtags_with_indices()\r\n\r\n entities = self._remove_overlapping_entities(entities)\r\n\r\n for entity in entities:\r\n entity = transform(entity)\r\n\r\n return entities","def encode(self, tokens):\n encoded = []\n for sentence in tqdm(tokens):\n tmp = []\n for token in sentence:\n try:\n index = self.vocab.index(token)\n tmp.append(index)\n except:\n tmp.append(self.unk_token_idx)\n encoded.append(tmp)\n return encoded","def getSentences(self, ser):\n\n tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n sentencesSer = ser.apply(tokenizer.tokenize)\n return sentencesSer","def emotions(self):\n return self._emotions","def tokenize_sentences(self, sentences, reset_stats=True, max_sentences=None):\n\n if max_sentences is None and not hasattr(sentences, '__len__'):\n raise ValueError('Either you must provide an array with a length'\n 'attribute (e.g. a list) or specify the maximum '\n 'length yourself using `max_sentences`!')\n n_sentences = (max_sentences if max_sentences is not None\n else len(sentences))\n\n if self.masking_value == 0:\n tokens = np.zeros((n_sentences, self.fixed_length), dtype='uint16')\n else:\n tokens = (np.ones((n_sentences, self.fixed_length), dtype='uint16') *\n self.masking_value)\n\n if reset_stats:\n self.wordgen.reset_stats()\n\n # With a custom word generator info can be extracted from each\n # sentence (e.g. labels)\n infos = []\n\n # Returns words as strings and then map them to vocabulary\n self.wordgen.stream = sentences\n next_insert = 0\n n_ignored_unknowns = 0\n for s_words, s_info in self.wordgen:\n s_tokens = self.find_tokens(s_words)\n\n if (self.ignore_sentences_with_only_custom and\n np.all([True if t < len(SPECIAL_TOKENS)\n else False for t in s_tokens])):\n n_ignored_unknowns += 1\n continue\n if len(s_tokens) > self.fixed_length:\n s_tokens = s_tokens[:self.fixed_length]\n tokens[next_insert, :len(s_tokens)] = s_tokens\n infos.append(s_info)\n next_insert += 1\n\n # For standard word generators all sentences should be tokenized\n # this is not necessarily the case for custom wordgenerators as they\n # may filter the sentences etc.\n if not self.uses_custom_wordgen and not self.ignore_sentences_with_only_custom:\n assert len(sentences) == next_insert\n else:\n # adjust based on actual tokens received\n tokens = tokens[:next_insert]\n infos = infos[:next_insert]\n return tokens, infos, self.wordgen.stats","def _tokenize(self, text):\n split_tokens = []\n for token in self.basic_tokenizer.tokenize(text):\n for sub_token in self.wordpiece_tokenizer.tokenize(token):\n split_tokens.append(sub_token)\n return split_tokens","def tokens(self):\n return tuple(self._tree.getWords())","def tokenize(lines, token='word'):\n if token == 'word':\n return [line.split() for line in lines]\n elif token == 'char':\n return [list(line) for line in lines]\n else:\n print('ERROR: unknown token type: ' + token)","def get_tokenized_texts(self, to_lower=True):\n # init pages for each url\n if not self.texts:\n self.pages = []\n # for rate, url, description\n for u in self.urls:\n self.pages.append(Page(u))\n # form texts\n self.texts = []\n for p in self.pages:\n # catch all excetptions of pages that we cannot parse\n try:\n tokens = p.get_list_of_tokens(to_lower=to_lower)\n except ParsingError:\n self.log.warn(u\"Failed to get tokens from text of url \"\n \"{url}. Skipping it.\".format(url=p.url))\n # preserve indexing for backward restoring possibility\n self.texts.append([])\n continue\n self.texts.append(tokens)\n\n # self.texts = [p.get_list_of_tokens() for p in self.pages]\n return self.texts","def _get_tokens(s: str) ->List[str]:\n return [] if not s else _normalize_text(s).split()","def _return_entities_indexes(self, seqs: List[List[str]], type_select: str) -> List[Tuple[str, int, int]]:\n\n entities = []\n sequences = [label for seq in seqs for label in seq + ['O']]\n sentences = [word for sentence in self.sentences for word in sentence + ['']]\n prev_top = 'O'\n prev_type = ''\n focus_idx = 0\n\n for i, (label, words) in enumerate(zip(sequences + ['O'], sentences + [''])):\n top = label[0]\n type_ = label.split('-')[-1]\n\n if self._is_end_of_label(prev_top, top, prev_type, type_) \\\n and type_select in [prev_type, ''] \\\n and self._check_add_entities(''.join(sentences[focus_idx: i]), type_select):\n entities.append((prev_type, focus_idx, i - 1))\n\n focus_idx = i if self._is_begin_of_label(prev_top, top, prev_type, type_) else focus_idx\n prev_top = top\n prev_type = type_\n\n return entities","def tokenize(text):\n sentence = Sentence(text)\n return sentence.tokens()","def to_tokens(self, indices: Union[int, List[int]]) -> Union[str, List[str]]:\n if isinstance(indices, list):\n return [self._idx_to_token[idx] for idx in indices]\n else:\n return self._idx_to_token[indices]","def extent(self) -> typing.Tuple[str, ...]:\n return self._extent.members()","def named_entity_list_stanford_nlp(self) -> List:\n stanza.download(self.lang, processors = 'tokenize,mwt,ner')\n #load file and convert input string\n nlp = stanza.Pipeline(self.lang, processors = 'tokenize,mwt,ner')\n doc = nlp(self.sent)\n #build the output list\n for sentence in doc.ents:\n #avoid that 'Abstract', 'Title' and 'Author' are counted between NEs\n if sentence.text != 'Abstract' and sentence.text != 'Title' and sentence.text != 'Author':\n self.named_entity_list.append((sentence.text, \n sentence.start_char, \n sentence.end_char, sentence.type))\n return self.named_entity_list","def split_sentences(self, text):\n assert isinstance(text, str)\n text = text.replace('\\n', '')\n\n if text.strip() == '':\n return []\n\n output = self._annotate(text, properties={\n \"annotators\": \"tokenize,ssplit\",\n \"coref.md.type\": \"dep\",\n \"coref.mode\": \"statistical\"\n })\n\n sentences = []\n for sentence in output['sentences']:\n num_token = len(sentence['tokens'])\n start_index = sentence['tokens'][0]['characterOffsetBegin']\n end_index = sentence['tokens'][num_token - 1]['characterOffsetEnd']\n sentences.append(text[start_index:end_index])\n return sentences","def all_tokens(self) -> List[Hashable]:\n return self._all_tokens","def collect_ners(self, ents):\r\n collected_ners = []\r\n for token in ents:\r\n if token.label_ in self.ners_label:\r\n collected_ners.append(token.text + '/' + token.label_)\r\n return collected_ners","def getIndices(self, trans, getPos=False, getToken=False):\n s0elems, s1elems, belems = [], [], []\n emptyIdx = self.vocabulary.getEmptyIdx(getPos=getPos, getToken=getToken)\n if trans.configuration.stack:\n s0Tokens = getTokens(trans.configuration.stack[-1])\n s0elems = self.vocabulary.getIndices(s0Tokens)\n if len(trans.configuration.stack) > 1:\n s1Tokens = getTokens(trans.configuration.stack[-2])\n s1elems = self.vocabulary.getIndices(s1Tokens)\n s0elems = padSequence(s0elems, 's0Padding', emptyIdx)\n s1elems = padSequence(s1elems, 's1Padding', emptyIdx)\n if trans.configuration.buffer:\n bTokens = trans.configuration.buffer[:2]\n belems = self.vocabulary.getIndices(bTokens)\n belems = padSequence(belems, 'bPadding', emptyIdx)\n words = np.concatenate((s0elems, s1elems, belems), axis=0)\n return words","def getTokens(self, text):\n\t\ttextWithoutPunctuation = self.removePunctuation(text)\n\t\ttextWithoutStopWords = self.removeStopWords(textWithoutPunctuation)\n\t\treturn textWithoutStopWords","def _tokenize(self, text: str) -> List[str]:\n return self.bert_model.tokenize(text.strip())","def get_entities(self):\n return list(self._entities.values())"],"string":"[\n \"def get_extent_tokens(self, extent=[\\\"discontiguous_triggers\\\"], source_order=True):\\n all_tokens = self.tokens.copy()\\n core_sen_idx = all_tokens[0].in_sentence.element_id # to ensure discont is in same sentence\\n\\n for ext in extent:\\n if getattr(self, ext):\\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens if t.in_sentence.element_id == core_sen_idx)\\n\\n all_tokens = list(\\n set(all_tokens)\\n ) # this is necessary because trigger and participant spans can overlap\\n if source_order: # return tokens in the order they appear in source text\\n all_tokens.sort(key=lambda x: x.begin)\\n\\n return all_tokens\",\n \"def get_extent_token_ids(self, **kwargs):\\n token_span = self.get_extent_tokens(**kwargs)\\n return [t.index for t in token_span]\",\n \"def get_tokens(tu, extent):\\r\\n tokens_memory = POINTER(Token)()\\r\\n tokens_count = c_uint()\\r\\n\\r\\n conf.lib.clang_tokenize(tu, extent, byref(tokens_memory),\\r\\n byref(tokens_count))\\r\\n\\r\\n count = int(tokens_count.value)\\r\\n\\r\\n # If we get no tokens, no memory was allocated. Be sure not to return\\r\\n # anything and potentially call a destructor on nothing.\\r\\n if count < 1:\\r\\n return\\r\\n\\r\\n tokens_array = cast(tokens_memory, POINTER(Token * count)).contents\\r\\n\\r\\n token_group = TokenGroup(tu, tokens_memory, tokens_count)\\r\\n\\r\\n for i in xrange(0, count):\\r\\n token = Token()\\r\\n token.int_data = tokens_array[i].int_data\\r\\n token.ptr_data = tokens_array[i].ptr_data\\r\\n token._tu = tu\\r\\n token._group = token_group\\r\\n\\r\\n yield token\",\n \"def get_tokens(self, locations=None, extent=None):\\r\\n if locations is not None:\\r\\n extent = SourceRange(start=locations[0], end=locations[1])\\r\\n\\r\\n return TokenGroup.get_tokens(self, extent)\",\n \"def getTokens(self):\\n list = []\\n for i in range(self.startIdx, self.endIdx + 1):\\n token = self.sentence[i]\\n list.append(token)\\n return list\",\n \"def get_tokens(self):\\r\\n return TokenGroup.get_tokens(self._tu, self.extent)\",\n \"def tokens(self):\\n tokens = []\\n for index in range(len(self.sentrep)):\\n tokens.append(self.sentrep.getWord(index).lexeme())\\n return tokens\",\n \"def tokens(self):\\n return self._sentrep.tokens()\",\n \"def getTokens(self):\\n # NOTE: seems to be used by the evitaNominalTrainer only\\n tokenList = []\\n for chunkOrToken in self.dtrs:\\n if chunkOrToken.isToken():\\n tokenList += [chunkOrToken]\\n elif chunkOrToken.isChunk():\\n tokenList += chunkOrToken.dtrs\\n else:\\n logger.warn(\\\"Sentence element that is not a chunk or token\\\")\\n return tokenList\",\n \"def sentences(self) -> List[str]:\\n\\t\\treturn [self.text[start:end] for start, end in self.tokenizations]\",\n \"def sentences(self) -> List[str]:\\n\\t\\treturn [self.text[start:end] for start, end in self.tokenizations]\",\n \"def tokenize(self):\\n tknzr = TweetTokenizer()\\n tkn = []\\n for tweet in self.tweets:\\n for word in tknzr.tokenize(tweet):\\n tkn.append(word)\\n return tkn\",\n \"def get_tokens(self) -> List[str]:\\n return self.tokens\",\n \"def get_tokens(self):\\r\\n return self.token_set\",\n \"def tokens(self):\\n # type: () -> List[Token]\\n return self._tokens\",\n \"def tokens(self):\\n\\t\\tlabels_and_synonyms = list(itertools.chain.from_iterable(list(self.term_to_tokens.values())))\\n\\t\\ttokens = set(list(itertools.chain.from_iterable([word_tokenize(x) for x in labels_and_synonyms])))\\n\\t\\treturn(list(tokens))\",\n \"def tokens(self):\\n return self._tokens\",\n \"def tokens(self):\\n return self._tokens\",\n \"def tokens(self):\\n return self._tokens\",\n \"def extent(self):\\r\\n return conf.lib.clang_getTokenExtent(self._tu, self)\",\n \"def tokenize_sentences(self, sents):\\n token_sentence_list = []\\n for sentence in sents:\\n token_sentence_list.append(self.tokenizer(sentence))\\n return token_sentence_list\",\n \"def get_tokens(self):\\n def _traverse_preorder(cursor, token_list): # There is a method called \\\"walk_preorder\\\" in Cursor class. Here we need to ignore some subtrees so we implement on our own.\\n if cursor.location.file and cursor.location.file.name != self.filepath: # exclude \\\"#include <...>\\\"\\n return\\n if (cursor.kind, cursor.spelling) in exclude_types: # exclude node in 'exclude_types'\\n return\\n \\n token_list.append(cursor)\\n for child in cursor.get_children():\\n _traverse_preorder(child, token_list)\\n\\n tokens = []\\n _traverse_preorder(self.cursor, tokens)\\n return tokens\",\n \"def tokens(self):\\n return self.__tokens\",\n \"def tokenize(self, texts: List[str]) -> List[Token]:\\n raise NotImplementedError\",\n \"def get_gene_tokens(self, classification: Classification) -> List[GeneToken]:\\n return self.get_protein_gene_symbol_tokens(classification)\",\n \"def multiword_tokens(self):\\n return self._mwts\",\n \"def get_sentences_list(text: str, model_type: str) -> t.List[str]:\\n sentences = []\\n sent_offsets = []\\n stok = SentenceTokenizer.from_type(model_type)\\n if isinstance(text, list):\\n sentences, sent_offsets = list(zip(*map(stok.tokenize, text)))\\n elif isinstance(text, str):\\n sentences, sent_offsets = stok.tokenize(text)\\n return sentences, sent_offsets\",\n \"def tokens(self):\\r\\n return self.iter_tokens(self._blob)\",\n \"def sents(self):\\n\\n text = str()\\n for file in os.listdir(self.path):\\n # checks if the given path contains a text file and opens it\\n if file.endswith(\\\".txt\\\"):\\n with open(self.path + \\\"/\\\" + file) as connection:\\n text += connection.read()\\n\\n # tokenizes the text to sentences and tokenizes the tokenized sentences to words\\n sentences_list = nltk.sent_tokenize(text)\\n word_list = [nltk.word_tokenize(sent) for sent in sentences_list]\\n\\n return word_list\",\n \"def return_tag_tokens(self, tags_indexes, observations):\\n tag_pred = []\\n for tag_index in tags_indexes:\\n tag_pred.append(observations.T.index[tag_index])\\n return tag_pred\",\n \"def math_tokens(cls, content):\\r\\n\\r\\n tokens = cls.math_pattern.findall(content)\\r\\n math = []\\r\\n\\r\\n for token in tokens:\\r\\n # print(\\\"Token = \\\"+token,flush=True)\\r\\n\\r\\n if token.endswith(\\\"math>\\\"): # MathML token\\r\\n ## # Does not handle the case where one math_tan expression is nested inside another\\r\\n ## # (likely with different namespaces)\\r\\n ## # N.B. Removing this check speeds up processing significantly (FWT)\\r\\n ## token = cls.inner_math.sub(r\\\"\\\\0\\\",token) # find innermost <*:math_tan\\r\\n token = cls.close_tag.sub(\\\"</\\\", token) # drop namespaces (FWT)\\r\\n token = cls.open_tag.sub(\\\"<\\\", token)\\r\\n math.append(token)\\r\\n\\r\\n else: # LaTeX math_tan expression\\r\\n tex = token.strip(\\\"$\\\") # TODO: handle other latex delimiters\\r\\n math.append(LatexToMathML.convert_to_mathml(tex))\\r\\n\\r\\n return math\",\n \"def entities(self):\\n if 'ner' not in self.annotators:\\n return None\\n return [t[self.NER] for t in self.data]\",\n \"def entities(self):\\n if 'ner' not in self.annotators:\\n return None\\n return [t[self.NER] for t in self.data]\",\n \"def importantTokenList(self, ignoreSemanticTagList=[]):\\n list = tokenlist.TokenList()\\n for token in self.tokens:\\n if token.isAcronym():\\n expansion = token.getAcronymExpansion()\\n \\n# print token.text, '=', \\n# for t in expansion:\\n# print t.text,\\n# print\\n\\n# list.append(token)\\n# for t in token.getAcronymExpansion():\\n# if self.isImportantToken(t):\\n# list.append(t)\\n\\n if expansion == None or len(expansion) == 0:\\n list.append(token)\\n else:\\n for t in token.getAcronymExpansion():\\n if self.isImportantToken(t, ignoreSemanticTagList=ignoreSemanticTagList):\\n list.append(t)\\n elif self.isImportantToken(token, ignoreSemanticTagList=ignoreSemanticTagList):\\n list.append(token)\\n \\n return list\",\n \"def get_myst_extensions(tokens: List[Token]) -> Set[str]:\\n extensions = set()\\n for token in tokens:\\n if token.type == \\\"substitution_inline\\\" or token.type == \\\"substitution_block\\\":\\n extensions.add(\\\"substitution\\\")\\n elif token.type == \\\"front_matter_key_open\\\":\\n key_path = token.meta.get(\\\"key_path\\\")\\n if key_path and key_path[0] == \\\"substitutions\\\":\\n extensions.add(\\\"substitution\\\")\\n elif token.type == \\\"directive_open\\\" and \\\":\\\" in token.markup:\\n extensions.add(\\\"colon_fence\\\")\\n elif (\\n token.type == \\\"math_inline\\\"\\n or token.type == \\\"math_block\\\"\\n or token.type == \\\"math_block_eqno\\\"\\n ):\\n extensions.add(\\\"dollarmath\\\")\\n elif token.type == \\\"dl_open\\\":\\n extensions.add(\\\"deflist\\\")\\n return extensions\",\n \"def get_tokens(self, numbered_lines):\\n tokens = []\\n tokens_append = tokens.append\\n\\n # simple tokenization: spaces and some punctuation\\n splitter = re.compile('[\\\\\\\\t =;]+').split\\n\\n for _line_number, line in numbered_lines:\\n line = line.strip()\\n if line:\\n line = prepare_text_line(line)\\n if line :\\n line = strip_markup(line)\\n if line and line.strip():\\n for tok in splitter(line):\\n # strip trailing quotes and ignore empties\\n tok = tok.strip(\\\"' \\\")\\n if not tok:\\n continue\\n # strip trailing colons: why?\\n tok = tok.rstrip(':').strip()\\n # strip leading @: : why?\\n tok = tok.lstrip('@').strip()\\n if tok and tok not in (':',):\\n tokens_append(tok)\\n logger.debug('CopyrightDetector:tokens: ' + repr(tokens))\\n return tokens\",\n \"def tokens(self) -> list:\\n if self._tokens is None:\\n tokens_ = sorted(list(self.elements()))\\n self._tokens = tokens_\\n return self._tokens\",\n \"def tokenize(self, sentences):\\n tokenized = []\\n en = []\\n prog = re.compile(r'\\\\s+')\\n\\n for sentence in tqdm(sentences):\\n sentence = unicodedata.normalize(\\\"NFKD\\\", sentence.strip())\\n\\n sentence = \\\"\\\".join([\\n \\\" \\\"+character+\\\" \\\"\\n if re.match(r\\\"[^a-zA-Z]\\\", character)\\n else character\\n for character in sentence\\n ])\\n tokenized.append(sentence.split())\\n\\n self.build_vocabulary(tokenized)\\n\\n return tokenized\",\n \"def get_Tokens(self):\\n return self._output.get('Tokens', None)\",\n \"def collect_sentences(self):\\n sentences = []\\n for document in self.documents:\\n for sentence_token in document.sentences:\\n sentences.append(sentence_token)\\n return sentences\",\n \"def get_tokens(self, document):\\n raise NotImplementedError()\",\n \"def extract_content(tweets):\\n result = []\\n for t in tweets:\\n text = t.text\\n result.append(tokenize(text))\\n return result\",\n \"def synth_tokens(self):\\n if self.lliagraph:\\n return self.lliagraph.synth_tokens.items()\\n else:\\n return []\",\n \"def tokenize_sents(sents, tokenizer):\\n return [tokenizer(sent) for sent in sents]\",\n \"def getTokens(self):\\n return self.__token\",\n \"def tokenize_helper(self, corpus_sentences):\\n tokenized_corpus = []\\n for sentence in corpus_sentences:\\n tokens = sentence.split()\\n tokenized_sentence = []\\n for token in tokens:\\n if token.isspace() is False:\\n tokenized_sentence.append(token)\\n tokenized_corpus.append(tokenized_sentence)\\n\\n return tokenized_corpus\",\n \"def get_token_types() -> List[type]:\\n return ExtensionTokenTypes.__TOKEN_TYPES\",\n \"def get_tokens(self, node, include_extra=False):\\n # type: (AstNode, bool) -> Iterator[Token]\\n return self.token_range(node.first_token, node.last_token, include_extra=include_extra)\",\n \"def tokens(self):\\n return self.rpc.call(MsfRpcMethod.AuthTokenList)['tokens']\",\n \"def pos_tag_sents(\\n sentences: List[List[str]], engine: str = \\\"perceptron\\\", corpus: str = \\\"orchid\\\"\\n) -> List[List[Tuple[str, str]]]:\\n if not sentences:\\n return []\\n\\n return [pos_tag(sent, engine=engine, corpus=corpus) for sent in sentences]\",\n \"def tokenize_sentences(sentences):\\r\\n \\r\\n # Initialize the list of lists of tokenized sentences\\r\\n tokenized_sentences = []\\r\\n \\r\\n for sentence in sentences:\\r\\n \\r\\n # Convert to lowercase letters\\r\\n sentence = sentence.lower()\\r\\n \\r\\n # Convert into a list of words\\r\\n \\r\\n tokenized = nltk.word_tokenize(sentence)\\r\\n \\r\\n # append the list of words to the list of lists\\r\\n tokenized_sentences.append(tokenized)\\r\\n \\r\\n return tokenized_sentences\",\n \"def get_tokens(self):\\n\\t\\treturn self.get_starttokens() + self.get_endtokens()\",\n \"def get_tokenized_sentences(snippets):\\n sentences = []\\n id2sentences = []\\n for snippet in snippets:\\n snippet_sent = split_single(snippet[1])\\n id2sentences.extend([snippet[0]] * len(snippet_sent))\\n sentences.extend(snippet_sent)\\n\\n return [Sentence(sentence, use_tokenizer=True) for sentence in sentences], id2sentences\",\n \"def tokens(self):\\n data, end = \\\\\\n self.pat.traverse(lambda obj, *args: obj.tokens(*args),\\n self.begin, self.data)\\n return data\",\n \"def _tokenize(self, sentence: str) -> List[str]: \\n tokens = word_tokenize(sentence)\\n tokens = [t.lower() for t in tokens if len(t) < 25 and self.pattern.match(t)]\\n \\n if self.include_stopword == True:\\n return tokens\\n stop_words = set(stopwords.words('english'))\\n res = []\\n for t in tokens:\\n if t in stop_words:\\n continue\\n res.append(t)\\n return res\",\n \"def sentences(self) -> List[str]:\\n\\t\\treturn [sentence for sentence in re.split('(?<=[.!?])', self.text)]\",\n \"def get_list_of_tokens(self, to_lower=True, remove_newlines=True):\\n if not self.tokens:\\n assert self.url, \\\"Wrong url provided!\\\"\\n self.text = self._get_text(remove_newlines=remove_newlines)\\n self.log.debug(u\\\"First 100 characters of text from {url}: {text}\\\"\\n \\\"\\\".format(url=self.url,\\n text=self.text[:100]))\\n self.tokens = self._get_tokens()\\n self.log.debug(u\\\"First 100 tokens of text from {url}: {tokens}\\\"\\n \\\"\\\".format(url=self.url,\\n tokens=self.tokens))\\n if to_lower:\\n # all words to lowercase\\n self.tokens = list(map(lambda s: s.lower(), self.tokens))\\n return self.tokens\",\n \"def get_tokens(self, *args, **kwargs) -> List[Token]:\\n for text in args:\\n for char in text:\\n self.set_tokens(char)\\n self.flush_temp()\\n\\n return self.tokens\",\n \"def tokens_and_tags(self):\\n return list(zip(self.tokens(), self.tags()))\",\n \"def get_sentences(self):\\n return [s for s in self.text.split('\\\\n')]\",\n \"def get_target_tags(self):\\n return ['en:' + self.tag_manager.normalize_tag_wtokenization(t, self.tries['en'], prefixed=False) for t in self.tag_manager.unprefixed_target_tags]\",\n \"def get_tokens(sent):\\n return word_tokenize(sent)\",\n \"def tokens(self):\\n tokens = [k for k in self.tok2ind.keys()\\n if k not in {'<NULL>', '<UNK>'}]\\n return tokens\",\n \"def get_nft_list(self):\\n tokens = self.api.find_all(\\\"nft\\\", \\\"nfts\\\", query={})\\n return tokens\",\n \"def tokenize(t):\\n tweet_tok = TweetTokenizer(strip_handles=True, reduce_len=True)\\n tokens = tweet_tok.tokenize(t)\\n wnl = WordNetLemmatizer()\\n stems = []\\n for item in tokens:\\n stems.append(wnl.lemmatize(item))\\n return stems\",\n \"def map_exp_ids(self, exp):\\n\\n ret = []\\n for x in exp:\\n ret.append(\\n (\\\"{}_{}_{}\\\".format(\\n self.model.tokenizer_._convert_id_to_token(int(self.example['input_ids'][x[0]])),\\n TT_NAMES[int(self.example['token_type_ids'][x[0]])],\\n int(self.example['position_ids'][x[0]])\\n ), x[1])\\n )\\n\\n return ret\",\n \"def _batch_tokenize(self, text: List[str]) -> List[List[str]]:\\n return self.bert_model.batch_tokenize([t.strip() for t in text])\",\n \"def get_token_list():\\n token_list = []\\n tokens_dir_path = os.path.join(BASE_DIR, TOKENS_DIR)\\n for dir, dirs, files in os.walk(tokens_dir_path):\\n for file_name in files:\\n file = open(os.path.join(tokens_dir_path, file_name), 'r')\\n token_list.append(file.read().strip())\\n file.close()\\n return token_list\",\n \"def get_token_names(self) -> List[str]:\\n return list(self._tokens.keys())\",\n \"def tokenize_en(text):\\n return [tok.text for tok in spacy_en.tokenizer(text)]\",\n \"def prepocess_pos_tagged_texts(tweet_tokens):\\n return [TextPreprocessor.additional_text_preprocessing_with_pos(json.loads(t)) for t in tweet_tokens]\",\n \"def tokenize(text: str):\\n # Create list of word tokens\\n token_list = []\\n # nlp = init_nlp(TaggingMethod.SPACY, Lang.ES, size=DictionarySize.MEDIUM)\\n doc = nlp(text)\\n token_list = [token.text for token in doc]\\n # for token in doc:\\n # token_list.append(token.text)\\n return token_list\",\n \"def tokenize(expression):\\n return [t.strip() for t in TOKEN_SEPARATOR.split(expression.strip()) if t]\",\n \"def get_tokens(self, text):\\n if text is not None:\\n text = text.strip()\\n words = self.safe_split(text)\\n return words\\n return []\",\n \"def get_token_list(text):\\n return text.split()\",\n \"def tokenize_tweets_by_word(self):\\n sent_tok_list = []\\n all_tok_list = []\\n\\n for t in range(len(self.all_tweets) - 1):\\n sent_list = sent_tokenize(self.all_tweets[t][0][1]) # all_tweets[tweet][look at tuple][look at text]\\n all_tok_list.append([])\\n for s in sent_list: # for each sentence\\n word_tok_list = word_tokenize(s)\\n pos_tag_list = pos_tag(word_tok_list)\\n all_tok_list[t].append(pos_tag_list)\\n print(pos_tag_list)\\n\\n return all_tok_list\",\n \"def tokens(self):\\r\\n return Tokens(self)\",\n \"def get_emotions(tweet):\\n classifier = train_model('NRC-Emotion-Intensity-Lexicon-v1.txt')\\n\\n emotions = []\\n probabilities = classifier.prob_classify(tweets_features(tweet))\\n for sample in probabilities.samples():\\n emotions.append((sample, probabilities.prob(sample)))\\n\\n label = classifier.classify(tweets_features(tweet))\\n\\n return emotions, label\",\n \"def extract_entities_with_indices(self, options = {}, transform = lambda x: x):\\r\\n if not self.text:\\r\\n return []\\r\\n\\r\\n # extract all entities\\r\\n entities = self.extract_urls_with_indices(options) + \\\\\\r\\n self.extract_hashtags_with_indices({'check_url_overlap': False}) + \\\\\\r\\n self.extract_mentions_or_lists_with_indices() + \\\\\\r\\n self.extract_cashtags_with_indices()\\r\\n\\r\\n entities = self._remove_overlapping_entities(entities)\\r\\n\\r\\n for entity in entities:\\r\\n entity = transform(entity)\\r\\n\\r\\n return entities\",\n \"def encode(self, tokens):\\n encoded = []\\n for sentence in tqdm(tokens):\\n tmp = []\\n for token in sentence:\\n try:\\n index = self.vocab.index(token)\\n tmp.append(index)\\n except:\\n tmp.append(self.unk_token_idx)\\n encoded.append(tmp)\\n return encoded\",\n \"def getSentences(self, ser):\\n\\n tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\\n sentencesSer = ser.apply(tokenizer.tokenize)\\n return sentencesSer\",\n \"def emotions(self):\\n return self._emotions\",\n \"def tokenize_sentences(self, sentences, reset_stats=True, max_sentences=None):\\n\\n if max_sentences is None and not hasattr(sentences, '__len__'):\\n raise ValueError('Either you must provide an array with a length'\\n 'attribute (e.g. a list) or specify the maximum '\\n 'length yourself using `max_sentences`!')\\n n_sentences = (max_sentences if max_sentences is not None\\n else len(sentences))\\n\\n if self.masking_value == 0:\\n tokens = np.zeros((n_sentences, self.fixed_length), dtype='uint16')\\n else:\\n tokens = (np.ones((n_sentences, self.fixed_length), dtype='uint16') *\\n self.masking_value)\\n\\n if reset_stats:\\n self.wordgen.reset_stats()\\n\\n # With a custom word generator info can be extracted from each\\n # sentence (e.g. labels)\\n infos = []\\n\\n # Returns words as strings and then map them to vocabulary\\n self.wordgen.stream = sentences\\n next_insert = 0\\n n_ignored_unknowns = 0\\n for s_words, s_info in self.wordgen:\\n s_tokens = self.find_tokens(s_words)\\n\\n if (self.ignore_sentences_with_only_custom and\\n np.all([True if t < len(SPECIAL_TOKENS)\\n else False for t in s_tokens])):\\n n_ignored_unknowns += 1\\n continue\\n if len(s_tokens) > self.fixed_length:\\n s_tokens = s_tokens[:self.fixed_length]\\n tokens[next_insert, :len(s_tokens)] = s_tokens\\n infos.append(s_info)\\n next_insert += 1\\n\\n # For standard word generators all sentences should be tokenized\\n # this is not necessarily the case for custom wordgenerators as they\\n # may filter the sentences etc.\\n if not self.uses_custom_wordgen and not self.ignore_sentences_with_only_custom:\\n assert len(sentences) == next_insert\\n else:\\n # adjust based on actual tokens received\\n tokens = tokens[:next_insert]\\n infos = infos[:next_insert]\\n return tokens, infos, self.wordgen.stats\",\n \"def _tokenize(self, text):\\n split_tokens = []\\n for token in self.basic_tokenizer.tokenize(text):\\n for sub_token in self.wordpiece_tokenizer.tokenize(token):\\n split_tokens.append(sub_token)\\n return split_tokens\",\n \"def tokens(self):\\n return tuple(self._tree.getWords())\",\n \"def tokenize(lines, token='word'):\\n if token == 'word':\\n return [line.split() for line in lines]\\n elif token == 'char':\\n return [list(line) for line in lines]\\n else:\\n print('ERROR: unknown token type: ' + token)\",\n \"def get_tokenized_texts(self, to_lower=True):\\n # init pages for each url\\n if not self.texts:\\n self.pages = []\\n # for rate, url, description\\n for u in self.urls:\\n self.pages.append(Page(u))\\n # form texts\\n self.texts = []\\n for p in self.pages:\\n # catch all excetptions of pages that we cannot parse\\n try:\\n tokens = p.get_list_of_tokens(to_lower=to_lower)\\n except ParsingError:\\n self.log.warn(u\\\"Failed to get tokens from text of url \\\"\\n \\\"{url}. Skipping it.\\\".format(url=p.url))\\n # preserve indexing for backward restoring possibility\\n self.texts.append([])\\n continue\\n self.texts.append(tokens)\\n\\n # self.texts = [p.get_list_of_tokens() for p in self.pages]\\n return self.texts\",\n \"def _get_tokens(s: str) ->List[str]:\\n return [] if not s else _normalize_text(s).split()\",\n \"def _return_entities_indexes(self, seqs: List[List[str]], type_select: str) -> List[Tuple[str, int, int]]:\\n\\n entities = []\\n sequences = [label for seq in seqs for label in seq + ['O']]\\n sentences = [word for sentence in self.sentences for word in sentence + ['']]\\n prev_top = 'O'\\n prev_type = ''\\n focus_idx = 0\\n\\n for i, (label, words) in enumerate(zip(sequences + ['O'], sentences + [''])):\\n top = label[0]\\n type_ = label.split('-')[-1]\\n\\n if self._is_end_of_label(prev_top, top, prev_type, type_) \\\\\\n and type_select in [prev_type, ''] \\\\\\n and self._check_add_entities(''.join(sentences[focus_idx: i]), type_select):\\n entities.append((prev_type, focus_idx, i - 1))\\n\\n focus_idx = i if self._is_begin_of_label(prev_top, top, prev_type, type_) else focus_idx\\n prev_top = top\\n prev_type = type_\\n\\n return entities\",\n \"def tokenize(text):\\n sentence = Sentence(text)\\n return sentence.tokens()\",\n \"def to_tokens(self, indices: Union[int, List[int]]) -> Union[str, List[str]]:\\n if isinstance(indices, list):\\n return [self._idx_to_token[idx] for idx in indices]\\n else:\\n return self._idx_to_token[indices]\",\n \"def extent(self) -> typing.Tuple[str, ...]:\\n return self._extent.members()\",\n \"def named_entity_list_stanford_nlp(self) -> List:\\n stanza.download(self.lang, processors = 'tokenize,mwt,ner')\\n #load file and convert input string\\n nlp = stanza.Pipeline(self.lang, processors = 'tokenize,mwt,ner')\\n doc = nlp(self.sent)\\n #build the output list\\n for sentence in doc.ents:\\n #avoid that 'Abstract', 'Title' and 'Author' are counted between NEs\\n if sentence.text != 'Abstract' and sentence.text != 'Title' and sentence.text != 'Author':\\n self.named_entity_list.append((sentence.text, \\n sentence.start_char, \\n sentence.end_char, sentence.type))\\n return self.named_entity_list\",\n \"def split_sentences(self, text):\\n assert isinstance(text, str)\\n text = text.replace('\\\\n', '')\\n\\n if text.strip() == '':\\n return []\\n\\n output = self._annotate(text, properties={\\n \\\"annotators\\\": \\\"tokenize,ssplit\\\",\\n \\\"coref.md.type\\\": \\\"dep\\\",\\n \\\"coref.mode\\\": \\\"statistical\\\"\\n })\\n\\n sentences = []\\n for sentence in output['sentences']:\\n num_token = len(sentence['tokens'])\\n start_index = sentence['tokens'][0]['characterOffsetBegin']\\n end_index = sentence['tokens'][num_token - 1]['characterOffsetEnd']\\n sentences.append(text[start_index:end_index])\\n return sentences\",\n \"def all_tokens(self) -> List[Hashable]:\\n return self._all_tokens\",\n \"def collect_ners(self, ents):\\r\\n collected_ners = []\\r\\n for token in ents:\\r\\n if token.label_ in self.ners_label:\\r\\n collected_ners.append(token.text + '/' + token.label_)\\r\\n return collected_ners\",\n \"def getIndices(self, trans, getPos=False, getToken=False):\\n s0elems, s1elems, belems = [], [], []\\n emptyIdx = self.vocabulary.getEmptyIdx(getPos=getPos, getToken=getToken)\\n if trans.configuration.stack:\\n s0Tokens = getTokens(trans.configuration.stack[-1])\\n s0elems = self.vocabulary.getIndices(s0Tokens)\\n if len(trans.configuration.stack) > 1:\\n s1Tokens = getTokens(trans.configuration.stack[-2])\\n s1elems = self.vocabulary.getIndices(s1Tokens)\\n s0elems = padSequence(s0elems, 's0Padding', emptyIdx)\\n s1elems = padSequence(s1elems, 's1Padding', emptyIdx)\\n if trans.configuration.buffer:\\n bTokens = trans.configuration.buffer[:2]\\n belems = self.vocabulary.getIndices(bTokens)\\n belems = padSequence(belems, 'bPadding', emptyIdx)\\n words = np.concatenate((s0elems, s1elems, belems), axis=0)\\n return words\",\n \"def getTokens(self, text):\\n\\t\\ttextWithoutPunctuation = self.removePunctuation(text)\\n\\t\\ttextWithoutStopWords = self.removeStopWords(textWithoutPunctuation)\\n\\t\\treturn textWithoutStopWords\",\n \"def _tokenize(self, text: str) -> List[str]:\\n return self.bert_model.tokenize(text.strip())\",\n \"def get_entities(self):\\n return list(self._entities.values())\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.79144996","0.66847426","0.64567703","0.64525014","0.6422023","0.63470286","0.6025481","0.5879312","0.5853642","0.58203405","0.58203405","0.55929494","0.5589577","0.55816835","0.5545209","0.553422","0.54197466","0.54197466","0.54197466","0.5392735","0.5365629","0.53329873","0.5325342","0.5286919","0.527805","0.5219621","0.5218858","0.52099985","0.5202829","0.5182594","0.51618606","0.5135769","0.5135769","0.5129257","0.51273215","0.51219094","0.50986534","0.5088169","0.50846684","0.50727326","0.50596476","0.5049838","0.50489944","0.5033674","0.5030834","0.50304556","0.50303733","0.50203365","0.4995761","0.49952","0.49898162","0.4978978","0.49711886","0.49640167","0.49545005","0.4945978","0.49429098","0.49141005","0.49074152","0.48984984","0.48906276","0.4863463","0.48575133","0.48497638","0.48402247","0.482892","0.48191184","0.48148084","0.48102647","0.4810081","0.480868","0.4799858","0.4799742","0.47922158","0.47898886","0.47855082","0.47769856","0.4768689","0.47669291","0.47637555","0.47593915","0.47549874","0.47539803","0.47524938","0.47499475","0.47278842","0.47208828","0.47161785","0.47152892","0.4709886","0.47055522","0.470268","0.47010085","0.4698258","0.46938026","0.46933722","0.46779516","0.4677516","0.46729112","0.46720645"],"string":"[\n \"0.79144996\",\n \"0.66847426\",\n \"0.64567703\",\n \"0.64525014\",\n \"0.6422023\",\n \"0.63470286\",\n \"0.6025481\",\n \"0.5879312\",\n \"0.5853642\",\n \"0.58203405\",\n \"0.58203405\",\n \"0.55929494\",\n \"0.5589577\",\n \"0.55816835\",\n \"0.5545209\",\n \"0.553422\",\n \"0.54197466\",\n \"0.54197466\",\n \"0.54197466\",\n \"0.5392735\",\n \"0.5365629\",\n \"0.53329873\",\n \"0.5325342\",\n \"0.5286919\",\n \"0.527805\",\n \"0.5219621\",\n \"0.5218858\",\n \"0.52099985\",\n \"0.5202829\",\n \"0.5182594\",\n \"0.51618606\",\n \"0.5135769\",\n \"0.5135769\",\n \"0.5129257\",\n \"0.51273215\",\n \"0.51219094\",\n \"0.50986534\",\n \"0.5088169\",\n \"0.50846684\",\n \"0.50727326\",\n \"0.50596476\",\n \"0.5049838\",\n \"0.50489944\",\n \"0.5033674\",\n \"0.5030834\",\n \"0.50304556\",\n \"0.50303733\",\n \"0.50203365\",\n \"0.4995761\",\n \"0.49952\",\n \"0.49898162\",\n \"0.4978978\",\n \"0.49711886\",\n \"0.49640167\",\n \"0.49545005\",\n \"0.4945978\",\n \"0.49429098\",\n \"0.49141005\",\n \"0.49074152\",\n \"0.48984984\",\n \"0.48906276\",\n \"0.4863463\",\n \"0.48575133\",\n \"0.48497638\",\n \"0.48402247\",\n \"0.482892\",\n \"0.48191184\",\n \"0.48148084\",\n \"0.48102647\",\n \"0.4810081\",\n \"0.480868\",\n \"0.4799858\",\n \"0.4799742\",\n \"0.47922158\",\n \"0.47898886\",\n \"0.47855082\",\n \"0.47769856\",\n \"0.4768689\",\n \"0.47669291\",\n \"0.47637555\",\n \"0.47593915\",\n \"0.47549874\",\n \"0.47539803\",\n \"0.47524938\",\n \"0.47499475\",\n \"0.47278842\",\n \"0.47208828\",\n \"0.47161785\",\n \"0.47152892\",\n \"0.4709886\",\n \"0.47055522\",\n \"0.470268\",\n \"0.47010085\",\n \"0.4698258\",\n \"0.46938026\",\n \"0.46933722\",\n \"0.46779516\",\n \"0.4677516\",\n \"0.46729112\",\n \"0.46720645\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.82137984"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":21892,"cells":{"query":{"kind":"string","value":"Set token id based on document id + token position in text"},"document":{"kind":"string","value":"def get_token_id(self):\n return f\"{self.document_title}_{self.index}\""},"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 correct_token_begin_position(self, tokens, text):\n beginning = 0\n for token in tokens:\n token.text_begin = text.find(token.text_content, beginning) \n beginning += len(token.text_content)\n return tokens","def doc2id(self, doc):\n doc = map(self.process_token, doc)\n return [self.token_to_id(token) for token in doc]","def tokenize(self, start_pos=0, text=None):\n pass","def tokenization(self, content: str, doc_id: str):\n self.metadata[doc_id] = dict()\n tokens = list()\n lines = content.splitlines()\n for i in range(200 if self.name == 'Novels' else 0, len(lines)):\n if self.name == 'HillaryEmails' or (lines[i] == '' and lines[i-1] != ''):\n words = 0\n self.metadata[doc_id]['Content'] = str()\n for j in range(i, len(lines)):\n line = lines[j]\n if line:\n words += len(line.split())\n self.metadata[doc_id]['Content'] += line + ''\n if words >= 75:\n self.metadata[doc_id]['Content'] += '...'\n break\n break\n keys = ['Title', 'Author', 'Release Date', 'Language', 'Character set encoding']\n for i, line in enumerate(lines):\n if self.name == 'Novels':\n if i < 30:\n for j in range(len(keys)):\n if keys[j] in line:\n self.metadata[doc_id][keys[j]] = line.strip().replace(keys[j]+': ', '')\n token = line.split() # default split by whitespace\n tokens.extend(zip(token, len(token) * [doc_id]))\n return tokens","def _id(self):\n result = ''\n while self.current_char is not None and self.current_char.isalnum() or self.current_char == '_':\n result += self.current_char\n self.advance()\n\n return Token(ID, result)","def map_token_to_id(self, token: str):\n if token not in self._token_to_id:\n token = self._unk_token\n return self._token_to_id[token]","def doc2id(self, doc):\n if isinstance(doc, string_types):\n raise TypeError(\"doc2idx expects an array of unicode tokens on input, not a single string\")\n doc = map(self.process_token, doc)\n return [self.token_to_id(token) for token in doc]","def default_idtoken_processing_hook(\n id_token, user, token, request, **kwargs):\n return id_token","def id(self, tokens):\n if len(tokens) != 1:\n raise Exception(\"Unexpected argument counts\")\n return tokens[0].value","def _convert_token_to_id(self, token):\n return self.vocab.get(token, self.vocab.get(self.unk_token))","def token_key(text):\n content2 = str(text.split())\n beginning = content2.find('access_token\":\"') + int(15)\n end = content2.find('token_type') - int(3)\n access_token = content2[beginning:end]\n return access_token","def token_key(text):\n content2 = str(text.split())\n beginning = content2.find('access_token\":\"') + int(15)\n end = content2.find('token_type') - int(3)\n access_token = content2[beginning:end]\n return access_token","def convert_idx(text, tokens):\n current = 0\n spans = []\n for token in tokens:\n current = text.find(token, current) # Find position of 1st occurrence; start search from 'current' \n if current < 0:\n raise Exception(f\"Token '{token}' cannot be found\")\n spans.append((current, current + len(token)))\n current += len(token) # next search start from the token afterwards\n return spans","def _token_to_id(self, sequence_tokens, token_map, char_map, ngram=0,\n token_ngram_map=None, max_char_sequence_length=-1,\n max_char_length_per_token=-1):\n token_id_list = []\n char_id_list = []\n char_in_token_id_list = []\n ngram_id_list = []\n for token in sequence_tokens:\n char_id = [char_map.get(x, self.VOCAB_UNKNOWN) for x in token]\n char_id_list.extend(char_id[0:max_char_sequence_length])\n char_in_token = [char_map.get(x, self.VOCAB_UNKNOWN)\n for x in token[0:max_char_length_per_token]]\n char_in_token_id_list.append(char_in_token)\n\n token_id_list.append(\n token_map.get(token, token_map[self.VOCAB_UNKNOWN]))\n if ngram > 1:\n for j in range(2, ngram + 1):\n ngram_id_list.extend(\n token_ngram_map[x] for x in\n [\"\".join(sequence_tokens[k:k + j]) for k in\n range(len(sequence_tokens) - j + 1)] if x in\n token_ngram_map)\n if not sequence_tokens:\n token_id_list.append(self.VOCAB_PADDING)\n char_id_list.append(self.VOCAB_PADDING)\n char_in_token_id_list.append([self.VOCAB_PADDING])\n if not ngram_id_list:\n ngram_id_list.append(token_ngram_map[self.VOCAB_PADDING])\n return token_id_list, char_id_list, char_in_token_id_list, ngram_id_list","def identifier(self):\n _id = ''\n while self.current_char is not None and self.current_char.isalpha():\n # inner loop to get alphanumeric characters\n while self.current_char is not None and\\\n self.current_char.isalnum():\n _id += self.current_char\n self.advance()\n return Token(self.tokentype['ID'], _id)","def process_id_from(self):\r\n return self._tokens[1]","def identity_tokenizer(text):\n return text","def token(self, id):\r\n return Token(self, id)","def _convert_id_to_token(self, index):\n return self.reverse_vocab.get(index, self.unk_token)","def add(self, token_docid):\n token = token_docid[0]\n doc_id = token_docid[1]\n # collapse identical tokens together\n if token in self.posting:\n self.posting[token].append(doc_id)\n else:\n self.posting[token] = [doc_id]","def _id(self, document):\n pass","def id_to_token(self, idx):\n return self._id2token[idx]","def id_to_token(self, idx):\n return self._id2token[idx]","def tokenization(text):\n\n global tokenizer_tree\n tokenised_document = tokenizer_tree.tokenize(text)\n return tokenised_document","def words_to_id(text, is_list=False, old_word_to_id=None):\n if is_list:\n x = \"\"\n for line in text:\n x += line + \" \"\n text = x\n \n uniq_words = set(text.split(\" \"))\n \n if old_word_to_id:\n word_to_id = old_word_to_id\n start = len(old_word_to_id)\n for word in uniq_words:\n if word not in word_to_id:\n word_to_id[word] = start\n start += 1\n else:\n word_to_id = {word:i for i, word in enumerate(uniq_words)}\n \n id_to_word = {str(v):k for k,v in word_to_id.items()}\n return word_to_id, id_to_word","def map_id_to_token(self, id: int):\n return self._id_to_token[id]","def _convert_token_to_id(self, token):\n return self.sp_model.PieceToId(str(token))","def id_to_token(self, index):\r\n return self.decoder.get(index)","def doc2idx(self, document, unknown_word_index=-1):\n if isinstance(document, string_types):\n raise TypeError(\"doc2idx expects an array of unicode tokens on input, not a single string\")\n\n document = [word if isinstance(word, unicode) else unicode(word, 'utf-8') for word in document]\n return [self.token2id.get(word, unknown_word_index) for word in document]","def tokenize_docs(self):\n if not hasattr(self,\"max_seq_len\"):\n self.max_seq_len = 0\n \n context_cols = self.cols\n\n for df in [self.train, self.val, self.test]:\n df[\"tok_context\"] = df[context_cols].values.tolist()\n df[\"tok_context\"] = df[\"tok_context\"].apply(self.docs_to_ids)\n self.max_seq_len = max(self.max_seq_len,\n df[\"tok_context\"].apply(\n lambda x: max(map(len,x))).max())\n \n self.train[\"tok_ending\"] = self.train[self.train_cols[-1]].apply(\n self.str_to_ids)\n self.val[\"tok_ending_1\"] = self.val[self.test_val_cols[-2]].apply(\n self.str_to_ids)\n self.val[\"tok_ending_2\"] = self.val[self.test_val_cols[-1]].apply(\n self.str_to_ids)\n self.test[\"tok_ending_1\"] = self.test[self.test_val_cols[-2]].apply(\n self.str_to_ids)\n self.test[\"tok_ending_2\"] = self.test[self.test_val_cols[-1]].apply(\n self.str_to_ids)\n self.max_seq_len = max(\n self.max_seq_len,\n self.train[\"tok_ending\"].apply(len).max(),\n self.val[\"tok_ending_1\"].apply(len).max(),\n self.val[\"tok_ending_2\"].apply(len).max(),\n self.test[\"tok_ending_1\"].apply(len).max(),\n self.test[\"tok_ending_2\"].apply(len).max(),\n )\n # Max_seq_len is without BOM, EOM\n self.max_seq_len += 2\n log(\"Maximum sequence length: {}\".format(self.max_seq_len))","def token(self, value):\r\n self._token = value","def _convert_id_to_token(self, index):\n if index in self.fairseq_ids_to_tokens:\n return self.fairseq_ids_to_tokens[index]\n return self.sp_model.IdToPiece(index - self.fairseq_offset)","def _tokenize_replace_pos(text, language_code, replace_pos):\n seq = annotool.raw2basicseq(text, language_code, pos=True)\n word_seq = seq['word']\n pos_seq = seq['pos']\n for i, pos in enumerate(pos_seq):\n if pos in replace_pos:\n word_seq[i] = '[{}]'.format(pos)\n return word_seq","def _convert_token_to_id(self, token):\n if token in self.fairseq_tokens_to_ids:\n return self.fairseq_tokens_to_ids[token]\n spm_id = self.sp_model.PieceToId(token)\n\n # Need to return unknown token if the SP model returned 0\n return spm_id + self.fairseq_offset if spm_id else self.unk_token_id","def token_to_id(self, token):\n token = self.process_token(token)\n return self.token2id.get(token, len(self.token2id) - 1)","def tokenize_document(doc_info: dict, tokenizer: BertTokenizer, max_doc_length: int = None) -> dict:\n sub_tokens: List[str] = [] # all sub tokens of a document\n sentence_map: List[int] = [] # collected tokenized tokens -> sentence id\n subtoken_map: List[int] = [] # collected tokenized tokens -> original token id\n\n word_idx = -1\n\n for sentence_id, sentence in enumerate(doc_info['sentences']):\n for token in sentence:\n word_idx += 1\n word_tokens = tokenizer.tokenize(token)\n sub_tokens.extend(word_tokens)\n sentence_map.extend([sentence_id] * len(word_tokens))\n subtoken_map.extend([word_idx] * len(word_tokens))\n if max_doc_length:\n num_to_pad = max_doc_length - len(sub_tokens)\n sub_tokens.extend([\"[PAD]\"] * num_to_pad)\n sentence_map.extend([sentence_map[-1]+1] * num_to_pad)\n subtoken_map.extend(list(range(word_idx+1, num_to_pad+1+word_idx)))\n # global MAX_LENGTH\n # if len(sub_tokens) > MAX_LENGTH:\n # print(len(sub_tokens))\n # MAX_LENGTH = len(sub_tokens)\n # print(MAX_LENGTH)\n # todo(yuxian): need pad speakers?\n speakers = {subtoken_map.index(word_index): tokenizer.tokenize(speaker)\n for word_index, speaker in doc_info['speakers']}\n clusters = [[(subtoken_map.index(start), len(subtoken_map) - 1 - subtoken_map[::-1].index(end))\n for start, end in cluster] for cluster in doc_info['clusters']]\n tokenized_document = {'sub_tokens': sub_tokens, 'sentence_map': sentence_map, 'subtoken_map': subtoken_map,\n 'speakers': speakers, 'clusters': clusters, 'doc_key': doc_info['doc_key']}\n return tokenized_document","def __getitem__(self, tokenid):\n if len(self.id2token) != len(self.token2id):\n # the word->id mapping has changed (presumably via add_documents);\n # recompute id->word accordingly\n self.id2token = utils.revdict(self.token2id)\n return self.id2token[tokenid] # will throw for non-existent ids","def token2id(data, mode):\n vocab_path = 'vocab.' + mode\n in_path = data + '.' + mode\n out_path = data + '_ids.' + mode\n _, vocab = load_vocab(os.path.join(config.PROCESSED_PATH, vocab_path))\n in_file = open(os.path.join(config.PROCESSED_PATH, in_path), 'rb')\n out_file = open(os.path.join(config.PROCESSED_PATH, out_path), 'wb')\n\n lines = in_file.read().splitlines()\n for line in lines:\n if mode == 'dec': # we only care about '<s>' and </s> in encoder\n ids = [vocab[b'<s>']]\n else:\n ids = []\n ids.extend(sentence2id(vocab, line))\n # ids.extend([vocab.get(token, vocab['<unk>']) for token in basic_tokenizer(line)])\n if mode == 'dec':\n ids.append(vocab[b'</s>'])\n out_file.write(b' '.join(str(id_).encode('ascii') for id_ in ids) + b'\\n')","def index_word(self, token):\n if token not in self.word_2_idx:\n idx = len(self.word_2_idx)\n self.word_2_idx[token] = idx\n self.idx_2_word[idx] = token","def replace_special_token(self, tokenizer_fields: Dict[str, torch.Tensor], positions: List[int], replace_id: int):\n token_ids, offsets = tokenizer_fields[\"token_ids\"], tokenizer_fields[\"offsets\"]\n for pos in positions:\n offset = offsets[pos].numpy().tolist()\n if offset[0] != offset[1]:\n warnings.warn(f\"replace normally expect token in `positions` has not been split to pieces.\"\n f\"This warning should NOT happen unless during batch prediction at evaluation\")\n token_ids[offset[0]] = replace_id","def _convert_token_to_id(self, token):\n return self.sp_model.PieceToId(token)","def add(self, document):\n #words=[word.lower() for word in words if word.isalpha()] #added on 0415\n for token in [t.lower() for t in nltk.word_tokenize(document)]:\n if not token.isalpha():\n continue\n\n if token in self.stopwords:\n continue\n \n if self.stemmer:\n token = self.stemmer.stem(token)\n \n if self.__unique_id not in self.index[token]:\n self.index[token].append(self.__unique_id)\n \n self.documents[self.__unique_id] = document\n self.__unique_id += 1","def token_id_from(self, token_id_from):\n\n self._token_id_from = token_id_from","def setUserIDRefs( self, text ):\n self.user_id_list= text.split()","def assign_textbook_id(textbook, used_ids=()):\r\n tid = Location.clean(textbook[\"tab_title\"])\r\n if not tid[0].isdigit():\r\n # stick a random digit in front\r\n tid = random.choice(string.digits) + tid\r\n while tid in used_ids:\r\n # add a random ASCII character to the end\r\n tid = tid + random.choice(string.ascii_lowercase)\r\n return tid","def doc2token(self, doc):\n return [self.word2idx[word] if self.word2idx.__contains__(word)\n else self.word2idx['UNK'] for word in doc]","def token_id_to(self, token_id_to):\n\n self._token_id_to = token_id_to","def word2idx(self, str):\n return self._tokens.get(str)","def token_key(token):\n morphotagged = analysis(token).get('raw')\n lemma_pos = (analysis(token).get('lemma'), analysis(token).get('partOfSpeech'))\n return morphotagged or lemma_pos","def _tokens_to_index(self,tokens):\n wids = []\n for tk in tokens:\n if tk in self.wtoi.keys():\n wids.append(self.wtoi[tk])\n else:\n wids.append(1) # <UNK>\n for _ in range(self.sentence_max_length - len(wids)):\n wids.append(0)\n if len(wids) > self.sentence_max_length:\n wids = wids[:self.sentence_max_length]\n return wids","def token_to_id(self, token):\r\n return self.encoder.get(token, self.encoder.get(self.unk_token))","def _id(self):\n result = ''\n while self.current_char is not None and self.current_char.isalnum():\n result += self.current_char\n self.advance()\n\n if self.current_char == '(' and self.is_declaration is False:\n self.advance()\n token = self.RESERVED_KEYWORDS.get(\n result.upper(), Token(CALL, result))\n else:\n token = self.RESERVED_KEYWORDS.get(\n result.upper(), Token(ID, result))\n self.is_declaration = False\n if token.type in (PROCEDURE, FUNCTION):\n self.is_declaration = True\n return token","def token_to_id(self, token):\n token = self.process_token(token)\n return self._token2id.get(token, len(self._token2id) - 1)","def getIdAt(self, pos):\n return self.sentence[pos].getId()","def process_id_to(self):\r\n return self._tokens[3]","def text_to_id(text, word_to_id_dict):\n return [word_to_id_dict[word] for word in text.split(\" \") if word in word_to_id_dict]","def token(self) -> str:","def get_ids(cls, text):\n tokens = TokenizerContainer.TOKENIZER.tokenize(text)\n token_ids = TokenizerContainer.TOKENIZER.convert_tokens_to_ids(tokens)\n input_ids = token_ids + [0] * (cls.MAX_LEN-len(token_ids))\n return tokens, input_ids","def get_id(self, word, add=False):\n if word not in self.word_to_id:\n if add:\n self._add_new_word(word)\n return self.word_to_id[constants.UNK_TOKEN]\n return self.word_to_id[word]","def __init__(self, tokenizer=None):\n self.tokenizer = tokenizer or (lambda x: x.split(' '))\n self._word2index = {\n self.PAD_TOKEN: 0,\n self.UNK_TOKEN: 1,\n self.START_TOKEN: 2,\n self.END_TOKEN: 3,\n }\n self._index2word = {}","def __init__(self, tokens, key=lambda x: x):\n self._tokens = tokens\n \"\"\"The document (list of tokens) that this concordance index\n was created from.\"\"\"\n\n self._key = key\n \"\"\"Function mapping each token to an index key (or None).\"\"\"\n\n self._offsets = defaultdict(list)\n \"\"\"Dictionary mapping words (or keys) to lists of offset indices.\"\"\"\n # Initialize the index (self._offsets)\n for index, word in enumerate(tokens):\n word = self._key(word)\n self._offsets[word].append(index)","def _internal2document_id(value):\n return 2*value + 1","def init_tokens(self):\n raise NotImplementedError('Abstract method.')","def text2ids(self, text: str, length: int):\n # Tokenize\n tokens = self.tokenizer.tokenize(text)\n token_ids = self.tokenizer.tokens2ids(tokens)\n # Padding\n while len(token_ids) < length:\n token_ids.append(0)\n # Truncate\n if len(token_ids) > length:\n token_ids = token_ids[:length]\n assert len(token_ids) == length\n return token_ids","def postag_doc(text_vector):\n\n tokenised_document_pos_tagged = pos_tag(text_vector)\n return tokenised_document_pos_tagged","def userDocumentId(self, id: str) -> str:","def get_mismatch_token_idx(self, words: List[str]):\n # todo(yuxian): this line is extremely slow, need optimization\n wordpieces, offsets = self._allennlp_tokenizer.intra_word_tokenize(words)\n\n # For tokens that don't correspond to any word pieces, we put (-1, -1) into the offsets.\n # That results in the embedding for the token to be all zeros.\n offsets = [x if x is not None else (-1, -1) for x in offsets]\n\n output = {\n \"token_ids\": torch.LongTensor([t.text_id for t in wordpieces]),\n \"word_mask\": torch.BoolTensor([True] * len(words)), # for original tokens (i.e. word-level)\n \"offsets\": torch.LongTensor(offsets),\n \"wordpiece_mask\": torch.BoolTensor([True] * len(wordpieces)), # for wordpieces (i.e. subword-level)\n }\n return output","def next_token(self, context, token):","def _convert_id_to_token(self, index, return_unicode=True):\n token = self.sp_model.IdToPiece(index)\n return token","def id2doc(self, ids):\n return [self.id_to_token(idx) for idx in ids]","def id2doc(self, ids):\n return [self.id_to_token(idx) for idx in ids]","def tokenize(text, token):\n text = [token(x) for x in text]\n return text","def __init__(self, text, idx):\n self.text = text\n self.idx = idx","def __gen_tokenization_file(self):\n paula_id = '{}.{}.tok'.format(self.corpus_name, self.name)\n E, tree = gen_paula_etree(paula_id)\n self.paulamap['tokenization'] = paula_id\n\n base_paula_id = '{}.{}.text'.format(self.corpus_name, self.name)\n mlist = E('markList',\n {'type': 'tok',\n XMLBASE: base_paula_id+'.xml'})\n tok_tuples = self.dg.get_tokens()\n for (tid, onset, tlen) in get_onsets(tok_tuples):\n # even SaltNPepper still uses xpointers for string-ranges!\n xp = \"#xpointer(string-range(//body,'',{},{}))\".format(onset, tlen)\n mlist.append(E('mark', {'id': tid,\n XLINKHREF: xp}))\n tree.append(mlist)\n self.files[paula_id] = tree\n self.file2dtd[paula_id] = PaulaDTDs.mark\n return paula_id","def _force_token_ids_generation(self, scores, token_id) -> None:\r\n scores[:, [x for x in range(self.config.vocab_size) if x != token_id]] = -float(\"inf\")","def get_tokens(self, document):\n raise NotImplementedError()","def create_token(self, token_id, data):\n raise exception.NotImplemented() # pragma: no cover","def generate_inverted_index(self, doc_id, tokenized_document):\n self.unique_doc_ids.add(doc_id)\n for t in tokenized_document:\n self.add_to_index(t, doc_id)","def setID(id, content):\n return content.replace(\"$ID\", str(id))","def replace_token_with_index(self, tokenized_tweet, max_length_dictionary):\n\n # should be replacing each token in a list of tokens by their corresponding index\n # Source: https://github.com/stanfordnlp/GloVe\n # Source: https://towardsdatascience.com/word-embeddings-for-sentiment-analysis-65f42ea5d26e\n\n if self.mode == \"zip\":\n for idx, word in enumerate(tokenized_tweet):\n if word not in self.corpus:\n tokenized_tweet[idx] = \"<unknown>\"\n else:\n for idx, word in enumerate(tokenized_tweet):\n if word not in self.corpus:\n tokenized_tweet[idx] = \"<unk>\"\n return [self.corpus.index(x) for x in tokenized_tweet]","def charIdx_to_tokenIdx(spans, ans_text, ans_start): \n # Convert answer 'char idxs' to 'token idxs' for one single record\n if ans_start != -999:\n ans_end = ans_start + len(ans_text) - 1 \n ans_token_idxs = []\n for token_idx, span in enumerate(spans):\n if span[0] < ans_end and span[1] > ans_start:\n ans_token_idxs.append(token_idx)\n y1, y2 = ans_token_idxs[0], ans_token_idxs[-1] \n else:\n y1, y2 = -999, -999\n \n return y1, y2","def tokenization(news, word_to_id):\n tokenized_news = []\n lengths = []\n for date in news:\n daily_headlines = []\n daily_lengths = []\n for headline in news[date]:\n daily_lengths.append(len(headline))\n token = []\n for word in headline:\n if word in word_to_id:\n token.append(word_to_id[word])\n else:\n token.append(word_to_id['<unk>'])\n daily_headlines.append(token)\n\n lengths.append(daily_lengths)\n tokenized_news.append(daily_headlines)\n #tokens = np.array(tokens).astype('int32')\n return tokenized_news, lengths","def tokenize_fast(self, text):\n\n ref_ids = prepare_ref([text], self.tokenizer_ltp, self.tokenizer_cn)\n\n tokens = self.tokenizer_cn.tokenize(text)\n\n ref_ids = cn_whole_word_mask(tokens, ref_ids[0])\n tokens, labels = random_word_wwm(tokens, ref_ids, self.tokenizer_cn)\n\n input_ids = self.tokenizer_cn.convert_tokens_to_ids(tokens)\n\n assert len(input_ids) == len(labels)\n\n return input_ids, labels","def __getitem__(self, i):\n if isinstance(i, int):\n start = i\n else:\n start = i.start\n if start is None:\n start = 0\n if start < 0:\n start = len(self.text) + start\n return Token(self.text[i], self.position + start, self.category)","def _parse_token(self, body):\n\n token_match = re.search('var\\s*token\\s*=[\\s\\']*(\\d+)', body)\n return int(token_match.group(1))","def _add_offsets_to_token_nodes(self):\n for edge_index in self._textual_relation_ids:\n token_node_index = self.edges[edge_index].source\n self.nodes[token_node_index].onset = self.edges[edge_index].onset\n self.nodes[token_node_index].offset = self.edges[edge_index].offset","def _get_token_ids(self, tokens):\n token_ids = self.tokenizer.convert_tokens_to_ids(tokens)\n pad_amount = self.max_seq_length - len(tokens)\n input_ids = token_ids + [0] * pad_amount\n return np.array(input_ids)","def tok2idx_data(token2id, tok_data):\n idx_data = []\n for toks in tok_data:\n idx_lst = [\n token2id[tok] if tok in token2id else UNK_IDX for tok in toks]\n idx_data.append(idx_lst)\n return idx_data","def __call__(self, tokenized_text):\n raise NotImplementedError()","def preprocess_text(self, input_text):\n input_text = self.clean_text(input_text)\n tokenization_list = self.tokenize_text(input_text)\n index_list = self.replace_token_with_index(tokenization_list, self.max_length_dictionary)\n index_list = self.pad_sequence(index_list, self.max_length_tweet)\n return index_list","def token(self, token):\n\n self._token = token","def token(self, token):\n\n self._token = token","def get_token(self, token_id):\n raise exception.NotImplemented() # pragma: no cover","def token_id(self) -> Optional[pulumi.Input[int]]:\n return pulumi.get(self, \"token_id\")","def test_wrap_tokenizer(self):\n input = \"this-string will be split@according to diff'rnt rules\"\n from enchant.tokenize import en\n tknzr = wrap_tokenizer(basic_tokenize, en.tokenize)\n tknzr = tknzr(input)\n self.assertEqual(tknzr._tokenizer.__class__, basic_tokenize)\n self.assertEqual(tknzr._tokenizer.offset, 0)\n for (n, (word, pos)) in enumerate(tknzr):\n if n == 0:\n self.assertEqual(pos, 0)\n self.assertEqual(word, \"this\")\n if n == 1:\n self.assertEqual(pos, 5)\n self.assertEqual(word, \"string\")\n if n == 2:\n self.assertEqual(pos, 12)\n self.assertEqual(word, \"will\")\n # Test setting offset to a previous token\n tknzr.set_offset(5)\n self.assertEqual(tknzr.offset, 5)\n self.assertEqual(tknzr._tokenizer.offset, 5)\n self.assertEqual(tknzr._curtok.__class__, empty_tokenize)\n if n == 3:\n self.assertEqual(word, \"string\")\n self.assertEqual(pos, 5)\n if n == 4:\n self.assertEqual(pos, 12)\n self.assertEqual(word, \"will\")\n if n == 5:\n self.assertEqual(pos, 17)\n self.assertEqual(word, \"be\")\n # Test setting offset past the current token\n tknzr.set_offset(20)\n self.assertEqual(tknzr.offset, 20)\n self.assertEqual(tknzr._tokenizer.offset, 20)\n self.assertEqual(tknzr._curtok.__class__, empty_tokenize)\n if n == 6:\n self.assertEqual(pos, 20)\n self.assertEqual(word, \"split\")\n if n == 7:\n self.assertEqual(pos, 26)\n self.assertEqual(word, \"according\")\n # Test setting offset to middle of current token\n tknzr.set_offset(23)\n self.assertEqual(tknzr.offset, 23)\n self.assertEqual(tknzr._tokenizer.offset, 23)\n self.assertEqual(tknzr._curtok.offset, 3)\n if n == 8:\n self.assertEqual(pos, 23)\n self.assertEqual(word, \"it\")\n # OK, I'm pretty happy with the behaviour, no need to\n # continue testing the rest of the string","def merged_tokens(self, i, tokenizer):\n out = []\n counts = []\n last_id = None\n tokens = tokenizer.convert_ids_to_tokens(self.token_ids[i].tolist())\n for token, current_id in zip(tokens[1:-1], self.conll_ids[i]):\n if last_id == current_id.item():\n if token.startswith(\"##\"):\n out[-1] += (token[2:])\n else:\n out[-1] += token\n counts[-1] += 1\n else:\n out.append(token)\n counts.append(1)\n last_id = current_id\n return out, counts","def __init__(self):\n self.tokens = []","def friendly_id(self):\n id = f\"{self.annotator_id}_{self.document_title.split('_')[0]}\"\n\n try: # try making an sentence identifier if there is an in_sentence attrib\n sen_id = \",\".join(str(se.element_id + 1) for se in self.in_sentence)\n id += f\"_s{sen_id}\"\n except Exception as e:\n print(e)\n pass\n\n if isinstance(self, Event):\n id += f\"_{self.event_fulltype}\"\n elif isinstance(self, Participant) or isinstance(self, Filler):\n id += f\"_{self.role}\"\n\n text_ellips = (\n (self.text[:15] + \"..\" + self.text[-15:])\n if len(self.text) > 32\n else self.text\n )\n id += f\"-{text_ellips}\"\n return id","def getWordIdx(token, word2Idx): \n if token in word2Idx:\n return word2Idx[token]\n elif token.lower() in word2Idx:\n return word2Idx[token.lower()]\n \n return word2Idx[\"UNKNOWN_TOKEN\"]","def convert_tokens_to_ids(self, tokens):\n ids = []\n for token in tokens:\n ids.append(self.vocab[token])\n if len(ids) > self.max_len:\n logger.warning('Token indices sequence length is longer than the specified maximum sequence length for this BERT model ({} > {}). Running this sequence through BERT will result in indexing errors'.format(len(ids), self.max_len))\n return ids"],"string":"[\n \"def correct_token_begin_position(self, tokens, text):\\n beginning = 0\\n for token in tokens:\\n token.text_begin = text.find(token.text_content, beginning) \\n beginning += len(token.text_content)\\n return tokens\",\n \"def doc2id(self, doc):\\n doc = map(self.process_token, doc)\\n return [self.token_to_id(token) for token in doc]\",\n \"def tokenize(self, start_pos=0, text=None):\\n pass\",\n \"def tokenization(self, content: str, doc_id: str):\\n self.metadata[doc_id] = dict()\\n tokens = list()\\n lines = content.splitlines()\\n for i in range(200 if self.name == 'Novels' else 0, len(lines)):\\n if self.name == 'HillaryEmails' or (lines[i] == '' and lines[i-1] != ''):\\n words = 0\\n self.metadata[doc_id]['Content'] = str()\\n for j in range(i, len(lines)):\\n line = lines[j]\\n if line:\\n words += len(line.split())\\n self.metadata[doc_id]['Content'] += line + ''\\n if words >= 75:\\n self.metadata[doc_id]['Content'] += '...'\\n break\\n break\\n keys = ['Title', 'Author', 'Release Date', 'Language', 'Character set encoding']\\n for i, line in enumerate(lines):\\n if self.name == 'Novels':\\n if i < 30:\\n for j in range(len(keys)):\\n if keys[j] in line:\\n self.metadata[doc_id][keys[j]] = line.strip().replace(keys[j]+': ', '')\\n token = line.split() # default split by whitespace\\n tokens.extend(zip(token, len(token) * [doc_id]))\\n return tokens\",\n \"def _id(self):\\n result = ''\\n while self.current_char is not None and self.current_char.isalnum() or self.current_char == '_':\\n result += self.current_char\\n self.advance()\\n\\n return Token(ID, result)\",\n \"def map_token_to_id(self, token: str):\\n if token not in self._token_to_id:\\n token = self._unk_token\\n return self._token_to_id[token]\",\n \"def doc2id(self, doc):\\n if isinstance(doc, string_types):\\n raise TypeError(\\\"doc2idx expects an array of unicode tokens on input, not a single string\\\")\\n doc = map(self.process_token, doc)\\n return [self.token_to_id(token) for token in doc]\",\n \"def default_idtoken_processing_hook(\\n id_token, user, token, request, **kwargs):\\n return id_token\",\n \"def id(self, tokens):\\n if len(tokens) != 1:\\n raise Exception(\\\"Unexpected argument counts\\\")\\n return tokens[0].value\",\n \"def _convert_token_to_id(self, token):\\n return self.vocab.get(token, self.vocab.get(self.unk_token))\",\n \"def token_key(text):\\n content2 = str(text.split())\\n beginning = content2.find('access_token\\\":\\\"') + int(15)\\n end = content2.find('token_type') - int(3)\\n access_token = content2[beginning:end]\\n return access_token\",\n \"def token_key(text):\\n content2 = str(text.split())\\n beginning = content2.find('access_token\\\":\\\"') + int(15)\\n end = content2.find('token_type') - int(3)\\n access_token = content2[beginning:end]\\n return access_token\",\n \"def convert_idx(text, tokens):\\n current = 0\\n spans = []\\n for token in tokens:\\n current = text.find(token, current) # Find position of 1st occurrence; start search from 'current' \\n if current < 0:\\n raise Exception(f\\\"Token '{token}' cannot be found\\\")\\n spans.append((current, current + len(token)))\\n current += len(token) # next search start from the token afterwards\\n return spans\",\n \"def _token_to_id(self, sequence_tokens, token_map, char_map, ngram=0,\\n token_ngram_map=None, max_char_sequence_length=-1,\\n max_char_length_per_token=-1):\\n token_id_list = []\\n char_id_list = []\\n char_in_token_id_list = []\\n ngram_id_list = []\\n for token in sequence_tokens:\\n char_id = [char_map.get(x, self.VOCAB_UNKNOWN) for x in token]\\n char_id_list.extend(char_id[0:max_char_sequence_length])\\n char_in_token = [char_map.get(x, self.VOCAB_UNKNOWN)\\n for x in token[0:max_char_length_per_token]]\\n char_in_token_id_list.append(char_in_token)\\n\\n token_id_list.append(\\n token_map.get(token, token_map[self.VOCAB_UNKNOWN]))\\n if ngram > 1:\\n for j in range(2, ngram + 1):\\n ngram_id_list.extend(\\n token_ngram_map[x] for x in\\n [\\\"\\\".join(sequence_tokens[k:k + j]) for k in\\n range(len(sequence_tokens) - j + 1)] if x in\\n token_ngram_map)\\n if not sequence_tokens:\\n token_id_list.append(self.VOCAB_PADDING)\\n char_id_list.append(self.VOCAB_PADDING)\\n char_in_token_id_list.append([self.VOCAB_PADDING])\\n if not ngram_id_list:\\n ngram_id_list.append(token_ngram_map[self.VOCAB_PADDING])\\n return token_id_list, char_id_list, char_in_token_id_list, ngram_id_list\",\n \"def identifier(self):\\n _id = ''\\n while self.current_char is not None and self.current_char.isalpha():\\n # inner loop to get alphanumeric characters\\n while self.current_char is not None and\\\\\\n self.current_char.isalnum():\\n _id += self.current_char\\n self.advance()\\n return Token(self.tokentype['ID'], _id)\",\n \"def process_id_from(self):\\r\\n return self._tokens[1]\",\n \"def identity_tokenizer(text):\\n return text\",\n \"def token(self, id):\\r\\n return Token(self, id)\",\n \"def _convert_id_to_token(self, index):\\n return self.reverse_vocab.get(index, self.unk_token)\",\n \"def add(self, token_docid):\\n token = token_docid[0]\\n doc_id = token_docid[1]\\n # collapse identical tokens together\\n if token in self.posting:\\n self.posting[token].append(doc_id)\\n else:\\n self.posting[token] = [doc_id]\",\n \"def _id(self, document):\\n pass\",\n \"def id_to_token(self, idx):\\n return self._id2token[idx]\",\n \"def id_to_token(self, idx):\\n return self._id2token[idx]\",\n \"def tokenization(text):\\n\\n global tokenizer_tree\\n tokenised_document = tokenizer_tree.tokenize(text)\\n return tokenised_document\",\n \"def words_to_id(text, is_list=False, old_word_to_id=None):\\n if is_list:\\n x = \\\"\\\"\\n for line in text:\\n x += line + \\\" \\\"\\n text = x\\n \\n uniq_words = set(text.split(\\\" \\\"))\\n \\n if old_word_to_id:\\n word_to_id = old_word_to_id\\n start = len(old_word_to_id)\\n for word in uniq_words:\\n if word not in word_to_id:\\n word_to_id[word] = start\\n start += 1\\n else:\\n word_to_id = {word:i for i, word in enumerate(uniq_words)}\\n \\n id_to_word = {str(v):k for k,v in word_to_id.items()}\\n return word_to_id, id_to_word\",\n \"def map_id_to_token(self, id: int):\\n return self._id_to_token[id]\",\n \"def _convert_token_to_id(self, token):\\n return self.sp_model.PieceToId(str(token))\",\n \"def id_to_token(self, index):\\r\\n return self.decoder.get(index)\",\n \"def doc2idx(self, document, unknown_word_index=-1):\\n if isinstance(document, string_types):\\n raise TypeError(\\\"doc2idx expects an array of unicode tokens on input, not a single string\\\")\\n\\n document = [word if isinstance(word, unicode) else unicode(word, 'utf-8') for word in document]\\n return [self.token2id.get(word, unknown_word_index) for word in document]\",\n \"def tokenize_docs(self):\\n if not hasattr(self,\\\"max_seq_len\\\"):\\n self.max_seq_len = 0\\n \\n context_cols = self.cols\\n\\n for df in [self.train, self.val, self.test]:\\n df[\\\"tok_context\\\"] = df[context_cols].values.tolist()\\n df[\\\"tok_context\\\"] = df[\\\"tok_context\\\"].apply(self.docs_to_ids)\\n self.max_seq_len = max(self.max_seq_len,\\n df[\\\"tok_context\\\"].apply(\\n lambda x: max(map(len,x))).max())\\n \\n self.train[\\\"tok_ending\\\"] = self.train[self.train_cols[-1]].apply(\\n self.str_to_ids)\\n self.val[\\\"tok_ending_1\\\"] = self.val[self.test_val_cols[-2]].apply(\\n self.str_to_ids)\\n self.val[\\\"tok_ending_2\\\"] = self.val[self.test_val_cols[-1]].apply(\\n self.str_to_ids)\\n self.test[\\\"tok_ending_1\\\"] = self.test[self.test_val_cols[-2]].apply(\\n self.str_to_ids)\\n self.test[\\\"tok_ending_2\\\"] = self.test[self.test_val_cols[-1]].apply(\\n self.str_to_ids)\\n self.max_seq_len = max(\\n self.max_seq_len,\\n self.train[\\\"tok_ending\\\"].apply(len).max(),\\n self.val[\\\"tok_ending_1\\\"].apply(len).max(),\\n self.val[\\\"tok_ending_2\\\"].apply(len).max(),\\n self.test[\\\"tok_ending_1\\\"].apply(len).max(),\\n self.test[\\\"tok_ending_2\\\"].apply(len).max(),\\n )\\n # Max_seq_len is without BOM, EOM\\n self.max_seq_len += 2\\n log(\\\"Maximum sequence length: {}\\\".format(self.max_seq_len))\",\n \"def token(self, value):\\r\\n self._token = value\",\n \"def _convert_id_to_token(self, index):\\n if index in self.fairseq_ids_to_tokens:\\n return self.fairseq_ids_to_tokens[index]\\n return self.sp_model.IdToPiece(index - self.fairseq_offset)\",\n \"def _tokenize_replace_pos(text, language_code, replace_pos):\\n seq = annotool.raw2basicseq(text, language_code, pos=True)\\n word_seq = seq['word']\\n pos_seq = seq['pos']\\n for i, pos in enumerate(pos_seq):\\n if pos in replace_pos:\\n word_seq[i] = '[{}]'.format(pos)\\n return word_seq\",\n \"def _convert_token_to_id(self, token):\\n if token in self.fairseq_tokens_to_ids:\\n return self.fairseq_tokens_to_ids[token]\\n spm_id = self.sp_model.PieceToId(token)\\n\\n # Need to return unknown token if the SP model returned 0\\n return spm_id + self.fairseq_offset if spm_id else self.unk_token_id\",\n \"def token_to_id(self, token):\\n token = self.process_token(token)\\n return self.token2id.get(token, len(self.token2id) - 1)\",\n \"def tokenize_document(doc_info: dict, tokenizer: BertTokenizer, max_doc_length: int = None) -> dict:\\n sub_tokens: List[str] = [] # all sub tokens of a document\\n sentence_map: List[int] = [] # collected tokenized tokens -> sentence id\\n subtoken_map: List[int] = [] # collected tokenized tokens -> original token id\\n\\n word_idx = -1\\n\\n for sentence_id, sentence in enumerate(doc_info['sentences']):\\n for token in sentence:\\n word_idx += 1\\n word_tokens = tokenizer.tokenize(token)\\n sub_tokens.extend(word_tokens)\\n sentence_map.extend([sentence_id] * len(word_tokens))\\n subtoken_map.extend([word_idx] * len(word_tokens))\\n if max_doc_length:\\n num_to_pad = max_doc_length - len(sub_tokens)\\n sub_tokens.extend([\\\"[PAD]\\\"] * num_to_pad)\\n sentence_map.extend([sentence_map[-1]+1] * num_to_pad)\\n subtoken_map.extend(list(range(word_idx+1, num_to_pad+1+word_idx)))\\n # global MAX_LENGTH\\n # if len(sub_tokens) > MAX_LENGTH:\\n # print(len(sub_tokens))\\n # MAX_LENGTH = len(sub_tokens)\\n # print(MAX_LENGTH)\\n # todo(yuxian): need pad speakers?\\n speakers = {subtoken_map.index(word_index): tokenizer.tokenize(speaker)\\n for word_index, speaker in doc_info['speakers']}\\n clusters = [[(subtoken_map.index(start), len(subtoken_map) - 1 - subtoken_map[::-1].index(end))\\n for start, end in cluster] for cluster in doc_info['clusters']]\\n tokenized_document = {'sub_tokens': sub_tokens, 'sentence_map': sentence_map, 'subtoken_map': subtoken_map,\\n 'speakers': speakers, 'clusters': clusters, 'doc_key': doc_info['doc_key']}\\n return tokenized_document\",\n \"def __getitem__(self, tokenid):\\n if len(self.id2token) != len(self.token2id):\\n # the word->id mapping has changed (presumably via add_documents);\\n # recompute id->word accordingly\\n self.id2token = utils.revdict(self.token2id)\\n return self.id2token[tokenid] # will throw for non-existent ids\",\n \"def token2id(data, mode):\\n vocab_path = 'vocab.' + mode\\n in_path = data + '.' + mode\\n out_path = data + '_ids.' + mode\\n _, vocab = load_vocab(os.path.join(config.PROCESSED_PATH, vocab_path))\\n in_file = open(os.path.join(config.PROCESSED_PATH, in_path), 'rb')\\n out_file = open(os.path.join(config.PROCESSED_PATH, out_path), 'wb')\\n\\n lines = in_file.read().splitlines()\\n for line in lines:\\n if mode == 'dec': # we only care about '<s>' and </s> in encoder\\n ids = [vocab[b'<s>']]\\n else:\\n ids = []\\n ids.extend(sentence2id(vocab, line))\\n # ids.extend([vocab.get(token, vocab['<unk>']) for token in basic_tokenizer(line)])\\n if mode == 'dec':\\n ids.append(vocab[b'</s>'])\\n out_file.write(b' '.join(str(id_).encode('ascii') for id_ in ids) + b'\\\\n')\",\n \"def index_word(self, token):\\n if token not in self.word_2_idx:\\n idx = len(self.word_2_idx)\\n self.word_2_idx[token] = idx\\n self.idx_2_word[idx] = token\",\n \"def replace_special_token(self, tokenizer_fields: Dict[str, torch.Tensor], positions: List[int], replace_id: int):\\n token_ids, offsets = tokenizer_fields[\\\"token_ids\\\"], tokenizer_fields[\\\"offsets\\\"]\\n for pos in positions:\\n offset = offsets[pos].numpy().tolist()\\n if offset[0] != offset[1]:\\n warnings.warn(f\\\"replace normally expect token in `positions` has not been split to pieces.\\\"\\n f\\\"This warning should NOT happen unless during batch prediction at evaluation\\\")\\n token_ids[offset[0]] = replace_id\",\n \"def _convert_token_to_id(self, token):\\n return self.sp_model.PieceToId(token)\",\n \"def add(self, document):\\n #words=[word.lower() for word in words if word.isalpha()] #added on 0415\\n for token in [t.lower() for t in nltk.word_tokenize(document)]:\\n if not token.isalpha():\\n continue\\n\\n if token in self.stopwords:\\n continue\\n \\n if self.stemmer:\\n token = self.stemmer.stem(token)\\n \\n if self.__unique_id not in self.index[token]:\\n self.index[token].append(self.__unique_id)\\n \\n self.documents[self.__unique_id] = document\\n self.__unique_id += 1\",\n \"def token_id_from(self, token_id_from):\\n\\n self._token_id_from = token_id_from\",\n \"def setUserIDRefs( self, text ):\\n self.user_id_list= text.split()\",\n \"def assign_textbook_id(textbook, used_ids=()):\\r\\n tid = Location.clean(textbook[\\\"tab_title\\\"])\\r\\n if not tid[0].isdigit():\\r\\n # stick a random digit in front\\r\\n tid = random.choice(string.digits) + tid\\r\\n while tid in used_ids:\\r\\n # add a random ASCII character to the end\\r\\n tid = tid + random.choice(string.ascii_lowercase)\\r\\n return tid\",\n \"def doc2token(self, doc):\\n return [self.word2idx[word] if self.word2idx.__contains__(word)\\n else self.word2idx['UNK'] for word in doc]\",\n \"def token_id_to(self, token_id_to):\\n\\n self._token_id_to = token_id_to\",\n \"def word2idx(self, str):\\n return self._tokens.get(str)\",\n \"def token_key(token):\\n morphotagged = analysis(token).get('raw')\\n lemma_pos = (analysis(token).get('lemma'), analysis(token).get('partOfSpeech'))\\n return morphotagged or lemma_pos\",\n \"def _tokens_to_index(self,tokens):\\n wids = []\\n for tk in tokens:\\n if tk in self.wtoi.keys():\\n wids.append(self.wtoi[tk])\\n else:\\n wids.append(1) # <UNK>\\n for _ in range(self.sentence_max_length - len(wids)):\\n wids.append(0)\\n if len(wids) > self.sentence_max_length:\\n wids = wids[:self.sentence_max_length]\\n return wids\",\n \"def token_to_id(self, token):\\r\\n return self.encoder.get(token, self.encoder.get(self.unk_token))\",\n \"def _id(self):\\n result = ''\\n while self.current_char is not None and self.current_char.isalnum():\\n result += self.current_char\\n self.advance()\\n\\n if self.current_char == '(' and self.is_declaration is False:\\n self.advance()\\n token = self.RESERVED_KEYWORDS.get(\\n result.upper(), Token(CALL, result))\\n else:\\n token = self.RESERVED_KEYWORDS.get(\\n result.upper(), Token(ID, result))\\n self.is_declaration = False\\n if token.type in (PROCEDURE, FUNCTION):\\n self.is_declaration = True\\n return token\",\n \"def token_to_id(self, token):\\n token = self.process_token(token)\\n return self._token2id.get(token, len(self._token2id) - 1)\",\n \"def getIdAt(self, pos):\\n return self.sentence[pos].getId()\",\n \"def process_id_to(self):\\r\\n return self._tokens[3]\",\n \"def text_to_id(text, word_to_id_dict):\\n return [word_to_id_dict[word] for word in text.split(\\\" \\\") if word in word_to_id_dict]\",\n \"def token(self) -> str:\",\n \"def get_ids(cls, text):\\n tokens = TokenizerContainer.TOKENIZER.tokenize(text)\\n token_ids = TokenizerContainer.TOKENIZER.convert_tokens_to_ids(tokens)\\n input_ids = token_ids + [0] * (cls.MAX_LEN-len(token_ids))\\n return tokens, input_ids\",\n \"def get_id(self, word, add=False):\\n if word not in self.word_to_id:\\n if add:\\n self._add_new_word(word)\\n return self.word_to_id[constants.UNK_TOKEN]\\n return self.word_to_id[word]\",\n \"def __init__(self, tokenizer=None):\\n self.tokenizer = tokenizer or (lambda x: x.split(' '))\\n self._word2index = {\\n self.PAD_TOKEN: 0,\\n self.UNK_TOKEN: 1,\\n self.START_TOKEN: 2,\\n self.END_TOKEN: 3,\\n }\\n self._index2word = {}\",\n \"def __init__(self, tokens, key=lambda x: x):\\n self._tokens = tokens\\n \\\"\\\"\\\"The document (list of tokens) that this concordance index\\n was created from.\\\"\\\"\\\"\\n\\n self._key = key\\n \\\"\\\"\\\"Function mapping each token to an index key (or None).\\\"\\\"\\\"\\n\\n self._offsets = defaultdict(list)\\n \\\"\\\"\\\"Dictionary mapping words (or keys) to lists of offset indices.\\\"\\\"\\\"\\n # Initialize the index (self._offsets)\\n for index, word in enumerate(tokens):\\n word = self._key(word)\\n self._offsets[word].append(index)\",\n \"def _internal2document_id(value):\\n return 2*value + 1\",\n \"def init_tokens(self):\\n raise NotImplementedError('Abstract method.')\",\n \"def text2ids(self, text: str, length: int):\\n # Tokenize\\n tokens = self.tokenizer.tokenize(text)\\n token_ids = self.tokenizer.tokens2ids(tokens)\\n # Padding\\n while len(token_ids) < length:\\n token_ids.append(0)\\n # Truncate\\n if len(token_ids) > length:\\n token_ids = token_ids[:length]\\n assert len(token_ids) == length\\n return token_ids\",\n \"def postag_doc(text_vector):\\n\\n tokenised_document_pos_tagged = pos_tag(text_vector)\\n return tokenised_document_pos_tagged\",\n \"def userDocumentId(self, id: str) -> str:\",\n \"def get_mismatch_token_idx(self, words: List[str]):\\n # todo(yuxian): this line is extremely slow, need optimization\\n wordpieces, offsets = self._allennlp_tokenizer.intra_word_tokenize(words)\\n\\n # For tokens that don't correspond to any word pieces, we put (-1, -1) into the offsets.\\n # That results in the embedding for the token to be all zeros.\\n offsets = [x if x is not None else (-1, -1) for x in offsets]\\n\\n output = {\\n \\\"token_ids\\\": torch.LongTensor([t.text_id for t in wordpieces]),\\n \\\"word_mask\\\": torch.BoolTensor([True] * len(words)), # for original tokens (i.e. word-level)\\n \\\"offsets\\\": torch.LongTensor(offsets),\\n \\\"wordpiece_mask\\\": torch.BoolTensor([True] * len(wordpieces)), # for wordpieces (i.e. subword-level)\\n }\\n return output\",\n \"def next_token(self, context, token):\",\n \"def _convert_id_to_token(self, index, return_unicode=True):\\n token = self.sp_model.IdToPiece(index)\\n return token\",\n \"def id2doc(self, ids):\\n return [self.id_to_token(idx) for idx in ids]\",\n \"def id2doc(self, ids):\\n return [self.id_to_token(idx) for idx in ids]\",\n \"def tokenize(text, token):\\n text = [token(x) for x in text]\\n return text\",\n \"def __init__(self, text, idx):\\n self.text = text\\n self.idx = idx\",\n \"def __gen_tokenization_file(self):\\n paula_id = '{}.{}.tok'.format(self.corpus_name, self.name)\\n E, tree = gen_paula_etree(paula_id)\\n self.paulamap['tokenization'] = paula_id\\n\\n base_paula_id = '{}.{}.text'.format(self.corpus_name, self.name)\\n mlist = E('markList',\\n {'type': 'tok',\\n XMLBASE: base_paula_id+'.xml'})\\n tok_tuples = self.dg.get_tokens()\\n for (tid, onset, tlen) in get_onsets(tok_tuples):\\n # even SaltNPepper still uses xpointers for string-ranges!\\n xp = \\\"#xpointer(string-range(//body,'',{},{}))\\\".format(onset, tlen)\\n mlist.append(E('mark', {'id': tid,\\n XLINKHREF: xp}))\\n tree.append(mlist)\\n self.files[paula_id] = tree\\n self.file2dtd[paula_id] = PaulaDTDs.mark\\n return paula_id\",\n \"def _force_token_ids_generation(self, scores, token_id) -> None:\\r\\n scores[:, [x for x in range(self.config.vocab_size) if x != token_id]] = -float(\\\"inf\\\")\",\n \"def get_tokens(self, document):\\n raise NotImplementedError()\",\n \"def create_token(self, token_id, data):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"def generate_inverted_index(self, doc_id, tokenized_document):\\n self.unique_doc_ids.add(doc_id)\\n for t in tokenized_document:\\n self.add_to_index(t, doc_id)\",\n \"def setID(id, content):\\n return content.replace(\\\"$ID\\\", str(id))\",\n \"def replace_token_with_index(self, tokenized_tweet, max_length_dictionary):\\n\\n # should be replacing each token in a list of tokens by their corresponding index\\n # Source: https://github.com/stanfordnlp/GloVe\\n # Source: https://towardsdatascience.com/word-embeddings-for-sentiment-analysis-65f42ea5d26e\\n\\n if self.mode == \\\"zip\\\":\\n for idx, word in enumerate(tokenized_tweet):\\n if word not in self.corpus:\\n tokenized_tweet[idx] = \\\"<unknown>\\\"\\n else:\\n for idx, word in enumerate(tokenized_tweet):\\n if word not in self.corpus:\\n tokenized_tweet[idx] = \\\"<unk>\\\"\\n return [self.corpus.index(x) for x in tokenized_tweet]\",\n \"def charIdx_to_tokenIdx(spans, ans_text, ans_start): \\n # Convert answer 'char idxs' to 'token idxs' for one single record\\n if ans_start != -999:\\n ans_end = ans_start + len(ans_text) - 1 \\n ans_token_idxs = []\\n for token_idx, span in enumerate(spans):\\n if span[0] < ans_end and span[1] > ans_start:\\n ans_token_idxs.append(token_idx)\\n y1, y2 = ans_token_idxs[0], ans_token_idxs[-1] \\n else:\\n y1, y2 = -999, -999\\n \\n return y1, y2\",\n \"def tokenization(news, word_to_id):\\n tokenized_news = []\\n lengths = []\\n for date in news:\\n daily_headlines = []\\n daily_lengths = []\\n for headline in news[date]:\\n daily_lengths.append(len(headline))\\n token = []\\n for word in headline:\\n if word in word_to_id:\\n token.append(word_to_id[word])\\n else:\\n token.append(word_to_id['<unk>'])\\n daily_headlines.append(token)\\n\\n lengths.append(daily_lengths)\\n tokenized_news.append(daily_headlines)\\n #tokens = np.array(tokens).astype('int32')\\n return tokenized_news, lengths\",\n \"def tokenize_fast(self, text):\\n\\n ref_ids = prepare_ref([text], self.tokenizer_ltp, self.tokenizer_cn)\\n\\n tokens = self.tokenizer_cn.tokenize(text)\\n\\n ref_ids = cn_whole_word_mask(tokens, ref_ids[0])\\n tokens, labels = random_word_wwm(tokens, ref_ids, self.tokenizer_cn)\\n\\n input_ids = self.tokenizer_cn.convert_tokens_to_ids(tokens)\\n\\n assert len(input_ids) == len(labels)\\n\\n return input_ids, labels\",\n \"def __getitem__(self, i):\\n if isinstance(i, int):\\n start = i\\n else:\\n start = i.start\\n if start is None:\\n start = 0\\n if start < 0:\\n start = len(self.text) + start\\n return Token(self.text[i], self.position + start, self.category)\",\n \"def _parse_token(self, body):\\n\\n token_match = re.search('var\\\\s*token\\\\s*=[\\\\s\\\\']*(\\\\d+)', body)\\n return int(token_match.group(1))\",\n \"def _add_offsets_to_token_nodes(self):\\n for edge_index in self._textual_relation_ids:\\n token_node_index = self.edges[edge_index].source\\n self.nodes[token_node_index].onset = self.edges[edge_index].onset\\n self.nodes[token_node_index].offset = self.edges[edge_index].offset\",\n \"def _get_token_ids(self, tokens):\\n token_ids = self.tokenizer.convert_tokens_to_ids(tokens)\\n pad_amount = self.max_seq_length - len(tokens)\\n input_ids = token_ids + [0] * pad_amount\\n return np.array(input_ids)\",\n \"def tok2idx_data(token2id, tok_data):\\n idx_data = []\\n for toks in tok_data:\\n idx_lst = [\\n token2id[tok] if tok in token2id else UNK_IDX for tok in toks]\\n idx_data.append(idx_lst)\\n return idx_data\",\n \"def __call__(self, tokenized_text):\\n raise NotImplementedError()\",\n \"def preprocess_text(self, input_text):\\n input_text = self.clean_text(input_text)\\n tokenization_list = self.tokenize_text(input_text)\\n index_list = self.replace_token_with_index(tokenization_list, self.max_length_dictionary)\\n index_list = self.pad_sequence(index_list, self.max_length_tweet)\\n return index_list\",\n \"def token(self, token):\\n\\n self._token = token\",\n \"def token(self, token):\\n\\n self._token = token\",\n \"def get_token(self, token_id):\\n raise exception.NotImplemented() # pragma: no cover\",\n \"def token_id(self) -> Optional[pulumi.Input[int]]:\\n return pulumi.get(self, \\\"token_id\\\")\",\n \"def test_wrap_tokenizer(self):\\n input = \\\"this-string will be split@according to diff'rnt rules\\\"\\n from enchant.tokenize import en\\n tknzr = wrap_tokenizer(basic_tokenize, en.tokenize)\\n tknzr = tknzr(input)\\n self.assertEqual(tknzr._tokenizer.__class__, basic_tokenize)\\n self.assertEqual(tknzr._tokenizer.offset, 0)\\n for (n, (word, pos)) in enumerate(tknzr):\\n if n == 0:\\n self.assertEqual(pos, 0)\\n self.assertEqual(word, \\\"this\\\")\\n if n == 1:\\n self.assertEqual(pos, 5)\\n self.assertEqual(word, \\\"string\\\")\\n if n == 2:\\n self.assertEqual(pos, 12)\\n self.assertEqual(word, \\\"will\\\")\\n # Test setting offset to a previous token\\n tknzr.set_offset(5)\\n self.assertEqual(tknzr.offset, 5)\\n self.assertEqual(tknzr._tokenizer.offset, 5)\\n self.assertEqual(tknzr._curtok.__class__, empty_tokenize)\\n if n == 3:\\n self.assertEqual(word, \\\"string\\\")\\n self.assertEqual(pos, 5)\\n if n == 4:\\n self.assertEqual(pos, 12)\\n self.assertEqual(word, \\\"will\\\")\\n if n == 5:\\n self.assertEqual(pos, 17)\\n self.assertEqual(word, \\\"be\\\")\\n # Test setting offset past the current token\\n tknzr.set_offset(20)\\n self.assertEqual(tknzr.offset, 20)\\n self.assertEqual(tknzr._tokenizer.offset, 20)\\n self.assertEqual(tknzr._curtok.__class__, empty_tokenize)\\n if n == 6:\\n self.assertEqual(pos, 20)\\n self.assertEqual(word, \\\"split\\\")\\n if n == 7:\\n self.assertEqual(pos, 26)\\n self.assertEqual(word, \\\"according\\\")\\n # Test setting offset to middle of current token\\n tknzr.set_offset(23)\\n self.assertEqual(tknzr.offset, 23)\\n self.assertEqual(tknzr._tokenizer.offset, 23)\\n self.assertEqual(tknzr._curtok.offset, 3)\\n if n == 8:\\n self.assertEqual(pos, 23)\\n self.assertEqual(word, \\\"it\\\")\\n # OK, I'm pretty happy with the behaviour, no need to\\n # continue testing the rest of the string\",\n \"def merged_tokens(self, i, tokenizer):\\n out = []\\n counts = []\\n last_id = None\\n tokens = tokenizer.convert_ids_to_tokens(self.token_ids[i].tolist())\\n for token, current_id in zip(tokens[1:-1], self.conll_ids[i]):\\n if last_id == current_id.item():\\n if token.startswith(\\\"##\\\"):\\n out[-1] += (token[2:])\\n else:\\n out[-1] += token\\n counts[-1] += 1\\n else:\\n out.append(token)\\n counts.append(1)\\n last_id = current_id\\n return out, counts\",\n \"def __init__(self):\\n self.tokens = []\",\n \"def friendly_id(self):\\n id = f\\\"{self.annotator_id}_{self.document_title.split('_')[0]}\\\"\\n\\n try: # try making an sentence identifier if there is an in_sentence attrib\\n sen_id = \\\",\\\".join(str(se.element_id + 1) for se in self.in_sentence)\\n id += f\\\"_s{sen_id}\\\"\\n except Exception as e:\\n print(e)\\n pass\\n\\n if isinstance(self, Event):\\n id += f\\\"_{self.event_fulltype}\\\"\\n elif isinstance(self, Participant) or isinstance(self, Filler):\\n id += f\\\"_{self.role}\\\"\\n\\n text_ellips = (\\n (self.text[:15] + \\\"..\\\" + self.text[-15:])\\n if len(self.text) > 32\\n else self.text\\n )\\n id += f\\\"-{text_ellips}\\\"\\n return id\",\n \"def getWordIdx(token, word2Idx): \\n if token in word2Idx:\\n return word2Idx[token]\\n elif token.lower() in word2Idx:\\n return word2Idx[token.lower()]\\n \\n return word2Idx[\\\"UNKNOWN_TOKEN\\\"]\",\n \"def convert_tokens_to_ids(self, tokens):\\n ids = []\\n for token in tokens:\\n ids.append(self.vocab[token])\\n if len(ids) > self.max_len:\\n logger.warning('Token indices sequence length is longer than the specified maximum sequence length for this BERT model ({} > {}). Running this sequence through BERT will result in indexing errors'.format(len(ids), self.max_len))\\n return ids\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.61441404","0.6103226","0.60713357","0.58848673","0.58489716","0.5830993","0.5819702","0.5808241","0.5795522","0.57590806","0.5744294","0.5744294","0.5727401","0.57221353","0.57152474","0.56934476","0.568862","0.56846684","0.5665206","0.5659678","0.56587857","0.56496584","0.56496584","0.5642953","0.5628797","0.56241965","0.5615611","0.56137437","0.5599643","0.55956554","0.55922836","0.5590387","0.5580661","0.5568915","0.55398303","0.5536963","0.55326146","0.5527609","0.55257326","0.5518399","0.5508236","0.5487912","0.5487076","0.54840785","0.547511","0.54708433","0.5460154","0.5437119","0.54245085","0.54099125","0.5392939","0.5383918","0.53615904","0.5354976","0.53363967","0.5331086","0.5329175","0.53277314","0.53186697","0.52732384","0.52702266","0.5255697","0.52511615","0.52500546","0.522213","0.5219467","0.5209904","0.520828","0.5207034","0.5201806","0.5201806","0.51965195","0.5180072","0.51704305","0.5167282","0.5161662","0.5159372","0.515059","0.51410526","0.5139033","0.5133528","0.5128091","0.51230913","0.5121311","0.5118494","0.5115897","0.5112129","0.5105947","0.51007026","0.5096491","0.5092434","0.5092434","0.5092258","0.5089812","0.5083672","0.50820094","0.5074091","0.5065143","0.5059858","0.50587857"],"string":"[\n \"0.61441404\",\n \"0.6103226\",\n \"0.60713357\",\n \"0.58848673\",\n \"0.58489716\",\n \"0.5830993\",\n \"0.5819702\",\n \"0.5808241\",\n \"0.5795522\",\n \"0.57590806\",\n \"0.5744294\",\n \"0.5744294\",\n \"0.5727401\",\n \"0.57221353\",\n \"0.57152474\",\n \"0.56934476\",\n \"0.568862\",\n \"0.56846684\",\n \"0.5665206\",\n \"0.5659678\",\n \"0.56587857\",\n \"0.56496584\",\n \"0.56496584\",\n \"0.5642953\",\n \"0.5628797\",\n \"0.56241965\",\n \"0.5615611\",\n \"0.56137437\",\n \"0.5599643\",\n \"0.55956554\",\n \"0.55922836\",\n \"0.5590387\",\n \"0.5580661\",\n \"0.5568915\",\n \"0.55398303\",\n \"0.5536963\",\n \"0.55326146\",\n \"0.5527609\",\n \"0.55257326\",\n \"0.5518399\",\n \"0.5508236\",\n \"0.5487912\",\n \"0.5487076\",\n \"0.54840785\",\n \"0.547511\",\n \"0.54708433\",\n \"0.5460154\",\n \"0.5437119\",\n \"0.54245085\",\n \"0.54099125\",\n \"0.5392939\",\n \"0.5383918\",\n \"0.53615904\",\n \"0.5354976\",\n \"0.53363967\",\n \"0.5331086\",\n \"0.5329175\",\n \"0.53277314\",\n \"0.53186697\",\n \"0.52732384\",\n \"0.52702266\",\n \"0.5255697\",\n \"0.52511615\",\n \"0.52500546\",\n \"0.522213\",\n \"0.5219467\",\n \"0.5209904\",\n \"0.520828\",\n \"0.5207034\",\n \"0.5201806\",\n \"0.5201806\",\n \"0.51965195\",\n \"0.5180072\",\n \"0.51704305\",\n \"0.5167282\",\n \"0.5161662\",\n \"0.5159372\",\n \"0.515059\",\n \"0.51410526\",\n \"0.5139033\",\n \"0.5133528\",\n \"0.5128091\",\n \"0.51230913\",\n \"0.5121311\",\n \"0.5118494\",\n \"0.5115897\",\n \"0.5112129\",\n \"0.5105947\",\n \"0.51007026\",\n \"0.5096491\",\n \"0.5092434\",\n \"0.5092434\",\n \"0.5092258\",\n \"0.5089812\",\n \"0.5083672\",\n \"0.50820094\",\n \"0.5074091\",\n \"0.5065143\",\n \"0.5059858\",\n \"0.50587857\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7065767"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":21893,"cells":{"query":{"kind":"string","value":"Append value to attribute list, create the attribute list if does not exist."},"document":{"kind":"string","value":"def append_attribute(myobj, attrib_k, val):\n vals = getattr(myobj, attrib_k, [])\n if val not in vals:\n vals.append(val)\n setattr(myobj, attrib_k, vals)"},"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 _setAttribute(self, attribute, value):\n\n # if multiple values found\n if hasattr(self, attribute):\n\n # make sure attribute is a list\n values = getattr(self, attribute)\n if not isinstance(values, list):\n setattr(self, attribute, [values])\n\n # append value to list\n getattr(self, attribute).append(value)\n\n # single value found\n else:\n setattr(self, attribute, value)","def add_attr(self, attr, value, position=None, extra=None):\n # pylint: disable=eval-used\n if attr.startswith(\"*\"):\n attr = attr[1:]\n if attr not in self._attributes:\n self._attributes[attr] = []\n if len(self._attributes[attr]) != position:\n raise TypeError(\"AST Node lost in conversion!\")\n self._attributes[attr].append(value)\n elif extra is not None:\n self._attributes[attr] = eval(extra)\n else:\n self._attributes[attr] = value","def add_value(self, key, value):\r\n if key in self:\r\n # We already have this key on the item.\r\n if not isinstance(self[key], list):\r\n # The key isn't already a list, take its current value and\r\n # convert it to a list with the only member being the\r\n # current value.\r\n self[key] = [self[key]]\r\n # Add the new value to the list.\r\n self[key].append(value)\r\n else:\r\n # This is a new attribute, just set it.\r\n self[key] = value","def __setitem__(self, key, value) :\n attributeslist = getattr(self.request, \"_%s_attributes\" % self.name)\n for i in range(len(attributeslist)) :\n attribute = attributeslist[i]\n for j in range(len(attribute)) :\n (attrname, attrvalue) = attribute[j]\n if attrname == key :\n attribute[j][1].append(value)\n return\n attribute.append((key, [value]))","def add_attribute(self, name, value):\n\t\tif name in self.__attr_hash:\n#\t\t\tattribue already exists\n\t\t\ta = self.__attr_hash[name]\n\t\t\tif name == 'class':\n#\t\t\t\t'class' is a magic attribute\n\t\t\t\tif a['value']:\n\t\t\t\t\tvalue = ' ' + value\n\t\t\t\ta['value'] += value\n\t\t\telse:\n\t\t\t\ta['value'] = value\n\t\telse:\n\t\t\ta = {'name': name, 'value': value}\n\t\t\tself.__attr_hash[name] = a\n\t\t\tself.attributes.append(a)","def add_attr(self, key: str, value):\n if key in self._attr_names():\n raise ValueError(\"Already have an attribute called '{}'\".format(key))\n self._attributes.append((key, value))","def add_attribute(self, attr):\n self.add(attr)","def add_attribute(node_proto, name, value):\n node_proto.attribute.extend([make_attribute(name, value)])","def batch_add(self, *args, **kwargs):\n new_attrobjs = []\n strattr = kwargs.get(\"strattr\", False)\n for tup in args:\n if not is_iter(tup) or len(tup) < 2:\n raise RuntimeError(\"batch_add requires iterables as arguments (got %r).\" % tup)\n ntup = len(tup)\n keystr = str(tup[0]).strip().lower()\n new_value = tup[1]\n category = str(tup[2]).strip().lower() if ntup > 2 and tup[2] is not None else None\n lockstring = tup[3] if ntup > 3 else \"\"\n\n attr_objs = self._getcache(keystr, category)\n\n if attr_objs:\n attr_obj = attr_objs[0]\n # update an existing attribute object\n attr_obj.db_category = category\n attr_obj.db_lock_storage = lockstring or \"\"\n attr_obj.save(update_fields=[\"db_category\", \"db_lock_storage\"])\n if strattr:\n # store as a simple string (will not notify OOB handlers)\n attr_obj.db_strvalue = new_value\n attr_obj.save(update_fields=[\"db_strvalue\"])\n else:\n # store normally (this will also notify OOB handlers)\n attr_obj.value = new_value\n else:\n # create a new Attribute (no OOB handlers can be notified)\n kwargs = {\n \"db_key\": keystr,\n \"db_category\": category,\n \"db_model\": self._model,\n \"db_attrtype\": self._attrtype,\n \"db_value\": None if strattr else to_pickle(new_value),\n \"db_strvalue\": new_value if strattr else None,\n \"db_lock_storage\": lockstring or \"\",\n }\n new_attr = Attribute(**kwargs)\n new_attr.save()\n new_attrobjs.append(new_attr)\n self._setcache(keystr, category, new_attr)\n if new_attrobjs:\n # Add new objects to m2m field all at once\n getattr(self.obj, self._m2m_fieldname).add(*new_attrobjs)","def append(self, value) -> None:\n key = getattr(value, self.keyattr)\n if callable(key):\n key = key()\n if key not in self.data:\n self.data[key] = []\n self.data[key].append(value)\n self.size += 1","def add_attribute(self, attr):\n self.attrs.add(attr)","def append(self, value):\n self.__field.validate_element(value)\n return list.append(self, value)","def add_attribute(self, attribute):\n if attribute not in self.attributes:\n self.attributes.add(attribute)\n self.attribute_list.append(attribute)\n return self","def add_attribute(self, attribute_name, attribute_value):\n self.attributes[attribute_name] = attribute_value","def add_attribute(self, name, value, modify=False, sources=None, published_at=None):\n\n # Find attributes and remove if matches\n if modify:\n if not self.current_attributes:\n res = self.tq.get('{}/attributes'.format(self._get_api_endpoint()))\n self.current_attributes = res.get('data', [])\n\n for attr in self.current_attributes:\n if attr['name'] == name and attr['value'].lower() != value.lower():\n attribute_id = attr['id']\n self.tq.delete(\n '{}/attributes/{}'.format(self._get_api_endpoint(), attribute_id))\n # break\n\n data = {'name': name, 'value': value}\n if sources and isinstance(sources, str):\n data['sources'] = [{'name': sources}]\n elif sources and isinstance(sources, dict):\n data['sources'] = [sources]\n elif sources and isinstance(sources, list):\n data['sources'] = sources\n if published_at:\n data['published_at'] = published_at\n\n res = self.tq.post('{}/attributes'.format(self._get_api_endpoint()), data=data)\n\n # Add the newly added indicator to the cache\n if res.get('total', 0) > 0:\n self.current_attributes.append(res['data'][0])","def add_attribute(self, key, value):\n self.attributes[key] = value","def record_attribute_set(self, typ, attr_name, node, value):\n serialized = self.serialize_type(typ)\n if serialized is None:\n return\n self.attributes_set[serialized].add(attr_name)\n self.merge_attribute_value(serialized, attr_name, value)","def add_attribute(self, name, value):\n\n self._attributes[name] = value","def add_attribute(self, name, value):\n\n self._attributes[name] = value","def add_attribute(self, name, value):\n\n self._attributes[name] = value","def add_attribute(self, attr):\n self.attrs.add_attribute(attr)","def append(self, value):\n self.list.append(value)","def append(self, value):\n self.__field.validate_element(value)\n return list.append(self, value)","def append(self):\n return AttributeFunctor(self, lambda a, b: a + b)","def createAttribute(nid, label, primary, list, x, y):\n attribute = Attribute(nid, label, primary, x, y)\n list.append(attribute)","def add_attribute(self, attribute_key, attribute_value):\n self.set_attribute_value(attribute_key, attribute_value) # record the input key-value pair","def add_attribute(self, attribute_key, attribute_value):\n self.set_attribute_value(attribute_key, attribute_value) # record the input key-value pair","def add_attribute(self, attribute_key, attribute_value):\n self.set_attribute_value(attribute_key, attribute_value) # record the input key-value pair","def append(self, value):\n\n list.append(self, value)\n self.changed()","def addattribute(self, uid, field, value):\n\n raise NotImplementedError","def add_value(self, value, attr):\n self.index[value] = attr","def add(self, attribute_name, value_name):\n self.__add[attribute_name] = value_name\n return self","def append(self, value):\n self.__list += [value]\n return self.__list","def addAttr(self, *args):\n return _libsbml.XMLToken_addAttr(self, *args)","def _add_attributo(spell_list):\n\n attr_list = [\"MU\", \"KL\", \"IN\", \"CH\", \"FF\", \"GE\", \"KO\", \"KK\"]\n # remove original attributo entry\n attributo_orig = spell_list.pop(-1)\n\n # change original attributo entry and add it to skill list\n for _, value in enumerate(attr_list):\n attributo_temp = copy.deepcopy(attributo_orig)\n attributo_temp.name = value + \" Attributo\"\n attributo_temp.attrs[2] = value\n spell_list.append(attributo_temp)\n\n return spell_list","def append_data_member(self, varName, valueName):\n hasList = self.start_data_member(varName, valueName)\n if hasList:\n listA = self.getVar(varName)\n if listA is not None:\n #print \"has %s\" %(varName)\n value = self.getVar(valueName)\n try:\n n = len(listA)\n except:\n print(\"listA = \", listA)\n raise\n listA.append(value)\n assert len(listA) == n + 1","def addAttr(self, attr_key, attr_val, replace=False, attr_pos=0):\n\t\tif attr_key in self.attributes:\n # replace the current value of this key with then new value\n\t\t\tif replace:\n\t\t\t\tdelAttr(attr_key)\n\t\t\t\tself.attributes[attr_key] = attr_val\n\t\t\t\tself.attributes_order.insert(attr_pos, attr_key)\n\t\t\t\tself.refreshAttrStr()\n # this key already exists in the attributes of the GFF3 file\n # add this value to the existing key's values with commas\n # separating values\n\t\t\telse:\n\t\t\t\tself.attributes[attr_key] = '{0},{1}'.format(\n self.attributes[attr_key], \n attr_val)\n\t\t\t\tself.refreshAttrStr()\n\t\telse:\n\t\t\tself.attributes[attr_key] = attr_val\n\t\t\tself.attributes_order.insert(attr_pos, attr_key)\n\t\t\tself.refreshAttrStr()","def add_attributes(self, attrs):\n for attr in attrs:\n self.add_attribute(attr)","def add_attributes(self, attrs):\n self.attrs.add_attributes(attrs)","def add_attr(self, section_name: str, attr_name: str, value: str) -> None:\n pass","def append_group_attr(self, analyte: str, group_name: str, key: str, value: Any):\n group_path = \"/\".join([\"\", ASSAYS, analyte, group_name])\n if group_path not in self.__file:\n raise ValueError(\"{} missing\".format(group_path))\n group = self.__file[group_path]\n if key in group.keys():\n key_path = group_path + \"/\" + key\n data = self.__file[key_path]\n normalized = normalize_attr_values(value)\n data[...] = normalized\n else:\n self.__write_value(group, key, value)","def append(self, value):\n self.values.append(value)\n return value","def merge_attrs(self):\n for aid in self.attrs:\n new_val = self.attrs[aid]\n if aid in self.attributes:\n if ('value' in self.attributes[aid] and\n self.attributes[aid]['value'] != new_val):\n pass\n # print \"Updating attribute %s[%s] %s -> %s\" % (\n # self.name, aid, self.attributes[aid]['value'], new_val)\n else:\n # print \"** Warning: non-declaired attribute %s['%s'] set to:\\n'%s'\" % (\n # self.name, aid, new_val)\n self.remember_custom_attribute(self.name, aid, new_val)\n self.attributes[aid] = {}\n self.attributes[aid]['nv'] = new_val","def add_attrib(self, key, func, func_args):\n if key in self.aux_attrib:\n raise KeyError(\"Attribute '{0}' already exists, please use 'set_attrib'.\".format(key))\n else:\n self.set_attrib(key, func, func_args)","def add_attributes(self, attribute_list, simulate=False):\n with LayerEditingManager(self.layer, 'Add attributes', DEBUG):\n # add attributes\n layer_pr = self.layer.dataProvider()\n proposed_attribute_dict = {}\n proposed_attribute_list = []\n for input_attribute in attribute_list:\n input_attribute_name = input_attribute.name()[:10]\n proposed_attribute_name = input_attribute_name\n i = 1\n while True:\n current_attribute_names = \\\n [attribute.name() for attribute in layer_pr.fields()]\n if proposed_attribute_name in current_attribute_names:\n # If the attribute is already assigned, change the\n # proposed_attribute_name\n i_num_digits = len(str(i))\n # 10 = shapefile limit\n # 1 = underscore\n max_name_len = 10 - i_num_digits - 1\n proposed_attribute_name = '%s_%d' % (\n input_attribute_name[:max_name_len], i)\n i += 1\n else:\n # If the attribute name is not already assigned,\n # add it to the proposed_attribute_dict\n proposed_attribute_dict[input_attribute_name] = \\\n proposed_attribute_name\n input_attribute.setName(proposed_attribute_name)\n proposed_attribute_list.append(input_attribute)\n break\n if not simulate:\n added_ok = layer_pr.addAttributes(proposed_attribute_list)\n if not added_ok:\n raise AttributeError(\n 'Unable to add attributes %s' %\n proposed_attribute_list)\n return proposed_attribute_dict","def __setitem__(self, attribute_name, value):\n pass # pragma: no cover","def add_edge_attributes(self, attribute_key, attribute_value):\n G_prime = self.__deepcopy__() # create a deepcopy of the undirected graph\n for edge in G_prime.get_edges(): # for every edge in the graph\n # add the attribute key-value pair to the attributes registry of the edge\n edge.add_attribute(attribute_key, attribute_value)\n return G_prime # return the modified graph","def add(self, attr):\n self.validate_type(attr)\n self.values.add(attr.value)","def add_attributes(self, attrs):\n self.attrs.add_container(attrs)","def append_value(self, value):\n self.value += value","def __setitem__(self, key, value):\r\n self._getAttrMap()\r\n self.attrMap[key] = value\r\n found = False\r\n for i in range(0, len(self.attrs)):\r\n if self.attrs[i][0] == key:\r\n self.attrs[i] = (key, value)\r\n found = True\r\n if not found:\r\n self.attrs.append((key, value))\r\n self._getAttrMap()[key] = value","def extend_attribute_dictionary(attributedict, ns, name, value):\n\n key = ns, name\n if value is None:\n if key in attributedict:\n del attributedict[key]\n else:\n if ns == 'html' and key in attributedict:\n if name == 'class':\n # CSS classes are appended by space-separated list\n value = attributedict[key] + ' ' + value\n elif name == 'style':\n # CSS styles are appended by semicolon-separated rules list\n value = attributedict[key] + '; ' + value\n elif name in _html_attribute_boolflags:\n # All attributes must have a value. According to XHTML those\n # traditionally used as flags should have their value set to\n # the same as the attribute name.\n value = name\n attributedict[key] = value","def add_attribute(self, subject_id, id=None, value=None):","def add_attribute(self, subject_id, id=None, value=None):","def add_attribute(self, subject_id, id=None, value=None):","def add_attribute(self, subject_id, id=None, value=None):","def add_attribute(self, subject_id, id=None, value=None):","def add(self, *args):\n return _libsbml.XMLAttributes_add(self, *args)","def add_attribute(obj, attribute, value):\n if not hasattr(obj, \"__dict__\"):\n raise TypeError(\"can't add new attribute\")\n setattr(obj, attribute, value)","def add_attribute(self, key: str, value: Union[str, int]) -> None:\n self.span_instance.add_attribute(key, value)","def add_attribute(self, attr_type, name, components):\n self.attributes[attr_type] = {\"name\": name, \"components\": components}","def _appendRecord(self, rec, reclistname):\n if reclistname not in self.__dict__: # if not already an attrib\n self.__dict__[reclistname] = [] # init it\n self.__dict__[reclistname].append(rec) # append this record to its list","def add_attribute(self, attr: ResourceAttributeDescriptor) -> None:\n self._attributes[assert_not_none(attr.name)] = attr.bind(self)","def append(self, val):\n self.val.append(val)","def set_attr_values(self):\n ats = self.attributes # convenient short name\n for aid in ats:\n value = ats[aid]['nv'] if 'nv' in ats[aid] else (\n ats[aid]['value'] if 'value' in ats[aid] else None)\n if value is not None:\n# self.h5node.attrs[aid] = value\n #- self.file.file_pointer[self.full_path].attrs[aid] = value\n self.file.set_attribute(self.full_path, aid, value)\n #- self.file.h5save_attribute(self.full_path, aid, value)\n #- self.file.h5commands.append(\"set attribute(%s:%s)-%s\" % (self.full_path,\n #- aid, value))","def add_value(self, value):\r\n self.resource_records.append(value)","def add(\n self,\n key,\n value,\n category=None,\n lockstring=\"\",\n strattr=False,\n accessing_obj=None,\n default_access=True,\n ):\n if accessing_obj and not self.obj.access(\n accessing_obj, self._attrcreate, default=default_access\n ):\n # check create access\n return\n\n if not key:\n return\n\n category = category.strip().lower() if category is not None else None\n keystr = key.strip().lower()\n attr_obj = self._getcache(key, category)\n\n if attr_obj:\n # update an existing attribute object\n attr_obj = attr_obj[0]\n if strattr:\n # store as a simple string (will not notify OOB handlers)\n attr_obj.db_strvalue = value\n attr_obj.save(update_fields=[\"db_strvalue\"])\n else:\n # store normally (this will also notify OOB handlers)\n attr_obj.value = value\n else:\n # create a new Attribute (no OOB handlers can be notified)\n kwargs = {\n \"db_key\": keystr,\n \"db_category\": category,\n \"db_model\": self._model,\n \"db_attrtype\": self._attrtype,\n \"db_value\": None if strattr else to_pickle(value),\n \"db_strvalue\": value if strattr else None,\n }\n new_attr = Attribute(**kwargs)\n new_attr.save()\n getattr(self.obj, self._m2m_fieldname).add(new_attr)\n # update cache\n self._setcache(keystr, category, new_attr)","def add_attribute(obj, attribute, value):\n if hasattr(obj, \"__dict__\"):\n setattr(obj, attribute, value)\n else:\n raise TypeError(\"can't add new attribute\")","def add_attribute(a, name, other):\n raise TypeError(\"can't add new attribute\")","def add_node_attributes(self, attribute_key, attribute_value):\n G_prime = self.__deepcopy__() # create a deepcopy of the undirected graph\n for node in G_prime.get_nodeset(): # for every node in the graph\n # add the attribute key-value pair to the attributes registry of the node\n node.add_attribute(attribute_key, attribute_value)\n return G_prime # return the modified graph","def add_symbol_attribute(self, symbol_attribute):\n self.symbol_attributes.append(symbol_attribute)","def append(self, value):\n assert isinstance(value, Item), type(value)\n list.append(self, value)\n self.emit('appened', value)\n self.emit('modified')","def attr(self, item: _PyteAugmentedValidator):\n self._attrs.append(item)\n return self","def append(self, val):\n self._values.push(val)","def append_value(self, value):\n value_wrapper = ValueWrapper(value)\n self.strict = value_wrapper.strict_value\n if self.strict is False:\n self.absolute = value_wrapper.absolute_value\n if value_wrapper.value not in self._values and value_wrapper.value is not None:\n self._values += [value_wrapper.value]\n for var_dependency in self.list_dependencies(value_wrapper.value):\n if not var_dependency in self.dependencies:\n self.dependencies.append(var_dependency)","def append(self, value: object) -> None:\n self.data.append(value)","def __setattr__(self, name, value):\n try:\n self[self.sig.argpos(name)] = value\n return value\n except:\n pass\n list.__setattr__(self, name, value)","def add_attr(self, event: Union[wx.CommandEvent, None],\n attr_label: str = '',\n attr_value: str = '') -> None:\n new_id = len(self.attr_ids)\n self.attr_ids[new_id] = attr_label\n button_remove = wx.Button(self, wx.ID_ANY, label='-')\n self.Bind(wx.EVT_BUTTON, partial(self.remove_attr, attr_id=new_id),\n button_remove)\n text_label = wx.TextCtrl(self, value=attr_label)\n text_value = wx.TextCtrl(self, value=str(attr_value))\n self.attr_labels[new_id] = text_label\n self.attr_values[new_id] = text_value\n self.attr_buttons[new_id] = button_remove\n if event is not None:\n self._update_attr_list()","def _handle_attr(self, attr, dev):\n attr_val = None\n list_flag = False\n\n if attr.name == \"os\":\n attr_val = self.OS_MAPPER[attr.val]\n elif attr.name == \"network\":\n attr_val = self._create_network(attr.val)\n list_flag = True\n elif attr.name == \"bluetooth\":\n attr_val = Bluetooth(version=attr.val.version)\n elif attr.name == \"cpu\":\n attr_val = CPU(cpu_family=attr.val.cpu_family,\n max_freq=float(attr.val.max_freq\n * self.FREQ_MULT[attr.val.unit]),\n fpu=attr.val.fpu)\n elif attr.name == \"memory\":\n attr_val = self._create_memory(attr.val)\n elif attr.name == \"type\":\n self._per_type = self.PER_MAPPER[attr.val]\n elif attr.name == \"pins\":\n list_flag = True\n attr_val = self._create_pins(attr.val)\n else:\n attr_val = attr.val\n\n # Set attribute\n if list_flag:\n getattr(dev, attr.name).extend(attr_val)\n elif attr_val:\n setattr(dev, attr.name, attr_val)","def append_to_list(self, metric_value_to_append):\n if type(metric_value_to_append)==MetricValue:\n self.__metric_value_list.append(metric_value_to_append)\n else:\n print(\"appended object must be a MetricValue, metric_value_to_append=\",metric_value_to_append)\n sys.exit() # stop entire program, because metric_value_to_append MUST be correct","def add(self, value):\n self._resolve_copies()\n self.data.append(value)","def addAttribute(self, attribute):\n\n if attribute.getName() in [x.getName() for x in self._attributes]:\n raise IndexError(\"Child with \" + attribute.getName() + \" already exists as a attribute.\")\n\n self._attributes.append(attribute)\n attribute.setParent(self)\n\n return True","def define_attribute(self, name, atype, data=None):\n self.attributes.append(name)\n self.attribute_types[name] = atype\n self.attribute_data[name] = data","def add_attribute(obj, name, value):\n if not hasattr(obj, \"__dict__\"):\n raise TypeError(\"can't add new attribute\")\n setattr(obj, name, value)","def safeAppend(values, value):\n\tif value is not None:\n\t\tvalues.append(value)","def append_jobs_attributes(self, attribute):\n # | - append_jobs_attributes\n att_new = attribute\n\n self.jobs_att.update(att_new)\n\n job_att_file = self.root_dir + \"/jobs_bin/job_attributes.csv\"\n pickle.dump(self.jobs_att, open(job_att_file, \"wb\"))\n # __|","def __setitem__(self, key, item):\n self.attrib[key] = item","def __setitem__(self, key, item):\n self.attrib[key] = item","def _return_attr(self, *args, **kwargs):\r\n self.attr = [args, kwargs]\r\n return []","def add_attributes(self, attributes):\n self.attributes = dict(self.attributes, **attributes)","def add_attribute(self, key, value):\n if key == \"process_step\":\n self.process_step = value\n elif key == \"source\":\n self.source = value\n else:\n self.attributes[key] = value","def append(self, value):\n self.data.append(None)\n self.data[len(self.data)-1] = value","def add_attribute(a_class, name, value):\n types = ['str', [], {}, (1, 1), 1.1, 1, None]\n for item in types:\n if type(a_class) == type(item):\n raise TypeError(\"can't add new attribute\")\n a_class.name = value","def add_attribute(obj, name, value):\n if hasattr(obj, \"__dict__\"):\n setattr(obj, name, value)\n else:\n raise TypeError(\"can't add new attribute\")","def append(self, item):\n self.update([item])","def appendlist(self, key, value):\n self.setlistdefault(key, [])\n dict.__setitem__(self, key, self.getlist(key) + [value])","def append(dest, field, value):\n if isinstance(dest[field], list):\n dest[field].append(value)\n else:\n dest[field] = [dest[field], value]","def append(self, name, value):\n # ...but only when the context has been entered (and locks acquired etc.)\n if not self.ready:\n raise RuntimeError(\"SnapshotView is a context manager. Never use it directly!\")\n # If this is the first entry under this key - create it\n try:\n target = self.data[name]\n except KeyError:\n target = []\n self.data[name] = target\n target.append(value)","def Add_Attribute(self, fc):\n\n updateFields = ['date', 'time']\n\n for field in updateFields:\n arcpy.AddField_management(fc, field, 'DATE')\n\n with arcpy.da.UpdateCursor(fc, updateFields) as cursor:\n for i, row in enumerate(cursor):\n for j, field in enumerate(updateFields):\n row[j] = self.__traectory_list[i][3+j]\n\n cursor.updateRow(row)\n\n\n print 'Complete Reading Attributes'\n return None","def add_attribute(self, subject_id, id=None, value=None):\n # ignore for now..."],"string":"[\n \"def _setAttribute(self, attribute, value):\\n\\n # if multiple values found\\n if hasattr(self, attribute):\\n\\n # make sure attribute is a list\\n values = getattr(self, attribute)\\n if not isinstance(values, list):\\n setattr(self, attribute, [values])\\n\\n # append value to list\\n getattr(self, attribute).append(value)\\n\\n # single value found\\n else:\\n setattr(self, attribute, value)\",\n \"def add_attr(self, attr, value, position=None, extra=None):\\n # pylint: disable=eval-used\\n if attr.startswith(\\\"*\\\"):\\n attr = attr[1:]\\n if attr not in self._attributes:\\n self._attributes[attr] = []\\n if len(self._attributes[attr]) != position:\\n raise TypeError(\\\"AST Node lost in conversion!\\\")\\n self._attributes[attr].append(value)\\n elif extra is not None:\\n self._attributes[attr] = eval(extra)\\n else:\\n self._attributes[attr] = value\",\n \"def add_value(self, key, value):\\r\\n if key in self:\\r\\n # We already have this key on the item.\\r\\n if not isinstance(self[key], list):\\r\\n # The key isn't already a list, take its current value and\\r\\n # convert it to a list with the only member being the\\r\\n # current value.\\r\\n self[key] = [self[key]]\\r\\n # Add the new value to the list.\\r\\n self[key].append(value)\\r\\n else:\\r\\n # This is a new attribute, just set it.\\r\\n self[key] = value\",\n \"def __setitem__(self, key, value) :\\n attributeslist = getattr(self.request, \\\"_%s_attributes\\\" % self.name)\\n for i in range(len(attributeslist)) :\\n attribute = attributeslist[i]\\n for j in range(len(attribute)) :\\n (attrname, attrvalue) = attribute[j]\\n if attrname == key :\\n attribute[j][1].append(value)\\n return\\n attribute.append((key, [value]))\",\n \"def add_attribute(self, name, value):\\n\\t\\tif name in self.__attr_hash:\\n#\\t\\t\\tattribue already exists\\n\\t\\t\\ta = self.__attr_hash[name]\\n\\t\\t\\tif name == 'class':\\n#\\t\\t\\t\\t'class' is a magic attribute\\n\\t\\t\\t\\tif a['value']:\\n\\t\\t\\t\\t\\tvalue = ' ' + value\\n\\t\\t\\t\\ta['value'] += value\\n\\t\\t\\telse:\\n\\t\\t\\t\\ta['value'] = value\\n\\t\\telse:\\n\\t\\t\\ta = {'name': name, 'value': value}\\n\\t\\t\\tself.__attr_hash[name] = a\\n\\t\\t\\tself.attributes.append(a)\",\n \"def add_attr(self, key: str, value):\\n if key in self._attr_names():\\n raise ValueError(\\\"Already have an attribute called '{}'\\\".format(key))\\n self._attributes.append((key, value))\",\n \"def add_attribute(self, attr):\\n self.add(attr)\",\n \"def add_attribute(node_proto, name, value):\\n node_proto.attribute.extend([make_attribute(name, value)])\",\n \"def batch_add(self, *args, **kwargs):\\n new_attrobjs = []\\n strattr = kwargs.get(\\\"strattr\\\", False)\\n for tup in args:\\n if not is_iter(tup) or len(tup) < 2:\\n raise RuntimeError(\\\"batch_add requires iterables as arguments (got %r).\\\" % tup)\\n ntup = len(tup)\\n keystr = str(tup[0]).strip().lower()\\n new_value = tup[1]\\n category = str(tup[2]).strip().lower() if ntup > 2 and tup[2] is not None else None\\n lockstring = tup[3] if ntup > 3 else \\\"\\\"\\n\\n attr_objs = self._getcache(keystr, category)\\n\\n if attr_objs:\\n attr_obj = attr_objs[0]\\n # update an existing attribute object\\n attr_obj.db_category = category\\n attr_obj.db_lock_storage = lockstring or \\\"\\\"\\n attr_obj.save(update_fields=[\\\"db_category\\\", \\\"db_lock_storage\\\"])\\n if strattr:\\n # store as a simple string (will not notify OOB handlers)\\n attr_obj.db_strvalue = new_value\\n attr_obj.save(update_fields=[\\\"db_strvalue\\\"])\\n else:\\n # store normally (this will also notify OOB handlers)\\n attr_obj.value = new_value\\n else:\\n # create a new Attribute (no OOB handlers can be notified)\\n kwargs = {\\n \\\"db_key\\\": keystr,\\n \\\"db_category\\\": category,\\n \\\"db_model\\\": self._model,\\n \\\"db_attrtype\\\": self._attrtype,\\n \\\"db_value\\\": None if strattr else to_pickle(new_value),\\n \\\"db_strvalue\\\": new_value if strattr else None,\\n \\\"db_lock_storage\\\": lockstring or \\\"\\\",\\n }\\n new_attr = Attribute(**kwargs)\\n new_attr.save()\\n new_attrobjs.append(new_attr)\\n self._setcache(keystr, category, new_attr)\\n if new_attrobjs:\\n # Add new objects to m2m field all at once\\n getattr(self.obj, self._m2m_fieldname).add(*new_attrobjs)\",\n \"def append(self, value) -> None:\\n key = getattr(value, self.keyattr)\\n if callable(key):\\n key = key()\\n if key not in self.data:\\n self.data[key] = []\\n self.data[key].append(value)\\n self.size += 1\",\n \"def add_attribute(self, attr):\\n self.attrs.add(attr)\",\n \"def append(self, value):\\n self.__field.validate_element(value)\\n return list.append(self, value)\",\n \"def add_attribute(self, attribute):\\n if attribute not in self.attributes:\\n self.attributes.add(attribute)\\n self.attribute_list.append(attribute)\\n return self\",\n \"def add_attribute(self, attribute_name, attribute_value):\\n self.attributes[attribute_name] = attribute_value\",\n \"def add_attribute(self, name, value, modify=False, sources=None, published_at=None):\\n\\n # Find attributes and remove if matches\\n if modify:\\n if not self.current_attributes:\\n res = self.tq.get('{}/attributes'.format(self._get_api_endpoint()))\\n self.current_attributes = res.get('data', [])\\n\\n for attr in self.current_attributes:\\n if attr['name'] == name and attr['value'].lower() != value.lower():\\n attribute_id = attr['id']\\n self.tq.delete(\\n '{}/attributes/{}'.format(self._get_api_endpoint(), attribute_id))\\n # break\\n\\n data = {'name': name, 'value': value}\\n if sources and isinstance(sources, str):\\n data['sources'] = [{'name': sources}]\\n elif sources and isinstance(sources, dict):\\n data['sources'] = [sources]\\n elif sources and isinstance(sources, list):\\n data['sources'] = sources\\n if published_at:\\n data['published_at'] = published_at\\n\\n res = self.tq.post('{}/attributes'.format(self._get_api_endpoint()), data=data)\\n\\n # Add the newly added indicator to the cache\\n if res.get('total', 0) > 0:\\n self.current_attributes.append(res['data'][0])\",\n \"def add_attribute(self, key, value):\\n self.attributes[key] = value\",\n \"def record_attribute_set(self, typ, attr_name, node, value):\\n serialized = self.serialize_type(typ)\\n if serialized is None:\\n return\\n self.attributes_set[serialized].add(attr_name)\\n self.merge_attribute_value(serialized, attr_name, value)\",\n \"def add_attribute(self, name, value):\\n\\n self._attributes[name] = value\",\n \"def add_attribute(self, name, value):\\n\\n self._attributes[name] = value\",\n \"def add_attribute(self, name, value):\\n\\n self._attributes[name] = value\",\n \"def add_attribute(self, attr):\\n self.attrs.add_attribute(attr)\",\n \"def append(self, value):\\n self.list.append(value)\",\n \"def append(self, value):\\n self.__field.validate_element(value)\\n return list.append(self, value)\",\n \"def append(self):\\n return AttributeFunctor(self, lambda a, b: a + b)\",\n \"def createAttribute(nid, label, primary, list, x, y):\\n attribute = Attribute(nid, label, primary, x, y)\\n list.append(attribute)\",\n \"def add_attribute(self, attribute_key, attribute_value):\\n self.set_attribute_value(attribute_key, attribute_value) # record the input key-value pair\",\n \"def add_attribute(self, attribute_key, attribute_value):\\n self.set_attribute_value(attribute_key, attribute_value) # record the input key-value pair\",\n \"def add_attribute(self, attribute_key, attribute_value):\\n self.set_attribute_value(attribute_key, attribute_value) # record the input key-value pair\",\n \"def append(self, value):\\n\\n list.append(self, value)\\n self.changed()\",\n \"def addattribute(self, uid, field, value):\\n\\n raise NotImplementedError\",\n \"def add_value(self, value, attr):\\n self.index[value] = attr\",\n \"def add(self, attribute_name, value_name):\\n self.__add[attribute_name] = value_name\\n return self\",\n \"def append(self, value):\\n self.__list += [value]\\n return self.__list\",\n \"def addAttr(self, *args):\\n return _libsbml.XMLToken_addAttr(self, *args)\",\n \"def _add_attributo(spell_list):\\n\\n attr_list = [\\\"MU\\\", \\\"KL\\\", \\\"IN\\\", \\\"CH\\\", \\\"FF\\\", \\\"GE\\\", \\\"KO\\\", \\\"KK\\\"]\\n # remove original attributo entry\\n attributo_orig = spell_list.pop(-1)\\n\\n # change original attributo entry and add it to skill list\\n for _, value in enumerate(attr_list):\\n attributo_temp = copy.deepcopy(attributo_orig)\\n attributo_temp.name = value + \\\" Attributo\\\"\\n attributo_temp.attrs[2] = value\\n spell_list.append(attributo_temp)\\n\\n return spell_list\",\n \"def append_data_member(self, varName, valueName):\\n hasList = self.start_data_member(varName, valueName)\\n if hasList:\\n listA = self.getVar(varName)\\n if listA is not None:\\n #print \\\"has %s\\\" %(varName)\\n value = self.getVar(valueName)\\n try:\\n n = len(listA)\\n except:\\n print(\\\"listA = \\\", listA)\\n raise\\n listA.append(value)\\n assert len(listA) == n + 1\",\n \"def addAttr(self, attr_key, attr_val, replace=False, attr_pos=0):\\n\\t\\tif attr_key in self.attributes:\\n # replace the current value of this key with then new value\\n\\t\\t\\tif replace:\\n\\t\\t\\t\\tdelAttr(attr_key)\\n\\t\\t\\t\\tself.attributes[attr_key] = attr_val\\n\\t\\t\\t\\tself.attributes_order.insert(attr_pos, attr_key)\\n\\t\\t\\t\\tself.refreshAttrStr()\\n # this key already exists in the attributes of the GFF3 file\\n # add this value to the existing key's values with commas\\n # separating values\\n\\t\\t\\telse:\\n\\t\\t\\t\\tself.attributes[attr_key] = '{0},{1}'.format(\\n self.attributes[attr_key], \\n attr_val)\\n\\t\\t\\t\\tself.refreshAttrStr()\\n\\t\\telse:\\n\\t\\t\\tself.attributes[attr_key] = attr_val\\n\\t\\t\\tself.attributes_order.insert(attr_pos, attr_key)\\n\\t\\t\\tself.refreshAttrStr()\",\n \"def add_attributes(self, attrs):\\n for attr in attrs:\\n self.add_attribute(attr)\",\n \"def add_attributes(self, attrs):\\n self.attrs.add_attributes(attrs)\",\n \"def add_attr(self, section_name: str, attr_name: str, value: str) -> None:\\n pass\",\n \"def append_group_attr(self, analyte: str, group_name: str, key: str, value: Any):\\n group_path = \\\"/\\\".join([\\\"\\\", ASSAYS, analyte, group_name])\\n if group_path not in self.__file:\\n raise ValueError(\\\"{} missing\\\".format(group_path))\\n group = self.__file[group_path]\\n if key in group.keys():\\n key_path = group_path + \\\"/\\\" + key\\n data = self.__file[key_path]\\n normalized = normalize_attr_values(value)\\n data[...] = normalized\\n else:\\n self.__write_value(group, key, value)\",\n \"def append(self, value):\\n self.values.append(value)\\n return value\",\n \"def merge_attrs(self):\\n for aid in self.attrs:\\n new_val = self.attrs[aid]\\n if aid in self.attributes:\\n if ('value' in self.attributes[aid] and\\n self.attributes[aid]['value'] != new_val):\\n pass\\n # print \\\"Updating attribute %s[%s] %s -> %s\\\" % (\\n # self.name, aid, self.attributes[aid]['value'], new_val)\\n else:\\n # print \\\"** Warning: non-declaired attribute %s['%s'] set to:\\\\n'%s'\\\" % (\\n # self.name, aid, new_val)\\n self.remember_custom_attribute(self.name, aid, new_val)\\n self.attributes[aid] = {}\\n self.attributes[aid]['nv'] = new_val\",\n \"def add_attrib(self, key, func, func_args):\\n if key in self.aux_attrib:\\n raise KeyError(\\\"Attribute '{0}' already exists, please use 'set_attrib'.\\\".format(key))\\n else:\\n self.set_attrib(key, func, func_args)\",\n \"def add_attributes(self, attribute_list, simulate=False):\\n with LayerEditingManager(self.layer, 'Add attributes', DEBUG):\\n # add attributes\\n layer_pr = self.layer.dataProvider()\\n proposed_attribute_dict = {}\\n proposed_attribute_list = []\\n for input_attribute in attribute_list:\\n input_attribute_name = input_attribute.name()[:10]\\n proposed_attribute_name = input_attribute_name\\n i = 1\\n while True:\\n current_attribute_names = \\\\\\n [attribute.name() for attribute in layer_pr.fields()]\\n if proposed_attribute_name in current_attribute_names:\\n # If the attribute is already assigned, change the\\n # proposed_attribute_name\\n i_num_digits = len(str(i))\\n # 10 = shapefile limit\\n # 1 = underscore\\n max_name_len = 10 - i_num_digits - 1\\n proposed_attribute_name = '%s_%d' % (\\n input_attribute_name[:max_name_len], i)\\n i += 1\\n else:\\n # If the attribute name is not already assigned,\\n # add it to the proposed_attribute_dict\\n proposed_attribute_dict[input_attribute_name] = \\\\\\n proposed_attribute_name\\n input_attribute.setName(proposed_attribute_name)\\n proposed_attribute_list.append(input_attribute)\\n break\\n if not simulate:\\n added_ok = layer_pr.addAttributes(proposed_attribute_list)\\n if not added_ok:\\n raise AttributeError(\\n 'Unable to add attributes %s' %\\n proposed_attribute_list)\\n return proposed_attribute_dict\",\n \"def __setitem__(self, attribute_name, value):\\n pass # pragma: no cover\",\n \"def add_edge_attributes(self, attribute_key, attribute_value):\\n G_prime = self.__deepcopy__() # create a deepcopy of the undirected graph\\n for edge in G_prime.get_edges(): # for every edge in the graph\\n # add the attribute key-value pair to the attributes registry of the edge\\n edge.add_attribute(attribute_key, attribute_value)\\n return G_prime # return the modified graph\",\n \"def add(self, attr):\\n self.validate_type(attr)\\n self.values.add(attr.value)\",\n \"def add_attributes(self, attrs):\\n self.attrs.add_container(attrs)\",\n \"def append_value(self, value):\\n self.value += value\",\n \"def __setitem__(self, key, value):\\r\\n self._getAttrMap()\\r\\n self.attrMap[key] = value\\r\\n found = False\\r\\n for i in range(0, len(self.attrs)):\\r\\n if self.attrs[i][0] == key:\\r\\n self.attrs[i] = (key, value)\\r\\n found = True\\r\\n if not found:\\r\\n self.attrs.append((key, value))\\r\\n self._getAttrMap()[key] = value\",\n \"def extend_attribute_dictionary(attributedict, ns, name, value):\\n\\n key = ns, name\\n if value is None:\\n if key in attributedict:\\n del attributedict[key]\\n else:\\n if ns == 'html' and key in attributedict:\\n if name == 'class':\\n # CSS classes are appended by space-separated list\\n value = attributedict[key] + ' ' + value\\n elif name == 'style':\\n # CSS styles are appended by semicolon-separated rules list\\n value = attributedict[key] + '; ' + value\\n elif name in _html_attribute_boolflags:\\n # All attributes must have a value. According to XHTML those\\n # traditionally used as flags should have their value set to\\n # the same as the attribute name.\\n value = name\\n attributedict[key] = value\",\n \"def add_attribute(self, subject_id, id=None, value=None):\",\n \"def add_attribute(self, subject_id, id=None, value=None):\",\n \"def add_attribute(self, subject_id, id=None, value=None):\",\n \"def add_attribute(self, subject_id, id=None, value=None):\",\n \"def add_attribute(self, subject_id, id=None, value=None):\",\n \"def add(self, *args):\\n return _libsbml.XMLAttributes_add(self, *args)\",\n \"def add_attribute(obj, attribute, value):\\n if not hasattr(obj, \\\"__dict__\\\"):\\n raise TypeError(\\\"can't add new attribute\\\")\\n setattr(obj, attribute, value)\",\n \"def add_attribute(self, key: str, value: Union[str, int]) -> None:\\n self.span_instance.add_attribute(key, value)\",\n \"def add_attribute(self, attr_type, name, components):\\n self.attributes[attr_type] = {\\\"name\\\": name, \\\"components\\\": components}\",\n \"def _appendRecord(self, rec, reclistname):\\n if reclistname not in self.__dict__: # if not already an attrib\\n self.__dict__[reclistname] = [] # init it\\n self.__dict__[reclistname].append(rec) # append this record to its list\",\n \"def add_attribute(self, attr: ResourceAttributeDescriptor) -> None:\\n self._attributes[assert_not_none(attr.name)] = attr.bind(self)\",\n \"def append(self, val):\\n self.val.append(val)\",\n \"def set_attr_values(self):\\n ats = self.attributes # convenient short name\\n for aid in ats:\\n value = ats[aid]['nv'] if 'nv' in ats[aid] else (\\n ats[aid]['value'] if 'value' in ats[aid] else None)\\n if value is not None:\\n# self.h5node.attrs[aid] = value\\n #- self.file.file_pointer[self.full_path].attrs[aid] = value\\n self.file.set_attribute(self.full_path, aid, value)\\n #- self.file.h5save_attribute(self.full_path, aid, value)\\n #- self.file.h5commands.append(\\\"set attribute(%s:%s)-%s\\\" % (self.full_path,\\n #- aid, value))\",\n \"def add_value(self, value):\\r\\n self.resource_records.append(value)\",\n \"def add(\\n self,\\n key,\\n value,\\n category=None,\\n lockstring=\\\"\\\",\\n strattr=False,\\n accessing_obj=None,\\n default_access=True,\\n ):\\n if accessing_obj and not self.obj.access(\\n accessing_obj, self._attrcreate, default=default_access\\n ):\\n # check create access\\n return\\n\\n if not key:\\n return\\n\\n category = category.strip().lower() if category is not None else None\\n keystr = key.strip().lower()\\n attr_obj = self._getcache(key, category)\\n\\n if attr_obj:\\n # update an existing attribute object\\n attr_obj = attr_obj[0]\\n if strattr:\\n # store as a simple string (will not notify OOB handlers)\\n attr_obj.db_strvalue = value\\n attr_obj.save(update_fields=[\\\"db_strvalue\\\"])\\n else:\\n # store normally (this will also notify OOB handlers)\\n attr_obj.value = value\\n else:\\n # create a new Attribute (no OOB handlers can be notified)\\n kwargs = {\\n \\\"db_key\\\": keystr,\\n \\\"db_category\\\": category,\\n \\\"db_model\\\": self._model,\\n \\\"db_attrtype\\\": self._attrtype,\\n \\\"db_value\\\": None if strattr else to_pickle(value),\\n \\\"db_strvalue\\\": value if strattr else None,\\n }\\n new_attr = Attribute(**kwargs)\\n new_attr.save()\\n getattr(self.obj, self._m2m_fieldname).add(new_attr)\\n # update cache\\n self._setcache(keystr, category, new_attr)\",\n \"def add_attribute(obj, attribute, value):\\n if hasattr(obj, \\\"__dict__\\\"):\\n setattr(obj, attribute, value)\\n else:\\n raise TypeError(\\\"can't add new attribute\\\")\",\n \"def add_attribute(a, name, other):\\n raise TypeError(\\\"can't add new attribute\\\")\",\n \"def add_node_attributes(self, attribute_key, attribute_value):\\n G_prime = self.__deepcopy__() # create a deepcopy of the undirected graph\\n for node in G_prime.get_nodeset(): # for every node in the graph\\n # add the attribute key-value pair to the attributes registry of the node\\n node.add_attribute(attribute_key, attribute_value)\\n return G_prime # return the modified graph\",\n \"def add_symbol_attribute(self, symbol_attribute):\\n self.symbol_attributes.append(symbol_attribute)\",\n \"def append(self, value):\\n assert isinstance(value, Item), type(value)\\n list.append(self, value)\\n self.emit('appened', value)\\n self.emit('modified')\",\n \"def attr(self, item: _PyteAugmentedValidator):\\n self._attrs.append(item)\\n return self\",\n \"def append(self, val):\\n self._values.push(val)\",\n \"def append_value(self, value):\\n value_wrapper = ValueWrapper(value)\\n self.strict = value_wrapper.strict_value\\n if self.strict is False:\\n self.absolute = value_wrapper.absolute_value\\n if value_wrapper.value not in self._values and value_wrapper.value is not None:\\n self._values += [value_wrapper.value]\\n for var_dependency in self.list_dependencies(value_wrapper.value):\\n if not var_dependency in self.dependencies:\\n self.dependencies.append(var_dependency)\",\n \"def append(self, value: object) -> None:\\n self.data.append(value)\",\n \"def __setattr__(self, name, value):\\n try:\\n self[self.sig.argpos(name)] = value\\n return value\\n except:\\n pass\\n list.__setattr__(self, name, value)\",\n \"def add_attr(self, event: Union[wx.CommandEvent, None],\\n attr_label: str = '',\\n attr_value: str = '') -> None:\\n new_id = len(self.attr_ids)\\n self.attr_ids[new_id] = attr_label\\n button_remove = wx.Button(self, wx.ID_ANY, label='-')\\n self.Bind(wx.EVT_BUTTON, partial(self.remove_attr, attr_id=new_id),\\n button_remove)\\n text_label = wx.TextCtrl(self, value=attr_label)\\n text_value = wx.TextCtrl(self, value=str(attr_value))\\n self.attr_labels[new_id] = text_label\\n self.attr_values[new_id] = text_value\\n self.attr_buttons[new_id] = button_remove\\n if event is not None:\\n self._update_attr_list()\",\n \"def _handle_attr(self, attr, dev):\\n attr_val = None\\n list_flag = False\\n\\n if attr.name == \\\"os\\\":\\n attr_val = self.OS_MAPPER[attr.val]\\n elif attr.name == \\\"network\\\":\\n attr_val = self._create_network(attr.val)\\n list_flag = True\\n elif attr.name == \\\"bluetooth\\\":\\n attr_val = Bluetooth(version=attr.val.version)\\n elif attr.name == \\\"cpu\\\":\\n attr_val = CPU(cpu_family=attr.val.cpu_family,\\n max_freq=float(attr.val.max_freq\\n * self.FREQ_MULT[attr.val.unit]),\\n fpu=attr.val.fpu)\\n elif attr.name == \\\"memory\\\":\\n attr_val = self._create_memory(attr.val)\\n elif attr.name == \\\"type\\\":\\n self._per_type = self.PER_MAPPER[attr.val]\\n elif attr.name == \\\"pins\\\":\\n list_flag = True\\n attr_val = self._create_pins(attr.val)\\n else:\\n attr_val = attr.val\\n\\n # Set attribute\\n if list_flag:\\n getattr(dev, attr.name).extend(attr_val)\\n elif attr_val:\\n setattr(dev, attr.name, attr_val)\",\n \"def append_to_list(self, metric_value_to_append):\\n if type(metric_value_to_append)==MetricValue:\\n self.__metric_value_list.append(metric_value_to_append)\\n else:\\n print(\\\"appended object must be a MetricValue, metric_value_to_append=\\\",metric_value_to_append)\\n sys.exit() # stop entire program, because metric_value_to_append MUST be correct\",\n \"def add(self, value):\\n self._resolve_copies()\\n self.data.append(value)\",\n \"def addAttribute(self, attribute):\\n\\n if attribute.getName() in [x.getName() for x in self._attributes]:\\n raise IndexError(\\\"Child with \\\" + attribute.getName() + \\\" already exists as a attribute.\\\")\\n\\n self._attributes.append(attribute)\\n attribute.setParent(self)\\n\\n return True\",\n \"def define_attribute(self, name, atype, data=None):\\n self.attributes.append(name)\\n self.attribute_types[name] = atype\\n self.attribute_data[name] = data\",\n \"def add_attribute(obj, name, value):\\n if not hasattr(obj, \\\"__dict__\\\"):\\n raise TypeError(\\\"can't add new attribute\\\")\\n setattr(obj, name, value)\",\n \"def safeAppend(values, value):\\n\\tif value is not None:\\n\\t\\tvalues.append(value)\",\n \"def append_jobs_attributes(self, attribute):\\n # | - append_jobs_attributes\\n att_new = attribute\\n\\n self.jobs_att.update(att_new)\\n\\n job_att_file = self.root_dir + \\\"/jobs_bin/job_attributes.csv\\\"\\n pickle.dump(self.jobs_att, open(job_att_file, \\\"wb\\\"))\\n # __|\",\n \"def __setitem__(self, key, item):\\n self.attrib[key] = item\",\n \"def __setitem__(self, key, item):\\n self.attrib[key] = item\",\n \"def _return_attr(self, *args, **kwargs):\\r\\n self.attr = [args, kwargs]\\r\\n return []\",\n \"def add_attributes(self, attributes):\\n self.attributes = dict(self.attributes, **attributes)\",\n \"def add_attribute(self, key, value):\\n if key == \\\"process_step\\\":\\n self.process_step = value\\n elif key == \\\"source\\\":\\n self.source = value\\n else:\\n self.attributes[key] = value\",\n \"def append(self, value):\\n self.data.append(None)\\n self.data[len(self.data)-1] = value\",\n \"def add_attribute(a_class, name, value):\\n types = ['str', [], {}, (1, 1), 1.1, 1, None]\\n for item in types:\\n if type(a_class) == type(item):\\n raise TypeError(\\\"can't add new attribute\\\")\\n a_class.name = value\",\n \"def add_attribute(obj, name, value):\\n if hasattr(obj, \\\"__dict__\\\"):\\n setattr(obj, name, value)\\n else:\\n raise TypeError(\\\"can't add new attribute\\\")\",\n \"def append(self, item):\\n self.update([item])\",\n \"def appendlist(self, key, value):\\n self.setlistdefault(key, [])\\n dict.__setitem__(self, key, self.getlist(key) + [value])\",\n \"def append(dest, field, value):\\n if isinstance(dest[field], list):\\n dest[field].append(value)\\n else:\\n dest[field] = [dest[field], value]\",\n \"def append(self, name, value):\\n # ...but only when the context has been entered (and locks acquired etc.)\\n if not self.ready:\\n raise RuntimeError(\\\"SnapshotView is a context manager. Never use it directly!\\\")\\n # If this is the first entry under this key - create it\\n try:\\n target = self.data[name]\\n except KeyError:\\n target = []\\n self.data[name] = target\\n target.append(value)\",\n \"def Add_Attribute(self, fc):\\n\\n updateFields = ['date', 'time']\\n\\n for field in updateFields:\\n arcpy.AddField_management(fc, field, 'DATE')\\n\\n with arcpy.da.UpdateCursor(fc, updateFields) as cursor:\\n for i, row in enumerate(cursor):\\n for j, field in enumerate(updateFields):\\n row[j] = self.__traectory_list[i][3+j]\\n\\n cursor.updateRow(row)\\n\\n\\n print 'Complete Reading Attributes'\\n return None\",\n \"def add_attribute(self, subject_id, id=None, value=None):\\n # ignore for now...\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.72828615","0.7063005","0.70154303","0.6982224","0.6949298","0.6837699","0.66487557","0.6644792","0.660447","0.6597281","0.65214103","0.64085853","0.639992","0.6395541","0.63902277","0.6352462","0.6338568","0.63299024","0.63299024","0.63299024","0.6323698","0.6318956","0.63034755","0.628151","0.62806904","0.627548","0.627548","0.627548","0.62751114","0.6269866","0.6231953","0.60875314","0.60651314","0.6063092","0.6053984","0.60431814","0.6032494","0.6028093","0.5976159","0.59726495","0.59680754","0.59580606","0.59482664","0.59256905","0.5864672","0.58407366","0.5825125","0.5809328","0.58013344","0.5779219","0.57733196","0.57577896","0.5753019","0.5753019","0.5753019","0.5753019","0.5753019","0.5750996","0.5745558","0.57352716","0.5729184","0.57266295","0.5714569","0.5705594","0.570475","0.5701007","0.5691539","0.5690051","0.56882584","0.568658","0.5670988","0.5667345","0.5663568","0.5662712","0.56611514","0.56461555","0.563646","0.5636386","0.5617011","0.5612184","0.5603098","0.5602324","0.5595355","0.5572369","0.55705893","0.55694515","0.5559893","0.5559893","0.5555446","0.5555326","0.5552751","0.5552597","0.55472445","0.55422306","0.55159825","0.55006725","0.5486439","0.54789144","0.5474413","0.5470221"],"string":"[\n \"0.72828615\",\n \"0.7063005\",\n \"0.70154303\",\n \"0.6982224\",\n \"0.6949298\",\n \"0.6837699\",\n \"0.66487557\",\n \"0.6644792\",\n \"0.660447\",\n \"0.6597281\",\n \"0.65214103\",\n \"0.64085853\",\n \"0.639992\",\n \"0.6395541\",\n \"0.63902277\",\n \"0.6352462\",\n \"0.6338568\",\n \"0.63299024\",\n \"0.63299024\",\n \"0.63299024\",\n \"0.6323698\",\n \"0.6318956\",\n \"0.63034755\",\n \"0.628151\",\n \"0.62806904\",\n \"0.627548\",\n \"0.627548\",\n \"0.627548\",\n \"0.62751114\",\n \"0.6269866\",\n \"0.6231953\",\n \"0.60875314\",\n \"0.60651314\",\n \"0.6063092\",\n \"0.6053984\",\n \"0.60431814\",\n \"0.6032494\",\n \"0.6028093\",\n \"0.5976159\",\n \"0.59726495\",\n \"0.59680754\",\n \"0.59580606\",\n \"0.59482664\",\n \"0.59256905\",\n \"0.5864672\",\n \"0.58407366\",\n \"0.5825125\",\n \"0.5809328\",\n \"0.58013344\",\n \"0.5779219\",\n \"0.57733196\",\n \"0.57577896\",\n \"0.5753019\",\n \"0.5753019\",\n \"0.5753019\",\n \"0.5753019\",\n \"0.5753019\",\n \"0.5750996\",\n \"0.5745558\",\n \"0.57352716\",\n \"0.5729184\",\n \"0.57266295\",\n \"0.5714569\",\n \"0.5705594\",\n \"0.570475\",\n \"0.5701007\",\n \"0.5691539\",\n \"0.5690051\",\n \"0.56882584\",\n \"0.568658\",\n \"0.5670988\",\n \"0.5667345\",\n \"0.5663568\",\n \"0.5662712\",\n \"0.56611514\",\n \"0.56461555\",\n \"0.563646\",\n \"0.5636386\",\n \"0.5617011\",\n \"0.5612184\",\n \"0.5603098\",\n \"0.5602324\",\n \"0.5595355\",\n \"0.5572369\",\n \"0.55705893\",\n \"0.55694515\",\n \"0.5559893\",\n \"0.5559893\",\n \"0.5555446\",\n \"0.5555326\",\n \"0.5552751\",\n \"0.5552597\",\n \"0.55472445\",\n \"0.55422306\",\n \"0.55159825\",\n \"0.55006725\",\n \"0.5486439\",\n \"0.54789144\",\n \"0.5474413\",\n \"0.5470221\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.74247986"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":21894,"cells":{"query":{"kind":"string","value":"Function to converts XML attributes into a dictionary"},"document":{"kind":"string","value":"def __convertAttributes__(xml_source):\n attributes = {}\n for attrName, attrValue in xml_source.attributes.items():\n attributes[attrName] = attrValue\n return attributes"},"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_attrs_dict(self, root_element):\n attr_elements = root_element.findall(\"attribute\")\n attrs_dict = {}\n for el in attr_elements:\n attrs_dict[el.attrib[\"name\"]] = {\n \"value\": el.attrib[\"value\"],\n \"type\": el.attrib.get(\"type\", None)\n }\n return attrs_dict","def extract_attrs(attr_string):\n attributes = {}\n for name, val in FIND_ATTRS.findall(attr_string):\n val = (\n val.replace(\"<\", \"<\")\n .replace(\">\", \">\")\n .replace(\""\", '\"')\n .replace(\"&\", \"&\")\n )\n attributes[name] = val\n return attributes","def dom2dict(element):\n keys = list(element.attributes.keys())\n values = [val.value for val in list(element.attributes.values())]\n return dict(list(zip(keys, values)))","def get_attrib_dict(self, attribs: Tuple[str]) -> Dict[str, str]:\n attrs = self.get_attribs(attribs)\n attrs = tuple(map(lambda a: (a[0][1:], a[1]), attrs))\n return dict(attrs)","def process_attrs(attrs):\n if attrs.getLength() == 0:\n return {}\n tmp_dict = {}\n for name in attrs.getNames():\n tmp_dict[name] = attrs.getValue(name)\n return tmp_dict","def dom2dict(element):\n keys = list(element.attributes.keys())\n values = [val.value for val in list(element.attributes.values())]\n return dict(list(zip(keys, values)))","def xml2dict( xml, sanitize=True, prefix=None):\n \n \n #Decode to avert parsing errors as some software dump large text\n #fields into the file that occasionally contain erronious chars\n xml=xml.decode('utf-8', errors='ignore')\n\n \n return etree2dict(etree.fromstring(xml), sanitize, prefix)","def attribute_to_dict(attr: onnx.AttributeProto) -> Dict:\n ret = {}\n for a in attr:\n value = get_attribute_value(a)\n if isinstance(value, bytes):\n value = str(value, 'utf-8')\n ret[a.name] = value\n return ret","def _get_attribute_dic(self, attrs):\n attr_dic = {}\n for attr_pair in attrs:\n attr_dic[attr_pair[0]] = attr_pair[1]\n return attr_dic","def convert_attr_to_dict(attr):\n\n\tresult = dict()\n\tattr = attr.split(';')\n\tattrlist = [a.split(':') for a in attr]\n\tfor pair in attrlist:\n\t\tif len(pair) == 2:\n\t\t\tkey = pair[0]\n\t\t\tvalue = pair[1]\n\t\t\tresult[key] = value\n\n\treturn result","def get_attributes(self):\n\t\treturn dict(list(self.__element.items()))","def load_attribute_dict(parent_element, tag_name):\n loaded_element = parent_element.find(tag_name)\n attribute_dict = {attr[0] : convert_to_int_if_numeric(attr[1])\n for attr in loaded_element.items()}\n return attribute_dict","def get_attributes(self) -> Dict[str, str]:\n pass","def attrs_to_dict(self, attrs):\n return {k: v for k, v in attrs}","def get_attributes_of_node(core, node):\n attributes = {}\n for attribute in core.get_attribute_names(node):\n attributes[attribute] = core.get_attribute(node,attribute)\n return attributes","def read_attribs(self):\n\n attribs = {}\n while self.index < self.length:\n self.ignore_whitespaces()\n if self.xtext[self.index] == '>':\n break\n name = self.read_until('=')\n self.index += 1\n self.read_until('\"')\n self.index += 1\n value = self.read_until('\"')\n self.index += 1\n\n attribs[name] = value\n\n return attribs","def test_xml_to_dict(self):\n xml = \"\"\"\n <a\n xmlns=\"urn:ietf:params:xml:ns:netconf:base:1.0\"\n xmlns:nm=\"s\"\n >\n <b m=\"g\">\n b\n </b>\n <c nm:nm=\"update\">\n <f>1</f>\n <e>str</e>\n <d>1</d>\n <d>2</d>\n <d>3</d>\n </c>\n </a>\n \"\"\"\n xmlns = {\n \"_\": utils.NETCONF_NAMESPACE\n }\n result = utils.generate_dict_node(etree.XML(xml), xmlns)\n # check dict\n self.assertDictEqual(\n self.SIMPLE_DICT,\n result\n )\n # check xmlns\n self.assertEqual(\n {\n '_': utils.NETCONF_NAMESPACE,\n 'nm': 's'\n }, xmlns\n )","def to_dict(xml):\n children = xml.getchildren()\n if not children:\n return xml.text\n else:\n out = {}\n for node in xml.getchildren():\n if node.tag in out:\n if not isinstance(out[node.tag], list):\n out[node.tag] = [out[node.tag]]\n out[node.tag].append(to_dict(node))\n else:\n out[node.tag] = to_dict(node)\n return out","def convert_attributes(cls, attrs):\n return {}","def parse_attributes(data, attributes):\n result = {}\n for key, val in attributes.items():\n if type(val) is list:\n attr_val = get_tree_data(data, val)\n else:\n attr_val = data.get(val, None)\n if attr_val is not None:\n result[key] = attr_val\n return result","def xml_to_dict(args):\n rdict = dict()\n args = re.sub(r'xmlns=\\\".+?\\\"', '', args)\n root = ET.fromstring(args)\n ifmtrunk = root.find('.//ifmtrunk')\n if ifmtrunk is not None:\n try:\n ifmtrunk_iter = ET.Element.iter(ifmtrunk)\n except AttributeError:\n ifmtrunk_iter = ifmtrunk.getiterator()\n\n for ele in ifmtrunk_iter:\n if ele.text is not None and len(ele.text.strip()) > 0:\n rdict[ele.tag] = ele.text\n return rdict","def parse_attributes(self, attr):\n result = {}\n annotations = []\n # Sanitize and split attributes up\n split_attr = attr.strip(' \\t\\n;').split(';')\n for pair in split_attr:\n splitpair = pair.split('=')\n if len(splitpair) != 2:\n continue\n if splitpair[0] == \"ID\":\n result['identifier'] = splitpair[1]\n elif splitpair[0] == \"Name\":\n result['name'] = splitpair[1]\n elif splitpair[0] == \"Parent\":\n result['parent_id'] = splitpair[1]\n elif splitpair[0] == \"Dbxref\" or splitpair[0] == \"Ontology_term\":\n annotations.append(splitpair)\n # Make sure we found an ID\n if \"identifier\" not in result:\n return {}\n # Add annotations if we found any\n if annotations:\n result[\"annotations\"] = annotations\n return result","def attrs(xml):\r\n return lxml.html.fromstring(xml).attrib","def getAttrsDict(attrs):\r\n attrsDict = json.loads(re.sub('/\\\"(?!(,\\s\"|}))','\\\\\"',attrs).replace(\"\\t\",\" \").replace(\"\\n\",\" \")) if len(attrs)>0 else {}\r\n return attrsDict","def xml_children_as_dict(node):\n return dict((e.tag, e.text) for e in node)","def parse_common_attributes(self, element, attributes):\n result_attributes = {}\n for attribute in attributes:\n attrib_info = ALL_ATTRIBUTES_MAP[attribute]\n if attrib_info.get(\"default\", MISSING) is not MISSING:\n default_value = attrib_info[\"default\"]\n else:\n default_value = MISSING\n attribute_name = attrib_info.get(\"attribute_name\", attribute)\n value = element.attrib.get(attribute_name, default_value)\n if value is MISSING or value == \"\":\n if attrib_info.get(\"use\") == \"optional\":\n continue\n\n else:\n raise ValueError(\n f\"element {element.tag} missing required \"\n f\"attribute {attribute_name}\"\n )\n\n if attrib_info.get(\"type\"):\n try:\n value = attrib_info[\"type\"](value)\n except ValueError as exc:\n raise ValueError(\n f\"element {element.tag} invalid value \"\n f\"{repr(value)} for attribute {attribute_name}\"\n ) from exc\n\n if attrib_info.get(\"minimumValue\"):\n if value < attrib_info[\"minimumValue\"]:\n raise ValueError(\n f\"element {element.tag} invalid value {repr(value)}\"\n f\" for attribute {attribute_name},\"\n \"less than allowed minimum \"\n f\"{repr(attrib_info['minimumValue'])}\"\n )\n\n if attrib_info.get(\"pattern\"):\n if not attrib_info[\"pattern\"].match(value):\n raise ValueError(\n f\"element {element.tag} invalid value {repr(value)} \"\n f\"for attribute {attribute_name},\"\n \"does not match expected pattern \"\n f\"{repr(attrib_info['pattern'])}\"\n )\n\n if attrib_info.get(\"map\"):\n try:\n value = attrib_info[\"map\"][value]\n except (KeyError, IndexError) as exc:\n raise ValueError(\n f\"element {element.tag} invalid value {repr(value)} \"\n f\"for attribute {attribute_name}\"\n f\", must be one of {repr(attrib_info['map'].keys())}\"\n ) from exc\n\n if attrib_info.get(\"rename\"):\n attribute = attrib_info[\"rename\"]\n\n result_attributes[attribute] = value\n\n return result_attributes","def get_attributes(self):\n \n retdict = {}\n retdict['s'] = str(self.s)\n if self.t != None:\n retdict['t'] = str(self.t)\n retdict['a'] = str(self.a)\n retdict['b'] = str(self.b)\n retdict['c'] = str(self.c)\n retdict['d'] = str(self.d)\n return retdict","def to_dict(\n self,\n attributes: Iterable[str] = (\"xyz\", \"viewdir\", \"imgsz\", \"f\", \"c\", \"k\", \"p\"),\n ) -> Dict[str, tuple]:\n return {key: helpers.numpy_to_native(getattr(self, key)) for key in attributes}","def _get_attribute_dict(self, attributes, classname=None):\n if attributes and isinstance(attributes, six.string_types):\n return {\n 'class': attributes\n }\n if not attributes:\n attributes = {}\n if not classname:\n classname = self.DEFAULT_CLASS_NAME\n attributes.setdefault('class', classname)\n return attributes","def proc_attr(inp):\n dic = {}\n for att in inp.attrs.keys():\n if getattr(inp.attrs[att], \"dtype\", None) is None:\n dic[att] = inp.attrs[att]\n elif inp.attrs[att].dtype.char == 'S':\n dic[att] = [\n x.strip() for x in inp.attrs[att].tostring().decode('ascii').split(',')\n ]\n else:\n dic[att] = (\n inp.attrs[att][0]\n if isinstance(inp.attrs[att],np.ndarray) and\n inp.attrs[att].size==1\n else inp.attrs[att]\n )\n return dic\n pass","def dict(self):\n return xmltodict.parse(str(self))","def get_attrs(self):\n req_attrv = self._ptr.contents.attrv\n attrs = {}\n if bool(req_attrv):\n i = 0\n while 1:\n s = bytestostr(req_attrv[i])\n i += 1\n if s == None:\n break\n try:\n k, v = s.split(\"=\", 1)\n attrs[k] = v\n except:\n pass\n return attrs","def dict(self) -> dict():\n\n dict_reg_hive = {}\n\n for _attribute in self.attributes.__dict__.items():\n if isinstance(_attribute[1], str):\n if not True in [_attribute[1].startswith(prefix) for prefix in ['<', 'providers.', 'None']]:\n _attribute_value = getattr(self, _attribute[1])\n dict_reg_hive.update({_attribute[1]: _attribute_value})\n\n return dict_reg_hive","def parse_one_xml(xml_file, fields=None):\r\n tree = etree.iterparse(xml_file)\r\n \r\n d = dict.fromkeys(fields)\r\n for event, elem in tree:\r\n if elem.tag == 'SAMPLE_ATTRIBUTE':\r\n for f in fields:\r\n if elem.getchildren()[0].text == f:\r\n d[f] = elem.getchildren()[1].text \r\n return d","def attribute_metadata(attr: Attr, parent_namespace: Optional[str]) -> Dict:\n metadata = dict(\n name=None\n if attr.is_nameless or attr.local_name == attribute_name(attr.name)\n else attr.local_name,\n type=attr.xml_type,\n namespace=attr.namespace\n if parent_namespace != attr.namespace or attr.is_attribute\n else None,\n **attr.restrictions.asdict(),\n )\n\n return {\n key: value\n for key, value in metadata.items()\n if value is not None and value is not False\n }","def get_attributes(doc):\n\treturn doc.keys()","def _attrs_map(self) -> \"dict[int, str]\":\n return {i: attr.name for i, attr in enumerate(self._attrs())}","def attributes(self, *args):\n kwargs = {}\n if args:\n kwargs[\"attributenames\"] = args\n\n r = self._token_id_request(urljoin(self._url, Client._attribute_resource), **kwargs)\n\n # parse contennt looking for all attributes\n attributes = []\n for line in r.text.splitlines():\n r = re.match(\"(userdetails\\.attribute\\.name)=(.*)\", line)\n if r:\n name = r.groups()[1]\n attributes.append([name, None])\n continue # next line\n\n r = re.match(\"(userdetails\\.attribute\\.value)=(.*)\", line)\n if r:\n value = r.groups()[1]\n # last name parsed is where it has to\n # be stacked\n if attributes[-1][1] == None:\n attributes[-1][1] = value\n if isinstance(attributes[-1][1], list):\n attributes[-1][1].append(value)\n else:\n # cast to list\n attributes[-1].append([attributes[-1][1], value])\n\n return dict([(item[0], item[1]) for item in attributes])","def getAttributeDict(self):\n result = {}\n dict = self.getDict()\n for key in dict.keys():\n value = dict.get(key)\n if value.__class__.__name__ != 'SpecialDict':\n result[key] = value\n return result","def to_dict(self):\n d = {}\n i = 0\n for entry in self.entries:\n d[i] = {}\n attributes = self.get_attribute_list()\n print (attributes)\n for data in attributes:\n d[i][data] = entry.__getattribute__(data)\n i = i + 1\n return d","def parseAttributes(self, ent_name, attrs_str):\r\n parts = []\r\n lastpos = 0\r\n \r\n while lastpos < len(attrs_str):\r\n newpos = self.nextString(attrs_str, lastpos)\r\n parts.extend(self.parseAttribute(attrs_str[lastpos:newpos-1]))\r\n lastpos = newpos\r\n \r\n schema_attributes = self.schema.getAttributes(ent_name)\r\n\r\n assert len(schema_attributes) == len(parts), \\\r\n \"Expected %s attributes, got %s (entity: %s\" % \\\r\n (len(schema_attributes), len(parts), ent_name)\r\n \r\n attribute_names = [a[0] for a in schema_attributes]\r\n \r\n return dict(zip(attribute_names, parts))","def etree_to_dict(t):\n d = {t.tag: {} if t.attrib else None}\n children = list(t)\n if children:\n dd = defaultdict(list)\n for dc in map(etree_to_dict, children):\n for k, v in dc.items():\n dd[k].append(v)\n d = {t.tag: {k: v[0] if len(v) == 1 else v\n for k, v in dd.items()}}\n if t.attrib:\n # treat DOI attributes differently for readability\n if (\"doi\"==t.tag):\n for k,v in t.attrib.items():\n d[\"doi_\"+k] = v\n else:\n d[t.tag].update(('@' + k, v)\n for k, v in t.attrib.items())\n if t.text:\n text = t.text.strip()\n if children or t.attrib:\n if text:\n d[t.tag] = text\n else:\n d[t.tag] = text\n return d","def parse_attributes(attribute_str):\n attributes = attribute_str.split(';')\n output = OrderedDict()\n\n for attribute in attributes:\n key, value = attribute.split('=')\n output[key] = value\n\n return output","def load_metadata(cls, xml_object):\r\n metadata = {'xml_attributes': {}}\r\n for attr, val in xml_object.attrib.iteritems():\r\n # VS[compat]. Remove after all key translations done\r\n attr = cls._translate(attr)\r\n\r\n if attr in cls.metadata_to_strip:\r\n # don't load these\r\n continue\r\n\r\n if attr not in cls.fields:\r\n metadata['xml_attributes'][attr] = val\r\n else:\r\n metadata[attr] = deserialize_field(cls.fields[attr], val)\r\n return metadata","def attributes_metadata(self):\n\n attribute_meta = collections.defaultdict(dict)\n\n for attribute in self.attributes:\n attribute_meta[attribute.name]['valuemap'] = attribute.valuemap\n attribute_meta[attribute.name]['qualifiers'] = attribute.qualifiers\n\n return dict(attribute_meta)","def test_parser_to_dict(self):\n xml = \"\"\"\\\n<foo>\n <bar>baz</bar>\n <quz>\n <wow>works!</wow>\n </quz>\n</foo>\n\"\"\"\n d = x.to_dict(xml, {})\n assert d.bar.text_ == u'baz'\n assert d.quz.wow.text_ == u'works!'","def GetAttributes(self):\n return dict(self._attrs)","def elem2dict(node):\n result = {}\n\n for element in node.iterchildren():\n # Remove namespace prefix\n key = element.tag.split('}')[1] if '}' in element.tag else element.tag\n key = key[:1].lower() + key[1:]\n\n # Process element as tree element if the inner XML contains non-whitespace content\n if element.text and element.text.strip():\n value = element.text\n else:\n value = elem2dict(element)\n if key in result:\n if type(result[key]) is list:\n result[key].append(value)\n else:\n tempvalue = result[key].copy()\n result[key] = [tempvalue, value]\n else:\n result[key] = value\n return result","def etree_to_dict(t):\n d = {t.tag: {} if t.attrib else None}\n children = list(t)\n if children:\n dd = defaultdict(list)\n for dc in map(etree_to_dict, children):\n for k, v in dc.items():\n dd[k].append(v)\n d = {t.tag: {k:v[0] if len(v) == 1 else v for k, v in dd.items()}}\n if t.attrib:\n d[t.tag].update(('@' + k, v) for k, v in t.attrib.items())\n if t.text:\n text = t.text.strip()\n if children or t.attrib:\n if text:\n d[t.tag]['#text'] = text\n else:\n d[t.tag] = text\n return d","def recursive_parse_xml_to_dict(xml):\n if not xml:\n return {xml.tag: xml.text}\n result = {}\n for child in xml:\n child_result = recursive_parse_xml_to_dict(child)\n if child.tag != 'object':\n result[child.tag] = child_result[child.tag]\n else:\n if child.tag not in result:\n result[child.tag] = []\n result[child.tag].append(child_result[child.tag])\n return {xml.tag: result}","def getXmlDict(oxml):\n lines = oxml.split(\"\\n\")\n rrd_d = {}\n # <cf> AVERAGE </cf>\n # <pdp_per_row> 288 </pdp_per_row> <!-- 86400 seconds -->\n\n # parse xml file\n key = \"\"\n rows = [] \n for line in lines:\n if (reMatchCF(line)):\n cf = line.split()[1]\n key += cf\n if (reMatchPDP(line)):\n pdp = line.split()[1]\n key += pdp\n if (reMatchRow(line)):\n ele = line.split()\n time = ele[5]\n val = ele[8]\n rows.append([time,val,line])\n # end of rra is reached, store to dict and rest vals\n if (reMatchDBEnd(line) and key and rows):\n rrd_d[key] = rows\n key = \"\"\n rows = []\n return rrd_d","def recursive_parse_xml_to_dict(xml):\n if not xml:\n return {xml.tag: xml.text}\n result = {}\n for child in xml:\n child_result = recursive_parse_xml_to_dict(child)\n if child.tag != 'object':\n result[child.tag] = child_result[child.tag]\n else:\n if child.tag not in result:\n result[child.tag] = []\n result[child.tag].append(child_result[child.tag])\n return {xml.tag: result}","def attributes(self):\n return dict(self.__attributes)","def _getAttrMap(self):\r\n if not getattr(self, 'attrMap'):\r\n self.attrMap = {}\r\n for (key, value) in self.attrs:\r\n self.attrMap[key] = value\r\n return self.attrMap","def _parse_attributes(self, attributes):\n\n var_value_pairs = attributes.split()\n\n self.logger.debug('attributes:{} pairs:{}'.format(attributes, var_value_pairs))\n\n for var_value_pair in var_value_pairs:\n (var, separator, value) = var_value_pair.partition('=')\n value = value.replace('\"', '')\n self._current_element.add_attribute(var, value)","def _parse_attr(self, attr_proto):\n attrs = {}\n for key, value in attr_proto.items():\n attrs[key] = self._get_attr(value)\n\n return attrs","def apply(self, attributes):\n return {\n self.attribute_names[k]: v\n for k, v in attributes.items()\n }","def __get_el_dict(activity_name, element_name):\n __set_xml()\n element_dict = activity.get(activity_name).get(element_name)\n return element_dict","def _xmlTreeToDict(cls, node):\n if not isinstance(node, ElementTree.Element):\n raise ATException('_xmlTreeToDict(), param: [node] expected a xml.etree.ElementTree.Element')\n\n nodeDict = {}\n\n if len(node.items()) > 0:\n nodeDict.update(dict(node.items()))\n\n for child in node:\n childItemDict = cls._xmlTreeToDict(child)\n if child.tag in nodeDict:\n if isinstance(nodeDict[child.tag], list):\n nodeDict[child.tag].append(childItemDict)\n else:\n nodeDict[child.tag] = [nodeDict[child.tag], childItemDict]\n else:\n nodeDict[child.tag] = childItemDict\n\n text = ''\n if node.text is not None:\n text = node.text.strip()\n\n if len(nodeDict) > 0:\n if len(text) > 0:\n nodeDict[node.tag + '_text'] = text\n else:\n nodeDict = text\n\n return nodeDict","def _attribs(self, name=None, description=None):\n a = {}\n if name:\n a['name'] = name\n if description:\n a['description'] = description\n return a","def _read_attributes(root):\n output_list = []\n for _, value in enumerate(root[0][2]):\n attr = Attribute(value)\n output_list.append(attr)\n return output_list","def parse_attributes(self):\n attrs = {}\n error = False\n for header, attr in self.app.config['SSO_ATTRIBUTE_MAP'].items():\n required, name = attr\n value = request.environ.get(header, None)\n\n attrs[name] = value\n if not value or value == '':\n if required:\n error = True\n return attrs, error","def etree_to_dict(t):\n from collections import defaultdict\n\n d = {t.tag: {} if t.attrib else None}\n children = list(t)\n if children:\n dd = defaultdict(list)\n for dc in map(etree_to_dict, children):\n for k, v in dc.items():\n dd[k].append(v)\n d = {t.tag: {k: v[0] if len(v) == 1 else v for k, v in dd.items()}}\n if t.attrib:\n d[t.tag].update((\"@\" + k, v) for k, v in t.attrib.items())\n if t.text:\n text = t.text.strip()\n if children or t.attrib:\n if text:\n d[t.tag][\"#text\"] = text\n else:\n d[t.tag] = text\n return d","def __build_dictionary(self):\n def recursive_generation(t):\n \"\"\"\n Nested helper function that recursively loops through an XML node to construct a dictionary.\n Solution from http://stackoverflow.com/a/10077069 (2013-01-19)\n \"\"\"\n d = {t.tag: {} if t.attrib else None}\n children = list(t)\n\n if children:\n dd = defaultdict(list)\n\n for dc in map(recursive_generation, children):\n for k, v in dc.iteritems():\n dd[k].append(v)\n\n d = {t.tag: {k:v[0] if len(v) == 1 else v for k, v in dd.iteritems()}}\n\n if t.attrib:\n d[t.tag].update(('@' + k, v) for k, v in t.attrib.iteritems())\n\n if t.text:\n text = t.text.strip()\n\n if children or t.attrib:\n if text:\n d[t.tag]['#text'] = text\n else:\n d[t.tag] = text\n\n return d\n \n string_repr = etree.tostring(self._config_file, pretty_print=True)\n element_tree = cElementTree.XML(string_repr)\n \n self._config_dict = recursive_generation(element_tree)\n self._config_dict = self._config_dict[self._config_dict.keys()[0]]","def parse_attr_str(attr_str):\r\n if not attr_str:\r\n print(\"Empty att_str in parse_attr_str()\")\r\n return {}\r\n\r\n if sys.version_info[0] == 3:\r\n string_types = str\r\n else:\r\n string_types = basestring\r\n\r\n if not isinstance(attr_str, string_types):\r\n print(\"Invalid attr_str while parsing:{0}\".format(attr_str))\r\n return {}\r\n\r\n # initialize a lexer, in POSIX mode (to properly handle escaping)\r\n lexer = shlex(attr_str, posix=True)\r\n # include '=' as a word character\r\n # (this is done so that the lexer returns a list of key-value pairs)\r\n # (if your option key or value contains any unquoted special character, you will need to add it here)\r\n lexer.wordchars += \"=\"\r\n # make sure attribute support 'data-parsley-length' attribute name\r\n lexer.wordchars += \"-\"\r\n # then we separate option keys and values to build the resulting dictionary\r\n # (maxsplit is required to make sure that '=' in value will not be a problem)\r\n # sometimes as HTML will has some key like attribute without value, like: 'required', 'disabled'...\r\n # it need extract those single attribute from the string\r\n pairs_attrs = []\r\n single_attrs = []\r\n for word in lexer:\r\n if \"=\" in word:\r\n # str.split() changed 'maxsplit' to keyword arguments\r\n # see: https://docs.python.org/3.3/library/stdtypes.html#str.split\r\n if sys.version_info >= (3, 3):\r\n pairs_attrs.append(word.split(\"=\", maxsplit=1))\r\n else:\r\n pairs_attrs.append(word.split(\"=\", 1))\r\n else:\r\n single_attrs.append(word)\r\n\r\n # convert pairs attribute list to dict\r\n pairs_attr_dict = dict(pairs_attrs)\r\n # add single atrribute to dict\r\n for attr in single_attrs:\r\n pairs_attr_dict[attr] = None\r\n\r\n return pairs_attr_dict","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def get_attributes(self):\n return dict(self.attributes) # return the attributes","def etree2dict(element):\n i = dict(element.items())\n i.update(_make_content(i, element.text, strip=True))\n\n for child in element:\n tag = child.tag\n value = etree2dict(child)\n i.update(_make_content(i, value, tag))\n\n if element.text and not set(i).difference([\"content\"]):\n # element is leaf node and doesn't have attributes\n i = i.get(\"content\")\n\n return i","def to_dict(self) -> Dict:\n _dict = {}\n if hasattr(self, 'attributes') and self.attributes is not None:\n _dict['attributes'] = [x.to_dict() for x in self.attributes]\n return _dict","def a_attr_dict (self) :\n return dict (href = self.abs_href)","def testAttributes(self):\n ddict = {\n \"group\": {\"dataset\": 100, \"@group_attr1\": 10},\n \"dataset\": 200,\n \"@root_attr\": 11,\n \"dataset@dataset_attr\": \"12\",\n \"group@group_attr2\": 13,\n }\n dictdump.dicttonx(ddict, self.h5_fname)\n ddict = dictdump.nxtodict(self.h5_fname, include_attributes=True)\n self.assertEqual(ddict[\"group\"][\"@group_attr1\"], 10)\n self.assertEqual(ddict[\"@root_attr\"], 11)\n self.assertEqual(ddict[\"dataset@dataset_attr\"], \"12\")\n self.assertEqual(ddict[\"group\"][\"@group_attr2\"], 13)","def parseGFFAttributes(attributeString):\n if attributeString == \".\":\n return {}\n ret = {}\n for attribute in attributeString.split(\";\"):\n key, value = attribute.split(\"=\")\n ret[urllib.unquote(key)] = urllib.unquote(value)\n return ret","def asdict(self):\n return attr.asdict(self)","def process_start_tag(tag_text):\n tokens = tokenise(tag_text.strip(\"<>\"))\n tag = tokens[0]\n attributes = {}\n if len(tokens) > 1:\n for token in tokens[1:]:\n try:\n i = token.index(\"=\")\n key = token[0:i]\n value = token[i + 1 :].strip(' \"')\n except ValueError:\n key = token\n value = \"\"\n attributes[key] = value\n return (tag, attributes)","def to_dict(self):\n if self._dict is not None:\n return self._dict\n\n result = {}\n for key in self.ATTRIBUTES:\n value = getattr(self, key)\n if value:\n result[key] = value\n self._dict = result\n return result","def voc_xml_to_dict(xml_path):\n\n xml = etree.parse(xml_path)\n root = xml.getroot()\n image_name = root.find('filename').text\n labels = []\n boxes = []\n\n for obj in root.findall('object'):\n labels.append(obj.find('name').text)\n bbox = obj.find('bndbox')\n bounds = [\n float(bbox.find('xmin').text),\n float(bbox.find('ymin').text),\n float(bbox.find('xmax').text),\n float(bbox.find('ymax').text)\n ]\n boxes.append(bounds)\n\n return {\n 'image': image_name,\n 'labels': labels,\n 'boxes': boxes,\n }","def transform(attrs: dict) -> dict:\n\n pass","def AttributesFromMojom(self, mojom):\n if not mojom.decl_data.attributes:\n return None\n\n return {attr.key: attr.value for attr in mojom.decl_data.attributes}","def to_dict(\n self, attributes: Sequence[str] = (\n 'demand_instruments', 'supply_instruments', 'inverse_covariance_matrix',\n 'expected_xi_by_theta_jacobian', 'expected_omega_by_theta_jacobian', 'expected_prices',\n 'expected_shares', 'computation_time', 'draws', 'fp_converged', 'fp_iterations',\n 'contraction_evaluations'\n )) -> dict:\n return {k: getattr(self, k) for k in attributes}","def get_el_dict(activity_name, element):\n set_xml()\n element_dict = activity.get(activity_name).get(element)\n print(element_dict)\n return element_dict","def getAttributeInfoDictionary(attr, format=None):\n format = format or _getDocFormat(attr)\n return {'name': attr.getName(),\n 'doc': renderText(attr.getDoc() or '', format=format)}","def as_attrdict(self) -> AttributeDict:\n return AttributeDict(\n {\n x: Configuration(v).as_attrdict() if isinstance(v, Mapping) else v\n for x, v in self.items(levels=1)\n }\n )","def get_attributes(self):\n\n endpoint = self._get_api_endpoint() + '/attributes'\n results = self.tq.get(endpoint, withp='attribute')\n if 'data' not in results:\n return {}\n\n return results['data']\n # tr = {}\n # for attribute in results['data']:\n # tr[attribute['attribute']['name']] = attribute['value']\n # return tr","def to_dict(self):\n return attr.asdict(self)","def get_attributes_from_child(child):\n return [{'element': child,\n 'attribute': x.attrib,\n 'tag': x.tag,\n 'keys': x.keys()} for x in child]","def ToDict(self):\n atributes_dictionary = {}\n for key, value in self.__dict__.iteritems():\n atributes_dictionary[key] = value\n return atributes_dictionary","def parse_product_full_attributes(advertise: Dict[str, Any]) -> Optional[Dict[str, Any]]:\n if \"product_full_attributes\" in advertise.keys():\n\n tmp_dict: Dict[str, Any] = dict()\n tmp: List[str] = re.split('[ \\t]{3,}', advertise[\"product_full_attributes\"])\n\n for term in tmp:\n key_val: List[str] = re.split('[ \\t]{2,}', term)\n if '' in key_val:\n key_val.remove('')\n if len(key_val) == 2:\n tmp_dict[re.sub(r'[^\\w\\s]', '', key_val[0]).strip()] = key_val[1]\n\n return tmp_dict","def tags_dict(self):\n return ({'name': 'tag', 'attrs': {'k': k, 'v': v}} for k, v in self.tags.items())","def parse_attribute(self,attr,table):\n \n attr_dict = {}\n \n for child in attr:\n name = child.attrib['name']\n \n #attributes can either have string or bool as the value we need\n #checking for boolean\n if 'val' in child[0].attrib:\n val = child[0].attrib['val']\n \n if val == 'true':\n flag = True\n else:\n flag = False\n \n attr_dict[name] = flag\n \n #else it's string stroed as text\n else:\n attr_dict[name] = super(DatabaseUmlParser,self).stripHashtags(child[0].text)\n \n attr = db_attribute.DbAttribute(table,attr_dict)\n \n return attr","def extract_request(self, attrs):\n try:\n _base = self.attributes()\n _new_dict = {}\n for key in attrs:\n if key in _base:\n _new_dict[key] = attrs[key]\n return _new_dict\n except Exception as e:\n print(e)","def rss_attributes(self):\n return {u\"version\": self._version,\n u\"xmlns:media\": u\"http://search.yahoo.com/mrss/\",\n \"xmlns:atom\": u\"http://www.w3.org/2005/Atom\"\n }","def get_attributes_config(node):\n # attrs_config = {}\n keyable_attrs = get_keyable_attribute(node)\n config_data = init_table_config_data()\n if keyable_attrs:\n # attrs_config[\"_attrs\"] = keyable_attrs\n for attr in keyable_attrs:\n config = get_single_attribute_config(node, attr)\n # attrs_config[attr] = config\n config_data[\"channels\"].append(config[\"fullName\"])\n config_data[\"channels_data\"][config[\"fullName\"]] = config\n\n return config_data","def load_attrs(self):\n return loads(self.get_attr().GetObject()) or {}","def attribute_dict(self):\n return self.__attribute_dict","def get_attribute_data(self, attrs):\n return {\n 'id': attrs['data-id'],\n }","def xml_to_dict(self):\n medicine_node = ['medicines', 'dose', 'unit', 'unit_price', 'goods_num', 'dose_that',\n 'remark', 'm_usage', 'goods_norms', 'goods_orgin', 'MedPerDos', 'MedPerDay']\n dict_data = {}\n n = 0\n xq_childs = 0\n for child in self.root.iter():\n # print(child.tag)\n # print(child.text)\n if child.tag not in medicine_node:\n if child.tag == 'xq':\n xq_childs = child.__len__() # __len__：返回元素大小，元素的大小为元素的子元素数量\n dict_data[child.tag] = child.text\n else:\n if n < xq_childs:\n dict_data[child.tag] = [child.text]\n n += 1\n else:\n dict_data[child.tag].append(child.text)\n return dict_data","def as_dict(self):\n result = {}\n for attr in self.__attr:\n result[attr] = getattr(self, attr)\n return result","def preprocess_attributes(business):\n\tattrs = business[ATTRIBUTES]\n\tattrs_dict = dict()\n\n\tfor (key, val) in attrs.items():\n\t\tval = ast.literal_eval(val)\n\t\tif val is None:\n\t\t\tcontinue\n\n\t\t# if val is a json object\n\t\tif isinstance(val, dict):\n\t\t\tinner_dict = dict()\n\t\t\tfor (inner_key, inner_val) in val.items():\n\t\t\t\tinner_key = inner_key\n\t\t\t\tinner_val = inner_val\n\t\t\t\tinner_dict[inner_key] = inner_val\n\t\t\tattrs_dict[key] = inner_dict\n\t\telse:\n\t\t\tattrs_dict[key] = val\n\tbusiness[ATTRIBUTES] = attrs_dict","def get_ns_dict(xml):\n \n nss = {} \n def_cnt = 0\n matches = re.findall(r'\\s+xmlns:?(\\w*?)\\s*=\\s*[\\'\"](.*?)[\\'\"]', xml)\n for match in matches:\n prefix = match[0]; ns = match[1]\n if prefix == '':\n def_cnt += 1\n prefix = '_' * def_cnt\n nss[prefix] = ns\n return nss"],"string":"[\n \"def get_attrs_dict(self, root_element):\\n attr_elements = root_element.findall(\\\"attribute\\\")\\n attrs_dict = {}\\n for el in attr_elements:\\n attrs_dict[el.attrib[\\\"name\\\"]] = {\\n \\\"value\\\": el.attrib[\\\"value\\\"],\\n \\\"type\\\": el.attrib.get(\\\"type\\\", None)\\n }\\n return attrs_dict\",\n \"def extract_attrs(attr_string):\\n attributes = {}\\n for name, val in FIND_ATTRS.findall(attr_string):\\n val = (\\n val.replace(\\\"<\\\", \\\"<\\\")\\n .replace(\\\">\\\", \\\">\\\")\\n .replace(\\\""\\\", '\\\"')\\n .replace(\\\"&\\\", \\\"&\\\")\\n )\\n attributes[name] = val\\n return attributes\",\n \"def dom2dict(element):\\n keys = list(element.attributes.keys())\\n values = [val.value for val in list(element.attributes.values())]\\n return dict(list(zip(keys, values)))\",\n \"def get_attrib_dict(self, attribs: Tuple[str]) -> Dict[str, str]:\\n attrs = self.get_attribs(attribs)\\n attrs = tuple(map(lambda a: (a[0][1:], a[1]), attrs))\\n return dict(attrs)\",\n \"def process_attrs(attrs):\\n if attrs.getLength() == 0:\\n return {}\\n tmp_dict = {}\\n for name in attrs.getNames():\\n tmp_dict[name] = attrs.getValue(name)\\n return tmp_dict\",\n \"def dom2dict(element):\\n keys = list(element.attributes.keys())\\n values = [val.value for val in list(element.attributes.values())]\\n return dict(list(zip(keys, values)))\",\n \"def xml2dict( xml, sanitize=True, prefix=None):\\n \\n \\n #Decode to avert parsing errors as some software dump large text\\n #fields into the file that occasionally contain erronious chars\\n xml=xml.decode('utf-8', errors='ignore')\\n\\n \\n return etree2dict(etree.fromstring(xml), sanitize, prefix)\",\n \"def attribute_to_dict(attr: onnx.AttributeProto) -> Dict:\\n ret = {}\\n for a in attr:\\n value = get_attribute_value(a)\\n if isinstance(value, bytes):\\n value = str(value, 'utf-8')\\n ret[a.name] = value\\n return ret\",\n \"def _get_attribute_dic(self, attrs):\\n attr_dic = {}\\n for attr_pair in attrs:\\n attr_dic[attr_pair[0]] = attr_pair[1]\\n return attr_dic\",\n \"def convert_attr_to_dict(attr):\\n\\n\\tresult = dict()\\n\\tattr = attr.split(';')\\n\\tattrlist = [a.split(':') for a in attr]\\n\\tfor pair in attrlist:\\n\\t\\tif len(pair) == 2:\\n\\t\\t\\tkey = pair[0]\\n\\t\\t\\tvalue = pair[1]\\n\\t\\t\\tresult[key] = value\\n\\n\\treturn result\",\n \"def get_attributes(self):\\n\\t\\treturn dict(list(self.__element.items()))\",\n \"def load_attribute_dict(parent_element, tag_name):\\n loaded_element = parent_element.find(tag_name)\\n attribute_dict = {attr[0] : convert_to_int_if_numeric(attr[1])\\n for attr in loaded_element.items()}\\n return attribute_dict\",\n \"def get_attributes(self) -> Dict[str, str]:\\n pass\",\n \"def attrs_to_dict(self, attrs):\\n return {k: v for k, v in attrs}\",\n \"def get_attributes_of_node(core, node):\\n attributes = {}\\n for attribute in core.get_attribute_names(node):\\n attributes[attribute] = core.get_attribute(node,attribute)\\n return attributes\",\n \"def read_attribs(self):\\n\\n attribs = {}\\n while self.index < self.length:\\n self.ignore_whitespaces()\\n if self.xtext[self.index] == '>':\\n break\\n name = self.read_until('=')\\n self.index += 1\\n self.read_until('\\\"')\\n self.index += 1\\n value = self.read_until('\\\"')\\n self.index += 1\\n\\n attribs[name] = value\\n\\n return attribs\",\n \"def test_xml_to_dict(self):\\n xml = \\\"\\\"\\\"\\n <a\\n xmlns=\\\"urn:ietf:params:xml:ns:netconf:base:1.0\\\"\\n xmlns:nm=\\\"s\\\"\\n >\\n <b m=\\\"g\\\">\\n b\\n </b>\\n <c nm:nm=\\\"update\\\">\\n <f>1</f>\\n <e>str</e>\\n <d>1</d>\\n <d>2</d>\\n <d>3</d>\\n </c>\\n </a>\\n \\\"\\\"\\\"\\n xmlns = {\\n \\\"_\\\": utils.NETCONF_NAMESPACE\\n }\\n result = utils.generate_dict_node(etree.XML(xml), xmlns)\\n # check dict\\n self.assertDictEqual(\\n self.SIMPLE_DICT,\\n result\\n )\\n # check xmlns\\n self.assertEqual(\\n {\\n '_': utils.NETCONF_NAMESPACE,\\n 'nm': 's'\\n }, xmlns\\n )\",\n \"def to_dict(xml):\\n children = xml.getchildren()\\n if not children:\\n return xml.text\\n else:\\n out = {}\\n for node in xml.getchildren():\\n if node.tag in out:\\n if not isinstance(out[node.tag], list):\\n out[node.tag] = [out[node.tag]]\\n out[node.tag].append(to_dict(node))\\n else:\\n out[node.tag] = to_dict(node)\\n return out\",\n \"def convert_attributes(cls, attrs):\\n return {}\",\n \"def parse_attributes(data, attributes):\\n result = {}\\n for key, val in attributes.items():\\n if type(val) is list:\\n attr_val = get_tree_data(data, val)\\n else:\\n attr_val = data.get(val, None)\\n if attr_val is not None:\\n result[key] = attr_val\\n return result\",\n \"def xml_to_dict(args):\\n rdict = dict()\\n args = re.sub(r'xmlns=\\\\\\\".+?\\\\\\\"', '', args)\\n root = ET.fromstring(args)\\n ifmtrunk = root.find('.//ifmtrunk')\\n if ifmtrunk is not None:\\n try:\\n ifmtrunk_iter = ET.Element.iter(ifmtrunk)\\n except AttributeError:\\n ifmtrunk_iter = ifmtrunk.getiterator()\\n\\n for ele in ifmtrunk_iter:\\n if ele.text is not None and len(ele.text.strip()) > 0:\\n rdict[ele.tag] = ele.text\\n return rdict\",\n \"def parse_attributes(self, attr):\\n result = {}\\n annotations = []\\n # Sanitize and split attributes up\\n split_attr = attr.strip(' \\\\t\\\\n;').split(';')\\n for pair in split_attr:\\n splitpair = pair.split('=')\\n if len(splitpair) != 2:\\n continue\\n if splitpair[0] == \\\"ID\\\":\\n result['identifier'] = splitpair[1]\\n elif splitpair[0] == \\\"Name\\\":\\n result['name'] = splitpair[1]\\n elif splitpair[0] == \\\"Parent\\\":\\n result['parent_id'] = splitpair[1]\\n elif splitpair[0] == \\\"Dbxref\\\" or splitpair[0] == \\\"Ontology_term\\\":\\n annotations.append(splitpair)\\n # Make sure we found an ID\\n if \\\"identifier\\\" not in result:\\n return {}\\n # Add annotations if we found any\\n if annotations:\\n result[\\\"annotations\\\"] = annotations\\n return result\",\n \"def attrs(xml):\\r\\n return lxml.html.fromstring(xml).attrib\",\n \"def getAttrsDict(attrs):\\r\\n attrsDict = json.loads(re.sub('/\\\\\\\"(?!(,\\\\s\\\"|}))','\\\\\\\\\\\"',attrs).replace(\\\"\\\\t\\\",\\\" \\\").replace(\\\"\\\\n\\\",\\\" \\\")) if len(attrs)>0 else {}\\r\\n return attrsDict\",\n \"def xml_children_as_dict(node):\\n return dict((e.tag, e.text) for e in node)\",\n \"def parse_common_attributes(self, element, attributes):\\n result_attributes = {}\\n for attribute in attributes:\\n attrib_info = ALL_ATTRIBUTES_MAP[attribute]\\n if attrib_info.get(\\\"default\\\", MISSING) is not MISSING:\\n default_value = attrib_info[\\\"default\\\"]\\n else:\\n default_value = MISSING\\n attribute_name = attrib_info.get(\\\"attribute_name\\\", attribute)\\n value = element.attrib.get(attribute_name, default_value)\\n if value is MISSING or value == \\\"\\\":\\n if attrib_info.get(\\\"use\\\") == \\\"optional\\\":\\n continue\\n\\n else:\\n raise ValueError(\\n f\\\"element {element.tag} missing required \\\"\\n f\\\"attribute {attribute_name}\\\"\\n )\\n\\n if attrib_info.get(\\\"type\\\"):\\n try:\\n value = attrib_info[\\\"type\\\"](value)\\n except ValueError as exc:\\n raise ValueError(\\n f\\\"element {element.tag} invalid value \\\"\\n f\\\"{repr(value)} for attribute {attribute_name}\\\"\\n ) from exc\\n\\n if attrib_info.get(\\\"minimumValue\\\"):\\n if value < attrib_info[\\\"minimumValue\\\"]:\\n raise ValueError(\\n f\\\"element {element.tag} invalid value {repr(value)}\\\"\\n f\\\" for attribute {attribute_name},\\\"\\n \\\"less than allowed minimum \\\"\\n f\\\"{repr(attrib_info['minimumValue'])}\\\"\\n )\\n\\n if attrib_info.get(\\\"pattern\\\"):\\n if not attrib_info[\\\"pattern\\\"].match(value):\\n raise ValueError(\\n f\\\"element {element.tag} invalid value {repr(value)} \\\"\\n f\\\"for attribute {attribute_name},\\\"\\n \\\"does not match expected pattern \\\"\\n f\\\"{repr(attrib_info['pattern'])}\\\"\\n )\\n\\n if attrib_info.get(\\\"map\\\"):\\n try:\\n value = attrib_info[\\\"map\\\"][value]\\n except (KeyError, IndexError) as exc:\\n raise ValueError(\\n f\\\"element {element.tag} invalid value {repr(value)} \\\"\\n f\\\"for attribute {attribute_name}\\\"\\n f\\\", must be one of {repr(attrib_info['map'].keys())}\\\"\\n ) from exc\\n\\n if attrib_info.get(\\\"rename\\\"):\\n attribute = attrib_info[\\\"rename\\\"]\\n\\n result_attributes[attribute] = value\\n\\n return result_attributes\",\n \"def get_attributes(self):\\n \\n retdict = {}\\n retdict['s'] = str(self.s)\\n if self.t != None:\\n retdict['t'] = str(self.t)\\n retdict['a'] = str(self.a)\\n retdict['b'] = str(self.b)\\n retdict['c'] = str(self.c)\\n retdict['d'] = str(self.d)\\n return retdict\",\n \"def to_dict(\\n self,\\n attributes: Iterable[str] = (\\\"xyz\\\", \\\"viewdir\\\", \\\"imgsz\\\", \\\"f\\\", \\\"c\\\", \\\"k\\\", \\\"p\\\"),\\n ) -> Dict[str, tuple]:\\n return {key: helpers.numpy_to_native(getattr(self, key)) for key in attributes}\",\n \"def _get_attribute_dict(self, attributes, classname=None):\\n if attributes and isinstance(attributes, six.string_types):\\n return {\\n 'class': attributes\\n }\\n if not attributes:\\n attributes = {}\\n if not classname:\\n classname = self.DEFAULT_CLASS_NAME\\n attributes.setdefault('class', classname)\\n return attributes\",\n \"def proc_attr(inp):\\n dic = {}\\n for att in inp.attrs.keys():\\n if getattr(inp.attrs[att], \\\"dtype\\\", None) is None:\\n dic[att] = inp.attrs[att]\\n elif inp.attrs[att].dtype.char == 'S':\\n dic[att] = [\\n x.strip() for x in inp.attrs[att].tostring().decode('ascii').split(',')\\n ]\\n else:\\n dic[att] = (\\n inp.attrs[att][0]\\n if isinstance(inp.attrs[att],np.ndarray) and\\n inp.attrs[att].size==1\\n else inp.attrs[att]\\n )\\n return dic\\n pass\",\n \"def dict(self):\\n return xmltodict.parse(str(self))\",\n \"def get_attrs(self):\\n req_attrv = self._ptr.contents.attrv\\n attrs = {}\\n if bool(req_attrv):\\n i = 0\\n while 1:\\n s = bytestostr(req_attrv[i])\\n i += 1\\n if s == None:\\n break\\n try:\\n k, v = s.split(\\\"=\\\", 1)\\n attrs[k] = v\\n except:\\n pass\\n return attrs\",\n \"def dict(self) -> dict():\\n\\n dict_reg_hive = {}\\n\\n for _attribute in self.attributes.__dict__.items():\\n if isinstance(_attribute[1], str):\\n if not True in [_attribute[1].startswith(prefix) for prefix in ['<', 'providers.', 'None']]:\\n _attribute_value = getattr(self, _attribute[1])\\n dict_reg_hive.update({_attribute[1]: _attribute_value})\\n\\n return dict_reg_hive\",\n \"def parse_one_xml(xml_file, fields=None):\\r\\n tree = etree.iterparse(xml_file)\\r\\n \\r\\n d = dict.fromkeys(fields)\\r\\n for event, elem in tree:\\r\\n if elem.tag == 'SAMPLE_ATTRIBUTE':\\r\\n for f in fields:\\r\\n if elem.getchildren()[0].text == f:\\r\\n d[f] = elem.getchildren()[1].text \\r\\n return d\",\n \"def attribute_metadata(attr: Attr, parent_namespace: Optional[str]) -> Dict:\\n metadata = dict(\\n name=None\\n if attr.is_nameless or attr.local_name == attribute_name(attr.name)\\n else attr.local_name,\\n type=attr.xml_type,\\n namespace=attr.namespace\\n if parent_namespace != attr.namespace or attr.is_attribute\\n else None,\\n **attr.restrictions.asdict(),\\n )\\n\\n return {\\n key: value\\n for key, value in metadata.items()\\n if value is not None and value is not False\\n }\",\n \"def get_attributes(doc):\\n\\treturn doc.keys()\",\n \"def _attrs_map(self) -> \\\"dict[int, str]\\\":\\n return {i: attr.name for i, attr in enumerate(self._attrs())}\",\n \"def attributes(self, *args):\\n kwargs = {}\\n if args:\\n kwargs[\\\"attributenames\\\"] = args\\n\\n r = self._token_id_request(urljoin(self._url, Client._attribute_resource), **kwargs)\\n\\n # parse contennt looking for all attributes\\n attributes = []\\n for line in r.text.splitlines():\\n r = re.match(\\\"(userdetails\\\\.attribute\\\\.name)=(.*)\\\", line)\\n if r:\\n name = r.groups()[1]\\n attributes.append([name, None])\\n continue # next line\\n\\n r = re.match(\\\"(userdetails\\\\.attribute\\\\.value)=(.*)\\\", line)\\n if r:\\n value = r.groups()[1]\\n # last name parsed is where it has to\\n # be stacked\\n if attributes[-1][1] == None:\\n attributes[-1][1] = value\\n if isinstance(attributes[-1][1], list):\\n attributes[-1][1].append(value)\\n else:\\n # cast to list\\n attributes[-1].append([attributes[-1][1], value])\\n\\n return dict([(item[0], item[1]) for item in attributes])\",\n \"def getAttributeDict(self):\\n result = {}\\n dict = self.getDict()\\n for key in dict.keys():\\n value = dict.get(key)\\n if value.__class__.__name__ != 'SpecialDict':\\n result[key] = value\\n return result\",\n \"def to_dict(self):\\n d = {}\\n i = 0\\n for entry in self.entries:\\n d[i] = {}\\n attributes = self.get_attribute_list()\\n print (attributes)\\n for data in attributes:\\n d[i][data] = entry.__getattribute__(data)\\n i = i + 1\\n return d\",\n \"def parseAttributes(self, ent_name, attrs_str):\\r\\n parts = []\\r\\n lastpos = 0\\r\\n \\r\\n while lastpos < len(attrs_str):\\r\\n newpos = self.nextString(attrs_str, lastpos)\\r\\n parts.extend(self.parseAttribute(attrs_str[lastpos:newpos-1]))\\r\\n lastpos = newpos\\r\\n \\r\\n schema_attributes = self.schema.getAttributes(ent_name)\\r\\n\\r\\n assert len(schema_attributes) == len(parts), \\\\\\r\\n \\\"Expected %s attributes, got %s (entity: %s\\\" % \\\\\\r\\n (len(schema_attributes), len(parts), ent_name)\\r\\n \\r\\n attribute_names = [a[0] for a in schema_attributes]\\r\\n \\r\\n return dict(zip(attribute_names, parts))\",\n \"def etree_to_dict(t):\\n d = {t.tag: {} if t.attrib else None}\\n children = list(t)\\n if children:\\n dd = defaultdict(list)\\n for dc in map(etree_to_dict, children):\\n for k, v in dc.items():\\n dd[k].append(v)\\n d = {t.tag: {k: v[0] if len(v) == 1 else v\\n for k, v in dd.items()}}\\n if t.attrib:\\n # treat DOI attributes differently for readability\\n if (\\\"doi\\\"==t.tag):\\n for k,v in t.attrib.items():\\n d[\\\"doi_\\\"+k] = v\\n else:\\n d[t.tag].update(('@' + k, v)\\n for k, v in t.attrib.items())\\n if t.text:\\n text = t.text.strip()\\n if children or t.attrib:\\n if text:\\n d[t.tag] = text\\n else:\\n d[t.tag] = text\\n return d\",\n \"def parse_attributes(attribute_str):\\n attributes = attribute_str.split(';')\\n output = OrderedDict()\\n\\n for attribute in attributes:\\n key, value = attribute.split('=')\\n output[key] = value\\n\\n return output\",\n \"def load_metadata(cls, xml_object):\\r\\n metadata = {'xml_attributes': {}}\\r\\n for attr, val in xml_object.attrib.iteritems():\\r\\n # VS[compat]. Remove after all key translations done\\r\\n attr = cls._translate(attr)\\r\\n\\r\\n if attr in cls.metadata_to_strip:\\r\\n # don't load these\\r\\n continue\\r\\n\\r\\n if attr not in cls.fields:\\r\\n metadata['xml_attributes'][attr] = val\\r\\n else:\\r\\n metadata[attr] = deserialize_field(cls.fields[attr], val)\\r\\n return metadata\",\n \"def attributes_metadata(self):\\n\\n attribute_meta = collections.defaultdict(dict)\\n\\n for attribute in self.attributes:\\n attribute_meta[attribute.name]['valuemap'] = attribute.valuemap\\n attribute_meta[attribute.name]['qualifiers'] = attribute.qualifiers\\n\\n return dict(attribute_meta)\",\n \"def test_parser_to_dict(self):\\n xml = \\\"\\\"\\\"\\\\\\n<foo>\\n <bar>baz</bar>\\n <quz>\\n <wow>works!</wow>\\n </quz>\\n</foo>\\n\\\"\\\"\\\"\\n d = x.to_dict(xml, {})\\n assert d.bar.text_ == u'baz'\\n assert d.quz.wow.text_ == u'works!'\",\n \"def GetAttributes(self):\\n return dict(self._attrs)\",\n \"def elem2dict(node):\\n result = {}\\n\\n for element in node.iterchildren():\\n # Remove namespace prefix\\n key = element.tag.split('}')[1] if '}' in element.tag else element.tag\\n key = key[:1].lower() + key[1:]\\n\\n # Process element as tree element if the inner XML contains non-whitespace content\\n if element.text and element.text.strip():\\n value = element.text\\n else:\\n value = elem2dict(element)\\n if key in result:\\n if type(result[key]) is list:\\n result[key].append(value)\\n else:\\n tempvalue = result[key].copy()\\n result[key] = [tempvalue, value]\\n else:\\n result[key] = value\\n return result\",\n \"def etree_to_dict(t):\\n d = {t.tag: {} if t.attrib else None}\\n children = list(t)\\n if children:\\n dd = defaultdict(list)\\n for dc in map(etree_to_dict, children):\\n for k, v in dc.items():\\n dd[k].append(v)\\n d = {t.tag: {k:v[0] if len(v) == 1 else v for k, v in dd.items()}}\\n if t.attrib:\\n d[t.tag].update(('@' + k, v) for k, v in t.attrib.items())\\n if t.text:\\n text = t.text.strip()\\n if children or t.attrib:\\n if text:\\n d[t.tag]['#text'] = text\\n else:\\n d[t.tag] = text\\n return d\",\n \"def recursive_parse_xml_to_dict(xml):\\n if not xml:\\n return {xml.tag: xml.text}\\n result = {}\\n for child in xml:\\n child_result = recursive_parse_xml_to_dict(child)\\n if child.tag != 'object':\\n result[child.tag] = child_result[child.tag]\\n else:\\n if child.tag not in result:\\n result[child.tag] = []\\n result[child.tag].append(child_result[child.tag])\\n return {xml.tag: result}\",\n \"def getXmlDict(oxml):\\n lines = oxml.split(\\\"\\\\n\\\")\\n rrd_d = {}\\n # <cf> AVERAGE </cf>\\n # <pdp_per_row> 288 </pdp_per_row> <!-- 86400 seconds -->\\n\\n # parse xml file\\n key = \\\"\\\"\\n rows = [] \\n for line in lines:\\n if (reMatchCF(line)):\\n cf = line.split()[1]\\n key += cf\\n if (reMatchPDP(line)):\\n pdp = line.split()[1]\\n key += pdp\\n if (reMatchRow(line)):\\n ele = line.split()\\n time = ele[5]\\n val = ele[8]\\n rows.append([time,val,line])\\n # end of rra is reached, store to dict and rest vals\\n if (reMatchDBEnd(line) and key and rows):\\n rrd_d[key] = rows\\n key = \\\"\\\"\\n rows = []\\n return rrd_d\",\n \"def recursive_parse_xml_to_dict(xml):\\n if not xml:\\n return {xml.tag: xml.text}\\n result = {}\\n for child in xml:\\n child_result = recursive_parse_xml_to_dict(child)\\n if child.tag != 'object':\\n result[child.tag] = child_result[child.tag]\\n else:\\n if child.tag not in result:\\n result[child.tag] = []\\n result[child.tag].append(child_result[child.tag])\\n return {xml.tag: result}\",\n \"def attributes(self):\\n return dict(self.__attributes)\",\n \"def _getAttrMap(self):\\r\\n if not getattr(self, 'attrMap'):\\r\\n self.attrMap = {}\\r\\n for (key, value) in self.attrs:\\r\\n self.attrMap[key] = value\\r\\n return self.attrMap\",\n \"def _parse_attributes(self, attributes):\\n\\n var_value_pairs = attributes.split()\\n\\n self.logger.debug('attributes:{} pairs:{}'.format(attributes, var_value_pairs))\\n\\n for var_value_pair in var_value_pairs:\\n (var, separator, value) = var_value_pair.partition('=')\\n value = value.replace('\\\"', '')\\n self._current_element.add_attribute(var, value)\",\n \"def _parse_attr(self, attr_proto):\\n attrs = {}\\n for key, value in attr_proto.items():\\n attrs[key] = self._get_attr(value)\\n\\n return attrs\",\n \"def apply(self, attributes):\\n return {\\n self.attribute_names[k]: v\\n for k, v in attributes.items()\\n }\",\n \"def __get_el_dict(activity_name, element_name):\\n __set_xml()\\n element_dict = activity.get(activity_name).get(element_name)\\n return element_dict\",\n \"def _xmlTreeToDict(cls, node):\\n if not isinstance(node, ElementTree.Element):\\n raise ATException('_xmlTreeToDict(), param: [node] expected a xml.etree.ElementTree.Element')\\n\\n nodeDict = {}\\n\\n if len(node.items()) > 0:\\n nodeDict.update(dict(node.items()))\\n\\n for child in node:\\n childItemDict = cls._xmlTreeToDict(child)\\n if child.tag in nodeDict:\\n if isinstance(nodeDict[child.tag], list):\\n nodeDict[child.tag].append(childItemDict)\\n else:\\n nodeDict[child.tag] = [nodeDict[child.tag], childItemDict]\\n else:\\n nodeDict[child.tag] = childItemDict\\n\\n text = ''\\n if node.text is not None:\\n text = node.text.strip()\\n\\n if len(nodeDict) > 0:\\n if len(text) > 0:\\n nodeDict[node.tag + '_text'] = text\\n else:\\n nodeDict = text\\n\\n return nodeDict\",\n \"def _attribs(self, name=None, description=None):\\n a = {}\\n if name:\\n a['name'] = name\\n if description:\\n a['description'] = description\\n return a\",\n \"def _read_attributes(root):\\n output_list = []\\n for _, value in enumerate(root[0][2]):\\n attr = Attribute(value)\\n output_list.append(attr)\\n return output_list\",\n \"def parse_attributes(self):\\n attrs = {}\\n error = False\\n for header, attr in self.app.config['SSO_ATTRIBUTE_MAP'].items():\\n required, name = attr\\n value = request.environ.get(header, None)\\n\\n attrs[name] = value\\n if not value or value == '':\\n if required:\\n error = True\\n return attrs, error\",\n \"def etree_to_dict(t):\\n from collections import defaultdict\\n\\n d = {t.tag: {} if t.attrib else None}\\n children = list(t)\\n if children:\\n dd = defaultdict(list)\\n for dc in map(etree_to_dict, children):\\n for k, v in dc.items():\\n dd[k].append(v)\\n d = {t.tag: {k: v[0] if len(v) == 1 else v for k, v in dd.items()}}\\n if t.attrib:\\n d[t.tag].update((\\\"@\\\" + k, v) for k, v in t.attrib.items())\\n if t.text:\\n text = t.text.strip()\\n if children or t.attrib:\\n if text:\\n d[t.tag][\\\"#text\\\"] = text\\n else:\\n d[t.tag] = text\\n return d\",\n \"def __build_dictionary(self):\\n def recursive_generation(t):\\n \\\"\\\"\\\"\\n Nested helper function that recursively loops through an XML node to construct a dictionary.\\n Solution from http://stackoverflow.com/a/10077069 (2013-01-19)\\n \\\"\\\"\\\"\\n d = {t.tag: {} if t.attrib else None}\\n children = list(t)\\n\\n if children:\\n dd = defaultdict(list)\\n\\n for dc in map(recursive_generation, children):\\n for k, v in dc.iteritems():\\n dd[k].append(v)\\n\\n d = {t.tag: {k:v[0] if len(v) == 1 else v for k, v in dd.iteritems()}}\\n\\n if t.attrib:\\n d[t.tag].update(('@' + k, v) for k, v in t.attrib.iteritems())\\n\\n if t.text:\\n text = t.text.strip()\\n\\n if children or t.attrib:\\n if text:\\n d[t.tag]['#text'] = text\\n else:\\n d[t.tag] = text\\n\\n return d\\n \\n string_repr = etree.tostring(self._config_file, pretty_print=True)\\n element_tree = cElementTree.XML(string_repr)\\n \\n self._config_dict = recursive_generation(element_tree)\\n self._config_dict = self._config_dict[self._config_dict.keys()[0]]\",\n \"def parse_attr_str(attr_str):\\r\\n if not attr_str:\\r\\n print(\\\"Empty att_str in parse_attr_str()\\\")\\r\\n return {}\\r\\n\\r\\n if sys.version_info[0] == 3:\\r\\n string_types = str\\r\\n else:\\r\\n string_types = basestring\\r\\n\\r\\n if not isinstance(attr_str, string_types):\\r\\n print(\\\"Invalid attr_str while parsing:{0}\\\".format(attr_str))\\r\\n return {}\\r\\n\\r\\n # initialize a lexer, in POSIX mode (to properly handle escaping)\\r\\n lexer = shlex(attr_str, posix=True)\\r\\n # include '=' as a word character\\r\\n # (this is done so that the lexer returns a list of key-value pairs)\\r\\n # (if your option key or value contains any unquoted special character, you will need to add it here)\\r\\n lexer.wordchars += \\\"=\\\"\\r\\n # make sure attribute support 'data-parsley-length' attribute name\\r\\n lexer.wordchars += \\\"-\\\"\\r\\n # then we separate option keys and values to build the resulting dictionary\\r\\n # (maxsplit is required to make sure that '=' in value will not be a problem)\\r\\n # sometimes as HTML will has some key like attribute without value, like: 'required', 'disabled'...\\r\\n # it need extract those single attribute from the string\\r\\n pairs_attrs = []\\r\\n single_attrs = []\\r\\n for word in lexer:\\r\\n if \\\"=\\\" in word:\\r\\n # str.split() changed 'maxsplit' to keyword arguments\\r\\n # see: https://docs.python.org/3.3/library/stdtypes.html#str.split\\r\\n if sys.version_info >= (3, 3):\\r\\n pairs_attrs.append(word.split(\\\"=\\\", maxsplit=1))\\r\\n else:\\r\\n pairs_attrs.append(word.split(\\\"=\\\", 1))\\r\\n else:\\r\\n single_attrs.append(word)\\r\\n\\r\\n # convert pairs attribute list to dict\\r\\n pairs_attr_dict = dict(pairs_attrs)\\r\\n # add single atrribute to dict\\r\\n for attr in single_attrs:\\r\\n pairs_attr_dict[attr] = None\\r\\n\\r\\n return pairs_attr_dict\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def get_attributes(self):\\n return dict(self.attributes) # return the attributes\",\n \"def etree2dict(element):\\n i = dict(element.items())\\n i.update(_make_content(i, element.text, strip=True))\\n\\n for child in element:\\n tag = child.tag\\n value = etree2dict(child)\\n i.update(_make_content(i, value, tag))\\n\\n if element.text and not set(i).difference([\\\"content\\\"]):\\n # element is leaf node and doesn't have attributes\\n i = i.get(\\\"content\\\")\\n\\n return i\",\n \"def to_dict(self) -> Dict:\\n _dict = {}\\n if hasattr(self, 'attributes') and self.attributes is not None:\\n _dict['attributes'] = [x.to_dict() for x in self.attributes]\\n return _dict\",\n \"def a_attr_dict (self) :\\n return dict (href = self.abs_href)\",\n \"def testAttributes(self):\\n ddict = {\\n \\\"group\\\": {\\\"dataset\\\": 100, \\\"@group_attr1\\\": 10},\\n \\\"dataset\\\": 200,\\n \\\"@root_attr\\\": 11,\\n \\\"dataset@dataset_attr\\\": \\\"12\\\",\\n \\\"group@group_attr2\\\": 13,\\n }\\n dictdump.dicttonx(ddict, self.h5_fname)\\n ddict = dictdump.nxtodict(self.h5_fname, include_attributes=True)\\n self.assertEqual(ddict[\\\"group\\\"][\\\"@group_attr1\\\"], 10)\\n self.assertEqual(ddict[\\\"@root_attr\\\"], 11)\\n self.assertEqual(ddict[\\\"dataset@dataset_attr\\\"], \\\"12\\\")\\n self.assertEqual(ddict[\\\"group\\\"][\\\"@group_attr2\\\"], 13)\",\n \"def parseGFFAttributes(attributeString):\\n if attributeString == \\\".\\\":\\n return {}\\n ret = {}\\n for attribute in attributeString.split(\\\";\\\"):\\n key, value = attribute.split(\\\"=\\\")\\n ret[urllib.unquote(key)] = urllib.unquote(value)\\n return ret\",\n \"def asdict(self):\\n return attr.asdict(self)\",\n \"def process_start_tag(tag_text):\\n tokens = tokenise(tag_text.strip(\\\"<>\\\"))\\n tag = tokens[0]\\n attributes = {}\\n if len(tokens) > 1:\\n for token in tokens[1:]:\\n try:\\n i = token.index(\\\"=\\\")\\n key = token[0:i]\\n value = token[i + 1 :].strip(' \\\"')\\n except ValueError:\\n key = token\\n value = \\\"\\\"\\n attributes[key] = value\\n return (tag, attributes)\",\n \"def to_dict(self):\\n if self._dict is not None:\\n return self._dict\\n\\n result = {}\\n for key in self.ATTRIBUTES:\\n value = getattr(self, key)\\n if value:\\n result[key] = value\\n self._dict = result\\n return result\",\n \"def voc_xml_to_dict(xml_path):\\n\\n xml = etree.parse(xml_path)\\n root = xml.getroot()\\n image_name = root.find('filename').text\\n labels = []\\n boxes = []\\n\\n for obj in root.findall('object'):\\n labels.append(obj.find('name').text)\\n bbox = obj.find('bndbox')\\n bounds = [\\n float(bbox.find('xmin').text),\\n float(bbox.find('ymin').text),\\n float(bbox.find('xmax').text),\\n float(bbox.find('ymax').text)\\n ]\\n boxes.append(bounds)\\n\\n return {\\n 'image': image_name,\\n 'labels': labels,\\n 'boxes': boxes,\\n }\",\n \"def transform(attrs: dict) -> dict:\\n\\n pass\",\n \"def AttributesFromMojom(self, mojom):\\n if not mojom.decl_data.attributes:\\n return None\\n\\n return {attr.key: attr.value for attr in mojom.decl_data.attributes}\",\n \"def to_dict(\\n self, attributes: Sequence[str] = (\\n 'demand_instruments', 'supply_instruments', 'inverse_covariance_matrix',\\n 'expected_xi_by_theta_jacobian', 'expected_omega_by_theta_jacobian', 'expected_prices',\\n 'expected_shares', 'computation_time', 'draws', 'fp_converged', 'fp_iterations',\\n 'contraction_evaluations'\\n )) -> dict:\\n return {k: getattr(self, k) for k in attributes}\",\n \"def get_el_dict(activity_name, element):\\n set_xml()\\n element_dict = activity.get(activity_name).get(element)\\n print(element_dict)\\n return element_dict\",\n \"def getAttributeInfoDictionary(attr, format=None):\\n format = format or _getDocFormat(attr)\\n return {'name': attr.getName(),\\n 'doc': renderText(attr.getDoc() or '', format=format)}\",\n \"def as_attrdict(self) -> AttributeDict:\\n return AttributeDict(\\n {\\n x: Configuration(v).as_attrdict() if isinstance(v, Mapping) else v\\n for x, v in self.items(levels=1)\\n }\\n )\",\n \"def get_attributes(self):\\n\\n endpoint = self._get_api_endpoint() + '/attributes'\\n results = self.tq.get(endpoint, withp='attribute')\\n if 'data' not in results:\\n return {}\\n\\n return results['data']\\n # tr = {}\\n # for attribute in results['data']:\\n # tr[attribute['attribute']['name']] = attribute['value']\\n # return tr\",\n \"def to_dict(self):\\n return attr.asdict(self)\",\n \"def get_attributes_from_child(child):\\n return [{'element': child,\\n 'attribute': x.attrib,\\n 'tag': x.tag,\\n 'keys': x.keys()} for x in child]\",\n \"def ToDict(self):\\n atributes_dictionary = {}\\n for key, value in self.__dict__.iteritems():\\n atributes_dictionary[key] = value\\n return atributes_dictionary\",\n \"def parse_product_full_attributes(advertise: Dict[str, Any]) -> Optional[Dict[str, Any]]:\\n if \\\"product_full_attributes\\\" in advertise.keys():\\n\\n tmp_dict: Dict[str, Any] = dict()\\n tmp: List[str] = re.split('[ \\\\t]{3,}', advertise[\\\"product_full_attributes\\\"])\\n\\n for term in tmp:\\n key_val: List[str] = re.split('[ \\\\t]{2,}', term)\\n if '' in key_val:\\n key_val.remove('')\\n if len(key_val) == 2:\\n tmp_dict[re.sub(r'[^\\\\w\\\\s]', '', key_val[0]).strip()] = key_val[1]\\n\\n return tmp_dict\",\n \"def tags_dict(self):\\n return ({'name': 'tag', 'attrs': {'k': k, 'v': v}} for k, v in self.tags.items())\",\n \"def parse_attribute(self,attr,table):\\n \\n attr_dict = {}\\n \\n for child in attr:\\n name = child.attrib['name']\\n \\n #attributes can either have string or bool as the value we need\\n #checking for boolean\\n if 'val' in child[0].attrib:\\n val = child[0].attrib['val']\\n \\n if val == 'true':\\n flag = True\\n else:\\n flag = False\\n \\n attr_dict[name] = flag\\n \\n #else it's string stroed as text\\n else:\\n attr_dict[name] = super(DatabaseUmlParser,self).stripHashtags(child[0].text)\\n \\n attr = db_attribute.DbAttribute(table,attr_dict)\\n \\n return attr\",\n \"def extract_request(self, attrs):\\n try:\\n _base = self.attributes()\\n _new_dict = {}\\n for key in attrs:\\n if key in _base:\\n _new_dict[key] = attrs[key]\\n return _new_dict\\n except Exception as e:\\n print(e)\",\n \"def rss_attributes(self):\\n return {u\\\"version\\\": self._version,\\n u\\\"xmlns:media\\\": u\\\"http://search.yahoo.com/mrss/\\\",\\n \\\"xmlns:atom\\\": u\\\"http://www.w3.org/2005/Atom\\\"\\n }\",\n \"def get_attributes_config(node):\\n # attrs_config = {}\\n keyable_attrs = get_keyable_attribute(node)\\n config_data = init_table_config_data()\\n if keyable_attrs:\\n # attrs_config[\\\"_attrs\\\"] = keyable_attrs\\n for attr in keyable_attrs:\\n config = get_single_attribute_config(node, attr)\\n # attrs_config[attr] = config\\n config_data[\\\"channels\\\"].append(config[\\\"fullName\\\"])\\n config_data[\\\"channels_data\\\"][config[\\\"fullName\\\"]] = config\\n\\n return config_data\",\n \"def load_attrs(self):\\n return loads(self.get_attr().GetObject()) or {}\",\n \"def attribute_dict(self):\\n return self.__attribute_dict\",\n \"def get_attribute_data(self, attrs):\\n return {\\n 'id': attrs['data-id'],\\n }\",\n \"def xml_to_dict(self):\\n medicine_node = ['medicines', 'dose', 'unit', 'unit_price', 'goods_num', 'dose_that',\\n 'remark', 'm_usage', 'goods_norms', 'goods_orgin', 'MedPerDos', 'MedPerDay']\\n dict_data = {}\\n n = 0\\n xq_childs = 0\\n for child in self.root.iter():\\n # print(child.tag)\\n # print(child.text)\\n if child.tag not in medicine_node:\\n if child.tag == 'xq':\\n xq_childs = child.__len__() # __len__：返回元素大小，元素的大小为元素的子元素数量\\n dict_data[child.tag] = child.text\\n else:\\n if n < xq_childs:\\n dict_data[child.tag] = [child.text]\\n n += 1\\n else:\\n dict_data[child.tag].append(child.text)\\n return dict_data\",\n \"def as_dict(self):\\n result = {}\\n for attr in self.__attr:\\n result[attr] = getattr(self, attr)\\n return result\",\n \"def preprocess_attributes(business):\\n\\tattrs = business[ATTRIBUTES]\\n\\tattrs_dict = dict()\\n\\n\\tfor (key, val) in attrs.items():\\n\\t\\tval = ast.literal_eval(val)\\n\\t\\tif val is None:\\n\\t\\t\\tcontinue\\n\\n\\t\\t# if val is a json object\\n\\t\\tif isinstance(val, dict):\\n\\t\\t\\tinner_dict = dict()\\n\\t\\t\\tfor (inner_key, inner_val) in val.items():\\n\\t\\t\\t\\tinner_key = inner_key\\n\\t\\t\\t\\tinner_val = inner_val\\n\\t\\t\\t\\tinner_dict[inner_key] = inner_val\\n\\t\\t\\tattrs_dict[key] = inner_dict\\n\\t\\telse:\\n\\t\\t\\tattrs_dict[key] = val\\n\\tbusiness[ATTRIBUTES] = attrs_dict\",\n \"def get_ns_dict(xml):\\n \\n nss = {} \\n def_cnt = 0\\n matches = re.findall(r'\\\\s+xmlns:?(\\\\w*?)\\\\s*=\\\\s*[\\\\'\\\"](.*?)[\\\\'\\\"]', xml)\\n for match in matches:\\n prefix = match[0]; ns = match[1]\\n if prefix == '':\\n def_cnt += 1\\n prefix = '_' * def_cnt\\n nss[prefix] = ns\\n return nss\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.7548967","0.722563","0.71245855","0.711149","0.7095163","0.7094138","0.70292187","0.6932637","0.69197536","0.68689096","0.6855458","0.68021226","0.68001693","0.6756407","0.6698822","0.6619554","0.6612907","0.6576497","0.6554866","0.6512518","0.6490863","0.6473228","0.6458487","0.6455899","0.6425571","0.6424577","0.6365587","0.6260193","0.62454116","0.61772186","0.6163393","0.6151673","0.61506486","0.61490095","0.6125614","0.6122195","0.6115212","0.6072505","0.6067048","0.60662335","0.60402817","0.6039937","0.60388666","0.60340244","0.60281557","0.6016827","0.60084313","0.59826005","0.5978846","0.594482","0.5937525","0.59325176","0.59021926","0.5898513","0.58872706","0.5887014","0.58813846","0.58785594","0.587819","0.58507437","0.5849078","0.58470184","0.5837797","0.5829972","0.5828754","0.58265245","0.58265245","0.58265245","0.58242154","0.582328","0.58093035","0.58010733","0.57981765","0.5797575","0.57967526","0.579664","0.5794363","0.579289","0.5789767","0.5784445","0.57842374","0.5778989","0.57729834","0.5748393","0.5748136","0.57315326","0.5726204","0.5725238","0.5722759","0.5721816","0.5721682","0.5706094","0.5698482","0.56910634","0.5671696","0.5664184","0.5645268","0.5630127","0.56195617","0.55992115"],"string":"[\n \"0.7548967\",\n \"0.722563\",\n \"0.71245855\",\n \"0.711149\",\n \"0.7095163\",\n \"0.7094138\",\n \"0.70292187\",\n \"0.6932637\",\n \"0.69197536\",\n \"0.68689096\",\n \"0.6855458\",\n \"0.68021226\",\n \"0.68001693\",\n \"0.6756407\",\n \"0.6698822\",\n \"0.6619554\",\n \"0.6612907\",\n \"0.6576497\",\n \"0.6554866\",\n \"0.6512518\",\n \"0.6490863\",\n \"0.6473228\",\n \"0.6458487\",\n \"0.6455899\",\n \"0.6425571\",\n \"0.6424577\",\n \"0.6365587\",\n \"0.6260193\",\n \"0.62454116\",\n \"0.61772186\",\n \"0.6163393\",\n \"0.6151673\",\n \"0.61506486\",\n \"0.61490095\",\n \"0.6125614\",\n \"0.6122195\",\n \"0.6115212\",\n \"0.6072505\",\n \"0.6067048\",\n \"0.60662335\",\n \"0.60402817\",\n \"0.6039937\",\n \"0.60388666\",\n \"0.60340244\",\n \"0.60281557\",\n \"0.6016827\",\n \"0.60084313\",\n \"0.59826005\",\n \"0.5978846\",\n \"0.594482\",\n \"0.5937525\",\n \"0.59325176\",\n \"0.59021926\",\n \"0.5898513\",\n \"0.58872706\",\n \"0.5887014\",\n \"0.58813846\",\n \"0.58785594\",\n \"0.587819\",\n \"0.58507437\",\n \"0.5849078\",\n \"0.58470184\",\n \"0.5837797\",\n \"0.5829972\",\n \"0.5828754\",\n \"0.58265245\",\n \"0.58265245\",\n \"0.58265245\",\n \"0.58242154\",\n \"0.582328\",\n \"0.58093035\",\n \"0.58010733\",\n \"0.57981765\",\n \"0.5797575\",\n \"0.57967526\",\n \"0.579664\",\n \"0.5794363\",\n \"0.579289\",\n \"0.5789767\",\n \"0.5784445\",\n \"0.57842374\",\n \"0.5778989\",\n \"0.57729834\",\n \"0.5748393\",\n \"0.5748136\",\n \"0.57315326\",\n \"0.5726204\",\n \"0.5725238\",\n \"0.5722759\",\n \"0.5721816\",\n \"0.5721682\",\n \"0.5706094\",\n \"0.5698482\",\n \"0.56910634\",\n \"0.5671696\",\n \"0.5664184\",\n \"0.5645268\",\n \"0.5630127\",\n \"0.56195617\",\n \"0.55992115\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.7341555"},"document_rank":{"kind":"string","value":"1"}}},{"rowIdx":21895,"cells":{"query":{"kind":"string","value":"Custom repr for easier debugging."},"document":{"kind":"string","value":"def __repr__(self):\n return f\"{self.document_id} {self.annotator_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 __str__(self):\r\n return repr(self)","def __repr__(self) -> str:\n items = (\"{}={}\".format(k, repr(v)) for k, v in self.__dict__.items())\n return \"<{}({})>\".format(self.__class__.__name__, \", \".join(items))","def __str__(self):\n return repr(self)","def __repr__(self):\n # this is a good default __repr__\n # Q: using the docs, can you figure out what this is doing?\n return f\"<{type(self).__name__} {self.__dict__}>\"","def __repr__(self):\n string_representation = self.__str__().replace(\"{\", \"(\").replace(\n \"}\", \")\").replace(\":\", \"=\")\n return f\"{type(self).__name__}{string_representation}\"","def __repr__(self) -> str:\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())","def __repr__(self) -> str:\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())","def __repr__(self) -> str:\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())","def _extra_repr(self):\n return ''","def repr(self):\n raise InterpreterError(\"Unimplemented repr()\")","def __repr__(self) -> str:\n return self.__str__()","def __repr__(self) -> str:\n return self.__str__()","def __repr__(self) -> str:\n return self.__str__()","def __repr__(self) -> str:\n return self.__str__()","def __repr__(self):\n return self.pretty_print(self.__dict__)","def _repr_(self):\n return repr(self.element())","def __repr__(self):\r\n return self.__str__()","def __repr__(self):\n return u\"(\" + str(self) + \", \" + str(dict(self)) + u')'","def __repr__(self) -> str:\n skip = ()\n items = (\n \"{}={}\".format(k, repr(v))\n for k, v in self.__dict__.items()\n if k not in skip\n )\n return \"<{}({})>\".format(self.__class__.__name__, \", \".join(items))","def repr_(object_):\n return repr(object_)","def repr(x) -> String:\n pass","def __repr__(self):\n\n rep = \"\"\n rep += str(self.literal)+\"\\n\"\n rep += str(self.bindings)+\"\\n\"\n rep += str(self.facts)+\"\\n\"\n return (rep)","def __repr__(self):\n repr_parts = ['<', self.__class__.__name__]\n self._add_type_specific_repr_fields(repr_parts)\n repr_parts.append('>')\n return ''.join(repr_parts)","def __repr__(self):\n\n return '%s(%s)' % (self, self._format_entries(),)","def __repr__(self):\n extras = self._extra_repr()\n if isinstance(extras, list):\n extras = ' '.join([str(e) for e in extras])\n try:\n if self.id:\n return '<%s %s 0x%x>' % (type(self).__name__,\n ('%s %s' % (self.id, extras)).strip(),\n hash(self))\n return '<%s %s 0x%x>' % (type(self).__name__, extras, hash(self))\n except Exception as e:\n return \"can't convert to string: %s\" % e","def __repr__(self) -> str:\n ...","def __repr__(self) -> str:\n ...","def __repr__(self) -> str:\n ...","def __repr__(self) -> str:\n ...","def __repr__(self) -> str:\n ...","def __repr__(self):\n args = [self.name]\n if self.propertiesstr:\n args.append(self.propertiesstr)\n args = map(repr, args)\n return \"%s(%s)\" % (type(self).__name__, \", \".join(args))","def __repr__(self):\n # type: () -> str\n return self.to_str()","def __repr__(self):\n # type: () -> str\n return self.to_str()","def __repr__(self):\n # type: () -> str\n return self.to_str()","def __repr__(self):\n args = []\n if self.name != \"alpha\":\n args.append(repr(self.name))\n if self.propertiesstr:\n args.append(repr(self.propertiesstr))\n elif self.propertiesstr:\n args.append(\"attr=%r\" % self.propertiesstr)\n return \"%s(%s)\" % (type(self).__name__, \", \".join(args))","def __repr__(self) -> str:\n return str(self)","def __repr__(self):\n args = [self.name, self.flags]\n if self.propertiesstr:\n args.append(self.propertiesstr)\n args = map(repr, args)\n return \"%s(%s)\" % (type(self).__name__, \", \".join(args))","def __repr__(self):\n cls = self.__class__.__name__\n return '%s(%s)' % (cls, repr(self.d))","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self):\n return self.__str__()","def __repr__(self) -> str:\n\t\treturn \"\"","def __repr__(self):\n str(self)","def __repr__(self):\n return \"\\'{}\\'\".format(self.__str__())","def __repr__(self):\n s = \"\"\n for v in self.V():\n s += f\"{v.__repr__()}\\n\"\n \n return s","def __repr__ (self):\n\t\tStr = \"\"\n\t\tfor i in self.structref:\n\t\t\tStr = Str + \"%-15s = \"%(i[self.NAME])\n\t\t\tvalue = self.value [i[self.NAME]]\n\t\t\tif isInteger(value):\n\t\t\t\tStr = Str + \"%d, 0x%X\"%(value,value)\n\t\t\t\tif value >= 0x20 and value <= 0xFF:\n\t\t\t\t\tStr = Str + \" '\" + chr (value) + \"'\"\n\t\t\telse:\n\t\t\t\tif type(value) == type(bytes(0)):\n\t\t\t\t\tStr = Str + value.decode(\"utf8\",\"ignore\")\n\t\t\t\telse:\n\t\t\t\t\tStr = Str + str(value) \n\t\t\t\t\t\n\t\t\tStr = Str + \"\\n\"\n\t\treturn Str","def __repr__(self):\r\n return self.to_str()","def __repr__(self):\r\n return self.to_str()","def __repr__(self):\n return pformat(vars(self))","def __repr__(self):\n\n return self._repr__base(rich_output=False)","def __repr__(self):\n\n return self._repr__base(rich_output=False)","def __repr__(self) -> str:\n return '{:s}({!s})'.format(self.__class__.__name__,\n super().__repr__())","def __repr__(self):\r\n return str(self)","def __repr__(self):\n return super().__repr__()","def __repr__(self):\n return '{}<{}, line {} char {}>'.format(\n self.__class__.__name__, repr(self._data),\n self._lineno, self._charno)","def __repr__(self):\n poargs = []\n kwargs = {}\n self._collect_repr_args(poargs, kwargs)\n arg_strs = []\n if poargs:\n arg_strs.append(', '.join('{!r}'.format(arg) for arg in poargs))\n if kwargs:\n arg_strs.append(', '.join('{}={!r}'.format(k, v)\n for k, v in kwargs.items()))\n arg_str = ', '.join(arg_strs)\n return '{}({})'.format(self.__class__.__name__, arg_str)","def __repr__(self):\r\n\t\treturn str(self)","def __repr__(self):\n return repr(self.__dict__['_obj'])","def __repr__(self):\n \n return \"%s(%s)\" % (self.__class__.__name__, self.__str__())","def __repr__(self) ->str:\n # Our __repr__ is recursive, because it can also be called\n # via repr...!\n return ('{}({}, {})'.format(self.__class__.__name__,\n repr(self.value),\n repr(self.children))\n if self.children\n else 'Tree({})'.format(repr(self.value)))","def _repr_(self):\n return repr(self.label())","def __repr__(self):\n return '%s(%r)' % (self.__class__.__name__, str(self))","def __repr__(self) -> str:\n\t\treturn \"- {}\\n{}\\n\".format(self.name, self.__str__())","def __repr__(self):\n\t\treturn str(self)","def __repr__(self):\n\t\treturn str(self)","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()","def __repr__(self):\n return self.to_str()"],"string":"[\n \"def __str__(self):\\r\\n return repr(self)\",\n \"def __repr__(self) -> str:\\n items = (\\\"{}={}\\\".format(k, repr(v)) for k, v in self.__dict__.items())\\n return \\\"<{}({})>\\\".format(self.__class__.__name__, \\\", \\\".join(items))\",\n \"def __str__(self):\\n return repr(self)\",\n \"def __repr__(self):\\n # this is a good default __repr__\\n # Q: using the docs, can you figure out what this is doing?\\n return f\\\"<{type(self).__name__} {self.__dict__}>\\\"\",\n \"def __repr__(self):\\n string_representation = self.__str__().replace(\\\"{\\\", \\\"(\\\").replace(\\n \\\"}\\\", \\\")\\\").replace(\\\":\\\", \\\"=\\\")\\n return f\\\"{type(self).__name__}{string_representation}\\\"\",\n \"def __repr__(self) -> str:\\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())\",\n \"def __repr__(self) -> str:\\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())\",\n \"def __repr__(self) -> str:\\n return '{:s}({!r})'.format(self.__class__.__name__, self.getvalue())\",\n \"def _extra_repr(self):\\n return ''\",\n \"def repr(self):\\n raise InterpreterError(\\\"Unimplemented repr()\\\")\",\n \"def __repr__(self) -> str:\\n return self.__str__()\",\n \"def __repr__(self) -> str:\\n return self.__str__()\",\n \"def __repr__(self) -> str:\\n return self.__str__()\",\n \"def __repr__(self) -> str:\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.pretty_print(self.__dict__)\",\n \"def _repr_(self):\\n return repr(self.element())\",\n \"def __repr__(self):\\r\\n return self.__str__()\",\n \"def __repr__(self):\\n return u\\\"(\\\" + str(self) + \\\", \\\" + str(dict(self)) + u')'\",\n \"def __repr__(self) -> str:\\n skip = ()\\n items = (\\n \\\"{}={}\\\".format(k, repr(v))\\n for k, v in self.__dict__.items()\\n if k not in skip\\n )\\n return \\\"<{}({})>\\\".format(self.__class__.__name__, \\\", \\\".join(items))\",\n \"def repr_(object_):\\n return repr(object_)\",\n \"def repr(x) -> String:\\n pass\",\n \"def __repr__(self):\\n\\n rep = \\\"\\\"\\n rep += str(self.literal)+\\\"\\\\n\\\"\\n rep += str(self.bindings)+\\\"\\\\n\\\"\\n rep += str(self.facts)+\\\"\\\\n\\\"\\n return (rep)\",\n \"def __repr__(self):\\n repr_parts = ['<', self.__class__.__name__]\\n self._add_type_specific_repr_fields(repr_parts)\\n repr_parts.append('>')\\n return ''.join(repr_parts)\",\n \"def __repr__(self):\\n\\n return '%s(%s)' % (self, self._format_entries(),)\",\n \"def __repr__(self):\\n extras = self._extra_repr()\\n if isinstance(extras, list):\\n extras = ' '.join([str(e) for e in extras])\\n try:\\n if self.id:\\n return '<%s %s 0x%x>' % (type(self).__name__,\\n ('%s %s' % (self.id, extras)).strip(),\\n hash(self))\\n return '<%s %s 0x%x>' % (type(self).__name__, extras, hash(self))\\n except Exception as e:\\n return \\\"can't convert to string: %s\\\" % e\",\n \"def __repr__(self) -> str:\\n ...\",\n \"def __repr__(self) -> str:\\n ...\",\n \"def __repr__(self) -> str:\\n ...\",\n \"def __repr__(self) -> str:\\n ...\",\n \"def __repr__(self) -> str:\\n ...\",\n \"def __repr__(self):\\n args = [self.name]\\n if self.propertiesstr:\\n args.append(self.propertiesstr)\\n args = map(repr, args)\\n return \\\"%s(%s)\\\" % (type(self).__name__, \\\", \\\".join(args))\",\n \"def __repr__(self):\\n # type: () -> str\\n return self.to_str()\",\n \"def __repr__(self):\\n # type: () -> str\\n return self.to_str()\",\n \"def __repr__(self):\\n # type: () -> str\\n return self.to_str()\",\n \"def __repr__(self):\\n args = []\\n if self.name != \\\"alpha\\\":\\n args.append(repr(self.name))\\n if self.propertiesstr:\\n args.append(repr(self.propertiesstr))\\n elif self.propertiesstr:\\n args.append(\\\"attr=%r\\\" % self.propertiesstr)\\n return \\\"%s(%s)\\\" % (type(self).__name__, \\\", \\\".join(args))\",\n \"def __repr__(self) -> str:\\n return str(self)\",\n \"def __repr__(self):\\n args = [self.name, self.flags]\\n if self.propertiesstr:\\n args.append(self.propertiesstr)\\n args = map(repr, args)\\n return \\\"%s(%s)\\\" % (type(self).__name__, \\\", \\\".join(args))\",\n \"def __repr__(self):\\n cls = self.__class__.__name__\\n return '%s(%s)' % (cls, repr(self.d))\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self):\\n return self.__str__()\",\n \"def __repr__(self) -> str:\\n\\t\\treturn \\\"\\\"\",\n \"def __repr__(self):\\n str(self)\",\n \"def __repr__(self):\\n return \\\"\\\\'{}\\\\'\\\".format(self.__str__())\",\n \"def __repr__(self):\\n s = \\\"\\\"\\n for v in self.V():\\n s += f\\\"{v.__repr__()}\\\\n\\\"\\n \\n return s\",\n \"def __repr__ (self):\\n\\t\\tStr = \\\"\\\"\\n\\t\\tfor i in self.structref:\\n\\t\\t\\tStr = Str + \\\"%-15s = \\\"%(i[self.NAME])\\n\\t\\t\\tvalue = self.value [i[self.NAME]]\\n\\t\\t\\tif isInteger(value):\\n\\t\\t\\t\\tStr = Str + \\\"%d, 0x%X\\\"%(value,value)\\n\\t\\t\\t\\tif value >= 0x20 and value <= 0xFF:\\n\\t\\t\\t\\t\\tStr = Str + \\\" '\\\" + chr (value) + \\\"'\\\"\\n\\t\\t\\telse:\\n\\t\\t\\t\\tif type(value) == type(bytes(0)):\\n\\t\\t\\t\\t\\tStr = Str + value.decode(\\\"utf8\\\",\\\"ignore\\\")\\n\\t\\t\\t\\telse:\\n\\t\\t\\t\\t\\tStr = Str + str(value) \\n\\t\\t\\t\\t\\t\\n\\t\\t\\tStr = Str + \\\"\\\\n\\\"\\n\\t\\treturn Str\",\n \"def __repr__(self):\\r\\n return self.to_str()\",\n \"def __repr__(self):\\r\\n return self.to_str()\",\n \"def __repr__(self):\\n return pformat(vars(self))\",\n \"def __repr__(self):\\n\\n return self._repr__base(rich_output=False)\",\n \"def __repr__(self):\\n\\n return self._repr__base(rich_output=False)\",\n \"def __repr__(self) -> str:\\n return '{:s}({!s})'.format(self.__class__.__name__,\\n super().__repr__())\",\n \"def __repr__(self):\\r\\n return str(self)\",\n \"def __repr__(self):\\n return super().__repr__()\",\n \"def __repr__(self):\\n return '{}<{}, line {} char {}>'.format(\\n self.__class__.__name__, repr(self._data),\\n self._lineno, self._charno)\",\n \"def __repr__(self):\\n poargs = []\\n kwargs = {}\\n self._collect_repr_args(poargs, kwargs)\\n arg_strs = []\\n if poargs:\\n arg_strs.append(', '.join('{!r}'.format(arg) for arg in poargs))\\n if kwargs:\\n arg_strs.append(', '.join('{}={!r}'.format(k, v)\\n for k, v in kwargs.items()))\\n arg_str = ', '.join(arg_strs)\\n return '{}({})'.format(self.__class__.__name__, arg_str)\",\n \"def __repr__(self):\\r\\n\\t\\treturn str(self)\",\n \"def __repr__(self):\\n return repr(self.__dict__['_obj'])\",\n \"def __repr__(self):\\n \\n return \\\"%s(%s)\\\" % (self.__class__.__name__, self.__str__())\",\n \"def __repr__(self) ->str:\\n # Our __repr__ is recursive, because it can also be called\\n # via repr...!\\n return ('{}({}, {})'.format(self.__class__.__name__,\\n repr(self.value),\\n repr(self.children))\\n if self.children\\n else 'Tree({})'.format(repr(self.value)))\",\n \"def _repr_(self):\\n return repr(self.label())\",\n \"def __repr__(self):\\n return '%s(%r)' % (self.__class__.__name__, str(self))\",\n \"def __repr__(self) -> str:\\n\\t\\treturn \\\"- {}\\\\n{}\\\\n\\\".format(self.name, self.__str__())\",\n \"def __repr__(self):\\n\\t\\treturn str(self)\",\n \"def __repr__(self):\\n\\t\\treturn str(self)\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\",\n \"def __repr__(self):\\n return self.to_str()\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.8258345","0.8253225","0.82328606","0.81909287","0.8136453","0.8132754","0.8132754","0.8132754","0.81184274","0.81119144","0.8045108","0.8045108","0.8045108","0.8045108","0.80425274","0.801632","0.79938865","0.7993399","0.79841006","0.79778564","0.7972018","0.79620326","0.7957288","0.7946096","0.7942049","0.7941991","0.7941991","0.7941991","0.7941991","0.7941991","0.7941284","0.7939565","0.7939565","0.7939565","0.79091156","0.7901264","0.79010755","0.78889626","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.78846014","0.7877741","0.78691125","0.7868904","0.78614336","0.784726","0.7843676","0.7843676","0.7842719","0.7832139","0.7832139","0.78286254","0.7825502","0.7814065","0.78122294","0.7801552","0.7793815","0.7792195","0.77804565","0.77800703","0.7776313","0.7770375","0.77687335","0.7764621","0.7764621","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794","0.77630794"],"string":"[\n \"0.8258345\",\n \"0.8253225\",\n \"0.82328606\",\n \"0.81909287\",\n \"0.8136453\",\n \"0.8132754\",\n \"0.8132754\",\n \"0.8132754\",\n \"0.81184274\",\n \"0.81119144\",\n \"0.8045108\",\n \"0.8045108\",\n \"0.8045108\",\n \"0.8045108\",\n \"0.80425274\",\n \"0.801632\",\n \"0.79938865\",\n \"0.7993399\",\n \"0.79841006\",\n \"0.79778564\",\n \"0.7972018\",\n \"0.79620326\",\n \"0.7957288\",\n \"0.7946096\",\n \"0.7942049\",\n \"0.7941991\",\n \"0.7941991\",\n \"0.7941991\",\n \"0.7941991\",\n \"0.7941991\",\n \"0.7941284\",\n \"0.7939565\",\n \"0.7939565\",\n \"0.7939565\",\n \"0.79091156\",\n \"0.7901264\",\n \"0.79010755\",\n \"0.78889626\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.78846014\",\n \"0.7877741\",\n \"0.78691125\",\n \"0.7868904\",\n \"0.78614336\",\n \"0.784726\",\n \"0.7843676\",\n \"0.7843676\",\n \"0.7842719\",\n \"0.7832139\",\n \"0.7832139\",\n \"0.78286254\",\n \"0.7825502\",\n \"0.7814065\",\n \"0.78122294\",\n \"0.7801552\",\n \"0.7793815\",\n \"0.7792195\",\n \"0.77804565\",\n \"0.77800703\",\n \"0.7776313\",\n \"0.7770375\",\n \"0.77687335\",\n \"0.7764621\",\n \"0.7764621\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\",\n \"0.77630794\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.0"},"document_rank":{"kind":"string","value":"-1"}}},{"rowIdx":21896,"cells":{"query":{"kind":"string","value":"Clean duplicate docs that are a consequence of opening them in WebAnno."},"document":{"kind":"string","value":"def clean_duplicate_documents(self):\n title_k = lambda x: x.title\n for k, g in groupby(sorted(self.annotation_documents, key=title_k), title_k):\n g = list(g)\n if len(g) > 1:\n # check first if one is in test set\n to_remove = [x for x in g if x not in self.test]\n if (\n len(to_remove) > 1\n ): # if test is not matched, make subselection based on annotation unit count\n select_k = lambda x: (\n len(x.events) + len(x.sentiment_expressions),\n x.annotator_id != \"gilles\",\n )\n to_remove.sort(key=select_k, reverse=True)\n to_remove = to_remove[1:]\n for docrm in to_remove:\n self.annotation_documents.remove(docrm)\n if docrm in self.dev:\n self.dev.remove(docrm)\n elif docrm in self.test:\n self.test.remove(docrm)\n print(f\"Duplicate doc removed: {docrm}\")"},"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_docs(self, docs):\n cleaned = [self.cleaning(doc) for doc in docs]\n print(cleaned[0])\n return cleaned","def clean_docs(c):\n c.run(f\"rm -fr {DOCS_BUILD_DIR}\")","def clean_all_documents(cls):\n for index, text in enumerate(cls.documents):\n text_processed = cls.clean_document(text)\n cls.processed_documents.append(text_processed)","def merge_docs(self):","def clear_doc():\n for obj in DOC.Objects:\n DOC.removeObject(obj.Name)","def _clean_doc(doc: Dict) -> Dict:\n doc['label'] = doc['labels'][config.LANG]['value']\n aliases = doc['aliases'][config.LANG] if config.LANG in doc['aliases'] else []\n\n doc['aliases'] = [alias['value'] for alias in aliases]\n\n for key in DOC_CLEAN_KEYS:\n try:\n del doc[key]\n except:\n continue\n return doc","def clean_documents():\n start = datetime.now()\n for i, raw_filename in enumerate(os.listdir(RAW_DIR)):\n fullpath = os.path.join(RAW_DIR, raw_filename)\n if os.path.isfile(fullpath):\n print(\"Cleaning {0} {1}\".format(i, fullpath), file=stderr)\n try:\n with open(fullpath, \"r\") as f:\n text = f.read()\n text = clean(text)\n soup = BeautifulSoup(text, \"html.parser\")\n cleaned = visible_text(soup)\n score = germanwings_score(cleaned)\n if not score:\n print(\"not germanwings: {0}\".format(raw_filename))\n else:\n clean_filename = os.path.join(CLEAN_DIR, raw_filename)\n with open(clean_filename, \"w\") as f:\n f.write(cleaned.encode(\"ascii\", \"ignore\"))\n except Exception as exc:\n print(\"{0}: {1}\".format(fullpath, exc), file=stderr)\n end = datetime.now()\n print(\"Elapsed time to clean: {0}\".format(end - start), file=stderr)","def clean_wikidata_docs(docs: List[Dict]) -> List[Dict]:\n for doc in docs:\n try:\n yield _clean_doc(doc)\n except:\n continue","def clean_docs(self,docs):\n\n # Remove numbers, but not words that contain numbers.\n docs = [[token for token in doc if not token.isnumeric()] for doc in docs]\n\n # Remove words that are only one character.\n docs = [[token for token in doc if len(token) > 1 and token not in stop_words] for doc in docs]\n\n # lemmatizer = WordNetLemmatizer()\n # docs = [[lemmatizer.lemmatize(token) for token in doc] for doc in docs]\n\n # Add bigrams and trigrams to docs (only ones that appear 20 times or more).\n bigram = Phrases(docs, min_count=20)\n for idx in range(len(docs)):\n for token in bigram[docs[idx]]:\n if '_' in token:\n # Token is a bigram, add to document.\n docs[idx].append(token)\n\n # Create a dictionary representation of the documents.\n dictionary = Dictionary(docs)\n\n # Filter out words that occur less than 20 documents, or more than 50% of the documents.\n dictionary.filter_extremes(no_below=20, no_above=0.5)\n\n # Bag-of-words representation of the documents.\n corpus = [dictionary.doc2bow(doc) for doc in docs]\n\n return docs,dictionary,corpus","def _clean_data(self, docs: []):\n print('Cleaning data...')\n preprocessed_data = []\n for doc in docs:\n if len(doc) <= constants.MIN_DOC_LENGTH:\n continue\n\n temp_doc = self._remove_urls(doc)\n temp_doc = self._remove_special_chars(temp_doc)\n temp_doc = self._transform_to_lowercase(temp_doc)\n temp_doc = self._remove_stopwords(temp_doc)\n\n preprocessed_data.append(temp_doc)\n\n return preprocessed_data","def cleanup(asciitest_out_dir, doc_file):\n # path join uses backslash win32 which is not cmake compatible\n filename = os.path.join(asciitest_out_dir, save_cmake_filename(doc_file)).replace(\"\\\\\",\"/\")\n \n #print(\"cleanup %s %s\" % (doc_file, filename))\n try:\n os.remove(filename)\n except:\n pass","def deleteconvert(self):\n filename = os.path.join(self.docx_path, self.name.docx)\n if os.path.isfile(filename):\n os.remove(filename)\n filename = os.path.join(self.html_path, self.name.html)\n if os.path.isfile(filename):\n os.remove(filename)\n filename = os.path.join(self.docbook_path, self.name.xml)\n if os.path.isfile(filename):\n os.remove(filename)\n filename = os.path.join(self.markdown_path, self.name.md)\n if os.path.isfile(filename):\n os.remove(filename)","def _remove_doc(xml_str):\n result = []\n do_add = True\n for line in xml_str.splitlines():\n if '<doc:doc>' in line:\n do_add = False\n if do_add:\n result.append(line)\n if '</doc:doc>' in line:\n do_add = True\n return '\\n'.join(result)","def clean(self):\n if os.path.exists(self.paths['build_dir']):\n shutil.rmtree(self.paths['build_dir'])\n if os.path.exists(os.path.join(self.base_dir, 'docs')):\n shutil.rmtree(os.path.join(self.base_dir, 'docs'))","def _clear_document(self, docid):\n doc = self.get_document(docid)\n for term, count in doc.get_terms():\n term_entry = self.sql_session.query(Term).get(term)\n term_entry.count -= abs(count)\n term_entry.distinct_docs -= 1\n any_term = self.sql_session.query(Term).get(ANY)\n any_term.distinct_docs -= 1\n doc.delete()","def clean(raw_file,clean_path,results_path):\n clean_file = clean_path + 'clean.pkl'\n stats_file = results_path + 'corpus_stats.pkl' \n raw_text = load_raw_text(raw_file) \n clean_docs = list()\n nlp = spacy.load('en')\n i = 0\n print('Cleaning documents...')\n for text in raw_text: \n words = nlp(text)\n raw_count = len(words)\n words = [w for w in words if not w.is_stop]\n words = [w for w in words if w.ent_type_ != 'PERSON' and w.pos_ != 'PROPN']\n words = [w for w in words if w.is_alpha and not w.is_digit]\n words = [w.lemma_ for w in words if w.text != '-PRON-']\n word_string = ' '.join(words)\n word_string = word_string.lower()\n doc = Document(word_string)\n doc.clean_count = len(words)\n doc.raw_count = raw_count\n clean_docs.append(doc)\n if i%10 == 0:\n print('Document: ' + str(i))\n i += 1\n clean_corpus = Corpus(clean_docs)\n clean_corpus.save_corpus_text(clean_file)\n clean_corpus.save_corpus_stats(stats_file)\n return clean_corpus","def reset_document(self):\n # FIXME: this state does not make sense\n self.doc_version_set = False\n self.doc_comment_set = False\n self.doc_namespace_set = False\n self.doc_data_lics_set = False\n self.doc_name_set = False\n self.doc_spdx_id_set = False","def clean(self):\r\n\r\n for _, data in self.composition.items():\r\n index_file = Path(data['file'] + '.fxi')\r\n if index_file.exists():\r\n index_file.unlink()","def remove_urls(self, doc):\n doc = re.sub(\n r'(?i)\\b((?:https?://|www\\d{0,3}[.]|[a-z0-9.\\-]+[.][a-z]{2,4}/)'\n r'(?:[^\\s()<>]+|\\(([^\\s()<>]+|'\n r'(\\([^\\s()<>]+\\)))*\\))+(?:\\(([^\\s()<>]+|(\\([^\\s()<>]+\\)))*\\)|'\n r'[^\\s`!()\\[\\]{};:\\'\".,<>]))',\n '',\n doc)\n return ' '.join(doc.split())","def html_clean(options):\r\n remake_directories(options.sphinx.doctrees, options.html.outdir)\r\n html(options)\r\n return","def preprocess_docs(self, docs_array):\n # Remove stopwords\n new_docs_list = []\n for doc in docs_array:\n temp = doc.lower()\n if \"\\\\\" in temp:\n temp = temp.replace(\"\\\\\", \"\")\n if \"<FONT face\" in temp:\n temp = re.sub(r\"<FONT face.*$\", \"\", doc)\n if \"a href=\" in temp:\n temp = re.sub(r\" ;a href.*/a>\", \"\", doc)\n if \"A HREF\" in temp:\n temp = re.sub(r\" ;A HREF.*/A>\", \"\", doc)\n if \"<\" in temp:\n temp = temp.replace(\"<\", \"\")\n if \"i>\" in temp:\n temp = temp.replace(\"i>\", \"\")\n if \" \" in temp:\n temp = temp.replace(\" \", \" \")\n if \"#151\" in temp:\n temp = temp.replace(\"#151\", \"\")\n if \"#36\" in temp:\n temp = temp.replace(\"#36\", \"\")\n if \"#39\" in temp:\n temp = temp.replace(\"#39\", \"\")\n new_docs_list.append(temp)\n return np.array(new_docs_list)","def _finalize_iteration(self, verbose: bool):\n super().delete_remote_files()\n self.comm.storyteller.document_task(task=\"adam_documentation\")","def clean():\n possible_outputs = (\n '{}.html'.format(CONFIG['FULL_PROJECT_NAME']),\n '{}.epub'.format(CONFIG['FULL_PROJECT_NAME']),\n '{}.pdf'.format(CONFIG['FULL_PROJECT_NAME']),\n '{}.docx'.format(CONFIG['FULL_PROJECT_NAME']),\n '{}.odt'.format(CONFIG['FULL_PROJECT_NAME']),\n )\n\n for filename in possible_outputs:\n if os.path.exists(filename):\n os.remove(filename)\n print(\"Removed {}\".format(filename))","async def remove_doc(self, *args, **kwargs):\n pass","def dummy(doc):\r\n return doc","def update_docs():\n site_path = os.path.join(PROJECTS_ROOT, CURRENT_SITE)\n docs_path = os.path.join(site_path, 'doc_src')\n with cd(docs_path):\n run('git reset --hard && git pull --all')\n run('workon djangopatterns && cd doc_src && make clean')\n run('workon djangopatterns && cd doc_src && make json')","def full_reset(self):\n for docid in self.iter_docids():\n self.delete(docid)\n self.client.delete(self.dbprefix + 'schema')\n self.client.delete(self.dbprefix + 'docs')\n self.client.delete(self.dbprefix + 'nextid')","def clean_document(doc_text,doc_type = \"text\",split_to_section = False,**kwargs):\n \n doc_text = clean_text(doc_text)\n doc_text = delete_lines(doc_text)\n return \"\".join(doc_text)\n \n # if split_to_section == True:\n # return doc_text\n # elif split_to_section == False:\n # return [\" \".join(doc_text)]","def _cleanup(self):\n os.system(\"rm -r %s/*\" %(self._snippet_index_dir))\n os.system(\"rm %s/*\" %(self._para_dir))\n os.system(\"rm %s/*\" %(self._temp_dir))\n os.system(\"rm %s/*\" %(self._snippet_result_dir))","def tearDown(self):\n super(SimpleDocumentTestCase, self).tearDown()\n del self.document","def triple_quote_docs():\n return","def cleanup(self):\n index_id = self.params[\"index_id\"]\n\n # Remove the index document from the database.\n self.db.indexes.delete_one({\"_id\": index_id})\n\n self.dispatch(\"indexes\", \"delete\", [index_id])\n\n query = {\n \"_id\": {\n \"$in\": self.db.history.distinct(\"_id\", {\"index.id\": index_id})\n }\n }\n\n # Set all the otus included in the build to \"unbuilt\" again.\n self.db.history.update_many(query, {\n \"$set\": {\n \"index\": {\n \"id\": \"unbuilt\",\n \"version\": \"unbuilt\"\n }\n }\n })\n\n id_list = self.db.history.distinct(\"_id\", query)\n\n self.dispatch(\"history\", \"update\", id_list)\n\n virtool.utils.rm(self.params[\"index_path\"], True)","def clean_doc(doc):\r\n # Replace regular enter (i.e. mere comment formatting in cpp file)\r\n # with space\r\n doc = doc.replace(\"\\n\", \" \")\r\n\r\n # The removal can cause a \"hard enter\" (literal \\n) to get an unintended\r\n # trailing space - trim those.\r\n doc = doc.replace(\"\\\\n \", \"\\\\n\")\r\n return '\"%s\"' % doc","def clean_pdf():\n xnt.build.tex.clean(path=\"./\", remove_pdf=True)","def _remove_urls(self, doc: str):\n processed_tweet = re.sub('(https?:)?\\/\\/[\\w\\.\\/-]+', '', doc)\n return processed_tweet","def clean(ctx):\n ctx.run(\"rm -rf build/html\")","def scrub():\n\n\tlocal(\"rm -fr dist build\")\n\tlocal(\"find . -name \\\"*.pyc\\\" -exec rm '{}' ';'\")","def prepare_dictionary_from_docs(self):\n if os.path.exists(self.DICT_PATH):\n return True\n self.logger.info(\"START PREPARING DICT\")\n for fn in os.listdir(self.wiki_path):\n self.logger.info(\"dict update {0}\".format(fn))\n content = self.get_processed_content(fn)\n self.dictionary.add_documents([content])\n self.dictionary.filter_extremes(no_below=20, no_above=0.1, keep_n=100000)\n self.dictionary.compactify()\n self.dictionary.save(self.DICT_PATH)\n return True","def clean_files(self):\n self.filenames.clear()","def clean_pofile(file):\r\n # Reading in the .po file and saving it again fixes redundancies.\r\n pomsgs = pofile(file)\r\n # The msgcat tool marks the metadata as fuzzy, but it's ok as it is.\r\n pomsgs.metadata_is_fuzzy = False\r\n for entry in pomsgs:\r\n # Remove line numbers\r\n entry.occurrences = [(filename, None) for (filename, lineno) in entry.occurrences]\r\n # Remove -format flags\r\n entry.flags = [f for f in entry.flags if not f.endswith(\"-format\")]\r\n pomsgs.save()","def filter_doc(doc_text):\n # remove stars\n filter_regex=re.compile(r\"[_*]\")\n doc=filter_regex.sub(\"\",doc_text)\n # substitute quotation marks\n double_quot_regex=re.compile(r\"[“”]\")\n single_quot_regex=re.compile(r\"[’‘]\")\n doc=double_quot_regex.sub('\"',doc)\n doc=single_quot_regex.sub(\"'\",doc)\n # substitute new lines inside the text for spaces\n # these new lines are usually caused by formatting texts to fit in 80 columns \n newline_quot_regex=re.compile(r\"(\\S)\\n(\\S)\")\n doc=newline_quot_regex.sub(r\"\\1 \\2\",doc)\n # remove illustration tag\n #illustration_regex=re.compile(r\"\\[Illustration.*]\")\n #doc=illustration_regex.sub(\"\",doc)\n return doc","def html():\n builtdocs = path(\"docs\") / options.sphinx.builddir / \"html\"\n destdir = path(PACKAGE) / \"docs\"\n destdir.rmtree()\n builtdocs.move(destdir)","def __del__(self):\n try:\n self._document.close()\n except:\n pass","def dev_clean():\n clean_files(\"csv\", True)\n clean_files(\"jsontxt\", True)","def get_outdated_docs(self):\n # This method is taken from TextBuilder.get_outdated_docs()\n # with minor changes to support :confval:`rst_file_transform`.\n for docname in self.env.found_docs:\n if docname not in self.env.all_docs:\n yield docname\n continue\n sourcename = path.join(self.env.srcdir, docname +\n self.file_suffix)\n targetname = path.join(self.outdir, self.file_transform(docname))\n print (sourcename, targetname)\n\n try:\n targetmtime = path.getmtime(targetname)\n except Exception:\n targetmtime = 0\n try:\n srcmtime = path.getmtime(sourcename)\n if srcmtime > targetmtime:\n yield docname\n except EnvironmentError:\n # source doesn't exist anymore\n pass","def dup_remove(doc):\n paragraphs_his = {}\n del_ids = []\n para_id = None\n if 'most_related_para' in doc:\n para_id = doc['most_related_para']\n doc['paragraphs_length'] = []\n for p_idx, (segmented_paragraph, paragraph_score) in \\\n enumerate(zip(doc[\"segmented_paragraphs\"], doc[\"segmented_paragraphs_scores\"])):\n doc['paragraphs_length'].append(len(segmented_paragraph))\n paragraph = ''.join(segmented_paragraph)\n if paragraph in paragraphs_his:\n del_ids.append(p_idx)\n if p_idx == para_id:\n para_id = paragraphs_his[paragraph]\n continue\n paragraphs_his[paragraph] = p_idx\n # delete\n prev_del_num = 0\n del_num = 0\n for p_idx in del_ids:\n if p_idx < para_id: \n prev_del_num += 1\n del doc[\"segmented_paragraphs\"][p_idx - del_num]\n del doc[\"segmented_paragraphs_scores\"][p_idx - del_num]\n del doc['paragraphs_length'][p_idx - del_num]\n del_num += 1\n if len(del_ids) != 0:\n if 'most_related_para' in doc:\n doc['most_related_para'] = para_id - prev_del_num\n doc['paragraphs'] = []\n for segmented_para in doc[\"segmented_paragraphs\"]:\n paragraph = ''.join(segmented_para)\n doc['paragraphs'].append(paragraph)\n return True\n else:\n return False","def tearDown(self):\n super(SimpleDocumentAnnotationTestCase, self).tearDown()\n del self.document","def removeFromNavBar(des):\n index = \"Xblog/docs/index.html\"\n title = des.split(\"/\")[-1].replace(\".html\", \"\")\n with open(index, 'r') as f:\n soup = BeautifulSoup(f, \"html.parser\")\n f.close()\n soup.select(\"#\"+title)[0].decompose()\n with open(index, 'w') as f:\n f.write(soup.prettify(formatter=\"html\"))\n f.close()\n ccc.success(\"removing \" + des + \" from navigation pallete\")","def empty_fn_docstr_pass():\n pass","def clean_doc(doc):\n tokens = doc.split()\n # Remove punctuation marks [., ;, :, \", e.t.c]\n re_punc = re.compile('[%s]' % re.escape(string.punctuation))\n tokens = [re_punc.sub('', w) for w in tokens]\n # Keep only alphabetic words (Remove words with numbers)\n tokens = [word for word in tokens if word.isalpha()]\n # Remove stop words like \"and, but, a, the, an\" e.t.c\n stop_words = set(stopwords.words('english'))\n tokens = [w for w in tokens if not w in stop_words]\n # Remove all other 1-character words if they're still in there\n tokens = [word for word in tokens if len(word) > 1]\n return tokens","def handle_cleaning():\n extra_fl = ['changesInfos', 'printerSettings']\n fld_path = f'{output_path}/ppt'\n out_rel_path = f'{fld_path}/_rels/presentation.xml.rels'\n root, tree = gen_tree(out_rel_path)\n \n for i in extra_fl:\n shutil.rmtree(f'{fld_path}/{i}')\n \n for relation in root:\n attrib = relation.attrib\n if i in attrib['Target']:\n root.remove(relation)\n \n tree.write(out_rel_path, pretty_print=True, xml_declaration=True, encoding='UTF-8', standalone=True)\n return","def clean(obj):\n clean_up_generated_files(obj)","def handleCleanMetadataRecon(self):\n logging.debug(\"Removing compromising personal info and remaking the file...\")\n filePath = self.filesList.selectedItems()[0].text(2)\n fileType = self.filesList.getFileObj(filePath).type\n self.printPdfPersonalData(filePath, \n fileType,\n AddedFile.changeBase(filePath, self.outputPath))\n self.tabArea.setCurrentIndex(1)\n self.changeCursor()\n self.filesList.getFileObj(filePath).reconMetaCleaned = True","def refresh(cls):\n # Flip the order of the links so that the first URL listed is the\n # highest priority and will take precedence\n for url in current_app.config['MATLAB_DOC_LINKS'][::-1]:\n resp = requests.get(url)\n soup = BeautifulSoup(resp.text, 'html.parser')\n\n terms = soup.findAll('td', {'class': 'term'})\n links = [term.find('a') for term in terms]\n\n for link in links:\n\n function = link.text.rstrip()\n\n doc = cls.query.filter_by(name=function).first()\n doc_url = urljoin(url, link['href'])\n\n # Create an entry if one doesn't already exist\n if doc is None:\n doc = cls(name=function)\n\n doc.link = doc_url\n doc.save()\n\n # Make sure to remove i and j entries\n toremove = cls.query.filter(or_(cls.name == 'i', cls.name == 'j')).all()\n for item in toremove:\n item.delete()\n\n return cls.query.all()","def split_doc_file(filename: str) -> List[str]:\n if not filename.endswith('.doc'):\n raise ValueError('File extension must be .doc')\n if not (os.path.exists('.antiword') and os.path.exists(filename)):\n raise FileNotFoundError('Unable to process .doc file')\n\n os.environ['HOME'] = '.'\n stream = Popen(\n [r'.antiword\\antiword.exe', '-m', 'UTF-8', filename],\n shell=True,\n stdout=PIPE,\n stderr=PIPE,\n stdin=PIPE,\n ).communicate()[0]\n\n text = stream.decode('utf-8').replace('[pic]', '')\n return drop_empty_lines(text.split('\\n'))","def cleanup() -> None:\n\n for fname in glob(os.path.join(tdir, 'alexandria.*')):\n if os.path.splitext(fname)[1] not in {'.c', '.h'}:\n os.unlink(fname)","def handleCleanMetadataKeep(self):\n logging.debug(\"Removing all metadata found...\")\n filePath = self.filesList.selectedItems()[0].text(2)\n self.filesList.removeAllMeta(filePath)","def purge_posts(app, env, docname):\n\n if hasattr(env, \"ablog_posts\"):\n env.ablog_posts.pop(docname, None)\n filename = os.path.split(docname)[1]\n env.domains[\"std\"].data[\"labels\"].pop(filename, None)\n env.domains[\"std\"].data[\"anonlabels\"].pop(filename, None)","def test_duplicate_word_removal(self):\n data = [{\"Header\": \"This is a Header, and this is a Header\", \"Paragraph\": \"This is a Paragraph, and this is a \"\n \"Paragraph\",\n \"Header_keywords\": [\"header\", \"header\"],\n \"Paragraph_keywords\": [\"paragraph\", \"paragraph\"], \"slide\": 10}]\n remove_duplicates = duplicate_word_removal(data)\n data[0][\"Header_keywords\"] = [\"header\"]\n data[0][\"Paragraph_keywords\"] = [\"paragraph\"]\n self.assertEqual(data, remove_duplicates)","def clean_PDF(submission):\n src = submission.file_upload.file.name\n pdf1 = PdfFileReader(src)\n merger = PdfFileMerger(strict=False, )\n merger.append(pdf1, import_bookmarks=False)\n merger.addMetadata({'/Title': '',\n '/Author': '',\n '/Creator': '',\n '/Producer': ''})\n fd, temp_file = tempfile.mkstemp(suffix='.pdf')\n merger.write(temp_file)\n merger.close()\n os.close(fd)\n shutil.move(temp_file, src) # replace the original PDF on the server","def clean_text(self, num='substitute'):\n for i, doc in enumerate(self.documents):\n if num is 'spell':\n doc = doc.replace('0', ' zero ')\n doc = doc.replace('1', ' one ')\n doc = doc.replace('2', ' two ')\n doc = doc.replace('3', ' three ')\n doc = doc.replace('4', ' four ')\n doc = doc.replace('5', ' five ')\n doc = doc.replace('6', ' six ')\n doc = doc.replace('7', ' seven ')\n doc = doc.replace('8', ' eight ')\n doc = doc.replace('9', ' nine ')\n elif num is 'substitute':\n doc = re.sub('(\\\\d+)', ' NUM ', doc)\n elif num is 'remove':\n doc = re.sub('[0-9]', ' ', doc)\n doc = doc.replace('$', ' dollar ')\n doc = doc.lower()\n doc = re.sub('[^a-z]', ' ', doc)\n doc = ' '.join(doc.split())\n self.documents[i] = doc","def horde_cleanup(self):","def clear_invalid_node_references(doc, ids_to_remove):\n start = time.time()\n for elem in doc.iter(\"nd\"):\n ref = elem.attrib[\"ref\"]\n\n if ref in ids_to_remove:\n tmp = elem\n elem = elem.getnext()\n tmp.getparent().remove(tmp)\n\n print 'Time Taken :: ', round(time.time() - start, 3), \" seconds\"\n return doc","def _clear_variables( self ):\r\n self.navigation = None\r\n self.resPath = None\r\n self.resolutions = None\r\n self.currentResolution = None\r\n self.resolution = None\r\n for doc in self.include_doc:\r\n try: doc.unlink()\r\n except: pass","def clean():\n clean_files()","def copy_docs():\n local('rsync -av --delete --exclude=.svn %s:%s/ /tmp/djangodocs/' %\n (env.hosts[0], env.deploy_base.child('docbuilds')))","def _clean_up(self):","def clean(self, quick_clean=False):\r\n date_time('Cleaning')\r\n self.cursor.execute('DELETE FROM citations WHERE citation IS NULL OR citation = \"\" ;')\r\n self.conn.execute(\"VACUUM\")\r\n self.conn.commit()\r\n gc.collect()","def clean_duplicate(self):\r\n self.elements = list(set(self.elements))\r\n self.elements = [e for e in self.elements if e != '']","def clean():\n for dirpath, dirnames, filenames in os.walk('.'):\n for filename in filenames:\n if filename.endswith('.pyc') or filename.endswith('.pyo'):\n full_pathname = os.path.join(dirpath, filename)\n click.echo('Removing {}'.format(full_pathname))\n os.remove(full_pathname)","def toLighter(self, del_spaces = True, del_comments = True, del_pi = True) :\n\t\tif self.doc is not None :\n\t\t\tself._purgeDOM(self.doc, del_spaces, del_comments, del_pi)\n\t\treturn self","def clean_content(sender, instance, **kwargs):\r\n if kwargs.get('raw'):\r\n # We're in loaddata (or something similar).\r\n return\r\n\r\n page = instance\r\n fields = dict(stemplates.get_fields_bare(page.template))\r\n current_contents = list(page.pagecontent_set.all())\r\n\r\n for content in current_contents:\r\n if content.title not in fields or fields[content.title] != content.typ:\r\n content.delete()\r\n\r\n existing_contents = dict([(pc.title, pc.typ)\r\n for pc in page.pagecontent_set.all()])\r\n\r\n for title, typ in fields.items():\r\n if title not in existing_contents or existing_contents[title] != typ:\r\n PageContent(page=page, title=title, typ=typ, content='').save()","def empty_fn_docstr():","def clean_stale_issues():\n from security_monkey.common.audit_issue_cleanup import clean_stale_issues\n clean_stale_issues()","def clean(corpus):\n # Initiate clean_corpus\n clean_corpus = [] \n \n for speech in corpus:\n \n # Removes meaningless intro \n speech = speech[5:] \n\n for i in range(len(speech)):\n # Removes 'meaningless text hear (min:sec)\\n' at the beginning of each paragraph\n speech[i] = speech[i][speech[i].find('\\n') + 1:] \n # Replaces brackets with paranthesis\n speech[i] = speech[i].replace('[', '(') \n speech[i] = speech[i].replace(']', ')')\n # Removes meaningless text in parantheses\n speech[i] = re.sub(r'\\([^)]*\\)', '', speech[i]) \n\n # Join all of the paragraphs into one speech\n speech = ','.join(speech) \n\n clean_corpus.append(speech)\n \n # Combined all of the speeches into one document\n \n if len(clean_corpus) == 1:\n clean_corpus = clean_corpus[0]\n if len(clean_corpus) == 2:\n clean_corpus = clean_corpus[0] + clean_corpus[1]\n if len(clean_corpus) == 3:\n clean_corpus = clean_corpus[0] + clean_corpus[1] + clean_corpus[2]\n if len(clean_corpus) == 8:\n clean_corpus = clean_corpus[0] + clean_corpus[1] + clean_corpus[2] + clean_corpus[3] + clean_corpus[4] + \\\n clean_corpus[5] + clean_corpus[6] + clean_corpus[7]\n \n return clean_corpus","def _remove_duplicates(self):\n self.search_query = remove_duplicates(self.search_query)","def rm_duplicates(self):\n # get uniq representation of existing detection documents\n existing = set(ed.uniq_data for ed in self.existing_detections)\n # remove duplicates\n for idx in xrange(len(self.new_detections)-1, -1, -1):\n nd = self.new_detections[idx]\n if nd.uniq_data in existing:\n self.new_detections.pop(idx)","def cleanUp(self):\n import evoware.fileutil as F\n F.tryRemove(self.f_project, verbose=(self.VERBOSITY>1), tree=1)","def preprocess(docs, nlp, min_length, min_counts, max_counts):\n\n def clean_and_tokenize(doc):\n text = ' '.join(doc.split()) # remove excessive spaces\n text = nlp(text, tag=True, parse=False, entity=False)\n #return [t.lemma_ for t in text if t.is_alpha and len(t) > 2 and not t.is_stop]\n return [t.lower_ for t in text if t.is_alpha and len(t) > 2 and not t.is_stop] # remove .lemma and add lower case operation\n\n tokenized_docs = [(i, clean_and_tokenize(doc)) for i, doc in tqdm(docs)]\n\n # remove short documents\n n_short_docs = sum(1 for i, doc in tokenized_docs if len(doc) < min_length)\n tokenized_docs = [(i, doc) for i, doc in tokenized_docs if len(doc) >= min_length]\n print('number of removed short documents:', n_short_docs)\n\n # remove some tokens\n counts = _count_unique_tokens(tokenized_docs)\n tokenized_docs = _remove_tokens(tokenized_docs, counts, min_counts, max_counts)\n n_short_docs = sum(1 for i, doc in tokenized_docs if len(doc) < min_length)\n tokenized_docs = [(i, doc) for i, doc in tokenized_docs if len(doc) >= min_length]\n print('number of additionally removed short documents:', n_short_docs)\n\n counts = _count_unique_tokens(tokenized_docs)\n encoder, decoder, word_counts = _create_token_encoder(counts)\n\n print('\\nminimum word count number:', word_counts[-1])\n print('this number can be less than MIN_COUNTS because of document removal')\n\n encoded_docs = _encode(tokenized_docs, encoder) # all the doc is encoded as indexes instead of words using ix2word\n return encoded_docs, decoder, word_counts","def remove_punctuation_and_multi_spaces_document(self, doc):\n regex = re.compile('[%s]' % re.escape(self.punctuation))\n doc = regex.sub(' ', doc)\n return ' '.join(doc.split())","def test_post_build_processing_remove_occurrences(self):\n it = [\n \"[[Chapter]] Chapter I\",\n \"This is chapter I text\",\n \"[[Article]] Article I\",\n \"This is article I text\",\n ]\n\n descriptor = {\n 'components': ['Chapter', 'Article'],\n 'patterns': ['Chapter', 'Article']\n }\n\n doc = parse_iterable(it, descriptor)\n\n descriptor = extend_internal_patterns(descriptor)\n descriptor = compile_patterns(descriptor)\n\n doc = post_build_process(doc, descriptor)\n\n result = [n['text'] for _, n in doc.traverse()]\n expected = [[], [\"Chapter I\", \"This is chapter I text\"], [\"Article I\", \"This is article I text\"]]\n self.assertListEqual(result, expected)","def cleanPDFs(self):\n to_delete = []\n pdfs = self.getPDFList()\n now = datetime.now()\n\n # First we compute the list of files to delete.\n for filename in pdfs:\n filedate = pdfs[filename]\n delta = now - filedate\n\n if delta.seconds > 7200:\n to_delete.append(filename)\n\n existing_files = os.listdir(self.tempdir)\n for filename in to_delete:\n del pdfs[filename]\n if filename in existing_files:\n os.remove('%s/%s' % (self.tempdir,\n filename))\n self.setPDFList(pdfs)\n metadata = self._getMetadata()\n metadata['last_clean'] = now","def extract_documents():\n client = MongoClient()\n conn = client.data\n coll = conn.germanwings\n\n query = {'text': {'$exists': 1}, 'exc': {'$exists': 0}}\n selection = {'text': 1, 'short_url': 1}\n for i, doc in enumerate(coll.find(query, selection)):\n short_url, text = tuple(doc[x] for x in (\"short_url\", \"text\"))\n print(\"Extracting {0} {1}\".format(i, short_url), file=stderr)\n filename = os.path.join(RAW_DIR, short_url)\n with open(filename, \"w\") as f:\n ascii = text.encode('ascii', 'ignore')\n f.write(ascii)","def PurgeAll(self):\n\t\tself.acad.ActiveDocument.PurgeAll()","def documents(iati_import, activity, project, activities_globals):\n imported_docs = []\n changes = []\n\n xml_ns = 'http://www.w3.org/XML/1998/namespace'\n first_image = True\n\n for doc_link in activity.findall('document-link'):\n url = ''\n doc_format = ''\n title = ''\n title_language = ''\n category = ''\n language = ''\n\n if 'url' in doc_link.attrib.keys():\n url = doc_link.attrib['url']\n\n # Check if it's the first image\n if url and url.rsplit('.', 1)[1].lower() in VALID_IMAGE_EXTENSIONS and first_image:\n first_image = False\n continue\n\n if 'format' in doc_link.attrib.keys():\n if not len(doc_link.attrib['format']) > 75:\n doc_format = doc_link.attrib['format']\n else:\n add_log(iati_import, 'document_link_format',\n 'format is too long (75 characters allowed)', project)\n\n # Check if the format is 'application/http'\n if doc_format == 'application/http':\n continue\n\n title_element = doc_link.find('title')\n if not title_element is None:\n title = get_text(title_element, activities_globals['version'])\n if len(title) > 100:\n add_log(iati_import, 'document_link_title',\n 'title is too long (100 characters allowed)', project,\n IatiImportLog.VALUE_PARTLY_SAVED)\n title = title[:100]\n\n if activities_globals['version'][0] == '1' and \\\n '{%s}lang' % xml_ns in title_element.attrib.keys():\n if not len(title_element.attrib['{%s}lang' % xml_ns]) > 2:\n title_language = title_element.attrib['{%s}lang' % xml_ns]\n else:\n add_log(iati_import, 'document_link_title_language',\n 'language is too long (2 characters allowed)', project)\n elif activities_globals['version'][0] == '2':\n narrative_element = title_element.find('narrative')\n if not narrative_element is None and \\\n '{%s}lang' % xml_ns in narrative_element.attrib.keys():\n if not len(narrative_element.attrib['{%s}lang' % xml_ns]) > 2:\n title_language = narrative_element.attrib['{%s}lang' % xml_ns]\n else:\n add_log(iati_import, 'document_link_title_language',\n 'language is too long (2 characters allowed)', project)\n\n category_element = doc_link.find('category')\n if not category_element is None and 'code' in category_element.attrib.keys():\n if not len(category_element.attrib['code']) > 3:\n category = category_element.attrib['code']\n else:\n add_log(iati_import, 'document_link_category',\n 'category is too long (3 characters allowed)', project)\n\n language_element = doc_link.find('language')\n if not language_element is None and 'code' in language_element.attrib.keys():\n if not len(language_element.attrib['code']) > 2:\n language = language_element.attrib['code']\n else:\n add_log(iati_import, 'document_link_language',\n 'language is too long (2 characters allowed)', project)\n\n doc, created = get_model('rsr', 'projectdocument').objects.get_or_create(\n project=project,\n url=url,\n format=doc_format,\n title=title,\n title_language=title_language,\n category=category,\n language=language\n )\n\n if created:\n changes.append(u'added project document (id: %s): %s' % (str(doc.pk), doc))\n\n imported_docs.append(doc)\n\n for doc_link in project.documents.all():\n if not doc_link in imported_docs:\n changes.append(u'deleted project document (id: %s): %s' %\n (str(doc_link.pk),\n doc_link.__unicode__()))\n doc_link.delete()\n\n return changes","def read_and_clean_files(clueweb_file, ann_file, data_dir, ann_dir):\n annotation_input = fileinput.FileInput(os.path.join(ann_dir, ann_file), openhook=fileinput.hook_compressed)\n annotation_list = []\n for line in annotation_input:\n\tannotation_list.append(Annotation.parse_annotation(line))\n\n warc_path = os.path.join(data_dir, clueweb_file)\n warc_file = warc.open(warc_path)\n print \"Replacing entity mentions for \", clueweb_file, \":\", ann_file, \"...\"\n start = time.time()\n warc_entry = WarcEntry(warc_path, warc_file, annotation_list)\n cleaned_records = warc_entry.replace_entity_mentions()\n end = time.time()\n print \"Time used: \", end - start\n warc_file.close()\n return cleaned_records","def keep_documents(self, idx):\n print('{} documents have been removed'.format(self.data.shape[0] - len(idx)))\n self.documents = [self.documents[i] for i in idx]\n self.labels = self.labels[idx]\n self.data = self.data[idx, :]","def clean_docstring(doc: str, unused: Literal[\"pre\", \"post\"] = None) -> str:\n doc = doc.split(\"\\n\")\n if unused == \"pre\":\n try:\n index = next(i for i, l in enumerate(doc) if l.strip())\n doc = doc[index:]\n except StopIteration:\n doc = []\n elif unused == \"post\":\n try:\n index = next(i for i, l in enumerate(reversed(doc)) if l.strip())\n doc = doc[: len(doc) - index]\n except StopIteration:\n doc = []\n if doc:\n first_line = doc[0]\n index = len(first_line) - len(first_line.lstrip())\n indent = first_line[:index]\n if all(l.startswith(indent) for l in doc if l.strip()):\n doc = [(l[index:] if l.strip() else l) for l in doc]\n return \"\\n\".join(doc)","def test_cleanup_html_lookup_file():\n # there are 4 duplicate entries, all having to do with stroke\n expected_lines = 63\n\n #original location\n test_filename = 'test/fixture/html_lookup_file.txt'\n #create a copy to maintain integrity of file\n test_filename_working = 'test/fixture/html_lookup_file_copy.txt'\n copyfile(test_filename, test_filename_working)\n\n dedup_medfind.cleanup_html_lookup_file(test_filename_working)\n # no return value, we must open and read the file to assert\n with open(test_filename_working, 'r', encoding='utf-8') as fs:\n lines = fs.readlines()\n # delete our working copy before performing asserts\n os.remove(test_filename_working)\n # finally, we do our asserts based upon the test file we create\n for line in lines:\n print(line.strip())\n assert expected_lines == len(lines)","def clean_text(doc):\n doc_clean = preprocess_text(doc)\n doc_clean = custom_tokenizer(doc_clean)\n \n doc_as_string = ' '.join(doc_clean)\n return doc_as_string","def _initDeleteEntryDocument(self, atomDoc): #@UnusedVariable #$NON-NLS-1$\r\n pass","def preprocess_docs():\n\n print(\"Getting started!\")\n stopwords.populate_stopwords(NLP, STOPWORD_URL)\n\n print(str.format(\"Using data dir:{}\", DATA_DIR))\n\n csv_file = open(os.path.join(DATA_DIR, 'PDFs.csv'))\n reader = csv.reader(csv_file, 'excel')\n rows = list(reader)\n\n filenames = [_get_filename(row) for row in rows]\n\n pool = Pool(multiprocessing.cpu_count())\n\n try:\n pool.map(_get_item, rows)\n pool.map(pdf.extract_text, filenames)\n docs = pool.map(_extract_questions, rows)\n docs = [d for d in docs if d is not None]\n\n _find_similar(docs, simdoc=compare.compare_doc_keywords)\n\n for doc in docs:\n if doc is None:\n continue\n doc.save_json()\n\n except KeyboardInterrupt:\n pool.terminate()\n print(\"You cancelled the program!\")\n sys.exit(1)\n\n print(\"Done\")","def delete_empty_documents(self, dataset):\n\t\tprint(\"Deleting empty documents in\", dataset)\n\t\tnumber_documents = len(self.raw_documents[dataset])\n\t\tindices = list(filter(lambda x:len(self.raw_documents[dataset][x].strip()), range(number_documents)))\n\n\t\tself.raw_documents[dataset] = [self.raw_documents[dataset][idx] for idx in indices]\n\t\tself.Y[dataset] = [self.Y[dataset][idx] for idx in indices]","def test_buildRemovesTemporaryLaTeXFiles(self):\n sections = range(1, 4)\n for sectionNumber in sections:\n self.howtoDir.child(\"%d.xhtml\" % (sectionNumber,)).setContent(\n self.getArbitraryLoreInput(sectionNumber))\n bookTeX = self._setupTeXBook(sections)\n bookPDF = FilePath(self.mktemp())\n\n builder = BookBuilder()\n builder.build(self.howtoDir, [self.howtoDir], bookTeX, bookPDF)\n\n self.assertEqual(\n set(self.howtoDir.listdir()),\n set([bookTeX.basename()] + [\"%d.xhtml\" % (n,) for n in sections]))","def get_clean_corpus(FILENAME):\n text = open(FILENAME)\n corpus = text.readlines()\n clean_corpus = []\n doc_no = 0\n prev_doc_no = 0\n doctype_flag = 0\n doctype_end_flag = 0\n doc_list = []\n punct = set(string.punctuation)\n tag = 0\n for i in range(len(corpus)):\n stmt = \"\"\n flag = 1\n sz = len(corpus[i])\n\n\n for j in range(sz-1):\n if(corpus[i][j] == '<'):\n tag = 1\n if(corpus[i][j] == '<' and j+1 < sz and corpus[i][j+1] == 'd'):\n doctype_flag = 1\n if(corpus[i][j] == 'c' and j+1 < sz and corpus[i][j+1] == '>'):\n doctype_end_flag = 1\n if(doctype_flag == 0 and doctype_end_flag == 0 and tag == 0):\n stmt = stmt + (corpus[i][j])\n if(corpus[i][j] == '>'):\n tag = 0\n if(doctype_flag == 1 and corpus[i][j] == '>'):\n doctype_flag = 0\n if(doctype_end_flag == 1 and corpus[i][j] == '>'):\n doctype_end_flag = 0\n doc_no = doc_no + 1\n break\n\n if(stmt != \"\"):\n if(doc_no != prev_doc_no):\n if(len(doc_list) != 0):\n clean_corpus.append(doc_list)\n doc_list = []\n prev_doc_no = doc_no\n stmt = stmt.lower()\n doc_list.append(stmt)\n return clean_corpus","def preprocess(self, documents):\n\n # A dict storing the frequency of each word\n word_freq = {}\n\n # Iterate for each document\n for doc in documents:\n # Split the document into a list of words and iterate on it\n for w in extract_words(doc):\n # Update word frequencies\n '''YOUR CODE HERE'''\n if w not in word_freq.keys():\n word_freq[w] = 1\n else:\n word_freq[w] += 1\n\n ''' END CODE FOR THIS LOOP '''\n\n\n # A set of words with frequency less than 'self.min_freq'\n remove_words = set()\n\n # Check frequency of each word and add to 'remove_words'\n # if it's frequency is below self.min_freq\n\n ''' YOUR CODE HERE '''\n for w in word_freq.keys():\n if word_freq[w] < self.min_freq:\n remove_words.add(w)\n\n # Delete the words in 'remove_words' from 'word_freq'\n for w in remove_words:\n del word_freq[w]\n\n # Fill 'self.word_to_idx' and 'self.idx_to_word' for\n # each word in 'word_freq' (dicts are explained above)\n\n i = 0\n for w in word_freq.keys():\n self.word_to_idx[w] = i\n self.idx_to_word[i] = w \n i += 1\n\n ''' END YOUR CODE HERE '''","def clean_doc(doc):\n tokens = doc.split()\n table = str.maketrans('', '', punctuation)\n tokens = [w.translate(table) for w in tokens]\n tokens = [word.lower() for word in tokens if word.isalpha()]\n stop_words = set(stopwords.words('english'))\n tokens = [w for w in tokens if not w in stop_words]\n tokens = [word for word in tokens if len(word) > 1]\n return tokens","def clean_old_data():\n logger.info('Cleaning standalone files on disk...')\n for absolute_path in glob.glob(MEDIA_URL + '*'):\n file_name = os.path.basename(absolute_path)\n try:\n relative_path = os.path.join(AUDIOS_URL, file_name)\n audio = Audio.objects.get(filename=relative_path)\n if audio.get_type() == 'episode':\n try:\n # If there are inactive audios on its being\n for e in audio.podcast.episode_set.exclude(pk=audio.podcast.active_episode.pk):\n if not e.is_active():\n logger.info('Inactive audio found in podcast set. Erasing files.')\n e.delete_files()\n except Exception, e:\n logger.exception(e.message)\n except ObjectDoesNotExist, e:\n logger.info('A file with no audio registered in database')\n if os.path.isfile(relative_path):\n logger.info('Erasing: %s' % relative_path)\n os.remove(relative_path)\n logger.info('... Done.')","def idempotence(args):\n title, posts = parse_markdown(os.path.join(args.root, 'index.md'))\n print_markdown(posts, title, os.path.join(args.dest, 'index.md'))","def assert_less_undocumented_wc(self, modulename, max_undoc_frac):\n undocumented, loc_len = get_undocumented_wildcards(modulename)\n undocumented_fraction = len(undocumented) / loc_len\n self.assertLessEqual(undocumented_fraction, max_undoc_frac, f\"{len(undocumented)/loc_len:.2%} of {modulename} imports undocumented. Missing Docstrings in {len(undocumented)}/{loc_len}:\\n- \" + \"\\n- \".join(undocumented))"],"string":"[\n \"def clean_docs(self, docs):\\n cleaned = [self.cleaning(doc) for doc in docs]\\n print(cleaned[0])\\n return cleaned\",\n \"def clean_docs(c):\\n c.run(f\\\"rm -fr {DOCS_BUILD_DIR}\\\")\",\n \"def clean_all_documents(cls):\\n for index, text in enumerate(cls.documents):\\n text_processed = cls.clean_document(text)\\n cls.processed_documents.append(text_processed)\",\n \"def merge_docs(self):\",\n \"def clear_doc():\\n for obj in DOC.Objects:\\n DOC.removeObject(obj.Name)\",\n \"def _clean_doc(doc: Dict) -> Dict:\\n doc['label'] = doc['labels'][config.LANG]['value']\\n aliases = doc['aliases'][config.LANG] if config.LANG in doc['aliases'] else []\\n\\n doc['aliases'] = [alias['value'] for alias in aliases]\\n\\n for key in DOC_CLEAN_KEYS:\\n try:\\n del doc[key]\\n except:\\n continue\\n return doc\",\n \"def clean_documents():\\n start = datetime.now()\\n for i, raw_filename in enumerate(os.listdir(RAW_DIR)):\\n fullpath = os.path.join(RAW_DIR, raw_filename)\\n if os.path.isfile(fullpath):\\n print(\\\"Cleaning {0} {1}\\\".format(i, fullpath), file=stderr)\\n try:\\n with open(fullpath, \\\"r\\\") as f:\\n text = f.read()\\n text = clean(text)\\n soup = BeautifulSoup(text, \\\"html.parser\\\")\\n cleaned = visible_text(soup)\\n score = germanwings_score(cleaned)\\n if not score:\\n print(\\\"not germanwings: {0}\\\".format(raw_filename))\\n else:\\n clean_filename = os.path.join(CLEAN_DIR, raw_filename)\\n with open(clean_filename, \\\"w\\\") as f:\\n f.write(cleaned.encode(\\\"ascii\\\", \\\"ignore\\\"))\\n except Exception as exc:\\n print(\\\"{0}: {1}\\\".format(fullpath, exc), file=stderr)\\n end = datetime.now()\\n print(\\\"Elapsed time to clean: {0}\\\".format(end - start), file=stderr)\",\n \"def clean_wikidata_docs(docs: List[Dict]) -> List[Dict]:\\n for doc in docs:\\n try:\\n yield _clean_doc(doc)\\n except:\\n continue\",\n \"def clean_docs(self,docs):\\n\\n # Remove numbers, but not words that contain numbers.\\n docs = [[token for token in doc if not token.isnumeric()] for doc in docs]\\n\\n # Remove words that are only one character.\\n docs = [[token for token in doc if len(token) > 1 and token not in stop_words] for doc in docs]\\n\\n # lemmatizer = WordNetLemmatizer()\\n # docs = [[lemmatizer.lemmatize(token) for token in doc] for doc in docs]\\n\\n # Add bigrams and trigrams to docs (only ones that appear 20 times or more).\\n bigram = Phrases(docs, min_count=20)\\n for idx in range(len(docs)):\\n for token in bigram[docs[idx]]:\\n if '_' in token:\\n # Token is a bigram, add to document.\\n docs[idx].append(token)\\n\\n # Create a dictionary representation of the documents.\\n dictionary = Dictionary(docs)\\n\\n # Filter out words that occur less than 20 documents, or more than 50% of the documents.\\n dictionary.filter_extremes(no_below=20, no_above=0.5)\\n\\n # Bag-of-words representation of the documents.\\n corpus = [dictionary.doc2bow(doc) for doc in docs]\\n\\n return docs,dictionary,corpus\",\n \"def _clean_data(self, docs: []):\\n print('Cleaning data...')\\n preprocessed_data = []\\n for doc in docs:\\n if len(doc) <= constants.MIN_DOC_LENGTH:\\n continue\\n\\n temp_doc = self._remove_urls(doc)\\n temp_doc = self._remove_special_chars(temp_doc)\\n temp_doc = self._transform_to_lowercase(temp_doc)\\n temp_doc = self._remove_stopwords(temp_doc)\\n\\n preprocessed_data.append(temp_doc)\\n\\n return preprocessed_data\",\n \"def cleanup(asciitest_out_dir, doc_file):\\n # path join uses backslash win32 which is not cmake compatible\\n filename = os.path.join(asciitest_out_dir, save_cmake_filename(doc_file)).replace(\\\"\\\\\\\\\\\",\\\"/\\\")\\n \\n #print(\\\"cleanup %s %s\\\" % (doc_file, filename))\\n try:\\n os.remove(filename)\\n except:\\n pass\",\n \"def deleteconvert(self):\\n filename = os.path.join(self.docx_path, self.name.docx)\\n if os.path.isfile(filename):\\n os.remove(filename)\\n filename = os.path.join(self.html_path, self.name.html)\\n if os.path.isfile(filename):\\n os.remove(filename)\\n filename = os.path.join(self.docbook_path, self.name.xml)\\n if os.path.isfile(filename):\\n os.remove(filename)\\n filename = os.path.join(self.markdown_path, self.name.md)\\n if os.path.isfile(filename):\\n os.remove(filename)\",\n \"def _remove_doc(xml_str):\\n result = []\\n do_add = True\\n for line in xml_str.splitlines():\\n if '<doc:doc>' in line:\\n do_add = False\\n if do_add:\\n result.append(line)\\n if '</doc:doc>' in line:\\n do_add = True\\n return '\\\\n'.join(result)\",\n \"def clean(self):\\n if os.path.exists(self.paths['build_dir']):\\n shutil.rmtree(self.paths['build_dir'])\\n if os.path.exists(os.path.join(self.base_dir, 'docs')):\\n shutil.rmtree(os.path.join(self.base_dir, 'docs'))\",\n \"def _clear_document(self, docid):\\n doc = self.get_document(docid)\\n for term, count in doc.get_terms():\\n term_entry = self.sql_session.query(Term).get(term)\\n term_entry.count -= abs(count)\\n term_entry.distinct_docs -= 1\\n any_term = self.sql_session.query(Term).get(ANY)\\n any_term.distinct_docs -= 1\\n doc.delete()\",\n \"def clean(raw_file,clean_path,results_path):\\n clean_file = clean_path + 'clean.pkl'\\n stats_file = results_path + 'corpus_stats.pkl' \\n raw_text = load_raw_text(raw_file) \\n clean_docs = list()\\n nlp = spacy.load('en')\\n i = 0\\n print('Cleaning documents...')\\n for text in raw_text: \\n words = nlp(text)\\n raw_count = len(words)\\n words = [w for w in words if not w.is_stop]\\n words = [w for w in words if w.ent_type_ != 'PERSON' and w.pos_ != 'PROPN']\\n words = [w for w in words if w.is_alpha and not w.is_digit]\\n words = [w.lemma_ for w in words if w.text != '-PRON-']\\n word_string = ' '.join(words)\\n word_string = word_string.lower()\\n doc = Document(word_string)\\n doc.clean_count = len(words)\\n doc.raw_count = raw_count\\n clean_docs.append(doc)\\n if i%10 == 0:\\n print('Document: ' + str(i))\\n i += 1\\n clean_corpus = Corpus(clean_docs)\\n clean_corpus.save_corpus_text(clean_file)\\n clean_corpus.save_corpus_stats(stats_file)\\n return clean_corpus\",\n \"def reset_document(self):\\n # FIXME: this state does not make sense\\n self.doc_version_set = False\\n self.doc_comment_set = False\\n self.doc_namespace_set = False\\n self.doc_data_lics_set = False\\n self.doc_name_set = False\\n self.doc_spdx_id_set = False\",\n \"def clean(self):\\r\\n\\r\\n for _, data in self.composition.items():\\r\\n index_file = Path(data['file'] + '.fxi')\\r\\n if index_file.exists():\\r\\n index_file.unlink()\",\n \"def remove_urls(self, doc):\\n doc = re.sub(\\n r'(?i)\\\\b((?:https?://|www\\\\d{0,3}[.]|[a-z0-9.\\\\-]+[.][a-z]{2,4}/)'\\n r'(?:[^\\\\s()<>]+|\\\\(([^\\\\s()<>]+|'\\n r'(\\\\([^\\\\s()<>]+\\\\)))*\\\\))+(?:\\\\(([^\\\\s()<>]+|(\\\\([^\\\\s()<>]+\\\\)))*\\\\)|'\\n r'[^\\\\s`!()\\\\[\\\\]{};:\\\\'\\\".,<>]))',\\n '',\\n doc)\\n return ' '.join(doc.split())\",\n \"def html_clean(options):\\r\\n remake_directories(options.sphinx.doctrees, options.html.outdir)\\r\\n html(options)\\r\\n return\",\n \"def preprocess_docs(self, docs_array):\\n # Remove stopwords\\n new_docs_list = []\\n for doc in docs_array:\\n temp = doc.lower()\\n if \\\"\\\\\\\\\\\" in temp:\\n temp = temp.replace(\\\"\\\\\\\\\\\", \\\"\\\")\\n if \\\"<FONT face\\\" in temp:\\n temp = re.sub(r\\\"<FONT face.*$\\\", \\\"\\\", doc)\\n if \\\"a href=\\\" in temp:\\n temp = re.sub(r\\\" ;a href.*/a>\\\", \\\"\\\", doc)\\n if \\\"A HREF\\\" in temp:\\n temp = re.sub(r\\\" ;A HREF.*/A>\\\", \\\"\\\", doc)\\n if \\\"<\\\" in temp:\\n temp = temp.replace(\\\"<\\\", \\\"\\\")\\n if \\\"i>\\\" in temp:\\n temp = temp.replace(\\\"i>\\\", \\\"\\\")\\n if \\\" \\\" in temp:\\n temp = temp.replace(\\\" \\\", \\\" \\\")\\n if \\\"#151\\\" in temp:\\n temp = temp.replace(\\\"#151\\\", \\\"\\\")\\n if \\\"#36\\\" in temp:\\n temp = temp.replace(\\\"#36\\\", \\\"\\\")\\n if \\\"#39\\\" in temp:\\n temp = temp.replace(\\\"#39\\\", \\\"\\\")\\n new_docs_list.append(temp)\\n return np.array(new_docs_list)\",\n \"def _finalize_iteration(self, verbose: bool):\\n super().delete_remote_files()\\n self.comm.storyteller.document_task(task=\\\"adam_documentation\\\")\",\n \"def clean():\\n possible_outputs = (\\n '{}.html'.format(CONFIG['FULL_PROJECT_NAME']),\\n '{}.epub'.format(CONFIG['FULL_PROJECT_NAME']),\\n '{}.pdf'.format(CONFIG['FULL_PROJECT_NAME']),\\n '{}.docx'.format(CONFIG['FULL_PROJECT_NAME']),\\n '{}.odt'.format(CONFIG['FULL_PROJECT_NAME']),\\n )\\n\\n for filename in possible_outputs:\\n if os.path.exists(filename):\\n os.remove(filename)\\n print(\\\"Removed {}\\\".format(filename))\",\n \"async def remove_doc(self, *args, **kwargs):\\n pass\",\n \"def dummy(doc):\\r\\n return doc\",\n \"def update_docs():\\n site_path = os.path.join(PROJECTS_ROOT, CURRENT_SITE)\\n docs_path = os.path.join(site_path, 'doc_src')\\n with cd(docs_path):\\n run('git reset --hard && git pull --all')\\n run('workon djangopatterns && cd doc_src && make clean')\\n run('workon djangopatterns && cd doc_src && make json')\",\n \"def full_reset(self):\\n for docid in self.iter_docids():\\n self.delete(docid)\\n self.client.delete(self.dbprefix + 'schema')\\n self.client.delete(self.dbprefix + 'docs')\\n self.client.delete(self.dbprefix + 'nextid')\",\n \"def clean_document(doc_text,doc_type = \\\"text\\\",split_to_section = False,**kwargs):\\n \\n doc_text = clean_text(doc_text)\\n doc_text = delete_lines(doc_text)\\n return \\\"\\\".join(doc_text)\\n \\n # if split_to_section == True:\\n # return doc_text\\n # elif split_to_section == False:\\n # return [\\\" \\\".join(doc_text)]\",\n \"def _cleanup(self):\\n os.system(\\\"rm -r %s/*\\\" %(self._snippet_index_dir))\\n os.system(\\\"rm %s/*\\\" %(self._para_dir))\\n os.system(\\\"rm %s/*\\\" %(self._temp_dir))\\n os.system(\\\"rm %s/*\\\" %(self._snippet_result_dir))\",\n \"def tearDown(self):\\n super(SimpleDocumentTestCase, self).tearDown()\\n del self.document\",\n \"def triple_quote_docs():\\n return\",\n \"def cleanup(self):\\n index_id = self.params[\\\"index_id\\\"]\\n\\n # Remove the index document from the database.\\n self.db.indexes.delete_one({\\\"_id\\\": index_id})\\n\\n self.dispatch(\\\"indexes\\\", \\\"delete\\\", [index_id])\\n\\n query = {\\n \\\"_id\\\": {\\n \\\"$in\\\": self.db.history.distinct(\\\"_id\\\", {\\\"index.id\\\": index_id})\\n }\\n }\\n\\n # Set all the otus included in the build to \\\"unbuilt\\\" again.\\n self.db.history.update_many(query, {\\n \\\"$set\\\": {\\n \\\"index\\\": {\\n \\\"id\\\": \\\"unbuilt\\\",\\n \\\"version\\\": \\\"unbuilt\\\"\\n }\\n }\\n })\\n\\n id_list = self.db.history.distinct(\\\"_id\\\", query)\\n\\n self.dispatch(\\\"history\\\", \\\"update\\\", id_list)\\n\\n virtool.utils.rm(self.params[\\\"index_path\\\"], True)\",\n \"def clean_doc(doc):\\r\\n # Replace regular enter (i.e. mere comment formatting in cpp file)\\r\\n # with space\\r\\n doc = doc.replace(\\\"\\\\n\\\", \\\" \\\")\\r\\n\\r\\n # The removal can cause a \\\"hard enter\\\" (literal \\\\n) to get an unintended\\r\\n # trailing space - trim those.\\r\\n doc = doc.replace(\\\"\\\\\\\\n \\\", \\\"\\\\\\\\n\\\")\\r\\n return '\\\"%s\\\"' % doc\",\n \"def clean_pdf():\\n xnt.build.tex.clean(path=\\\"./\\\", remove_pdf=True)\",\n \"def _remove_urls(self, doc: str):\\n processed_tweet = re.sub('(https?:)?\\\\/\\\\/[\\\\w\\\\.\\\\/-]+', '', doc)\\n return processed_tweet\",\n \"def clean(ctx):\\n ctx.run(\\\"rm -rf build/html\\\")\",\n \"def scrub():\\n\\n\\tlocal(\\\"rm -fr dist build\\\")\\n\\tlocal(\\\"find . -name \\\\\\\"*.pyc\\\\\\\" -exec rm '{}' ';'\\\")\",\n \"def prepare_dictionary_from_docs(self):\\n if os.path.exists(self.DICT_PATH):\\n return True\\n self.logger.info(\\\"START PREPARING DICT\\\")\\n for fn in os.listdir(self.wiki_path):\\n self.logger.info(\\\"dict update {0}\\\".format(fn))\\n content = self.get_processed_content(fn)\\n self.dictionary.add_documents([content])\\n self.dictionary.filter_extremes(no_below=20, no_above=0.1, keep_n=100000)\\n self.dictionary.compactify()\\n self.dictionary.save(self.DICT_PATH)\\n return True\",\n \"def clean_files(self):\\n self.filenames.clear()\",\n \"def clean_pofile(file):\\r\\n # Reading in the .po file and saving it again fixes redundancies.\\r\\n pomsgs = pofile(file)\\r\\n # The msgcat tool marks the metadata as fuzzy, but it's ok as it is.\\r\\n pomsgs.metadata_is_fuzzy = False\\r\\n for entry in pomsgs:\\r\\n # Remove line numbers\\r\\n entry.occurrences = [(filename, None) for (filename, lineno) in entry.occurrences]\\r\\n # Remove -format flags\\r\\n entry.flags = [f for f in entry.flags if not f.endswith(\\\"-format\\\")]\\r\\n pomsgs.save()\",\n \"def filter_doc(doc_text):\\n # remove stars\\n filter_regex=re.compile(r\\\"[_*]\\\")\\n doc=filter_regex.sub(\\\"\\\",doc_text)\\n # substitute quotation marks\\n double_quot_regex=re.compile(r\\\"[“”]\\\")\\n single_quot_regex=re.compile(r\\\"[’‘]\\\")\\n doc=double_quot_regex.sub('\\\"',doc)\\n doc=single_quot_regex.sub(\\\"'\\\",doc)\\n # substitute new lines inside the text for spaces\\n # these new lines are usually caused by formatting texts to fit in 80 columns \\n newline_quot_regex=re.compile(r\\\"(\\\\S)\\\\n(\\\\S)\\\")\\n doc=newline_quot_regex.sub(r\\\"\\\\1 \\\\2\\\",doc)\\n # remove illustration tag\\n #illustration_regex=re.compile(r\\\"\\\\[Illustration.*]\\\")\\n #doc=illustration_regex.sub(\\\"\\\",doc)\\n return doc\",\n \"def html():\\n builtdocs = path(\\\"docs\\\") / options.sphinx.builddir / \\\"html\\\"\\n destdir = path(PACKAGE) / \\\"docs\\\"\\n destdir.rmtree()\\n builtdocs.move(destdir)\",\n \"def __del__(self):\\n try:\\n self._document.close()\\n except:\\n pass\",\n \"def dev_clean():\\n clean_files(\\\"csv\\\", True)\\n clean_files(\\\"jsontxt\\\", True)\",\n \"def get_outdated_docs(self):\\n # This method is taken from TextBuilder.get_outdated_docs()\\n # with minor changes to support :confval:`rst_file_transform`.\\n for docname in self.env.found_docs:\\n if docname not in self.env.all_docs:\\n yield docname\\n continue\\n sourcename = path.join(self.env.srcdir, docname +\\n self.file_suffix)\\n targetname = path.join(self.outdir, self.file_transform(docname))\\n print (sourcename, targetname)\\n\\n try:\\n targetmtime = path.getmtime(targetname)\\n except Exception:\\n targetmtime = 0\\n try:\\n srcmtime = path.getmtime(sourcename)\\n if srcmtime > targetmtime:\\n yield docname\\n except EnvironmentError:\\n # source doesn't exist anymore\\n pass\",\n \"def dup_remove(doc):\\n paragraphs_his = {}\\n del_ids = []\\n para_id = None\\n if 'most_related_para' in doc:\\n para_id = doc['most_related_para']\\n doc['paragraphs_length'] = []\\n for p_idx, (segmented_paragraph, paragraph_score) in \\\\\\n enumerate(zip(doc[\\\"segmented_paragraphs\\\"], doc[\\\"segmented_paragraphs_scores\\\"])):\\n doc['paragraphs_length'].append(len(segmented_paragraph))\\n paragraph = ''.join(segmented_paragraph)\\n if paragraph in paragraphs_his:\\n del_ids.append(p_idx)\\n if p_idx == para_id:\\n para_id = paragraphs_his[paragraph]\\n continue\\n paragraphs_his[paragraph] = p_idx\\n # delete\\n prev_del_num = 0\\n del_num = 0\\n for p_idx in del_ids:\\n if p_idx < para_id: \\n prev_del_num += 1\\n del doc[\\\"segmented_paragraphs\\\"][p_idx - del_num]\\n del doc[\\\"segmented_paragraphs_scores\\\"][p_idx - del_num]\\n del doc['paragraphs_length'][p_idx - del_num]\\n del_num += 1\\n if len(del_ids) != 0:\\n if 'most_related_para' in doc:\\n doc['most_related_para'] = para_id - prev_del_num\\n doc['paragraphs'] = []\\n for segmented_para in doc[\\\"segmented_paragraphs\\\"]:\\n paragraph = ''.join(segmented_para)\\n doc['paragraphs'].append(paragraph)\\n return True\\n else:\\n return False\",\n \"def tearDown(self):\\n super(SimpleDocumentAnnotationTestCase, self).tearDown()\\n del self.document\",\n \"def removeFromNavBar(des):\\n index = \\\"Xblog/docs/index.html\\\"\\n title = des.split(\\\"/\\\")[-1].replace(\\\".html\\\", \\\"\\\")\\n with open(index, 'r') as f:\\n soup = BeautifulSoup(f, \\\"html.parser\\\")\\n f.close()\\n soup.select(\\\"#\\\"+title)[0].decompose()\\n with open(index, 'w') as f:\\n f.write(soup.prettify(formatter=\\\"html\\\"))\\n f.close()\\n ccc.success(\\\"removing \\\" + des + \\\" from navigation pallete\\\")\",\n \"def empty_fn_docstr_pass():\\n pass\",\n \"def clean_doc(doc):\\n tokens = doc.split()\\n # Remove punctuation marks [., ;, :, \\\", e.t.c]\\n re_punc = re.compile('[%s]' % re.escape(string.punctuation))\\n tokens = [re_punc.sub('', w) for w in tokens]\\n # Keep only alphabetic words (Remove words with numbers)\\n tokens = [word for word in tokens if word.isalpha()]\\n # Remove stop words like \\\"and, but, a, the, an\\\" e.t.c\\n stop_words = set(stopwords.words('english'))\\n tokens = [w for w in tokens if not w in stop_words]\\n # Remove all other 1-character words if they're still in there\\n tokens = [word for word in tokens if len(word) > 1]\\n return tokens\",\n \"def handle_cleaning():\\n extra_fl = ['changesInfos', 'printerSettings']\\n fld_path = f'{output_path}/ppt'\\n out_rel_path = f'{fld_path}/_rels/presentation.xml.rels'\\n root, tree = gen_tree(out_rel_path)\\n \\n for i in extra_fl:\\n shutil.rmtree(f'{fld_path}/{i}')\\n \\n for relation in root:\\n attrib = relation.attrib\\n if i in attrib['Target']:\\n root.remove(relation)\\n \\n tree.write(out_rel_path, pretty_print=True, xml_declaration=True, encoding='UTF-8', standalone=True)\\n return\",\n \"def clean(obj):\\n clean_up_generated_files(obj)\",\n \"def handleCleanMetadataRecon(self):\\n logging.debug(\\\"Removing compromising personal info and remaking the file...\\\")\\n filePath = self.filesList.selectedItems()[0].text(2)\\n fileType = self.filesList.getFileObj(filePath).type\\n self.printPdfPersonalData(filePath, \\n fileType,\\n AddedFile.changeBase(filePath, self.outputPath))\\n self.tabArea.setCurrentIndex(1)\\n self.changeCursor()\\n self.filesList.getFileObj(filePath).reconMetaCleaned = True\",\n \"def refresh(cls):\\n # Flip the order of the links so that the first URL listed is the\\n # highest priority and will take precedence\\n for url in current_app.config['MATLAB_DOC_LINKS'][::-1]:\\n resp = requests.get(url)\\n soup = BeautifulSoup(resp.text, 'html.parser')\\n\\n terms = soup.findAll('td', {'class': 'term'})\\n links = [term.find('a') for term in terms]\\n\\n for link in links:\\n\\n function = link.text.rstrip()\\n\\n doc = cls.query.filter_by(name=function).first()\\n doc_url = urljoin(url, link['href'])\\n\\n # Create an entry if one doesn't already exist\\n if doc is None:\\n doc = cls(name=function)\\n\\n doc.link = doc_url\\n doc.save()\\n\\n # Make sure to remove i and j entries\\n toremove = cls.query.filter(or_(cls.name == 'i', cls.name == 'j')).all()\\n for item in toremove:\\n item.delete()\\n\\n return cls.query.all()\",\n \"def split_doc_file(filename: str) -> List[str]:\\n if not filename.endswith('.doc'):\\n raise ValueError('File extension must be .doc')\\n if not (os.path.exists('.antiword') and os.path.exists(filename)):\\n raise FileNotFoundError('Unable to process .doc file')\\n\\n os.environ['HOME'] = '.'\\n stream = Popen(\\n [r'.antiword\\\\antiword.exe', '-m', 'UTF-8', filename],\\n shell=True,\\n stdout=PIPE,\\n stderr=PIPE,\\n stdin=PIPE,\\n ).communicate()[0]\\n\\n text = stream.decode('utf-8').replace('[pic]', '')\\n return drop_empty_lines(text.split('\\\\n'))\",\n \"def cleanup() -> None:\\n\\n for fname in glob(os.path.join(tdir, 'alexandria.*')):\\n if os.path.splitext(fname)[1] not in {'.c', '.h'}:\\n os.unlink(fname)\",\n \"def handleCleanMetadataKeep(self):\\n logging.debug(\\\"Removing all metadata found...\\\")\\n filePath = self.filesList.selectedItems()[0].text(2)\\n self.filesList.removeAllMeta(filePath)\",\n \"def purge_posts(app, env, docname):\\n\\n if hasattr(env, \\\"ablog_posts\\\"):\\n env.ablog_posts.pop(docname, None)\\n filename = os.path.split(docname)[1]\\n env.domains[\\\"std\\\"].data[\\\"labels\\\"].pop(filename, None)\\n env.domains[\\\"std\\\"].data[\\\"anonlabels\\\"].pop(filename, None)\",\n \"def test_duplicate_word_removal(self):\\n data = [{\\\"Header\\\": \\\"This is a Header, and this is a Header\\\", \\\"Paragraph\\\": \\\"This is a Paragraph, and this is a \\\"\\n \\\"Paragraph\\\",\\n \\\"Header_keywords\\\": [\\\"header\\\", \\\"header\\\"],\\n \\\"Paragraph_keywords\\\": [\\\"paragraph\\\", \\\"paragraph\\\"], \\\"slide\\\": 10}]\\n remove_duplicates = duplicate_word_removal(data)\\n data[0][\\\"Header_keywords\\\"] = [\\\"header\\\"]\\n data[0][\\\"Paragraph_keywords\\\"] = [\\\"paragraph\\\"]\\n self.assertEqual(data, remove_duplicates)\",\n \"def clean_PDF(submission):\\n src = submission.file_upload.file.name\\n pdf1 = PdfFileReader(src)\\n merger = PdfFileMerger(strict=False, )\\n merger.append(pdf1, import_bookmarks=False)\\n merger.addMetadata({'/Title': '',\\n '/Author': '',\\n '/Creator': '',\\n '/Producer': ''})\\n fd, temp_file = tempfile.mkstemp(suffix='.pdf')\\n merger.write(temp_file)\\n merger.close()\\n os.close(fd)\\n shutil.move(temp_file, src) # replace the original PDF on the server\",\n \"def clean_text(self, num='substitute'):\\n for i, doc in enumerate(self.documents):\\n if num is 'spell':\\n doc = doc.replace('0', ' zero ')\\n doc = doc.replace('1', ' one ')\\n doc = doc.replace('2', ' two ')\\n doc = doc.replace('3', ' three ')\\n doc = doc.replace('4', ' four ')\\n doc = doc.replace('5', ' five ')\\n doc = doc.replace('6', ' six ')\\n doc = doc.replace('7', ' seven ')\\n doc = doc.replace('8', ' eight ')\\n doc = doc.replace('9', ' nine ')\\n elif num is 'substitute':\\n doc = re.sub('(\\\\\\\\d+)', ' NUM ', doc)\\n elif num is 'remove':\\n doc = re.sub('[0-9]', ' ', doc)\\n doc = doc.replace('$', ' dollar ')\\n doc = doc.lower()\\n doc = re.sub('[^a-z]', ' ', doc)\\n doc = ' '.join(doc.split())\\n self.documents[i] = doc\",\n \"def horde_cleanup(self):\",\n \"def clear_invalid_node_references(doc, ids_to_remove):\\n start = time.time()\\n for elem in doc.iter(\\\"nd\\\"):\\n ref = elem.attrib[\\\"ref\\\"]\\n\\n if ref in ids_to_remove:\\n tmp = elem\\n elem = elem.getnext()\\n tmp.getparent().remove(tmp)\\n\\n print 'Time Taken :: ', round(time.time() - start, 3), \\\" seconds\\\"\\n return doc\",\n \"def _clear_variables( self ):\\r\\n self.navigation = None\\r\\n self.resPath = None\\r\\n self.resolutions = None\\r\\n self.currentResolution = None\\r\\n self.resolution = None\\r\\n for doc in self.include_doc:\\r\\n try: doc.unlink()\\r\\n except: pass\",\n \"def clean():\\n clean_files()\",\n \"def copy_docs():\\n local('rsync -av --delete --exclude=.svn %s:%s/ /tmp/djangodocs/' %\\n (env.hosts[0], env.deploy_base.child('docbuilds')))\",\n \"def _clean_up(self):\",\n \"def clean(self, quick_clean=False):\\r\\n date_time('Cleaning')\\r\\n self.cursor.execute('DELETE FROM citations WHERE citation IS NULL OR citation = \\\"\\\" ;')\\r\\n self.conn.execute(\\\"VACUUM\\\")\\r\\n self.conn.commit()\\r\\n gc.collect()\",\n \"def clean_duplicate(self):\\r\\n self.elements = list(set(self.elements))\\r\\n self.elements = [e for e in self.elements if e != '']\",\n \"def clean():\\n for dirpath, dirnames, filenames in os.walk('.'):\\n for filename in filenames:\\n if filename.endswith('.pyc') or filename.endswith('.pyo'):\\n full_pathname = os.path.join(dirpath, filename)\\n click.echo('Removing {}'.format(full_pathname))\\n os.remove(full_pathname)\",\n \"def toLighter(self, del_spaces = True, del_comments = True, del_pi = True) :\\n\\t\\tif self.doc is not None :\\n\\t\\t\\tself._purgeDOM(self.doc, del_spaces, del_comments, del_pi)\\n\\t\\treturn self\",\n \"def clean_content(sender, instance, **kwargs):\\r\\n if kwargs.get('raw'):\\r\\n # We're in loaddata (or something similar).\\r\\n return\\r\\n\\r\\n page = instance\\r\\n fields = dict(stemplates.get_fields_bare(page.template))\\r\\n current_contents = list(page.pagecontent_set.all())\\r\\n\\r\\n for content in current_contents:\\r\\n if content.title not in fields or fields[content.title] != content.typ:\\r\\n content.delete()\\r\\n\\r\\n existing_contents = dict([(pc.title, pc.typ)\\r\\n for pc in page.pagecontent_set.all()])\\r\\n\\r\\n for title, typ in fields.items():\\r\\n if title not in existing_contents or existing_contents[title] != typ:\\r\\n PageContent(page=page, title=title, typ=typ, content='').save()\",\n \"def empty_fn_docstr():\",\n \"def clean_stale_issues():\\n from security_monkey.common.audit_issue_cleanup import clean_stale_issues\\n clean_stale_issues()\",\n \"def clean(corpus):\\n # Initiate clean_corpus\\n clean_corpus = [] \\n \\n for speech in corpus:\\n \\n # Removes meaningless intro \\n speech = speech[5:] \\n\\n for i in range(len(speech)):\\n # Removes 'meaningless text hear (min:sec)\\\\n' at the beginning of each paragraph\\n speech[i] = speech[i][speech[i].find('\\\\n') + 1:] \\n # Replaces brackets with paranthesis\\n speech[i] = speech[i].replace('[', '(') \\n speech[i] = speech[i].replace(']', ')')\\n # Removes meaningless text in parantheses\\n speech[i] = re.sub(r'\\\\([^)]*\\\\)', '', speech[i]) \\n\\n # Join all of the paragraphs into one speech\\n speech = ','.join(speech) \\n\\n clean_corpus.append(speech)\\n \\n # Combined all of the speeches into one document\\n \\n if len(clean_corpus) == 1:\\n clean_corpus = clean_corpus[0]\\n if len(clean_corpus) == 2:\\n clean_corpus = clean_corpus[0] + clean_corpus[1]\\n if len(clean_corpus) == 3:\\n clean_corpus = clean_corpus[0] + clean_corpus[1] + clean_corpus[2]\\n if len(clean_corpus) == 8:\\n clean_corpus = clean_corpus[0] + clean_corpus[1] + clean_corpus[2] + clean_corpus[3] + clean_corpus[4] + \\\\\\n clean_corpus[5] + clean_corpus[6] + clean_corpus[7]\\n \\n return clean_corpus\",\n \"def _remove_duplicates(self):\\n self.search_query = remove_duplicates(self.search_query)\",\n \"def rm_duplicates(self):\\n # get uniq representation of existing detection documents\\n existing = set(ed.uniq_data for ed in self.existing_detections)\\n # remove duplicates\\n for idx in xrange(len(self.new_detections)-1, -1, -1):\\n nd = self.new_detections[idx]\\n if nd.uniq_data in existing:\\n self.new_detections.pop(idx)\",\n \"def cleanUp(self):\\n import evoware.fileutil as F\\n F.tryRemove(self.f_project, verbose=(self.VERBOSITY>1), tree=1)\",\n \"def preprocess(docs, nlp, min_length, min_counts, max_counts):\\n\\n def clean_and_tokenize(doc):\\n text = ' '.join(doc.split()) # remove excessive spaces\\n text = nlp(text, tag=True, parse=False, entity=False)\\n #return [t.lemma_ for t in text if t.is_alpha and len(t) > 2 and not t.is_stop]\\n return [t.lower_ for t in text if t.is_alpha and len(t) > 2 and not t.is_stop] # remove .lemma and add lower case operation\\n\\n tokenized_docs = [(i, clean_and_tokenize(doc)) for i, doc in tqdm(docs)]\\n\\n # remove short documents\\n n_short_docs = sum(1 for i, doc in tokenized_docs if len(doc) < min_length)\\n tokenized_docs = [(i, doc) for i, doc in tokenized_docs if len(doc) >= min_length]\\n print('number of removed short documents:', n_short_docs)\\n\\n # remove some tokens\\n counts = _count_unique_tokens(tokenized_docs)\\n tokenized_docs = _remove_tokens(tokenized_docs, counts, min_counts, max_counts)\\n n_short_docs = sum(1 for i, doc in tokenized_docs if len(doc) < min_length)\\n tokenized_docs = [(i, doc) for i, doc in tokenized_docs if len(doc) >= min_length]\\n print('number of additionally removed short documents:', n_short_docs)\\n\\n counts = _count_unique_tokens(tokenized_docs)\\n encoder, decoder, word_counts = _create_token_encoder(counts)\\n\\n print('\\\\nminimum word count number:', word_counts[-1])\\n print('this number can be less than MIN_COUNTS because of document removal')\\n\\n encoded_docs = _encode(tokenized_docs, encoder) # all the doc is encoded as indexes instead of words using ix2word\\n return encoded_docs, decoder, word_counts\",\n \"def remove_punctuation_and_multi_spaces_document(self, doc):\\n regex = re.compile('[%s]' % re.escape(self.punctuation))\\n doc = regex.sub(' ', doc)\\n return ' '.join(doc.split())\",\n \"def test_post_build_processing_remove_occurrences(self):\\n it = [\\n \\\"[[Chapter]] Chapter I\\\",\\n \\\"This is chapter I text\\\",\\n \\\"[[Article]] Article I\\\",\\n \\\"This is article I text\\\",\\n ]\\n\\n descriptor = {\\n 'components': ['Chapter', 'Article'],\\n 'patterns': ['Chapter', 'Article']\\n }\\n\\n doc = parse_iterable(it, descriptor)\\n\\n descriptor = extend_internal_patterns(descriptor)\\n descriptor = compile_patterns(descriptor)\\n\\n doc = post_build_process(doc, descriptor)\\n\\n result = [n['text'] for _, n in doc.traverse()]\\n expected = [[], [\\\"Chapter I\\\", \\\"This is chapter I text\\\"], [\\\"Article I\\\", \\\"This is article I text\\\"]]\\n self.assertListEqual(result, expected)\",\n \"def cleanPDFs(self):\\n to_delete = []\\n pdfs = self.getPDFList()\\n now = datetime.now()\\n\\n # First we compute the list of files to delete.\\n for filename in pdfs:\\n filedate = pdfs[filename]\\n delta = now - filedate\\n\\n if delta.seconds > 7200:\\n to_delete.append(filename)\\n\\n existing_files = os.listdir(self.tempdir)\\n for filename in to_delete:\\n del pdfs[filename]\\n if filename in existing_files:\\n os.remove('%s/%s' % (self.tempdir,\\n filename))\\n self.setPDFList(pdfs)\\n metadata = self._getMetadata()\\n metadata['last_clean'] = now\",\n \"def extract_documents():\\n client = MongoClient()\\n conn = client.data\\n coll = conn.germanwings\\n\\n query = {'text': {'$exists': 1}, 'exc': {'$exists': 0}}\\n selection = {'text': 1, 'short_url': 1}\\n for i, doc in enumerate(coll.find(query, selection)):\\n short_url, text = tuple(doc[x] for x in (\\\"short_url\\\", \\\"text\\\"))\\n print(\\\"Extracting {0} {1}\\\".format(i, short_url), file=stderr)\\n filename = os.path.join(RAW_DIR, short_url)\\n with open(filename, \\\"w\\\") as f:\\n ascii = text.encode('ascii', 'ignore')\\n f.write(ascii)\",\n \"def PurgeAll(self):\\n\\t\\tself.acad.ActiveDocument.PurgeAll()\",\n \"def documents(iati_import, activity, project, activities_globals):\\n imported_docs = []\\n changes = []\\n\\n xml_ns = 'http://www.w3.org/XML/1998/namespace'\\n first_image = True\\n\\n for doc_link in activity.findall('document-link'):\\n url = ''\\n doc_format = ''\\n title = ''\\n title_language = ''\\n category = ''\\n language = ''\\n\\n if 'url' in doc_link.attrib.keys():\\n url = doc_link.attrib['url']\\n\\n # Check if it's the first image\\n if url and url.rsplit('.', 1)[1].lower() in VALID_IMAGE_EXTENSIONS and first_image:\\n first_image = False\\n continue\\n\\n if 'format' in doc_link.attrib.keys():\\n if not len(doc_link.attrib['format']) > 75:\\n doc_format = doc_link.attrib['format']\\n else:\\n add_log(iati_import, 'document_link_format',\\n 'format is too long (75 characters allowed)', project)\\n\\n # Check if the format is 'application/http'\\n if doc_format == 'application/http':\\n continue\\n\\n title_element = doc_link.find('title')\\n if not title_element is None:\\n title = get_text(title_element, activities_globals['version'])\\n if len(title) > 100:\\n add_log(iati_import, 'document_link_title',\\n 'title is too long (100 characters allowed)', project,\\n IatiImportLog.VALUE_PARTLY_SAVED)\\n title = title[:100]\\n\\n if activities_globals['version'][0] == '1' and \\\\\\n '{%s}lang' % xml_ns in title_element.attrib.keys():\\n if not len(title_element.attrib['{%s}lang' % xml_ns]) > 2:\\n title_language = title_element.attrib['{%s}lang' % xml_ns]\\n else:\\n add_log(iati_import, 'document_link_title_language',\\n 'language is too long (2 characters allowed)', project)\\n elif activities_globals['version'][0] == '2':\\n narrative_element = title_element.find('narrative')\\n if not narrative_element is None and \\\\\\n '{%s}lang' % xml_ns in narrative_element.attrib.keys():\\n if not len(narrative_element.attrib['{%s}lang' % xml_ns]) > 2:\\n title_language = narrative_element.attrib['{%s}lang' % xml_ns]\\n else:\\n add_log(iati_import, 'document_link_title_language',\\n 'language is too long (2 characters allowed)', project)\\n\\n category_element = doc_link.find('category')\\n if not category_element is None and 'code' in category_element.attrib.keys():\\n if not len(category_element.attrib['code']) > 3:\\n category = category_element.attrib['code']\\n else:\\n add_log(iati_import, 'document_link_category',\\n 'category is too long (3 characters allowed)', project)\\n\\n language_element = doc_link.find('language')\\n if not language_element is None and 'code' in language_element.attrib.keys():\\n if not len(language_element.attrib['code']) > 2:\\n language = language_element.attrib['code']\\n else:\\n add_log(iati_import, 'document_link_language',\\n 'language is too long (2 characters allowed)', project)\\n\\n doc, created = get_model('rsr', 'projectdocument').objects.get_or_create(\\n project=project,\\n url=url,\\n format=doc_format,\\n title=title,\\n title_language=title_language,\\n category=category,\\n language=language\\n )\\n\\n if created:\\n changes.append(u'added project document (id: %s): %s' % (str(doc.pk), doc))\\n\\n imported_docs.append(doc)\\n\\n for doc_link in project.documents.all():\\n if not doc_link in imported_docs:\\n changes.append(u'deleted project document (id: %s): %s' %\\n (str(doc_link.pk),\\n doc_link.__unicode__()))\\n doc_link.delete()\\n\\n return changes\",\n \"def read_and_clean_files(clueweb_file, ann_file, data_dir, ann_dir):\\n annotation_input = fileinput.FileInput(os.path.join(ann_dir, ann_file), openhook=fileinput.hook_compressed)\\n annotation_list = []\\n for line in annotation_input:\\n\\tannotation_list.append(Annotation.parse_annotation(line))\\n\\n warc_path = os.path.join(data_dir, clueweb_file)\\n warc_file = warc.open(warc_path)\\n print \\\"Replacing entity mentions for \\\", clueweb_file, \\\":\\\", ann_file, \\\"...\\\"\\n start = time.time()\\n warc_entry = WarcEntry(warc_path, warc_file, annotation_list)\\n cleaned_records = warc_entry.replace_entity_mentions()\\n end = time.time()\\n print \\\"Time used: \\\", end - start\\n warc_file.close()\\n return cleaned_records\",\n \"def keep_documents(self, idx):\\n print('{} documents have been removed'.format(self.data.shape[0] - len(idx)))\\n self.documents = [self.documents[i] for i in idx]\\n self.labels = self.labels[idx]\\n self.data = self.data[idx, :]\",\n \"def clean_docstring(doc: str, unused: Literal[\\\"pre\\\", \\\"post\\\"] = None) -> str:\\n doc = doc.split(\\\"\\\\n\\\")\\n if unused == \\\"pre\\\":\\n try:\\n index = next(i for i, l in enumerate(doc) if l.strip())\\n doc = doc[index:]\\n except StopIteration:\\n doc = []\\n elif unused == \\\"post\\\":\\n try:\\n index = next(i for i, l in enumerate(reversed(doc)) if l.strip())\\n doc = doc[: len(doc) - index]\\n except StopIteration:\\n doc = []\\n if doc:\\n first_line = doc[0]\\n index = len(first_line) - len(first_line.lstrip())\\n indent = first_line[:index]\\n if all(l.startswith(indent) for l in doc if l.strip()):\\n doc = [(l[index:] if l.strip() else l) for l in doc]\\n return \\\"\\\\n\\\".join(doc)\",\n \"def test_cleanup_html_lookup_file():\\n # there are 4 duplicate entries, all having to do with stroke\\n expected_lines = 63\\n\\n #original location\\n test_filename = 'test/fixture/html_lookup_file.txt'\\n #create a copy to maintain integrity of file\\n test_filename_working = 'test/fixture/html_lookup_file_copy.txt'\\n copyfile(test_filename, test_filename_working)\\n\\n dedup_medfind.cleanup_html_lookup_file(test_filename_working)\\n # no return value, we must open and read the file to assert\\n with open(test_filename_working, 'r', encoding='utf-8') as fs:\\n lines = fs.readlines()\\n # delete our working copy before performing asserts\\n os.remove(test_filename_working)\\n # finally, we do our asserts based upon the test file we create\\n for line in lines:\\n print(line.strip())\\n assert expected_lines == len(lines)\",\n \"def clean_text(doc):\\n doc_clean = preprocess_text(doc)\\n doc_clean = custom_tokenizer(doc_clean)\\n \\n doc_as_string = ' '.join(doc_clean)\\n return doc_as_string\",\n \"def _initDeleteEntryDocument(self, atomDoc): #@UnusedVariable #$NON-NLS-1$\\r\\n pass\",\n \"def preprocess_docs():\\n\\n print(\\\"Getting started!\\\")\\n stopwords.populate_stopwords(NLP, STOPWORD_URL)\\n\\n print(str.format(\\\"Using data dir:{}\\\", DATA_DIR))\\n\\n csv_file = open(os.path.join(DATA_DIR, 'PDFs.csv'))\\n reader = csv.reader(csv_file, 'excel')\\n rows = list(reader)\\n\\n filenames = [_get_filename(row) for row in rows]\\n\\n pool = Pool(multiprocessing.cpu_count())\\n\\n try:\\n pool.map(_get_item, rows)\\n pool.map(pdf.extract_text, filenames)\\n docs = pool.map(_extract_questions, rows)\\n docs = [d for d in docs if d is not None]\\n\\n _find_similar(docs, simdoc=compare.compare_doc_keywords)\\n\\n for doc in docs:\\n if doc is None:\\n continue\\n doc.save_json()\\n\\n except KeyboardInterrupt:\\n pool.terminate()\\n print(\\\"You cancelled the program!\\\")\\n sys.exit(1)\\n\\n print(\\\"Done\\\")\",\n \"def delete_empty_documents(self, dataset):\\n\\t\\tprint(\\\"Deleting empty documents in\\\", dataset)\\n\\t\\tnumber_documents = len(self.raw_documents[dataset])\\n\\t\\tindices = list(filter(lambda x:len(self.raw_documents[dataset][x].strip()), range(number_documents)))\\n\\n\\t\\tself.raw_documents[dataset] = [self.raw_documents[dataset][idx] for idx in indices]\\n\\t\\tself.Y[dataset] = [self.Y[dataset][idx] for idx in indices]\",\n \"def test_buildRemovesTemporaryLaTeXFiles(self):\\n sections = range(1, 4)\\n for sectionNumber in sections:\\n self.howtoDir.child(\\\"%d.xhtml\\\" % (sectionNumber,)).setContent(\\n self.getArbitraryLoreInput(sectionNumber))\\n bookTeX = self._setupTeXBook(sections)\\n bookPDF = FilePath(self.mktemp())\\n\\n builder = BookBuilder()\\n builder.build(self.howtoDir, [self.howtoDir], bookTeX, bookPDF)\\n\\n self.assertEqual(\\n set(self.howtoDir.listdir()),\\n set([bookTeX.basename()] + [\\\"%d.xhtml\\\" % (n,) for n in sections]))\",\n \"def get_clean_corpus(FILENAME):\\n text = open(FILENAME)\\n corpus = text.readlines()\\n clean_corpus = []\\n doc_no = 0\\n prev_doc_no = 0\\n doctype_flag = 0\\n doctype_end_flag = 0\\n doc_list = []\\n punct = set(string.punctuation)\\n tag = 0\\n for i in range(len(corpus)):\\n stmt = \\\"\\\"\\n flag = 1\\n sz = len(corpus[i])\\n\\n\\n for j in range(sz-1):\\n if(corpus[i][j] == '<'):\\n tag = 1\\n if(corpus[i][j] == '<' and j+1 < sz and corpus[i][j+1] == 'd'):\\n doctype_flag = 1\\n if(corpus[i][j] == 'c' and j+1 < sz and corpus[i][j+1] == '>'):\\n doctype_end_flag = 1\\n if(doctype_flag == 0 and doctype_end_flag == 0 and tag == 0):\\n stmt = stmt + (corpus[i][j])\\n if(corpus[i][j] == '>'):\\n tag = 0\\n if(doctype_flag == 1 and corpus[i][j] == '>'):\\n doctype_flag = 0\\n if(doctype_end_flag == 1 and corpus[i][j] == '>'):\\n doctype_end_flag = 0\\n doc_no = doc_no + 1\\n break\\n\\n if(stmt != \\\"\\\"):\\n if(doc_no != prev_doc_no):\\n if(len(doc_list) != 0):\\n clean_corpus.append(doc_list)\\n doc_list = []\\n prev_doc_no = doc_no\\n stmt = stmt.lower()\\n doc_list.append(stmt)\\n return clean_corpus\",\n \"def preprocess(self, documents):\\n\\n # A dict storing the frequency of each word\\n word_freq = {}\\n\\n # Iterate for each document\\n for doc in documents:\\n # Split the document into a list of words and iterate on it\\n for w in extract_words(doc):\\n # Update word frequencies\\n '''YOUR CODE HERE'''\\n if w not in word_freq.keys():\\n word_freq[w] = 1\\n else:\\n word_freq[w] += 1\\n\\n ''' END CODE FOR THIS LOOP '''\\n\\n\\n # A set of words with frequency less than 'self.min_freq'\\n remove_words = set()\\n\\n # Check frequency of each word and add to 'remove_words'\\n # if it's frequency is below self.min_freq\\n\\n ''' YOUR CODE HERE '''\\n for w in word_freq.keys():\\n if word_freq[w] < self.min_freq:\\n remove_words.add(w)\\n\\n # Delete the words in 'remove_words' from 'word_freq'\\n for w in remove_words:\\n del word_freq[w]\\n\\n # Fill 'self.word_to_idx' and 'self.idx_to_word' for\\n # each word in 'word_freq' (dicts are explained above)\\n\\n i = 0\\n for w in word_freq.keys():\\n self.word_to_idx[w] = i\\n self.idx_to_word[i] = w \\n i += 1\\n\\n ''' END YOUR CODE HERE '''\",\n \"def clean_doc(doc):\\n tokens = doc.split()\\n table = str.maketrans('', '', punctuation)\\n tokens = [w.translate(table) for w in tokens]\\n tokens = [word.lower() for word in tokens if word.isalpha()]\\n stop_words = set(stopwords.words('english'))\\n tokens = [w for w in tokens if not w in stop_words]\\n tokens = [word for word in tokens if len(word) > 1]\\n return tokens\",\n \"def clean_old_data():\\n logger.info('Cleaning standalone files on disk...')\\n for absolute_path in glob.glob(MEDIA_URL + '*'):\\n file_name = os.path.basename(absolute_path)\\n try:\\n relative_path = os.path.join(AUDIOS_URL, file_name)\\n audio = Audio.objects.get(filename=relative_path)\\n if audio.get_type() == 'episode':\\n try:\\n # If there are inactive audios on its being\\n for e in audio.podcast.episode_set.exclude(pk=audio.podcast.active_episode.pk):\\n if not e.is_active():\\n logger.info('Inactive audio found in podcast set. Erasing files.')\\n e.delete_files()\\n except Exception, e:\\n logger.exception(e.message)\\n except ObjectDoesNotExist, e:\\n logger.info('A file with no audio registered in database')\\n if os.path.isfile(relative_path):\\n logger.info('Erasing: %s' % relative_path)\\n os.remove(relative_path)\\n logger.info('... Done.')\",\n \"def idempotence(args):\\n title, posts = parse_markdown(os.path.join(args.root, 'index.md'))\\n print_markdown(posts, title, os.path.join(args.dest, 'index.md'))\",\n \"def assert_less_undocumented_wc(self, modulename, max_undoc_frac):\\n undocumented, loc_len = get_undocumented_wildcards(modulename)\\n undocumented_fraction = len(undocumented) / loc_len\\n self.assertLessEqual(undocumented_fraction, max_undoc_frac, f\\\"{len(undocumented)/loc_len:.2%} of {modulename} imports undocumented. Missing Docstrings in {len(undocumented)}/{loc_len}:\\\\n- \\\" + \\\"\\\\n- \\\".join(undocumented))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.71175075","0.682035","0.64427346","0.64351165","0.64301306","0.6388901","0.6304049","0.6276406","0.6156239","0.60863686","0.60153323","0.5972687","0.59360844","0.5934813","0.5888154","0.58511215","0.58008873","0.5787599","0.5778761","0.5765141","0.5758671","0.5746751","0.57399243","0.57202435","0.5708542","0.56873626","0.56724405","0.5666658","0.56390446","0.56380576","0.5631387","0.5609544","0.5592184","0.55878466","0.5585641","0.5584403","0.5568311","0.5561784","0.55487937","0.5548203","0.55178833","0.5513134","0.55127096","0.5490264","0.5478062","0.5466751","0.54582584","0.54567796","0.54465604","0.54453164","0.54424894","0.54367954","0.5430171","0.54269373","0.54069316","0.54045177","0.5402177","0.5400693","0.5394209","0.53922975","0.5392206","0.53596187","0.5358645","0.53535646","0.53531325","0.53507435","0.5347069","0.5343324","0.5340611","0.53382856","0.53152996","0.5311586","0.53101003","0.53050816","0.529861","0.52948374","0.5288364","0.5284011","0.52520305","0.52460116","0.5243469","0.52428263","0.5240725","0.5235468","0.5226347","0.5225649","0.5224238","0.52203953","0.5217436","0.52170575","0.51880616","0.51824","0.5180262","0.5178733","0.51775694","0.51757586","0.51744026","0.5171993","0.51548725","0.51479435"],"string":"[\n \"0.71175075\",\n \"0.682035\",\n \"0.64427346\",\n \"0.64351165\",\n \"0.64301306\",\n \"0.6388901\",\n \"0.6304049\",\n \"0.6276406\",\n \"0.6156239\",\n \"0.60863686\",\n \"0.60153323\",\n \"0.5972687\",\n \"0.59360844\",\n \"0.5934813\",\n \"0.5888154\",\n \"0.58511215\",\n \"0.58008873\",\n \"0.5787599\",\n \"0.5778761\",\n \"0.5765141\",\n \"0.5758671\",\n \"0.5746751\",\n \"0.57399243\",\n \"0.57202435\",\n \"0.5708542\",\n \"0.56873626\",\n \"0.56724405\",\n \"0.5666658\",\n \"0.56390446\",\n \"0.56380576\",\n \"0.5631387\",\n \"0.5609544\",\n \"0.5592184\",\n \"0.55878466\",\n \"0.5585641\",\n \"0.5584403\",\n \"0.5568311\",\n \"0.5561784\",\n \"0.55487937\",\n \"0.5548203\",\n \"0.55178833\",\n \"0.5513134\",\n \"0.55127096\",\n \"0.5490264\",\n \"0.5478062\",\n \"0.5466751\",\n \"0.54582584\",\n \"0.54567796\",\n \"0.54465604\",\n \"0.54453164\",\n \"0.54424894\",\n \"0.54367954\",\n \"0.5430171\",\n \"0.54269373\",\n \"0.54069316\",\n \"0.54045177\",\n \"0.5402177\",\n \"0.5400693\",\n \"0.5394209\",\n \"0.53922975\",\n \"0.5392206\",\n \"0.53596187\",\n \"0.5358645\",\n \"0.53535646\",\n \"0.53531325\",\n \"0.53507435\",\n \"0.5347069\",\n \"0.5343324\",\n \"0.5340611\",\n \"0.53382856\",\n \"0.53152996\",\n \"0.5311586\",\n \"0.53101003\",\n \"0.53050816\",\n \"0.529861\",\n \"0.52948374\",\n \"0.5288364\",\n \"0.5284011\",\n \"0.52520305\",\n \"0.52460116\",\n \"0.5243469\",\n \"0.52428263\",\n \"0.5240725\",\n \"0.5235468\",\n \"0.5226347\",\n \"0.5225649\",\n \"0.5224238\",\n \"0.52203953\",\n \"0.5217436\",\n \"0.52170575\",\n \"0.51880616\",\n \"0.51824\",\n \"0.5180262\",\n \"0.5178733\",\n \"0.51775694\",\n \"0.51757586\",\n \"0.51744026\",\n \"0.5171993\",\n \"0.51548725\",\n \"0.51479435\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.67015225"},"document_rank":{"kind":"string","value":"2"}}},{"rowIdx":21897,"cells":{"query":{"kind":"string","value":"Run the Spacy processing pipeline on the annotation documents. Add processed docs so they can be accessed."},"document":{"kind":"string","value":"def process_spacy(self):\n\n def prevent_sentence_boundary_detection(doc):\n for token in doc:\n # This will entirely disable spaCy's sentence detection\n token.is_sent_start = False\n return doc\n\n def process_sentence(sen_tokens):\n doc = spacy.tokens.Doc(nlp.vocab, words=sen_tokens)\n tagger(doc)\n prevent_sbd(doc)\n ner(doc)\n parser(doc)\n return doc\n\n # setup spacy nlp pipeline\n nlp = spacy.load(\"en_core_web_lg\")\n parser = nlp.get_pipe(\"parser\")\n nlp.add_pipe(\n prevent_sentence_boundary_detection, name=\"prevent-sbd\", before=\"parser\"\n )\n\n tagger = nlp.get_pipe(\"tagger\")\n prevent_sbd = nlp.get_pipe(\"prevent-sbd\")\n parser = nlp.get_pipe(\"parser\")\n ner = nlp.get_pipe(\"ner\")\n\n for doc in self.annotation_documents:\n doc.sentences_processed = []\n for sen in doc.sentences:\n sen_tokens = [t.text for t in sen.tokens]\n sen_proc = process_sentence(sen_tokens)\n # add processed sentence to doc\n doc.sentences_processed.append(sen_proc)\n\n print(f\"Processed with Spacy: {doc.document_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 process(self, doc):\n # don't try to process null notes\n if not doc[1]:\n if self.verbose:\n print(\"Error segmenting doc\",doc[0])\n return []\n # odd notes may throw an error. Just continue rather than stopping the entire process\n try:\n sentences = self.sentence_tokenizer.segToSentenceSpans(doc[1])\n except KeyError:\n if self.verbose:\n print(\"Error segmenting doc\",doc[0])\n return []\n\n #context_doc = pyConTextGraph.ConTextDocument() # ConTextDoc not needed for simple usage\n\n doc_annots = list()\n\n for sentence in sentences:\n # run sentence tokenizer on input text, return the spans\n sentence_text = doc[1][sentence.begin:sentence.end]\n # process every sentence by adding markup\n markup = pyConTextGraph.ConTextMarkup()\n markup.setRawText(sentence_text)\n markup.cleanText()\n # apply targets and modifiers\n markup.markItems(self.targets, mode=\"target\")\n markup.markItems(self.modifiers, mode=\"modifier\")\n # address scope of modifiers to targets, remove inactive modifiers and self-modifying relationships\n markup.pruneMarks()\n markup.applyModifiers()\n markup.pruneSelfModifyingRelationships()\n markup.dropInactiveModifiers()\n\n marked_targets = markup.getMarkedTargets()\n for marked_target in marked_targets:\n modifiers = markup.getModifiers(marked_target)\n if not modifiers:\n span = (sentence.begin+marked_target.getSpan()[0],sentence.begin+marked_target.getSpan()[1])\n if self.mode == 'combined':\n annot = (doc[0], marked_target.getPhrase(), span[0], span[1], marked_target.getCategory()[0]+'_unspecified', marked_target.getCode())\n elif self.mode == 'separate':\n annot = (doc[0], marked_target.getPhrase(), span[0], span[1], marked_target.getCategory()[0], 'unspecified', marked_target.getCode())\n if annot not in doc_annots:\n doc_annots.append(annot)\n else:\n for modifier in modifiers:\n if marked_target.getSpan()[0] < modifier.getSpan()[0]:\n span = (sentence.begin+marked_target.getSpan()[0],sentence.begin+modifier.getSpan()[1])\n else:\n span = (sentence.begin+modifier.getSpan()[0],sentence.begin+marked_target.getSpan()[1])\n if self.mode == 'combined':\n annot = (doc[0], doc[1][span[0]:span[1]], span[0], span[1], marked_target.getCategory()[0]+'_'+modifier.getCategory()[0], marked_target.getCode())\n elif self.mode == 'separate':\n annot = (doc[0], doc[1][span[0]:span[1]], span[0], span[1], marked_target.getCategory()[0], modifier.getCategory()[0], marked_target.getCode())\n if annot not in doc_annots:\n doc_annots.append(annot)\n\n #context_doc.addMarkup(markup)\n\n return doc_annots","def run(self, mapping={}, *args, **kwargs):\n self.processed = 0\n for batch in self._process_by_batch(self.load(*args, **kwargs)):\n batch = list(map(lambda doc: self._apply_mapping(doc, mapping), batch))\n for doc in batch:\n self._ingest(iterable=doc, doctype=doc[\"doctype\"])\n self.processed += 1\n logger.info(\"Added {} documents to the database.\".format(self.processed))","def process_document(self, document, **kwargs):\n \n start = time.time()\n\n if self._log_file is not None:\n map_annotations_logger.addHandler(file_handler(self._log_file))\n map_annotations_logger.setLevel(self._log_level)\n \n ref_annotation = document.annotation(self._annotation_name)\n ref_annotations = ref_annotation.annotations\n values = set([a.value for a in ref_annotations])\n new_annotations = [Tag(self._mapping.get(annotation.value, annotation.value), annotation.lb, annotation.ub) for annotation in ref_annotations if self._mapping.get(annotation.value, None) != u\"\"]\n \n document.add_annotation(Annotation(self._annotation_name, reference=ref_annotation.reference, annotations=new_annotations))\n \n laps = time.time() - start\n map_annotations_logger.info('in %s', timedelta(seconds=laps))","def add_documents(self, docs):\n if 'sentences' in docs:\n for sent in docs.sentences:\n sent = map(self.process_token, [t for t in sent.tokens if not t.is_stopword])\n self._token_count.update(sent)\n\n else:\n sent = list(map(self.process_token, [t for t in docs.tokens if not t.is_stopword]))\n self._token_count.update(sent)","def processed_bulk(self, pipeline):\n docs = [Document([], text=t) for t in EN_DOCS]\n return pipeline(docs)","def multiple_document_processing(self) -> List:\n batch_list = []\n for doc, idx in self.__documents:\n entities_idx = {'idx': idx}\n entities_result = self.create_entity(document=doc)\n word_cleaned = self.clean_words(doc)\n entities_idx[self.key_spacy_text] = str(word_cleaned)\n entities_idx.update(entities_result)\n batch_list.append(entities_idx)\n return batch_list","def pre_process(self, documents):\n\n return documents","def processed_doc(self, pipeline):\n return [pipeline(text) for text in EN_DOCS]","def parse(self, doc):\n self.preprocessor.preprocess(doc)\n\n for extractor in self.extractors:\n extractor.extract(doc)\n\n return doc","def _process_texts(self, texts, generator = False):\n wordlists = [\n x for x in [self.tokenizer.tokenize(doc) for doc in texts] if x != []\n ]\n self.vocab = corpora.Dictionary(wordlists)\n self.has_vocab = True\n self.corpus = (self.vocab.doc2bow(doc) for doc in wordlists)\n if not generator:\n self.corpus = list(self.corpus)\n self.has_corpus = True","def process(self):\n self.extract()\n self.transform()\n self.load()","def pipe(self, docs: Iterable[Doc]) -> Iterable[Doc]:\n\n stream1, stream2 = itertools.tee(docs, 2)\n\n # Remove existing entities from the document\n stream2 = (self.create_new_doc(d) for d in stream2)\n \n # And run the model\n for _, proc in self.model.pipeline:\n stream2 = proc.pipe(stream2)\n \n for doc, doc_copy in zip(stream1, stream2):\n\n doc.spans[self.name] = []\n\n # Add the annotation\n for ent in doc_copy.ents:\n doc.spans[self.name].append(Span(doc, ent.start, ent.end, ent.label_))\n\n yield doc","def process(self, fulltext=None):\r\n raise NotImplementedError(\"Please implement this in your importer\")","def preprocess_docs():\n\n print(\"Getting started!\")\n stopwords.populate_stopwords(NLP, STOPWORD_URL)\n\n print(str.format(\"Using data dir:{}\", DATA_DIR))\n\n csv_file = open(os.path.join(DATA_DIR, 'PDFs.csv'))\n reader = csv.reader(csv_file, 'excel')\n rows = list(reader)\n\n filenames = [_get_filename(row) for row in rows]\n\n pool = Pool(multiprocessing.cpu_count())\n\n try:\n pool.map(_get_item, rows)\n pool.map(pdf.extract_text, filenames)\n docs = pool.map(_extract_questions, rows)\n docs = [d for d in docs if d is not None]\n\n _find_similar(docs, simdoc=compare.compare_doc_keywords)\n\n for doc in docs:\n if doc is None:\n continue\n doc.save_json()\n\n except KeyboardInterrupt:\n pool.terminate()\n print(\"You cancelled the program!\")\n sys.exit(1)\n\n print(\"Done\")","def clean_all_documents(cls):\n for index, text in enumerate(cls.documents):\n text_processed = cls.clean_document(text)\n cls.processed_documents.append(text_processed)","def process(self):\n # tokenize, then filter & otherwise process words in each document\n # using steps in preprocess_doc()\n\n all_posts_count = self.postman.posts_read.find({'subreddit': self.postman.subreddit}).count()\n\n for post_idx, post in enumerate(self.postman.posts_read.find({'subreddit': self.postman.subreddit})):\n # preprocess the post and add the new words to the corpus\n new_words = self.preprocess_post(post)\n self.corpus.update(new_words)\n\n # print on every Nth post so you know it's alive\n if post_idx % 100 == 0:\n print 'done post %i out of %i' % (post_idx, all_posts_count)\n\n #TODO:\n print 'word count and other corpus-level filters not implemented, skipping...'\n # corpus-level filtering\n # get rid of invalid documents (based on word count)\n # self.corpus = [doc for doc in self.corpus if self.doc_has_valid_wc(doc)]\n # print 'filtered out %i out of %i documents' % (pre_corpus_len - len(self.corpus), pre_corpus_len)\n # stem or lemmatize\n # if self.stem_or_lemma_callback:\n # self.corpus = [self.perform_stem_or_lem(doc) for doc in self.corpus]\n # for chaining\n #######################################################\n\n return self","def process_docs(directory, vocab):\n for filename in listdir(directory):\n if not filename.endswith('.txt'):\n continue\n path = directory + '/' + filename\n add_doc_to_vocab(path, vocab)","def annotate(self,corpus):\n\n\t\tassert corpus.parsed == True, \"Corpus must already be parsed before entity recognition\"\n\n\t\tfor doc in corpus.documents:\n\t\t\tentityCount = len(doc.entities)\n\t\t\tfor sentence in doc.sentences:\n\t\t\t\twords = [ t.word for t in sentence.tokens ]\n\t\t\t\t\n\t\t\t\tfor i,t in enumerate(sentence.tokens):\n\t\t\t\t\tif not isNumber(t.word):\n\t\t\t\t\t\tcontinue\n\t\t\t\t\t\n\t\t\t\t\tsourceEntityID = \"T%d\" % (entityCount+1)\n\t\t\t\t\ttext = doc.text[t.startPos:t.endPos]\n\t\t\t\t\tloc = [i]\n\n\t\t\t\t\te = kindred.Entity('quantity',text,[(t.startPos,t.endPos)],sourceEntityID=sourceEntityID)\n\t\t\t\t\tdoc.addEntity(e)\n\t\t\t\t\tsentence.addEntityAnnotation(e,loc)\n\t\t\t\t\tentityCount += 1","def ingest_annotations(directory, annotation_type):\n in_golden = annotation_type == \"manual\"\n doc_type = None\n issue_number = None\n bid = None\n annotated_pages = []\n file_prefix = \"page\"\n try:\n bid = directory.split('/')[len(directory.split('/'))-1:][0]\n record = Metadata.objects(bid=bid).first()\n assert record is not None\n except Exception as e:\n bid,issue_number = directory.split('/')[len(directory.split('/'))-2:]\n record = Metadata.objects(bid=bid).first()\n try:\n doc_type = record[\"type_document\"]\n except Exception as e:\n doc_type = None\n logger.warning('The record for %s is not in MongoDB'%bid)\n try:\n page_numbers = {int(os.path.basename(fname).replace(\"page-\",\"\").split('.')[0]):os.path.basename(fname).split('.')[0] \n for fname in glob.glob(\"%s/*.ann\"%directory)}\n except Exception as e:\n page_numbers = {int(os.path.basename(fname).replace(\"image-\",\"\").split('.')[0]):os.path.basename(fname).split('.')[0] \n for fname in glob.glob(\"%s/*.ann\"%directory)}\n file_prefix = \"image\"\n # TODO: handle the exception of document not in the DB\n logger.info(\"Ingesting the annotations from directory \\\"%s\\\"\"%directory)\n if(issue_number != None):\n logger.info(\"Found document %s-%s [type=%s]\"%(bid,issue_number,doc_type))\n else:\n logger.info(\"Found document %s [type=%s]\"%(bid,doc_type))\n try:\n if(doc_type==\"journal\"):\n doc = LBDocument.objects(internal_id=\"%s-%s\"%(bid,issue_number)).first()\n if doc is None:\n doc = LBDocument.objects(internal_id=\"%s-%s\"%(bid, convert_issue_number(issue_number))).first()\n elif(doc_type==\"monograph\"):\n doc = LBDocument.objects(bid=bid).first()\n logger.info(\"%s has %i pages\"%(doc.internal_id, len(doc.pages)))\n for page_n in sorted(page_numbers):\n logger.debug(\"Reading in annotations for page %i from file %s/ %s\"%(page_n,directory,page_numbers[page_n]))\n entities_with_continuations = {}\n entities,relations = read_ann_file(page_numbers[page_n],directory)\n fulltext = codecs.open(\"%s/%s.txt\"%(directory,page_number2image_name(page_n, string=file_prefix)),'r', 'utf-8').read()\n line_breaks = find_newlines(fulltext)\n #logger.info(\"Found %i entities, %i relation in %s\"%(len(entities), len(relations), directory))\n doc_id = \"%s-%s-%s\"%(bid, issue_number, page_numbers[page_n])\n try:\n page = next((page for page in doc.pages if page.single_page_file_number==page_n))\n if(page[\"in_golden\"]==True):\n annotated_pages.append(page.id)\n logger.info(\"found %i entities in %s (p. %i)\"%(len(entities),doc_id,page_n))\n logger.info(\"found %i relations in %s (p. %i)\"%(len(relations.keys()),doc_id,page_n))\n \"\"\"\n Parse the `ContainedIN` relations and identify annotations that should be merged together.\n IDs of candidates for merging are stored in a dict(), e.g. {\"T1\":[\"T2\",\"T4\"]}\n \"\"\"\n entities_with_continuations = {}\n if len(relations.keys())>0:\n for relation_key in relations:\n args = relations[relation_key][\"arguments\"]\n if args[0] in entities_with_continuations:\n entities_with_continuations[args[0]].append(args[1])\n else:\n entities_with_continuations[args[0]] = [args[1]]\n logger.debug(\"(%s-%s) entities to be merged: %s\"%(doc_id,page_n,entities_with_continuations))\n \"\"\"\n Create the annotations (`entities` dict). \n Later they will be stored into the MongoDB\n \"\"\"\n for entity in entities:\n entities[entity][\"ingestion_timestamp\"] = datetime.utcnow()\n entities[entity][\"annotation_ingester_version\"] = __version__\n entities[entity][\"entity_type\"] = entities[entity][\"entity_type\"].lower( )\n entities[entity][\"filename\"] = \"%s/%s%s\"%(directory,page_numbers[page_n],\".ann\")\n if(doc_type==\"journal\"):\n entities[entity][\"bid\"] = bid\n entities[entity][\"pageid\"] = doc_id\n elif(doc_type==\"monograph\"):\n entities[entity][\"bid\"] = bid\n entities[entity][\"pageid\"] = \"%s-%s\"%(bid,page_numbers[page_n])\n entities[entity][\"container\"] = entities[entity][\"entity_type\"] in container_annotations\n # ref to page_id (from content_loader) ✓\n for position in entities[entity][\"positions\"]:\n line_number = find_linenumber_for_string(position[\"start\"],position[\"end\"], line_breaks)\n logger.debug(\"%s is found at line %s\"%(entity,line_number))\n position[\"line_n\"] = line_number\n position[\"page_id\"] = page.id\n positions_by_offset = sorted(entities[entity][\"positions\"]\n ,key=lambda position: position['start'])\n entities[entity][\"positions\"] = sorted(positions_by_offset\n , key=lambda position: Page.objects(id=position['page_id']).first().single_page_file_number)\n logger.debug(\"Annotations %s %s\"%(entity,entities[entity]))\n \"\"\"\n Now take the candidates for merging identified above and populate the annotations.\n Still nothing is saved into MongoDB at this stage.\n \"\"\"\n for ann_id in entities_with_continuations:\n try:\n logger.debug(\"Starting to merge SP and SF entities into meta-annotations (%s-%s)\"%(doc_id, page_n))\n logger.debug(\"%s will be merged with %s\"%(ann_id,\"+\".join(entities_with_continuations[ann_id])))\n top_entity_types = \"_\".join([entities[ann_id][\"entity_type\"]]+[entities[annid][\"entity_type\"] \n for annid in entities_with_continuations[ann_id]])\n logger.debug(\"%s\"%top_entity_types)\n new_entity = copy.deepcopy(entities)[ann_id] \n #container = True \n new_entity[\"ann_id\"] = \"%s+%s\"%(ann_id,\"+\".join(entities_with_continuations[ann_id]))\n new_entity[\"entity_type\"] = \"meta-annotation\"\n new_entity[\"top_entity_types\"] = top_entity_types\n new_entity[\"top_entities_ids\"] = [ann_id]\n new_entity[\"top_entities_ids\"] += [id for id in entities_with_continuations[ann_id]]\n fname = new_entity[\"filename\"]\n new_entity[\"filename\"] = [fname]\n for to_merge_id in entities_with_continuations[ann_id]:\n to_merge = dict(entities)[to_merge_id]\n new_entity[\"filename\"]+= [to_merge[\"filename\"]]\n new_entity[\"positions\"] = new_entity[\"positions\"] + to_merge[\"positions\"]\n positions_by_offset = sorted(new_entity[\"positions\"]\n ,key=lambda position: position['start'])\n new_entity[\"positions\"] = sorted(positions_by_offset\n ,key=lambda position: Page.objects(id=position['page_id']).first().single_page_file_number)\n new_entity[\"filename\"] = \", \".join(list(set(new_entity[\"filename\"])))\n surface_start = new_entity[\"positions\"][0][\"start\"]\n surface_end = new_entity[\"positions\"][-1][\"end\"]\n new_entity[\"surface\"] = fulltext[surface_start:surface_end]\n entities[new_entity[\"ann_id\"]] = new_entity\n logger.debug(new_entity)\n except Exception as e:\n logger.error(\"The merging of %s in (%s-%s) failed with error\\\"%s\\\"\"%(new_entity[\"ann_id\"],bid,page_n,e))\n \"\"\"\n Now all annotations will be stored into the MongoDB. \n And some specific fields (e.g. `top_entities`) are sorted, and annotations updated \n accordingly in the DB. \n \"\"\"\n try:\n annotations = []\n for entity in entities.values():\n annotation = Annotation(**entity)\n annotation.positions = [PagePosition(**position) for position in entity[\"positions\"]]\n annotation.save()\n annotations.append(annotation)\n page.annotations_ids = [] #TODO\n page.annotations_ids = annotations\n page.is_annotated = True\n page.save()\n logger.debug(\"Following annotations were inserted into MongoDB: %s\"%([annotation.id for annotation in annotations]))\n logger.info(\"%i annotations were inserted into MongoDB\"%len(annotations))\n except Exception as e:\n raise e\n containers = [annotation for annotation in annotations if annotation[\"container\"]] \n contained = [annotation for annotation in annotations if not annotation[\"container\"]]\n meta_annotations = [annotation for annotation in annotations if annotation[\"entity_type\"]==\"meta-annotation\"]\n logger.debug(\"meta annotations: %s\"%meta_annotations)\n \"\"\"\n Resolve the top entities in the meta-annotations: replace entity IDs with \n a reference to the annotation in the MongoDB.\n \"\"\"\n for annotation in meta_annotations:\n top_entities_ids = annotation[\"top_entities_ids\"]\n logger.debug('resolving top_entities')\n top_entities = [Annotation.objects(ann_id=ann_id, pageid=annotation.pageid).first() for ann_id in top_entities_ids]\n #top_entities = list([db_conn.annotations.find_one({\"ann_id\":ann_id,\"pageid\":annotation[\"pageid\"]}) for ann_id in top_entities_ids])\n logger.debug(\"Top entities before sorting %s\"%[ann.id for ann in top_entities])\n annotation[\"top_entities\"] = sort_annotations_by_offset(top_entities)\n logger.debug(\"Top entities after sorting %s\"%[ann.id for ann in top_entities])\n annotation[\"top_entities\"] = top_entities\n annotation.save()\n logger.debug(\"Updating meta-annotation: %s\"%annotation.id)\n \"\"\"\n Transform contains relations between entities into references between annotations \n in the MongoDB.\n \"\"\"\n for annotation in sort_annotations_by_offset(containers):\n if(len(annotation[\"positions\"]) > 1):\n start = annotation[\"positions\"][0][\"start\"]\n end = annotation[\"positions\"][len(annotation[\"positions\"])-1][\"end\"]\n else:\n start = annotation[\"positions\"][0][\"start\"]\n end = annotation[\"positions\"][0][\"end\"]\n annotation[\"contains\"] = []\n for contained_annotation in sort_annotations_by_offset(contained):\n if(len(contained_annotation[\"positions\"])>1):\n if(contained_annotation[\"positions\"][0][\"start\"] >= start\n and contained_annotation[\"positions\"][len(contained_annotation[\"positions\"])-1][\"end\"] <= end):\n annotation[\"contains\"].append(contained_annotation)\n logger.debug(\"[%s] Annotation %s (%s) contains %s (%s)\"%(\n doc_id\n ,annotation[\"ann_id\"]\n ,annotation[\"id\"]\n ,contained_annotation[\"ann_id\"]\n ,contained_annotation[\"id\"]))\n annotation.save()\n else:\n if(contained_annotation[\"positions\"][0][\"start\"] >= start\n and contained_annotation[\"positions\"][0][\"end\"] <= end):\n annotation[\"contains\"].append(contained_annotation)\n logger.debug(\"[%s] Annotation %s (%s) contains %s (%s)\"%(\n doc_id\n ,annotation[\"ann_id\"]\n ,annotation[\"id\"]\n ,contained_annotation[\"ann_id\"]\n ,contained_annotation[\"id\"]))\n annotation.save()\n else:\n page.is_annotated = False\n logger.info(\"%s was ignored because it's not in the golden set\"%doc_id)\n except StopIteration as e:\n logger.error(\"The annotations for %s-%s p. %i can't be ingested\"%(bid, issue_number, page_n))\n except Exception as e:\n logger.error(\"The annotations for %s-%s can't be ingested. Got error %s\"%(bid, issue_number, e))\n return annotated_pages","def preproc_doc(document):\n\n # Each document is a list of lines\n tokenizer = tokenization.FullTokenizer(\n vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)\n\n # set a random seed for reproducability\n # since this function is run in parallel, if we hardcode a seed, all\n # documents will have the same permutations. Instead we use the hash of the\n # first sentence as the seed so it is different for each document and it\n # is still reproducible.\n hash_object = hashlib.md5(document[0])\n rng = random.Random(int(hash_object.hexdigest(), 16) % (10**8))\n\n # Each document is composed of a list of sentences. We create paragraphs\n # by keeping together sentences on the same line and adding adjacent sentences\n # if there are fewer than 5 to form the paragraph.\n # The utility functions below expect the document to be split by paragraphs.\n list_of_paragraphs = []\n paragraph = []\n for line in document:\n line = tokenization.convert_to_unicode(line)\n line = line.replace(u\"\\u2018\", \"'\").replace(u\"\\u2019\", \"'\")\n sents = split_line_by_sentences(line)\n for sent in sents:\n tokens = tokenizer.tokenize(sent)\n if tokens:\n paragraph.append(tokens)\n if len(paragraph) > 5:\n list_of_paragraphs.append(paragraph)\n paragraph = []\n\n # In case of any empty paragraphs, remove them.\n list_of_paragraphs = [x for x in list_of_paragraphs if x]\n\n # Convert the list of paragraphs into TrainingInstance object\n # See preprocessing_utils.py for definition\n if FLAGS.format == FORMAT_BINARY:\n instances = create_instances_from_document(list_of_paragraphs,\n FLAGS.max_seq_length, rng)\n elif FLAGS.format == FORMAT_PARAGRAPH:\n instances = create_paragraph_order_from_document(list_of_paragraphs,\n FLAGS.max_seq_length, rng)\n\n # Convert token lists into ids and add any needed tokens and padding for BERT\n tf_examples = [\n convert_instance_to_tf_example(tokenizer, instance,\n FLAGS.max_seq_length)[0]\n for instance in instances\n ]\n\n # Serialize TFExample for writing to file.\n tf_examples = [example.SerializeToString() for example in tf_examples]\n\n return tf_examples","def process(self, doc_data):\n self.doc_data = doc_data\n self.process_text(self.auto_link_messages)\n self.process_text(self.auto_link_xips)\n self.add_type_sizes()\n return self.doc_data","def add_documents(self, docs):\n for sent in docs:\n sent = map(self.process_token, sent)\n self._token_count.update(sent)","def process(self, doc):\n self.doc = doc\n if self.replace_words is True:\n self.replace_words_fun()\n if self.remove_html_tags is True:\n self.remove_html_tags_fun()\n if self.remove_stopwords is True:\n self.remove_stopwords_fun()\n if self.remove_numbers is True:\n self.remove_numbers_fun()\n if self.remove_punctations is True:\n self.remove_punctations_fun() \n if self.lemmatize is True:\n self.lemmatize_fun()\n return self.doc","def postprocess_docs(self, doc_scores, docs, input_strings, add_eos, prefix, print_docs=False):\n\n def cat_input_and_doc(doc_score, domain, entity_name, doc_title, doc_text, input_string, add_eos, prefix, print_docs=False):\n # TODO(Patrick): if we train more RAG models, I want to put the input first to take advantage of effortless truncation\n # TODO(piktus): better handling of truncation\n if doc_title.startswith('\"'):\n doc_title = doc_title[1:]\n if doc_title.endswith('\"'):\n doc_title = doc_title[:-1]\n if prefix is None:\n prefix = \"\"\n if entity_name is None:\n entity_name = \"*\"\n suffix = self.generator_tokenizer.eos_token if add_eos else \"\"\n out = (\n prefix + domain + self.config.title_sep + entity_name + self.config.title_sep + doc_title + self.config.title_sep + doc_text + self.config.doc_sep + input_string + suffix\n ).replace(\" \", \" \")\n if print_docs:\n logger.info(\"{} {}\".format(doc_score, out))\n return out\n\n rag_input_strings = [\n cat_input_and_doc(\n doc_scores[i][j],\n docs[i][j]['domain'],\n docs[i][j]['entity_name'],\n docs[i][j]['doc']['title'],\n docs[i][j]['doc']['body'],\n input_strings[i],\n add_eos,\n prefix,\n print_docs,\n )\n for i in range(len(docs))\n for j in range(self.n_docs)\n ]\n\n contextualized_inputs = self.generator_tokenizer.batch_encode_plus(\n rag_input_strings,\n max_length=self.config.max_combined_length,\n return_tensors=\"pt\",\n padding=\"longest\",\n truncation=False,\n ).to(doc_scores.device)\n\n return contextualized_inputs[\"input_ids\"], contextualized_inputs[\"attention_mask\"]","def _preprocess(self):\n self.data['sentences'] = self.data['text'].apply(self._tokenize_sent)\n self.data['nouns'] = self.data['sentences'].apply(self._get_nouns)\n # self._get_frequent_features()\n # self._compactness_pruning()\n # self._redundancy_pruning()\n # self._get_features()\n self._extract_opinions()","def run_docs(self, *docs):\n self.docs = docs\n self.run()","def structure_PBDMS_annotations(documents, kb_data):\n \n doc_annotations = list()\n partial_func = partial(parse_PBDMS_doc, kb_data)\n \n with multiprocessing.Pool(processes=10) as pool:\n doc_annotations = pool.map(partial_func, documents)\n \n return doc_annotations","def process(self, doc):\n raise multisearch.errors.FeatureNotAvailableError","def process_docs(directory, vocab):\n for file_name in listdir(directory):\n file_path = directory + '/' + file_name\n add_doc_to_vocab(file_path, vocab)","def process_corpus(args):\n\n fs = open(args.input,'r')\n out = list()\n for line in fs:\n blob = TextBlob(line.strip())\n result_info = dict()\n result_info\n result_info['correct'] = str(blob.correct())\n if args.parse :\n result_info['parse'] = get_parsed_text(blob)\n if args.tokenize:\n result_info['tokenize'] = get_tokenizer_result(blob)\n if args.sentiment:\n result_info['sentiment'] = analyze_sentiment(blob)\n if args.sentence_sentiment:\n result_info['sentence_sentiment'] = analyze_sentence_sentiment(blob)\n if args.noun_phrase:\n result_info['noun_phrase'] = get_noun_phrases(blob)\n if args.pos:\n result_info['pos'] = get_pos_tags(blob)\n\n out.append(result_info)\n print out\n json.dump(out,open('out.json','w'))\n fs.close()\n print '******************************* Execution completed *********************************'","def processCorpus(self):\n # Fit tokenizer on corpus\n self.tokenizer.fit_on_texts(self.corpus)\n input_sequences = []\n for line in self.corpus:\n token_list = self.tokenizer.texts_to_sequences([line])[0]\n for i in range(1, len(token_list)):\n n_gram_sequence = token_list[:i+1]\n input_sequences.append(n_gram_sequence)\n # Pad shorter sequences\n max_sequence_len = max([len(x) for x in input_sequences])\n input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))\n # Assemble training data\n X_train, labels = input_sequences[:,:-1], input_sequences[:,-1]\n y_train = tf.keras.utils.to_categorical(labels, num_classes=len(self.tokenizer.word_index) + 1)\n return max_sequence_len, X_train, y_train","def main():\n global collection\n #args = argparse.ArgumentParser()\n #args.add_argument('directory', help='Directory in which the files'\n #'are stored.')\n #args.add_argument('collection', help='The collection to use.')\n #parser = args.parse_args()\n collection = get_collection()\n #documents = glob.glob('*.asm')\n documents = collection.find()\n num_cores = multiprocessing.cpu_count()\n print('Running code on %d processors' % num_cores)\n Parallel(n_jobs=num_cores)(\\\n delayed(save_comments)(doc) for doc in documents)","def _process_one(self, to_process: NewsArticle) -> NewsArticle:\n text = (to_process.title + to_process.desc_title + to_process.desc_para).lower()\n\n # rake\n self.r.extract_keywords_from_text(text)\n rakes = self.r.get_ranked_phrases()\n\n # spacy\n result = []\n doc = self.nlp(text)\n\n for token in doc:\n if token.text in self.nlp.Defaults.stop_words or token.text in punctuation:\n continue\n\n if token.pos_ in self.pos_tag:\n result.append(token.text)\n\n spacys = [x[0] for x in Counter(result).most_common()]\n\n to_process.tags = list(set(rakes + spacys))\n\n return to_process","def do_docs(self, path):\n print(\"scaraping documentation\")\n for p in path.glob(\"**/*\"):\n if p.is_file():\n parts = p.relative_to(path).parts\n if parts[-1].endswith(\"rst\"):\n data = tsparse(p.read_bytes())\n blob = DocBlob()\n blob.arbitrary = data\n blob.content = {}\n\n blob.ordered_sections = []\n blob.item_file = None\n blob.item_line = None\n blob.item_type = None\n blob.aliases = []\n blob.example_section_data = Section()\n blob.see_also = []\n blob.signature = None\n blob.references = None\n blob.refs = []\n\n self.docs[parts] = json.dumps(blob.to_json(), indent=2)\n else:\n pass\n # data = p.read_bytes()","def annotate_documents(asynch=False):\n \n files = glob.glob(os.path.join(cfg.INPUT_PATH, cfg.INPUT_PATTERN))\n logger.info('found %d files with pattern %s', len(files), cfg.INPUT_PATTERN) \n \n if asynch:\n with concurrent.futures.ThreadPoolExecutor(max_workers=cfg.ASYNCH_MAX_WORKERS) as executor:\n executor.map(annotate_one, files)\n \n else: \n for f_json in files:\n annotate_one(f_json)\n \n \n return","def preproc_doc(document):\n tokenizer = tokenization.FullTokenizer(\n vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)\n\n # document = [\n # tokenization.convert_to_unicode(\n # unidecode.unidecode(line.decode(\"utf-8\"))) for line in document\n # ]\n\n sent_tokens = [tokenizer.tokenize(sent) for sent in document if sent]\n sent_tokens = [sent for sent in sent_tokens if len(sent) > 1]\n if len(sent_tokens) < 8:\n return []\n\n # Convert token lists into ids and add any needed tokens and padding for BERT\n tf_example = convert_instance_to_tf_example(tokenizer, sent_tokens,\n FLAGS.max_sent_length,\n FLAGS.max_para_length)\n\n # Serialize TFExample for writing to file.\n tf_examples = [tf_example.SerializeToString()]\n\n return tf_examples","def preprocess(self, documents):\n\n # Store the total number of documents\n num_docs = np.float(len(documents))\n\n # A dict storing the frequency of each word across all documents\n total_word_freq = {}\n\n # A dict storing the number of documents that word appears in\n doc_word_freq = {}\n\n # Iterate over all documents\n for doc in documents:\n # Split the string into a list of words\n words = extract_words(doc)\n\n # Update the 'total_word_freq' dict using all words in 'words'\n for w in words:\n ''' YOUR CODE HERE '''\n if w not in total_word_freq.keys():\n total_word_freq[w] = 1\n else:\n total_word_freq[w] += 1\n\n ''' END CODE FOR THIS LOOP '''\n\n # Update the 'doc_word_freq' dict. Remember to only add '1' corresponding to\n # each word in a document. In case a word appears twice in a document, then\n # it should be ignored. We use the set() data structure to achieve this.\n for w in set(words):\n ''' YOUR CODE HERE '''\n if w not in doc_word_freq:\n doc_word_freq[w] = 1\n else:\n doc_word_freq[w] += 1\n\n ''' END CODE FOR THIS LOOP '''\n\n # A set of words with total frequency less than 'self.min_freq'\n remove_words = set()\n\n ''' YOUR CODE HERE '''\n\n # Check frequency of each word and add to 'remove_words'\n for w in total_word_freq.keys():\n if total_word_freq[w] < self.min_freq:\n remove_words.add(w)\n\n # Delete the words in 'remove_words' from 'total_word_freq' and\n # 'doc_word_freq'.\n for w in remove_words:\n del total_word_freq[w]\n del doc_word_freq[w]\n\n # Create a numpy array to store frequencies from which\n # we can create the 'self.idf' preprocessed numpy array.\n word_freq_tensor = np.zeros(len(doc_word_freq))\n\n # For each word in 'doc_word_freq' dict, update\n # 'self.word_to_idx' and 'self.idx_to_word' and\n # 'word_freq_tensor'.\n i = 0\n for w in doc_word_freq.keys():\n self.word_to_idx[w] = i \n self.idx_to_word[i] = w\n word_freq_tensor[i] = doc_word_freq[w]\n i+=1\n \n #print(word_freq_tensor.shape)\n #print(word_freq_tensor)\n # Calculate 'self.idf' (see hint.pdf for formula)\n self.idf = -1*np.log(word_freq_tensor/(len(documents)))\n ''' END YOUR CODE HERE '''","def extract_and_process(input_dir, pdf_path, json_output):\n\tprint('Extracting text from: ', pdf_path)\n\toutput_dir = input_dir + \"/output\"\n\ttry:\n\t\t# Extract PDF to HTML format\n\t\textracted_text = txt_ext.extract_pdf_to_html(pdf_path)\n\t\t# Write raw HTML\n\t\t#pre_proc.create_text_file(output_dir + \"/raw_\" + path_leaf(pdf_path) + \".html\", extracted_text)\n\t\t\n\t\tprint(\"Extraction finished: \"+ pdf_path + \", starting processing\")\n\t\tprocess(extracted_text, output_dir, path_leaf(pdf_path), json_output)\n\n\texcept PDFSyntaxError:\n\t\tprint(\"PDFSyntaxError: Is this really a PDF? \", pdf_path)\n\texcept PDFTextExtractionNotAllowed as e:\n\t\tprint(e)","def run(self):\n cmds = (self.clean_docs_cmd, self.html_docs_cmd, self.view_docs_cmd)\n self.call_in_sequence(cmds)","def process(self, doc: Doc) -> Doc:\n\n matches = self.get_matches(doc)\n\n terminations = get_spans(matches, \"termination\")\n boundaries = self._boundaries(doc, terminations)\n\n # Removes duplicate matches and pseudo-expressions in one statement\n matches = filter_spans(matches, label_to_remove=\"pseudo\")\n\n entities = list(self.get_spans(doc))\n ents = None\n\n for start, end in boundaries:\n\n ents, entities = consume_spans(\n entities,\n filter=lambda s: check_inclusion(s, start, end),\n second_chance=ents,\n )\n\n sub_matches, matches = consume_spans(\n matches, lambda s: start <= s.start < end\n )\n\n if self.on_ents_only and not ents:\n continue\n\n sub_preceding = get_spans(sub_matches, \"preceding\")\n sub_following = get_spans(sub_matches, \"following\")\n # Verbs preceding negated content\n sub_preceding += get_spans(sub_matches, \"verbs_preceding\")\n # Verbs following negated content\n sub_following += get_spans(sub_matches, \"verbs_following\")\n\n if not sub_preceding + sub_following:\n continue\n\n if not self.on_ents_only:\n for token in doc[start:end]:\n token._.negation = any(\n m.end <= token.i for m in sub_preceding\n ) or any(m.start > token.i for m in sub_following)\n\n for ent in ents:\n self.annotate_entity(\n ent=ent,\n sub_preceding=sub_preceding,\n sub_following=sub_following,\n )\n\n return doc","def preprocess(doc_in, doc_out):\n def output(text, doc_id):\n doc_out.write(doc_id + \"\\n\")\n doc_out.write(text.replace(\"\\n\", \" \") + \"\\n\\n\")\n\n def filter_text(t):\n filtered_out = [\"<P>\", \"</P>\"]\n r = t\n for f in filtered_out:\n r = r.replace(f, \" \")\n return r\n\n\n doc_id = None\n reading_text = False\n text = \"\"\n for line in doc_in:\n if(str_text_start in line):\n if(reading_text):\n warning(\"Found \" + str_text_start + \" in text\")\n if(not doc_id):\n warning(\"Reading text without knowing id\")\n continue\n reading_text = True\n continue\n if((str_text_stop in line) and reading_text):\n output(text, doc_id)\n text = \"\"\n reading_text = False\n doc_id = None\n doc_id_match = pat_doc_no.match(line)\n if(doc_id_match):\n doc_id = doc_id_match.group(1)\n if(reading_text):\n warning(\"Found doc id in text\")\n continue\n if(reading_text):\n text = text + filter_text(line)","def __parse_docs(self, docs, analyses=True):\n # iter over docs\n for i, doc in enumerate(docs):\n _meta = doc.attrib['title']\n # iter over examples in *doc*\n for snip in doc.getchildren()[1:]:\n _text = str()\n _idx = 0\n _target_idxs = list()\n _ana = list()\n # iter over words in cur example\n for word in snip.getchildren():\n if word.tag == 'text':\n _text += word.text\n _idx += len(word.text)\n \n if len(word.attrib) > 0:\n _text += word.attrib['text']\n # process target\n if word.attrib.get('target') is not None:\n _target_idxs.append((_idx, _idx + len(word.attrib['text'])))\n if analyses:\n _ana.append(self.__get_ana(word))\n \n _idx += len(word.attrib['text'])\n \n if _target_idxs:\n for i, ixs in enumerate(_target_idxs):\n if analyses:\n yield _text, ixs, _meta, _ana[i]\n else:\n yield _text, ixs, _meta, _ana\n else:\n continue","def process(self, file=False):\n if file:\n doc = self.corpus.getDocument(file)\n print \"process Document:\", doc.path\n print\n self._process(doc)\n return\n\n count = 0\n\n while True:\n count += 1\n doc = self.corpus.getDocument()\n if doc == None or count > self.limit:\n break\n\n self._process(doc)","def build_corpus(self):\n print(\"Inside the build_corpus >>>>>\")\n documentsCount = 0\n documents = self.documents\n\t\t\n with open(self.documents_path) as file:\n for documents in file.readlines():\n documents = documents.rstrip('}\\n ').strip('0\\t').strip('1\\t').split(' ')\n documentsCount = documentsCount +1\n self.documents.append(documents)\n\t\t\t\n self.number_of_documents = documentsCount","def ingest_all(self, docs):\n for doc in docs:\n self.ingest(doc)","def _transform(self, document):\n pass","def updateAnnotations(self):\n self.backupDatafiles()\n print(\"Updating annotation files \", self.field(\"trainDir\"))\n listOfDataFiles = QDir(self.field(\"trainDir\")).entryList(['*.data'])\n for file in listOfDataFiles:\n # Read the annotation\n segments = Segment.SegmentList()\n newsegments = Segment.SegmentList()\n segments.parseJSON(os.path.join(self.field(\"trainDir\"), file))\n allSpSegs = np.arange(len(segments)).tolist()\n newsegments.metadata = segments.metadata\n for segix in allSpSegs:\n seg = segments[segix]\n if self.field(\"species\") not in [fil[\"species\"] for fil in seg[4]]:\n newsegments.addSegment(seg) # leave non-target segments unchanged\n else:\n for seg2 in self.segments:\n if seg2[1] == seg:\n # find the index of target sp and update call type\n seg[4][[fil[\"species\"] for fil in seg[4]].index(self.field(\"species\"))][\"calltype\"] = self.clusters[seg2[-1]]\n newsegments.addSegment(seg)\n newsegments.saveJSON(os.path.join(self.field(\"trainDir\"), file))","def train_pipeline(nlp: spacy.language.Language) -> None:\n if TEXTCAT not in nlp.pipe_names:\n textcat = nlp.create_pipe(TEXTCAT, config={\"exclusive_classes\": False})\n nlp.add_pipe(textcat, last=True)\n else:\n textcat = nlp.get_pipe(TEXTCAT)\n\n for category in CATEGORIES:\n textcat.add_label(category.value)\n\n pipe_exceptions = {TEXTCAT, \"trf_wordpiecer\", \"trf_tok2vec\"}\n other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]\n with nlp.disable_pipes(*other_pipes): # only train textcat\n all_data = list(get_classification_training_data())\n random.shuffle(all_data)\n\n training_data = all_data[: len(all_data) - 2]\n validation_data = all_data[len(all_data) - 2 :]\n\n optimizer = nlp.begin_training()\n for itn in range(20):\n losses: Dict[str, Any] = {}\n random.shuffle(training_data)\n batches = minibatch(training_data, size=compounding(4.0, 32.0, 1.001))\n\n for batch in batches:\n texts, annotations = zip(*batch)\n nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)","def transform(self, docs):\n return [doc for doc in docs]","def save_annotated_text_to_xml(self):\n #initialise file to write the output\n outfile = open(('annotated_text_' + self.lang + '_' + \n self.method + '.xml'), 'w')\n #initialise xml\n annotated_doc = etree.Element('Annotated_document')\n main_text = ''\n #counter for the sentences\n counter_sentence = 0\n #counter for the paragraphs\n counter_paragraph = 0\n #open txt file\n with open(self.lang + '.txt') as file:\n for paragraph in file:\n paragraph_string = ''\n sentences = tokenize.sent_tokenize(paragraph)\n for sentence in sentences:\n #build lists with the ends of the tokens with NE and the NEs\n end_list = [0]\n end_list += [i[2] for i in \n self.named_entity_list_total[counter_sentence]]\n ne_list = [i[3] for i in \n self.named_entity_list_total[counter_sentence]]\n counter_sentence += 1\n #build new string\n new_string = ''\n for i in range(len(end_list)-1):\n new_string += (sentence[end_list[i]:end_list[i+1]]+\n '<annotation class=\"'+ne_list[i]+'\"/>')\n new_string += sentence[end_list[-1]:len(sentence)]\n paragraph_string += new_string+'\\n'\n #print title, author, abstract and main text differently to xml\n if counter_paragraph == 0:\n title_text = etree.SubElement(annotated_doc, \"Title\")\n #add text to the node\n init_text = \"<text>{0}</text>\".format(paragraph_string[6:])\n fin_text = etree.fromstring(init_text)\n title_text.append(fin_text)\n elif counter_paragraph == 1:\n author_text = etree.SubElement(annotated_doc, \"Author\")\n #add text to the node\n init_text = \"<text>{0}</text>\".format(paragraph_string[7:])\n fin_text = etree.fromstring(init_text)\n author_text.append(fin_text)\n elif counter_paragraph == 2:\n abstract_text = etree.SubElement(annotated_doc, \"Abstract\")\n #add text to the node\n init_text = \"<text>{0}</text>\".format(paragraph_string[9:])\n fin_text = etree.fromstring(init_text)\n abstract_text.append(fin_text)\n else: \n main_text += paragraph_string\n counter_paragraph += 1\n main_text_xml = etree.SubElement(annotated_doc, \"Main_text\")\n #add text to the node\n init_text = \"<text>{0}</text>\".format(main_text)\n fin_text = etree.fromstring(init_text)\n main_text_xml.append(fin_text)\n #convert and write to outfile\n xml_bytes = etree.tostring(annotated_doc, encoding='UTF-8', \n pretty_print=True, xml_declaration=True)\n xml_str = xml_bytes.decode(\"utf-8\")\n outfile.write(xml_str)\n outfile.close()\n return","def process_text(model_name: str, text: str) -> spacy.tokens.Doc:\r\n nlp = load_model(model_name)\r\n return nlp(text)","def run(self, iteration_key):\n record_provider = SqlDocumentProvider(iteration_key, self.case_accessor())\n processor = BulkDocProcessor(record_provider, self.doc_processor(self.domain))\n return processor.run()","def gather_documents(self):\n self.document_gatherer.gather_and_save_everything(Constants.path_cord, \n Constants.path_metadata, \n Constants.path_linked_documents,\n Constants.path_unlinked_documents,\n Constants.path_parsed_documents,\n Constants.path_all_documents)\n \n print(\"Done gathering documents.\")","def process(self):\n\n self.copy_supported_files()\n tex = self.replace_includes(file_name=self.source_tex_file)\n\n try:\n tex = self.replace_bibliography(tex)\n except Exception, ex:\n print '[e] exception {}'.format(str(ex))\n print '[i] bibliography was not replaced - run firstly \"build-latex-win\\\\_build.bat\" for bbl file'\n\n self.dir_helper.save_file(file_name=self.dest_tex_file, text=tex)\n self.create_bat_file()\n\n print '\\n[i] publication is ready - IN CASE NO ERROR ACCURED'\n print '[i] check foloder: \"{}\"\\n'.format(self.temp_dir)","def train(self, documents, total_examples=None, total_words=None,\n epochs=None, start_alpha=None, end_alpha=None,\n word_count=0, queue_factor=2, report_delay=1.0, callbacks=()):\n super(Doc2Vec, self).train(\n documents, total_examples=total_examples, total_words=total_words,\n epochs=epochs, start_alpha=start_alpha, end_alpha=end_alpha, word_count=word_count,\n queue_factor=queue_factor, report_delay=report_delay, callbacks=callbacks)","def learn(self, documents, labels):\n for i in xrange(len(documents)):\n text = documents[i]\n\n words = text.split()\n self.learn_from_one(words)","def process_all_documents(self,\n n_jobs: Optional[int] = None,\n ) -> List[Optional[Document]]:\n return self.process_documents(self.doc_ids, n_jobs)","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))","def prepare_spacy_pipeline():\n global nlp \n nlp = spacy.load(\"en_core_web_lg\", disable = [\"ner\"])\n \n return","def runDocumentReduce(self):\n # run as usual\n self.runDocument(reduceApplication=True)","def update_from_document(self, document_path):\n with open(document_path, 'r') as document_file:\n for sentence in document_file:\n words = sentence.strip().split()\n for word in words:\n self._add_new_word(word)","def _prepare_analysis_input(self, documents):\n subdoc_to_doc_map = {}\n wordtype_to_number = {}\n number_to_wordtype = []\n wordtypes = {}\n \n # prevent duplicating work\n if os.path.exists(self.wordtype_file):\n return\n \n try:\n # First find singletons\n if self.remove_singletons:\n word_type_count_threshold = max(1, int(math.log(documents.count(), 10)) - 2)\n temp_word_type_counts = {}\n for doc_index, doc in enumerate(documents):\n tokens = self.tokenize(doc.get_content())\n for token, token_start in tokens:\n temp_word_type_counts[token] = temp_word_type_counts.setdefault(token, 0) + 1\n for word_type, count in temp_word_type_counts.iteritems(): # add singletons to stopword list\n if count <= word_type_count_threshold:\n self._excluded_words[word_type] = True\n with io.open(self.excluded_words_file, 'w', encoding='utf-8') as ex_f:\n ex_f.write(unicode(json.dumps(self._excluded_words)))\n \n haltwords = dict(self.stopwords)\n haltwords.update(self._excluded_words)\n # Second find bigrams, iterate through documents and train.\n if self.find_bigrams:\n from import_tool.analysis.bigram_finder import BigramFinder\n bigram_finder = BigramFinder(stopwords=haltwords)\n for doc_index, doc in enumerate(documents):\n bigram_finder.train(doc_index, self.tokenize(doc.get_content()))\n bigram_finder.print()\n \n # Third, we're going to stem words\n if self.stem_words:\n from import_tool.analysis.stemmer import Stemmer\n stemmer = Stemmer(self._working_dir, self.base_dir)\n \n # for each document tokenize and map tokens to numbers to avoid regex problems before passing data to Mallet\n with io.open(self.mallet_input_file, 'w', encoding='utf-8') as w:\n with io.open(self.start_index_file, 'w', encoding='utf-8') as w2:\n count = 0\n subcount = 0\n for doc_index, doc in enumerate(documents):\n doc_content = unicode(doc.get_content())\n count += 1\n subdocuments = self.create_subdocuments(doc_index, doc_content)\n token_start_index_offset = 0 # needed to make sure the start index remains correct once the document is re-merged\n for subdoc_name, subdoc_content in subdocuments:\n if subcount > 0:\n w2.write(u'\\n')\n subcount += 1\n subdoc_to_doc_map[subdoc_name] = doc_index\n tokens = self.tokenize(subdoc_content)\n \n if self.find_bigrams:\n tokens = bigram_finder.combine(tokens, subdoc_content)\n \n token_numbers = []\n token_start_indices = []\n only_tokens = []\n tokens_temp = []\n for tok, tok_start in tokens:\n only_tokens.append(tok)\n tokens_temp.append([tok, tok_start + token_start_index_offset])\n tokens = tokens_temp\n tokens_temp = None\n if self.stem_words:\n stemmed_tokens = stemmer.stem(only_tokens)\n else:\n stemmed_tokens = only_tokens\n for tup, tok_stem in zip(tokens, stemmed_tokens):\n tok, tok_start = tup\n wordtypes[tok] = True\n wordtypes[tok_stem] = True\n try:\n tok_num = wordtype_to_number[tok_stem]\n except:\n tok_num = len(wordtype_to_number)\n number_to_wordtype.append(tok_stem)\n wordtype_to_number[tok_stem] = tok_num\n token_numbers.append(unicode(tok_num))\n token_start_indices.append([tok, tok_start])\n text = u' '.join(token_numbers)\n w.write(u'{0} all {1}\\n'.format(subdoc_name, text))\n w2.write(unicode(json.dumps(token_start_indices)))\n token_start_index_offset += len(subdoc_content)\n for tok, tok_start in tokens:\n try:\n assert doc_content[tok_start:tok_start+len(tok)].lower() == tok.lower()\n except:\n print(tok_start)\n print(len(tok))\n print('\"'+doc_content[tok_start:tok_start+len(tok)].lower()+'\"')\n print('\"'+tok.lower()+'\"')\n raise\n if not count:\n raise Exception('No files processed.')\n # record which subdocuments belong to which documents\n with io.open(self.subdoc_to_doc_map_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(subdoc_to_doc_map)))\n with io.open(self.wordtype_to_number_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(wordtype_to_number)))\n with io.open(self.number_to_wordtype_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(number_to_wordtype)))\n with io.open(self.wordtype_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(wordtypes)))\n except: # cleanup\n self._cleanup(self.mallet_input_file)\n self._cleanup(self.subdoc_to_doc_map_file)\n self._cleanup(self.wordtype_to_number_file)\n self._cleanup(self.number_to_wordtype_file)\n self._cleanup(self.wordtype_file)\n self._cleanup(self.excluded_words_file)\n raise","def batch_parser(texts, raw_output=False, corenlp_path=CORENLP_PATH, memory=\"3g\", outputformat=\"xml\",\n annotators = [\"tokenize\",\"ssplit\",\"pos\",\"lemma\",\"ner\",\"parse\",\"dcoref\"], **kwargs):\n\n # First, we change to the directory where we place the xml files from the parser\n input_dir = tempfile.mkdtemp()\n output_dir = tempfile.mkdtemp()\n files = []\n file_list = tempfile.NamedTemporaryFile()\n\n for i, text in enumerate(texts):\n filename = os.path.join(input_dir, \"{0}.txt\".format(i))\n files.append(filename)\n f = open(filename, \"w\")\n f.write(text.encode(\"utf-8\"))\n f.close()\n\n # creating the file list of files to parse\n file_list.write('\\n'.join(files))\n file_list.seek(0)\n\n command = generate_corenlp_command(corenlp_path, memory=memory,\n filelist=file_list.name,\n outputdir=output_dir,\n outputformat=outputformat,\n annotators=annotators, **kwargs)\n\n # print \"executing...\"\n # print command\n\n # creates the xml file of parser output:\n call(command, shell=True)\n\n # reading in the raw xml file:\n try:\n result = []\n for output_file in sorted(os.listdir(output_dir),\n key=lambda x: int(x.split('.')[0])):\n #print output_file\n with open(output_dir + '/' + output_file, 'r') as xml:\n file_name = re.sub('.xml$', '', os.path.basename(output_file))\n parsed = parse_xml_result(xml.read())\n # add only the first document in the parse\n result.append(parsed[0])\n return result\n finally:\n file_list.close()\n shutil.rmtree(output_dir)","def __call__(self, doc):\n matches = self.matcher(doc)\n spans = [] # keep spans here to merge them later\n for match_id, start, end in matches:\n span = doc[start : end]\n for token in span:\n token._.set(self._is_url, True)\n token.lemma_ = get_domain(token.text)\n spans.append(span)\n\n return doc","def process_input_files(list_input_files):\n global dict_models_results\n global list_spacy_docs\n \n for input_file in list_input_files:\n prefix = prefix_from_filename(input_file)\n \n with open(input_file) as f:\n list_cases = json.load(f)\n dict_models_results[prefix] = list_cases\n \n \n #extract list of questions from all vignettes and create a mapping page -> vignette question\n dict_questions = {}\n for prefix, list_cases in dict_models_results.items():\n for vignette in list_cases:\n dict_questions[vignette[\"book_page\"]] = vignette[\"question\"]\n \n \n for book_page,question in dict_questions.items():\n doc_q = load_bner_onto_tokens_extension(question, book_page)\n list_spacy_docs.append(doc_q)\n \n return","def preprocess_text(self, seq):\n if self.text_preprocess_fn:\n seq = list(map(self.text_preprocess_fn, seq))\n return seq","def process_docs_2(directory, vocab):\n lines = []\n for filename in listdir(directory):\n if not filename.endswith('.txt'):\n continue\n path = directory + '/' + filename\n line = doc_to_line(path, vocab)\n lines.append(line)\n return lines","def run(self):\n logging.info('Perform automatic annotations')\n\n # The procedure outcome report file.\n self.__param.set_report_filename(self.__log_report.get_filename())\n self.__log_report.increment()\n\n # Create the progress bar then run the annotations\n wx.BeginBusyCursor()\n p = sppasAnnotProgressDialog()\n self.__manager.annotate(self.__param, p)\n p.close()\n wx.EndBusyCursor()\n\n self.__update_log_text()\n self.Refresh()\n\n # send to parent\n evt = DataChangedEvent(data=self.__param.get_workspace())\n evt.SetEventObject(self)\n wx.PostEvent(self.GetParent(), evt)","def forward(self, doc):\n out = torch.tensor([]).float().to(self.device)\n\n for i in range(len(doc)):\n sentences_raw = sentencesplit(cleantxt(doc[i]))\n sentences_ready = torch.tensor([]).float().to(self.device)\n for sentence in sentences_raw:\n sentence = sentence.split()\n if sentence == []:\n continue\n lookup_tensor = torch.tensor([]).long().to(self.device)\n for word in sentence:\n if word in self.embedd_dict:\n lookup_tensor = torch.cat((lookup_tensor,\n torch.LongTensor([self.embedd_dict[word]])), 0)\n else:\n lookup_tensor = torch.cat((lookup_tensor, torch.LongTensor([0])), 0)\n # Word embedding\n xw = self.word_embedding(lookup_tensor).view(1, -1, self.embedding_dim).to(self.device)\n # Word GRU\n self.hidden_gru_words = self.init_hidden_words()\n hw, self.hidden_gru_words = self.gru_word(xw, self.hidden_gru_words)\n # Word MLP\n uw = nn.Tanh()(self.MLP_word(hw)).to(self.device)\n # Word attention\n attention_score = torch.matmul(uw, self.attention_word).squeeze().to(self.device)\n attention_score = F.softmax(attention_score, dim=0).view(uw.size(0), uw.size(1), 1).to(self.device)\n scored_x = (hw * attention_score).to(self.device)\n s = torch.sum(scored_x, dim=1).to(self.device)\n #collecting sentences\n sentences_ready = torch.cat((sentences_ready, s), 0)\n # Sentence GRU\n if len(sentences_ready) == 0:\n out = torch.cat((out,\n torch.randn(1, self.number_cat).to(self.device)), 0).to(self.device)\n continue\n sentences_ready_gru = sentences_ready.view(1, -1, self.embedding_dim).to(self.device)\n self.hidden_gru_sentences = self.init_hidden_sentences()\n hs, self.hidden_gru_sentences = self.gru_sentence(torch.tensor(sentences_ready_gru), self.hidden_gru_sentences)\n # SENTENCE MLP\n us = nn.Tanh()(self.MLP_sentence(hs)).to(self.device)\n # Sentence attention\n attention_score = torch.matmul(us, self.attention_sentence).squeeze().to(self.device)\n attention_score = F.softmax(attention_score, dim=0).view(us.size(0), us.size(1), 1).to(self.device)\n scored_x = (hs * attention_score).to(self.device)\n v = torch.sum(scored_x, dim=1).to(self.device)\n # classification\n p = self.MLP_classification(v).to(self.device)\n out = torch.cat((out, p.float()), 0).float().to(self.device)\n return out","def processCorpus(self, texts: [Text]):\n \n remaining_texts = texts\n curr_texts = []\n processed_texts = []\n cases = []\n num_failed_test_cases = []\n num_failed_test_cases_per_asr = {}\n num_processed_texts = []\n for asr in self.asrs:\n num_failed_test_cases_per_asr[asr.getName()] = []\n \n for i in range(self.num_iteration):\n # print(f\"Iteration: {i+1}\")\n \n if self.text_batch_size :\n curr_texts = remaining_texts[:self.text_batch_size]\n remaining_texts = remaining_texts[self.text_batch_size:]\n else : # use global visibility\n curr_texts = remaining_texts\n\n if len(curr_texts) > 0 :\n \n curr_cases, curr_processsed_texts, unprocessed_texts = self.processOneIteration(curr_texts, processed_texts, cases)\n cases.extend(curr_cases)\n processed_texts.extend(curr_processsed_texts)\n if self.text_batch_size :\n remaining_texts.extend(unprocessed_texts)\n else :\n remaining_texts = unprocessed_texts\n\n num_failed_test_cases.append(calculate_cases(cases, mode=FAILED_TEST_CASE))\n for asr in self.asrs:\n num_failed_test_cases_per_asr[asr.getName()].append(calculate_cases_per_asr(\n cases, mode=FAILED_TEST_CASE, asr_name=asr.getName()))\n num_processed_texts.append(len(processed_texts))\n else :\n print(\"Texts are not enough!\")\n \n # shuffle the remaining texts\n np.random.shuffle(remaining_texts)\n \n data = {}\n data[\"number_of_failed_test_cases_all\"] = num_failed_test_cases\n data[\"number_of_failed_test_cases_per_asr\"] = num_failed_test_cases_per_asr\n data[\"number_of_processed_texts\"] = num_processed_texts\n with open(self.outputfile_failed_test_case, 'w') as outfile:\n json.dump(data, outfile, indent=2, sort_keys=True)\n\n if self.target_asr :\n self.saveFailedTestCases(processed_texts, cases)","def transform(self, documents, pad_length=None, add_start=False,\n add_end=False):\n indices = [\n self.process_text(text=doc, add_start=add_start, add_end=add_end)\n for doc in documents\n ]\n if pad_length:\n if pad_length == 'max':\n pad_length = max(map(len, indices))\n indices = keras.preprocessing.sequence.pad_sequences(\n sequences=indices,\n maxlen=pad_length,\n dtype=int,\n padding='pre',\n truncating='pre'\n ).tolist()\n return indices","def docs_from_clinical_record(\n lines: List[str], annotations: List[str], nlp: Language, merge_docs: bool = False\n) -> List[Doc]:\n docs = []\n spans_by_line = defaultdict(list)\n\n for row in annotations:\n row = row.split(\"||\")\n text_info = row[0]\n type_info = row[1]\n\n offset_start = text_info.split(\" \")[-2]\n offset_end = text_info.split(\" \")[-1]\n\n start_line, word_start = offset_start.split(\":\")\n end_line, word_end = offset_end.split(\":\")\n\n label = type_info.split('\"')[-2]\n\n if start_line != end_line:\n # This happens very infrequently (only about 10 times in total)\n # so we just skip these annotations\n continue\n else:\n spans_by_line[int(start_line)].append(\n (int(word_start), int(word_end), label)\n )\n\n for i, line in enumerate(lines):\n n = i + 1\n doc = nlp.make_doc(line)\n if n in spans_by_line:\n ents = [\n Span(doc, start, end + 1, label=label)\n for (start, end, label) in spans_by_line[n]\n ]\n ents = [\n e for e in ents if bool(e.text.strip()) and e.text.strip() == e.text\n ]\n doc.ents = filter_spans(ents)\n\n docs.append(doc)\n\n return [Doc.from_docs(docs)] if merge_docs else docs","def run_indexer(self, corpus):\n with open(corpus, 'r') as fp:\n for line in tqdm(fp.readlines()):\n doc_id, document = self.preprocessor.get_doc_id(line)\n tokenized_document = self.preprocessor.tokenizer(document)\n self.indexer.generate_inverted_index(doc_id, tokenized_document)\n self.indexer.sort_terms()\n self.indexer.add_skip_connections()\n self.indexer.calculate_tf_idf()","def openie_prepare_files(document_file, no_entity_filter=False, consider_sections=False):\n temp_dir = tempfile.mkdtemp()\n temp_in_dir = os.path.join(temp_dir, \"input\")\n filelist_fn = os.path.join(temp_dir, \"filelist.txt\")\n out_fn = os.path.join(temp_dir, \"output.txt\")\n os.mkdir(temp_in_dir)\n input_files = []\n\n amount_skipped_files = 0\n doc_count = count_documents(document_file)\n logging.info('counting files to process....')\n if no_entity_filter:\n for document_content in read_pubtator_documents(document_file):\n doc = TaggedDocument(from_str=document_content)\n if not doc or not doc.title or not doc.abstract:\n amount_skipped_files += 1\n else:\n doc_count += 1\n # TODO: Not beautiful but join sections via a '.' to ensure sentence splitting in CoreNLP\n content = '. '.join([te for te, _ in doc.iterate_over_text_elements(sections=consider_sections)])\n input_file = os.path.join(temp_in_dir, \"{}.txt\".format(doc.id))\n input_files.append(input_file)\n with open(input_file, \"w\") as f:\n f.write(content)\n else:\n logging.info('Init spacy nlp...')\n spacy_nlp = English() # just the language with no model\n spacy_nlp.add_pipe(\"sentencizer\")\n\n doc2sentences, doc2tags = filter_document_sentences_without_tags(doc_count, document_file, spacy_nlp,\n consider_sections=consider_sections)\n doc_count = len(doc2tags)\n for doc_id, sentences in doc2sentences.items():\n if sentences:\n input_file = os.path.join(temp_in_dir, \"{}.txt\".format(doc_id))\n input_files.append(input_file)\n with open(input_file, 'wt') as f:\n f.write(' '.join(sentences))\n\n logging.info('{} files need to be processed. {} files skipped.'.format(doc_count, amount_skipped_files))\n with open(filelist_fn, \"w\") as f:\n f.write(\"\\n\".join(input_files))\n return filelist_fn, out_fn, doc_count","def fit(self):\n if not self.paragraphs or len(self.paragraphs) == 0:\n self.paragraphs = self._get_all_paragraphs()\n if not self.paragraphs or len(self.paragraphs) == 0:\n logger.warning(\"Fit method called with empty document store\")\n return\n\n self.df = pd.DataFrame.from_dict(self.paragraphs)\n self.df[\"text\"] = self.df[\"text\"].apply(lambda x: \" \".join(x))\n self.tfidf_matrix = self.vectorizer.fit_transform(self.df[\"text\"])","def run():\n assert os.path.exists(args.input_path), \"input_path doesn't exist\"\n assert os.path.exists(args.output_path), \"output_path doesn't exist\"\n\n # read all the paths to the input documents\n doc_files = []\n for root, dirs, files in os.walk(args.input_path):\n for file in files:\n if not file.endswith('gz') and not file.endswith('xml'):\n continue\n doc_files.append(os.path.join(root, file))\n print('{} medline files found from {}'\n ''.format(len(doc_files), args.input_path))\n\n print('converting...')\n pool = Pool(processes=args.num_workers)\n total_doc = 0\n total_batch = 0\n total_empty = 0\n for d, b, n in tqdm(pool.imap_unordered(partial(convert), doc_files),\n total=len(doc_files)):\n total_doc += d\n total_batch += b\n total_empty += n\n\n print('total docs: {}, total batches: {} created (empty doc {})'\n ''.format(total_doc, total_batch, total_empty))","def prepare_dictionary_from_docs(self):\n if os.path.exists(self.DICT_PATH):\n return True\n self.logger.info(\"START PREPARING DICT\")\n for fn in os.listdir(self.wiki_path):\n self.logger.info(\"dict update {0}\".format(fn))\n content = self.get_processed_content(fn)\n self.dictionary.add_documents([content])\n self.dictionary.filter_extremes(no_below=20, no_above=0.1, keep_n=100000)\n self.dictionary.compactify()\n self.dictionary.save(self.DICT_PATH)\n return True","def process_documents(self,\n doc_ids: Iterable[str],\n n_jobs: Optional[int] = None,\n ) -> List[Optional[Document]]:\n if n_jobs is None:\n n_jobs = self.n_jobs\n elif n_jobs == -1:\n n_jobs = os.cpu_count()\n elif n_jobs < -1:\n raise ValueError(f'n_jobs must be >= 0 or -1, but got {n_jobs}')\n if self.archive_handler is not None:\n assert n_jobs == 0\n with (self.archive_handler.open() if self.archive_handler else nullcontext()) as archive:\n process_document = partial(KyotoReader.process_document, self, archive=archive)\n if n_jobs > 0:\n with futures.ProcessPoolExecutor(max_workers=n_jobs) as executor:\n rets: Iterable[Optional[Document]] = executor.map(process_document, doc_ids)\n else:\n rets: Iterable[Optional[Document]] = map(process_document, doc_ids)\n return list(rets)","def fit(self, texts):\n print('Processing text and fitting LDA...')\n\n texts = preprocess_text(texts)\n stemmed_texts = [\n list(set(self.tokenizer.stem(text))) for text in texts]\n self.lda_dictionary = Dictionary(stemmed_texts)\n lda_corpus = [\n self.lda_dictionary.doc2bow(text) for text in stemmed_texts]\n self.lda = LdaModel(lda_corpus, num_topics=self.n_topics)\n print('Done.')\n\n return self","def _process_reviews(self):\n params = {'add_special_tokens': True, 'max_length': self._max_len,\n 'return_token_type_ids': False, 'pad_to_max_length': True,\n 'return_attention_mask': True, 'return_tensors': 'pt'}\n\n self._tokens = self._review.apply(self._tokenizer.encode_plus, **params)","def preprocess(self, doc):\n self.stopwords = []\n doc = doc.split()\n new_doc = []\n for word in doc:\n if word.isalpha() and word.lower() not in self.stopwords:\n word = word.lower()\n if self.stemmer != None:\n word = self.stemmer.stem(word)\n new_doc.append(word)\n return new_doc","def analyze_text(self, text, doc_num=None):\n doc_words = frozenset(x[0] for x in text)\n top_ids_in_doc = self.relevant_ids.intersection(doc_words)\n for word_id in top_ids_in_doc:\n self._inverted_index[self.id2contiguous[word_id]].add(self._num_docs)","def perform_parse(self):\n # get folder of pdf files\n folder = QFileDialog.getExistingDirectory(\n parent=self.parent(),\n caption='Get folder with PDF documents to parse'\n )\n if folder:\n # get list of fields and patterns\n field_list = self._get_fields()\n # performing parse\n results = make_parse(folder, field_list)\n self.open_result(results)","def iter_documents(self):\n raise NotImplementedError","def process_augm_data(text_file, dataset_file, augm_dataset_file, doc_num=-1, ctx_size=5 ):\r\n num_lines = 0\r\n num_words = 0\r\n full_counter = Counter()\r\n \r\n vocabulary = []\r\n \r\n # Disable NER and categorisation to lighten processing\r\n nlp = spacy.load('en_core_web_sm', disable=['ner', 'textcat'])\r\n \r\n # If the dataset file does not exist, add a header\r\n if os.path.exists(dataset_file):\r\n dataset = []\r\n else:\r\n dataset = [['focus_word', 'context_word', 'sent_num', 'focus_index', 'context_position']]\r\n if os.path.exists(augm_dataset_file):\r\n augm_dataset = []\r\n else:\r\n augm_dataset = [['synonym', 'context_word', 'sent_num', 'focus_index', 'context_position', 'focus_word']]\r\n \r\n # Open the file with UTF-8 encoding. If a character\r\n # can't be read, it gets replaced by a standard token\r\n with open(text_file, 'r', encoding='utf-8', errors='replace') as f:\r\n print('Cleaning and processing ', text_file)\r\n \r\n # Go through all sentences tokenised by spaCy\r\n # Word pairs are constrained to sentence appearances,\r\n # i.e. no inter-sentence word pairs\r\n for sent_num, raw_sent in enumerate(data.readlines()):\r\n sent = nlp(raw_sent)\r\n \r\n token_list = [token for token in sent]\r\n num_tokens = len(token_list)\r\n \r\n # Skip processing if sentence is only one word\r\n if len(token_list) > 1:\r\n for focus_i, token in enumerate(token_list):\r\n t_pos = token.pos_\r\n \r\n # List of ignored tags\r\n ignore_pos = ['PUNCT', 'SYM', 'X']\r\n \r\n # Temporary list of generated pairs for the current\r\n # focus word. Will be used later when searching for\r\n # synonyms for the focus word\r\n word_pairs = []\r\n augment_pairs = []\r\n \r\n # Only process if focus word is not punctuation\r\n # (PUNCT), symbol (SYM), or unclassified (X), and\r\n # if token is not only a symbol (not caught by spaCy)\r\n if (t_pos not in ignore_pos\r\n and re.sub(r'[^\\w\\s]', '', token.text).strip() != ''):\r\n \r\n # BYPASSED: original formulation, sampling context\r\n # size, from 1 to ctx_size\r\n #context_size = random.randint(1, ctx_size)\r\n context_size = ctx_size\r\n \r\n context_min = focus_i - context_size if (focus_i - context_size >= 0) else 0\r\n \r\n context_max = focus_i + context_size if (focus_i + context_size < num_tokens-1) else num_tokens-1\r\n \r\n focus_word = token.text.lower()\r\n \r\n # Go through every context word in the window\r\n for ctx_i in range(context_min, context_max+1):\r\n # Check that context index is not the same as\r\n # focus, that the context word is not in our\r\n # 'ignore' list, and that the context is not\r\n # white space or punctuation\r\n if (ctx_i != focus_i\r\n and token_list[ctx_i].pos_ not in ignore_pos\r\n and re.sub(r'[^\\w\\s]', '', token_list[ctx_i].text).strip() != ''):\r\n # Changing everything to lower case\r\n # A more principled approach would uppercase\r\n # named entities such as persons, companies\r\n # or countries:\r\n # if token.ent_iob_ != 'O':\r\n # token.text.capitalize()\r\n context_word = token_list[ctx_i].text.lower()\r\n \r\n ctx_pos = ctx_i - focus_i\r\n \r\n # If passes all checks (context different\r\n # from target and neither tagged as)\r\n word_pairs.append([focus_word, context_word,sent_num, focus_i, ctx_pos])\r\n \r\n # If word_pairs is not empty, that means there is\r\n # at least one valid word pair. For every non-stop focus\r\n # word in these pairs, augment the dataset with external\r\n # knowledge bases\r\n if len(word_pairs) > 0 and not token.is_stop:\r\n \r\n # Convert to list of text words for NLTK\r\n text_list = [token.text for token in token_list]\r\n # POS tag with NLTK\r\n nltk_pos = [pos_tag(text_list)]\r\n \r\n # We create nltk_pos with a single sentence\r\n # so we can access it directly. We are accessing:\r\n # - Sentence 0\r\n # - Word number focus index\r\n # - POS tag (second column)\r\n nltk_pos_tag = nltk_pos[0][focus_i][1]\r\n \r\n # If the POS tags of spaCy and NLTK agree\r\n # then continue with the augmentation\r\n if token.tag_ == nltk_pos_tag:\r\n #print('Tags agree for ', nltk_pos[0][focus_i][1])\r\n \r\n # Convert universal POS tags to WordNet types\r\n # https://universaldependencies.org/u/pos/all.html\r\n # (skip proper nouns)\r\n wn_tag_dict = {\r\n 'ADJ': wn.ADJ,\r\n 'ADV': wn.ADV,\r\n 'NOUN': wn.NOUN,\r\n #'PROPN': wn.NOUN,\r\n 'VERB': wn.VERB\r\n }\r\n \r\n # If the POS tag is part of the\r\n # pre-specified tags\r\n if token.pos_ in wn_tag_dict:\r\n synsets = wn.synsets(focus_word, wn_tag_dict[token.pos_])\r\n \r\n # Keep track of accepted synonyms,\r\n # to avoid adding the same synonym\r\n # multiple times to the dataset\r\n accepted_synonyms = []\r\n \r\n # Cycle through the possible synonym\r\n # sets in WordNet\r\n for syn_num, syn in enumerate(synsets):\r\n # Cycle through all the lemmas in\r\n # every synset\r\n for lem in syn.lemmas():\r\n # Get the synonym in lowercase\r\n synonym = lem.name().lower()\r\n \r\n # Removes multi-word synonyms\r\n # as well as repeated synonyms\r\n if not re.search('[-_]+', synonym) and focus_word != synonym and synonym not in accepted_synonyms:\r\n accepted_synonyms.append(synonym)\r\n \r\n for fw, c, sn, fi, cp, _ in word_pairs:\r\n augment_pairs.append([synonym, c, sn, fi, cp, fw])\r\n \r\n if len(word_pairs) > 0:\r\n dataset.extend(word_pairs)\r\n \r\n if len(augment_pairs) > 0:\r\n augm_dataset.extend(augment_pairs)\r\n \r\n\r\n if len(dataset) > 0: print('Original dataset: ', len(dataset), len(dataset[0]))\r\n if len(augm_dataset) > 0: print('Augmented dataset: ', len(augm_dataset), len(augm_dataset[0]))\r\n \r\n # Look for the dataset file, if it doesn't exist create it ('a+')\r\n with open(dataset_file, 'a+', newline='') as f:\r\n wr = csv.writer(f, quoting=csv.QUOTE_ALL)\r\n wr.writerows(dataset)\r\n \r\n with open(augm_dataset_file, 'a+', newline='') as f:\r\n wr = csv.writer(f, quoting=csv.QUOTE_ALL)\r\n wr.writerows(augm_dataset) \r\n \r\n return True","def process(self, terms):\n for entry in self.files:\n try:\n logger.info('file - {0}'.format(entry.path))\n\n # notional output file path\n path_sentences = self.path.joinpath('{0}.csv'.format(entry.path.stem))\n path_summary = self.path.joinpath('{0}-summary.csv'.format(entry.path.stem))\n logger.info('will save to - {0}'.format(path_sentences.resolve()))\n\n reports = self.inspect_doc(entry, terms)\n\n # receiving a list of dicts\n # therefore pandas can package into a useful outcome\n if len(reports) > 0:\n frame_sentences = pd.DataFrame(reports)\n\n frame_sentences = frame_sentences[['page', 'term', 'sentence']]\n logger.info('saving sentence file to - {0}'.format(path_sentences.resolve()))\n frame_sentences.to_csv(str(path_sentences.resolve()))\n \n frame_summary = frame_sentences.pivot_table(\n index='page',\n columns='term',\n aggfunc='size',\n fill_value=0\n )\n logger.info('saving summary file to - {0}'.format(path_sentences.resolve()))\n frame_summary.to_csv(str(path_summary.resolve()))\n\n\n except Exception as e:\n logger.error(e)","def add_document_lists(self, docs):\n for sent in docs:\n sent = map(self.process_token, sent)\n self._token_count.update(sent)","def process_commands():\n # Parse and handle each different command\n args = parse_arguments()\n\n pdfsplit.pdf_split(args.file, args.pages, args.offset)","def ie_preprocess(document):\n sentences = nltk.sent_tokenize(document) #NLTK default sentence segmenter\n #print sentences # sentences are segmented\n sentences = [nltk.word_tokenize(sent) for sent in sentences] # NLTK word tokenizer \n #print sentences # sentences are tokenized\n sentences = [nltk.pos_tag(sent) for sent in sentences] # NLTK POS tagger \n #print sentences # sentences are POS tagged\n return sentences","def parse_documents():\n\n\tcount_before = control.find().count()\n\n\tprint \"There are currently %i unprocessed records.\" % count_before\n\n\t#dispatch\n\t# executor = concurrent.futures.ThreadPoolExecutor(10)\n\t# futures = [executor.submit(analyze_message, document) for document in control.find()]\n\t# concurrent.futures.wait(futures)\n\n\tfor document in control.find():\n\t\tanalyze_message(document)\n\n\tcount_after = control.count()\n\tprint \"There are now %i stored records.\" % control.count()","def __call__(self, *input_data):\n \n pb_ann = Any()\n pb_ann.Pack(annotation_to_protobuf.convert_annotation(input_data))\n request = annotation_pb2.ProcessRequest(pipeline_name = self._pipeline_name, \n input_annotations = pb_ann)\n \n response = self._stub.process(request)\n return annotation_from_protobuf.convert_annotation(response.output_annotations)","def process(self, request, context):\n \n logger.info('Processing incoming request with \"{}\"...'.format(request.pipeline_name))\n ppl = self._ppls[request.pipeline_name]\n input_annotations = annotation_from_protobuf.convert_annotation(request.input_annotations)\n res = ppl(*input_annotations)\n logger.info('Done.')\n \n pb_res = annotation_to_protobuf.convert_annotation(res)\n reply = pb.ProcessReply()\n reply.output_annotations.Pack(pb_res)\n \n return reply","def annotateFiles(listOfFilesPath=None, annotatedOutputFolder=u'./002manuallyAnnotated/', dumpSP=True):\n referencePathLine = []\n listOfAnnotations = []\n # get the list containing the file paths\n if listOfFilesPath is None:\n listOfFilesPath = randomlySelectNDocsFromPath(b000path.getBtFolderPath(flagFolder=None), n=100)\n makeLocalFolderPaths(listOfFilesPath)\n elif type(listOfFilesPath) is str:\n if u'.json' in listOfFilesPath:\n listOfFilesPath = utilsOs.openJsonFileAsDict(listOfFilesPath)\n else:\n listOfFilesPath = [listOfFilesPath]\n # get rid of the files we have already annotated\n if utilsOs.theFileExists(u'{0}sampleReference.tsv'.format(annotatedOutputFolder)):\n refLines = utilsOs.readAllLinesFromFile(u'{0}sampleReference.tsv'.format(annotatedOutputFolder),\n noNewLineChar=True)\n annotatedFiles = set([line.split(u'\\t')[0] for line in refLines])\n listOfFilesPath = [file for file in listOfFilesPath if file not in annotatedFiles]\n # print the annotator cheat sheet\n printCheatSheet()\n # open each file in EN and FR and show it in the terminal\n for filePath in listOfFilesPath:\n print(u'############# {0} ##############'.format(filePath.replace(u'/data/rali8/Tmp/rali/bt/burtrad/corpus_renamed/', u'')))\n # get the path for the source and target\n fileSourcePath = u'{0}.fr'.format(filePath) if u'fr-en' in filePath else u'{0}.en'.format(filePath)\n fileTargetPath = u'{0}.en'.format(filePath) if u'fr-en' in filePath else u'{0}.fr'.format(filePath)\n with open(fileSourcePath) as fileSource:\n with open(fileTargetPath) as fileTarget:\n # show the context of the annotated sentence\n beforeSentSource = fileSource.readline()\n duringSentSource = fileSource.readline()\n beforeSentTarget = fileTarget.readline()\n duringSentTarget = fileTarget.readline()\n # annotate the first sentence pair\n listOfAnnotations = annotateFirstSP(beforeSentSource, duringSentSource, beforeSentTarget,\n duringSentTarget, listOfAnnotations, lineLength=137)\n # save the reference\n # if the filepath is the reference\n if u'burtrad' in filePath:\n referencePathLine.append(u'{0}\\t{1}'.format(filePath, 0))\n # otherwise we get it from a reference file\n else:\n with open(u'{0}.tsv'.format(filePath)) as refFile:\n refLns = [ln.replace(u'\\n', u'') for ln in refFile.readlines()]\n referencePathLine.append(refLns[0])\n # dump the first SP\n if dumpSP is True:\n enSent = beforeSentSource if u'.en' in fileSourcePath else beforeSentTarget\n frSent = beforeSentTarget if u'.en' in fileSourcePath else beforeSentSource\n utilsOs.appendLineToFile(enSent, u'{0}sample.en'.format(annotatedOutputFolder), addNewLine=False)\n utilsOs.appendLineToFile(frSent, u'{0}sample.fr'.format(annotatedOutputFolder), addNewLine=False)\n duringIndex = 1\n # for each line\n while duringSentSource or duringSentTarget:\n # get the correct terminal line length\n lineLength = 137-len(str(len(listOfAnnotations)+1))\n # get the sentences\n afterSentSource = fileSource.readline()\n afterSentTarget = fileTarget.readline()\n # color in red the during lines\n redDuringSource = u'\\033[1;31m{0}\\033[0m'.format(duringSentSource)\n redDuringTarget = u'\\033[1;31m{0}\\033[0m'.format(duringSentTarget)\n # print the sentences\n print(u'{0} - {1}'.format(len(listOfAnnotations)-1, beforeSentSource))\n print(u'{0} - {1}'.format(len(listOfAnnotations)-1, beforeSentTarget))\n print(u'{0} - {1}'.format(len(listOfAnnotations), redDuringSource))\n print(u'{0} - {1}'.format(len(listOfAnnotations), redDuringTarget))\n print(u'{0} - {1}'.format(len(listOfAnnotations)+1, afterSentSource))\n print(u'{0} - {1}'.format(len(listOfAnnotations)+1, afterSentTarget))\n print()\n # count if the lines that take the space of 2 lines\n longLines = getNbLongLines([beforeSentSource, beforeSentTarget, duringSentSource,\n duringSentTarget, afterSentSource, afterSentTarget], lineLength)\n # get the first part of the annotation (aligned or not)\n annotatorGeneralInput = input(u'Aligned-Misaligned annotation: ')\n # make sure to have the right general annotation\n while True:\n if annotatorGeneralInput in [u'0', u'1', u'0.0', u'0.1', u'0.2',\n u'1.0', u'1.1', u'1.2', u'1.3', u'1.4', u'c', u'correction']:\n break\n else:\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n annotatorGeneralInput = input(u'Repeat annotation: ')\n if annotatorGeneralInput in [u'c', u'correct']:\n annotatorGeneralInput, listOfAnnotations = correctionToAnnotation(listOfAnnotations)\n # if we still need to specify what type of alignment or misalignment\n if annotatorGeneralInput in [u'0', u'1']:\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n # get the second part of the annotation (aligned or not)\n annotatorSpecificInput = input(u'Specific type annotation: ')\n typeAnswers = [u'0', u'1', u'2'] if annotatorGeneralInput == 0 else [u'0', u'1', u'2', u'3', u'4']\n # make sure to have the right specific annotation\n while True:\n if annotatorSpecificInput in typeAnswers:\n break\n else:\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\n annotatorSpecificInput = input(u'Repeat type annotation: ')\n # save to the list of annotations\n listOfAnnotations.append(float(u'{0}.{1}'.format(annotatorGeneralInput, annotatorSpecificInput)))\n # if the right answer was given in the right format right away\n else:\n # save to the list of annotations\n listOfAnnotations.append(float(annotatorGeneralInput))\n # remove the lines from the terminal before getting to the next pair\n utilsOs.moveUpAndLeftNLines(14+longLines, slowly=False)\n # erase all remainder of the previous sentences and go back up again\n for e in range(14+longLines):\n print(u' '*(lineLength+4))\n utilsOs.moveUpAndLeftNLines(14 + longLines, slowly=False)\n # next line source\n beforeSentSource = duringSentSource\n duringSentSource = afterSentSource\n # next line target\n beforeSentTarget = duringSentTarget\n duringSentTarget = afterSentTarget\n # append the reference to the file\n # if the filepath is the reference\n if u'burtrad' in filePath:\n referencePathLine.append(u'{0}\\t{1}'.format(filePath, duringIndex))\n # otherwise we get it from a reference file\n else:\n with open(u'{0}.tsv'.format(filePath)) as refFile:\n refLns = [ln.replace(u'\\n', u'') for ln in refFile.readlines()]\n referencePathLine.append(refLns[duringIndex])\n # add 1 to index\n duringIndex += 1\n # dump the file line by line, to be sure in case of error\n # dump the reference\n utilsOs.dumpRawLines(referencePathLine, u'{0}sampleReference.tsv'.format(annotatedOutputFolder),\n addNewline=True, rewrite=True)\n # dump the annotation\n utilsOs.dumpRawLines(listOfAnnotations, u'{0}sampleAnnotation.tsv'.format(annotatedOutputFolder),\n addNewline=True, rewrite=True)\n # dump the SP\n if dumpSP is True:\n enSent = beforeSentSource if u'.en' in fileSourcePath else beforeSentTarget\n frSent = beforeSentTarget if u'.en' in fileSourcePath else beforeSentSource\n utilsOs.appendLineToFile(enSent, u'{0}sample.en'.format(annotatedOutputFolder), addNewLine=False)\n utilsOs.appendLineToFile(frSent, u'{0}sample.fr'.format(annotatedOutputFolder), addNewLine=False)\n # clear part of terminal\n utilsOs.moveUpAndLeftNLines(2, slowly=False)","def process_corpus(data_dir, feature_source):\n raw_dir = os.path.join(data_dir, 'text/', feature_source)\n stem_dir = os.path.join(data_dir, 'text/stemmed_{0}/'.format(feature_source))\n proc_dir = os.path.join(data_dir, 'text/cleaned_{0}/'.format(feature_source))\n spell_file = os.path.join('/home/data/nbc/athena/athena-data/misc/english_spellings.csv')\n spell_df = pd.read_csv(spell_file, index_col='UK')\n spell_dict = spell_df['US'].to_dict()\n\n # Defines functions that are from the NLTK, I assume?\n stemmer = EnglishStemmer()\n test_stemmer = PorterStemmer()\n\n # Cycles through each raw .txt file\n for file_ in glob(os.path.join(raw_dir, '*.txt')):\n filename = os.path.basename(file_)\n print('Processing {0}'.format(filename))\n with open(file_, 'rb') as fo:\n text = fo.read()\n\n text = text.decode('utf8', 'ignore').encode('ascii', 'ignore')\n\n # Clean text\n text = abbr.clean_str(text)\n\n # Detect and expand abbreviations\n text = abbr.expandall(text)\n\n # Remove periods (for abbreviations)\n text = text.replace('.', '')\n\n # Replace British words with American words.\n pattern = re.compile(r'\\b(' + '|'.join(spell_dict.keys()) + r')\\b')\n text = pattern.sub(lambda x: spell_dict[x.group()], text)\n\n # Defines stem_list which will be a list of all the words in the file,\n # not including spaces\n stem_list = []\n for word in text.split():\n # Use Porter stemmer to test for string unicode encoding, then use\n # English stemmer to perform stemming\n try:\n ' '.join(['kdkd', test_stemmer.stem(word), 'kdkd'])\n except:\n word = word.decode('utf8', 'ignore').encode('ascii', 'ignore')\n stem_list.append(stemmer.stem(word))\n\n # Writes the stem_list\n with open(os.path.join(stem_dir, filename), 'wb') as fo:\n fo.write(' '.join(stem_list))\n\n # Writes the processed text\n with open(os.path.join(proc_dir, filename), 'wb') as fo:\n fo.write(text)","def train(\n train_texts: List[str],\n train_labels: List[str],\n pretrain_params: Any = None) -> Any:\n train_texts = preprocessing(train_texts)\n train_tokenized_texts = text_to_tokens(train_texts)\n\n train_pos = [train_tokenized_texts[i] for i in range(len(train_labels)) if train_labels[i] == 'pos']\n train_neg = [train_tokenized_texts[i] for i in range(len(train_labels)) if train_labels[i] == 'neg']\n \n cnt_pos_docs = len(train_pos)\n cnt_neg_docs = len(train_neg)\n\n\n all_words_freq = defaultdict(int)\n all_words = set()\n\n pos_dict = defaultdict(int)\n neg_dict = defaultdict(int)\n sum_len_pos = 0\n sum_len_neg = 0\n\n for text in train_pos:\n for token in text:\n all_words.add(token)\n all_words_freq[token] += text[token]\n pos_dict[token] += text[token]\n sum_len_pos += text[token]\n \n for text in train_neg:\n for token in text:\n all_words.add(token)\n all_words_freq[token] += text[token]\n neg_dict[token] += text[token]\n sum_len_neg += text[token]\n \n alpha = 1 #For additive smoothing\n M = len(all_words)\n sum_len = 0\n print(\"____________\")\n print(\"Sum of text lens\", sum_len)\n print(\"____________\")\n print(\"Words quantity\", M)\n print(\"____________\")\n\n token_probs_pos = defaultdict(int)\n token_probs_neg = defaultdict(int)\n print(\"Calculate probablity for\", M, \"tokens\")\n\n i = 0\n for token in all_words:\n if (i % 5000 == 0):\n print(\"__________\")\n print(\"Calculated\", i, \"tokens\")\n print(\"__________\")\n token_probs_pos[token] = (alpha + pos_dict[token]) / (alpha * M + sum_len_pos)\n token_probs_neg[token] = (alpha + neg_dict[token]) / (alpha * M + sum_len_neg)\n i += 1\n \n return {\n \"token_probs_pos\": token_probs_pos,\n \"token_probs_neg\": token_probs_neg,\n \"all_words\": all_words,\n \"sum_len_pos\": sum_len_pos,\n \"sum_len_neg\": sum_len_neg,\n \"cnt_pos_docs\": cnt_pos_docs,\n \"cnt_neg_docs\": cnt_pos_docs,\n \"pos_dict\": pos_dict,\n \"neg_dict\": neg_dict\n }","def learn(self, documents, labels):\n raise NotImplementedError('FeatureExtractorBase:learn(self, text_list) is not defined')","def postpare_anno(infolder, outfolder, mode=\"fl\"):\n\tprint(\"Starting...\")\n\t\n\tif not os.path.exists(infolder):\n\t\traise ValueError(\"The input folder could not be found.\")\n\t\t\n\tin_temp = os.path.join(infolder, \"temp\")\n\tin_anno = os.path.join(infolder, \"annotated_temp\")\n\t\n\tif not os.path.exists(in_temp):\n\t\traise ValueError(\"The folder 'temp' could not be found inside the input folder.\")\n\tif not os.path.exists(in_anno):\n\t\traise ValueError(\"The folder 'annotated_temp' could not be found inside the input folder.\")\n\tif not os.path.exists(outfolder):\n\t\tos.makedirs(outfolder)\n\t\t\n\tfilecounter = 0\t\n\n\t# fetch annotated snippets for each TEI template file\n\tfor filepath in glob.glob(os.path.join(in_temp, \"*.xml\")):\n\t\tprint(\"doing file \" + filepath)\n\t\tfilecounter+= 1\n\t\tfn = os.path.basename(filepath)\n\t\tannofolder = os.path.join(Path(os.path.join(infolder, \"annotated_temp\")).as_uri(), \"\")\n\t\t# which annotation mode are we in?\n\t\tannomode = mode\n\t\t\n\t\tparser = etree.XMLParser(encoding=\"UTF-8\")\n\t\tparser.resolvers.add(FileResolver())\n\t\t\n\t\tdoc = etree.parse(filepath, parser)\n\t\txslt_root = etree.parse(io.StringIO(xslt_joinDIVs), parser)\n\t\t\n\t\ttransform = etree.XSLT(xslt_root)\n\t\t\n\t\tresult_tree = transform(doc, annofolder= \"'\" + annofolder + \"'\", mode= \"'\" + annomode + \"'\")\n\t\tresult = str(result_tree)\n\t\t\n\t\t# save the results\n\t\twith open(os.path.join(outfolder, fn), \"w\") as output:\n\t\t\toutput.write(result)\n\t\n\tprint(\"Done. \" + str(filecounter) + \" files treated.\")","def annotate_document(self, document, analyzer_steps):\n assert isinstance(analyzer_steps, list)\n if isinstance(document, dict):\n document = json.dumps(document)\n r = requests.post('/'.join([self.base_url,\n self.ANNOTATE_ENDPOINT,\n '+'.join(analyzer_steps)]),\n data=document,\n headers={'Content-Type': 'application/json'})\n return(r.json())","def test_pipeline_data(self, doc_list):\n summaries = []\n for item in doc_list:\n summaries.append(self.preprocess(item))\n return summaries","def process_raw(self):\n\n for i in range(self._length):\n word = self._words[i].lower()\n if word not in self._data:\n self._data[word] = Word(word)\n self._data[word].add_occurrence(i / self._length)\n\n # Notify view\n for view in self._views:\n view.notify_progress(1 / self._length)\n\n # process\n for word in self._data:\n self._data[word].update()\n\n # allow access\n self._canOperate = True"],"string":"[\n \"def process(self, doc):\\n # don't try to process null notes\\n if not doc[1]:\\n if self.verbose:\\n print(\\\"Error segmenting doc\\\",doc[0])\\n return []\\n # odd notes may throw an error. Just continue rather than stopping the entire process\\n try:\\n sentences = self.sentence_tokenizer.segToSentenceSpans(doc[1])\\n except KeyError:\\n if self.verbose:\\n print(\\\"Error segmenting doc\\\",doc[0])\\n return []\\n\\n #context_doc = pyConTextGraph.ConTextDocument() # ConTextDoc not needed for simple usage\\n\\n doc_annots = list()\\n\\n for sentence in sentences:\\n # run sentence tokenizer on input text, return the spans\\n sentence_text = doc[1][sentence.begin:sentence.end]\\n # process every sentence by adding markup\\n markup = pyConTextGraph.ConTextMarkup()\\n markup.setRawText(sentence_text)\\n markup.cleanText()\\n # apply targets and modifiers\\n markup.markItems(self.targets, mode=\\\"target\\\")\\n markup.markItems(self.modifiers, mode=\\\"modifier\\\")\\n # address scope of modifiers to targets, remove inactive modifiers and self-modifying relationships\\n markup.pruneMarks()\\n markup.applyModifiers()\\n markup.pruneSelfModifyingRelationships()\\n markup.dropInactiveModifiers()\\n\\n marked_targets = markup.getMarkedTargets()\\n for marked_target in marked_targets:\\n modifiers = markup.getModifiers(marked_target)\\n if not modifiers:\\n span = (sentence.begin+marked_target.getSpan()[0],sentence.begin+marked_target.getSpan()[1])\\n if self.mode == 'combined':\\n annot = (doc[0], marked_target.getPhrase(), span[0], span[1], marked_target.getCategory()[0]+'_unspecified', marked_target.getCode())\\n elif self.mode == 'separate':\\n annot = (doc[0], marked_target.getPhrase(), span[0], span[1], marked_target.getCategory()[0], 'unspecified', marked_target.getCode())\\n if annot not in doc_annots:\\n doc_annots.append(annot)\\n else:\\n for modifier in modifiers:\\n if marked_target.getSpan()[0] < modifier.getSpan()[0]:\\n span = (sentence.begin+marked_target.getSpan()[0],sentence.begin+modifier.getSpan()[1])\\n else:\\n span = (sentence.begin+modifier.getSpan()[0],sentence.begin+marked_target.getSpan()[1])\\n if self.mode == 'combined':\\n annot = (doc[0], doc[1][span[0]:span[1]], span[0], span[1], marked_target.getCategory()[0]+'_'+modifier.getCategory()[0], marked_target.getCode())\\n elif self.mode == 'separate':\\n annot = (doc[0], doc[1][span[0]:span[1]], span[0], span[1], marked_target.getCategory()[0], modifier.getCategory()[0], marked_target.getCode())\\n if annot not in doc_annots:\\n doc_annots.append(annot)\\n\\n #context_doc.addMarkup(markup)\\n\\n return doc_annots\",\n \"def run(self, mapping={}, *args, **kwargs):\\n self.processed = 0\\n for batch in self._process_by_batch(self.load(*args, **kwargs)):\\n batch = list(map(lambda doc: self._apply_mapping(doc, mapping), batch))\\n for doc in batch:\\n self._ingest(iterable=doc, doctype=doc[\\\"doctype\\\"])\\n self.processed += 1\\n logger.info(\\\"Added {} documents to the database.\\\".format(self.processed))\",\n \"def process_document(self, document, **kwargs):\\n \\n start = time.time()\\n\\n if self._log_file is not None:\\n map_annotations_logger.addHandler(file_handler(self._log_file))\\n map_annotations_logger.setLevel(self._log_level)\\n \\n ref_annotation = document.annotation(self._annotation_name)\\n ref_annotations = ref_annotation.annotations\\n values = set([a.value for a in ref_annotations])\\n new_annotations = [Tag(self._mapping.get(annotation.value, annotation.value), annotation.lb, annotation.ub) for annotation in ref_annotations if self._mapping.get(annotation.value, None) != u\\\"\\\"]\\n \\n document.add_annotation(Annotation(self._annotation_name, reference=ref_annotation.reference, annotations=new_annotations))\\n \\n laps = time.time() - start\\n map_annotations_logger.info('in %s', timedelta(seconds=laps))\",\n \"def add_documents(self, docs):\\n if 'sentences' in docs:\\n for sent in docs.sentences:\\n sent = map(self.process_token, [t for t in sent.tokens if not t.is_stopword])\\n self._token_count.update(sent)\\n\\n else:\\n sent = list(map(self.process_token, [t for t in docs.tokens if not t.is_stopword]))\\n self._token_count.update(sent)\",\n \"def processed_bulk(self, pipeline):\\n docs = [Document([], text=t) for t in EN_DOCS]\\n return pipeline(docs)\",\n \"def multiple_document_processing(self) -> List:\\n batch_list = []\\n for doc, idx in self.__documents:\\n entities_idx = {'idx': idx}\\n entities_result = self.create_entity(document=doc)\\n word_cleaned = self.clean_words(doc)\\n entities_idx[self.key_spacy_text] = str(word_cleaned)\\n entities_idx.update(entities_result)\\n batch_list.append(entities_idx)\\n return batch_list\",\n \"def pre_process(self, documents):\\n\\n return documents\",\n \"def processed_doc(self, pipeline):\\n return [pipeline(text) for text in EN_DOCS]\",\n \"def parse(self, doc):\\n self.preprocessor.preprocess(doc)\\n\\n for extractor in self.extractors:\\n extractor.extract(doc)\\n\\n return doc\",\n \"def _process_texts(self, texts, generator = False):\\n wordlists = [\\n x for x in [self.tokenizer.tokenize(doc) for doc in texts] if x != []\\n ]\\n self.vocab = corpora.Dictionary(wordlists)\\n self.has_vocab = True\\n self.corpus = (self.vocab.doc2bow(doc) for doc in wordlists)\\n if not generator:\\n self.corpus = list(self.corpus)\\n self.has_corpus = True\",\n \"def process(self):\\n self.extract()\\n self.transform()\\n self.load()\",\n \"def pipe(self, docs: Iterable[Doc]) -> Iterable[Doc]:\\n\\n stream1, stream2 = itertools.tee(docs, 2)\\n\\n # Remove existing entities from the document\\n stream2 = (self.create_new_doc(d) for d in stream2)\\n \\n # And run the model\\n for _, proc in self.model.pipeline:\\n stream2 = proc.pipe(stream2)\\n \\n for doc, doc_copy in zip(stream1, stream2):\\n\\n doc.spans[self.name] = []\\n\\n # Add the annotation\\n for ent in doc_copy.ents:\\n doc.spans[self.name].append(Span(doc, ent.start, ent.end, ent.label_))\\n\\n yield doc\",\n \"def process(self, fulltext=None):\\r\\n raise NotImplementedError(\\\"Please implement this in your importer\\\")\",\n \"def preprocess_docs():\\n\\n print(\\\"Getting started!\\\")\\n stopwords.populate_stopwords(NLP, STOPWORD_URL)\\n\\n print(str.format(\\\"Using data dir:{}\\\", DATA_DIR))\\n\\n csv_file = open(os.path.join(DATA_DIR, 'PDFs.csv'))\\n reader = csv.reader(csv_file, 'excel')\\n rows = list(reader)\\n\\n filenames = [_get_filename(row) for row in rows]\\n\\n pool = Pool(multiprocessing.cpu_count())\\n\\n try:\\n pool.map(_get_item, rows)\\n pool.map(pdf.extract_text, filenames)\\n docs = pool.map(_extract_questions, rows)\\n docs = [d for d in docs if d is not None]\\n\\n _find_similar(docs, simdoc=compare.compare_doc_keywords)\\n\\n for doc in docs:\\n if doc is None:\\n continue\\n doc.save_json()\\n\\n except KeyboardInterrupt:\\n pool.terminate()\\n print(\\\"You cancelled the program!\\\")\\n sys.exit(1)\\n\\n print(\\\"Done\\\")\",\n \"def clean_all_documents(cls):\\n for index, text in enumerate(cls.documents):\\n text_processed = cls.clean_document(text)\\n cls.processed_documents.append(text_processed)\",\n \"def process(self):\\n # tokenize, then filter & otherwise process words in each document\\n # using steps in preprocess_doc()\\n\\n all_posts_count = self.postman.posts_read.find({'subreddit': self.postman.subreddit}).count()\\n\\n for post_idx, post in enumerate(self.postman.posts_read.find({'subreddit': self.postman.subreddit})):\\n # preprocess the post and add the new words to the corpus\\n new_words = self.preprocess_post(post)\\n self.corpus.update(new_words)\\n\\n # print on every Nth post so you know it's alive\\n if post_idx % 100 == 0:\\n print 'done post %i out of %i' % (post_idx, all_posts_count)\\n\\n #TODO:\\n print 'word count and other corpus-level filters not implemented, skipping...'\\n # corpus-level filtering\\n # get rid of invalid documents (based on word count)\\n # self.corpus = [doc for doc in self.corpus if self.doc_has_valid_wc(doc)]\\n # print 'filtered out %i out of %i documents' % (pre_corpus_len - len(self.corpus), pre_corpus_len)\\n # stem or lemmatize\\n # if self.stem_or_lemma_callback:\\n # self.corpus = [self.perform_stem_or_lem(doc) for doc in self.corpus]\\n # for chaining\\n #######################################################\\n\\n return self\",\n \"def process_docs(directory, vocab):\\n for filename in listdir(directory):\\n if not filename.endswith('.txt'):\\n continue\\n path = directory + '/' + filename\\n add_doc_to_vocab(path, vocab)\",\n \"def annotate(self,corpus):\\n\\n\\t\\tassert corpus.parsed == True, \\\"Corpus must already be parsed before entity recognition\\\"\\n\\n\\t\\tfor doc in corpus.documents:\\n\\t\\t\\tentityCount = len(doc.entities)\\n\\t\\t\\tfor sentence in doc.sentences:\\n\\t\\t\\t\\twords = [ t.word for t in sentence.tokens ]\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tfor i,t in enumerate(sentence.tokens):\\n\\t\\t\\t\\t\\tif not isNumber(t.word):\\n\\t\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\t\\t\\n\\t\\t\\t\\t\\tsourceEntityID = \\\"T%d\\\" % (entityCount+1)\\n\\t\\t\\t\\t\\ttext = doc.text[t.startPos:t.endPos]\\n\\t\\t\\t\\t\\tloc = [i]\\n\\n\\t\\t\\t\\t\\te = kindred.Entity('quantity',text,[(t.startPos,t.endPos)],sourceEntityID=sourceEntityID)\\n\\t\\t\\t\\t\\tdoc.addEntity(e)\\n\\t\\t\\t\\t\\tsentence.addEntityAnnotation(e,loc)\\n\\t\\t\\t\\t\\tentityCount += 1\",\n \"def ingest_annotations(directory, annotation_type):\\n in_golden = annotation_type == \\\"manual\\\"\\n doc_type = None\\n issue_number = None\\n bid = None\\n annotated_pages = []\\n file_prefix = \\\"page\\\"\\n try:\\n bid = directory.split('/')[len(directory.split('/'))-1:][0]\\n record = Metadata.objects(bid=bid).first()\\n assert record is not None\\n except Exception as e:\\n bid,issue_number = directory.split('/')[len(directory.split('/'))-2:]\\n record = Metadata.objects(bid=bid).first()\\n try:\\n doc_type = record[\\\"type_document\\\"]\\n except Exception as e:\\n doc_type = None\\n logger.warning('The record for %s is not in MongoDB'%bid)\\n try:\\n page_numbers = {int(os.path.basename(fname).replace(\\\"page-\\\",\\\"\\\").split('.')[0]):os.path.basename(fname).split('.')[0] \\n for fname in glob.glob(\\\"%s/*.ann\\\"%directory)}\\n except Exception as e:\\n page_numbers = {int(os.path.basename(fname).replace(\\\"image-\\\",\\\"\\\").split('.')[0]):os.path.basename(fname).split('.')[0] \\n for fname in glob.glob(\\\"%s/*.ann\\\"%directory)}\\n file_prefix = \\\"image\\\"\\n # TODO: handle the exception of document not in the DB\\n logger.info(\\\"Ingesting the annotations from directory \\\\\\\"%s\\\\\\\"\\\"%directory)\\n if(issue_number != None):\\n logger.info(\\\"Found document %s-%s [type=%s]\\\"%(bid,issue_number,doc_type))\\n else:\\n logger.info(\\\"Found document %s [type=%s]\\\"%(bid,doc_type))\\n try:\\n if(doc_type==\\\"journal\\\"):\\n doc = LBDocument.objects(internal_id=\\\"%s-%s\\\"%(bid,issue_number)).first()\\n if doc is None:\\n doc = LBDocument.objects(internal_id=\\\"%s-%s\\\"%(bid, convert_issue_number(issue_number))).first()\\n elif(doc_type==\\\"monograph\\\"):\\n doc = LBDocument.objects(bid=bid).first()\\n logger.info(\\\"%s has %i pages\\\"%(doc.internal_id, len(doc.pages)))\\n for page_n in sorted(page_numbers):\\n logger.debug(\\\"Reading in annotations for page %i from file %s/ %s\\\"%(page_n,directory,page_numbers[page_n]))\\n entities_with_continuations = {}\\n entities,relations = read_ann_file(page_numbers[page_n],directory)\\n fulltext = codecs.open(\\\"%s/%s.txt\\\"%(directory,page_number2image_name(page_n, string=file_prefix)),'r', 'utf-8').read()\\n line_breaks = find_newlines(fulltext)\\n #logger.info(\\\"Found %i entities, %i relation in %s\\\"%(len(entities), len(relations), directory))\\n doc_id = \\\"%s-%s-%s\\\"%(bid, issue_number, page_numbers[page_n])\\n try:\\n page = next((page for page in doc.pages if page.single_page_file_number==page_n))\\n if(page[\\\"in_golden\\\"]==True):\\n annotated_pages.append(page.id)\\n logger.info(\\\"found %i entities in %s (p. %i)\\\"%(len(entities),doc_id,page_n))\\n logger.info(\\\"found %i relations in %s (p. %i)\\\"%(len(relations.keys()),doc_id,page_n))\\n \\\"\\\"\\\"\\n Parse the `ContainedIN` relations and identify annotations that should be merged together.\\n IDs of candidates for merging are stored in a dict(), e.g. {\\\"T1\\\":[\\\"T2\\\",\\\"T4\\\"]}\\n \\\"\\\"\\\"\\n entities_with_continuations = {}\\n if len(relations.keys())>0:\\n for relation_key in relations:\\n args = relations[relation_key][\\\"arguments\\\"]\\n if args[0] in entities_with_continuations:\\n entities_with_continuations[args[0]].append(args[1])\\n else:\\n entities_with_continuations[args[0]] = [args[1]]\\n logger.debug(\\\"(%s-%s) entities to be merged: %s\\\"%(doc_id,page_n,entities_with_continuations))\\n \\\"\\\"\\\"\\n Create the annotations (`entities` dict). \\n Later they will be stored into the MongoDB\\n \\\"\\\"\\\"\\n for entity in entities:\\n entities[entity][\\\"ingestion_timestamp\\\"] = datetime.utcnow()\\n entities[entity][\\\"annotation_ingester_version\\\"] = __version__\\n entities[entity][\\\"entity_type\\\"] = entities[entity][\\\"entity_type\\\"].lower( )\\n entities[entity][\\\"filename\\\"] = \\\"%s/%s%s\\\"%(directory,page_numbers[page_n],\\\".ann\\\")\\n if(doc_type==\\\"journal\\\"):\\n entities[entity][\\\"bid\\\"] = bid\\n entities[entity][\\\"pageid\\\"] = doc_id\\n elif(doc_type==\\\"monograph\\\"):\\n entities[entity][\\\"bid\\\"] = bid\\n entities[entity][\\\"pageid\\\"] = \\\"%s-%s\\\"%(bid,page_numbers[page_n])\\n entities[entity][\\\"container\\\"] = entities[entity][\\\"entity_type\\\"] in container_annotations\\n # ref to page_id (from content_loader) ✓\\n for position in entities[entity][\\\"positions\\\"]:\\n line_number = find_linenumber_for_string(position[\\\"start\\\"],position[\\\"end\\\"], line_breaks)\\n logger.debug(\\\"%s is found at line %s\\\"%(entity,line_number))\\n position[\\\"line_n\\\"] = line_number\\n position[\\\"page_id\\\"] = page.id\\n positions_by_offset = sorted(entities[entity][\\\"positions\\\"]\\n ,key=lambda position: position['start'])\\n entities[entity][\\\"positions\\\"] = sorted(positions_by_offset\\n , key=lambda position: Page.objects(id=position['page_id']).first().single_page_file_number)\\n logger.debug(\\\"Annotations %s %s\\\"%(entity,entities[entity]))\\n \\\"\\\"\\\"\\n Now take the candidates for merging identified above and populate the annotations.\\n Still nothing is saved into MongoDB at this stage.\\n \\\"\\\"\\\"\\n for ann_id in entities_with_continuations:\\n try:\\n logger.debug(\\\"Starting to merge SP and SF entities into meta-annotations (%s-%s)\\\"%(doc_id, page_n))\\n logger.debug(\\\"%s will be merged with %s\\\"%(ann_id,\\\"+\\\".join(entities_with_continuations[ann_id])))\\n top_entity_types = \\\"_\\\".join([entities[ann_id][\\\"entity_type\\\"]]+[entities[annid][\\\"entity_type\\\"] \\n for annid in entities_with_continuations[ann_id]])\\n logger.debug(\\\"%s\\\"%top_entity_types)\\n new_entity = copy.deepcopy(entities)[ann_id] \\n #container = True \\n new_entity[\\\"ann_id\\\"] = \\\"%s+%s\\\"%(ann_id,\\\"+\\\".join(entities_with_continuations[ann_id]))\\n new_entity[\\\"entity_type\\\"] = \\\"meta-annotation\\\"\\n new_entity[\\\"top_entity_types\\\"] = top_entity_types\\n new_entity[\\\"top_entities_ids\\\"] = [ann_id]\\n new_entity[\\\"top_entities_ids\\\"] += [id for id in entities_with_continuations[ann_id]]\\n fname = new_entity[\\\"filename\\\"]\\n new_entity[\\\"filename\\\"] = [fname]\\n for to_merge_id in entities_with_continuations[ann_id]:\\n to_merge = dict(entities)[to_merge_id]\\n new_entity[\\\"filename\\\"]+= [to_merge[\\\"filename\\\"]]\\n new_entity[\\\"positions\\\"] = new_entity[\\\"positions\\\"] + to_merge[\\\"positions\\\"]\\n positions_by_offset = sorted(new_entity[\\\"positions\\\"]\\n ,key=lambda position: position['start'])\\n new_entity[\\\"positions\\\"] = sorted(positions_by_offset\\n ,key=lambda position: Page.objects(id=position['page_id']).first().single_page_file_number)\\n new_entity[\\\"filename\\\"] = \\\", \\\".join(list(set(new_entity[\\\"filename\\\"])))\\n surface_start = new_entity[\\\"positions\\\"][0][\\\"start\\\"]\\n surface_end = new_entity[\\\"positions\\\"][-1][\\\"end\\\"]\\n new_entity[\\\"surface\\\"] = fulltext[surface_start:surface_end]\\n entities[new_entity[\\\"ann_id\\\"]] = new_entity\\n logger.debug(new_entity)\\n except Exception as e:\\n logger.error(\\\"The merging of %s in (%s-%s) failed with error\\\\\\\"%s\\\\\\\"\\\"%(new_entity[\\\"ann_id\\\"],bid,page_n,e))\\n \\\"\\\"\\\"\\n Now all annotations will be stored into the MongoDB. \\n And some specific fields (e.g. `top_entities`) are sorted, and annotations updated \\n accordingly in the DB. \\n \\\"\\\"\\\"\\n try:\\n annotations = []\\n for entity in entities.values():\\n annotation = Annotation(**entity)\\n annotation.positions = [PagePosition(**position) for position in entity[\\\"positions\\\"]]\\n annotation.save()\\n annotations.append(annotation)\\n page.annotations_ids = [] #TODO\\n page.annotations_ids = annotations\\n page.is_annotated = True\\n page.save()\\n logger.debug(\\\"Following annotations were inserted into MongoDB: %s\\\"%([annotation.id for annotation in annotations]))\\n logger.info(\\\"%i annotations were inserted into MongoDB\\\"%len(annotations))\\n except Exception as e:\\n raise e\\n containers = [annotation for annotation in annotations if annotation[\\\"container\\\"]] \\n contained = [annotation for annotation in annotations if not annotation[\\\"container\\\"]]\\n meta_annotations = [annotation for annotation in annotations if annotation[\\\"entity_type\\\"]==\\\"meta-annotation\\\"]\\n logger.debug(\\\"meta annotations: %s\\\"%meta_annotations)\\n \\\"\\\"\\\"\\n Resolve the top entities in the meta-annotations: replace entity IDs with \\n a reference to the annotation in the MongoDB.\\n \\\"\\\"\\\"\\n for annotation in meta_annotations:\\n top_entities_ids = annotation[\\\"top_entities_ids\\\"]\\n logger.debug('resolving top_entities')\\n top_entities = [Annotation.objects(ann_id=ann_id, pageid=annotation.pageid).first() for ann_id in top_entities_ids]\\n #top_entities = list([db_conn.annotations.find_one({\\\"ann_id\\\":ann_id,\\\"pageid\\\":annotation[\\\"pageid\\\"]}) for ann_id in top_entities_ids])\\n logger.debug(\\\"Top entities before sorting %s\\\"%[ann.id for ann in top_entities])\\n annotation[\\\"top_entities\\\"] = sort_annotations_by_offset(top_entities)\\n logger.debug(\\\"Top entities after sorting %s\\\"%[ann.id for ann in top_entities])\\n annotation[\\\"top_entities\\\"] = top_entities\\n annotation.save()\\n logger.debug(\\\"Updating meta-annotation: %s\\\"%annotation.id)\\n \\\"\\\"\\\"\\n Transform contains relations between entities into references between annotations \\n in the MongoDB.\\n \\\"\\\"\\\"\\n for annotation in sort_annotations_by_offset(containers):\\n if(len(annotation[\\\"positions\\\"]) > 1):\\n start = annotation[\\\"positions\\\"][0][\\\"start\\\"]\\n end = annotation[\\\"positions\\\"][len(annotation[\\\"positions\\\"])-1][\\\"end\\\"]\\n else:\\n start = annotation[\\\"positions\\\"][0][\\\"start\\\"]\\n end = annotation[\\\"positions\\\"][0][\\\"end\\\"]\\n annotation[\\\"contains\\\"] = []\\n for contained_annotation in sort_annotations_by_offset(contained):\\n if(len(contained_annotation[\\\"positions\\\"])>1):\\n if(contained_annotation[\\\"positions\\\"][0][\\\"start\\\"] >= start\\n and contained_annotation[\\\"positions\\\"][len(contained_annotation[\\\"positions\\\"])-1][\\\"end\\\"] <= end):\\n annotation[\\\"contains\\\"].append(contained_annotation)\\n logger.debug(\\\"[%s] Annotation %s (%s) contains %s (%s)\\\"%(\\n doc_id\\n ,annotation[\\\"ann_id\\\"]\\n ,annotation[\\\"id\\\"]\\n ,contained_annotation[\\\"ann_id\\\"]\\n ,contained_annotation[\\\"id\\\"]))\\n annotation.save()\\n else:\\n if(contained_annotation[\\\"positions\\\"][0][\\\"start\\\"] >= start\\n and contained_annotation[\\\"positions\\\"][0][\\\"end\\\"] <= end):\\n annotation[\\\"contains\\\"].append(contained_annotation)\\n logger.debug(\\\"[%s] Annotation %s (%s) contains %s (%s)\\\"%(\\n doc_id\\n ,annotation[\\\"ann_id\\\"]\\n ,annotation[\\\"id\\\"]\\n ,contained_annotation[\\\"ann_id\\\"]\\n ,contained_annotation[\\\"id\\\"]))\\n annotation.save()\\n else:\\n page.is_annotated = False\\n logger.info(\\\"%s was ignored because it's not in the golden set\\\"%doc_id)\\n except StopIteration as e:\\n logger.error(\\\"The annotations for %s-%s p. %i can't be ingested\\\"%(bid, issue_number, page_n))\\n except Exception as e:\\n logger.error(\\\"The annotations for %s-%s can't be ingested. Got error %s\\\"%(bid, issue_number, e))\\n return annotated_pages\",\n \"def preproc_doc(document):\\n\\n # Each document is a list of lines\\n tokenizer = tokenization.FullTokenizer(\\n vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)\\n\\n # set a random seed for reproducability\\n # since this function is run in parallel, if we hardcode a seed, all\\n # documents will have the same permutations. Instead we use the hash of the\\n # first sentence as the seed so it is different for each document and it\\n # is still reproducible.\\n hash_object = hashlib.md5(document[0])\\n rng = random.Random(int(hash_object.hexdigest(), 16) % (10**8))\\n\\n # Each document is composed of a list of sentences. We create paragraphs\\n # by keeping together sentences on the same line and adding adjacent sentences\\n # if there are fewer than 5 to form the paragraph.\\n # The utility functions below expect the document to be split by paragraphs.\\n list_of_paragraphs = []\\n paragraph = []\\n for line in document:\\n line = tokenization.convert_to_unicode(line)\\n line = line.replace(u\\\"\\\\u2018\\\", \\\"'\\\").replace(u\\\"\\\\u2019\\\", \\\"'\\\")\\n sents = split_line_by_sentences(line)\\n for sent in sents:\\n tokens = tokenizer.tokenize(sent)\\n if tokens:\\n paragraph.append(tokens)\\n if len(paragraph) > 5:\\n list_of_paragraphs.append(paragraph)\\n paragraph = []\\n\\n # In case of any empty paragraphs, remove them.\\n list_of_paragraphs = [x for x in list_of_paragraphs if x]\\n\\n # Convert the list of paragraphs into TrainingInstance object\\n # See preprocessing_utils.py for definition\\n if FLAGS.format == FORMAT_BINARY:\\n instances = create_instances_from_document(list_of_paragraphs,\\n FLAGS.max_seq_length, rng)\\n elif FLAGS.format == FORMAT_PARAGRAPH:\\n instances = create_paragraph_order_from_document(list_of_paragraphs,\\n FLAGS.max_seq_length, rng)\\n\\n # Convert token lists into ids and add any needed tokens and padding for BERT\\n tf_examples = [\\n convert_instance_to_tf_example(tokenizer, instance,\\n FLAGS.max_seq_length)[0]\\n for instance in instances\\n ]\\n\\n # Serialize TFExample for writing to file.\\n tf_examples = [example.SerializeToString() for example in tf_examples]\\n\\n return tf_examples\",\n \"def process(self, doc_data):\\n self.doc_data = doc_data\\n self.process_text(self.auto_link_messages)\\n self.process_text(self.auto_link_xips)\\n self.add_type_sizes()\\n return self.doc_data\",\n \"def add_documents(self, docs):\\n for sent in docs:\\n sent = map(self.process_token, sent)\\n self._token_count.update(sent)\",\n \"def process(self, doc):\\n self.doc = doc\\n if self.replace_words is True:\\n self.replace_words_fun()\\n if self.remove_html_tags is True:\\n self.remove_html_tags_fun()\\n if self.remove_stopwords is True:\\n self.remove_stopwords_fun()\\n if self.remove_numbers is True:\\n self.remove_numbers_fun()\\n if self.remove_punctations is True:\\n self.remove_punctations_fun() \\n if self.lemmatize is True:\\n self.lemmatize_fun()\\n return self.doc\",\n \"def postprocess_docs(self, doc_scores, docs, input_strings, add_eos, prefix, print_docs=False):\\n\\n def cat_input_and_doc(doc_score, domain, entity_name, doc_title, doc_text, input_string, add_eos, prefix, print_docs=False):\\n # TODO(Patrick): if we train more RAG models, I want to put the input first to take advantage of effortless truncation\\n # TODO(piktus): better handling of truncation\\n if doc_title.startswith('\\\"'):\\n doc_title = doc_title[1:]\\n if doc_title.endswith('\\\"'):\\n doc_title = doc_title[:-1]\\n if prefix is None:\\n prefix = \\\"\\\"\\n if entity_name is None:\\n entity_name = \\\"*\\\"\\n suffix = self.generator_tokenizer.eos_token if add_eos else \\\"\\\"\\n out = (\\n prefix + domain + self.config.title_sep + entity_name + self.config.title_sep + doc_title + self.config.title_sep + doc_text + self.config.doc_sep + input_string + suffix\\n ).replace(\\\" \\\", \\\" \\\")\\n if print_docs:\\n logger.info(\\\"{} {}\\\".format(doc_score, out))\\n return out\\n\\n rag_input_strings = [\\n cat_input_and_doc(\\n doc_scores[i][j],\\n docs[i][j]['domain'],\\n docs[i][j]['entity_name'],\\n docs[i][j]['doc']['title'],\\n docs[i][j]['doc']['body'],\\n input_strings[i],\\n add_eos,\\n prefix,\\n print_docs,\\n )\\n for i in range(len(docs))\\n for j in range(self.n_docs)\\n ]\\n\\n contextualized_inputs = self.generator_tokenizer.batch_encode_plus(\\n rag_input_strings,\\n max_length=self.config.max_combined_length,\\n return_tensors=\\\"pt\\\",\\n padding=\\\"longest\\\",\\n truncation=False,\\n ).to(doc_scores.device)\\n\\n return contextualized_inputs[\\\"input_ids\\\"], contextualized_inputs[\\\"attention_mask\\\"]\",\n \"def _preprocess(self):\\n self.data['sentences'] = self.data['text'].apply(self._tokenize_sent)\\n self.data['nouns'] = self.data['sentences'].apply(self._get_nouns)\\n # self._get_frequent_features()\\n # self._compactness_pruning()\\n # self._redundancy_pruning()\\n # self._get_features()\\n self._extract_opinions()\",\n \"def run_docs(self, *docs):\\n self.docs = docs\\n self.run()\",\n \"def structure_PBDMS_annotations(documents, kb_data):\\n \\n doc_annotations = list()\\n partial_func = partial(parse_PBDMS_doc, kb_data)\\n \\n with multiprocessing.Pool(processes=10) as pool:\\n doc_annotations = pool.map(partial_func, documents)\\n \\n return doc_annotations\",\n \"def process(self, doc):\\n raise multisearch.errors.FeatureNotAvailableError\",\n \"def process_docs(directory, vocab):\\n for file_name in listdir(directory):\\n file_path = directory + '/' + file_name\\n add_doc_to_vocab(file_path, vocab)\",\n \"def process_corpus(args):\\n\\n fs = open(args.input,'r')\\n out = list()\\n for line in fs:\\n blob = TextBlob(line.strip())\\n result_info = dict()\\n result_info\\n result_info['correct'] = str(blob.correct())\\n if args.parse :\\n result_info['parse'] = get_parsed_text(blob)\\n if args.tokenize:\\n result_info['tokenize'] = get_tokenizer_result(blob)\\n if args.sentiment:\\n result_info['sentiment'] = analyze_sentiment(blob)\\n if args.sentence_sentiment:\\n result_info['sentence_sentiment'] = analyze_sentence_sentiment(blob)\\n if args.noun_phrase:\\n result_info['noun_phrase'] = get_noun_phrases(blob)\\n if args.pos:\\n result_info['pos'] = get_pos_tags(blob)\\n\\n out.append(result_info)\\n print out\\n json.dump(out,open('out.json','w'))\\n fs.close()\\n print '******************************* Execution completed *********************************'\",\n \"def processCorpus(self):\\n # Fit tokenizer on corpus\\n self.tokenizer.fit_on_texts(self.corpus)\\n input_sequences = []\\n for line in self.corpus:\\n token_list = self.tokenizer.texts_to_sequences([line])[0]\\n for i in range(1, len(token_list)):\\n n_gram_sequence = token_list[:i+1]\\n input_sequences.append(n_gram_sequence)\\n # Pad shorter sequences\\n max_sequence_len = max([len(x) for x in input_sequences])\\n input_sequences = np.array(pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre'))\\n # Assemble training data\\n X_train, labels = input_sequences[:,:-1], input_sequences[:,-1]\\n y_train = tf.keras.utils.to_categorical(labels, num_classes=len(self.tokenizer.word_index) + 1)\\n return max_sequence_len, X_train, y_train\",\n \"def main():\\n global collection\\n #args = argparse.ArgumentParser()\\n #args.add_argument('directory', help='Directory in which the files'\\n #'are stored.')\\n #args.add_argument('collection', help='The collection to use.')\\n #parser = args.parse_args()\\n collection = get_collection()\\n #documents = glob.glob('*.asm')\\n documents = collection.find()\\n num_cores = multiprocessing.cpu_count()\\n print('Running code on %d processors' % num_cores)\\n Parallel(n_jobs=num_cores)(\\\\\\n delayed(save_comments)(doc) for doc in documents)\",\n \"def _process_one(self, to_process: NewsArticle) -> NewsArticle:\\n text = (to_process.title + to_process.desc_title + to_process.desc_para).lower()\\n\\n # rake\\n self.r.extract_keywords_from_text(text)\\n rakes = self.r.get_ranked_phrases()\\n\\n # spacy\\n result = []\\n doc = self.nlp(text)\\n\\n for token in doc:\\n if token.text in self.nlp.Defaults.stop_words or token.text in punctuation:\\n continue\\n\\n if token.pos_ in self.pos_tag:\\n result.append(token.text)\\n\\n spacys = [x[0] for x in Counter(result).most_common()]\\n\\n to_process.tags = list(set(rakes + spacys))\\n\\n return to_process\",\n \"def do_docs(self, path):\\n print(\\\"scaraping documentation\\\")\\n for p in path.glob(\\\"**/*\\\"):\\n if p.is_file():\\n parts = p.relative_to(path).parts\\n if parts[-1].endswith(\\\"rst\\\"):\\n data = tsparse(p.read_bytes())\\n blob = DocBlob()\\n blob.arbitrary = data\\n blob.content = {}\\n\\n blob.ordered_sections = []\\n blob.item_file = None\\n blob.item_line = None\\n blob.item_type = None\\n blob.aliases = []\\n blob.example_section_data = Section()\\n blob.see_also = []\\n blob.signature = None\\n blob.references = None\\n blob.refs = []\\n\\n self.docs[parts] = json.dumps(blob.to_json(), indent=2)\\n else:\\n pass\\n # data = p.read_bytes()\",\n \"def annotate_documents(asynch=False):\\n \\n files = glob.glob(os.path.join(cfg.INPUT_PATH, cfg.INPUT_PATTERN))\\n logger.info('found %d files with pattern %s', len(files), cfg.INPUT_PATTERN) \\n \\n if asynch:\\n with concurrent.futures.ThreadPoolExecutor(max_workers=cfg.ASYNCH_MAX_WORKERS) as executor:\\n executor.map(annotate_one, files)\\n \\n else: \\n for f_json in files:\\n annotate_one(f_json)\\n \\n \\n return\",\n \"def preproc_doc(document):\\n tokenizer = tokenization.FullTokenizer(\\n vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)\\n\\n # document = [\\n # tokenization.convert_to_unicode(\\n # unidecode.unidecode(line.decode(\\\"utf-8\\\"))) for line in document\\n # ]\\n\\n sent_tokens = [tokenizer.tokenize(sent) for sent in document if sent]\\n sent_tokens = [sent for sent in sent_tokens if len(sent) > 1]\\n if len(sent_tokens) < 8:\\n return []\\n\\n # Convert token lists into ids and add any needed tokens and padding for BERT\\n tf_example = convert_instance_to_tf_example(tokenizer, sent_tokens,\\n FLAGS.max_sent_length,\\n FLAGS.max_para_length)\\n\\n # Serialize TFExample for writing to file.\\n tf_examples = [tf_example.SerializeToString()]\\n\\n return tf_examples\",\n \"def preprocess(self, documents):\\n\\n # Store the total number of documents\\n num_docs = np.float(len(documents))\\n\\n # A dict storing the frequency of each word across all documents\\n total_word_freq = {}\\n\\n # A dict storing the number of documents that word appears in\\n doc_word_freq = {}\\n\\n # Iterate over all documents\\n for doc in documents:\\n # Split the string into a list of words\\n words = extract_words(doc)\\n\\n # Update the 'total_word_freq' dict using all words in 'words'\\n for w in words:\\n ''' YOUR CODE HERE '''\\n if w not in total_word_freq.keys():\\n total_word_freq[w] = 1\\n else:\\n total_word_freq[w] += 1\\n\\n ''' END CODE FOR THIS LOOP '''\\n\\n # Update the 'doc_word_freq' dict. Remember to only add '1' corresponding to\\n # each word in a document. In case a word appears twice in a document, then\\n # it should be ignored. We use the set() data structure to achieve this.\\n for w in set(words):\\n ''' YOUR CODE HERE '''\\n if w not in doc_word_freq:\\n doc_word_freq[w] = 1\\n else:\\n doc_word_freq[w] += 1\\n\\n ''' END CODE FOR THIS LOOP '''\\n\\n # A set of words with total frequency less than 'self.min_freq'\\n remove_words = set()\\n\\n ''' YOUR CODE HERE '''\\n\\n # Check frequency of each word and add to 'remove_words'\\n for w in total_word_freq.keys():\\n if total_word_freq[w] < self.min_freq:\\n remove_words.add(w)\\n\\n # Delete the words in 'remove_words' from 'total_word_freq' and\\n # 'doc_word_freq'.\\n for w in remove_words:\\n del total_word_freq[w]\\n del doc_word_freq[w]\\n\\n # Create a numpy array to store frequencies from which\\n # we can create the 'self.idf' preprocessed numpy array.\\n word_freq_tensor = np.zeros(len(doc_word_freq))\\n\\n # For each word in 'doc_word_freq' dict, update\\n # 'self.word_to_idx' and 'self.idx_to_word' and\\n # 'word_freq_tensor'.\\n i = 0\\n for w in doc_word_freq.keys():\\n self.word_to_idx[w] = i \\n self.idx_to_word[i] = w\\n word_freq_tensor[i] = doc_word_freq[w]\\n i+=1\\n \\n #print(word_freq_tensor.shape)\\n #print(word_freq_tensor)\\n # Calculate 'self.idf' (see hint.pdf for formula)\\n self.idf = -1*np.log(word_freq_tensor/(len(documents)))\\n ''' END YOUR CODE HERE '''\",\n \"def extract_and_process(input_dir, pdf_path, json_output):\\n\\tprint('Extracting text from: ', pdf_path)\\n\\toutput_dir = input_dir + \\\"/output\\\"\\n\\ttry:\\n\\t\\t# Extract PDF to HTML format\\n\\t\\textracted_text = txt_ext.extract_pdf_to_html(pdf_path)\\n\\t\\t# Write raw HTML\\n\\t\\t#pre_proc.create_text_file(output_dir + \\\"/raw_\\\" + path_leaf(pdf_path) + \\\".html\\\", extracted_text)\\n\\t\\t\\n\\t\\tprint(\\\"Extraction finished: \\\"+ pdf_path + \\\", starting processing\\\")\\n\\t\\tprocess(extracted_text, output_dir, path_leaf(pdf_path), json_output)\\n\\n\\texcept PDFSyntaxError:\\n\\t\\tprint(\\\"PDFSyntaxError: Is this really a PDF? \\\", pdf_path)\\n\\texcept PDFTextExtractionNotAllowed as e:\\n\\t\\tprint(e)\",\n \"def run(self):\\n cmds = (self.clean_docs_cmd, self.html_docs_cmd, self.view_docs_cmd)\\n self.call_in_sequence(cmds)\",\n \"def process(self, doc: Doc) -> Doc:\\n\\n matches = self.get_matches(doc)\\n\\n terminations = get_spans(matches, \\\"termination\\\")\\n boundaries = self._boundaries(doc, terminations)\\n\\n # Removes duplicate matches and pseudo-expressions in one statement\\n matches = filter_spans(matches, label_to_remove=\\\"pseudo\\\")\\n\\n entities = list(self.get_spans(doc))\\n ents = None\\n\\n for start, end in boundaries:\\n\\n ents, entities = consume_spans(\\n entities,\\n filter=lambda s: check_inclusion(s, start, end),\\n second_chance=ents,\\n )\\n\\n sub_matches, matches = consume_spans(\\n matches, lambda s: start <= s.start < end\\n )\\n\\n if self.on_ents_only and not ents:\\n continue\\n\\n sub_preceding = get_spans(sub_matches, \\\"preceding\\\")\\n sub_following = get_spans(sub_matches, \\\"following\\\")\\n # Verbs preceding negated content\\n sub_preceding += get_spans(sub_matches, \\\"verbs_preceding\\\")\\n # Verbs following negated content\\n sub_following += get_spans(sub_matches, \\\"verbs_following\\\")\\n\\n if not sub_preceding + sub_following:\\n continue\\n\\n if not self.on_ents_only:\\n for token in doc[start:end]:\\n token._.negation = any(\\n m.end <= token.i for m in sub_preceding\\n ) or any(m.start > token.i for m in sub_following)\\n\\n for ent in ents:\\n self.annotate_entity(\\n ent=ent,\\n sub_preceding=sub_preceding,\\n sub_following=sub_following,\\n )\\n\\n return doc\",\n \"def preprocess(doc_in, doc_out):\\n def output(text, doc_id):\\n doc_out.write(doc_id + \\\"\\\\n\\\")\\n doc_out.write(text.replace(\\\"\\\\n\\\", \\\" \\\") + \\\"\\\\n\\\\n\\\")\\n\\n def filter_text(t):\\n filtered_out = [\\\"<P>\\\", \\\"</P>\\\"]\\n r = t\\n for f in filtered_out:\\n r = r.replace(f, \\\" \\\")\\n return r\\n\\n\\n doc_id = None\\n reading_text = False\\n text = \\\"\\\"\\n for line in doc_in:\\n if(str_text_start in line):\\n if(reading_text):\\n warning(\\\"Found \\\" + str_text_start + \\\" in text\\\")\\n if(not doc_id):\\n warning(\\\"Reading text without knowing id\\\")\\n continue\\n reading_text = True\\n continue\\n if((str_text_stop in line) and reading_text):\\n output(text, doc_id)\\n text = \\\"\\\"\\n reading_text = False\\n doc_id = None\\n doc_id_match = pat_doc_no.match(line)\\n if(doc_id_match):\\n doc_id = doc_id_match.group(1)\\n if(reading_text):\\n warning(\\\"Found doc id in text\\\")\\n continue\\n if(reading_text):\\n text = text + filter_text(line)\",\n \"def __parse_docs(self, docs, analyses=True):\\n # iter over docs\\n for i, doc in enumerate(docs):\\n _meta = doc.attrib['title']\\n # iter over examples in *doc*\\n for snip in doc.getchildren()[1:]:\\n _text = str()\\n _idx = 0\\n _target_idxs = list()\\n _ana = list()\\n # iter over words in cur example\\n for word in snip.getchildren():\\n if word.tag == 'text':\\n _text += word.text\\n _idx += len(word.text)\\n \\n if len(word.attrib) > 0:\\n _text += word.attrib['text']\\n # process target\\n if word.attrib.get('target') is not None:\\n _target_idxs.append((_idx, _idx + len(word.attrib['text'])))\\n if analyses:\\n _ana.append(self.__get_ana(word))\\n \\n _idx += len(word.attrib['text'])\\n \\n if _target_idxs:\\n for i, ixs in enumerate(_target_idxs):\\n if analyses:\\n yield _text, ixs, _meta, _ana[i]\\n else:\\n yield _text, ixs, _meta, _ana\\n else:\\n continue\",\n \"def process(self, file=False):\\n if file:\\n doc = self.corpus.getDocument(file)\\n print \\\"process Document:\\\", doc.path\\n print\\n self._process(doc)\\n return\\n\\n count = 0\\n\\n while True:\\n count += 1\\n doc = self.corpus.getDocument()\\n if doc == None or count > self.limit:\\n break\\n\\n self._process(doc)\",\n \"def build_corpus(self):\\n print(\\\"Inside the build_corpus >>>>>\\\")\\n documentsCount = 0\\n documents = self.documents\\n\\t\\t\\n with open(self.documents_path) as file:\\n for documents in file.readlines():\\n documents = documents.rstrip('}\\\\n ').strip('0\\\\t').strip('1\\\\t').split(' ')\\n documentsCount = documentsCount +1\\n self.documents.append(documents)\\n\\t\\t\\t\\n self.number_of_documents = documentsCount\",\n \"def ingest_all(self, docs):\\n for doc in docs:\\n self.ingest(doc)\",\n \"def _transform(self, document):\\n pass\",\n \"def updateAnnotations(self):\\n self.backupDatafiles()\\n print(\\\"Updating annotation files \\\", self.field(\\\"trainDir\\\"))\\n listOfDataFiles = QDir(self.field(\\\"trainDir\\\")).entryList(['*.data'])\\n for file in listOfDataFiles:\\n # Read the annotation\\n segments = Segment.SegmentList()\\n newsegments = Segment.SegmentList()\\n segments.parseJSON(os.path.join(self.field(\\\"trainDir\\\"), file))\\n allSpSegs = np.arange(len(segments)).tolist()\\n newsegments.metadata = segments.metadata\\n for segix in allSpSegs:\\n seg = segments[segix]\\n if self.field(\\\"species\\\") not in [fil[\\\"species\\\"] for fil in seg[4]]:\\n newsegments.addSegment(seg) # leave non-target segments unchanged\\n else:\\n for seg2 in self.segments:\\n if seg2[1] == seg:\\n # find the index of target sp and update call type\\n seg[4][[fil[\\\"species\\\"] for fil in seg[4]].index(self.field(\\\"species\\\"))][\\\"calltype\\\"] = self.clusters[seg2[-1]]\\n newsegments.addSegment(seg)\\n newsegments.saveJSON(os.path.join(self.field(\\\"trainDir\\\"), file))\",\n \"def train_pipeline(nlp: spacy.language.Language) -> None:\\n if TEXTCAT not in nlp.pipe_names:\\n textcat = nlp.create_pipe(TEXTCAT, config={\\\"exclusive_classes\\\": False})\\n nlp.add_pipe(textcat, last=True)\\n else:\\n textcat = nlp.get_pipe(TEXTCAT)\\n\\n for category in CATEGORIES:\\n textcat.add_label(category.value)\\n\\n pipe_exceptions = {TEXTCAT, \\\"trf_wordpiecer\\\", \\\"trf_tok2vec\\\"}\\n other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]\\n with nlp.disable_pipes(*other_pipes): # only train textcat\\n all_data = list(get_classification_training_data())\\n random.shuffle(all_data)\\n\\n training_data = all_data[: len(all_data) - 2]\\n validation_data = all_data[len(all_data) - 2 :]\\n\\n optimizer = nlp.begin_training()\\n for itn in range(20):\\n losses: Dict[str, Any] = {}\\n random.shuffle(training_data)\\n batches = minibatch(training_data, size=compounding(4.0, 32.0, 1.001))\\n\\n for batch in batches:\\n texts, annotations = zip(*batch)\\n nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)\",\n \"def transform(self, docs):\\n return [doc for doc in docs]\",\n \"def save_annotated_text_to_xml(self):\\n #initialise file to write the output\\n outfile = open(('annotated_text_' + self.lang + '_' + \\n self.method + '.xml'), 'w')\\n #initialise xml\\n annotated_doc = etree.Element('Annotated_document')\\n main_text = ''\\n #counter for the sentences\\n counter_sentence = 0\\n #counter for the paragraphs\\n counter_paragraph = 0\\n #open txt file\\n with open(self.lang + '.txt') as file:\\n for paragraph in file:\\n paragraph_string = ''\\n sentences = tokenize.sent_tokenize(paragraph)\\n for sentence in sentences:\\n #build lists with the ends of the tokens with NE and the NEs\\n end_list = [0]\\n end_list += [i[2] for i in \\n self.named_entity_list_total[counter_sentence]]\\n ne_list = [i[3] for i in \\n self.named_entity_list_total[counter_sentence]]\\n counter_sentence += 1\\n #build new string\\n new_string = ''\\n for i in range(len(end_list)-1):\\n new_string += (sentence[end_list[i]:end_list[i+1]]+\\n '<annotation class=\\\"'+ne_list[i]+'\\\"/>')\\n new_string += sentence[end_list[-1]:len(sentence)]\\n paragraph_string += new_string+'\\\\n'\\n #print title, author, abstract and main text differently to xml\\n if counter_paragraph == 0:\\n title_text = etree.SubElement(annotated_doc, \\\"Title\\\")\\n #add text to the node\\n init_text = \\\"<text>{0}</text>\\\".format(paragraph_string[6:])\\n fin_text = etree.fromstring(init_text)\\n title_text.append(fin_text)\\n elif counter_paragraph == 1:\\n author_text = etree.SubElement(annotated_doc, \\\"Author\\\")\\n #add text to the node\\n init_text = \\\"<text>{0}</text>\\\".format(paragraph_string[7:])\\n fin_text = etree.fromstring(init_text)\\n author_text.append(fin_text)\\n elif counter_paragraph == 2:\\n abstract_text = etree.SubElement(annotated_doc, \\\"Abstract\\\")\\n #add text to the node\\n init_text = \\\"<text>{0}</text>\\\".format(paragraph_string[9:])\\n fin_text = etree.fromstring(init_text)\\n abstract_text.append(fin_text)\\n else: \\n main_text += paragraph_string\\n counter_paragraph += 1\\n main_text_xml = etree.SubElement(annotated_doc, \\\"Main_text\\\")\\n #add text to the node\\n init_text = \\\"<text>{0}</text>\\\".format(main_text)\\n fin_text = etree.fromstring(init_text)\\n main_text_xml.append(fin_text)\\n #convert and write to outfile\\n xml_bytes = etree.tostring(annotated_doc, encoding='UTF-8', \\n pretty_print=True, xml_declaration=True)\\n xml_str = xml_bytes.decode(\\\"utf-8\\\")\\n outfile.write(xml_str)\\n outfile.close()\\n return\",\n \"def process_text(model_name: str, text: str) -> spacy.tokens.Doc:\\r\\n nlp = load_model(model_name)\\r\\n return nlp(text)\",\n \"def run(self, iteration_key):\\n record_provider = SqlDocumentProvider(iteration_key, self.case_accessor())\\n processor = BulkDocProcessor(record_provider, self.doc_processor(self.domain))\\n return processor.run()\",\n \"def gather_documents(self):\\n self.document_gatherer.gather_and_save_everything(Constants.path_cord, \\n Constants.path_metadata, \\n Constants.path_linked_documents,\\n Constants.path_unlinked_documents,\\n Constants.path_parsed_documents,\\n Constants.path_all_documents)\\n \\n print(\\\"Done gathering documents.\\\")\",\n \"def process(self):\\n\\n self.copy_supported_files()\\n tex = self.replace_includes(file_name=self.source_tex_file)\\n\\n try:\\n tex = self.replace_bibliography(tex)\\n except Exception, ex:\\n print '[e] exception {}'.format(str(ex))\\n print '[i] bibliography was not replaced - run firstly \\\"build-latex-win\\\\\\\\_build.bat\\\" for bbl file'\\n\\n self.dir_helper.save_file(file_name=self.dest_tex_file, text=tex)\\n self.create_bat_file()\\n\\n print '\\\\n[i] publication is ready - IN CASE NO ERROR ACCURED'\\n print '[i] check foloder: \\\"{}\\\"\\\\n'.format(self.temp_dir)\",\n \"def train(self, documents, total_examples=None, total_words=None,\\n epochs=None, start_alpha=None, end_alpha=None,\\n word_count=0, queue_factor=2, report_delay=1.0, callbacks=()):\\n super(Doc2Vec, self).train(\\n documents, total_examples=total_examples, total_words=total_words,\\n epochs=epochs, start_alpha=start_alpha, end_alpha=end_alpha, word_count=word_count,\\n queue_factor=queue_factor, report_delay=report_delay, callbacks=callbacks)\",\n \"def learn(self, documents, labels):\\n for i in xrange(len(documents)):\\n text = documents[i]\\n\\n words = text.split()\\n self.learn_from_one(words)\",\n \"def process_all_documents(self,\\n n_jobs: Optional[int] = None,\\n ) -> List[Optional[Document]]:\\n return self.process_documents(self.doc_ids, n_jobs)\",\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 prepare_spacy_pipeline():\\n global nlp \\n nlp = spacy.load(\\\"en_core_web_lg\\\", disable = [\\\"ner\\\"])\\n \\n return\",\n \"def runDocumentReduce(self):\\n # run as usual\\n self.runDocument(reduceApplication=True)\",\n \"def update_from_document(self, document_path):\\n with open(document_path, 'r') as document_file:\\n for sentence in document_file:\\n words = sentence.strip().split()\\n for word in words:\\n self._add_new_word(word)\",\n \"def _prepare_analysis_input(self, documents):\\n subdoc_to_doc_map = {}\\n wordtype_to_number = {}\\n number_to_wordtype = []\\n wordtypes = {}\\n \\n # prevent duplicating work\\n if os.path.exists(self.wordtype_file):\\n return\\n \\n try:\\n # First find singletons\\n if self.remove_singletons:\\n word_type_count_threshold = max(1, int(math.log(documents.count(), 10)) - 2)\\n temp_word_type_counts = {}\\n for doc_index, doc in enumerate(documents):\\n tokens = self.tokenize(doc.get_content())\\n for token, token_start in tokens:\\n temp_word_type_counts[token] = temp_word_type_counts.setdefault(token, 0) + 1\\n for word_type, count in temp_word_type_counts.iteritems(): # add singletons to stopword list\\n if count <= word_type_count_threshold:\\n self._excluded_words[word_type] = True\\n with io.open(self.excluded_words_file, 'w', encoding='utf-8') as ex_f:\\n ex_f.write(unicode(json.dumps(self._excluded_words)))\\n \\n haltwords = dict(self.stopwords)\\n haltwords.update(self._excluded_words)\\n # Second find bigrams, iterate through documents and train.\\n if self.find_bigrams:\\n from import_tool.analysis.bigram_finder import BigramFinder\\n bigram_finder = BigramFinder(stopwords=haltwords)\\n for doc_index, doc in enumerate(documents):\\n bigram_finder.train(doc_index, self.tokenize(doc.get_content()))\\n bigram_finder.print()\\n \\n # Third, we're going to stem words\\n if self.stem_words:\\n from import_tool.analysis.stemmer import Stemmer\\n stemmer = Stemmer(self._working_dir, self.base_dir)\\n \\n # for each document tokenize and map tokens to numbers to avoid regex problems before passing data to Mallet\\n with io.open(self.mallet_input_file, 'w', encoding='utf-8') as w:\\n with io.open(self.start_index_file, 'w', encoding='utf-8') as w2:\\n count = 0\\n subcount = 0\\n for doc_index, doc in enumerate(documents):\\n doc_content = unicode(doc.get_content())\\n count += 1\\n subdocuments = self.create_subdocuments(doc_index, doc_content)\\n token_start_index_offset = 0 # needed to make sure the start index remains correct once the document is re-merged\\n for subdoc_name, subdoc_content in subdocuments:\\n if subcount > 0:\\n w2.write(u'\\\\n')\\n subcount += 1\\n subdoc_to_doc_map[subdoc_name] = doc_index\\n tokens = self.tokenize(subdoc_content)\\n \\n if self.find_bigrams:\\n tokens = bigram_finder.combine(tokens, subdoc_content)\\n \\n token_numbers = []\\n token_start_indices = []\\n only_tokens = []\\n tokens_temp = []\\n for tok, tok_start in tokens:\\n only_tokens.append(tok)\\n tokens_temp.append([tok, tok_start + token_start_index_offset])\\n tokens = tokens_temp\\n tokens_temp = None\\n if self.stem_words:\\n stemmed_tokens = stemmer.stem(only_tokens)\\n else:\\n stemmed_tokens = only_tokens\\n for tup, tok_stem in zip(tokens, stemmed_tokens):\\n tok, tok_start = tup\\n wordtypes[tok] = True\\n wordtypes[tok_stem] = True\\n try:\\n tok_num = wordtype_to_number[tok_stem]\\n except:\\n tok_num = len(wordtype_to_number)\\n number_to_wordtype.append(tok_stem)\\n wordtype_to_number[tok_stem] = tok_num\\n token_numbers.append(unicode(tok_num))\\n token_start_indices.append([tok, tok_start])\\n text = u' '.join(token_numbers)\\n w.write(u'{0} all {1}\\\\n'.format(subdoc_name, text))\\n w2.write(unicode(json.dumps(token_start_indices)))\\n token_start_index_offset += len(subdoc_content)\\n for tok, tok_start in tokens:\\n try:\\n assert doc_content[tok_start:tok_start+len(tok)].lower() == tok.lower()\\n except:\\n print(tok_start)\\n print(len(tok))\\n print('\\\"'+doc_content[tok_start:tok_start+len(tok)].lower()+'\\\"')\\n print('\\\"'+tok.lower()+'\\\"')\\n raise\\n if not count:\\n raise Exception('No files processed.')\\n # record which subdocuments belong to which documents\\n with io.open(self.subdoc_to_doc_map_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(subdoc_to_doc_map)))\\n with io.open(self.wordtype_to_number_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(wordtype_to_number)))\\n with io.open(self.number_to_wordtype_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(number_to_wordtype)))\\n with io.open(self.wordtype_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(wordtypes)))\\n except: # cleanup\\n self._cleanup(self.mallet_input_file)\\n self._cleanup(self.subdoc_to_doc_map_file)\\n self._cleanup(self.wordtype_to_number_file)\\n self._cleanup(self.number_to_wordtype_file)\\n self._cleanup(self.wordtype_file)\\n self._cleanup(self.excluded_words_file)\\n raise\",\n \"def batch_parser(texts, raw_output=False, corenlp_path=CORENLP_PATH, memory=\\\"3g\\\", outputformat=\\\"xml\\\",\\n annotators = [\\\"tokenize\\\",\\\"ssplit\\\",\\\"pos\\\",\\\"lemma\\\",\\\"ner\\\",\\\"parse\\\",\\\"dcoref\\\"], **kwargs):\\n\\n # First, we change to the directory where we place the xml files from the parser\\n input_dir = tempfile.mkdtemp()\\n output_dir = tempfile.mkdtemp()\\n files = []\\n file_list = tempfile.NamedTemporaryFile()\\n\\n for i, text in enumerate(texts):\\n filename = os.path.join(input_dir, \\\"{0}.txt\\\".format(i))\\n files.append(filename)\\n f = open(filename, \\\"w\\\")\\n f.write(text.encode(\\\"utf-8\\\"))\\n f.close()\\n\\n # creating the file list of files to parse\\n file_list.write('\\\\n'.join(files))\\n file_list.seek(0)\\n\\n command = generate_corenlp_command(corenlp_path, memory=memory,\\n filelist=file_list.name,\\n outputdir=output_dir,\\n outputformat=outputformat,\\n annotators=annotators, **kwargs)\\n\\n # print \\\"executing...\\\"\\n # print command\\n\\n # creates the xml file of parser output:\\n call(command, shell=True)\\n\\n # reading in the raw xml file:\\n try:\\n result = []\\n for output_file in sorted(os.listdir(output_dir),\\n key=lambda x: int(x.split('.')[0])):\\n #print output_file\\n with open(output_dir + '/' + output_file, 'r') as xml:\\n file_name = re.sub('.xml$', '', os.path.basename(output_file))\\n parsed = parse_xml_result(xml.read())\\n # add only the first document in the parse\\n result.append(parsed[0])\\n return result\\n finally:\\n file_list.close()\\n shutil.rmtree(output_dir)\",\n \"def __call__(self, doc):\\n matches = self.matcher(doc)\\n spans = [] # keep spans here to merge them later\\n for match_id, start, end in matches:\\n span = doc[start : end]\\n for token in span:\\n token._.set(self._is_url, True)\\n token.lemma_ = get_domain(token.text)\\n spans.append(span)\\n\\n return doc\",\n \"def process_input_files(list_input_files):\\n global dict_models_results\\n global list_spacy_docs\\n \\n for input_file in list_input_files:\\n prefix = prefix_from_filename(input_file)\\n \\n with open(input_file) as f:\\n list_cases = json.load(f)\\n dict_models_results[prefix] = list_cases\\n \\n \\n #extract list of questions from all vignettes and create a mapping page -> vignette question\\n dict_questions = {}\\n for prefix, list_cases in dict_models_results.items():\\n for vignette in list_cases:\\n dict_questions[vignette[\\\"book_page\\\"]] = vignette[\\\"question\\\"]\\n \\n \\n for book_page,question in dict_questions.items():\\n doc_q = load_bner_onto_tokens_extension(question, book_page)\\n list_spacy_docs.append(doc_q)\\n \\n return\",\n \"def preprocess_text(self, seq):\\n if self.text_preprocess_fn:\\n seq = list(map(self.text_preprocess_fn, seq))\\n return seq\",\n \"def process_docs_2(directory, vocab):\\n lines = []\\n for filename in listdir(directory):\\n if not filename.endswith('.txt'):\\n continue\\n path = directory + '/' + filename\\n line = doc_to_line(path, vocab)\\n lines.append(line)\\n return lines\",\n \"def run(self):\\n logging.info('Perform automatic annotations')\\n\\n # The procedure outcome report file.\\n self.__param.set_report_filename(self.__log_report.get_filename())\\n self.__log_report.increment()\\n\\n # Create the progress bar then run the annotations\\n wx.BeginBusyCursor()\\n p = sppasAnnotProgressDialog()\\n self.__manager.annotate(self.__param, p)\\n p.close()\\n wx.EndBusyCursor()\\n\\n self.__update_log_text()\\n self.Refresh()\\n\\n # send to parent\\n evt = DataChangedEvent(data=self.__param.get_workspace())\\n evt.SetEventObject(self)\\n wx.PostEvent(self.GetParent(), evt)\",\n \"def forward(self, doc):\\n out = torch.tensor([]).float().to(self.device)\\n\\n for i in range(len(doc)):\\n sentences_raw = sentencesplit(cleantxt(doc[i]))\\n sentences_ready = torch.tensor([]).float().to(self.device)\\n for sentence in sentences_raw:\\n sentence = sentence.split()\\n if sentence == []:\\n continue\\n lookup_tensor = torch.tensor([]).long().to(self.device)\\n for word in sentence:\\n if word in self.embedd_dict:\\n lookup_tensor = torch.cat((lookup_tensor,\\n torch.LongTensor([self.embedd_dict[word]])), 0)\\n else:\\n lookup_tensor = torch.cat((lookup_tensor, torch.LongTensor([0])), 0)\\n # Word embedding\\n xw = self.word_embedding(lookup_tensor).view(1, -1, self.embedding_dim).to(self.device)\\n # Word GRU\\n self.hidden_gru_words = self.init_hidden_words()\\n hw, self.hidden_gru_words = self.gru_word(xw, self.hidden_gru_words)\\n # Word MLP\\n uw = nn.Tanh()(self.MLP_word(hw)).to(self.device)\\n # Word attention\\n attention_score = torch.matmul(uw, self.attention_word).squeeze().to(self.device)\\n attention_score = F.softmax(attention_score, dim=0).view(uw.size(0), uw.size(1), 1).to(self.device)\\n scored_x = (hw * attention_score).to(self.device)\\n s = torch.sum(scored_x, dim=1).to(self.device)\\n #collecting sentences\\n sentences_ready = torch.cat((sentences_ready, s), 0)\\n # Sentence GRU\\n if len(sentences_ready) == 0:\\n out = torch.cat((out,\\n torch.randn(1, self.number_cat).to(self.device)), 0).to(self.device)\\n continue\\n sentences_ready_gru = sentences_ready.view(1, -1, self.embedding_dim).to(self.device)\\n self.hidden_gru_sentences = self.init_hidden_sentences()\\n hs, self.hidden_gru_sentences = self.gru_sentence(torch.tensor(sentences_ready_gru), self.hidden_gru_sentences)\\n # SENTENCE MLP\\n us = nn.Tanh()(self.MLP_sentence(hs)).to(self.device)\\n # Sentence attention\\n attention_score = torch.matmul(us, self.attention_sentence).squeeze().to(self.device)\\n attention_score = F.softmax(attention_score, dim=0).view(us.size(0), us.size(1), 1).to(self.device)\\n scored_x = (hs * attention_score).to(self.device)\\n v = torch.sum(scored_x, dim=1).to(self.device)\\n # classification\\n p = self.MLP_classification(v).to(self.device)\\n out = torch.cat((out, p.float()), 0).float().to(self.device)\\n return out\",\n \"def processCorpus(self, texts: [Text]):\\n \\n remaining_texts = texts\\n curr_texts = []\\n processed_texts = []\\n cases = []\\n num_failed_test_cases = []\\n num_failed_test_cases_per_asr = {}\\n num_processed_texts = []\\n for asr in self.asrs:\\n num_failed_test_cases_per_asr[asr.getName()] = []\\n \\n for i in range(self.num_iteration):\\n # print(f\\\"Iteration: {i+1}\\\")\\n \\n if self.text_batch_size :\\n curr_texts = remaining_texts[:self.text_batch_size]\\n remaining_texts = remaining_texts[self.text_batch_size:]\\n else : # use global visibility\\n curr_texts = remaining_texts\\n\\n if len(curr_texts) > 0 :\\n \\n curr_cases, curr_processsed_texts, unprocessed_texts = self.processOneIteration(curr_texts, processed_texts, cases)\\n cases.extend(curr_cases)\\n processed_texts.extend(curr_processsed_texts)\\n if self.text_batch_size :\\n remaining_texts.extend(unprocessed_texts)\\n else :\\n remaining_texts = unprocessed_texts\\n\\n num_failed_test_cases.append(calculate_cases(cases, mode=FAILED_TEST_CASE))\\n for asr in self.asrs:\\n num_failed_test_cases_per_asr[asr.getName()].append(calculate_cases_per_asr(\\n cases, mode=FAILED_TEST_CASE, asr_name=asr.getName()))\\n num_processed_texts.append(len(processed_texts))\\n else :\\n print(\\\"Texts are not enough!\\\")\\n \\n # shuffle the remaining texts\\n np.random.shuffle(remaining_texts)\\n \\n data = {}\\n data[\\\"number_of_failed_test_cases_all\\\"] = num_failed_test_cases\\n data[\\\"number_of_failed_test_cases_per_asr\\\"] = num_failed_test_cases_per_asr\\n data[\\\"number_of_processed_texts\\\"] = num_processed_texts\\n with open(self.outputfile_failed_test_case, 'w') as outfile:\\n json.dump(data, outfile, indent=2, sort_keys=True)\\n\\n if self.target_asr :\\n self.saveFailedTestCases(processed_texts, cases)\",\n \"def transform(self, documents, pad_length=None, add_start=False,\\n add_end=False):\\n indices = [\\n self.process_text(text=doc, add_start=add_start, add_end=add_end)\\n for doc in documents\\n ]\\n if pad_length:\\n if pad_length == 'max':\\n pad_length = max(map(len, indices))\\n indices = keras.preprocessing.sequence.pad_sequences(\\n sequences=indices,\\n maxlen=pad_length,\\n dtype=int,\\n padding='pre',\\n truncating='pre'\\n ).tolist()\\n return indices\",\n \"def docs_from_clinical_record(\\n lines: List[str], annotations: List[str], nlp: Language, merge_docs: bool = False\\n) -> List[Doc]:\\n docs = []\\n spans_by_line = defaultdict(list)\\n\\n for row in annotations:\\n row = row.split(\\\"||\\\")\\n text_info = row[0]\\n type_info = row[1]\\n\\n offset_start = text_info.split(\\\" \\\")[-2]\\n offset_end = text_info.split(\\\" \\\")[-1]\\n\\n start_line, word_start = offset_start.split(\\\":\\\")\\n end_line, word_end = offset_end.split(\\\":\\\")\\n\\n label = type_info.split('\\\"')[-2]\\n\\n if start_line != end_line:\\n # This happens very infrequently (only about 10 times in total)\\n # so we just skip these annotations\\n continue\\n else:\\n spans_by_line[int(start_line)].append(\\n (int(word_start), int(word_end), label)\\n )\\n\\n for i, line in enumerate(lines):\\n n = i + 1\\n doc = nlp.make_doc(line)\\n if n in spans_by_line:\\n ents = [\\n Span(doc, start, end + 1, label=label)\\n for (start, end, label) in spans_by_line[n]\\n ]\\n ents = [\\n e for e in ents if bool(e.text.strip()) and e.text.strip() == e.text\\n ]\\n doc.ents = filter_spans(ents)\\n\\n docs.append(doc)\\n\\n return [Doc.from_docs(docs)] if merge_docs else docs\",\n \"def run_indexer(self, corpus):\\n with open(corpus, 'r') as fp:\\n for line in tqdm(fp.readlines()):\\n doc_id, document = self.preprocessor.get_doc_id(line)\\n tokenized_document = self.preprocessor.tokenizer(document)\\n self.indexer.generate_inverted_index(doc_id, tokenized_document)\\n self.indexer.sort_terms()\\n self.indexer.add_skip_connections()\\n self.indexer.calculate_tf_idf()\",\n \"def openie_prepare_files(document_file, no_entity_filter=False, consider_sections=False):\\n temp_dir = tempfile.mkdtemp()\\n temp_in_dir = os.path.join(temp_dir, \\\"input\\\")\\n filelist_fn = os.path.join(temp_dir, \\\"filelist.txt\\\")\\n out_fn = os.path.join(temp_dir, \\\"output.txt\\\")\\n os.mkdir(temp_in_dir)\\n input_files = []\\n\\n amount_skipped_files = 0\\n doc_count = count_documents(document_file)\\n logging.info('counting files to process....')\\n if no_entity_filter:\\n for document_content in read_pubtator_documents(document_file):\\n doc = TaggedDocument(from_str=document_content)\\n if not doc or not doc.title or not doc.abstract:\\n amount_skipped_files += 1\\n else:\\n doc_count += 1\\n # TODO: Not beautiful but join sections via a '.' to ensure sentence splitting in CoreNLP\\n content = '. '.join([te for te, _ in doc.iterate_over_text_elements(sections=consider_sections)])\\n input_file = os.path.join(temp_in_dir, \\\"{}.txt\\\".format(doc.id))\\n input_files.append(input_file)\\n with open(input_file, \\\"w\\\") as f:\\n f.write(content)\\n else:\\n logging.info('Init spacy nlp...')\\n spacy_nlp = English() # just the language with no model\\n spacy_nlp.add_pipe(\\\"sentencizer\\\")\\n\\n doc2sentences, doc2tags = filter_document_sentences_without_tags(doc_count, document_file, spacy_nlp,\\n consider_sections=consider_sections)\\n doc_count = len(doc2tags)\\n for doc_id, sentences in doc2sentences.items():\\n if sentences:\\n input_file = os.path.join(temp_in_dir, \\\"{}.txt\\\".format(doc_id))\\n input_files.append(input_file)\\n with open(input_file, 'wt') as f:\\n f.write(' '.join(sentences))\\n\\n logging.info('{} files need to be processed. {} files skipped.'.format(doc_count, amount_skipped_files))\\n with open(filelist_fn, \\\"w\\\") as f:\\n f.write(\\\"\\\\n\\\".join(input_files))\\n return filelist_fn, out_fn, doc_count\",\n \"def fit(self):\\n if not self.paragraphs or len(self.paragraphs) == 0:\\n self.paragraphs = self._get_all_paragraphs()\\n if not self.paragraphs or len(self.paragraphs) == 0:\\n logger.warning(\\\"Fit method called with empty document store\\\")\\n return\\n\\n self.df = pd.DataFrame.from_dict(self.paragraphs)\\n self.df[\\\"text\\\"] = self.df[\\\"text\\\"].apply(lambda x: \\\" \\\".join(x))\\n self.tfidf_matrix = self.vectorizer.fit_transform(self.df[\\\"text\\\"])\",\n \"def run():\\n assert os.path.exists(args.input_path), \\\"input_path doesn't exist\\\"\\n assert os.path.exists(args.output_path), \\\"output_path doesn't exist\\\"\\n\\n # read all the paths to the input documents\\n doc_files = []\\n for root, dirs, files in os.walk(args.input_path):\\n for file in files:\\n if not file.endswith('gz') and not file.endswith('xml'):\\n continue\\n doc_files.append(os.path.join(root, file))\\n print('{} medline files found from {}'\\n ''.format(len(doc_files), args.input_path))\\n\\n print('converting...')\\n pool = Pool(processes=args.num_workers)\\n total_doc = 0\\n total_batch = 0\\n total_empty = 0\\n for d, b, n in tqdm(pool.imap_unordered(partial(convert), doc_files),\\n total=len(doc_files)):\\n total_doc += d\\n total_batch += b\\n total_empty += n\\n\\n print('total docs: {}, total batches: {} created (empty doc {})'\\n ''.format(total_doc, total_batch, total_empty))\",\n \"def prepare_dictionary_from_docs(self):\\n if os.path.exists(self.DICT_PATH):\\n return True\\n self.logger.info(\\\"START PREPARING DICT\\\")\\n for fn in os.listdir(self.wiki_path):\\n self.logger.info(\\\"dict update {0}\\\".format(fn))\\n content = self.get_processed_content(fn)\\n self.dictionary.add_documents([content])\\n self.dictionary.filter_extremes(no_below=20, no_above=0.1, keep_n=100000)\\n self.dictionary.compactify()\\n self.dictionary.save(self.DICT_PATH)\\n return True\",\n \"def process_documents(self,\\n doc_ids: Iterable[str],\\n n_jobs: Optional[int] = None,\\n ) -> List[Optional[Document]]:\\n if n_jobs is None:\\n n_jobs = self.n_jobs\\n elif n_jobs == -1:\\n n_jobs = os.cpu_count()\\n elif n_jobs < -1:\\n raise ValueError(f'n_jobs must be >= 0 or -1, but got {n_jobs}')\\n if self.archive_handler is not None:\\n assert n_jobs == 0\\n with (self.archive_handler.open() if self.archive_handler else nullcontext()) as archive:\\n process_document = partial(KyotoReader.process_document, self, archive=archive)\\n if n_jobs > 0:\\n with futures.ProcessPoolExecutor(max_workers=n_jobs) as executor:\\n rets: Iterable[Optional[Document]] = executor.map(process_document, doc_ids)\\n else:\\n rets: Iterable[Optional[Document]] = map(process_document, doc_ids)\\n return list(rets)\",\n \"def fit(self, texts):\\n print('Processing text and fitting LDA...')\\n\\n texts = preprocess_text(texts)\\n stemmed_texts = [\\n list(set(self.tokenizer.stem(text))) for text in texts]\\n self.lda_dictionary = Dictionary(stemmed_texts)\\n lda_corpus = [\\n self.lda_dictionary.doc2bow(text) for text in stemmed_texts]\\n self.lda = LdaModel(lda_corpus, num_topics=self.n_topics)\\n print('Done.')\\n\\n return self\",\n \"def _process_reviews(self):\\n params = {'add_special_tokens': True, 'max_length': self._max_len,\\n 'return_token_type_ids': False, 'pad_to_max_length': True,\\n 'return_attention_mask': True, 'return_tensors': 'pt'}\\n\\n self._tokens = self._review.apply(self._tokenizer.encode_plus, **params)\",\n \"def preprocess(self, doc):\\n self.stopwords = []\\n doc = doc.split()\\n new_doc = []\\n for word in doc:\\n if word.isalpha() and word.lower() not in self.stopwords:\\n word = word.lower()\\n if self.stemmer != None:\\n word = self.stemmer.stem(word)\\n new_doc.append(word)\\n return new_doc\",\n \"def analyze_text(self, text, doc_num=None):\\n doc_words = frozenset(x[0] for x in text)\\n top_ids_in_doc = self.relevant_ids.intersection(doc_words)\\n for word_id in top_ids_in_doc:\\n self._inverted_index[self.id2contiguous[word_id]].add(self._num_docs)\",\n \"def perform_parse(self):\\n # get folder of pdf files\\n folder = QFileDialog.getExistingDirectory(\\n parent=self.parent(),\\n caption='Get folder with PDF documents to parse'\\n )\\n if folder:\\n # get list of fields and patterns\\n field_list = self._get_fields()\\n # performing parse\\n results = make_parse(folder, field_list)\\n self.open_result(results)\",\n \"def iter_documents(self):\\n raise NotImplementedError\",\n \"def process_augm_data(text_file, dataset_file, augm_dataset_file, doc_num=-1, ctx_size=5 ):\\r\\n num_lines = 0\\r\\n num_words = 0\\r\\n full_counter = Counter()\\r\\n \\r\\n vocabulary = []\\r\\n \\r\\n # Disable NER and categorisation to lighten processing\\r\\n nlp = spacy.load('en_core_web_sm', disable=['ner', 'textcat'])\\r\\n \\r\\n # If the dataset file does not exist, add a header\\r\\n if os.path.exists(dataset_file):\\r\\n dataset = []\\r\\n else:\\r\\n dataset = [['focus_word', 'context_word', 'sent_num', 'focus_index', 'context_position']]\\r\\n if os.path.exists(augm_dataset_file):\\r\\n augm_dataset = []\\r\\n else:\\r\\n augm_dataset = [['synonym', 'context_word', 'sent_num', 'focus_index', 'context_position', 'focus_word']]\\r\\n \\r\\n # Open the file with UTF-8 encoding. If a character\\r\\n # can't be read, it gets replaced by a standard token\\r\\n with open(text_file, 'r', encoding='utf-8', errors='replace') as f:\\r\\n print('Cleaning and processing ', text_file)\\r\\n \\r\\n # Go through all sentences tokenised by spaCy\\r\\n # Word pairs are constrained to sentence appearances,\\r\\n # i.e. no inter-sentence word pairs\\r\\n for sent_num, raw_sent in enumerate(data.readlines()):\\r\\n sent = nlp(raw_sent)\\r\\n \\r\\n token_list = [token for token in sent]\\r\\n num_tokens = len(token_list)\\r\\n \\r\\n # Skip processing if sentence is only one word\\r\\n if len(token_list) > 1:\\r\\n for focus_i, token in enumerate(token_list):\\r\\n t_pos = token.pos_\\r\\n \\r\\n # List of ignored tags\\r\\n ignore_pos = ['PUNCT', 'SYM', 'X']\\r\\n \\r\\n # Temporary list of generated pairs for the current\\r\\n # focus word. Will be used later when searching for\\r\\n # synonyms for the focus word\\r\\n word_pairs = []\\r\\n augment_pairs = []\\r\\n \\r\\n # Only process if focus word is not punctuation\\r\\n # (PUNCT), symbol (SYM), or unclassified (X), and\\r\\n # if token is not only a symbol (not caught by spaCy)\\r\\n if (t_pos not in ignore_pos\\r\\n and re.sub(r'[^\\\\w\\\\s]', '', token.text).strip() != ''):\\r\\n \\r\\n # BYPASSED: original formulation, sampling context\\r\\n # size, from 1 to ctx_size\\r\\n #context_size = random.randint(1, ctx_size)\\r\\n context_size = ctx_size\\r\\n \\r\\n context_min = focus_i - context_size if (focus_i - context_size >= 0) else 0\\r\\n \\r\\n context_max = focus_i + context_size if (focus_i + context_size < num_tokens-1) else num_tokens-1\\r\\n \\r\\n focus_word = token.text.lower()\\r\\n \\r\\n # Go through every context word in the window\\r\\n for ctx_i in range(context_min, context_max+1):\\r\\n # Check that context index is not the same as\\r\\n # focus, that the context word is not in our\\r\\n # 'ignore' list, and that the context is not\\r\\n # white space or punctuation\\r\\n if (ctx_i != focus_i\\r\\n and token_list[ctx_i].pos_ not in ignore_pos\\r\\n and re.sub(r'[^\\\\w\\\\s]', '', token_list[ctx_i].text).strip() != ''):\\r\\n # Changing everything to lower case\\r\\n # A more principled approach would uppercase\\r\\n # named entities such as persons, companies\\r\\n # or countries:\\r\\n # if token.ent_iob_ != 'O':\\r\\n # token.text.capitalize()\\r\\n context_word = token_list[ctx_i].text.lower()\\r\\n \\r\\n ctx_pos = ctx_i - focus_i\\r\\n \\r\\n # If passes all checks (context different\\r\\n # from target and neither tagged as)\\r\\n word_pairs.append([focus_word, context_word,sent_num, focus_i, ctx_pos])\\r\\n \\r\\n # If word_pairs is not empty, that means there is\\r\\n # at least one valid word pair. For every non-stop focus\\r\\n # word in these pairs, augment the dataset with external\\r\\n # knowledge bases\\r\\n if len(word_pairs) > 0 and not token.is_stop:\\r\\n \\r\\n # Convert to list of text words for NLTK\\r\\n text_list = [token.text for token in token_list]\\r\\n # POS tag with NLTK\\r\\n nltk_pos = [pos_tag(text_list)]\\r\\n \\r\\n # We create nltk_pos with a single sentence\\r\\n # so we can access it directly. We are accessing:\\r\\n # - Sentence 0\\r\\n # - Word number focus index\\r\\n # - POS tag (second column)\\r\\n nltk_pos_tag = nltk_pos[0][focus_i][1]\\r\\n \\r\\n # If the POS tags of spaCy and NLTK agree\\r\\n # then continue with the augmentation\\r\\n if token.tag_ == nltk_pos_tag:\\r\\n #print('Tags agree for ', nltk_pos[0][focus_i][1])\\r\\n \\r\\n # Convert universal POS tags to WordNet types\\r\\n # https://universaldependencies.org/u/pos/all.html\\r\\n # (skip proper nouns)\\r\\n wn_tag_dict = {\\r\\n 'ADJ': wn.ADJ,\\r\\n 'ADV': wn.ADV,\\r\\n 'NOUN': wn.NOUN,\\r\\n #'PROPN': wn.NOUN,\\r\\n 'VERB': wn.VERB\\r\\n }\\r\\n \\r\\n # If the POS tag is part of the\\r\\n # pre-specified tags\\r\\n if token.pos_ in wn_tag_dict:\\r\\n synsets = wn.synsets(focus_word, wn_tag_dict[token.pos_])\\r\\n \\r\\n # Keep track of accepted synonyms,\\r\\n # to avoid adding the same synonym\\r\\n # multiple times to the dataset\\r\\n accepted_synonyms = []\\r\\n \\r\\n # Cycle through the possible synonym\\r\\n # sets in WordNet\\r\\n for syn_num, syn in enumerate(synsets):\\r\\n # Cycle through all the lemmas in\\r\\n # every synset\\r\\n for lem in syn.lemmas():\\r\\n # Get the synonym in lowercase\\r\\n synonym = lem.name().lower()\\r\\n \\r\\n # Removes multi-word synonyms\\r\\n # as well as repeated synonyms\\r\\n if not re.search('[-_]+', synonym) and focus_word != synonym and synonym not in accepted_synonyms:\\r\\n accepted_synonyms.append(synonym)\\r\\n \\r\\n for fw, c, sn, fi, cp, _ in word_pairs:\\r\\n augment_pairs.append([synonym, c, sn, fi, cp, fw])\\r\\n \\r\\n if len(word_pairs) > 0:\\r\\n dataset.extend(word_pairs)\\r\\n \\r\\n if len(augment_pairs) > 0:\\r\\n augm_dataset.extend(augment_pairs)\\r\\n \\r\\n\\r\\n if len(dataset) > 0: print('Original dataset: ', len(dataset), len(dataset[0]))\\r\\n if len(augm_dataset) > 0: print('Augmented dataset: ', len(augm_dataset), len(augm_dataset[0]))\\r\\n \\r\\n # Look for the dataset file, if it doesn't exist create it ('a+')\\r\\n with open(dataset_file, 'a+', newline='') as f:\\r\\n wr = csv.writer(f, quoting=csv.QUOTE_ALL)\\r\\n wr.writerows(dataset)\\r\\n \\r\\n with open(augm_dataset_file, 'a+', newline='') as f:\\r\\n wr = csv.writer(f, quoting=csv.QUOTE_ALL)\\r\\n wr.writerows(augm_dataset) \\r\\n \\r\\n return True\",\n \"def process(self, terms):\\n for entry in self.files:\\n try:\\n logger.info('file - {0}'.format(entry.path))\\n\\n # notional output file path\\n path_sentences = self.path.joinpath('{0}.csv'.format(entry.path.stem))\\n path_summary = self.path.joinpath('{0}-summary.csv'.format(entry.path.stem))\\n logger.info('will save to - {0}'.format(path_sentences.resolve()))\\n\\n reports = self.inspect_doc(entry, terms)\\n\\n # receiving a list of dicts\\n # therefore pandas can package into a useful outcome\\n if len(reports) > 0:\\n frame_sentences = pd.DataFrame(reports)\\n\\n frame_sentences = frame_sentences[['page', 'term', 'sentence']]\\n logger.info('saving sentence file to - {0}'.format(path_sentences.resolve()))\\n frame_sentences.to_csv(str(path_sentences.resolve()))\\n \\n frame_summary = frame_sentences.pivot_table(\\n index='page',\\n columns='term',\\n aggfunc='size',\\n fill_value=0\\n )\\n logger.info('saving summary file to - {0}'.format(path_sentences.resolve()))\\n frame_summary.to_csv(str(path_summary.resolve()))\\n\\n\\n except Exception as e:\\n logger.error(e)\",\n \"def add_document_lists(self, docs):\\n for sent in docs:\\n sent = map(self.process_token, sent)\\n self._token_count.update(sent)\",\n \"def process_commands():\\n # Parse and handle each different command\\n args = parse_arguments()\\n\\n pdfsplit.pdf_split(args.file, args.pages, args.offset)\",\n \"def ie_preprocess(document):\\n sentences = nltk.sent_tokenize(document) #NLTK default sentence segmenter\\n #print sentences # sentences are segmented\\n sentences = [nltk.word_tokenize(sent) for sent in sentences] # NLTK word tokenizer \\n #print sentences # sentences are tokenized\\n sentences = [nltk.pos_tag(sent) for sent in sentences] # NLTK POS tagger \\n #print sentences # sentences are POS tagged\\n return sentences\",\n \"def parse_documents():\\n\\n\\tcount_before = control.find().count()\\n\\n\\tprint \\\"There are currently %i unprocessed records.\\\" % count_before\\n\\n\\t#dispatch\\n\\t# executor = concurrent.futures.ThreadPoolExecutor(10)\\n\\t# futures = [executor.submit(analyze_message, document) for document in control.find()]\\n\\t# concurrent.futures.wait(futures)\\n\\n\\tfor document in control.find():\\n\\t\\tanalyze_message(document)\\n\\n\\tcount_after = control.count()\\n\\tprint \\\"There are now %i stored records.\\\" % control.count()\",\n \"def __call__(self, *input_data):\\n \\n pb_ann = Any()\\n pb_ann.Pack(annotation_to_protobuf.convert_annotation(input_data))\\n request = annotation_pb2.ProcessRequest(pipeline_name = self._pipeline_name, \\n input_annotations = pb_ann)\\n \\n response = self._stub.process(request)\\n return annotation_from_protobuf.convert_annotation(response.output_annotations)\",\n \"def process(self, request, context):\\n \\n logger.info('Processing incoming request with \\\"{}\\\"...'.format(request.pipeline_name))\\n ppl = self._ppls[request.pipeline_name]\\n input_annotations = annotation_from_protobuf.convert_annotation(request.input_annotations)\\n res = ppl(*input_annotations)\\n logger.info('Done.')\\n \\n pb_res = annotation_to_protobuf.convert_annotation(res)\\n reply = pb.ProcessReply()\\n reply.output_annotations.Pack(pb_res)\\n \\n return reply\",\n \"def annotateFiles(listOfFilesPath=None, annotatedOutputFolder=u'./002manuallyAnnotated/', dumpSP=True):\\n referencePathLine = []\\n listOfAnnotations = []\\n # get the list containing the file paths\\n if listOfFilesPath is None:\\n listOfFilesPath = randomlySelectNDocsFromPath(b000path.getBtFolderPath(flagFolder=None), n=100)\\n makeLocalFolderPaths(listOfFilesPath)\\n elif type(listOfFilesPath) is str:\\n if u'.json' in listOfFilesPath:\\n listOfFilesPath = utilsOs.openJsonFileAsDict(listOfFilesPath)\\n else:\\n listOfFilesPath = [listOfFilesPath]\\n # get rid of the files we have already annotated\\n if utilsOs.theFileExists(u'{0}sampleReference.tsv'.format(annotatedOutputFolder)):\\n refLines = utilsOs.readAllLinesFromFile(u'{0}sampleReference.tsv'.format(annotatedOutputFolder),\\n noNewLineChar=True)\\n annotatedFiles = set([line.split(u'\\\\t')[0] for line in refLines])\\n listOfFilesPath = [file for file in listOfFilesPath if file not in annotatedFiles]\\n # print the annotator cheat sheet\\n printCheatSheet()\\n # open each file in EN and FR and show it in the terminal\\n for filePath in listOfFilesPath:\\n print(u'############# {0} ##############'.format(filePath.replace(u'/data/rali8/Tmp/rali/bt/burtrad/corpus_renamed/', u'')))\\n # get the path for the source and target\\n fileSourcePath = u'{0}.fr'.format(filePath) if u'fr-en' in filePath else u'{0}.en'.format(filePath)\\n fileTargetPath = u'{0}.en'.format(filePath) if u'fr-en' in filePath else u'{0}.fr'.format(filePath)\\n with open(fileSourcePath) as fileSource:\\n with open(fileTargetPath) as fileTarget:\\n # show the context of the annotated sentence\\n beforeSentSource = fileSource.readline()\\n duringSentSource = fileSource.readline()\\n beforeSentTarget = fileTarget.readline()\\n duringSentTarget = fileTarget.readline()\\n # annotate the first sentence pair\\n listOfAnnotations = annotateFirstSP(beforeSentSource, duringSentSource, beforeSentTarget,\\n duringSentTarget, listOfAnnotations, lineLength=137)\\n # save the reference\\n # if the filepath is the reference\\n if u'burtrad' in filePath:\\n referencePathLine.append(u'{0}\\\\t{1}'.format(filePath, 0))\\n # otherwise we get it from a reference file\\n else:\\n with open(u'{0}.tsv'.format(filePath)) as refFile:\\n refLns = [ln.replace(u'\\\\n', u'') for ln in refFile.readlines()]\\n referencePathLine.append(refLns[0])\\n # dump the first SP\\n if dumpSP is True:\\n enSent = beforeSentSource if u'.en' in fileSourcePath else beforeSentTarget\\n frSent = beforeSentTarget if u'.en' in fileSourcePath else beforeSentSource\\n utilsOs.appendLineToFile(enSent, u'{0}sample.en'.format(annotatedOutputFolder), addNewLine=False)\\n utilsOs.appendLineToFile(frSent, u'{0}sample.fr'.format(annotatedOutputFolder), addNewLine=False)\\n duringIndex = 1\\n # for each line\\n while duringSentSource or duringSentTarget:\\n # get the correct terminal line length\\n lineLength = 137-len(str(len(listOfAnnotations)+1))\\n # get the sentences\\n afterSentSource = fileSource.readline()\\n afterSentTarget = fileTarget.readline()\\n # color in red the during lines\\n redDuringSource = u'\\\\033[1;31m{0}\\\\033[0m'.format(duringSentSource)\\n redDuringTarget = u'\\\\033[1;31m{0}\\\\033[0m'.format(duringSentTarget)\\n # print the sentences\\n print(u'{0} - {1}'.format(len(listOfAnnotations)-1, beforeSentSource))\\n print(u'{0} - {1}'.format(len(listOfAnnotations)-1, beforeSentTarget))\\n print(u'{0} - {1}'.format(len(listOfAnnotations), redDuringSource))\\n print(u'{0} - {1}'.format(len(listOfAnnotations), redDuringTarget))\\n print(u'{0} - {1}'.format(len(listOfAnnotations)+1, afterSentSource))\\n print(u'{0} - {1}'.format(len(listOfAnnotations)+1, afterSentTarget))\\n print()\\n # count if the lines that take the space of 2 lines\\n longLines = getNbLongLines([beforeSentSource, beforeSentTarget, duringSentSource,\\n duringSentTarget, afterSentSource, afterSentTarget], lineLength)\\n # get the first part of the annotation (aligned or not)\\n annotatorGeneralInput = input(u'Aligned-Misaligned annotation: ')\\n # make sure to have the right general annotation\\n while True:\\n if annotatorGeneralInput in [u'0', u'1', u'0.0', u'0.1', u'0.2',\\n u'1.0', u'1.1', u'1.2', u'1.3', u'1.4', u'c', u'correction']:\\n break\\n else:\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n annotatorGeneralInput = input(u'Repeat annotation: ')\\n if annotatorGeneralInput in [u'c', u'correct']:\\n annotatorGeneralInput, listOfAnnotations = correctionToAnnotation(listOfAnnotations)\\n # if we still need to specify what type of alignment or misalignment\\n if annotatorGeneralInput in [u'0', u'1']:\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n # get the second part of the annotation (aligned or not)\\n annotatorSpecificInput = input(u'Specific type annotation: ')\\n typeAnswers = [u'0', u'1', u'2'] if annotatorGeneralInput == 0 else [u'0', u'1', u'2', u'3', u'4']\\n # make sure to have the right specific annotation\\n while True:\\n if annotatorSpecificInput in typeAnswers:\\n break\\n else:\\n utilsOs.moveUpAndLeftNLines(1, slowly=False)\\n annotatorSpecificInput = input(u'Repeat type annotation: ')\\n # save to the list of annotations\\n listOfAnnotations.append(float(u'{0}.{1}'.format(annotatorGeneralInput, annotatorSpecificInput)))\\n # if the right answer was given in the right format right away\\n else:\\n # save to the list of annotations\\n listOfAnnotations.append(float(annotatorGeneralInput))\\n # remove the lines from the terminal before getting to the next pair\\n utilsOs.moveUpAndLeftNLines(14+longLines, slowly=False)\\n # erase all remainder of the previous sentences and go back up again\\n for e in range(14+longLines):\\n print(u' '*(lineLength+4))\\n utilsOs.moveUpAndLeftNLines(14 + longLines, slowly=False)\\n # next line source\\n beforeSentSource = duringSentSource\\n duringSentSource = afterSentSource\\n # next line target\\n beforeSentTarget = duringSentTarget\\n duringSentTarget = afterSentTarget\\n # append the reference to the file\\n # if the filepath is the reference\\n if u'burtrad' in filePath:\\n referencePathLine.append(u'{0}\\\\t{1}'.format(filePath, duringIndex))\\n # otherwise we get it from a reference file\\n else:\\n with open(u'{0}.tsv'.format(filePath)) as refFile:\\n refLns = [ln.replace(u'\\\\n', u'') for ln in refFile.readlines()]\\n referencePathLine.append(refLns[duringIndex])\\n # add 1 to index\\n duringIndex += 1\\n # dump the file line by line, to be sure in case of error\\n # dump the reference\\n utilsOs.dumpRawLines(referencePathLine, u'{0}sampleReference.tsv'.format(annotatedOutputFolder),\\n addNewline=True, rewrite=True)\\n # dump the annotation\\n utilsOs.dumpRawLines(listOfAnnotations, u'{0}sampleAnnotation.tsv'.format(annotatedOutputFolder),\\n addNewline=True, rewrite=True)\\n # dump the SP\\n if dumpSP is True:\\n enSent = beforeSentSource if u'.en' in fileSourcePath else beforeSentTarget\\n frSent = beforeSentTarget if u'.en' in fileSourcePath else beforeSentSource\\n utilsOs.appendLineToFile(enSent, u'{0}sample.en'.format(annotatedOutputFolder), addNewLine=False)\\n utilsOs.appendLineToFile(frSent, u'{0}sample.fr'.format(annotatedOutputFolder), addNewLine=False)\\n # clear part of terminal\\n utilsOs.moveUpAndLeftNLines(2, slowly=False)\",\n \"def process_corpus(data_dir, feature_source):\\n raw_dir = os.path.join(data_dir, 'text/', feature_source)\\n stem_dir = os.path.join(data_dir, 'text/stemmed_{0}/'.format(feature_source))\\n proc_dir = os.path.join(data_dir, 'text/cleaned_{0}/'.format(feature_source))\\n spell_file = os.path.join('/home/data/nbc/athena/athena-data/misc/english_spellings.csv')\\n spell_df = pd.read_csv(spell_file, index_col='UK')\\n spell_dict = spell_df['US'].to_dict()\\n\\n # Defines functions that are from the NLTK, I assume?\\n stemmer = EnglishStemmer()\\n test_stemmer = PorterStemmer()\\n\\n # Cycles through each raw .txt file\\n for file_ in glob(os.path.join(raw_dir, '*.txt')):\\n filename = os.path.basename(file_)\\n print('Processing {0}'.format(filename))\\n with open(file_, 'rb') as fo:\\n text = fo.read()\\n\\n text = text.decode('utf8', 'ignore').encode('ascii', 'ignore')\\n\\n # Clean text\\n text = abbr.clean_str(text)\\n\\n # Detect and expand abbreviations\\n text = abbr.expandall(text)\\n\\n # Remove periods (for abbreviations)\\n text = text.replace('.', '')\\n\\n # Replace British words with American words.\\n pattern = re.compile(r'\\\\b(' + '|'.join(spell_dict.keys()) + r')\\\\b')\\n text = pattern.sub(lambda x: spell_dict[x.group()], text)\\n\\n # Defines stem_list which will be a list of all the words in the file,\\n # not including spaces\\n stem_list = []\\n for word in text.split():\\n # Use Porter stemmer to test for string unicode encoding, then use\\n # English stemmer to perform stemming\\n try:\\n ' '.join(['kdkd', test_stemmer.stem(word), 'kdkd'])\\n except:\\n word = word.decode('utf8', 'ignore').encode('ascii', 'ignore')\\n stem_list.append(stemmer.stem(word))\\n\\n # Writes the stem_list\\n with open(os.path.join(stem_dir, filename), 'wb') as fo:\\n fo.write(' '.join(stem_list))\\n\\n # Writes the processed text\\n with open(os.path.join(proc_dir, filename), 'wb') as fo:\\n fo.write(text)\",\n \"def train(\\n train_texts: List[str],\\n train_labels: List[str],\\n pretrain_params: Any = None) -> Any:\\n train_texts = preprocessing(train_texts)\\n train_tokenized_texts = text_to_tokens(train_texts)\\n\\n train_pos = [train_tokenized_texts[i] for i in range(len(train_labels)) if train_labels[i] == 'pos']\\n train_neg = [train_tokenized_texts[i] for i in range(len(train_labels)) if train_labels[i] == 'neg']\\n \\n cnt_pos_docs = len(train_pos)\\n cnt_neg_docs = len(train_neg)\\n\\n\\n all_words_freq = defaultdict(int)\\n all_words = set()\\n\\n pos_dict = defaultdict(int)\\n neg_dict = defaultdict(int)\\n sum_len_pos = 0\\n sum_len_neg = 0\\n\\n for text in train_pos:\\n for token in text:\\n all_words.add(token)\\n all_words_freq[token] += text[token]\\n pos_dict[token] += text[token]\\n sum_len_pos += text[token]\\n \\n for text in train_neg:\\n for token in text:\\n all_words.add(token)\\n all_words_freq[token] += text[token]\\n neg_dict[token] += text[token]\\n sum_len_neg += text[token]\\n \\n alpha = 1 #For additive smoothing\\n M = len(all_words)\\n sum_len = 0\\n print(\\\"____________\\\")\\n print(\\\"Sum of text lens\\\", sum_len)\\n print(\\\"____________\\\")\\n print(\\\"Words quantity\\\", M)\\n print(\\\"____________\\\")\\n\\n token_probs_pos = defaultdict(int)\\n token_probs_neg = defaultdict(int)\\n print(\\\"Calculate probablity for\\\", M, \\\"tokens\\\")\\n\\n i = 0\\n for token in all_words:\\n if (i % 5000 == 0):\\n print(\\\"__________\\\")\\n print(\\\"Calculated\\\", i, \\\"tokens\\\")\\n print(\\\"__________\\\")\\n token_probs_pos[token] = (alpha + pos_dict[token]) / (alpha * M + sum_len_pos)\\n token_probs_neg[token] = (alpha + neg_dict[token]) / (alpha * M + sum_len_neg)\\n i += 1\\n \\n return {\\n \\\"token_probs_pos\\\": token_probs_pos,\\n \\\"token_probs_neg\\\": token_probs_neg,\\n \\\"all_words\\\": all_words,\\n \\\"sum_len_pos\\\": sum_len_pos,\\n \\\"sum_len_neg\\\": sum_len_neg,\\n \\\"cnt_pos_docs\\\": cnt_pos_docs,\\n \\\"cnt_neg_docs\\\": cnt_pos_docs,\\n \\\"pos_dict\\\": pos_dict,\\n \\\"neg_dict\\\": neg_dict\\n }\",\n \"def learn(self, documents, labels):\\n raise NotImplementedError('FeatureExtractorBase:learn(self, text_list) is not defined')\",\n \"def postpare_anno(infolder, outfolder, mode=\\\"fl\\\"):\\n\\tprint(\\\"Starting...\\\")\\n\\t\\n\\tif not os.path.exists(infolder):\\n\\t\\traise ValueError(\\\"The input folder could not be found.\\\")\\n\\t\\t\\n\\tin_temp = os.path.join(infolder, \\\"temp\\\")\\n\\tin_anno = os.path.join(infolder, \\\"annotated_temp\\\")\\n\\t\\n\\tif not os.path.exists(in_temp):\\n\\t\\traise ValueError(\\\"The folder 'temp' could not be found inside the input folder.\\\")\\n\\tif not os.path.exists(in_anno):\\n\\t\\traise ValueError(\\\"The folder 'annotated_temp' could not be found inside the input folder.\\\")\\n\\tif not os.path.exists(outfolder):\\n\\t\\tos.makedirs(outfolder)\\n\\t\\t\\n\\tfilecounter = 0\\t\\n\\n\\t# fetch annotated snippets for each TEI template file\\n\\tfor filepath in glob.glob(os.path.join(in_temp, \\\"*.xml\\\")):\\n\\t\\tprint(\\\"doing file \\\" + filepath)\\n\\t\\tfilecounter+= 1\\n\\t\\tfn = os.path.basename(filepath)\\n\\t\\tannofolder = os.path.join(Path(os.path.join(infolder, \\\"annotated_temp\\\")).as_uri(), \\\"\\\")\\n\\t\\t# which annotation mode are we in?\\n\\t\\tannomode = mode\\n\\t\\t\\n\\t\\tparser = etree.XMLParser(encoding=\\\"UTF-8\\\")\\n\\t\\tparser.resolvers.add(FileResolver())\\n\\t\\t\\n\\t\\tdoc = etree.parse(filepath, parser)\\n\\t\\txslt_root = etree.parse(io.StringIO(xslt_joinDIVs), parser)\\n\\t\\t\\n\\t\\ttransform = etree.XSLT(xslt_root)\\n\\t\\t\\n\\t\\tresult_tree = transform(doc, annofolder= \\\"'\\\" + annofolder + \\\"'\\\", mode= \\\"'\\\" + annomode + \\\"'\\\")\\n\\t\\tresult = str(result_tree)\\n\\t\\t\\n\\t\\t# save the results\\n\\t\\twith open(os.path.join(outfolder, fn), \\\"w\\\") as output:\\n\\t\\t\\toutput.write(result)\\n\\t\\n\\tprint(\\\"Done. \\\" + str(filecounter) + \\\" files treated.\\\")\",\n \"def annotate_document(self, document, analyzer_steps):\\n assert isinstance(analyzer_steps, list)\\n if isinstance(document, dict):\\n document = json.dumps(document)\\n r = requests.post('/'.join([self.base_url,\\n self.ANNOTATE_ENDPOINT,\\n '+'.join(analyzer_steps)]),\\n data=document,\\n headers={'Content-Type': 'application/json'})\\n return(r.json())\",\n \"def test_pipeline_data(self, doc_list):\\n summaries = []\\n for item in doc_list:\\n summaries.append(self.preprocess(item))\\n return summaries\",\n \"def process_raw(self):\\n\\n for i in range(self._length):\\n word = self._words[i].lower()\\n if word not in self._data:\\n self._data[word] = Word(word)\\n self._data[word].add_occurrence(i / self._length)\\n\\n # Notify view\\n for view in self._views:\\n view.notify_progress(1 / self._length)\\n\\n # process\\n for word in self._data:\\n self._data[word].update()\\n\\n # allow access\\n self._canOperate = True\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.6669254","0.6452818","0.6272669","0.6263057","0.6151627","0.6133174","0.6116166","0.60921097","0.6062387","0.6043837","0.5952495","0.5947582","0.5938026","0.5930227","0.58644366","0.5852183","0.58298236","0.5801322","0.57992077","0.57630277","0.57474416","0.57435316","0.570151","0.56982166","0.5677628","0.5668026","0.56464124","0.5608801","0.5586619","0.55241585","0.5501787","0.54910195","0.54766923","0.5460091","0.54577714","0.5456323","0.54468805","0.54225606","0.5420165","0.5417305","0.5416637","0.5415304","0.54121166","0.541199","0.5398066","0.5380248","0.5363726","0.5340256","0.5339846","0.5333221","0.53204286","0.5317933","0.5312138","0.53089625","0.5280222","0.52717066","0.526565","0.52652","0.52638805","0.5262895","0.5256581","0.52459425","0.52442473","0.521662","0.51993585","0.519551","0.51911527","0.5178816","0.5177213","0.51679206","0.5166985","0.5156334","0.5153154","0.51481766","0.5141068","0.5140034","0.5136455","0.5135716","0.51230025","0.51155263","0.5109624","0.5105854","0.5103781","0.51009154","0.50988394","0.507975","0.50784165","0.50776595","0.5076291","0.5072753","0.50727355","0.50701267","0.50694335","0.5069024","0.5067996","0.50498503","0.50485384","0.50470215","0.50467914","0.50393903"],"string":"[\n \"0.6669254\",\n \"0.6452818\",\n \"0.6272669\",\n \"0.6263057\",\n \"0.6151627\",\n \"0.6133174\",\n \"0.6116166\",\n \"0.60921097\",\n \"0.6062387\",\n \"0.6043837\",\n \"0.5952495\",\n \"0.5947582\",\n \"0.5938026\",\n \"0.5930227\",\n \"0.58644366\",\n \"0.5852183\",\n \"0.58298236\",\n \"0.5801322\",\n \"0.57992077\",\n \"0.57630277\",\n \"0.57474416\",\n \"0.57435316\",\n \"0.570151\",\n \"0.56982166\",\n \"0.5677628\",\n \"0.5668026\",\n \"0.56464124\",\n \"0.5608801\",\n \"0.5586619\",\n \"0.55241585\",\n \"0.5501787\",\n \"0.54910195\",\n \"0.54766923\",\n \"0.5460091\",\n \"0.54577714\",\n \"0.5456323\",\n \"0.54468805\",\n \"0.54225606\",\n \"0.5420165\",\n \"0.5417305\",\n \"0.5416637\",\n \"0.5415304\",\n \"0.54121166\",\n \"0.541199\",\n \"0.5398066\",\n \"0.5380248\",\n \"0.5363726\",\n \"0.5340256\",\n \"0.5339846\",\n \"0.5333221\",\n \"0.53204286\",\n \"0.5317933\",\n \"0.5312138\",\n \"0.53089625\",\n \"0.5280222\",\n \"0.52717066\",\n \"0.526565\",\n \"0.52652\",\n \"0.52638805\",\n \"0.5262895\",\n \"0.5256581\",\n \"0.52459425\",\n \"0.52442473\",\n \"0.521662\",\n \"0.51993585\",\n \"0.519551\",\n \"0.51911527\",\n \"0.5178816\",\n \"0.5177213\",\n \"0.51679206\",\n \"0.5166985\",\n \"0.5156334\",\n \"0.5153154\",\n \"0.51481766\",\n \"0.5141068\",\n \"0.5140034\",\n \"0.5136455\",\n \"0.5135716\",\n \"0.51230025\",\n \"0.51155263\",\n \"0.5109624\",\n \"0.5105854\",\n \"0.5103781\",\n \"0.51009154\",\n \"0.50988394\",\n \"0.507975\",\n \"0.50784165\",\n \"0.50776595\",\n \"0.5076291\",\n \"0.5072753\",\n \"0.50727355\",\n \"0.50701267\",\n \"0.50694335\",\n \"0.5069024\",\n \"0.5067996\",\n \"0.50498503\",\n \"0.50485384\",\n \"0.50470215\",\n \"0.50467914\",\n \"0.50393903\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.8031649"},"document_rank":{"kind":"string","value":"0"}}},{"rowIdx":21898,"cells":{"query":{"kind":"string","value":"Replaces all participant arguments in project with canonical referent links."},"document":{"kind":"string","value":"def replace_canonical_referents(self, **kwargs):\n replaced_count = 0\n for doc in self.annotation_documents:\n for sen in doc.sentences:\n for part in sen.participants:\n replaced_count += part.replace_by_canonical_referent(**kwargs) #this will replace in-place in project, but returns a count too.\n\n return replaced_count"},"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 fix_links():\n pass","def replace_by_canonical_referent(self, cross_sentence=True):\n replaced_count = 0\n\n # replace the participant in their containers\n def replace_in_containers(myobj, replacing):\n containers = {\n \"event\": \"participants\",\n \"sentiment_expression\": \"targets\",\n \"sentence\": \"participants\",\n }\n for c_name, attrib_n in containers.items():\n c_name = f\"in_{c_name}\"\n if hasattr(myobj, c_name):\n for c in getattr(myobj, c_name):\n to_replace_in = getattr(c, attrib_n)\n to_replace_in.append(replacing)\n if myobj in to_replace_in:\n to_replace_in.remove(myobj)\n to_replace_in.sort(key=lambda x: x.begin)\n\n if self.canonical_referents and self.canonical_referents != \"from_canonref\":\n # sometimes there are multiple canonrefs tagged\n # this can be a) annotation mistake or\n # b) multiple reference to a group, e.g. \"all\" refers to three companies.\n for canonref in self.canonical_referents:\n # check whether canonical referent is in same sentence\n same_sentence = [s.element_id for s in self.in_sentence] == [\n s.element_id for s in canonref.in_sentence\n ]\n # always replace when cross_sentence is true\n # if cross_sentence is False (disallowed) only replace when canonref is in same sentence\n if cross_sentence or same_sentence:\n # replace element\n replacing_participant = Participant(\n canonref.text,\n canonref.begin,\n canonref.end,\n canonref.element_id,\n canonref.annotator_id,\n canonref.document_title,\n canonref.in_document,\n self.role,\n \"from_canonref\",\n self.link_id,\n canonref.tokens,\n )\n replacing_participant.in_sentence = self.in_sentence\n replacing_participant.in_document = self.in_document\n if hasattr(self, \"in_sentiment_expression\"):\n replacing_participant.in_sentiment_expression = (\n self.in_sentiment_expression\n )\n replacing_participant.from_original_participant = copy(self)\n print(\n f\"Replaced {self} with {canonref}. (in same sentence:{same_sentence})\"\n )\n\n replace_in_containers(self, replacing_participant)\n\n # replace on document\n self.in_document.participants.append(replacing_participant)\n if self in self.in_document.participants:\n self.in_document.participants.remove(self)\n\n replaced_count += 1\n return replaced_count","def to_projectlink(self):\n\n thumb_image_url = reverse('project_serve_file', args=[self.short_name,self.logo])\n\n args = {\"abreviation\":self.short_name,\n \"title\":self.short_name,\n \"description\":self.description,\n \"URL\":reverse('comicsite.views.site', args=[self.short_name]),\n \"download URL\":\"\",\n \"submission URL\":self.get_submission_URL(),\n \"event name\":self.event_name,\n \"year\":\"\",\n \"event URL\":self.event_url,\n \"image URL\":self.logo,\n \"thumb_image_url\":thumb_image_url,\n \"website section\":\"active challenges\",\n \"overview article url\":self.publication_url,\n \"overview article journal\":self.publication_journal_name,\n \"overview article citations\":\"\",\n \"overview article date\":\"\",\n \"submission deadline\":\"\",\n \"workshop date\":self.workshop_date,\n \"open for submission\":\"yes\" if self.is_open_for_submissions else \"no\",\n \"data download\":\"yes\" if self.offers_data_download else \"no\",\n \"dataset downloads\":self.number_of_downloads,\n \"registered teams\":\"\",\n \"submitted results\":self.number_of_submissions,\n \"last submission date\":self.last_submission_date,\n \"hosted on comic\":True,\n \"created at\":self.created_at\n }\n\n projectlink = ProjectLink(args)\n return projectlink","def reproject(self, *args, **kwargs):\n pass","def replace_with_arg(self, src_arg, tgt_arg):","def _fix_links(self, text, page_names):\n for n in page_names:\n text = text.replace(f\"]({n})\", f\"]({n}.html)\")\n text = text.replace(f\"]({n}.md)\", f\"]({n}.html)\")\n return text","def _make_links(tweet):\n for pattern, repl in (USER_SUB, KEYWORD_SUB):\n tweet = re.sub(pattern, repl, tweet)\n return tweet","def _build_links(links):\n for link in links:\n link['href'] = link['href'].replace('servers', 'instances')\n return links","def generate_clean_url(self):\n\n\t\tspaces_replaced = self.title.replace(' ', '-')\n\t\tpattern = re.compile('[^a-zA-Z0-9-]+')\n\t\tstripped = pattern.sub('', spaces_replaced)\n\t\tself.cleanurl = '-'.join([str(self.pid), stripped.lower()])","def test_replace_project(self):\n pass","def setUp_extra(self):\n [self.testproject,\n self.root,\n self.projectadmin,\n self.participant,\n self.signedup_user] = self._create_dummy_project(\"linkreplacer-test\")\n\n self.replacer = HtmlLinkReplacer()","def replace_local_hyperlinks(\n text,\n base_url=\"https://github.com/project-rig/nengo_spinnaker/blob/master/\"\n ):\n def get_new_url(url):\n return base_url + url[2:]\n\n # Deal with anonymous URLS\n for match in re.finditer(r\"^__ (?P<url>\\./.*)\", text, re.MULTILINE):\n orig_url = match.groupdict()[\"url\"]\n url = get_new_url(orig_url)\n\n text = re.sub(\"^__ {}\".format(orig_url),\n \"__ {}\".format(url), text, flags=re.MULTILINE)\n\n # Deal with named URLS\n for match in re.finditer(r\"^\\.\\. _(?P<identifier>[^:]*): (?P<url>\\./.*)\",\n text, re.MULTILINE):\n identifier = match.groupdict()[\"identifier\"]\n orig_url = match.groupdict()[\"url\"]\n url = get_new_url(orig_url)\n\n text = re.sub(\n \"^\\.\\. _{}: {}\".format(identifier, orig_url),\n \".. _{}: {}\".format(identifier, url),\n text, flags=re.MULTILINE)\n\n # Deal with image URLS\n for match in re.finditer(r\"^\\.\\. image:: (?P<url>\\./.*)\",\n text, re.MULTILINE):\n orig_url = match.groupdict()[\"url\"]\n url = get_new_url(orig_url)\n\n text = text.replace(\".. image:: {}\".format(orig_url),\n \".. image:: {}\".format(url))\n\n return text","def _fixup_find_links(find_links):\n if isinstance(find_links, str):\n return find_links.split()\n assert isinstance(find_links, (tuple, list))\n return find_links","def get_absolute_url(self):\n return reverse('plan_proposal',\n kwargs = {'project_name': self.project.slug,\n 'proposal_name': self.slug})","def get_absolute_url(self):\n return reverse('project-detail', kwargs={'pk': self.pk})","def adjust_anchors(self):\n pass","def replace(self, name, *args):\n\n self._add(True, self.authority, name, *args)","def replace(self, *args, **kwargs): # real signature unknown\r\n pass","def fix_genindex(self, tree: list[tuple[str, list[tuple[str, Any]]]]) -> None:\n # XXX: modifies tree inline\n # Logic modeled from themes/basic/genindex.html\n for _key, columns in tree:\n for _entryname, (links, subitems, _key) in columns:\n for (i, (ismain, link)) in enumerate(links):\n m = self.refuri_re.match(link)\n if m:\n links[i] = (ismain,\n self.fix_fragment(m.group(1), m.group(2)))\n for _subentryname, subentrylinks in subitems:\n for (i, (ismain, link)) in enumerate(subentrylinks):\n m = self.refuri_re.match(link)\n if m:\n subentrylinks[i] = (ismain,\n self.fix_fragment(m.group(1), m.group(2)))","def simplify_links(proj,exp,links):\n simple_links =[] \n\n for key in links:\n (node_name,x,y) = key.rpartition(':')\n node_name = node_name+\".\"+exp+\".\"+proj+\".emulab.net\"\n simple_links.append((node_name,links[key]['ipaddr']))\n\n return simple_links","def rewrite_relative_links(soup: bs4.BeautifulSoup, base_url: str):\n for tag_name, attrib in URL_REWRITE_PAIRS:\n for tag in soup.find_all(tag_name, attrs={attrib: True}):\n try:\n tag[attrib] = urllib.parse.urljoin(base_url, tag[attrib])\n except ValueError as e:\n logger.info('Could not rewrite link: %s', e)","def get_absolute_url(self):\n return ('project_detail', (), {\n 'name': self.title\n })","def get_absolute_url(self):\n return reverse(\"projects-detail\", kwargs={'pk': self.pk})","def _transform_github_url(self):\n self.url = (\n self.url\n .replace('/blob/', '/')\n .replace(self.GITHUB_NETLOC, self.GITHUB_RAW_NETLOC)\n )","def reverse_discussion_link(viewname, args):\r\n if viewname == \"django_comment_client.forum.views.forum_form_discussion\" and args == [course.id.to_deprecated_string()]:\r\n return \"default_discussion_link\"","def _remove_invalid_links(text):\n\n for reply_number in re.finditer(REGEX_REPLY, text):\n post_id = reply_number.group(1)\n post = Post.objects.filter(id=post_id)\n if not post.exists():\n text = string.replace(text, REFLINK_PREFIX + post_id, post_id)\n\n return text","def link_residues(self) -> None:\n ...","def get_absolute_url(self):\n return reverse('participant-detail', args=[str(self.participantId)])","def make_absolute_query_link(db,**params):\n pass","def get_absolute_url(self):\n return reverse('authors', args=[str(self.id)])","def extendMarkdown(self, md, md_globals):\r\n md.inlinePatterns['autolink'] = UrlizePattern(URLIZE_RE, md)","def fix_references(self) -> None:\n\n\t\tself.log_info(\"--- Searching for references to variants ---\")\n\t\tfor publication in Publication.objects.filter(variant_of__isnull=False):\n\t\t\tvariant = publication.variant_of\n\t\t\torigs = PublicationReference.objects.filter(reference=publication)\n\t\t\tfor orig in origs:\n\t\t\t\tif PublicationReference.objects.filter(reference=variant, publication=orig.publication).exists():\n\t\t\t\t\tcontinue\n\t\t\t\tfixed = PublicationReference(\n\t\t\t\t\treference=variant,\n\t\t\t\t\tpublication=orig.publication,\n\t\t\t\t\tidentifier=('' if orig.identifier is None else orig.identifier) + \"*\",\n\t\t\t\t)\n\t\t\t\ttry:\n\t\t\t\t\tfixed.full_clean()\n\t\t\t\t\tfixed.save()\n\t\t\t\t\tself.log_success(f\"Added reference: {publication} -- {fixed.identifier} -> {variant}\")\n\t\t\t\texcept ValidationError as e:\n\t\t\t\t\traise CommandError(f\"{publication} -- {fixed.identifier} -> {variant}: {e}\")","def normalize_reviewer(ctx, reviewer):\n name_re = re.compile(r'^\\w+ [^<]+ <.*@.*\\..*>$')\n if name_re.match(reviewer):\n return reviewer\n rbranch = '%s/master' % ctx.config['remote']\n cmd = ['git',\n '-c', 'mailmap.blob=origin/master:.mailmap',\n 'shortlog', '-sen', rbranch]\n names = ctx.runprocess(cmd).stdout.splitlines()\n names = (name.split('\\t', 1)[-1] for name in names)\n names = (name for name in names if name_re.match(name))\n names = [name for name in names if reviewer.lower() in name.lower()]\n if not names:\n ctx.die('Reviewer %s not found' % reviewer)\n elif len(names) > 1:\n print(ctx.term.red('Reviewer %s could be:' % reviewer))\n for name in names:\n print('- %s' % name)\n ctx.die('Multiple matches found for reviewer')\n else:\n name = unidecode.unidecode(names[0])\n return name","def fix_project_keys(apps, schema_editor):\n RemoteProject = apps.get_model('projectroles', 'RemoteProject')\n Project = apps.get_model('projectroles', 'Project')\n\n for rp in RemoteProject.objects.all():\n if not rp.project:\n rp.project = Project.objects.filter(\n sodar_uuid=rp.project_uuid\n ).first()\n rp.save()","def canonicalize(self, url):\n pass","def _annotate_remote_from_args(self) -> None:\n if self._local_remote_pairs:\n if self._remote_side:\n raise sa_exc.ArgumentError(\n \"remote_side argument is redundant \"\n \"against more detailed _local_remote_side \"\n \"argument.\"\n )\n\n remote_side = [r for (l, r) in self._local_remote_pairs]\n else:\n remote_side = self._remote_side\n\n if self._refers_to_parent_table():\n self._annotate_selfref(lambda col: col in remote_side, True)\n else:\n\n def repl(element: _CE, **kw: Any) -> Optional[_CE]:\n # use set() to avoid generating ``__eq__()`` expressions\n # against each element\n if element in set(remote_side):\n return element._annotate({\"remote\": True})\n return None\n\n self.primaryjoin = visitors.replacement_traverse(\n self.primaryjoin, {}, repl\n )","def replaceArgument(self, *args):\n return _libsbml.ASTNode_replaceArgument(self, *args)","def _transform_args(self) -> None:\n self.args = None if self.args == [] else self.args","def replaceLetra(a): \n global palavraOculta\n palavraSecreta = list(palavraOculta)\n\n for m in re.finditer(a, palavraDoJogo):\n palavraSecreta[m.start()] = a\n\n palavraOculta = \"\".join(palavraSecreta)","def remake_challenge_url(self, text_signature, ch_id):\n text_head = \"https://www.iesdouyin.com/web/api/v2/challenge/aweme/?\"\n text_music_id = \"ch_id={}&\".format(ch_id)\n text_count = \"count=45&\"\n text_cursor = \"cursor={}&\".format(self.cursor)\n text_aid = \"aid=1128&screen_limit=3&download_click_limit=0&_\"\n\n remake = text_head + text_music_id + text_count + text_cursor + text_aid + text_signature\n return remake","def __apply_reflexive_pronoun(self, conjugation_notes, explicit_accent_already_applied):\n if conjugation_notes.tense in Tense.imperative():\n self.__apply_imperative_reflexive_pronoun(conjugation_notes, explicit_accent_already_applied) \n elif conjugation_notes.tense not in Tense.Person_Agnostic():\n conjugation_notes.change(operation=\"apply_reflexive\", conjugation = conjugation_notes.person.indirect_pronoun +\" \"+ conjugation_notes.conjugation,\n irregular_nature=IrregularNature.regular)\n elif conjugation_notes.tense == Tense.gerund:\n conjugation_notes.change(operation=\"apply_reflexive\",conjugation = Vowels.accent(conjugation_notes.conjugation)+Person.third_person_plural.indirect_pronoun,\n irregular_nature=IrregularNature.regular)","def uncanonicalize(self, url):\n pass","def get_absolute_url(self) -> str:\n return reverse(\n \"cagnottesolidaire:proposition\",\n kwargs={\"slug\": self.slug, \"p_slug\": self.cagnotte.slug},\n )","def project(self, other, normalized=...):\n ...","def repl_onebox(mat=None, li=[]):\n if mat is None:\n li[:] = []\n return\n if mat.group(1):\n url = mat.group(1).strip()\n if '/project/' in url:\n # Try to parse a project link\n return box_project(url) or mat.group()\n return mat.group()","def makeRelativePathsAbsolute(cmdargs):\n for i in range(len(cmdargs)):\n if relativePathRE.match(cmdargs[i]):\n cmdargs[i]=os.path.abspath(cmdargs[i])","def add_project_link(self, proj_curr, link):\r\n for proj in self.__projects:\r\n if proj == proj_curr: # Find the project with the same name\r\n proj.update_links(link) # Add the photo\r","def replace_relative_links(soup,base_url,debug=False):\n # only extract hyperlinks with href atributte\n if soup is None:\n return None\n\n links = soup.findAll('a', {\"href\" : True})\n links_replaced = 0\n for link in links: \n url = link['href']\n if url.startswith(\"./\"):\n link['href'] = base_url + url[1:len(url)] \n links_replaced += 1\n if debug is True:\n print(f\"soup_converter.replace_relative_links: {links_replaced} links replaced\")\n return soup","def resolve(self, anchors):\n\n for anchor in anchors:\n if self.node[DuAttrRefid] in anchor.ids():\n self.toAnchor = anchor\n break","def compose_url(base_url, anno, chimico):\n \n return base_url + chimico + '_' + anno + '.txt'","async def link_to(self, *args):\n pass","def remove_link():","def follow_project(cls, user, project):\r\n pass","def mutate_urls(self, mutator):","def _urljoin(self, *args):\r\n\t\treturn \"/\".join(map(lambda x: str(x).rstrip('/'), args))","def replace_links(\n soup: BeautifulSoup,\n resource_dir: str,\n replace_data: dict,\n) -> str:\n resource_tags = soup.findAll(name=list(TAGS_ATTR.keys()))\n change_tags(resource_tags, replace_data, resource_dir)\n return soup.prettify()","def _map_arguments(self, args):\n data = args.get('data')\n comp = args.get('comp')\n library = args.get('library')\n dry_run = args.get('dry_run', False)\n\n self._set_link('sum-rings', SumRings_SG,\n library=library,\n outdir=args['outdir'],\n dry_run=dry_run)\n\n self._set_link('srcmaps-diffuse', SrcmapsDiffuse_SG,\n comp=comp, data=data,\n library=library,\n make_xml=args['make_xml'],\n dry_run=dry_run)\n\n self._set_link('vstack-diffuse', Vstack_SG,\n comp=comp, data=data,\n library=library,\n dry_run=dry_run)","def get_absolute_url(self):\n # return reverse('tutor-detail', args=[str(self.email_address)])\n return reverse('tutor-detail', args=[str(self.username)])","def _get_reproducing_arguments(self):\n reproducing_arguments = {\n 'include': self.include,\n 'exclude': self.exclude,\n 'copy': self.copy,\n }\n args_names = {name: getattr(self, name) for name in self.args_names}\n reproducing_arguments.update(args_names)\n return reproducing_arguments","def fixAuthor(self, pageText):\n informationRegex = re.compile(\n r'\\|Author=Original uploader was '\n r'(?P<author>\\[\\[:\\w+:\\w+:\\w+\\|\\w+\\]\\] at \\[.+\\])')\n selfRegex = re.compile(\n r'{{self\\|author='\n r'(?P<author>\\[\\[:\\w+:\\w+:\\w+\\|\\w+\\]\\] at \\[.+\\])\\|')\n\n # Find the |Author=Original uploader was ....\n informationMatch = informationRegex.search(pageText)\n\n # Find the {{self|author=\n selfMatch = selfRegex.search(pageText)\n\n # Check if both are found and are equal\n if (informationMatch and selfMatch):\n if informationMatch.group('author') == selfMatch.group('author'):\n # Replace |Author=Original uploader was ... with |Author= ...\n pageText = informationRegex.sub(r'|Author=\\g<author>',\n pageText)\n return pageText","def mirrorJoint(*args, mirrorBehavior: bool=True, mirrorXY: bool=True, mirrorXZ: bool=True,\n mirrorYZ: bool=True, searchReplace: List[AnyStr, AnyStr]=None,\n **kwargs)->List[AnyStr]:\n pass","def test_indirect_parameters_update(renku_cli, project):\n with chdir(project.path):\n Path(\".renku/tmp\").mkdir(exist_ok=True)\n\n Path(\"script.sh\").write_text(\n \"\"\"\n echo param 1: \"forty-two\" >> .renku/tmp/parameters.yml\n echo param-2: 42.42 >> .renku/tmp/parameters.yml\n echo param3: 42 >> .renku/tmp/parameters.yml\n \"\"\"\n )\n\n project.repository.add(all=True)\n project.repository.commit(\"test setup\")\n\n renku_cli(\"run\", \"sh\", \"script.sh\", stdout=\"result\")\n\n with chdir(project.path):\n Path(\".renku/tmp\").mkdir(exist_ok=True)\n\n Path(\"script.sh\").write_text(\n \"\"\"\n echo param 1: \"forty-two-updated\" >> .renku/tmp/parameters.yml\n echo param-2: 42.42 >> .renku/tmp/parameters.yml\n \"\"\"\n )\n\n project.repository.add(all=True)\n project.repository.commit(\"test setup\")\n\n exit_code, activity = renku_cli(\"update\", \"--all\")\n\n assert 0 == exit_code\n assert {\"forty-two-updated\", \"42.42\", \"42\"} == {a.default_value for a in activity.association.plan.parameters}","def correct_links(html_file, schema_name):\n return html_file.replace(schema_name.replace(\".\", \"_\") + \"_xsd.html#\", \"#\").replace(\"target=\\\"mainFrame\\\"\", \"\")","def participate_url(self) -> str:\n return create_absolute_url(\n reverse(\"arrange_videochat:participate\", args=[self.pk])\n )","def recreate_rel_url(url, parsed):\n if parsed.query:\n url = u'%s?%s' % (url, parsed.query)\n if parsed.fragment:\n url = u'%s#%s' % (url, parsed.fragment)\n return url","def _fwd_set_ruri(target, fwd_result):\n # The Request-URI in the copy's start line MUST be replaced with the URI for this target. If the URI contains\n # any parameters not allowed in a Request-URI, they MUST be removed.\n sip_msg, opts = fwd_result\n sip_msg.ruri = target.clear_not_allowed_parts('ruri')\n return sip_msg, opts","def test_remove_trusted_project1(self):\n pass","def do_p(self, arg):\n self.do_project(arg)","def shorten_link(post):\n return f\"redd.it/{post.id}\"","def make_urls(row):\n mapping = {\n 'base': self.course.moodle.base_url,\n 'cmid': row['cmid'],\n 'subid': row['subid'],\n }\n url = self._submission_url.format(**mapping)\n return url","def _map_arguments(self, args):\n data = args.get('data')\n comp = args.get('comp')\n library = args.get('library')\n dry_run = args.get('dry_run', False)\n\n self._set_link('srcmaps-catalog', SrcmapsCatalog_SG,\n comp=comp, data=data,\n library=library,\n nsrc=args.get('nsrc', 500),\n dry_run=dry_run)\n\n self._set_link('gather-srcmaps', GatherSrcmaps_SG,\n comp=comp, data=data,\n library=library,\n dry_run=dry_run)\n \n self._set_link('merge-srcmaps', MergeSrcmaps_SG,\n comp=comp, data=data,\n library=library,\n dry_run=dry_run)","def rebase_program(*args):\n return _ida_segment.rebase_program(*args)","def _enrich_changes(changes: List[Dict[str, Any]], root_url: str) -> None:\n for change in changes:\n change[\"link\"] = f\"{root_url}#L{change['line']}\"\n if change.get(\"children\") is not None:\n _enrich_changes(change[\"children\"], root_url)","def normalize(seed_url, link):\n link, _ = urldefrag(link) # remove hash to avoid duplicates\n return urljoin(seed_url, link)","def get_absolute_url(self):\n\t\treturn reverse('author-detail', args=[str(self.id)])","def rewrite_local_links_to_relative(db_data: Optional[DbData], link: str) -> str:\n\n if db_data:\n realm_uri_prefix = db_data.realm_uri + \"/\"\n if link.startswith((realm_uri_prefix + \"#\", realm_uri_prefix + \"user_uploads/\")):\n return link[len(realm_uri_prefix) :]\n\n return link","def _build_reverse_url(self, name, args=None, kwargs=None): \n pk = kwargs.pop('pk')\n object = self.obj_get(pk=pk)\n kwargs['user__username'] = object.user.username\n return reverse(name, args=args, kwargs=kwargs)","def replace_urls(text, replace_with=\"_URL_\"):\n return RE_SHORT_URL.sub(replace_with, RE_URL.sub(replace_with, text))","def follow_project(cls, user, project):\n pass","def test_remove_trusted_project3(self):\n pass","def test_urlintagwithmultipleamps(self):\n tag = tags.invisible[tags.a(href=url.URL.fromString('http://localhost/').add('foo', 'bar').add('baz', 'spam'))]\n self.assertEqual(flatten(tag), '<a href=\"http://localhost/?foo=bar&baz=spam\"></a>')\n\n tag = tags.invisible[loaders.xmlstr('<a xmlns:n=\"http://nevow.com/ns/nevow/0.1\" href=\"#\"><n:attr name=\"href\"><n:slot name=\"href\"/></n:attr></a>')]\n tag.fillSlots('href', url.URL.fromString('http://localhost/').add('foo', 'bar').add('baz', 'spam'))\n self.assertEqual(flatten(tag), '<a href=\"http://localhost/?foo=bar&baz=spam\"></a>')","def _make_url(self):\n ...","def __replace_href(self, tag):\n if \"<a href=\" in str(tag.contents):\n if len(tag.contents) > 1:\n tag = concatenate(list(tag.contents))\n else:\n tag = str(tag.contents)[1:len(str(tag.contents)) - 1]\n for k, v in self.rules.items():\n tag = tag.replace(k, v)\n else:\n tag = tag.get_text()\n\n return tag","def filter_url_parse_partial_links(match):\n dname = html.unescape(match.group(1))\n dname = html.escape(dname)\n punctuation = match.group(2)\n caption = filter_url_trim(dname, filter_url_length)\n return '<a href=\"http://' + dname + '\">' + caption + '</a>' + punctuation","def edit_url(self, inplace=False,show_url = 0, **kwargs):\n\n if len(kwargs) > 0 :\n other_args = [\"{}={}\".format(k,str(v).replace(\" \",\"+\")) for k,v in kwargs.items()]\n new_url = self.url + \"&\" + \"&\".join(other_args)\n if show_url: print(new_url) \n\n if \"maxresults\" not in kwargs : \n print(\"Be careful : This request will only display the first 100 results.\")\n\n if inplace:\n self.url = new_url","def make_args(self, args):\n result_str = \"?\"\n for k, v in args.iteritems():\n result_str = result_str + k + \"=\" + v + \"&\"\n return result_str","def __invert(self, args):","def test_update_contact_multiple_href_match(mock_app, gpx4_patients):\n\n runner = mock_app.test_cli_runner()\n patients_collection = mock_app.db.patients\n\n assert len(gpx4_patients) == 2\n # GIVEN a database with 2 patients with sligthly different contact href\n gpx4_patients[0][\"contact\"][\"href\"] = \"test_1@mail.com\"\n gpx4_patients[0][\"contact\"][\"href\"] = \"test_2@mail.com\"\n patients_collection.insert_many(gpx4_patients)\n\n # WHEN their contact info is updated using the cli but the search for the old href returns multiple contacts\n old_href = \"test_\"\n new_href = \"test_3@mail.com\"\n result = runner.invoke(\n cli,\n [\n \"update\",\n \"contact\",\n \"--old-href\",\n old_href,\n \"--href\",\n new_href,\n \"--name\",\n NEW_NAME,\n \"--institution\",\n TEST_INST,\n ],\n )\n\n # THEN no patients contact should be updated\n assert patients_collection.find_one({CONTACT_HREF: \":\".join([\"mailto\", new_href])}) is None","def test_remove_trusted_project2(self):\n pass","def test_remove_trusted_project4(self):\n pass","def build_url_long(self, obj):\n if obj.slug:\n url = self.request.build_absolute_uri(reverse('build_repo', args=(obj.slug,)))\n return '<a href=\"%s\" target=\"_blank\">%s<a>' % (url, url)\n else:\n return ''","def remove_aliases(self):\n for k, v in iteritems(self.argspec):\n if 'aliases' in v:\n for alias in v['aliases']:\n if alias in self.params:\n self.params.pop(alias)","def test_review_citation_wrt_canonical_uri(self):\n\n self.review_mono.doi = None\n self.issue_mono.setDoiRegistrationActive(False)\n self.review_jour.doi = None\n self.issue_jour.setDoiRegistrationActive(False)\n\n monograph_citation = self.review_mono.get_citation_string()\n self.assertEquals('<a href=\"http://nohost/plone' in monograph_citation, True)\n\n journal_citation = self.review_jour.get_citation_string()\n self.assertEquals('<a href=\"http://nohost/plone' in journal_citation, True)\n\n self.review_mono.canonical_uri = \"http://example.com\"\n monograph_citation = self.review_mono.get_citation_string()\n self.assertEquals('<a href=\"http://nohost/plone' in monograph_citation, False)\n\n self.review_jour.canonical_uri = \"http://example.com\"\n journal_citation = self.review_jour.get_citation_string()\n self.assertEquals('<a href=\"http://nohost/plone' in journal_citation, False)","def get_absolute_url(self):\n return reverse('blogs-by-author', args=[str(self.id)])","def get_proper_url(self):\n if self.owner.custom_domain:\n path = reverse(\"post_detail\", kwargs={\"slug\": self.slug})\n return f\"//{self.owner.custom_domain}{path}\"\n else:\n return self.get_absolute_url(self)","def _monkey_patch_python_domain_to_resolve_params():\n\n PythonDomain = sphinx.domains.python.PythonDomain # pylint: disable=invalid-name\n orig_resolve_xref = PythonDomain.resolve_xref\n\n def resolve_xref(\n self: PythonDomain, env: sphinx.environment.BuildEnvironment,\n fromdocname: str, builder: sphinx.builders.Builder, typ: str, target: str,\n node: sphinx.addnodes.pending_xref,\n contnode: docutils.nodes.Element) -> Optional[docutils.nodes.Element]:\n if typ == 'param':\n func_name = node.get('py:func')\n if func_name and '.' not in target:\n return orig_resolve_xref(self, env, fromdocname, builder, typ,\n '%s.%s' % (func_name, target), node, contnode)\n\n return orig_resolve_xref(self, env, fromdocname, builder, typ, target, node,\n contnode)\n\n PythonDomain.resolve_xref = resolve_xref\n\n orig_resolve_any_xref = PythonDomain.resolve_any_xref\n\n def resolve_any_xref(\n self: PythonDomain, env: sphinx.environment.BuildEnvironment,\n fromdocname: str, builder: sphinx.builders.Builder, target: str,\n node: sphinx.addnodes.pending_xref, contnode: docutils.nodes.Element\n ) -> List[Tuple[str, docutils.nodes.Element]]:\n results = orig_resolve_any_xref(self, env, fromdocname, builder, target,\n node, contnode)\n # Don't resolve parameters as any refs, as they introduce too many\n # ambiguities.\n return [r for r in results if r[0] != 'py:param']\n\n PythonDomain.resolve_any_xref = resolve_any_xref","def replace_urls(text):\n text = re.sub('(?P<url>https?://[^\\s]+)', 'URL', text)\n return text","def main(argv=None):\n if argv is None:\n argv = sys.argv\n\n inspire_outdir = \"\"\n correct_outdir = \"\"\n indir = \"\"\n inspire_pattern = \"\"\n\n helpshort = (\n \"python fix_arxiv.py -p '037__9:arxiv - 037__c:**' [-o 'tmp/from_inspire'\"\n \"-c 'tmp/correct' -i 'tmp/from_inspire']\"\n )\n\n # Parse arguments\n try:\n opts, _ = getopt.getopt(\n argv,\n \"hp:o:c:i:\",\n [\"help\", \"pattern=\", \"inspire_outdir=\", \"correct_outdir=\", \"indir=\"]\n )\n except getopt.GetoptError as err:\n print(err)\n print(helpshort)\n sys.exit(2)\n for opt, arg in opts:\n if opt == \"-h\":\n print(helpshort)\n sys.exit()\n elif opt in (\"-p\", \"--pattern\"):\n inspire_pattern = arg\n elif opt in (\"-o\", \"--inspire_outdir\"):\n # For saving the fetched INSPIRE records\n inspire_outdir = os.path.join(arg, \"\")\n elif opt in (\"-c\", \"--correct_outdir\"):\n # For saving the corrected MARCXML\n correct_outdir = os.path.join(arg, \"\")\n elif opt in (\"-i\", \"--indir\"):\n # For using previously fetched and saved local files\n indir = os.path.join(arg, \"\")\n if not argv:\n print(helpshort)\n sys.exit()\n if not (inspire_pattern or indir):\n print(helpshort)\n print(\"\\nPlease give INSPIRE search pattern or the path to local files\")\n sys.exit()\n\n create_corrected_marcs(\n inspire_pattern=inspire_pattern,\n inspire_outdir=inspire_outdir,\n correct_outdir=correct_outdir,\n indir=indir\n )","def __str__(self):\r\n self.query = urllib.urlencode(self.args)\r\n self.query = urllib.unquote(self.query)\r\n return urlparse.urlunparse((self.scheme, self.netloc, self.path, self.params, self.query, self.fragment))","def get_absolute_url(self):\n return reverse('author-detail', args=[str(self.id)])"],"string":"[\n \"def fix_links():\\n pass\",\n \"def replace_by_canonical_referent(self, cross_sentence=True):\\n replaced_count = 0\\n\\n # replace the participant in their containers\\n def replace_in_containers(myobj, replacing):\\n containers = {\\n \\\"event\\\": \\\"participants\\\",\\n \\\"sentiment_expression\\\": \\\"targets\\\",\\n \\\"sentence\\\": \\\"participants\\\",\\n }\\n for c_name, attrib_n in containers.items():\\n c_name = f\\\"in_{c_name}\\\"\\n if hasattr(myobj, c_name):\\n for c in getattr(myobj, c_name):\\n to_replace_in = getattr(c, attrib_n)\\n to_replace_in.append(replacing)\\n if myobj in to_replace_in:\\n to_replace_in.remove(myobj)\\n to_replace_in.sort(key=lambda x: x.begin)\\n\\n if self.canonical_referents and self.canonical_referents != \\\"from_canonref\\\":\\n # sometimes there are multiple canonrefs tagged\\n # this can be a) annotation mistake or\\n # b) multiple reference to a group, e.g. \\\"all\\\" refers to three companies.\\n for canonref in self.canonical_referents:\\n # check whether canonical referent is in same sentence\\n same_sentence = [s.element_id for s in self.in_sentence] == [\\n s.element_id for s in canonref.in_sentence\\n ]\\n # always replace when cross_sentence is true\\n # if cross_sentence is False (disallowed) only replace when canonref is in same sentence\\n if cross_sentence or same_sentence:\\n # replace element\\n replacing_participant = Participant(\\n canonref.text,\\n canonref.begin,\\n canonref.end,\\n canonref.element_id,\\n canonref.annotator_id,\\n canonref.document_title,\\n canonref.in_document,\\n self.role,\\n \\\"from_canonref\\\",\\n self.link_id,\\n canonref.tokens,\\n )\\n replacing_participant.in_sentence = self.in_sentence\\n replacing_participant.in_document = self.in_document\\n if hasattr(self, \\\"in_sentiment_expression\\\"):\\n replacing_participant.in_sentiment_expression = (\\n self.in_sentiment_expression\\n )\\n replacing_participant.from_original_participant = copy(self)\\n print(\\n f\\\"Replaced {self} with {canonref}. (in same sentence:{same_sentence})\\\"\\n )\\n\\n replace_in_containers(self, replacing_participant)\\n\\n # replace on document\\n self.in_document.participants.append(replacing_participant)\\n if self in self.in_document.participants:\\n self.in_document.participants.remove(self)\\n\\n replaced_count += 1\\n return replaced_count\",\n \"def to_projectlink(self):\\n\\n thumb_image_url = reverse('project_serve_file', args=[self.short_name,self.logo])\\n\\n args = {\\\"abreviation\\\":self.short_name,\\n \\\"title\\\":self.short_name,\\n \\\"description\\\":self.description,\\n \\\"URL\\\":reverse('comicsite.views.site', args=[self.short_name]),\\n \\\"download URL\\\":\\\"\\\",\\n \\\"submission URL\\\":self.get_submission_URL(),\\n \\\"event name\\\":self.event_name,\\n \\\"year\\\":\\\"\\\",\\n \\\"event URL\\\":self.event_url,\\n \\\"image URL\\\":self.logo,\\n \\\"thumb_image_url\\\":thumb_image_url,\\n \\\"website section\\\":\\\"active challenges\\\",\\n \\\"overview article url\\\":self.publication_url,\\n \\\"overview article journal\\\":self.publication_journal_name,\\n \\\"overview article citations\\\":\\\"\\\",\\n \\\"overview article date\\\":\\\"\\\",\\n \\\"submission deadline\\\":\\\"\\\",\\n \\\"workshop date\\\":self.workshop_date,\\n \\\"open for submission\\\":\\\"yes\\\" if self.is_open_for_submissions else \\\"no\\\",\\n \\\"data download\\\":\\\"yes\\\" if self.offers_data_download else \\\"no\\\",\\n \\\"dataset downloads\\\":self.number_of_downloads,\\n \\\"registered teams\\\":\\\"\\\",\\n \\\"submitted results\\\":self.number_of_submissions,\\n \\\"last submission date\\\":self.last_submission_date,\\n \\\"hosted on comic\\\":True,\\n \\\"created at\\\":self.created_at\\n }\\n\\n projectlink = ProjectLink(args)\\n return projectlink\",\n \"def reproject(self, *args, **kwargs):\\n pass\",\n \"def replace_with_arg(self, src_arg, tgt_arg):\",\n \"def _fix_links(self, text, page_names):\\n for n in page_names:\\n text = text.replace(f\\\"]({n})\\\", f\\\"]({n}.html)\\\")\\n text = text.replace(f\\\"]({n}.md)\\\", f\\\"]({n}.html)\\\")\\n return text\",\n \"def _make_links(tweet):\\n for pattern, repl in (USER_SUB, KEYWORD_SUB):\\n tweet = re.sub(pattern, repl, tweet)\\n return tweet\",\n \"def _build_links(links):\\n for link in links:\\n link['href'] = link['href'].replace('servers', 'instances')\\n return links\",\n \"def generate_clean_url(self):\\n\\n\\t\\tspaces_replaced = self.title.replace(' ', '-')\\n\\t\\tpattern = re.compile('[^a-zA-Z0-9-]+')\\n\\t\\tstripped = pattern.sub('', spaces_replaced)\\n\\t\\tself.cleanurl = '-'.join([str(self.pid), stripped.lower()])\",\n \"def test_replace_project(self):\\n pass\",\n \"def setUp_extra(self):\\n [self.testproject,\\n self.root,\\n self.projectadmin,\\n self.participant,\\n self.signedup_user] = self._create_dummy_project(\\\"linkreplacer-test\\\")\\n\\n self.replacer = HtmlLinkReplacer()\",\n \"def replace_local_hyperlinks(\\n text,\\n base_url=\\\"https://github.com/project-rig/nengo_spinnaker/blob/master/\\\"\\n ):\\n def get_new_url(url):\\n return base_url + url[2:]\\n\\n # Deal with anonymous URLS\\n for match in re.finditer(r\\\"^__ (?P<url>\\\\./.*)\\\", text, re.MULTILINE):\\n orig_url = match.groupdict()[\\\"url\\\"]\\n url = get_new_url(orig_url)\\n\\n text = re.sub(\\\"^__ {}\\\".format(orig_url),\\n \\\"__ {}\\\".format(url), text, flags=re.MULTILINE)\\n\\n # Deal with named URLS\\n for match in re.finditer(r\\\"^\\\\.\\\\. _(?P<identifier>[^:]*): (?P<url>\\\\./.*)\\\",\\n text, re.MULTILINE):\\n identifier = match.groupdict()[\\\"identifier\\\"]\\n orig_url = match.groupdict()[\\\"url\\\"]\\n url = get_new_url(orig_url)\\n\\n text = re.sub(\\n \\\"^\\\\.\\\\. _{}: {}\\\".format(identifier, orig_url),\\n \\\".. _{}: {}\\\".format(identifier, url),\\n text, flags=re.MULTILINE)\\n\\n # Deal with image URLS\\n for match in re.finditer(r\\\"^\\\\.\\\\. image:: (?P<url>\\\\./.*)\\\",\\n text, re.MULTILINE):\\n orig_url = match.groupdict()[\\\"url\\\"]\\n url = get_new_url(orig_url)\\n\\n text = text.replace(\\\".. image:: {}\\\".format(orig_url),\\n \\\".. image:: {}\\\".format(url))\\n\\n return text\",\n \"def _fixup_find_links(find_links):\\n if isinstance(find_links, str):\\n return find_links.split()\\n assert isinstance(find_links, (tuple, list))\\n return find_links\",\n \"def get_absolute_url(self):\\n return reverse('plan_proposal',\\n kwargs = {'project_name': self.project.slug,\\n 'proposal_name': self.slug})\",\n \"def get_absolute_url(self):\\n return reverse('project-detail', kwargs={'pk': self.pk})\",\n \"def adjust_anchors(self):\\n pass\",\n \"def replace(self, name, *args):\\n\\n self._add(True, self.authority, name, *args)\",\n \"def replace(self, *args, **kwargs): # real signature unknown\\r\\n pass\",\n \"def fix_genindex(self, tree: list[tuple[str, list[tuple[str, Any]]]]) -> None:\\n # XXX: modifies tree inline\\n # Logic modeled from themes/basic/genindex.html\\n for _key, columns in tree:\\n for _entryname, (links, subitems, _key) in columns:\\n for (i, (ismain, link)) in enumerate(links):\\n m = self.refuri_re.match(link)\\n if m:\\n links[i] = (ismain,\\n self.fix_fragment(m.group(1), m.group(2)))\\n for _subentryname, subentrylinks in subitems:\\n for (i, (ismain, link)) in enumerate(subentrylinks):\\n m = self.refuri_re.match(link)\\n if m:\\n subentrylinks[i] = (ismain,\\n self.fix_fragment(m.group(1), m.group(2)))\",\n \"def simplify_links(proj,exp,links):\\n simple_links =[] \\n\\n for key in links:\\n (node_name,x,y) = key.rpartition(':')\\n node_name = node_name+\\\".\\\"+exp+\\\".\\\"+proj+\\\".emulab.net\\\"\\n simple_links.append((node_name,links[key]['ipaddr']))\\n\\n return simple_links\",\n \"def rewrite_relative_links(soup: bs4.BeautifulSoup, base_url: str):\\n for tag_name, attrib in URL_REWRITE_PAIRS:\\n for tag in soup.find_all(tag_name, attrs={attrib: True}):\\n try:\\n tag[attrib] = urllib.parse.urljoin(base_url, tag[attrib])\\n except ValueError as e:\\n logger.info('Could not rewrite link: %s', e)\",\n \"def get_absolute_url(self):\\n return ('project_detail', (), {\\n 'name': self.title\\n })\",\n \"def get_absolute_url(self):\\n return reverse(\\\"projects-detail\\\", kwargs={'pk': self.pk})\",\n \"def _transform_github_url(self):\\n self.url = (\\n self.url\\n .replace('/blob/', '/')\\n .replace(self.GITHUB_NETLOC, self.GITHUB_RAW_NETLOC)\\n )\",\n \"def reverse_discussion_link(viewname, args):\\r\\n if viewname == \\\"django_comment_client.forum.views.forum_form_discussion\\\" and args == [course.id.to_deprecated_string()]:\\r\\n return \\\"default_discussion_link\\\"\",\n \"def _remove_invalid_links(text):\\n\\n for reply_number in re.finditer(REGEX_REPLY, text):\\n post_id = reply_number.group(1)\\n post = Post.objects.filter(id=post_id)\\n if not post.exists():\\n text = string.replace(text, REFLINK_PREFIX + post_id, post_id)\\n\\n return text\",\n \"def link_residues(self) -> None:\\n ...\",\n \"def get_absolute_url(self):\\n return reverse('participant-detail', args=[str(self.participantId)])\",\n \"def make_absolute_query_link(db,**params):\\n pass\",\n \"def get_absolute_url(self):\\n return reverse('authors', args=[str(self.id)])\",\n \"def extendMarkdown(self, md, md_globals):\\r\\n md.inlinePatterns['autolink'] = UrlizePattern(URLIZE_RE, md)\",\n \"def fix_references(self) -> None:\\n\\n\\t\\tself.log_info(\\\"--- Searching for references to variants ---\\\")\\n\\t\\tfor publication in Publication.objects.filter(variant_of__isnull=False):\\n\\t\\t\\tvariant = publication.variant_of\\n\\t\\t\\torigs = PublicationReference.objects.filter(reference=publication)\\n\\t\\t\\tfor orig in origs:\\n\\t\\t\\t\\tif PublicationReference.objects.filter(reference=variant, publication=orig.publication).exists():\\n\\t\\t\\t\\t\\tcontinue\\n\\t\\t\\t\\tfixed = PublicationReference(\\n\\t\\t\\t\\t\\treference=variant,\\n\\t\\t\\t\\t\\tpublication=orig.publication,\\n\\t\\t\\t\\t\\tidentifier=('' if orig.identifier is None else orig.identifier) + \\\"*\\\",\\n\\t\\t\\t\\t)\\n\\t\\t\\t\\ttry:\\n\\t\\t\\t\\t\\tfixed.full_clean()\\n\\t\\t\\t\\t\\tfixed.save()\\n\\t\\t\\t\\t\\tself.log_success(f\\\"Added reference: {publication} -- {fixed.identifier} -> {variant}\\\")\\n\\t\\t\\t\\texcept ValidationError as e:\\n\\t\\t\\t\\t\\traise CommandError(f\\\"{publication} -- {fixed.identifier} -> {variant}: {e}\\\")\",\n \"def normalize_reviewer(ctx, reviewer):\\n name_re = re.compile(r'^\\\\w+ [^<]+ <.*@.*\\\\..*>$')\\n if name_re.match(reviewer):\\n return reviewer\\n rbranch = '%s/master' % ctx.config['remote']\\n cmd = ['git',\\n '-c', 'mailmap.blob=origin/master:.mailmap',\\n 'shortlog', '-sen', rbranch]\\n names = ctx.runprocess(cmd).stdout.splitlines()\\n names = (name.split('\\\\t', 1)[-1] for name in names)\\n names = (name for name in names if name_re.match(name))\\n names = [name for name in names if reviewer.lower() in name.lower()]\\n if not names:\\n ctx.die('Reviewer %s not found' % reviewer)\\n elif len(names) > 1:\\n print(ctx.term.red('Reviewer %s could be:' % reviewer))\\n for name in names:\\n print('- %s' % name)\\n ctx.die('Multiple matches found for reviewer')\\n else:\\n name = unidecode.unidecode(names[0])\\n return name\",\n \"def fix_project_keys(apps, schema_editor):\\n RemoteProject = apps.get_model('projectroles', 'RemoteProject')\\n Project = apps.get_model('projectroles', 'Project')\\n\\n for rp in RemoteProject.objects.all():\\n if not rp.project:\\n rp.project = Project.objects.filter(\\n sodar_uuid=rp.project_uuid\\n ).first()\\n rp.save()\",\n \"def canonicalize(self, url):\\n pass\",\n \"def _annotate_remote_from_args(self) -> None:\\n if self._local_remote_pairs:\\n if self._remote_side:\\n raise sa_exc.ArgumentError(\\n \\\"remote_side argument is redundant \\\"\\n \\\"against more detailed _local_remote_side \\\"\\n \\\"argument.\\\"\\n )\\n\\n remote_side = [r for (l, r) in self._local_remote_pairs]\\n else:\\n remote_side = self._remote_side\\n\\n if self._refers_to_parent_table():\\n self._annotate_selfref(lambda col: col in remote_side, True)\\n else:\\n\\n def repl(element: _CE, **kw: Any) -> Optional[_CE]:\\n # use set() to avoid generating ``__eq__()`` expressions\\n # against each element\\n if element in set(remote_side):\\n return element._annotate({\\\"remote\\\": True})\\n return None\\n\\n self.primaryjoin = visitors.replacement_traverse(\\n self.primaryjoin, {}, repl\\n )\",\n \"def replaceArgument(self, *args):\\n return _libsbml.ASTNode_replaceArgument(self, *args)\",\n \"def _transform_args(self) -> None:\\n self.args = None if self.args == [] else self.args\",\n \"def replaceLetra(a): \\n global palavraOculta\\n palavraSecreta = list(palavraOculta)\\n\\n for m in re.finditer(a, palavraDoJogo):\\n palavraSecreta[m.start()] = a\\n\\n palavraOculta = \\\"\\\".join(palavraSecreta)\",\n \"def remake_challenge_url(self, text_signature, ch_id):\\n text_head = \\\"https://www.iesdouyin.com/web/api/v2/challenge/aweme/?\\\"\\n text_music_id = \\\"ch_id={}&\\\".format(ch_id)\\n text_count = \\\"count=45&\\\"\\n text_cursor = \\\"cursor={}&\\\".format(self.cursor)\\n text_aid = \\\"aid=1128&screen_limit=3&download_click_limit=0&_\\\"\\n\\n remake = text_head + text_music_id + text_count + text_cursor + text_aid + text_signature\\n return remake\",\n \"def __apply_reflexive_pronoun(self, conjugation_notes, explicit_accent_already_applied):\\n if conjugation_notes.tense in Tense.imperative():\\n self.__apply_imperative_reflexive_pronoun(conjugation_notes, explicit_accent_already_applied) \\n elif conjugation_notes.tense not in Tense.Person_Agnostic():\\n conjugation_notes.change(operation=\\\"apply_reflexive\\\", conjugation = conjugation_notes.person.indirect_pronoun +\\\" \\\"+ conjugation_notes.conjugation,\\n irregular_nature=IrregularNature.regular)\\n elif conjugation_notes.tense == Tense.gerund:\\n conjugation_notes.change(operation=\\\"apply_reflexive\\\",conjugation = Vowels.accent(conjugation_notes.conjugation)+Person.third_person_plural.indirect_pronoun,\\n irregular_nature=IrregularNature.regular)\",\n \"def uncanonicalize(self, url):\\n pass\",\n \"def get_absolute_url(self) -> str:\\n return reverse(\\n \\\"cagnottesolidaire:proposition\\\",\\n kwargs={\\\"slug\\\": self.slug, \\\"p_slug\\\": self.cagnotte.slug},\\n )\",\n \"def project(self, other, normalized=...):\\n ...\",\n \"def repl_onebox(mat=None, li=[]):\\n if mat is None:\\n li[:] = []\\n return\\n if mat.group(1):\\n url = mat.group(1).strip()\\n if '/project/' in url:\\n # Try to parse a project link\\n return box_project(url) or mat.group()\\n return mat.group()\",\n \"def makeRelativePathsAbsolute(cmdargs):\\n for i in range(len(cmdargs)):\\n if relativePathRE.match(cmdargs[i]):\\n cmdargs[i]=os.path.abspath(cmdargs[i])\",\n \"def add_project_link(self, proj_curr, link):\\r\\n for proj in self.__projects:\\r\\n if proj == proj_curr: # Find the project with the same name\\r\\n proj.update_links(link) # Add the photo\\r\",\n \"def replace_relative_links(soup,base_url,debug=False):\\n # only extract hyperlinks with href atributte\\n if soup is None:\\n return None\\n\\n links = soup.findAll('a', {\\\"href\\\" : True})\\n links_replaced = 0\\n for link in links: \\n url = link['href']\\n if url.startswith(\\\"./\\\"):\\n link['href'] = base_url + url[1:len(url)] \\n links_replaced += 1\\n if debug is True:\\n print(f\\\"soup_converter.replace_relative_links: {links_replaced} links replaced\\\")\\n return soup\",\n \"def resolve(self, anchors):\\n\\n for anchor in anchors:\\n if self.node[DuAttrRefid] in anchor.ids():\\n self.toAnchor = anchor\\n break\",\n \"def compose_url(base_url, anno, chimico):\\n \\n return base_url + chimico + '_' + anno + '.txt'\",\n \"async def link_to(self, *args):\\n pass\",\n \"def remove_link():\",\n \"def follow_project(cls, user, project):\\r\\n pass\",\n \"def mutate_urls(self, mutator):\",\n \"def _urljoin(self, *args):\\r\\n\\t\\treturn \\\"/\\\".join(map(lambda x: str(x).rstrip('/'), args))\",\n \"def replace_links(\\n soup: BeautifulSoup,\\n resource_dir: str,\\n replace_data: dict,\\n) -> str:\\n resource_tags = soup.findAll(name=list(TAGS_ATTR.keys()))\\n change_tags(resource_tags, replace_data, resource_dir)\\n return soup.prettify()\",\n \"def _map_arguments(self, args):\\n data = args.get('data')\\n comp = args.get('comp')\\n library = args.get('library')\\n dry_run = args.get('dry_run', False)\\n\\n self._set_link('sum-rings', SumRings_SG,\\n library=library,\\n outdir=args['outdir'],\\n dry_run=dry_run)\\n\\n self._set_link('srcmaps-diffuse', SrcmapsDiffuse_SG,\\n comp=comp, data=data,\\n library=library,\\n make_xml=args['make_xml'],\\n dry_run=dry_run)\\n\\n self._set_link('vstack-diffuse', Vstack_SG,\\n comp=comp, data=data,\\n library=library,\\n dry_run=dry_run)\",\n \"def get_absolute_url(self):\\n # return reverse('tutor-detail', args=[str(self.email_address)])\\n return reverse('tutor-detail', args=[str(self.username)])\",\n \"def _get_reproducing_arguments(self):\\n reproducing_arguments = {\\n 'include': self.include,\\n 'exclude': self.exclude,\\n 'copy': self.copy,\\n }\\n args_names = {name: getattr(self, name) for name in self.args_names}\\n reproducing_arguments.update(args_names)\\n return reproducing_arguments\",\n \"def fixAuthor(self, pageText):\\n informationRegex = re.compile(\\n r'\\\\|Author=Original uploader was '\\n r'(?P<author>\\\\[\\\\[:\\\\w+:\\\\w+:\\\\w+\\\\|\\\\w+\\\\]\\\\] at \\\\[.+\\\\])')\\n selfRegex = re.compile(\\n r'{{self\\\\|author='\\n r'(?P<author>\\\\[\\\\[:\\\\w+:\\\\w+:\\\\w+\\\\|\\\\w+\\\\]\\\\] at \\\\[.+\\\\])\\\\|')\\n\\n # Find the |Author=Original uploader was ....\\n informationMatch = informationRegex.search(pageText)\\n\\n # Find the {{self|author=\\n selfMatch = selfRegex.search(pageText)\\n\\n # Check if both are found and are equal\\n if (informationMatch and selfMatch):\\n if informationMatch.group('author') == selfMatch.group('author'):\\n # Replace |Author=Original uploader was ... with |Author= ...\\n pageText = informationRegex.sub(r'|Author=\\\\g<author>',\\n pageText)\\n return pageText\",\n \"def mirrorJoint(*args, mirrorBehavior: bool=True, mirrorXY: bool=True, mirrorXZ: bool=True,\\n mirrorYZ: bool=True, searchReplace: List[AnyStr, AnyStr]=None,\\n **kwargs)->List[AnyStr]:\\n pass\",\n \"def test_indirect_parameters_update(renku_cli, project):\\n with chdir(project.path):\\n Path(\\\".renku/tmp\\\").mkdir(exist_ok=True)\\n\\n Path(\\\"script.sh\\\").write_text(\\n \\\"\\\"\\\"\\n echo param 1: \\\"forty-two\\\" >> .renku/tmp/parameters.yml\\n echo param-2: 42.42 >> .renku/tmp/parameters.yml\\n echo param3: 42 >> .renku/tmp/parameters.yml\\n \\\"\\\"\\\"\\n )\\n\\n project.repository.add(all=True)\\n project.repository.commit(\\\"test setup\\\")\\n\\n renku_cli(\\\"run\\\", \\\"sh\\\", \\\"script.sh\\\", stdout=\\\"result\\\")\\n\\n with chdir(project.path):\\n Path(\\\".renku/tmp\\\").mkdir(exist_ok=True)\\n\\n Path(\\\"script.sh\\\").write_text(\\n \\\"\\\"\\\"\\n echo param 1: \\\"forty-two-updated\\\" >> .renku/tmp/parameters.yml\\n echo param-2: 42.42 >> .renku/tmp/parameters.yml\\n \\\"\\\"\\\"\\n )\\n\\n project.repository.add(all=True)\\n project.repository.commit(\\\"test setup\\\")\\n\\n exit_code, activity = renku_cli(\\\"update\\\", \\\"--all\\\")\\n\\n assert 0 == exit_code\\n assert {\\\"forty-two-updated\\\", \\\"42.42\\\", \\\"42\\\"} == {a.default_value for a in activity.association.plan.parameters}\",\n \"def correct_links(html_file, schema_name):\\n return html_file.replace(schema_name.replace(\\\".\\\", \\\"_\\\") + \\\"_xsd.html#\\\", \\\"#\\\").replace(\\\"target=\\\\\\\"mainFrame\\\\\\\"\\\", \\\"\\\")\",\n \"def participate_url(self) -> str:\\n return create_absolute_url(\\n reverse(\\\"arrange_videochat:participate\\\", args=[self.pk])\\n )\",\n \"def recreate_rel_url(url, parsed):\\n if parsed.query:\\n url = u'%s?%s' % (url, parsed.query)\\n if parsed.fragment:\\n url = u'%s#%s' % (url, parsed.fragment)\\n return url\",\n \"def _fwd_set_ruri(target, fwd_result):\\n # The Request-URI in the copy's start line MUST be replaced with the URI for this target. If the URI contains\\n # any parameters not allowed in a Request-URI, they MUST be removed.\\n sip_msg, opts = fwd_result\\n sip_msg.ruri = target.clear_not_allowed_parts('ruri')\\n return sip_msg, opts\",\n \"def test_remove_trusted_project1(self):\\n pass\",\n \"def do_p(self, arg):\\n self.do_project(arg)\",\n \"def shorten_link(post):\\n return f\\\"redd.it/{post.id}\\\"\",\n \"def make_urls(row):\\n mapping = {\\n 'base': self.course.moodle.base_url,\\n 'cmid': row['cmid'],\\n 'subid': row['subid'],\\n }\\n url = self._submission_url.format(**mapping)\\n return url\",\n \"def _map_arguments(self, args):\\n data = args.get('data')\\n comp = args.get('comp')\\n library = args.get('library')\\n dry_run = args.get('dry_run', False)\\n\\n self._set_link('srcmaps-catalog', SrcmapsCatalog_SG,\\n comp=comp, data=data,\\n library=library,\\n nsrc=args.get('nsrc', 500),\\n dry_run=dry_run)\\n\\n self._set_link('gather-srcmaps', GatherSrcmaps_SG,\\n comp=comp, data=data,\\n library=library,\\n dry_run=dry_run)\\n \\n self._set_link('merge-srcmaps', MergeSrcmaps_SG,\\n comp=comp, data=data,\\n library=library,\\n dry_run=dry_run)\",\n \"def rebase_program(*args):\\n return _ida_segment.rebase_program(*args)\",\n \"def _enrich_changes(changes: List[Dict[str, Any]], root_url: str) -> None:\\n for change in changes:\\n change[\\\"link\\\"] = f\\\"{root_url}#L{change['line']}\\\"\\n if change.get(\\\"children\\\") is not None:\\n _enrich_changes(change[\\\"children\\\"], root_url)\",\n \"def normalize(seed_url, link):\\n link, _ = urldefrag(link) # remove hash to avoid duplicates\\n return urljoin(seed_url, link)\",\n \"def get_absolute_url(self):\\n\\t\\treturn reverse('author-detail', args=[str(self.id)])\",\n \"def rewrite_local_links_to_relative(db_data: Optional[DbData], link: str) -> str:\\n\\n if db_data:\\n realm_uri_prefix = db_data.realm_uri + \\\"/\\\"\\n if link.startswith((realm_uri_prefix + \\\"#\\\", realm_uri_prefix + \\\"user_uploads/\\\")):\\n return link[len(realm_uri_prefix) :]\\n\\n return link\",\n \"def _build_reverse_url(self, name, args=None, kwargs=None): \\n pk = kwargs.pop('pk')\\n object = self.obj_get(pk=pk)\\n kwargs['user__username'] = object.user.username\\n return reverse(name, args=args, kwargs=kwargs)\",\n \"def replace_urls(text, replace_with=\\\"_URL_\\\"):\\n return RE_SHORT_URL.sub(replace_with, RE_URL.sub(replace_with, text))\",\n \"def follow_project(cls, user, project):\\n pass\",\n \"def test_remove_trusted_project3(self):\\n pass\",\n \"def test_urlintagwithmultipleamps(self):\\n tag = tags.invisible[tags.a(href=url.URL.fromString('http://localhost/').add('foo', 'bar').add('baz', 'spam'))]\\n self.assertEqual(flatten(tag), '<a href=\\\"http://localhost/?foo=bar&baz=spam\\\"></a>')\\n\\n tag = tags.invisible[loaders.xmlstr('<a xmlns:n=\\\"http://nevow.com/ns/nevow/0.1\\\" href=\\\"#\\\"><n:attr name=\\\"href\\\"><n:slot name=\\\"href\\\"/></n:attr></a>')]\\n tag.fillSlots('href', url.URL.fromString('http://localhost/').add('foo', 'bar').add('baz', 'spam'))\\n self.assertEqual(flatten(tag), '<a href=\\\"http://localhost/?foo=bar&baz=spam\\\"></a>')\",\n \"def _make_url(self):\\n ...\",\n \"def __replace_href(self, tag):\\n if \\\"<a href=\\\" in str(tag.contents):\\n if len(tag.contents) > 1:\\n tag = concatenate(list(tag.contents))\\n else:\\n tag = str(tag.contents)[1:len(str(tag.contents)) - 1]\\n for k, v in self.rules.items():\\n tag = tag.replace(k, v)\\n else:\\n tag = tag.get_text()\\n\\n return tag\",\n \"def filter_url_parse_partial_links(match):\\n dname = html.unescape(match.group(1))\\n dname = html.escape(dname)\\n punctuation = match.group(2)\\n caption = filter_url_trim(dname, filter_url_length)\\n return '<a href=\\\"http://' + dname + '\\\">' + caption + '</a>' + punctuation\",\n \"def edit_url(self, inplace=False,show_url = 0, **kwargs):\\n\\n if len(kwargs) > 0 :\\n other_args = [\\\"{}={}\\\".format(k,str(v).replace(\\\" \\\",\\\"+\\\")) for k,v in kwargs.items()]\\n new_url = self.url + \\\"&\\\" + \\\"&\\\".join(other_args)\\n if show_url: print(new_url) \\n\\n if \\\"maxresults\\\" not in kwargs : \\n print(\\\"Be careful : This request will only display the first 100 results.\\\")\\n\\n if inplace:\\n self.url = new_url\",\n \"def make_args(self, args):\\n result_str = \\\"?\\\"\\n for k, v in args.iteritems():\\n result_str = result_str + k + \\\"=\\\" + v + \\\"&\\\"\\n return result_str\",\n \"def __invert(self, args):\",\n \"def test_update_contact_multiple_href_match(mock_app, gpx4_patients):\\n\\n runner = mock_app.test_cli_runner()\\n patients_collection = mock_app.db.patients\\n\\n assert len(gpx4_patients) == 2\\n # GIVEN a database with 2 patients with sligthly different contact href\\n gpx4_patients[0][\\\"contact\\\"][\\\"href\\\"] = \\\"test_1@mail.com\\\"\\n gpx4_patients[0][\\\"contact\\\"][\\\"href\\\"] = \\\"test_2@mail.com\\\"\\n patients_collection.insert_many(gpx4_patients)\\n\\n # WHEN their contact info is updated using the cli but the search for the old href returns multiple contacts\\n old_href = \\\"test_\\\"\\n new_href = \\\"test_3@mail.com\\\"\\n result = runner.invoke(\\n cli,\\n [\\n \\\"update\\\",\\n \\\"contact\\\",\\n \\\"--old-href\\\",\\n old_href,\\n \\\"--href\\\",\\n new_href,\\n \\\"--name\\\",\\n NEW_NAME,\\n \\\"--institution\\\",\\n TEST_INST,\\n ],\\n )\\n\\n # THEN no patients contact should be updated\\n assert patients_collection.find_one({CONTACT_HREF: \\\":\\\".join([\\\"mailto\\\", new_href])}) is None\",\n \"def test_remove_trusted_project2(self):\\n pass\",\n \"def test_remove_trusted_project4(self):\\n pass\",\n \"def build_url_long(self, obj):\\n if obj.slug:\\n url = self.request.build_absolute_uri(reverse('build_repo', args=(obj.slug,)))\\n return '<a href=\\\"%s\\\" target=\\\"_blank\\\">%s<a>' % (url, url)\\n else:\\n return ''\",\n \"def remove_aliases(self):\\n for k, v in iteritems(self.argspec):\\n if 'aliases' in v:\\n for alias in v['aliases']:\\n if alias in self.params:\\n self.params.pop(alias)\",\n \"def test_review_citation_wrt_canonical_uri(self):\\n\\n self.review_mono.doi = None\\n self.issue_mono.setDoiRegistrationActive(False)\\n self.review_jour.doi = None\\n self.issue_jour.setDoiRegistrationActive(False)\\n\\n monograph_citation = self.review_mono.get_citation_string()\\n self.assertEquals('<a href=\\\"http://nohost/plone' in monograph_citation, True)\\n\\n journal_citation = self.review_jour.get_citation_string()\\n self.assertEquals('<a href=\\\"http://nohost/plone' in journal_citation, True)\\n\\n self.review_mono.canonical_uri = \\\"http://example.com\\\"\\n monograph_citation = self.review_mono.get_citation_string()\\n self.assertEquals('<a href=\\\"http://nohost/plone' in monograph_citation, False)\\n\\n self.review_jour.canonical_uri = \\\"http://example.com\\\"\\n journal_citation = self.review_jour.get_citation_string()\\n self.assertEquals('<a href=\\\"http://nohost/plone' in journal_citation, False)\",\n \"def get_absolute_url(self):\\n return reverse('blogs-by-author', args=[str(self.id)])\",\n \"def get_proper_url(self):\\n if self.owner.custom_domain:\\n path = reverse(\\\"post_detail\\\", kwargs={\\\"slug\\\": self.slug})\\n return f\\\"//{self.owner.custom_domain}{path}\\\"\\n else:\\n return self.get_absolute_url(self)\",\n \"def _monkey_patch_python_domain_to_resolve_params():\\n\\n PythonDomain = sphinx.domains.python.PythonDomain # pylint: disable=invalid-name\\n orig_resolve_xref = PythonDomain.resolve_xref\\n\\n def resolve_xref(\\n self: PythonDomain, env: sphinx.environment.BuildEnvironment,\\n fromdocname: str, builder: sphinx.builders.Builder, typ: str, target: str,\\n node: sphinx.addnodes.pending_xref,\\n contnode: docutils.nodes.Element) -> Optional[docutils.nodes.Element]:\\n if typ == 'param':\\n func_name = node.get('py:func')\\n if func_name and '.' not in target:\\n return orig_resolve_xref(self, env, fromdocname, builder, typ,\\n '%s.%s' % (func_name, target), node, contnode)\\n\\n return orig_resolve_xref(self, env, fromdocname, builder, typ, target, node,\\n contnode)\\n\\n PythonDomain.resolve_xref = resolve_xref\\n\\n orig_resolve_any_xref = PythonDomain.resolve_any_xref\\n\\n def resolve_any_xref(\\n self: PythonDomain, env: sphinx.environment.BuildEnvironment,\\n fromdocname: str, builder: sphinx.builders.Builder, target: str,\\n node: sphinx.addnodes.pending_xref, contnode: docutils.nodes.Element\\n ) -> List[Tuple[str, docutils.nodes.Element]]:\\n results = orig_resolve_any_xref(self, env, fromdocname, builder, target,\\n node, contnode)\\n # Don't resolve parameters as any refs, as they introduce too many\\n # ambiguities.\\n return [r for r in results if r[0] != 'py:param']\\n\\n PythonDomain.resolve_any_xref = resolve_any_xref\",\n \"def replace_urls(text):\\n text = re.sub('(?P<url>https?://[^\\\\s]+)', 'URL', text)\\n return text\",\n \"def main(argv=None):\\n if argv is None:\\n argv = sys.argv\\n\\n inspire_outdir = \\\"\\\"\\n correct_outdir = \\\"\\\"\\n indir = \\\"\\\"\\n inspire_pattern = \\\"\\\"\\n\\n helpshort = (\\n \\\"python fix_arxiv.py -p '037__9:arxiv - 037__c:**' [-o 'tmp/from_inspire'\\\"\\n \\\"-c 'tmp/correct' -i 'tmp/from_inspire']\\\"\\n )\\n\\n # Parse arguments\\n try:\\n opts, _ = getopt.getopt(\\n argv,\\n \\\"hp:o:c:i:\\\",\\n [\\\"help\\\", \\\"pattern=\\\", \\\"inspire_outdir=\\\", \\\"correct_outdir=\\\", \\\"indir=\\\"]\\n )\\n except getopt.GetoptError as err:\\n print(err)\\n print(helpshort)\\n sys.exit(2)\\n for opt, arg in opts:\\n if opt == \\\"-h\\\":\\n print(helpshort)\\n sys.exit()\\n elif opt in (\\\"-p\\\", \\\"--pattern\\\"):\\n inspire_pattern = arg\\n elif opt in (\\\"-o\\\", \\\"--inspire_outdir\\\"):\\n # For saving the fetched INSPIRE records\\n inspire_outdir = os.path.join(arg, \\\"\\\")\\n elif opt in (\\\"-c\\\", \\\"--correct_outdir\\\"):\\n # For saving the corrected MARCXML\\n correct_outdir = os.path.join(arg, \\\"\\\")\\n elif opt in (\\\"-i\\\", \\\"--indir\\\"):\\n # For using previously fetched and saved local files\\n indir = os.path.join(arg, \\\"\\\")\\n if not argv:\\n print(helpshort)\\n sys.exit()\\n if not (inspire_pattern or indir):\\n print(helpshort)\\n print(\\\"\\\\nPlease give INSPIRE search pattern or the path to local files\\\")\\n sys.exit()\\n\\n create_corrected_marcs(\\n inspire_pattern=inspire_pattern,\\n inspire_outdir=inspire_outdir,\\n correct_outdir=correct_outdir,\\n indir=indir\\n )\",\n \"def __str__(self):\\r\\n self.query = urllib.urlencode(self.args)\\r\\n self.query = urllib.unquote(self.query)\\r\\n return urlparse.urlunparse((self.scheme, self.netloc, self.path, self.params, self.query, self.fragment))\",\n \"def get_absolute_url(self):\\n return reverse('author-detail', args=[str(self.id)])\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56018543","0.5512885","0.5474612","0.53858924","0.52240896","0.5085354","0.49265683","0.49210376","0.49045438","0.4873174","0.48181435","0.47943506","0.4749334","0.46768937","0.46495956","0.46395046","0.45719907","0.45718217","0.45712867","0.45704007","0.45686272","0.456786","0.45671862","0.4564249","0.45574588","0.45502433","0.45488122","0.45461404","0.45394742","0.45346704","0.45343384","0.45162714","0.4515999","0.45064765","0.4505156","0.45032722","0.4494022","0.44817087","0.4479598","0.44657594","0.44350734","0.44280165","0.44245213","0.4423534","0.44154868","0.44100416","0.44072253","0.440672","0.44033903","0.44011247","0.44003493","0.4396178","0.438943","0.43890983","0.43850556","0.4378608","0.4372024","0.43717176","0.43693417","0.43692935","0.43661118","0.43658364","0.4364087","0.43597716","0.433681","0.43316966","0.4328858","0.43225512","0.43204826","0.43201983","0.43180314","0.43169382","0.43134922","0.4308281","0.43079883","0.4306587","0.43017423","0.43005255","0.42931905","0.42830184","0.42667884","0.42662245","0.4264463","0.42639777","0.4263204","0.42573613","0.42520258","0.42463896","0.4243378","0.4234604","0.42327753","0.42312574","0.42312482","0.42312217","0.42292443","0.42188665","0.42177963","0.42168275","0.42163968","0.42148173"],"string":"[\n \"0.56018543\",\n \"0.5512885\",\n \"0.5474612\",\n \"0.53858924\",\n \"0.52240896\",\n \"0.5085354\",\n \"0.49265683\",\n \"0.49210376\",\n \"0.49045438\",\n \"0.4873174\",\n \"0.48181435\",\n \"0.47943506\",\n \"0.4749334\",\n \"0.46768937\",\n \"0.46495956\",\n \"0.46395046\",\n \"0.45719907\",\n \"0.45718217\",\n \"0.45712867\",\n \"0.45704007\",\n \"0.45686272\",\n \"0.456786\",\n \"0.45671862\",\n \"0.4564249\",\n \"0.45574588\",\n \"0.45502433\",\n \"0.45488122\",\n \"0.45461404\",\n \"0.45394742\",\n \"0.45346704\",\n \"0.45343384\",\n \"0.45162714\",\n \"0.4515999\",\n \"0.45064765\",\n \"0.4505156\",\n \"0.45032722\",\n \"0.4494022\",\n \"0.44817087\",\n \"0.4479598\",\n \"0.44657594\",\n \"0.44350734\",\n \"0.44280165\",\n \"0.44245213\",\n \"0.4423534\",\n \"0.44154868\",\n \"0.44100416\",\n \"0.44072253\",\n \"0.440672\",\n \"0.44033903\",\n \"0.44011247\",\n \"0.44003493\",\n \"0.4396178\",\n \"0.438943\",\n \"0.43890983\",\n \"0.43850556\",\n \"0.4378608\",\n \"0.4372024\",\n \"0.43717176\",\n \"0.43693417\",\n \"0.43692935\",\n \"0.43661118\",\n \"0.43658364\",\n \"0.4364087\",\n \"0.43597716\",\n \"0.433681\",\n \"0.43316966\",\n \"0.4328858\",\n \"0.43225512\",\n \"0.43204826\",\n \"0.43201983\",\n \"0.43180314\",\n \"0.43169382\",\n \"0.43134922\",\n \"0.4308281\",\n \"0.43079883\",\n \"0.4306587\",\n \"0.43017423\",\n \"0.43005255\",\n \"0.42931905\",\n \"0.42830184\",\n \"0.42667884\",\n \"0.42662245\",\n \"0.4264463\",\n \"0.42639777\",\n \"0.4263204\",\n \"0.42573613\",\n \"0.42520258\",\n \"0.42463896\",\n \"0.4243378\",\n \"0.4234604\",\n \"0.42327753\",\n \"0.42312574\",\n \"0.42312482\",\n \"0.42312217\",\n \"0.42292443\",\n \"0.42188665\",\n \"0.42177963\",\n \"0.42168275\",\n \"0.42163968\",\n \"0.42148173\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.5472057"},"document_rank":{"kind":"string","value":"3"}}},{"rowIdx":21899,"cells":{"query":{"kind":"string","value":"Parses the main corpus and IAA gold standard files and joins them in one WebAnnoProject."},"document":{"kind":"string","value":"def parse_main_iaa(main_dirp, iaa_dirp, opt_fp):\n\n # moderator_id = \"gilles\"\n # exclude_moderator = lambda x: moderator_id not in Path(x.path).stem\n # include_moderator = lambda x: moderator_id in Path(x.path).stem\n\n main_project = WebannoProject(main_dirp)\n # # exclude moderator and trial files which start with two digits\n # main_anndocs_final = [p for p in main_project.annotation_document_fps\n # if moderator_id not in Path(p).stem and not Path(p).parents[1].stem[0:1].isdigit()]\n #\n # iaa_project = WebannoProject(iaa_dirp)\n # # exclude all annotators except moderator and trial files which start with two digits\n # iaa_anndocs_final = [p for p in iaa_project.annotation_document_fps\n # if moderator_id in Path(p).stem and not Path(p).parents[1].stem[0:1].isdigit()]\n\n main_project.annotation_document_fps.extend(main_project.annotation_document_fps)\n\n main_project.parse_annotation_project()\n\n main_project.dump_pickle(opt_fp)\n print(f\"Written project object pickle to {opt_fp}\")"},"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 main(url, inputFile, directory, rss, opml, output, verbose, debug, relevanceAlgorithm):\n\n if (len(argv) < 2):\n print(\n \"Usage: python3 ctirt.py [options] [target files]\\n\\n Use --> ctirt.py --help for more details...\"\n )\n exit(1)\n\n if (verbose and url) or (url and debug):\n print(\"URL is mutually exclusive with verbose and debug\")\n exit(1)\n \n \n \n # INITIALIZE DOCUMENTS LIST\n documents = [] # list of document objects\n\n # OPML FILE INPUT\n\n if opml:\n printLogo()\n print(\"\\033[0;34m\" + \"Parsing provided opml file: \" + \"\\033[0m\" + \"\\033[1m\" + opml + \"\\033[0m\")\n\n rssList = parser.parseOpml(opml)\n\n for rss in rssList:\n print(\"Parsing RSS feed: \" + \"\\033[1m\" + rss + \"\\033[0m\")\n\n feed = parser.parseRss(rss)\n \n if not verbose:\n # progress bar\n progressBar = IncrementalBar('\\tParsing URLs in RSS feed:', max=len(feed.entries), suffix='%(index)d / %(max)d')\n\n for entry in feed.entries:\n document = Document()\n\n document.path = entry.link\n \n document.name, document.text = parser.parseUrl(document.path)\n \n document.wordCount = parser.countWords(document.text)\n \n # Add document object to list, add document wordcount to list\n documents.append(document)\n\n if not verbose:\n progressBar.next()\n else:\n print(\"Done.\")\n \n print(\"\\n\\t\" + \"\\033[0;32m\" + u'\\u2713' + \" Done parsing RSS feed: \" + \"\\033[0m\" + \"\\033[1m\" + rss + \"\\033[0m\")\n # RSS INPUT\n\n elif rss:\n printLogo()\n print(\"Parsing\", rss)\n\n feed = parser.parseRss(rss)\n if not verbose:\n # progress bar\n progressBar = IncrementalBar('Parsing URLs', max=len(feed.entries), suffix='%(index)d / %(max)d')\n\n for entry in feed.entries:\n document = Document()\n\n document.path = entry.link\n \n document.name, document.text = parser.parseUrl(document.path)\n \n document.wordCount = parser.countWords(document.text)\n \n # Add document object to list, add document wordcount to list\n documents.append(document)\n\n if not verbose:\n progressBar.next()\n else:\n print(\"Done.\")\n \n if not verbose:\n progressBar.finish()\n\n print(\"Done.\")\n \n # URL INPUT\n \n elif url:\n printLogo()\n print(\"Parsing...\")\n\n document = Document()\n\n document.path = url\n \n document.name, document.text = parser.parseUrl(url)\n \n document.wordCount = parser.countWords(document.text)\n \n # Add document object to list, add document wordcount to list\n documents.append(document)\n\n print(\"Done.\")\n\n \n # SINGLE FILE INPUT\n\n elif inputFile:\n printLogo()\n print(\"Parsing...\")\n\n document = Document()\n\n document.name = os.path.splitext(inputFile)[0]\n document.path = inputFile\n\n if inputFile.lower().endswith(\".pdf\"): # PDF Parsing\n document.text = parser.parsePdf(inputFile)\n elif inputFile.lower().endswith(\".html\"): # HTML Parsing\n document.text = parser.parseHtml(inputFile)\n\n document.wordCount = parser.countWords(document.text) # Document word count\n\n # Add document object to list, add document wordcount to list\n documents.append(document)\n\n print(\"Done.\")\n\n\n # DIRECTORY INPUT\n\n elif directory:\n printLogo()\n if not verbose:\n # progress bar\n progressBar = IncrementalBar('Parsing', max=len(\n os.listdir(directory)), suffix='%(index)d / %(max)d')\n\n # Loop through files in directory\n for inputFile in os.scandir(directory):\n beginningTime = time.time()\n\n if verbose:\n timeStamp = time.time()\n print(\"***[\" + inputFile.name[0:50] + \"]***\", \"is currently being parsed\",\n \"-->\", (timeStamp - beginningTime), \"seconds have elapsed...\")\n\n document = Document()\n\n document.name = os.path.splitext(inputFile.name)[0]\n document.path = inputFile.path\n\n if verbose:\n print(inputFile.name)\n\n if inputFile.name.lower().endswith(\".pdf\"): # PDF Parsing\n document.text = parser.parsePdf(inputFile.path)\n elif inputFile.name.lower().endswith(\".html\"): # HTML Parsing\n document.text = parser.parseHtml(inputFile.path)\n\n document.wordCount = parser.countWords(\n document.text) # Document word count\n\n # Add document object to list, add document wordcount to list\n documents.append(document)\n\n if not verbose:\n progressBar.next()\n else:\n print(\"Done.\")\n \n if not verbose:\n progressBar.finish()\n\n\n # BASIC RELEVANCE CALCULATION\n\n for document in documents:\n document.relevance = relevance.computeBasicRelevance(document.text)\n\n\n # TF-IDF RELEVANCE CALCULATION\n\n if directory and (verbose or debug or relevanceAlgorithm == \"tfidf\"):\n dirWordCount = parser.countDirectoryWords(documents)\n\n wordList = {}\n with open('./assets/wordlist.json') as f:\n jsonWordList = load(f)\n for pair in jsonWordList.items():\n wordList[pair[0]] = float(pair[1])\n\n for document in documents:\n # TODO Figure out how to run - fix arguments (ex. import wordlist), make debug work better by allowing it to work not in verbose\n idfs = relevance.computeIDF(documents, dirWordCount)\n print(\"**************** IDFS ****************\")\n print(idfs)\n tf = relevance.computeTF(wordList, document.wordCount)\n print(\"**************** TF DICT ****************\")\n print(tf)\n\n tfidf = relevance.computeTFIDF(tf, idfs)\n print(\"**************** TF-IDF Values ****************\")\n print(tfidf)\n\n relevanceScore = 0\n\n for word, val in tfidf.items():\n relevanceScore += val\n \n document.tfidf = relevanceScore * 100\n\n\n # OUTPUT SECTION\n\n documents.sort(key=lambda document: document.relevance, reverse=True)\n\n table = []\n tableHeaders = []\n outputData = []\n # print(\"**************** RELEVANCE SCORES ****************\")\n for document in documents:\n outputData.append({'name': document.name[0:30], 'relevance': document.relevance,'path': document.path, 'topTerms': list(document.wordCount.items())[:10]})\n if url or rss or opml: \n table.append([document.name[0:30], document.relevance, document.path])\n tableHeaders = [\"Document\",\"Relevance Score\",\"URL\"]\n elif not verbose:\n table.append([document.name[0:70], document.relevance])\n tableHeaders=[\"Document\",\"Relevance Score\"]\n elif verbose and directory:\n table.append([document.name[0:70], document.relevance, document.tfidf, list(document.wordCount.items())[:10]])\n tableHeaders=[\"Document\",\"Relevance Score\", \"TF-IDF Score\", \"Top Terms\"]\n else:\n table.append([document.name[0:70], document.relevance, list(document.wordCount.items())[:10]])\n tableHeaders=[\"Document\",\"Relevance Score\", \"Top Terms\"]\n\n print(tabulate(table, headers=tableHeaders, tablefmt=\"fancy_grid\"))\n\n # OUTPUT TO FILE\n\n with open(output, 'w', encoding='utf-8') as o:\n dump(outputData, o, indent=3)","def main(args):\n\tif not os.path.isdir(args.dir):\n\t\tprint \"The specified folder is not a directory.\"\n\t\tsys.exit(1)\n\tNUMBER_OF_FILES = len(os.listdir(args.dir))\n\tif args.num_of_files:\n\t\tNUMBER_OF_FILES = args.num_of_files\n\tprint \"Parsing\", NUMBER_OF_FILES, \"files\"\n\tsql = None\n\tif not args.stdout:\n\t\tsql = sqlite3.connect(args.database)\n\t\tsql.execute(\"\"\"PRAGMA foreign_keys = ON;\"\"\")\n\t\tsql.execute(\"\"\"CREATE TABLE airdates(\n\t\t\tgame INTEGER PRIMARY KEY,\n\t\t\tairdate TEXT\n\t\t);\"\"\")\n\t\tsql.execute(\"\"\"CREATE TABLE documents(\n\t\t\tid INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\tclue TEXT,\n\t\t\tanswer TEXT\n\t\t);\"\"\")\n\t\tsql.execute(\"\"\"CREATE TABLE categories(\n\t\t\tid INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\tcategory TEXT UNIQUE\n\t\t);\"\"\")\n\t\tsql.execute(\"\"\"CREATE TABLE clues(\n\t\t\tid INTEGER PRIMARY KEY AUTOINCREMENT,\n\t\t\tgame INTEGER,\n\t\t\tround INTEGER,\n\t\t\tvalue INTEGER,\n\t\t\tFOREIGN KEY(id) REFERENCES documents(id),\n\t\t\tFOREIGN KEY(game) REFERENCES airdates(game)\n\t\t);\"\"\")\n\t\tsql.execute(\"\"\"CREATE TABLE classifications(\n\t\t\tclue_id INTEGER,\n\t\t\tcategory_id INTEGER,\n\t\t\tFOREIGN KEY(clue_id) REFERENCES clues(id),\n\t\t\tFOREIGN KEY(category_id) REFERENCES categories(id)\n\t\t);\"\"\")\n\tfor i, file_name in enumerate(glob(os.path.join(args.dir, \"*.html\")), 1):\n\t\twith open(os.path.abspath(file_name)) as f:\n\t\t\tparse_game(f, sql, i)\n\tif not args.stdout:\n\t\tsql.commit()\n\tprint \"All done\"","def main():\n LESSONS_PATH = os.path.join(LESSON_LOCATOR_DATA, LESSON_SETS[0])\n ORIGINAL_LESSONS_PATH = os.path.join(LESSONS_PATH, \"original\")\n ANNOTATED_LESSONS_PATH = os.path.join(LESSONS_PATH, \"annotated\")\n\n if not os.path.exists(ANNOTATED_LESSONS_PATH):\n os.mkdir(ANNOTATED_LESSONS_PATH)\n\n print(\"Scanning original lessons in %s...\" % ORIGINAL_LESSONS_PATH)\n\n for item in os.listdir(ORIGINAL_LESSONS_PATH):\n if item == \".DS_Store\": continue\n\n print(\" found: %s\" % item)\n\n item_path = os.path.join(ORIGINAL_LESSONS_PATH, item)\n\n lesson_number = None\n lesson_description = None\n mobj = re.search(r'^AY\\s+(\\d+)\\s*-\\s*(.+)\\.txt$', item)\n if mobj:\n lesson_number = mobj.group(1)\n lesson_description = mobj.group(2)\n\n print(\" number: %s\" % lesson_number)\n print(\" description: %s\" % lesson_description)\n\n lesson = dict()\n lesson['number'] = lesson_number\n lesson['description'] = lesson_description\n\n fh = open(item_path)\n lesson_raw_text = fh.read()\n fh.close()\n lesson_text = re.split(r'\\n', lesson_raw_text)\n# lesson_raw_text_reencoded = lesson_raw_text.decode('mac-roman').encode('utf-8')\n# lesson_text = re.split(r'\\n', lesson_raw_text_reencoded)\n\n lesson['text'] = lesson_text\n lesson['parsed'] = parseLesson(lesson_text)\n\n if lesson['parsed']['end_of_lesson'] is None:\n print(\" lesson has no 'end of lesson' marker\")\n\n lesson_json = json.dumps(lesson, indent=4)\n annotated_lesson_path = os.path.join(ANNOTATED_LESSONS_PATH, \"ay_%04d.json\" % int(lesson_number))\n fh = open(annotated_lesson_path, \"w\")\n fh.write(lesson_json)\n fh.close()\n\n else:\n print(\"ERROR: File name not understood: %s\" % item)\n\n return 0","def documents(iati_import, activity, project, activities_globals):\n imported_docs = []\n changes = []\n\n xml_ns = 'http://www.w3.org/XML/1998/namespace'\n first_image = True\n\n for doc_link in activity.findall('document-link'):\n url = ''\n doc_format = ''\n title = ''\n title_language = ''\n category = ''\n language = ''\n\n if 'url' in doc_link.attrib.keys():\n url = doc_link.attrib['url']\n\n # Check if it's the first image\n if url and url.rsplit('.', 1)[1].lower() in VALID_IMAGE_EXTENSIONS and first_image:\n first_image = False\n continue\n\n if 'format' in doc_link.attrib.keys():\n if not len(doc_link.attrib['format']) > 75:\n doc_format = doc_link.attrib['format']\n else:\n add_log(iati_import, 'document_link_format',\n 'format is too long (75 characters allowed)', project)\n\n # Check if the format is 'application/http'\n if doc_format == 'application/http':\n continue\n\n title_element = doc_link.find('title')\n if not title_element is None:\n title = get_text(title_element, activities_globals['version'])\n if len(title) > 100:\n add_log(iati_import, 'document_link_title',\n 'title is too long (100 characters allowed)', project,\n IatiImportLog.VALUE_PARTLY_SAVED)\n title = title[:100]\n\n if activities_globals['version'][0] == '1' and \\\n '{%s}lang' % xml_ns in title_element.attrib.keys():\n if not len(title_element.attrib['{%s}lang' % xml_ns]) > 2:\n title_language = title_element.attrib['{%s}lang' % xml_ns]\n else:\n add_log(iati_import, 'document_link_title_language',\n 'language is too long (2 characters allowed)', project)\n elif activities_globals['version'][0] == '2':\n narrative_element = title_element.find('narrative')\n if not narrative_element is None and \\\n '{%s}lang' % xml_ns in narrative_element.attrib.keys():\n if not len(narrative_element.attrib['{%s}lang' % xml_ns]) > 2:\n title_language = narrative_element.attrib['{%s}lang' % xml_ns]\n else:\n add_log(iati_import, 'document_link_title_language',\n 'language is too long (2 characters allowed)', project)\n\n category_element = doc_link.find('category')\n if not category_element is None and 'code' in category_element.attrib.keys():\n if not len(category_element.attrib['code']) > 3:\n category = category_element.attrib['code']\n else:\n add_log(iati_import, 'document_link_category',\n 'category is too long (3 characters allowed)', project)\n\n language_element = doc_link.find('language')\n if not language_element is None and 'code' in language_element.attrib.keys():\n if not len(language_element.attrib['code']) > 2:\n language = language_element.attrib['code']\n else:\n add_log(iati_import, 'document_link_language',\n 'language is too long (2 characters allowed)', project)\n\n doc, created = get_model('rsr', 'projectdocument').objects.get_or_create(\n project=project,\n url=url,\n format=doc_format,\n title=title,\n title_language=title_language,\n category=category,\n language=language\n )\n\n if created:\n changes.append(u'added project document (id: %s): %s' % (str(doc.pk), doc))\n\n imported_docs.append(doc)\n\n for doc_link in project.documents.all():\n if not doc_link in imported_docs:\n changes.append(u'deleted project document (id: %s): %s' %\n (str(doc_link.pk),\n doc_link.__unicode__()))\n doc_link.delete()\n\n return changes","def read_and_clean_files(clueweb_file, ann_file, data_dir, ann_dir):\n annotation_input = fileinput.FileInput(os.path.join(ann_dir, ann_file), openhook=fileinput.hook_compressed)\n annotation_list = []\n for line in annotation_input:\n\tannotation_list.append(Annotation.parse_annotation(line))\n\n warc_path = os.path.join(data_dir, clueweb_file)\n warc_file = warc.open(warc_path)\n print \"Replacing entity mentions for \", clueweb_file, \":\", ann_file, \"...\"\n start = time.time()\n warc_entry = WarcEntry(warc_path, warc_file, annotation_list)\n cleaned_records = warc_entry.replace_entity_mentions()\n end = time.time()\n print \"Time used: \", end - start\n warc_file.close()\n return cleaned_records","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')","def main():\n download_insert_title_basics()\n download_insert_title_principals()\n download_insert_name_basics()\n download_insert_title_ratings()\n scrap_keywords()\n create_and_insert_soup()\n return","def load_known_corpora():\n\n print (\"Loading Corpora\")\n\n # For debugging purposes, comment out the definition of all but say the first\n # corpus, to speed the ingest time.\n shenoute_a22 = Corpus()\n shenoute_a22.annis_corpus_name = \"shenoute.a22\"\n shenoute_a22.title = \"Acephalous Work 22\"\n shenoute_a22.slug = \"acephalous_work_22\"\n shenoute_a22.urn_code = \"shenoute.a22\"\n shenoute_a22.github = \"https://github.com/CopticScriptorium/corpora/tree/master/shenoute-a22\"\n\n patrum = Corpus()\n patrum.annis_corpus_name = \"apophthegmata.patrum\"\n patrum.title = \"Apophthegmata Patrum\"\n patrum.slug = \"ap\"\n patrum.urn_code = \"ap\"\n patrum.github = \"https://github.com/CopticScriptorium/corpora/tree/master/AP\"\n\n saof = Corpus()\n saof.annis_corpus_name = \"shenoute.abraham.our.father\"\n saof.title = \"Abraham Our Father\"\n saof.slug = \"abraham_our_father\"\n saof.urn_code = \"shenoute.abraham\"\n saof.github = \"https://github.com/CopticScriptorium/corpora/tree/master/abraham\"\n\n besa_ap = Corpus()\n besa_ap.annis_corpus_name = \"besa.letters\"\n besa_ap.title = \"Letter to Aphthonia\"\n besa_ap.slug = \"to_aphthonia\"\n besa_ap.urn_code = \"besa.aphthonia\"\n besa_ap.github = \"https://github.com/CopticScriptorium/corpora/tree/master/besa-letters\"\n\n fox = Corpus()\n fox.annis_corpus_name = \"shenoute.fox\"\n fox.title = \"Not Because a Fox Barks\"\n fox.slug = \"not_because_a_fox_barks\"\n fox.urn_code = \"shenoute.fox\"\n fox.github = \"https://github.com/CopticScriptorium/corpora/tree/master/shenoute-fox\"\n\n mark = Corpus()\n mark.annis_corpus_name = \"sahidica.mark\"\n mark.title = \"Gospel of Mark\"\n mark.slug = \"gospel_of_mark\"\n mark.urn_code = \"nt.mark\"\n mark.github = \"https://github.com/CopticScriptorium/corpora/tree/master/bible\"\n\n corinth = Corpus()\n corinth.annis_corpus_name = \"sahidica.1corinthians\"\n corinth.title = \"1 Corinthians\"\n corinth.slug = \"1st_corinthians\"\n corinth.urn_code = \"nt.1cor\"\n corinth.github = \"https://github.com/CopticScriptorium/corpora/tree/master/bible\"\n\n snt = Corpus()\n snt.annis_corpus_name = \"sahidica.nt\"\n snt.title = \"New Testament\"\n snt.slug = \"new-testament\"\n snt.urn_code = \"nt\"\n snt.github = \"https://github.com/CopticScriptorium/corpora/tree/master/bible\"\n\n eager = Corpus()\n eager.annis_corpus_name = \"shenoute.eagerness\"\n eager.title = \"I See Your Eagerness\"\n eager.slug = \"eagernesss\"\n eager.urn_code = \"shenoute.eagerness\"\n eager.github = \"https://github.com/CopticScriptorium/corpora/tree/master/shenoute-eagerness\"\n\n besa_nuns = Corpus()\n besa_nuns.annis_corpus_name = \"besa.letters\"\n besa_nuns.title = \"Letter to Thieving Nuns\"\n besa_nuns.slug = \"to_thieving_nuns\"\n besa_nuns.urn_code = \"besa.thieving\"\n besa_nuns.github = \"https://github.com/CopticScriptorium/corpora/tree/master/besa-letters\"\n\n doc_pap = Corpus()\n doc_pap.annis_corpus_name = \"doc.papyri\"\n doc_pap.title = \"Documentary Papyri\"\n doc_pap.slug = \"papyri\"\n doc_pap.urn_code = \"copticDoc:papyri_info\"\n doc_pap.github = \"https://github.com/CopticScriptorium/corpora/tree/master/doc-papyri\"\n\n known_corpora = [shenoute_a22, patrum, saof, besa_ap, fox, mark, corinth, snt, eager, besa_nuns, doc_pap]\n# known_corpora = [shenoute_a22]\n \n for one in known_corpora:\n try:\n Corpus.objects.get(slug__exact=one.slug)\n except Corpus.DoesNotExist:\n one.save()","def process(self):\n\n linelang = defaultdict(int)\n wordlang = defaultdict(int)\n\n linefont = defaultdict(int)\n wordfont = defaultdict(int)\n\n inputfiles = self.input_files\n for input_file in inputfiles:\n\n alignurl = input_file.url\n pcgts = parse(alignurl, True)\n page = pcgts.get_Page()\n regions = page.get_TextRegion()\n\n for region in regions:\n lines = region.get_TextLine()\n\n for line in lines:\n try:\n llang = line.primaryLanguage\n linelang[llang] += 1\n except TypeError:\n pass\n\n try:\n lfont = line.fontFamily\n linefont[lfont] += 1\n except TypeError:\n pass\n\n words = line.get_Word()\n for word in words:\n try:\n wlang = word.language\n wordlang[wlang] += 1\n except TypeError:\n pass\n\n try:\n wfont = word.get_TextStyle().fontFamily\n wordfont[wfont] += 1\n except TypeError:\n pass\n\n #predominant language\n try:\n lang = max(linelang, key=lambda k: linelang[k])\n except TypeError:\n try:\n lang = max(wordlang, key=lambda k: wordlang[k])\n except TypeError:\n lang = 'German'\n\n #predominant font\n try:\n font = max(linefont, key=lambda k: linefont[k])\n except TypeError:\n try:\n font = max(wordfont, key=lambda k: wordfont[k])\n except TypeError:\n font = 'Antiqua'\n\n\n print(lang)\n print(font)","def merge_articles(docs_folder):\n\n s = \"\"\n \n for doc in os.listdir(docs_folder):\n try:\n with open(docs_folder + doc ,'r') as f:\n\n lines = f.readlines()\n raw_doc = \"\".join(txt for txt in lines)\n left_idx_headline = [ m.end(0) for m in re.finditer(r\"<HEADLINE>\",raw_doc)]\n right_idx_headline = [ m.start(0) for m in re.finditer(r\"</HEADLINE>\",raw_doc)]\n\n left_idx_text = [ m.end(0) for m in re.finditer(r\"<TEXT>\",raw_doc)]\n right_idx_text = [ m.start(0) for m in re.finditer(r\"</TEXT>\",raw_doc)]\n\n raw_headline = raw_doc[left_idx_headline[0]:right_idx_headline[0]]\n raw_text = raw_doc[left_idx_text[0]:right_idx_text[0]]\n\n left_idx_paragraph_headline = [ m.end(0) for m in re.finditer(r\"<P>\",raw_headline)]\n right_idx_paragraph_headline = [ m.start(0) for m in re.finditer(r\"</P>\",raw_headline)]\n\n left_idx_paragraph_text = [ m.end(0) for m in re.finditer(r\"<P>\",raw_text)]\n right_idx_paragraph_text = [ m.start(0) for m in re.finditer(r\"</P>\",raw_text)]\n\n for i in range(len(left_idx_paragraph_headline)):\n s += raw_headline[left_idx_paragraph_headline[i]:right_idx_paragraph_headline[i]-2] + \".\"\n\n for i in range(len(left_idx_paragraph_text)):\n s += raw_text[left_idx_paragraph_text[i]:right_idx_paragraph_text[i]-1]\n except:\n pass\n\n return s","def main():\n token_dict_dict = {}\n all_dict = {}\n pronoun_proportion_list = []\n tag = 'PRP' # base tag for all pronouns, see 'https://www.clips.uantwerpen.be/pages/MBSP-tags' for more info\n\n for text in glob.glob(file_loc):\n file_title = os.path.basename(text).split('.')[0]\n\n with open(text, 'r') as f:\n speech = f.read()\n text_dict = {}\n\n try:\n #TextBlob goodness that tags all the words for me\n speech_blob = TextBlob(clean(speech))\n speech_blob.tags\n except:\n #for some reason Trump's address contained a unicode 128 character that I couldn't find\n #instead of getting rid of it in a single file, i decided to have an except that could catch that case in\n #all sitations and handle them accordingly\n\n #lets the user know that there was an issue, and that it's been handled\n print file_title,\n print \"contains unexpected unicode characters. they have been removed and the document has been processed\"\n\n #gets rid of all unicode characters. i could do this by default, but all the other files ran fine\n #so i didn't think it was worth it\n speech_blob = TextBlob(clean(speech.decode('unicode_escape').encode('ascii','ignore')))\n\n for token in speech_blob.tags:\n # builds the inital dictionary of data, only looks at words with a specified tag\n if tag in token[1]:\n try:\n text_dict[token[0]] += 1\n except:\n text_dict[token[0]] = 1\n try:\n all_dict[token[0]] += 1\n except:\n all_dict[token[0]] = 1\n #breaks the title into 3 pieces: number, president, date\n token_dict_dict[file_title] = text_dict\n partial_split, date = string.rsplit(file_title, '_', 1)\n num_pres, pres = string.split(partial_split, '_', 1)\n\n pronoun_proportion_list.append(\n (pres, date, total_to_proportion(pronoun_breakdown(token_dict_dict[file_title])))\n )\n create_pronoun_graph(sort_list_by_president_order(pronoun_proportion_list))","def main():\n\tfl = '/home/rupesh20/ProjectFinal/IITB/conv.txt'\n\tPconv = '/home/rupesh20/ProjectFinal/IITB/prevconv.txt'\n filename = '/home/rupesh20/ProjectFinal/IITB/NEw.txt'\n outfile = '/home/rupesh20/ProjectFinal/IITB/Demo.txt'\n outputfile ='/home/rupesh20/ProjectFinal/IITB/Test.txt'\n\n\t\"\"\" \n\t\tOther comments are for debugging.\n\t\"\"\"\n #filename = '/home/rupesh20/ProjectFinal/final/en_US/en_US.blogs.txt'\n #newfilename = '/home/rupesh20/ProjectFinal/IITB/NEw.txt'\n #file = open(filename,'r')\n #newfile = open(newfilename,'w')\n\n #text = file.read()\n #file.close()\n #words = text.split()\n\t\n #new = []\n #new=lowercase(words,0,new)\n #new=url(new,0)\n #new=unwanted(new)\n #new=DictLook(new)\n\t\t\n\twith open(Pconv) as f:\n \t\tlines = f.readlines()\n\t\"\"\" \n\t\tlines : contains list of strings in english for conversion \n\t\"\"\"\n #Writer(new,newfile)\n #newfile.close()\n\n\t\"\"\" \n\t\tObjects for files are opened\n\t\"\"\"\n\ttxt = open(fl,'w')\n Tobj1=open(outputfile,'w')\n #Ttext1=Tobj1.read()\n #Tobj1.close()1\n Nobj=open(filename,'r')\n Ntext=Nobj.read()\n Nobj.close()\n Nobj1=open(outfile,'r')\n Ntext1=Nobj1.read()\n Nobj1.close()\n\t\"\"\" \n\t\tSentences are Tokenized \n\t\"\"\"\n sentences=sent_tokenize(Ntext)\n testSentences=sent_tokenize(Ntext1)\n \n Csize=0\n\t\"\"\" \n\t\tAfter Tokenize the text in sentences \n\t\tWe have calculated the Ngram \n\t\t1,2 3,4,5 grams\n\t\tGram1 list contains uni-gram\n\t\t||ly Gram2,3,4,5 contains further grams\n\t\t\n\t\"\"\"\n for sent in sentences:\n for j in xrange(1,6):\n Csize= Ngram(sent,j,Csize)\n print Csize \n FreqCount(Gram)\n for sent in testSentences:\n TestNgram(sent,5)\n #print Gram5\n #print Sliced(x)\n \n\t\"\"\"\" \n\t\tCalcScore : function to ecaluate the score using the \n\t\t\t stupid backoff algo\n\t\toutput file : generated after the calcscore module , sent : Score\n\t\t\t\n\t\"\"\"\n\tCalcScore(Csize,Tobj1)\n\n\t\"\"\" \n\t\tProcesscontent : module is for Alignment, S-O-V \n\t\t\t\tdetermization.\n\t\n\t\"\"\"\n\n\t\"\"\" \n\t\tbelow code translate, del list[:]( delete whole list )\n\t\n\t\"\"\"\n\tfor line in lines:\n\t\ttemp = []\n\t\ttemp.append(line)\t\n\t\tprocessContent(temp)\n\t\tfor i in Subj:\n\t\t\tBilingDict(txt,i)\n\t\tfor i in obj:\n\t\t\tBilingDict(txt,i)\n\t\tfor i in verb:\n\t\t\tBilingDict(txt,i)\t\n\t\tdel Subj[:]\n\t\tdel verb[:]\n\t\tdel obj[:]\n\t\t\n\t\ttxt.write('\\n')\n\n\t#print Subj[0] \n\t#print verb\n\t#print obj\n\t\"\"\" \n\t\tBilingdict : module contain JSON lookup table.\n\t\t\t\tgenrates a file with HINDI text.\n\n\t\"\"\"\n\t\t\t \n\t#print StupidBackoff(x,Sliced(x),Csize)\n #print CountGram1\n #print CountGram2\n #print CountGram3\n #print CountGram4\n #freq.plot(50)","def main() -> None:\n\n # Retrieving the wiki URL\n url = get_api_url()\n print(url)\n\n # Creates file if it does not exist\n open(TEXT_FILE, \"a\")\n\n with open(TEXT_FILE, \"r\") as f:\n last_title = f.readline().strip()\n print(\"Starting from:\", last_title)\n\n # Retrieving the pages JSON and extracting page titles\n pages_json = get_pages_json(url, last_title)\n pages = pages_json[\"query\"][\"allpages\"]\n print(\"Pages to be scanned:\", pages)\n\n # Tagging operations\n for page in pages:\n curr_title = page[\"title\"]\n cats_to_add = get_categories(curr_title)\n if cats_to_add:\n print(\"Adding categories\", cats_to_add, \"to '%s'\" % curr_title)\n for cat in cats_to_add:\n add_category(curr_title, \"[[Category:\" + cat + \"]]\")\n\n # Extracting title to continue iterating from next run\n if \"continue\" in pages_json:\n continue_from_title = pages_json[\"continue\"][\"apcontinue\"]\n print(\"Continuing from:\", continue_from_title, \"next run.\")\n else:\n continue_from_title = \"\"\n\n with open(TEXT_FILE, \"w+\") as f:\n f.write(continue_from_title)\n print(\"Wrote\", continue_from_title, \"in\", TEXT_FILE)\n\n print(\"No pages left to be tagged\")","def main():\n files = init.file_list\n citations = load_citations.load_files(files)\n citations_a = citations[0]\n citations_b = citations[1]\n\n common_citations, num_common = compare_citations.common_citations(citations_a, citations_b)\n save_citations.save_citations(common_citations)","def build_training_data():\r\n for i in range(len(FILE_NAMES)):\r\n input_text = read_file(FILE_NAMES[i])\r\n list_of_word_lines = limiting_sentence_length(input_text)\r\n data = create_training_data_file(list_of_word_lines, LANGUAGE[i])\r\n write_training_data(data, LANGUAGE[i])\r\n merge_training_data()","def Main(root_directory):\n filepaths = GetAllFilepaths(root_directory)\n for filepath in filepaths:\n parser = fileparser.CreateParser(filepath)\n if not parser:\n ReportWarning('cannot find a parser for file %s, skipping...' %\n filepath)\n continue\n old_file_contents = ReadFileIntoString(filepath)\n comment_blocks = parser.FindAllCommentBlocks(old_file_contents)\n if not comment_blocks:\n ReportWarning('cannot find any comment blocks in file %s' %\n filepath)\n old_copyright_block = parser.FindCopyrightBlock(comment_blocks)\n if not old_copyright_block:\n ReportWarning('cannot find copyright block in file %s' % filepath)\n (year, holder) = parser.GetCopyrightBlockAttributes(old_copyright_block)\n if holder and not ConfirmAllowedCopyrightHolder(holder):\n ReportWarning(\n 'unrecognized copyright holder \"%s\" in file %s, skipping...' % (\n holder, filepath))\n continue\n new_copyright_block = parser.CreateCopyrightBlock(year, holder)\n if old_copyright_block:\n new_file_contents = old_file_contents.replace(\n old_copyright_block, new_copyright_block, 1)\n else:\n new_file_contents = new_copyright_block + old_file_contents\n WriteStringToFile(new_file_contents, filepath)","def test2_basic_info(self):\n\t\tprint \"\\nTEST 2: Extracting basic info from each ontology in %s folder.\\n=================\" % DATA_FOLDER\n\n\t\tfor f in os.listdir(DATA_FOLDER):\n\t\t\tif not f.startswith('.'):\n\t\t\t\tprint \"Loading... >\", f\n\t\t\t\t\n\t\t\t\t# divert output to a file temporarily \n\t\t\t\tsaveout = sys.stdout \n\t\t\t\tfsock = open('out.log', 'w') \n\t\t\t\tsys.stdout = fsock \n\t\t\t\t\n\t\t\t\to = ontospy.Ontology(DATA_FOLDER + f)\n\t\t\t\tprintBasicInfo(o)\t\t\t\t\n\t\t\t\t\n\t\t\t\tsys.stdout = saveout\n\t\t\t\tfsock.close()\n\t\t\t\tprint \"Success.\"","def createStructuredTranscript_Non_Core_Doc():\n\n #create a temporary folder that will hold the data transformed from doc to docx\n os.system('mkdir ' + INPUT_FOLDER+'temp')\n\n core_doc_asset = []\n missing_count = 0\n missing_files=[]\n # get all the docx files that are part of the core asset\n for file in glob.glob(INPUT_FOLDER+\"*.doc\"):\n\n # RG numbers for the core asset\n if (\"RG-50.030\" not in file and\n \"RG-50.106\" not in file and\n \"RG-50.549\" not in file):\n \n\n \n # convert file to docx, storing it in an untracked folder called temp\n file_docx = file + 'x'\n command = 'textutil -convert docx ' + file + ' -output ' + INPUT_FOLDER+'temp/'+ file_docx.split('/')[-1]\n call(command, shell=True)\n\n # append to the array\n core_doc_asset.append(file_docx)\n \n\n \n\n # get the units for each file, store them and update tracker\n core_doc_asset=create_dictionary_of_file_list(core_doc_asset)\n \n not_processed=0\n processed_doc=0\n \n # get the units for each file, store them and update tracker \n for mongo_rg in core_doc_asset:\n # get text units for this entry\n processed=[]\n result=[]\n \n for file in core_doc_asset[mongo_rg]:\n \n \n \n units = getTextUnits(INPUT_FOLDER+'temp/'+file.split('/')[-1])\n \n if units:\n #replace white spaces\n for i,element in enumerate(units):\n units[i]['unit']=' '.join(element['unit'].split())\n result.extend(units)\n \n processed.append(True)\n else:\n #check if processed\n processed.append(False)\n\n #set the method used to transform the transcript\n h.update_field(DB, TRACKER, \"rg_number\", mongo_rg, \"method\", \"transcribe_non_core_doc\")\n\n not_processed=not_processed+1\n\n if False in processed:\n\n h.update_field(DB, TRACKER, \"rg_number\", mongo_rg, \"status\", \"Unprocessed\")\n not_processed=not_processed+1\n missing_files.append(' '.join(core_doc_asset[mongo_rg]))\n else:\n # insert units on the output collection\n h.update_field(DB, OUTPUT, \"shelfmark\", 'USHMM '+mongo_rg, \"structured_transcript\", result)\n\n \n # update status on the stracker\n \n h.update_field(DB, TRACKER, \"rg_number\", mongo_rg, \"status\", \"Processed\")\n processed_doc=processed_doc+1\n \n\n #delete the temporary folder\n os.system('rm -r ' + INPUT_FOLDER+'temp')\n\n \n #write the missing files to text file\n file = open(OUTPUT_FOLDER_USHMM_PROCESSING_LOGS+'transcribe_non_core_doc_failed.txt','w')\n file.write('\\n'.join(missing_files))\n\n \n # success\n pprint.pprint(\"Non-core doc files were successfully processed, but there are \" + str(missing_count) + \" missing\")","def main():\n # %%\n CFG.profiles_yamls_path.mkdir(parents=True, exist_ok=True)\n fpaths = list( _Config.raw_profiles_path.glob('*.html') )\n print( f'{len(fpaths)} htmls found' )\n # %%\n fpath = CFG.raw_profiles_path / 'luis-mario-urrea-murillo.html'\n # %%\n fpath = CFG.raw_profiles_path / 'cristian-david-montoya-saldarriaga-09638514a.html'\n # %%\n fpaths = [ CFG.raw_profiles_path / 'ricardo-alarcon-44079b105.html' ]\n # %%\n fpaths = [ Path('/home/teo/_data/talent/linkedin_raw_profiles/israellaguan.html')]\n # %%\n dics = {}\n # %%\n\n for i, fpath in enumerate(fpaths):\n if fpath in dics:\n continue\n\n with fpath.open('rt') as f_in:\n html = f_in.read()\n\n print( f'\\n***{i+1}/{len(fpaths)} {fpath.name}:')\n dic = extract_one( html, fpath )\n dic['linkedin_url'] = f\"https://www.linkedin.com/in/{fpath.name.split('.')[0]}\"\n dic['scraped_at'] = dt.datetime.fromtimestamp( fpath.stat().st_ctime )\n # pprint(dic['work_stats'])\n dics[fpath] = dic\n\n dics_arr = list(dics.values())\n # %%\n del dics\n # %%\n\n with (CFG.profiles_yamls_path / 'all_profiles.json').open('wt') as f_out:\n json.dump( dics_arr, f_out, cls=DateTimeEncoder, indent=4 )\n # %%\n with (CFG.profiles_yamls_path / 'all_profiles.yaml').open('wt') as f_out:\n yaml.safe_dump( dics_arr, f_out )\n # %%\n df = produce_summary_table( dics_arr )\n df.to_excel( CFG.raw_profiles_path.parent / 'mined_ruby_candidates_sample.xlsx',\n index=False)\n # %%","def mk_eng_txt_files(self, **rewrite):\n\n\n print \"mk_eng_txt_files: RETRIEVING PAGE_LIST..........\"\n page_list = c_m.l_of_l_read(self.page_list_path)\n\n if len(page_list) < 1: # handling empty page_list case\n print \"mk_eng_txt_files: PAGE LIST IS NOT POPULATED, RUN HTML_File_Maker AND Text_Extractor MODULES FIRST\"\n else: # handling page_list for partially transalted sites\n print \"mk_eng_txt_files: IN CASE PAGE LIST ALREADY HAD SOME ENG_TEXT ENTRIES SETTING INITIAL new_page_list TO LAST KNOWN PAGE_LIST VERSION\"\n self.new_page_list = copy(page_list)\n\n\n\n # iterating throug unique text per page txts\n for data_set in page_list:\n self.p_text_f_name = data_set[2]\n print \"mk_eng_txt_files: TRANSLATING TEXT FROM FILE %s\" % self.p_text_f_name\n\n self.eng_p_text_f_name = \"eng_\" + self.p_text_f_name\n self.eng_p_text_f_path = self.text_eng_folder_path + self.eng_p_text_f_name\n\n self.page_text = c_m.simply_read(self.text_folder_path, self.p_text_f_name)\n\n # if page has less than 10 symbols it is not translated\n if len(self.page_text) < 10:\n print \"mk_eng_txt_files: NOT WORTH TRANSLATING, WRITING AS IS AND SKIPPING...\"\n c_m.simply_write(self.page_text, self.eng_p_text_f_path)\n\n elif len(self.page_text) > self.max_page_length:\n print \"mk_eng_txt_files: PAGE TEXT IS TOO LONG DEVIDING TO PARTS, TRANSLATING AND GETTING BACK FULL PAGE TEXT\"\n text_output = self.get_text_parts(**rewrite)\n\n else: # 10 < len(page_text) < 2000\n\n if rewrite[\"rewrite\"]:\n print \"mk_eng_txt_files: TRANSLATING IN REWRITE MODE\"\n text_output = self.get_text()\n\n\n elif not os.path.exists(self.eng_p_text_f_path):\n print \"mk_eng_txt_files: TRANSLATING IN ONLY ONCE MODE\"\n text_output = self.get_text()\n\n else:\n print \"mk_eng_txt_files: SKIPPING FILE, ALREADY TRANSLATED\"\n # continue\n\n # print \"WRITING TRANSLATED OUTPUT TO FILE: \", self.eng_p_text_f_name\n # c_m.simply_write(text_output, self.text_eng_folder_path, self.eng_p_text_f_name) \n data_set.append(self.eng_p_text_f_name) # updating dataset with eng_text file name\n self.new_page_list.append(data_set) # updating page list with updated entry\n\n\n print \"mk_eng_txt_files: DONE TRANSLATING SITE %s \" % self.domain\n print \"mk_eng_txt_files: UPDATING PAGE LIST WITH ENG TEXT FILE NAMES\"\n c_m.l_of_l_write(self.new_page_list, self.page_list_path)\n print \"mk_eng_txt_files: SITE TRANSLATION FINISHED, CLOSING CHROME WEBDIRVER\"\n self.loaded_driver.quit()","def preMain(dictADT):\r\n data_addr = \"..\\\\csv\\\\normal.csv\"\r\n\r\n userPref = dictADT\r\n mainPref(\"base.txt\", userPref)\r\n\r\n sample_path = \"base.txt\"\r\n webing(sample_path, data_addr)\r\n\r\n path_in = \"base.txt\"\r\n path_out = \"..\\\\resultHTML\\\\result.html\"\r\n mover(path_in, path_out)\r\n\r\n baseDel(\"base.txt\")\r\n # baseDel(\"resultHTML\\\\result.html\")\r\n baseDel(\"..\\\\csv\\\\normal.csv\")\r\n return True","def main():\n\n # Remove the files if existing before creating new ones\n OUT_VOCAB = \"results/vocabs.txt\"\n OUT_SYN = \"results/syn.txt\"\n OUT_EXCP = \"results/exception.txt\"\n for f in [OUT_SYN, OUT_VOCAB, OUT_EXCP]:\n if os.path.isfile(f):\n os.remove(f)\n\n \"\"\"\n Now we do the recursion:\n Step 1: Find the children link;\n Step 2: Add to the url_list;\n Step: Move to next entry of the url_list and Go to Step 1;\n \"\"\"\n with open(DATA, \"r\") as fin:\n url_list = fin.readlines()\n\n # Write down the urls as tuples, 0 as the first level\n url_list = [((PREFIX_URL + subURL).strip(), 0) for subURL in url_list]\n\n with open(OUT_VOCAB, \"w\") as vocab_file, \\\n open(OUT_SYN, \"w\") as syn_file, \\\n open(OUT_EXCP, \"w\") as exp_file:\n cur_index = 0\n while cur_index < len(url_list):\n # Current url of the term, current level number (hierarchy) of the term\n cur_url, cur_hrc = url_list[cur_index]\n try:\n # Get the current sub-url\n time.sleep(0.1)\n r = requests.get(cur_url + \"?\" + API_KEY)\n source_dict = json.loads(r.text)\n\n # Getting the vocabulary and the synonym from this page.\n vocab, syn = parse_words(source_dict=source_dict)\n if cur_hrc + 1 < int(level):\n # Fetch all the children's name and url\n children_urls = fetch_children_url(source_dict=source_dict, hrc=cur_hrc + 1)\n # print children_urls\n url_list = url_list + children_urls # Concatenate the lists\n\n if vocab is not None:\n syn_file.write(json.dumps({vocab: syn}) + \"\\n\")\n vocab_file.write(vocab + \"\\n\")\n\n if cur_index % 100 == 0:\n print \"\\tNow at index # {}, {} in total.\".format(str(cur_index), str(len(url_list)))\n except UnicodeEncodeError as unicode_err:\n exp_file.write(str(unicode_err))\n except KeyError as key_err:\n exp_file.write(str(key_err))\n except:\n pass\n cur_index += 1","def main():\n with open(\"page_data.yaml\", 'r') as inputstr:\n config_data = yaml.safe_load(inputstr)\n ointf = OutputInterface('template.txt')\n table_data = get_song_artist_matches()\n ofilen = config_data['directory'] + os.sep + 'common_songs.html'\n title = 'Song Titles and Band Name Overlap'\n header = ['No.', 'Artist', 'Peak', 'Date', 'Song/Artist', 'Peak',\n 'Date', 'Song']\n ointf.build_page(ofilen, title, header, fmt_table(table_data))\n ointf.inject(XTRAEDIT)\n ointf.output()","def test_project(self, doi_dataset, doi_bib, orcid, metadata_parser):\n apply_mock(doi_dataset, doi_bib, orcid, metadata_parser)\n os.chdir(\"input/\")\n _set_args(\"-i\",\"yamls/project.yaml\",\"-o\",\"../out\")\n with HiddenPrints():\n ya2ro.main()\n\n data = yaml.load(\"yamls/project.yaml\", Loader=SafeLoader)\n with open(\"../out/project/index-en.html\") as f:\n web = f.read()\n\n self.assert_data_in_web(data, web)","def index_file(self, file_name):\n self.contents = []\n article_text = \"\"\n article_annots = [] # for annot-only index\n\n f = open(file_name, \"r\")\n for line in f:\n line = line.replace(\"#redirect\", \"\")\n # ------ Reaches the end tag for an article ---------\n if re.search(r'</doc>', line):\n # ignores null titles\n if wiki_uri is None:\n print \"\\tINFO: Null Wikipedia title!\"\n # ignores disambiguation pages\n elif (wiki_uri.endswith(\"(disambiguation)>\")) or \\\n ((len(article_text) < 200) and (\"may refer to:\" in article_text)):\n print \"\\tINFO: disambiguation page \" + wiki_uri + \" ignored!\"\n # ignores list pages\n elif (wiki_uri.startswith(\"<wikipedia:List_of\")) or (wiki_uri.startswith(\"<wikipedia:Table_of\")):\n print \"\\tINFO: List page \" + wiki_uri + \" ignored!\"\n # adds the document to the index\n else:\n self.__add_to_contents(Lucene.FIELDNAME_ID, wiki_uri, Lucene.FIELDTYPE_ID)\n if self.annot_only:\n self.__add_to_contents(Lucene.FIELDNAME_CONTENTS, article_annots, Lucene.FIELDTYPE_ID_TV)\n else:\n self.__add_to_contents(Lucene.FIELDNAME_CONTENTS, article_text, Lucene.FIELDTYPE_TEXT_TVP)\n self.lucene.add_document(self.contents)\n self.contents = []\n article_text = \"\"\n article_annots = []\n\n # ------ Process other lines of article ---------\n tag_iter = list(self.tagRE.finditer(line))\n # adds line to content if there is no annotation\n if len(tag_iter) == 0:\n article_text += line\n continue\n # A tag is detected in the line\n for t in tag_iter:\n tag = t.group(3)\n if tag == \"doc\":\n doc_title = self.titleRE.search(t.group(2))\n wiki_uri = WikipediaUtils.wiki_title_to_uri(doc_title.group(1)) if doc_title else None\n if tag == \"a\":\n article_text += t.group(1) + t.group(4) # resolves annotations and replace them with mention\n # extracts only annotations\n if self.annot_only:\n link_title = self.linkRE.search(t.group(2))\n link_uri = WikipediaUtils.wiki_title_to_uri(unquote(link_title.group(1))) if link_title else None\n if link_uri is not None:\n article_annots.append(link_uri)\n else:\n print \"\\nINFO: link to the annotation not found in \" + file_name\n last_span = tag_iter[-1].span()\n article_text += line[last_span[1]:]\n f.close()","def main():\n\n global APP_NAME\n\n # Defines Argument Parser and fefines flags and expected inputs\n parser = argparse.ArgumentParser(\n description='Converts specified html files or all html files to \\\n\t\t\tdjango format within a \\n specified directory.'\n )\n # Defines the -f flag, standing for files, to gey file nameof the HTML\n # file to convert\n parser.add_argument(\n 'files',\n metavar='f',\n type=str,\n nargs='*',\n help='provide file names to convert'\n )\n # Defines the -a flag, for defining the APP_NAME, you want the file\n # converted to, for.\n parser.add_argument(\n '-a',\n dest='app_name',\n type=str,\n nargs='?',\n help='provide django app name'\n )\n # Defines the -d flag, standing for directory, which accepts the path\n # to a directory containing the files to be translated\n parser.add_argument(\n '-d',\n dest='base_directory',\n type=str,\n nargs='?',\n help='Provide base directory'\n )\n\n # Parse the Arguments from the user\n args = parser.parse_args()\n\n # Deconstruct the arguments from the parser\n files = args.files\n directory = args.base_directory\n app_name = args.app_name\n\n # If APP_NAME is not passes in as an argument, leave it as ''(empty)\n if app_name is not None:\n APP_NAME = app_name + \"/\"\n\n # If directory is not passed in as an argument, use the current working\n # directory to fetch files\n if directory is None:\n directory = os.getcwd()\n\n logging.info(\"Directory : \" + str(directory))\n logging.info(\"app_name : \" + str(app_name))\n\n # Check if the directory passed in as argument already has the directory \n # 'Modified_files', else create it.\n if not os.path.exists(os.path.join(directory, \"Modified_files\")):\n os.mkdir(os.path.join(directory, \"Modified_files\"))\n\n if files != []:\n for file in files:\n processFile(directory, directory + \"/\" + file, file)\n\n else:\n # If no file was passed in as input, then extract all files in the \n # directory passed in, with extension '.html'\n for file in os.listdir(directory):\n if file.endswith(\".html\"):\n processFile(directory, directory + \"/\" + file, file)","def main(base_path):\n current = os.getcwd()\n try:\n if not(os.path.exists(base_path)):\n ans = 'y'\n if p_out:\n print(\"Do you want to create \" + base_path + \"?(y/n)\")\n ans = sys.stdin.read(1)\n print(\"\")\n if ans in ('y', 'Y'):\n pass\n elif ans in ('n', 'N'):\n raise NoneOutput\n else:\n raise InputError\n else:\n m_path = os.path.join(base_path, 'nzmath/manual')\n if os.path.exists(m_path):\n ans = 'y'\n if p_out:\n print(\"Do you want to remove \" + m_path + \"?(y/n)\")\n ans = sys.stdin.read(1)\n print(\"\")\n if ans in ('y', 'Y'):\n for root, dirs, files in os.walk(m_path, topdown=False):\n for name in files:\n os.remove(os.path.join(root, name))\n for name in dirs:\n os.rmdir(os.path.join(root, name))\n elif ans in ('n', 'N'):\n raise NoneOutput\n else:\n raise InputError\n dirname = os.path.join(base_path, 'nzmath/manual/modules')\n if not(os.path.exists(dirname)):\n os.makedirs(dirname)\n os.chdir(os.path.join(base_path, 'nzmath/manual/'))\n csspage = convertHPURL('manual/default.css')\n if p_out:\n print(\"get css from \" + csspage)\n retryConnection(urllib.request.urlretrieve, csspage, 'default.css')\n while ad_list:\n files = ad_list.pop()\n MyWikiParser(files).feeds()\n if p_out:\n print(\"\\n\" + \"All process is done!\" + \"\\n\")\n print(\"Ok, now created nzmath-current manual located to\")\n print(os.path.join(base_path, \"nzmath\"))\n print(\"if you check difference between nzmath-cvs manual, with GNU diff,\")\n print(\"$ diff -ubBr /tmp/nzmath/manual {your-nzmathcvs-repo}/manual\")\n print(\"or you check only new version files,\")\n print(\"$ diff -r --brief /tmp/nzmath/manual {your-nzmathcvs-repo}/manual .\")\n except NoneOutput:\n if p_out:\n print('end.')\n except InputError:\n print(\"Error: Invalid input!\")\n except LookupError:\n print(\"Error: Maybe, Japanese encodings(ex.euc_jp) is not supported.\")\n except:\n if p_out:\n print(\"Check \" + base_path + \" (dir? truly path? and so on.)\")\n print(\"Delete \" + base_path + \" and try again.\")\n print(\"(Maybe, caused by problem of network connection)\\n\")\n print(sys.exc_info()[0])\n os.chdir(current)","def parse_and_analyse_corenlp_coref(input_dir = 'CoreNLP_coref_anno/dev', gold_annotations_folder = '../../../data/baseline/dev'):\n\tmentions = []\n\n\n\twith open('coref_analyse_output.txt', 'w') as out_file:\n\n\t\tfor file_name in os.listdir(input_dir):\n\t\t\tif re.match(r'(.+)\\.xml', file_name)!= None:\n\t\t\t\tokr_graph = load_graph_from_file(gold_annotations_folder + '/'+ re.match(r'(.+)\\.xml', file_name).group(1)[:-4]+'.xml')\n\n\t\t\t\ttree = ET.parse(input_dir + '/' + file_name)\n\t\t\t\tdocument = tree.getroot()[0]\n\t\t\t\tsentence_wise_predicted_mentions = defaultdict(list)\n\t\t\t\tsentence_wise_gold_mentions = defaultdict(list)\n\t\t\t\tpredicted_coref_dict = defaultdict(list)\n\t\t\t\tgold_coref_dict = defaultdict(list)\n\n\t\t\t\tcoref_node = document.find('coreference')\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tfor coref_id, coref_chain in enumerate(coref_node):\n\t\t\t\t\tfor mention in coref_chain:\n\t\t\t\t\t\tsent_num = int(mention[0].text)\n\t\t\t\t\t\tstart = int(mention[1].text)-1\n\t\t\t\t\t\tend = int(mention[2].text)-1\n\t\t\t\t\t\ttext = mention[4].text\n\t\t\t\t\t\tsentence_wise_predicted_mentions[sent_num].append({\"indices\":range(start, end),\"coref\":coref_id+1, \"text\":text})\n\t\t\t\t\t\tpredicted_coref_dict[coref_id+1].append({\"indices\":range(start, end), \"s_num\":sent_num, \"text\":text })\n\n\n\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t\t\tfor entity in okr_graph.entities.values():\n\t\t\t\t\tfor mention in entity.mentions.values():\n\t\t\t\t\t\tsentence_wise_gold_mentions[mention.sentence_id].append({\"indices\":mention.indices,\"coref\":entity.id, 'text':mention.terms})\n\n\t\t\t\tprint'###'+ file_name + '\\n'\t\n\t\t\t\tfor sentence_id, sentence in enumerate(okr_graph.sentences.values()):\n\t\t\t\t\tprint 'Sentence: ', ' '.join(sentence) \n\t\t\t\t\tprint 'Predicted entities: ', [element['text'] for element in sentence_wise_predicted_mentions[sentence_id+1]]\n\t\t\t\t\tprint 'Gold entities: ', [element['text'] for element in sentence_wise_gold_mentions[sentence_id+1]]\n\t\t\t\t\tprint ' '\n\t\t\t\n\t\t\t\tprint \"Not printing singletons\"\n\t\t\t\tprint('\\nThe predicted clusters: ')\n\t\t\t\tfor cluster_id, cluster in enumerate(predicted_coref_dict.values()):\n\t\t\t\t\tprint('Cluster id: ', cluster_id +1)\n\t\t\t\t\tprint([[okr_graph.sentences[mention['s_num']][index] for index in mention['indices']]for mention in predicted_coref_dict[cluster_id+1]] )\n\n\t\t\t\tprint('\\n The Gold clusters:')\t\n\t\t\t\tfor entity in okr_graph.entities.values():\n\t\t\t\t\tprint('cluster_id: ', entity.id )\n\t\t\t\t\tprint([mention.terms for mention in entity.mentions.values()])\n\n\t\t\t\tprint '**********'","def main():\n\n for lang, family, host, db, footer, summary in projects:\n wiki = wikipedia.Site(lang, family)\n conn = MySQLdb.connect(host=host, db=db, read_default_file='~/.my.cnf', use_unicode=True)\n cursor = conn.cursor()\n cursor.execute(\"SELECT page_title from page where page_namespace=0 and page_title regexp \\\".*[–-].*\\\";\")\n row = cursor.fetchone()\n endashes = sets.Set()\n hyphens = sets.Set()\n while row:\n pagetitle = re.sub(ur\"_\", ur\" \", unicode(row[0], \"utf-8\"))\n if re.search(ur\"–\", pagetitle) and not re.search(ur\"[-\\(\\)]\", pagetitle): #descartamos las que tienen paréntesis, (cación) (desambiguación)...\n endashes.add(pagetitle)\n if not re.search(ur\"–\", pagetitle) and re.search(ur\"-\", pagetitle):\n hyphens.add(pagetitle)\n row = cursor.fetchone() \n cursor.close()\n conn.close()\n\n print len(endashes), \"endashes\"\n\n for pagetitle in endashes:\n footer2=footer\n pagetitle_ = re.sub(ur\"–\", ur\"-\", pagetitle)\n if pagetitle_ not in hyphens:\n #creamos\n redirect = wikipedia.Page(wiki, pagetitle_)\n if not redirect.exists():\n time.sleep(3)\n target = wikipedia.Page(wiki, pagetitle)\n if target.exists():\n if target.isRedirectPage():\n footer2=\"\"\n target = target.getRedirectTarget()\n if target.exists() and not target.isRedirectPage():\n redirect.put(ur\"#REDIRECT [[%s]]%s\" % (target.title(), footer2), u\"BOT - %s [[%s]]\" % (summary, target.title()))\n print ur\"#REDIRECT [[%s]]%s\" % (target.title(), footer2)\n else:\n pass #demasiadas redirecciones anidadas\n else:\n redirect.put(ur\"#REDIRECT [[%s]]%s\" % (target.title(), footer2), u\"BOT - %s [[%s]]\" % (summary, target.title()))\n print ur\"#REDIRECT [[%s]]%s\" % (target.title(), footer2)","def derive_project(\n session_directory, corpus_filter, session_filter, pre_select=1, post_select=0\n):\n # Change to the session directory\n with change_dir(session_directory):\n # Get the parent directory of the session directory\n parent_directory = os.path.dirname(session_directory)\n\n # Check if 'project.xml' exists in the parent directory\n if \"project.xml\" not in os.listdir(parent_directory):\n raise FileNotFoundError(\"project.xml not found in parent project directory\")\n\n # Determine excluded 'Pre' files based on the pre-select option\n if pre_select == 1:\n # Exclude 'Pre' files with a single digit\n exclude_pre_list = [\n e for e in os.listdir(session_directory) if re.search(r\"Pre\\s?\\d\", e)\n ]\n if pre_select == 2:\n # Exclude 'Pre' files with a single digit and rename files with ' 2' suffix\n multi_pre_list = [\n e for e in os.listdir(session_directory) if re.search(r\"Pre\\s?\\d\", e)\n ]\n exclude_pre_list = [e.replace(\" 2\", \"\") for e in multi_pre_list]\n\n # Determine excluded 'Post' files based on the post-select option\n if post_select == 1:\n # Exclude 'Post II' files\n exclude_post_list = [\n e for e in os.listdir(session_directory) if re.search(r\"Post\\s?II\", e)\n ]\n if post_select == 2:\n # Exclude 'Post II' files and remove ' II' suffix\n multi_post_list = [\n e for e in os.listdir(session_directory) if re.search(r\"Post\\s?II\", e)\n ]\n exclude_post_list = [e.replace(\" II\", \"\") for e in multi_post_list]\n # Create corpus folders\n for corpus in corpus_filter:\n try:\n os.mkdir(os.path.join(parent_directory, corpus))\n except FileExistsError:\n print(f\"{corpus} directory already exists. Adding to folder\")\n # Organize all files\n for e in os.listdir(session_directory):\n assert os.path.isfile(e), \"Error: Subdirectory found.\"\n for corpus in corpus_filter:\n # Don't include excluded Pre files specified by pre-select option\n if pre_select > 0:\n if e in exclude_pre_list:\n continue\n # Don't include excluded Post files specified by pre-select option\n if post_select > 0:\n if e in exclude_post_list:\n continue\n # Copy the file to the appropriate corpus folder if it matches the filters\n if fnmatch.fnmatch(e, \"*\" + corpus + \"*\"):\n for key in session_filter:\n if fnmatch.fnmatch(e, \"*\" + key + \"*\"):\n shutil.copy(e, os.path.join(parent_directory, corpus, e))\n\n else:\n continue\n else:\n continue\n\n return","def merge():\n result = []\n for f in glob.glob(f\"{DATA_DIR}/COP*.json\"):\n with open(f, \"r\") as infile:\n result.append(json.load(infile))\n\n with open(f\"{DATA_DIR}/corpus.json\", \"w\", encoding=\"utf-8\") as outfile:\n json.dump(result, outfile)","def build_corpus(self):\n # #############################\n\n doc = metapy.index.Document()\n tok = metapy.analyzers.ICUTokenizer(suppress_tags=True)\n tok = metapy.analyzers.LowercaseFilter(tok)\n tok = metapy.analyzers.LengthFilter(tok, min=3, max=1000)\n tok = metapy.analyzers.Porter2Filter(tok)\n tok = metapy.analyzers.ListFilter(tok, \"lemur-stopwords.txt\", metapy.analyzers.ListFilter.Type.Reject)\n collection = -1\n\n with open(self.documents_path) as file:\n for num, line in enumerate(file):\n l = line.strip()\n c = int(l[0])\n l = l[2:]\n doc.content(l)\n tok.set_content(doc.content())\n if c != collection:\n self.documents.append([])\n collection = c\n self.documents[c].append([token for token in tok])\n self.number_of_collections = len(self.documents)\n self.number_of_documents = len(self.documents[0])\n #print(self.number_of_collections)\n #print(self.number_of_documents)\n #print(self.documents[0])","def main() -> None:\n\n\t# Retrieving the wiki URL\n\turl = get_api_url()\n\tprint(url)\n\n\t# Creates file if it does not exist\n\topen(TEXT_FILE, \"a\")\n\n\twith open(TEXT_FILE, \"r\") as f:\n\t\tlast_title = f.readline().strip()\n\t\tprint(\"Starting from:\", last_title)\n\n\tmodify_pages(url, last_title)\n\n\tprint(\"\\nNo pages left to be tagged\")","def read_news(old=True, recent=True):\n corpus = \"\"\n doc = Doc()\n if old:\n # Read news csv files\n print(\"Preparing old news\")\n path = '/data/WorkData/media_and_judging/data/collected/nytimes/'\n newsfiles = glob.glob(path + '*.csv')\n print(newsfiles)\n tf = len(newsfiles)\n print(\"Total old news files: {}\".format(tf))\n\n # concate all cleaned news text\n for news in newsfiles:\n df = pd.read_csv(news)\n count = 0\n for index, row in df.iterrows():\n count += 1\n # append text\n try:\n cleaned_text = normalizeString(row[5].replace(\"LEAD: \", \"\"))\n corpus += cleaned_text\n\n # add to lang row[4]: title, row[5]: content\n date = formatDate(row[0], '%Y/%m/%d')\n doc.add_news(row[4], cleaned_text, date)\n except:\n pass\n if count % 500 == 0:\n print(\"Progress Report: Old news {}, total {}\".\n format(count, len(news)))\n tf -= 1\n print(\"Files Remaining: \", tf)\n\n if recent:\n # Read json\n # {title: {content:, ptime:, utime:, }..}\n print(\"Preparing recent news\")\n path = '/data/WorkData/media_and_judging/data/collected/nytimes/'\n newsfiles = glob.glob(path + '*.json')\n tf = len(newsfiles)\n print(\"Length of recent news: {}\".format(tf))\n\n # concate all cleaned news text\n for news in newsfiles:\n count = 0\n n = json_load(news)\n for title in n:\n count += 1\n # append text\n cleaned_text = normalizeString(clean_text(n[title]['content']\n .replace(\"LEAD: \", \"\")))\n\n corpus += cleaned_text\n # add to lang\n try:\n if 'ptime' in n[title]:\n date = formatDate(n[title]['ptime'], '%Y%m%d%H%M%S%f')\n else:\n date = formatDate(n[title]['DATE'], '%m. %d, %Y')\n except:\n date = 'UNKNOWN'\n doc.add_news(title, cleaned_text, date)\n if count % 500 == 0:\n print(\"Progress Report: Recent news, {}, total {}\".\n format(count, len(news)))\n tf -= 1\n print(\"Files Remaining: \", tf)\n\n return corpus, doc","def main(path: str):\n output_dir = Path(path)\n output_dir.mkdir(exist_ok=True)\n\n for lang in LANGS:\n lang_dir = output_dir / lang\n lang_dir.mkdir(exist_ok=True)\n analysis_paths: List[str] = []\n for datasets, set_name in ((DEV_FILES, \"dev\"), (TRAIN_FILES, \"train\")):\n for year, url in datasets[lang].items():\n filename = f\"{set_name}_analyses.{year}\"\n analysis_path = lang_dir / filename\n urllib.request.urlretrieve(url, analysis_path)\n analysis_paths.append(str(analysis_path))\n\n # Concatenate all the files and filter for duplicates\n all_analyses_path = lang_dir / \"all_analyses\"\n cmd = f\"cat {' '.join(analysis_paths)} | sort | uniq > {all_analyses_path}\"\n subprocess.check_call(cmd, shell=True)","def doiparse(newdir):\n with open(newdir + 'EC.csv', 'r') as ECdata:\n reader = csv.DictReader(ECdata)\n data = [x for x in reader]\n print(\"creating ANVL files in \" + newdir)\n for record in data:\n dirid = record['id']\n output = open(newdir + dirid + '.txt', 'a')\n handle = record['dc.identifier.uri']\n # Target URL field\n output.write('_target: ' + handle + '\\n')\n # Datacite Schema\n output.write('_profile: datacite \\n')\n # Handle\n output.write('datacite.ObjectLocationURL: ' + handle + '\\n')\n\n # Author\n if record['dc.contributor.author']:\n output.write('datacite.creator: ' + record['dc.contributor.author'] + '\\n')\n \n # Title\n try:\n if record['dc.title']:\n output.write('datacite.title: ' + record['dc.title'] + '\\n')\n else:\n output.write('datacite.title: None')\n with open(newdir + 'notitlesETDs.txt', 'a') as fother:\n fother.write(record['id'])\n fother.write('\\n')\n except KeyError:\n pass\n\n # Publisher\n output.write('datacite.publisher: Cornell University Library' + '\\n')\n\n # Publication Year\n if record['dc.date.issued']:\n output.write('datacite.publicationyear: ' + record['dc.date.issued'][:4] + '\\n')\n else:\n output.write('datacite.publicationyear: None')\n with open(newdir + 'noyearsETDs.txt', 'a') as fyrother:\n fyrother.write(record['id'])\n fyrother.write('\\n')\n\n # Resource Type\n output.write('datacite.resourcetype: Text \\n')\n print('ANVL txt files created.')\n return(data)","def parseArnetminerDataset():\n\n inputFile = open(os.path.join(projectRoot, 'data','DBLP-citation-Feb21.txt'))\n graph = MultiDiGraph()\n\n # Sets for authors, papers, conferences, and terms found so far\n indexToPaperIdMap = {}\n citationCountMap = {}\n indexSet = set()\n\n beginning = inputFile.tell()\n\n print \"Parsing nodes for graph...\"\n\n # Counts for statistics\n VALID_PAPERS = 1566322 # 99.62% of total papers in DBLP dataset\n papersProcessed = 0\n skippedPaperIndices = set()\n invalidPaperIndices = set()\n\n # Add each paper to graph (adding missing associated terms, authors, and conferences)\n for title, authors, conference, terms, citationCount, index in __papersFromFile(inputFile, skippedPaperIndices, invalidPaperIndices):\n\n # Check that index is unique, and record it\n assert index not in indexSet\n indexSet.add(index)\n\n # Create unique identifier with paper index & title\n paperId = '%d----%s' % (index, title)\n citationCountMap[paperId] = citationCount\n indexToPaperIdMap[index] = paperId\n\n # Add symmetric edges & nodes (if they don't already exist in the network)\n for author in authors:\n graph.add_edges_from([(author, paperId), (paperId, author)])\n graph.add_edges_from([(conference, paperId), (paperId, conference)])\n for term in terms:\n graph.add_edges_from([(term, paperId), (paperId, term)])\n\n # Output progress\n papersProcessed += 1\n sys.stdout.write(\"\\r Processed %d / %d papers...\" % (papersProcessed, VALID_PAPERS))\n\n # Rewind file\n inputFile.seek(beginning)\n\n print \"Parsing citations for graph...\"\n\n # Counts for statistics\n papersProcessed = 0\n successfulCitations = 0\n omittedPaperCitations = 0\n invalidPaperCitations = 0\n invalidCitations = 0\n\n # Add citations to the graph\n for title, index, citations in __citationsFromFile(inputFile):\n citingId = '%d----%s' % (index, title)\n for citationIndex in citations:\n\n # Add citation edge if it was found\n if citationIndex in indexToPaperIdMap:\n successfulCitations += 1\n graph.add_edge(citingId, indexToPaperIdMap[citationIndex])\n\n # Tally missing citation appropriately\n elif citationIndex in skippedPaperIndices:\n omittedPaperCitations += 1\n elif citationIndex in invalidPaperIndices:\n invalidPaperCitations += 1\n else:\n print \"\\nCitation '%d' not found for '%s'\" % (citationIndex, title)\n invalidCitations += 1\n\n # Output progress\n papersProcessed += 1\n sys.stdout.write(\"\\r Processed Citations for %d / %d papers...\" % (papersProcessed, VALID_PAPERS))\n\n # Basic statistics about cleanliness of citations\n totalCitations = invalidCitations + successfulCitations\n successfulCitationsPercent = 100 * float(successfulCitations) / totalCitations\n omittedPaperCitationsPercent = 100 * float(omittedPaperCitations) / totalCitations\n invalidPaperCitationsPercent = 100 * float(invalidPaperCitations) / totalCitations\n invalidCitationsPercent = 100 * float(invalidCitations) / totalCitations\n print \"\\n\\nTotal Citations: %d\" % totalCitations\n print \" Citations Added (Successful): %d (%2.2f%%)\" % (successfulCitations, successfulCitationsPercent)\n print \" Citations Skipped (Skipped Paper): %d (%2.2f%%)\" % (omittedPaperCitations, omittedPaperCitationsPercent)\n print \" Citations Skipped (Invalid Paper): %d (%2.2f%%)\" % (invalidPaperCitations, invalidPaperCitationsPercent)\n print \" Citations Invalid (Unknown): %d (%2.2f%%)\" % (invalidCitations, invalidCitationsPercent)\n\n return graph","def openie_prepare_files(document_file, no_entity_filter=False, consider_sections=False):\n temp_dir = tempfile.mkdtemp()\n temp_in_dir = os.path.join(temp_dir, \"input\")\n filelist_fn = os.path.join(temp_dir, \"filelist.txt\")\n out_fn = os.path.join(temp_dir, \"output.txt\")\n os.mkdir(temp_in_dir)\n input_files = []\n\n amount_skipped_files = 0\n doc_count = count_documents(document_file)\n logging.info('counting files to process....')\n if no_entity_filter:\n for document_content in read_pubtator_documents(document_file):\n doc = TaggedDocument(from_str=document_content)\n if not doc or not doc.title or not doc.abstract:\n amount_skipped_files += 1\n else:\n doc_count += 1\n # TODO: Not beautiful but join sections via a '.' to ensure sentence splitting in CoreNLP\n content = '. '.join([te for te, _ in doc.iterate_over_text_elements(sections=consider_sections)])\n input_file = os.path.join(temp_in_dir, \"{}.txt\".format(doc.id))\n input_files.append(input_file)\n with open(input_file, \"w\") as f:\n f.write(content)\n else:\n logging.info('Init spacy nlp...')\n spacy_nlp = English() # just the language with no model\n spacy_nlp.add_pipe(\"sentencizer\")\n\n doc2sentences, doc2tags = filter_document_sentences_without_tags(doc_count, document_file, spacy_nlp,\n consider_sections=consider_sections)\n doc_count = len(doc2tags)\n for doc_id, sentences in doc2sentences.items():\n if sentences:\n input_file = os.path.join(temp_in_dir, \"{}.txt\".format(doc_id))\n input_files.append(input_file)\n with open(input_file, 'wt') as f:\n f.write(' '.join(sentences))\n\n logging.info('{} files need to be processed. {} files skipped.'.format(doc_count, amount_skipped_files))\n with open(filelist_fn, \"w\") as f:\n f.write(\"\\n\".join(input_files))\n return filelist_fn, out_fn, doc_count","def index_file(self, anns_file):\n print \"Indexing \" + anns_file + \"... \",\n\n with open(anns_file, 'rb') as tsvfile:\n reader = csv.reader(tsvfile, delimiter=\"\\t\", quoting=csv.QUOTE_NONE)\n file_dict = defaultdict(list)\n # Reads tsv lines\n for line in reader:\n doc_id, en = line[0], line[7]\n file_dict[doc_id].append(en)\n\n for doc_id, en_list in file_dict.iteritems():\n contents = self.__get_lucene_contents(doc_id, en_list)\n self.lucene.add_document(contents)\n\n print \"done\"","def html_reader(input_dir):\r\n #read data from the html file\r\n with open(input_dir,'r') as html_file:\r\n content = html_file.read()\r\n content = (content.split('\\n'))[4:-4]\r\n num = re.compile(\"(.*\\t\\d.*)|(\\d*\\d\\.\\d*)\")\r\n information = []\r\n for i in range(len(content)):\r\n if num.match(content[i])==None:\r\n information.append(content[i])\r\n information = information[:-1]\r\n #data parsing\r\n Date = re.compile('( ?CACM|June)')\r\n Meta = re.compile(\"(CA\\d\\d\\d\\d\\d\\d|June)\")\r\n #get date and meta index\r\n for i in range(len(information)):\r\n if Date.match(information[i])!=None:\r\n index_date = i\r\n if Meta.match(information[i])!=None:\r\n index_meta =i\r\n content = information[:index_date]\r\n others = information[index_date+2:index_meta]\r\n for i in range(len(content)):\r\n if content[i]==\"\":\r\n title = content[:i]\r\n abstract = content[i+1:]\r\n break\r\n #get author and other\r\n author = []\r\n other = []\r\n for i in range(len(others)):\r\n if others[i]==\"\":\r\n if re.match(\"[A-Z].*, ?[A-Z].*\\..*\",others[0]) != None:\r\n author = others[:i]\r\n other = others[i+1:]\r\n else:\r\n other = others\r\n break\r\n for i in range(len(author)):\r\n if re.match(\"[A-Z].*, ?[A-Z].*\\..*\",author[i]) != None:\r\n name = author[i].split(\",\")\r\n author[i] = (name[1]+name[0])\r\n author[i] = author[i].replace(\" \",\"\")\r\n author[i] = author[i].replace(\"\\t\",\"\")\r\n author[i] = author[i].lower()\r\n\r\n #parse date\r\n date = []\r\n date.append(re.search(\"19\\d\\d\", information[index_date]).group())\r\n date.append(re.search(\"(January|February|March|April|May|June|JUly|July|August|September|October|November|December)\",information[index_date]).group().lower())\r\n\r\n #parse meta data\r\n meta = []\r\n meta.append(re.search(\"CA\\d\\d\\d\\d\\d\\d\\w?\",information[index_meta]).group().lower())#0\r\n meta.append(re.search(\"[a-z0-9] [A-Z]{2}[A-Z]?\",information[index_meta]).group()[2:].lower())#1\r\n meta.append(re.search(\"(January|February|March|April|May|June|JUly|July|August|September|October|November|December)\",information[index_meta]).group().lower())#2\r\n meta.append(re.search(\"\\w \\d\\d?\",information[index_meta]).group()[2:])#3\r\n meta.append(re.search(\"\\d?\\d:\\d\\d\",information[index_meta]).group())#4\r\n meta.append(re.search(\"(AM|PM)\",information[index_meta]).group().lower())#5\r\n meta.append(re.search(\"19\\d\\d\",information[index_meta]).group())#6\r\n\r\n #build corpus\r\n corpus = set()\r\n lemmatizer = WordNetLemmatizer()\r\n for i in range(len(title)):\r\n title[i] = re.sub(\"\\(|\\)|-|\\d\\d?\\d?|:|/|\\.|`|\\?\",\" \",title[i])\r\n words = word_tokenize(title[i])\r\n for word in words:\r\n normal_word = word.lower()\r\n if normal_word not in stopwords.words(\"english\"):\r\n corpus.add(lemmatizer.lemmatize(normal_word))\r\n\r\n for i in range(len(abstract)):\r\n abstract[i] = re.sub(\"\\(|\\)|-|\\d\\d?\\d?|:|/|\\.|`|\\?|,\",\" \",abstract[i])\r\n words = word_tokenize(abstract[i])\r\n for word in words:\r\n normal_word = word.lower()\r\n if normal_word not in stopwords.words(\"english\"):\r\n corpus.add(lemmatizer.lemmatize(normal_word))\r\n\r\n for i in range(len(other)):\r\n other[i] = re.sub(\"\\(|\\)|-|\\d\\d?\\d?|:|/|\\.|`|\\?|,\",\" \",other[i])\r\n words = word_tokenize(other[i])\r\n for word in words:\r\n normal_word = word.lower()\r\n if normal_word not in stopwords.words(\"english\"):\r\n corpus.add(lemmatizer.lemmatize(normal_word))\r\n\r\n corpus = list(corpus)\r\n\r\n return paper(author= author, other= other, metadata= meta,date = date,title = title,abstract = abstract,id=int(input_dir[-9:-5]),corpus = corpus)","def collect_data(self, data_folder):\n print(\"Start collecting\")\n input_file_path = os.path.join(data_folder, \"dblp.xml\")\n with codecs.open(input_file_path, \"r\", encoding=\"iso-8859-1\") as file:\n start = time.time()\n tmp = start\n\n title = \"\"\n key_prefix = \"\"\n authors = []\n personal_information = False\n publications_type = [\"article\", \"inproceedings\", \"proceedings\", \"book\",\n \"incollection\", \"phdthesis\", \"mastersthesis\"]\n start_publication_regex = r\"<([^\\/]*?) (.*)>\" # </article><article mdate=\"2017-05-28\" key=\"journals/acta/Simon83\">\n end_publication_regex = r\"<\\/(.*?)>\" # </article><article mdate=\"2017-05-28\" key=\"journals/acta/Simon83\">\n author_regex = r\"<author>(.*)</author>\" # <author>Katsuyuki Tateishi</author>\n title_regex = r\"<title>(.*)</title>\" # <title>A quadratic speedup theorem ...</title>\n start_person_regex = r'<www.*key=\"(.*)\">' #<www mdate=\"2009-06-10\" key=\"homepages/32/3977\">\n end_person_regex = r\"<\\/www>\"\n note_url_regex = r\"<note.*?>|<url.*?>\"\n inside_publication = False\n inside_person = False\n for i, line in tqdm(enumerate(file)):\n # Test end of publication, if true add publication\n result = regex.search(end_publication_regex, line)\n if result:\n if result.group(1) in publications_type:\n if inside_publication and authors and title:\n # This a a publication, lets add it to the dictionnay author -> title\n self.__add_publication(authors, title)\n title = \"\"\n authors = []\n inside_publication = False\n personal_information = False\n key_prefix = \"\"\n\n #Test beginning of a publication\n result = regex.search(start_publication_regex, line)\n if result and result.group(1) in publications_type:\n inside_publication = True\n title = \"\"\n authors = []\n personal_information = False\n key_prefix = \"\"\n\n #Check if author\n result = regex.search(author_regex, line)\n if result and (inside_publication or inside_person):\n authors.append(result.group(1))\n #Check if title\n result = regex.search(title_regex, line)\n if result and (inside_publication or inside_person):\n title = result.group(1)\n #Check if containt url or note\n result = regex.search(note_url_regex, line)\n if result:\n #if inside_person and (\"<note\" in line or \"<url\" in line):\n personal_information = True\n # Check if end of person, if true add author\n result = regex.search(end_person_regex, line)\n if result and inside_person:\n if authors and \"homepages/\" in key_prefix and title == \"Home Page\":\n self.__add_author(key_prefix, authors, personal_information)\n key_prefix = \"\"\n authors = []\n inside_person = False\n personal_information = False\n title = \"\"\n #Check start person\n result = regex.search(start_person_regex, line)\n if result:\n inside_person = True\n key_prefix = result.group(1)\n\n\n self.__save_everything(data_folder)\n self.__merge_data()\n self.__save_everything(data_folder)","def main(db=\"dbs/readme.db\"):\r\n\r\n firefox_profile_folder = read_firefox_cookies.get_profile_folder()\r\n cjar = read_firefox_cookies.get_cookie_jar(firefox_profile_folder)\r\n\r\n if cjar is False:\r\n sys.exit()\r\n\r\n # Try to make connections less noisy here.\r\n logging.getLogger(\r\n 'urllib3.connectionpool').setLevel(logging.ERROR)\r\n\r\n try:\r\n works_page = \"https://archiveofourown.org/users/RockSunner/works\"\r\n with PageGetter(cookie_jar=cjar) as pgetter:\r\n # Note: will need to be able to plug in a name.\r\n tree = pgetter.get_page(works_page)\r\n find_navigation = \\\r\n etree.XPath('//ol[@class = \"pagination actions\"]/li/a')\r\n page_nums = find_navigation(tree)\r\n keys = []\r\n for res in page_nums:\r\n res_t = res.text_content()\r\n if RepresentsInt(res_t):\r\n keys.append(int(res_t))\r\n else:\r\n break\r\n\r\n scraper = Ao3Scraper()\r\n recs = []\r\n\r\n scraper.parse_tree(tree, recs)\r\n\r\n for key in keys:\r\n payload = {\"page\": key}\r\n tree = pgetter.get_page(works_page, payload)\r\n scraper.parse_tree(tree, recs)\r\n\r\n report_gen = ReportGen(\"A03\")\r\n with ReadMeDb(db, echo=False) as read_db:\r\n for rec in recs:\r\n story = read_db.get_or_create_ao3_story(rec[\"title\"])\r\n changed = compare_ao3_rec(story, rec, report_gen)\r\n if \"kudos\" in changed:\r\n eprint(\"Could look up kudos here\")\r\n report_gen.print_report()\r\n read_db.set_commit_flag()\r\n\r\n pgetter.stop_sleep()\r\n\r\n except ConnectionRefusedError as exc:\r\n eprint(\"ConnectionRefused to archiveofourown.org\", exc)\r\n except ConnectionAbortedError as exc:\r\n eprint(\"Connection problem\", exc)\r\n except Exception as exc:\r\n eprint(\"Cannot connect to archiveofourown.org\", exc)","def main():\r\n # Create clean output files\r\n open(args.outdir2+\"/\"+args.outfile1, 'w').close()\r\n open(args.outdir2+\"/\"+args.outfile2, 'w').close()\r\n\r\n # Run functions\r\n taxonList = nsrTaxonomy()\r\n synonymList, synonymDict = nsrSynonyms()\r\n generaList = nsrGenera(taxonList, synonymList)\r\n boldExtract(generaList)\r\n zipDirectory(args.outdir1, zip_path)\r\n boldNSR(taxonList, synonymList, synonymDict, zip_path)\r\n print(\"Done\")","def main():\n papers_with_references = add_references_to_papers(PAPERS_FILE, TEXT_DIR)\n citations = match_citations_with_papers(papers_with_references)\n insert_into_citation_table(citations)","def doTheJob(input_file):\n\n Parse.parseFile(input_file)\n Write.writeFile(input_file.replace(\".txt\", \".html\"))","def parse_PGR(kb_data):\n\n print(\"Parsing corpus...\")\n output_PGR = dict()\n filepaths = [\"./retrieved_data/corpora/PGR/train.tsv\", \"./retrieved_data/corpora/PGR/test.tsv\"]\n\n for filepath in filepaths:\n \n with open(filepath, encoding=\"utf-8\") as corpus_file:\n reader = csv.reader(corpus_file, delimiter=\"\\t\")\n row_count = int()\n\n for row in reader:\n row_count += 1\n \n if row_count > 1: # Skip the header\n doc_id = row[0]\n annotation_str = row[3]\n hp_id = row[5].replace(\"_\", \":\")\n start_pos, end_pos = row[8], row[9]\n\n if hp_id in kb_data.child_to_parent.keys(): # Only consider concept ids with ONE direct ancestor\n direct_ancestor = kb_data.child_to_parent[hp_id]\n annotation = (annotation_str, start_pos, end_pos, hp_id, direct_ancestor)\n output_PGR = add_annotation_to_output_dict(doc_id, annotation, output_PGR)\n \n print(\"...Done!\") \n return output_PGR","def clean_documents():\n start = datetime.now()\n for i, raw_filename in enumerate(os.listdir(RAW_DIR)):\n fullpath = os.path.join(RAW_DIR, raw_filename)\n if os.path.isfile(fullpath):\n print(\"Cleaning {0} {1}\".format(i, fullpath), file=stderr)\n try:\n with open(fullpath, \"r\") as f:\n text = f.read()\n text = clean(text)\n soup = BeautifulSoup(text, \"html.parser\")\n cleaned = visible_text(soup)\n score = germanwings_score(cleaned)\n if not score:\n print(\"not germanwings: {0}\".format(raw_filename))\n else:\n clean_filename = os.path.join(CLEAN_DIR, raw_filename)\n with open(clean_filename, \"w\") as f:\n f.write(cleaned.encode(\"ascii\", \"ignore\"))\n except Exception as exc:\n print(\"{0}: {1}\".format(fullpath, exc), file=stderr)\n end = datetime.now()\n print(\"Elapsed time to clean: {0}\".format(end - start), file=stderr)","def main(file: str, db: WikiDB, filter: str = \"births\") -> None:\n pageCount = 0\n for event, elem in etree.iterparse(file, events=('end',)):\n elem.tag = strip_tag_name(elem.tag)\n if elem.tag != \"page\": continue # Only parses a page once you finish getting all the elem attrib\n # print(elem.find(\"title\").text) # For debugging\n pageCount += 1\n try:\n parsePage(elem, db, filter)\n # Some articles throw an exception\n # Other articles run forever and hit the time limit, which throws a keyboard interrupt\n except BaseException as e:\n print(e)\n elem.clear() # Clear elem for next use\n if not pageCount % 100000: # Prints out periodic progress statements\n elapsed_time = time.time() - start_time\n print(\"{} articles parsed\".format((pageCount / 100000) * 100000), end=\" \")\n print(\"Elapsed time: {}\".format(hms_string(elapsed_time)))\n db.commit()\n\n elapsed_time = time.time() - start_time\n\n print(\"Elapsed time: {}\".format(hms_string(elapsed_time)))","def make_well_project(laspath='data/las/', stripath='data/tops/'):\n wells = {}\n lasfiles = glob(laspath + '*.LAS')\n stripfiles = glob(stripath + '*.csv')\n for fname, sname in zip(lasfiles, stripfiles):\n name = Path(fname).stem\n wells[name] = Well.from_las(fname)\n wells[name].data['tops'] = Striplog.from_csv(sname)\n proj = Project(list(wells.values()))\n return proj","def loadFiles():\n all_chapters = []\n for name in names:\n f = open(file_path + name, \"r\", encoding=\"utf-8\")\n html_file = f.read()\n f.close()\n chap_text = extractText(html_file)\n new_text = cleanText(chap_text)\n all_chapters.append(new_text)\n concatenated_chapters = \" \".join(all_chapters)\n return concatenated_chapters","def prepare_anno(infolder, outfolder, mode=\"split\"):\n\tprint(\"Starting...\")\n\t\n\tinpath = os.path.join(infolder, \"*.xml\")\n\tfilecounter = 0\n\t\n\t# check output folders\n\tif not os.path.exists(outfolder):\n\t\tos.makedirs(outfolder)\n\t\t\n\tout_tei = os.path.join(outfolder, \"temp\")\n\tout_txt = os.path.join(outfolder, \"txt\")\n\t\n\tif not os.path.exists(out_tei):\n\t\tos.makedirs(out_tei)\n\tif not os.path.exists(out_txt):\n\t\tos.makedirs(out_txt)\n\t\t\n\t\n\tfor filepath in glob.glob(inpath):\n\t\tfilecounter+= 1\n\t\tfn = os.path.basename(filepath)[:-4]\n\t\toutfile_x = fn + \".xml\"\n\t\t\n\t\tdoc = etree.parse(filepath)\n\t\t\n\t\tif mode == \"split-1\":\n\t\t\ttransform = etree.XSLT(xslt_TEIwrapper_1)\n\t\telse:\n\t\t\ttransform = etree.XSLT(xslt_TEIwrapper)\n\n\t\tresult_tree = transform(doc)\n\t\tresult = str(result_tree)\n\t\t\n\t\t# create TEI wrapper for future annotation results\n\t\twith open(os.path.join(outfolder, \"temp\", outfile_x), \"w\") as output:\n\t\t\toutput.write(result)\n\t\t\t\n\t\t# create one full text file per chapter (or for the whole text)\n\t\ttei = {'tei':'http://www.tei-c.org/ns/1.0'}\n\t\tcligs_id = doc.xpath(\"//tei:idno[@type='cligs']/text()\", namespaces=tei)\n\t\tif mode == \"split-1\":\n\t\t\tresults = doc.xpath(\"//tei:text/tei:body\", namespaces=tei)\n\t\telse:\n\t\t\tresults = doc.xpath(\"//tei:div[ancestor::tei:body][not(descendant::tei:div[not(ancestor::tei:floatingText)])][not(ancestor::tei:floatingText)]\", namespaces=tei)\n\t\t\n\t\tif isinstance(cligs_id, list):\n\t\t\tcligs_id = cligs_id[0]\n\t\telif isinstance(cligs_id, str) == False:\n\t\t\traise ValueError(\"This type (\" + str(type(cligs_id)) + \") is not supported for cligs_id. Must be list or string.\")\n\t\t\n\t\tfor i,r in enumerate(results):\n\t\t\ttransform = etree.XSLT(xslt_extractDIVs)\n\t\t\tresult_tree = transform(r)\n\t\t\tresult = str(result_tree)\n\t\t\t\n\t\t\toutfile = cligs_id + \"_d\" + str(i + 1) + \".txt\"\n\t\t\t\n\t\t\twith open(os.path.join(outfolder, \"txt\", outfile), \"w\") as output:\n\t\t\t\toutput.write(result)\n\t\n\tprint(\"Done. \" + str(filecounter) + \" files treated.\")","def combine_corpus(include_whole_abstracts=False):\r\n\tif load_combined_corpus_maps():\r\n\t\treturn\r\n\t\r\n\tglobal gensim_dictionary\r\n\tglobal search_criteria_dict, solution_dict, linked_abstracts_dict, abstracts_dict\r\n\tglobal common_corpus_list, question_map, abstract_map, abstract_whole_map\r\n\tglobal corpus_layout\r\n\t\r\n\tprint('\\nbuilding common corpus')\r\n\t\r\n\tcommon_corpus_list = list()\r\n\tquestion_map = dict()\r\n\tabstract_map = dict()\r\n\tabstract_whole_map = dict()\r\n\t\r\n\tsentence_count = 0\r\n\t\r\n\tcorpus_layout = []\r\n\t\r\n\tfor qi,qv in search_criteria_dict.items():\r\n\t\tquestions = qv\r\n\t\t\r\n\t\tsentence_incr = len(questions)\r\n\t\tquestion_map[qi] = (sentence_count,sentence_count + sentence_incr)\r\n\t\tsentence_count += sentence_incr\r\n\t\t\r\n\t\tfor st in questions:\r\n\t\t\tcommon_corpus_list.append(st)\r\n\t\r\n\tcorpus_layout.append(('Q',sentence_count))\r\n\t\r\n\tfor ai,av in abstracts_dict.items():\r\n\t\tabstract = av\r\n\t\t\r\n\t\tsentence_incr = len(abstract)\r\n\t\tabstract_map[ai] = (sentence_count,sentence_count + sentence_incr)\r\n\t\tsentence_count += sentence_incr\r\n\r\n\t\tfor st in abstract:\r\n\t\t\tcommon_corpus_list.append(st)\r\n\t\r\n\tcorpus_layout.append(('A',sentence_count))\r\n\r\n\tif include_whole_abstracts:\r\n\t\tfor ai,av in abstracts_dict.items():\r\n\t\t\tabstract = av\r\n\t\t\t\r\n\t\t\tabstract_whole_map[ai] = (sentence_count,sentence_count + 1)\r\n\t\t\tsentence_count += 1\r\n\r\n\t\t\tst = tuple('\\n'.join(s) for s in zip(*abstract))\r\n\t\t\tif len(st) < 2:\r\n\t\t\t\tst = ('','')\r\n\r\n\t\t\tcommon_corpus_list.append(st)\r\n\t\t\r\n\t\tcorpus_layout.append(('W',sentence_count))\r\n\t\r\n\tpickle.dump(question_map,open(paths.path_data_map_questions_pickle,\"wb\"))\r\n\tpickle.dump(abstract_map,open(paths.path_data_map_abstracts_pickle,\"wb\"))\r\n\tpickle.dump(abstract_whole_map,open(paths.path_data_map_abstracts_whole_pickle,\"wb\"))\t\r\n\tpickle.dump(common_corpus_list,open(paths.path_data_corpus_pickle,\"wb\"))\r\n\tpickle.dump(corpus_layout,open(paths.path_data_corpus_layout_pickle,\"wb\"))","def setup():\n\n p4_slackbots = model.Project(\"Merch Website\", \"http://76.176.109.127:6969/\", \"/static/img/p4_slackbots.png\",\n \"P4Slackbots\",\n [\"Abhijay Deevi\", \"Kevin Do\", \"Travis Medley\", \"Paul Bokelman\", \"Gavin Theriault\"],\n \"This project is a merch website that we created for our Youtube channels, \"\n \"GodlyGoats and \"\n \"Albertpani Compani. We have a lot of merch you can buy and other information.\")\n\n p4_hangman = model.Project(\"Music Website\", url_for('p4_hangman_bp.index'), \"/static/img/p4hangman.png\",\n \"P4 Hangman\",\n [\"Charlie Zhu\", \"Rohan Nallapati\", \"Rivan Nayak\", \"Sarah Xie\", \"Noah Pidding\"],\n \"This website includes a portfolio of our projects we worked on this trimester as well \"\n \"as a music section including three different genres of music with multiple examples \"\n \"and descriptions of each.\")\n\n p4_fruitycoders = model.Project(\"Photography Website\", \"google.com\", \"/static/img/p4_fruitycoders.png\",\n \"P4 fruitycoders\",\n [\"Sophie Lee\", \"Linda Long\", \"Maggie Killada\", \"Adam Holbel\", \"Wenshi Bao\"],\n \"Our website (Fruity Photos) features the history of photography, as well as the \"\n \"works \"\n \"and biographies of several famous photographers, such as Ansel Adams and Annie \"\n \"Leibovitz.\")\n\n \"\"\"p4_coderjoes = model.Project(\"CoderJoes Store\", url_for('p4_coderjoes_bp.index'), \"/static/img/p4_coderjoes.png\",\n \"P4 Guessers\",\n [\"Lola Bulkin\", \"Grace Le\", \"Ryan Moghaddas\", \"William Cherres\", \"Brayden Basinger\"],\n \"CoderJoes is a virtual store where you can find recipes, ideas, and descriptions, \"\n \"as well as a group portfolio of our work over the trimester.\")\"\"\"\n\n projects = [p4_slackbots, p4_hangman, p4_fruitycoders]#, p4_coderjoes\n period = model.Period(\"Period 4\", \"AP Principles of Computer Science - Python\", projects)\n return period","def loadLinks(self):\n\t\tmainPageFile = open(self.mainPageFilePath, 'r')\n\t\ttry:\n\t\t\tlines = mainPageFile.readlines()\n\t\t\tif len(lines) > 0:\n\t\t\t\tself.mainPage = lines[0]\n\t\t\telse:\n\t\t\t\tprint(\"No link in : \", self.mainPageFilePath)\n\t\tfinally:\n\t\t mainPageFile.close()\n\n\t\tlinkFile = open(self.linkFilePath, 'r')\n\t\ttry:\n\t\t\t# add all links to a list\n\t\t\tfor line in linkFile.readlines():\n\t\t\t\tif \"http\" in line:\n\t\t\t\t\tsubPageName = line.split(\".de\")[1].replace(\"/\", \"\")\n\t\t\t\t\tself.subpages.append( Subpage(line.strip(' \\t\\n\\r'),subPageName,self.mainPage) )\n\t\tfinally:\n\t\t linkFile.close()","def main():\r\n\tlang = get_arguments()\r\n\twiki_analyzer(lang)","def run():\n assert os.path.exists(args.input_path), \"input_path doesn't exist\"\n assert os.path.exists(args.output_path), \"output_path doesn't exist\"\n\n # read all the paths to the input documents\n doc_files = []\n for root, dirs, files in os.walk(args.input_path):\n for file in files:\n if not file.endswith('gz') and not file.endswith('xml'):\n continue\n doc_files.append(os.path.join(root, file))\n print('{} medline files found from {}'\n ''.format(len(doc_files), args.input_path))\n\n print('converting...')\n pool = Pool(processes=args.num_workers)\n total_doc = 0\n total_batch = 0\n total_empty = 0\n for d, b, n in tqdm(pool.imap_unordered(partial(convert), doc_files),\n total=len(doc_files)):\n total_doc += d\n total_batch += b\n total_empty += n\n\n print('total docs: {}, total batches: {} created (empty doc {})'\n ''.format(total_doc, total_batch, total_empty))","def organize_bib():\n\n parser = argparse.ArgumentParser(description='Organize papers')\n parser.add_argument('bibfile', help='The bibtex file to be processed.')\n subparsers = parser.add_subparsers(help='Available commands')\n\n help_tmpl = 'Create a YAML file describing metadata for the organization tasks.'\n parser_tmpl = subparsers.add_parser('genmetadata', help=help_tmpl)\n parser_tmpl.add_argument('--PI', default='Qi, Fei',\n help='The name of the PI.')\n parser_tmpl.add_argument('--metadata', '-m',\n help='Output metadata in a YAML file.')\n parser_tmpl.set_defaults(func=gen_metadata)\n\n help_patch = 'Create attachements for uploading to NSFC website.'\n parser_attach = subparsers.add_parser('genattach', help=help_patch)\n parser_attach.add_argument('--metadata', '-m',\n help='Metadata in YAML format specifying the task.')\n parser_attach.add_argument('--script', '-s',\n help='Bash script for creating tasks.')\n parser_attach.set_defaults(func=gen_attachments) \n\n help_extract = 'Extract bib items based on a given aux file.'\n parser_extract = subparsers.add_parser('extract', help=help_extract)\n parser_extract.add_argument('--auxfile', '-x',\n help='AUX file created by LaTeX.')\n parser_extract.add_argument('--output', '-o',\n help='Output of cited references in BibTeX format.')\n parser_extract.set_defaults(func=extract_bibtex)\n\n help_simplify = 'Simplify a BibTeX file.'\n parser_simplify = subparsers.add_parser('simplify', help=help_simplify)\n parser_simplify.add_argument('--bib-journal', '-b',\n help='BibTeX of canonical journal/conference titles.')\n parser_simplify.add_argument('--output', '-o',\n help='Output of simplified BibTeX.')\n parser_simplify.set_defaults(func=simplify_bibtex)\n\n args = parser.parse_args()\n # print(args)\n args.func(args)","def parse(input_path, output_dir, func_validate = None, target_tag = None, size_limit_KB = None):\n if not os.path.exists(output_dir): os.makedirs(output_dir)\n\n if os.path.isfile(input_path):\n print (\"The input file is\", input_path)\n print (\"The output directory is\", output_dir)\n files = [[input_path, os.path.basename(input_path)]]\n else:\n input_dir = input_path\n print (\"The input directory is\", input_dir)\n print (\"The output directory is\", output_dir)\n print (\"Searching HTML files in the input directory...\")\n files = file_utils.get_filelist_in_path(\"html\", input_dir, True)\n\n def process(files):\n corpus_store = corpus_utils.CorpusStore(func_validate = func_validate)\n\n for idx, file in enumerate(files):\n f_abst = file[0] # absolute path\n f_rel = file[1] # relative path\n print (\"(\", idx, \"of\", len(files), \") file\", f_rel)\n file_content = file_utils.read_file_any_encoding(f_abst)\n if (len(file_content) == 0):\n continue\n\n # 1st, process the data with Atomic Parser\n parser = pat.Parser(corpus_store)\n parser.parse(file_content)\n\n # Process the same data with Atomic HeaderBody Parser\n # parser = pah.Parser(corpus_store)\n # parser.parse(file_content)\n\n # Process the same data with HeaderBody Parser\n parser = phb.Parser(corpus_store, target_tag)\n parser.parse(file_content)\n\n # Export the parsed data into file\n print (\"Exporting the result...\")\n return corpus_store.export_corpus(output_dir, size_limit_KB = size_limit_KB)\n\n print (\"Total\", len(files), \"files to process.\")\n exported_files = process(files)\n print (\"Exported:\", exported_files)","def get_parses(corpus_sents, corpus_name, target_dir):\n print('Generating the parse files for the {:s} corpus ...'.format(corpus_name))\n # Define POS tag inventory separated along the open/ closed class axis;\n # exclude tags not associated with either class (such as 'filler' words), due to their relatively low frequency\n # and low relevance for the contrastive analysis of the two ID-variant corpora\n open_class_tags = ['FW', 'GW', 'JJ', 'JJR', 'JJS', 'NN', 'NNP', 'NNPS', 'NNS', 'PDT', 'RB', 'RBR', 'RBS', 'UH',\n 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WRB']\n closed_class_tags = ['AFX', 'BES', 'CC', 'CD', 'DT', 'EX', 'HVS', 'IN', 'MD', 'POS', 'PRP', 'PRP$', 'RP', 'SYM',\n 'TO', 'WDT', 'WP', 'WP$']\n all_tags = open_class_tags + closed_class_tags\n\n # Parse corpus contents with SpaCy\n model = sc.load('en')\n parses = [model(sent) for sent in corpus_sents]\n # Obtain tag counts for the specified tag inventory\n flat_tags = [parse.tag_ for parsed_sent in parses for parse in parsed_sent]\n unique_tags = set(flat_tags)\n tag_counts = sorted([(tag, flat_tags.count(tag)) for tag in unique_tags if tag in all_tags],\n reverse=True, key=lambda x: x[1])\n\n # Calculate open class fraction (total and top 50%), to determine whether open classes are distributed differently\n # in sentences of varying ID; intuitively one may expect high-ID sentences to contain a greater portion of open\n # class words, as they exhibit greater variation and are therefore less predictable in sentential context\n top_open = [tag_tpl[1] for tag_tpl in tag_counts[: len(tag_counts) // 2] if tag_tpl[0] in open_class_tags]\n top_closed = [tag_tpl[1] for tag_tpl in tag_counts[: len(tag_counts) // 2] if tag_tpl[0] in closed_class_tags]\n top_all = [tag_tpl[1] for tag_tpl in tag_counts[: len(tag_counts) // 2]]\n top_open_fraction = sum(top_open) / sum(top_all)\n top_closed_fraction = sum(top_closed) / sum(top_all)\n\n full_open = [tag_tpl[1] for tag_tpl in tag_counts if tag_tpl[0] in open_class_tags]\n full_closed = [tag_tpl[1] for tag_tpl in tag_counts if tag_tpl[0] in closed_class_tags]\n full_all = [tag_tpl[1] for tag_tpl in tag_counts]\n full_open_fraction = sum(full_open) / sum(full_all)\n full_closed_fraction = sum(full_closed) / sum(full_all)\n\n # Write tag counts to file\n tag_log_path = os.path.join(target_dir, '{:s}_tag_counts.txt'.format(corpus_name))\n with codecs.open(tag_log_path, 'w', encoding='utf8') as tag_file:\n for i in range(len(tag_counts)):\n tag_file.write('{:s}\\t{:d}\\n'.format(tag_counts[i][0], tag_counts[i][1]))\n # Calculate corpus statistics\n tag_file.write('=' * 10 + '\\n')\n tag_file.write('Open class fraction of most frequent 50% POS tags: {:.4f}\\n'.format(top_open_fraction))\n tag_file.write('Closed class fraction of most frequent 50% POS tags: {:.4f}\\n'.format(top_closed_fraction))\n tag_file.write('Open class fraction of all identified POS tags: {:.4f}\\n'.format(full_open_fraction))\n tag_file.write('Closed class fraction of all identified POS tags: {:.4f}'.format(full_closed_fraction))\n print('Done with POS-tagging.')\n\n # Perform dependency parsing related analysis\n def _get_dlt(_parent, _children):\n \"\"\" Computes the integration cost at the head of dependency relations identified within the input sentence,\n according to the Dependency Locality Theory. \"\"\"\n dlt_cost = 0\n for child in _children:\n # Determine the span length between the child and parent node\n left = min(_parent.i, child.i)\n right = max(_parent.i, child.i)\n for j in range(left + 1, right):\n # Identify discourse referents present within the determined span\n if 'NN' in parse[j].tag_ or 'VB' in parse[j].tag_:\n dlt_cost += 1\n # Check if the parent node is also occupied by a new discourse referent\n if 'NN' in _parent.tag_ or 'VB' in _parent.tag_:\n dlt_cost += 1\n return dlt_cost\n\n corpus_spans = list()\n corpus_costs = list()\n # Compute the mean dependency arc length and DLT integration cost for each sentence within the corpus\n for parse in parses:\n sent_spans = list()\n sent_costs = list()\n for parent in parse:\n children = [w for w in parent.lefts] + [w for w in parent.rights]\n if len(children) == 0:\n continue\n parent_spans = [abs(parent.i - child.i) for child in children]\n sent_spans += parent_spans\n sent_costs += [_get_dlt(parent, children)]\n # Collect means\n corpus_spans += [np.mean(sent_spans)]\n corpus_costs += [np.mean(sent_costs)]\n\n # Calculate SVO fraction (ultimately did not yield any interesting insights)\n clause_triples = list()\n svo_count = 0\n other_count = 0\n for parse in parses:\n # Identify subjects, predicates, and objects\n subjects = [[word.i, word.head.i] for word in parse if 'subj' in word.dep_ and word.head.pos_ == 'VERB']\n objects = [[word.head.i, word.i] for word in parse if 'obj' in word.dep_]\n for subj_list in subjects:\n for obj_list in objects:\n if subj_list[-1] == obj_list[0]:\n clause_triple = subj_list + obj_list[-1:]\n clause_triples.append(clause_triple)\n # Check if isolated triples are in the SVO order, increment counter if so\n if clause_triple[0] < clause_triple[1] < clause_triple[2]:\n svo_count += 1\n else:\n other_count += 1\n # Compute word order fractions\n svo_fraction = svo_count / len(clause_triples)\n other_fraction = other_count / len(clause_triples)\n\n # Write mean sentence-wise dependency arc lengths and DLT integration costs to file\n parse_log_path = os.path.join(target_dir, '{:s}_parse_stats.txt'.format(corpus_name))\n with codecs.open(parse_log_path, 'w', encoding='utf8') as parse_file:\n # Document mean sentence dependency arc length and mean sentence DLT integration cost\n for i in range(len(corpus_spans)):\n parse_file.write('{:.4f}\\t{:.4f}\\n'.format(corpus_spans[i], corpus_costs[i]))\n # Calculate corpus statistics\n parse_file.write('=' * 10 + '\\n')\n parse_file.write('Span length max: {:.4f}\\n'.format(np.max(corpus_spans)))\n parse_file.write('Span length min: {:.4f}\\n'.format(np.min(corpus_spans)))\n parse_file.write('Span length mean: {:.4f}\\n'.format(np.mean(corpus_spans)))\n parse_file.write('Span length standard deviation: {:.4f}\\n'.format(np.std(corpus_spans)))\n parse_file.write('=' * 10 + '\\n')\n parse_file.write('DLT cost max: {:.4f}\\n'.format(np.max(corpus_costs)))\n parse_file.write('DLT cost min: {:.4f}\\n'.format(np.min(corpus_costs)))\n parse_file.write('DLT cost mean: {:.4f}\\n'.format(np.mean(corpus_costs)))\n parse_file.write('DLT cost standard deviation: {:.4f}\\n'.format(np.std(corpus_costs)))\n # Document word order distribution\n parse_file.write('=' * 10 + '\\n')\n parse_file.write('SVO clauses count: {:d}\\n'.format(svo_count))\n parse_file.write('SVO clauses fraction: {:.4f}\\n'.format(svo_fraction))\n parse_file.write('Other clauses count: {:d}\\n'.format(other_count))\n parse_file.write('Other clauses fraction: {:.4f}'.format(other_fraction))\n print('Done with dependency parsing.')","def build_corpus(self):\n print(\"Inside the build_corpus >>>>>\")\n documentsCount = 0\n documents = self.documents\n\t\t\n with open(self.documents_path) as file:\n for documents in file.readlines():\n documents = documents.rstrip('}\\n ').strip('0\\t').strip('1\\t').split(' ')\n documentsCount = documentsCount +1\n self.documents.append(documents)\n\t\t\t\n self.number_of_documents = documentsCount","def overall_annotation_function(path_to_species_trees, path_to_gene_trees, project_name = \"myproject\", path_to_ranger_outputs = \"\", ):\r\n\r\n #initially gather the names of the datasets from the species_trees folder\r\n dataset_names = gather_dataset_names(path_to_species_trees, project_name, \"_CC\")\r\n #create an object of class PhyloData for each unique dataset\r\n phylodata_objects = []\r\n for name in dataset_names:\r\n phylodata_objects.append(annotate_classes.PhyloData(name))\r\n #for each object, have it try and assign itself the correct files\r\n print(\"populating phylodata objects\")\r\n populate_objects(phylodata_objects, project_name, path_to_species_trees, path_to_gene_trees, path_to_ranger_outputs)\r\n #run the visualizer for each object\r\n parse_and_visualize(phylodata_objects, project_name)","def parse(file_name, user, agenda_type, db):\n db.execute('''select section from roll where onyen=%(onyen)s''', dict(onyen=user))\n row = db.fetchone()\n section = None if row is None else row.section\n\n # Get Recitation zoom\n db.execute(\"\"\"select url from zoom where type='recitation'\"\"\")\n row = db.fetchone()\n recitation_zoom_url = row.url if row else None\n\n # Get lecture zoom\n lecture_zoom_urls = []\n if section in ['001', '003']:\n db.execute(\"\"\"select url from zoom where type='lecture' and section='001'\"\"\")\n lecture_zoom_urls.append(db.fetchone().url)\n if section in ['002', '003']:\n db.execute(\"\"\"select url from zoom where type='lecture' and section='002'\"\"\")\n lecture_zoom_urls.append(db.fetchone().url)\n\n # Get checklist information\n checklist_info = get_checklist_info(db, user, agenda_type)\n\n if agenda_type == 'la':\n first_day_of_class = date(2021, 1, 12)\n else:\n first_day_of_class = date(2021, 1, 19)\n last_day_of_classes = date(2021, 5, 5)\n today = date.today()\n with open(file_name, \"rt\") as fp:\n agenda = fp.read().split(\"\\n| \")\n day = first_day_of_class\n result = []\n for one_days_details in agenda:\n lines = one_days_details.split(\"\\n\")\n title = lines[0]\n output_lines = []\n for line in lines[1:]:\n if line.startswith(\"S \"):\n line = slide_line(line)\n elif line.startswith(\"#\"):\n line = comment_line(line, user)\n elif line.startswith(\"Z\"):\n line = zoom_line(line, day, section, lecture_zoom_urls, recitation_zoom_url)\n elif line.startswith(\"CL\"):\n line = checklist_line(line, day, checklist_info)\n output_lines.append(line)\n when = set_when(day, today)\n\n result.append(\n {\"date\": day, \"title\": title, \"when\": when,\n \"body\": renderMarkdown(renderTemplate(\"\\n\".join(output_lines)))})\n day = increment_day(day, last_day_of_classes, result, agenda_type)\n return result","def _prepare_analysis_input(self, documents):\n subdoc_to_doc_map = {}\n wordtype_to_number = {}\n number_to_wordtype = []\n wordtypes = {}\n \n # prevent duplicating work\n if os.path.exists(self.wordtype_file):\n return\n \n try:\n # First find singletons\n if self.remove_singletons:\n word_type_count_threshold = max(1, int(math.log(documents.count(), 10)) - 2)\n temp_word_type_counts = {}\n for doc_index, doc in enumerate(documents):\n tokens = self.tokenize(doc.get_content())\n for token, token_start in tokens:\n temp_word_type_counts[token] = temp_word_type_counts.setdefault(token, 0) + 1\n for word_type, count in temp_word_type_counts.iteritems(): # add singletons to stopword list\n if count <= word_type_count_threshold:\n self._excluded_words[word_type] = True\n with io.open(self.excluded_words_file, 'w', encoding='utf-8') as ex_f:\n ex_f.write(unicode(json.dumps(self._excluded_words)))\n \n haltwords = dict(self.stopwords)\n haltwords.update(self._excluded_words)\n # Second find bigrams, iterate through documents and train.\n if self.find_bigrams:\n from import_tool.analysis.bigram_finder import BigramFinder\n bigram_finder = BigramFinder(stopwords=haltwords)\n for doc_index, doc in enumerate(documents):\n bigram_finder.train(doc_index, self.tokenize(doc.get_content()))\n bigram_finder.print()\n \n # Third, we're going to stem words\n if self.stem_words:\n from import_tool.analysis.stemmer import Stemmer\n stemmer = Stemmer(self._working_dir, self.base_dir)\n \n # for each document tokenize and map tokens to numbers to avoid regex problems before passing data to Mallet\n with io.open(self.mallet_input_file, 'w', encoding='utf-8') as w:\n with io.open(self.start_index_file, 'w', encoding='utf-8') as w2:\n count = 0\n subcount = 0\n for doc_index, doc in enumerate(documents):\n doc_content = unicode(doc.get_content())\n count += 1\n subdocuments = self.create_subdocuments(doc_index, doc_content)\n token_start_index_offset = 0 # needed to make sure the start index remains correct once the document is re-merged\n for subdoc_name, subdoc_content in subdocuments:\n if subcount > 0:\n w2.write(u'\\n')\n subcount += 1\n subdoc_to_doc_map[subdoc_name] = doc_index\n tokens = self.tokenize(subdoc_content)\n \n if self.find_bigrams:\n tokens = bigram_finder.combine(tokens, subdoc_content)\n \n token_numbers = []\n token_start_indices = []\n only_tokens = []\n tokens_temp = []\n for tok, tok_start in tokens:\n only_tokens.append(tok)\n tokens_temp.append([tok, tok_start + token_start_index_offset])\n tokens = tokens_temp\n tokens_temp = None\n if self.stem_words:\n stemmed_tokens = stemmer.stem(only_tokens)\n else:\n stemmed_tokens = only_tokens\n for tup, tok_stem in zip(tokens, stemmed_tokens):\n tok, tok_start = tup\n wordtypes[tok] = True\n wordtypes[tok_stem] = True\n try:\n tok_num = wordtype_to_number[tok_stem]\n except:\n tok_num = len(wordtype_to_number)\n number_to_wordtype.append(tok_stem)\n wordtype_to_number[tok_stem] = tok_num\n token_numbers.append(unicode(tok_num))\n token_start_indices.append([tok, tok_start])\n text = u' '.join(token_numbers)\n w.write(u'{0} all {1}\\n'.format(subdoc_name, text))\n w2.write(unicode(json.dumps(token_start_indices)))\n token_start_index_offset += len(subdoc_content)\n for tok, tok_start in tokens:\n try:\n assert doc_content[tok_start:tok_start+len(tok)].lower() == tok.lower()\n except:\n print(tok_start)\n print(len(tok))\n print('\"'+doc_content[tok_start:tok_start+len(tok)].lower()+'\"')\n print('\"'+tok.lower()+'\"')\n raise\n if not count:\n raise Exception('No files processed.')\n # record which subdocuments belong to which documents\n with io.open(self.subdoc_to_doc_map_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(subdoc_to_doc_map)))\n with io.open(self.wordtype_to_number_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(wordtype_to_number)))\n with io.open(self.number_to_wordtype_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(number_to_wordtype)))\n with io.open(self.wordtype_file, 'w', encoding='utf-8') as w:\n w.write(unicode(json.dumps(wordtypes)))\n except: # cleanup\n self._cleanup(self.mallet_input_file)\n self._cleanup(self.subdoc_to_doc_map_file)\n self._cleanup(self.wordtype_to_number_file)\n self._cleanup(self.number_to_wordtype_file)\n self._cleanup(self.wordtype_file)\n self._cleanup(self.excluded_words_file)\n raise","def makeHTMLIndexFile(self):\n part1 = \"\"\"<html>\n <body>\n <title>Index</title>\n <div id=\"pg_body\">\n <div id=\"testSuitesTitle\">TestSuites</div>\n <div id=\"resultsTitle\">Results</div>\n <div id=\"testSuites\">\n \"\"\"\n part2 = self.makeLinks()\n part3 = \"\"\"</div>\n <div id=\"results\">\n <iframe id=\"loadHTMLResults\" name=\"loadHTMLResults\" frameborder=\"0\" src=\"statistics.html\" style=\"height:100%;width:100%;\"></iframe>\n </div>\n <div id=\"footer\">Test Engineer(s) :\"\"\"+testEngineers+\"\"\"</div>\n </div>\n </body>\n </html>\n\t\t<style>\n\t\tbody{\n margin:0;\n }\n\t\t#pg_body{\n\t\twidth=100%;\n\t\ttext-align:center;\n\t\t}\n\t\t#testSuitesTitle{\n\t\twidth:25%;\n\t\tfloat:left;\n\t\tbackground-color:#6495ED;\n\t\tfont-weight:bold;\n\t\tcolor:white;\n\t\t}\n\t\t#resultsTitle{\n\t\twidth:75%;\n\t\tfloat:right;\n\t\tbackground-color:#6495ED;\n\t\tfont-weight:bold;\n\t\tcolor:white;\n\t\t}\n\t\t#testSuites{\n\t\twidth:25%;\n\t\tfloat:left;\n\t\tbackground-color:lightgrey;\n\t\tfont-weight:bold;\n\t\ttext-align:left;\n\t\theight:94%;\n\t\toverflow:scroll;\n\t\t}\n\t\t#results{\n\t\twidth:75%;\n\t\tbackground-color:white;\n\t\tfloat:right;\n\t\ttext-align:left;\n\t\theight:94%;\n\t\toverflow:scroll;\n\t\t}\n\t\t#footer{\n\t\twidth:100%;\n\t\ttext-align:left;\n\t\tcolor:lightgrey;\n\t\tbackground-color:#6495ED;\n\t\t}\n\t\t</style>\n \"\"\"\n \n page = (part1+part2+part3)\n f = open(self.dir+'/index.html','w')\n f.write(page)\n f.close","def append_corpus(output):\n files = []\n output_path = output + \"/ig/\" + \"ig_corpus.txt\"\n for root, directories, filenames in os.walk(output + \"/ig/\"):\n for filename in filenames:\n files.append(os.path.join(root, filename))\n corpusfiles = filter(lambda x: \".txt\" in x, files)\n if not os.path.exists(os.path.dirname(output_path)):\n os.makedirs(os.path.dirname(output_path))\n with open(output_path, \"w+\") as corpusFile:\n for file in corpusfiles:\n fileH = open(file, \"r\")\n corpusFile.write(fileH.read())","def summarize_corpus():\n\t\n\t# get metadata\n\t#get_metadata.from_TEIP5(wdir, corpus_inpath, \"metadata\", md_mode)\n\t\n\t# visualize some metadata\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"author-continent\")\n\tvisualize_metadata.describe_corpus(wdir, md_csv, \"author-country\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"language\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"subgenre_hist\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"subgenre_x\")\n\tvisualize_metadata.plot_pie(wdir, md_csv, \"subgenre\")\n\n\tvisualize_metadata.describe_corpus(wdir, md_csv, \"subgenre\")\n\t#visualize_metadata.describe_corpus(wdir, md_csv, \"gender\")\n\t\n\t# make some counts\n\tmd_table = pd.DataFrame.from_csv(os.path.join(wdir, md_csv), header=0)\n\tnum_texts = len(md_table)\n\t#num_language = len(md_table.groupby([\"language\"]))\n\t#num_continent = len(md_table.groupby([\"author-continent\"]))\n\t#num_countries = len(md_table.groupby([\"author-country\"]))\n\t#num_authors = len(md_table.groupby([\"author-name\"]))\n\tnum_authors = len(md_table.groupby([\"author-name\"]))\n\tnum_subgenre = len(md_table.groupby([\"subgenre\"]))\n\t#num_subgenre_x = len(md_table.groupby([\"subgenre_x\"]))\n\t#fr_subgenre_hist = md_table.groupby([\"subgenre_hist\"]).count()\n\t#num_historical = fr_subgenre_hist[\"idno\"][\"historical\"]\n\t#num_not_historical = fr_subgenre_hist[\"idno\"][\"not_historical\"]\n\t\n\t\n\td = {\"texts\":[num_texts], \n\t#\"languages\":[num_language],\n\t#\"continents\":[num_continent],\n\t#\"countries\":[num_countries],\n\t\"authors\":[num_authors],\n\t#\"subgenre_x\":[num_subgenre_x],\n\t\"subgenre\":[num_subgenre]}\n\t#\"num_historical\":[num_historical],\n\t#\"num_not_historical\":[num_not_historical]}\n\t\n\t\n\t\n\tcount_fr = pd.DataFrame(d)\n\tcount_fr.to_csv(os.path.join(wdir, \"corpus-description.csv\"), sep=\",\", header=True)\n\tprint(\"Done: summarize corpus\")","def fileparser(file_path,graph_path,keyword_path,nkeyword_path,anchor_path):\n\n\tf = open(file_path, 'r')\n\n\t#TODO perhaps a sanity check, see if file exists and is well formated, also checking dirs\n\n\t#main event\n\t#feed the file contents to our HTML parser, to process it\n\thtml_code = f.read().decode('utf-8')\n\thtml_parser = OurHTMLParser()\n\thtml_parser.feed(html_code)\n\n\t#keep the results\n\tanchors = html_parser.anchors\n\tkeyword_idx = html_parser.keyword_idx\n\tnkeyword_idx = html_parser.nkeyword_idx\n\n\t#get the main file name: /home/user/fileX.html -> fileX\n\tfullname = os.path.basename(file_path)\n\tname = os.path.splitext(fullname)[0]\n\n\t#converting graphs and indices to save\n\tgraph = gengraph(keyword_idx)\n\tkeyword_dic = idx2json(keyword_idx)\n\tnkeyword_dic = idx2json(nkeyword_idx)\n\n\tgraph_filepath = os.path.join(graph_path,name) + '_graph' + '.gml'\n\tkeyword_filepath = os.path.join(keyword_path,name) + '_keywords' + '.json'\n\tnkeyword_filepath = os.path.join(nkeyword_path,name) + '_non_keywords' + '.json'\n\tanchor_filepath = os.path.join(anchor_path,name) + '_anchors' + '.json'\n\n\n\tnx.write_gml(graph,graph_filepath)\n\t\t\n\twith open(keyword_filepath, 'w') as keyword_f:\n\t\tjson.dump(keyword_dic,keyword_f)\n\n\twith open(nkeyword_filepath, 'w') as nkeyword_f:\n\t\tjson.dump(nkeyword_dic,nkeyword_f)\n\n\twith open(anchor_filepath, 'w') as anchor_f:\n\t\tjson.dump(anchors,anchor_f)","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)","def main():\n args = utils.read_arguments(__doc__)\n documents = []\n filenames = list(traverse_directory(args[\"input_dirpath\"],'*clean*.txt'))\n labels_dirname = args[\"labels_dirpath\"]\n labels_from_json = get_all_labels_from_json(labels_dirname)\n for filename in tqdm(filenames):\n with AnnotatedIBMFactory(filename) as instance_extractor:\n filename_key = filename.split(\"/\")[-1]\n document = instance_extractor.build_document(\n labels_from_json[filename_key])\n documents.append(document)\n utils.pickle_to_file(documents, args['output_file'])","def load_enron():\n connection=apsw.Connection(\"enron.db\")\n cursor=connection.cursor()\n\n \n cursor.execute(\"CREATE VIRTUAL TABLE corpus USING fts4(rowid, title, body);\")\n\n\n enrondir = \"/Users/jonas/Downloads/enron_mail_20110402/maildir\"\n\n pi = 0\n \n for dirpath, dirnames, filenames in os.walk(enrondir):\n for filename in filenames:\n try:\n subject, body = enron.parse_file(os.path.join(dirpath, filename))\n \n cursor.execute(\"insert into corpus values(?,?,?)\", (pi, subject, body))\n except:\n pass\n \n if pi % 100 == 0:\n print pi\n pi += 1","def boldExtract(genera):\r\n # Prepare Web Service Endpoint for BOLD's Public Data Portal API\r\n # Appending BOLD's base URL to each genera from the NSR list\r\n base_url = 'http://v4.boldsystems.org/index.php/API_Public/combined?taxon='\r\n source_urls = list(map(lambda x: \"{}{}{}\".\r\n format(base_url, x, '&format=tsv'), genera))\r\n\r\n # Download sequence data from BOLD using list of url's\r\n print('Beginning sequence data retrieval...')\r\n counter = 0\r\n for url in source_urls:\r\n r = http.request('GET', url)\r\n name = genera[counter]\r\n counter += 1\r\n with open(args.outdir1+\"/\"+name+\".tsv\", \"wb\") as fcont:\r\n fcont.write(r.data)","def processFiles(self, testDataFiles, sourceDirectory, sourceFilename, outputFile, language, IncludeSourceInOutput, maxRunTime, interpreter, maxOutputLines, AutoGraderVersion):\n \n print (\"***Start***\")\n self.sourceDirectory = sourceDirectory\n #self.TopLevelFilesFound = []\n #self.subdirs = {} #dictionary of sbudirectories\n \n\n #delete the output file\n self._removeFile(outputFile)\n\n #create the html header. Use the name of the source directory as the header text.\n self._MakeHtmlHeader(outputFile, language, \"AutoGrader\", os.path.split(sourceDirectory)[-1])\n\n #--------- C++ ---------\n if language == 'C++':\n #call _recursivelyFindFiles to generate the self.TopLevelFilesFound and self.SubDirsFound lists\n (self.TopLevelFilesFound, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, \".cpp\")\n (tlf, sd) = self._recursivelyFindFiles(sourceDirectory, \".cc\")\n\n self.TopLevelFilesFound += tlf\n self.SubDirsFound += sd\n\n exeFile = sourceDirectory + '/' + 'AG.out'\n\n\n def doInnerCppProcessing(sourceFiles, gradingTextLabel): #sourceFiles is a list of filenames\n #get/report the analytics on the source files\n self._reportFileAnalytics(sourceFiles, outputFile, language)\n \n #include source code here if selected\n if IncludeSourceInOutput == True:\n self._formatSource(sourceFiles, outputFile, language)\n\n #compile the file\n self._removeFile(exeFile)\n self._compileCppFiles(interpreter, sourceFiles, outputFile, exeFile, maxOutputLines)\n\n if os.path.isfile(exeFile): #did the compilation succeed?\n print(\"Compilation succeeded.\")\n self._reportErrorMsg(\"Compilation succeeded.<br>\", outputFile) \n if len(testDataFiles) == 0: #no input data required\n exec_time = self._shellExec('\"'+exeFile+'\"', '', '', outputFile, maxRunTime, maxOutputLines, sourceDirectory)\n print (format(\"%0.4f\" % exec_time) + \" secs.\")\n else:\n for dataFile in testDataFiles:\n self._reportDataFile(dataFile, outputFile)\n \n #print the name of the datafile to indicate progress. This is a temporary solution to allow us to identify\n #programs that don't end. Ultimately, we will want to use a fork()/wait() pair and be able to set a max run time.\n _, filename = os.path.split(dataFile)\n print (\"processing '\" + filename + \"'...\")\n exec_time = self._shellExec('\"'+exeFile+'\"', '', dataFile, outputFile, maxRunTime, maxOutputLines, sourceDirectory)\n\n print (format(\"%0.4f\" % exec_time) + \" secs.\")\n self._reportExecTime(exec_time, outputFile)\n print ()\n else:\n print(\"Executable not found. Check compiler output.\")\n self._reportErrorMsg(\"Executable not found. Check compiler output.<br>\", outputFile)\n\n self._removeFile(exeFile)\n self._gradingBox(sourceDirectory, sourceFiles[0], outputFile, gradingTextLabel)\n return\n\n\n #compile the names of single-file programs and multi-file programs into a single list to enable sorting.\n #note that this is a list of tuples. The first element is the name of the file or directory.\n #the second element specifies if the first is a filename or directory name.\n filesAndDirs = []\n for x in self.TopLevelFilesFound:\n filesAndDirs.append((x,'file')) #add files\n\n for x in self.SubDirsFound:\n filesAndDirs.append((x,'dir')) #add directories\n\n filesAndDirs.sort() #sort the list\n\n print (\"**********************************\")\n for x in filesAndDirs:\n print (x)\n print (\"**********************************\")\n\n #now, process the sorted list depending on whether it is a single-file or directory.\n for n, x in enumerate(filesAndDirs):\n if x[1] == 'file': #this is a file\n #we will process source code in the top-level directory as single-file programs \n print ('=======================================================')\n print (x[0])\n print ('=======================================================')\n #doInnerCppProcessing([x[0]], 'student'+str(n))\n doInnerCppProcessing([x[0]], 'student')\n elif x[1] == 'dir': #this is a directory\n #we will process source code in sub-directories of the top-level directory as multi-file programs\n print ('=======================================================')\n print (x[0])\n print ('=======================================================')\n sourceFiles = []\n self._findFilesInDir(x[0], \".cpp\", sourceFiles) #add .h files to the files list\n self._findFilesInDir(x[0], \".cc\", sourceFiles) #add .h files to the files list\n self._findFilesInDir(x[0], \".h\", sourceFiles) #add .h files to the files list\n self._findFilesInDir(x[0], \".hpp\", sourceFiles) #add .hpp files to the files list\n #doInnerCppProcessing(sourceFiles, 'student'+str(n))\n doInnerCppProcessing(sourceFiles, 'student')\n else:\n print (\"***** EXCEPTION: Entity for processing is neither a file nor a directory. *****\")\n \n \n self._reportErrorMsg('<br><br><b>**** ' + str(len(self.TopLevelFilesFound) + len(self.SubDirsFound)) + ' project(s) processed. ****</b>', outputFile)\n\n #--------- Python ---------\n elif language == 'Python':\n print ('Find sub dirs')\n #first, find all the subdirectories that contain a python source file that matches <sourceFilename>\n #(_, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, sourceFilename)\n #(_, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, '/' + sourceFilename)\n self.SubDirsFound = self._recursivelyFindFile(sourceDirectory, sourceFilename)\n\n print ('Find TLFs')\n #now, find all .py files in the top level directory.\n self.TopLevelFilesFound = self._findFilesInDir(sourceDirectory, \".py\", [])\n #(self.TopLevelFilesFound, _) = self._recursivelyFindFiles(sourceDirectory, \".py\")\n\n\n #does this block do anything??? 9/25/16\n #if sourceFilename == \"\" or sourceFilename == None:\n #self._recursivelyFindFiles(sourceDirectory, \".py\")\n #else:\n #self._recursivelyFindFiles(sourceDirectory, '/' + sourceFilename)\n\n\n def doInnerPythonProcessing(sourceFiles, topLevelModule, gradingTextLabel): #sourceFiles is a list of filenames\n #get/report the analytics on the source files\n self._reportFileAnalytics(sourceFiles, outputFile, language)\n \n #include source code here if selected\n if IncludeSourceInOutput == True:\n self._formatSource(sourceFiles, outputFile, language)\n\n\n if len(testDataFiles) == 0: #no input data required\n exec_time = self._shellExec('\"'+interpreter+'\"', topLevelModule, '', outputFile, maxRunTime, maxOutputLines, sourceDirectory)\n print (format(\"%0.4f\" % exec_time) + \" secs.\")\n self._reportExecTime(exec_time, outputFile)\n else:\n for dataFile in testDataFiles:\n self._reportDataFile(dataFile, outputFile)\n \n #print the name of the datafile to indicate progress. This is a temporary solution to allow us to identify\n #programs that don't end. Ultimately, we will want to use a fork()/wait() pair and be able to set a max run time.\n _, filename = os.path.split(dataFile)\n print (\"processing '\" + filename + \"'...\")\n exec_time = self._shellExec('\"'+interpreter+'\"', topLevelModule, dataFile, outputFile, maxRunTime, maxOutputLines, sourceDirectory)\n\n print (format(\"%0.4f\" % exec_time) + \" secs.\")\n self._reportExecTime(exec_time, outputFile)\n print ()\n \n self._gradingBox(sourceDirectory, sourceFiles[0], outputFile, gradingTextLabel)\n return\n\n\n #compile the names of single-file programs and multi-file programs into a single list to enable sorting.\n #note that this is a list of tuples. The first element is the name of the file or directory.\n #the second element specifies if the first is a filename or directory name.\n filesAndDirs = []\n for x in self.TopLevelFilesFound:\n filesAndDirs.append((x,'file')) #add files\n\n for x in self.SubDirsFound:\n filesAndDirs.append((x,'dir')) #add directories\n\n filesAndDirs.sort() #sort the list\n\n print (\"**********************************\")\n for x in filesAndDirs:\n print (x)\n print (\"**********************************\")\n\n\n #now, process the sorted list depending on whether it is a single-file or directory\n for n, x in enumerate(filesAndDirs):\n if x[1] == 'file': #this is a file\n #we will process source code in the top-level directory as single-file programs \n print ('=======================================================')\n print (x[0])\n print ('=======================================================')\n #doInnerPythonProcessing([x[0]], x[0], 'student'+str(n))\n doInnerPythonProcessing([x[0]], x[0], 'student')\n elif x[1] == 'dir': #this is a directory\n #we will process source code in sub-directories of the top-level directory as multi-file programs\n print ('=======================================================')\n print (x[0])\n print ('=======================================================')\n #doInnerPythonProcessing(self._findFilesInDir(x[0],\".py\", []), x[0]+'/'+sourceFilename, 'student'+str(n))\n doInnerPythonProcessing(self._findFilesInDir(x[0],\".py\", []), x[0]+'/'+sourceFilename, 'student')\n else:\n print (\"***** EXCEPTION: Entity for processing is neither a file nor a directory. *****\")\n \n \n self._reportErrorMsg('<br><br><b>**** ' + str(len(self.TopLevelFilesFound) + len(self.SubDirsFound)) + ' project(s) processed. ****</b>', outputFile)\n\n\n\n #--------- Unknown language ---------\n else:\n print ('Unknown language choice: ' + language)\n return\n\n \n self._reportErrorMsg('<br><font face=\"verdana\">', outputFile)\n self._reportErrorMsg('Report Generator: AutoGrader v' + AutoGraderVersion + '<br>', outputFile)\n if language == 'C++':\n self._reportErrorMsg('C++ Compiler: ' + interpreter + '<br>', outputFile)\n elif language == 'Python':\n self._reportErrorMsg('Python Interpreter: ' + interpreter + '<br>', outputFile)\n else:\n self._reportErrorMsg('Compiler/Interpreter: Not Specified <br>', outputFile)\n self._reportErrorMsg('<br></font>', outputFile)\n\n self._writeOutput('''<font size='+6'><input type=\"button\" style=\"font-size:20px;width:250px\" value=\"Download Feedback\" OnClick=\"download_feedback_file()\">\n <br><br></font></form>''', outputFile)\n\n\n feedback_filename = 'feedback.txt'\n download_script = '''\n <script type=\"text/javascript\">\n\n function download_feedback_file()\n {\n \n x = document.getElementsByName(\"student\").length\n msg = \"\"\n for (i=0; i<x; i++)\n {\n msg = msg + document.getElementsByName(\"student\")[i].value\n msg = msg + '\\\\n\\\\n-------------------------------------------------------\\\\n'\n }\n\n var element = document.createElement('a');\n element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(msg));\n element.setAttribute('download', \"''' + feedback_filename + '''\");\n\n element.style.display = 'none';\n document.body.appendChild(element);\n\n element.click();\n\n document.body.removeChild(element);\n }\n\n </script>\n </body></html>\n\n '''\n self._writeOutput(download_script, outputFile)\n\n self._writeOutput('</body></html>', outputFile)\n\n \n #open the output file using the default application\n cmd = 'open \"' + outputFile + '\"'\n os.system(cmd)\n \n print (\"***End***\\n\")\n\n\n\n #--------- ---------","def copy_project_docs(srctree):\n docdir = os.path.join(srctree, 'Doc')\n\n # This block shouldn't be here, but I do not yet know how to\n # embed this in ReST files.\n extra_info = {}\n if os.path.exists(os.path.join(docdir, 'website.lst')):\n fd = open(os.path.join(docdir, 'website.lst'))\n for ln in fd.readlines():\n if ln.startswith('#'): continue\n fields = ln.split(',')\n extra_info[fields[0].strip()] = {\n 'section': fields[1].strip(),\n 'priority': int(fields[2].strip()),\n }\n\n docs = [ os.path.join(docdir, fn)\n for fn in os.listdir(docdir) if fn.endswith('.txt') ]\n docs.append(os.path.join(srctree, 'Install.txt'))\n docs.append(os.path.join(srctree, 'NEWS.txt'))\n docs.append(os.path.join(docdir, 'tutorial', 'tutorial.txt'))\n docs.append(os.path.join(docdir, 'tutorial_embed', 'extending_objc_with_python.txt'))\n NAMES = {\n os.path.join(srctree, 'Examples', '00ReadMe.txt') : 'Examples.txt',\n }\n docs.extend(NAMES)\n\n alldocs = {}\n\n for fname in docs:\n print \"-\", fname\n docinfo = {}\n\n bn = NAMES.get(fname)\n if bn is None:\n bn = os.path.split(fname)[-1]\n if bn in ('index.txt', 'announcement.txt'):\n continue\n if extra_info.has_key(bn):\n docinfo.update(extra_info[bn])\n\n if bn.endswith('.txt'):\n bn = bn[:-3].lower() + \"php\"\n else:\n bn = bn.lower() + '.php'\n fd = open(fname)\n input = fd.read()\n fd.close()\n output = docutils.core.publish_string(\n source = input,\n source_path = fname,\n destination_path = bn,\n writer_name = 'hthtml')\n \n output_lines = output.split('\\n')\n for i in range(len(output_lines)):\n if output_lines[i] == '':\n break\n idx = output_lines[i].find(':')\n if idx == -1:\n break\n\n key = output_lines[i][:idx].strip()\n value = output_lines[i][idx+1:].strip()\n docinfo[key] = value\n\n output = '\\n'.join(output_lines[i:])\n if not docinfo.has_key('title'):\n docinfo['title'] = bn\n alldocs[bn] = docinfo\n \n fd = open(os.path.join('docroot', 'doc', bn), 'w')\n fd.write(PHP_HEADER%docinfo)\n\n fd.write(output);\n\n fd.write(PHP_FOOTER)\n\n # Calculate indices for user and developer documentation\n docs = alldocs.keys()\n developer_docs = []\n user_docs = []\n\n for doc in alldocs:\n if not alldocs[doc].has_key('section'):\n print \"Skipping\", doc\n continue\n\n if alldocs[doc]['section'] == 'user':\n user_docs.append([alldocs[doc]['title'], doc])\n elif alldocs[doc]['section'] == 'developer':\n developer_docs.append([alldocs[doc]['title'], doc])\n\n def doccmp(a, b):\n r = cmp(alldocs[a[1]]['priority'], alldocs[b[1]]['priority'])\n if r != 0: return r\n\n return cmp(a[1], b[1])\n user_docs.sort(doccmp)\n developer_docs.sort(doccmp)\n \n # Rewrite the indices (substitute the current document lists)\n for fname in ('index.php', 'usage.php', 'developer.php'):\n fd = open(os.path.join('docroot', 'doc', fname), 'r')\n index_php = fd.readlines()\n fd.close()\n\n fd = open(os.path.join('docroot', 'doc', fname), 'w')\n skip = 0\n for ln in index_php:\n if not skip:\n fd.write(ln)\n if ln.find('/USERDOC') != -1:\n skip = 0\n fd.write(ln)\n elif ln.find('USERDOC') != -1:\n skip = 1\n for title, link in user_docs:\n fd.write('<LI><A HREF=\"%s\">%s</A>\\n'%(link, title))\n if ln.find('/DEVDOC') != -1:\n skip = 0\n fd.write(ln)\n elif ln.find('DEVDOC') != -1:\n skip = 1\n for title, link in developer_docs:\n fd.write('<LI><A HREF=\"%s\">%s</A>\\n'%(link, title))\n\n EXAMPLES = os.path.join('docroot', 'doc', 'examples.php')\n OUTEXAMPLES = os.path.join('docroot', 'examples', 'index.php')\n replace_examples_svn(EXAMPLES)\n if os.path.exists(OUTEXAMPLES):\n os.unlink(OUTEXAMPLES)\n shutil.copyfile(EXAMPLES, OUTEXAMPLES)\n\n # Copy tutorial files\n TUTORIAL_ENDINGS = ['.nib', '.py', '-src', '.h', '.m']\n tutdir = os.path.join(docdir, 'tutorial')\n files = os.listdir(tutdir)\n replacements = []\n for fn in files:\n for ext in TUTORIAL_ENDINGS:\n if fn.endswith(ext):\n dstname = os.path.join('docroot', 'doc', fn)\n replacements.append(copy_tutorial_file(fn, tutdir, dstname))\n break\n replace_tutorial_zips(os.path.join('docroot', 'doc', 'tutorial.php'), replacements)\n \n tutdir = os.path.join(docdir, 'tutorial_embed', 'src')\n files = os.listdir(tutdir)\n if not os.path.exists(os.path.join('docroot', 'doc', 'src')):\n os.mkdir(os.path.join('docroot', 'doc', 'src'))\n for fn in files:\n for ext in TUTORIAL_ENDINGS:\n if fn.endswith(ext):\n dstname = os.path.join('docroot', 'doc', 'src', fn)\n replacements.append(copy_tutorial_file(fn, tutdir, dstname))\n break\n replace_tutorial_zips(os.path.join('docroot', 'doc', 'tutorial.php'), replacements)\n\n #print \"Don't forget to update docroot/doc/tutorial.php: it's references to\"\n #print \"'step3-MainMenu.nib' and 'step12-src' should be changed to ZIP files\"","def load_gold_standard():\n\tstandard = {}\n\n\tfor dirpath, dirnames, filenames in os.walk(PATH_TO_DATA):\n\t\tfor basename in filenames:\n\t\t\tif basename.endswith(\".apf.xml\"):\n\t\t\t\tprint(os.path.join(dirpath, basename))\n\t\t\t\tcounts = numpy.array(numpy.zeros(len(EVENT_TYPES)))\n\t\t\t\tcounts, all_events = count_events(os.path.join(dirpath, basename), counts)\n\t\t\t\t\n\t\t\t\t\"\"\" Excluse all documents with less than 2 event types \"\"\"\n\t\t\t\tif numpy.sum(counts) < MIN_EVENT_TYPES:\n\t\t\t\t\tcontinue\n\n\t\t\t\tname = os.path.splitext(os.path.splitext(basename)[0])[0]\n\t\t\t\tstandard[name] = (counts, all_events)\n\n\tprint(\"Loaded documents: \" + str(len(standard)))\n\treturn standard","def build_index():\n for site in get_sites():\n text = read_site(site)\n while text == False:\n text = read_site(site) # keep attempting to read until successful\n index_site(site, text)","def build_index():\n for site in get_sites():\n text = read_site(site)\n while text == False:\n text = read_site(site) # keep attempting to read until successful\n index_site(site, text)","def read_doc(self):\n self.data_read[:] = []\n for para in self.document.paragraphs:\n text = para.text\n # skip blank lines\n if text.strip():\n # remove duplicated spaces\n text = ' '.join(text.split())\n # for older versions of final CAPA's\n self.fill_project_info(text, new_format=False)\n self.data_read.append(text)\n\n # Constant in old & new report format\n # Batch/Project name\n # Lead(s)'s name\n # Reported date\n for i in range(0, len(self.data_read)):\n if next((x for x in self.leads if x in self.data_read[i]), None):\n self.project_info.update({'Project Name': self.data_read[i - 1]})\n self.project_info.update({'Lead(s)': self.data_read[i]})\n self.project_info.update({'Date Reported': self.data_read[i + 1]})\n break","def main(argv):\n\n defaults = {\n 'uspto_url': 'http://www.google.com/googlebooks/uspto-patents-applications-biblio.html',\n 'html_page': None,\n 'requested_years': [],\n 'write_to_stdout': False,\n 'print_to_file': False,\n 'download': False,\n 'output_directory': '.'\n }\n\n validate_input(argv, defaults);\n\n if not defaults['html_page']:\n defaults['html_page'] = load_url(uspto_url)\n soup = BeautifulSoup(html_page)\n\n links_per_year = defaultdict(list)\n links = soup.findAll('a', attrs={ 'href': re.compile('zip$') })\n logging.info(' found ' + str(len(links)) + ' links')\n for link in links:\n logging.debug(' . ' + link['href'])\n matched_year = re.search( '/([0-9]{4})/', link['href'])\n if matched_year:\n links_per_year[matched_year.group(1)].append(link['href'])\n filtered_dict = links_per_year\n if requested_years:\n filtered_dict = { year : links_per_year[year] for year in requested_years }\n if write_to_stdout:\n for links in sorted(filtered_dict.itervalues()):\n for link in links:\n print link\n if print_to_file:\n makedirs(output_directory)\n for k,v in filtered_dict.iteritems():\n basename = k + '.txt'\n filename = output_directory + '/' + basename\n if os.path.isfile(filename):\n os.remove(filename)\n with open(filename, 'a') as text_file:\n for link in sorted(v):\n text_file.write(link + '\\n')\n logging.debug(' file ' + basename + ' written to disk')\n logging.info(' all files written to disk')\n if download:\n for year, links in filtered_dict.iteritems():\n makedirs(os.path.join(output_directory, year))\n for link in links:\n try:\n filename = os.path.join(output_directory, year, link.split('/')[-1])\n prog = urlgrabber.progress.text_progress_meter()\n urlgrabber.urlgrab(str(link), filename, progress_obj=prog)\n except Exception, e:\n logging.warn(' error while downloading %s: %s' % (link, e))","def gather_documents(self):\n self.document_gatherer.gather_and_save_everything(Constants.path_cord, \n Constants.path_metadata, \n Constants.path_linked_documents,\n Constants.path_unlinked_documents,\n Constants.path_parsed_documents,\n Constants.path_all_documents)\n \n print(\"Done gathering documents.\")","def readcorpus(index):\n for docid in range(0, 1001):\n file = open(PATH + \"Document-\" + str(docid) + \".txt\", 'r', encoding=\"utf-8\")\n allcontent = file.readlines()\n stoplist = getstopwords()\n corpus = []\n stemmer = PorterStemmer()\n getatitle(allcontent, corpus)\n getmetakeywords(allcontent, corpus)\n getcontent(allcontent, corpus)\n flagfordate = 0\n for i in range(0, len(corpus)):\n if flagfordate == 1:\n flagfordate = 0\n continue\n word = corpus[i]\n if word in MONTH:\n if expressionfordateb(corpus, i) is True or expressionfordatef(corpus, i) is True:\n word = constructdate_expression(corpus, i, index)\n increasecount(index[0], word, docid)\n if word in stoplist:\n continue\n increasecount(index[1], word, docid)\n increasecount(index[2], processword(stemmer.stem(word)), docid)","def parse_data(self, file_data):\n if self.data:\n all_api_reference = self.data.findAll([\"h4\",\"h3\"])\n for api in all_api_reference: \n title = api.text.replace(' #', '')\n\n href = self.parse_link(file_data, api)\n\n content = self.parse_content(api)\n\n example = self.parse_example(api)\n\n section = {\n 'title': title,\n 'href': href,\n 'content': content,\n 'example': example\n }\n self.parsed_data.append(section)","def _load_project(self, _thefile='', _lang=''):\n# messagebox.showwarning('_load_project', 'Entered')\n# def _load_project(self, thefile):\n# \"\"\"loads an existing project (.prj) file,adapting it's contents\n# to the current Simple/Advanced choice\"\"\"\n #set current project label\n if len(_lang) > 0:\n lang = _lang\n else:\n lang = self.selected_lang.get()\n if lang == '':\n lang = 'en-US'\n# messagebox.showwarning('_load_project', \\\n# '_lang=>{}<, lang=>{}<'.format(_lang, lang))\n self.lblProject['text'] = '{} {}'.\\\n format(LOCALIZED_TEXT[lang]['Current Project>'], \\\n self.current_project.get())\n self.update()\n\n linesin = list()\n #thefile comes from current project label or test\n if _thefile:\n thefile = _thefile\n else:\n thefile = ospath.normpath(\\\n self.BibTerm + '/' + self.current_project + '.prj')\n if os.path.exists(thefile):\n filein = codecs.open(thefile, mode='r', encoding='utf-8')\n for aline in filein.readlines():\n # if len(aline.strip()) > 0:\n if aline.strip():\n linesin.extend([aline.strip()])\n filein.close()\n lines = ''.join(linesin)\n self.root = etree.fromstring(lines)\n self.settings = self.root.find(\"settings\")\n self.sf0 = self.settings.find(\"f0\")\n if self.sf0.get('fallback') != self.dict_in.get():\n self.dict_in.set(self.sf0.get('fallback'))\n self.dict_in_changed.set(1)\n else:\n self.dict_in_changed.set(0)\n if self.sf0.get('terms') != self.terms_in.get():\n self.terms_in.set(self.sf0.get('terms'))\n self.terms_in_changed.set(1)\n else:\n self.terms_in_changed.set(0)\n if self.sf0.get('old') != self.terms_in.get():\n self.old_dict.set(self.sf0.get('old'))\n self.old_dict_changed.set(1)\n else:\n self.old_dict_changed.set(0)\n self.sf1 = self.settings.find(\"f1\")\n self.sf2 = self.settings.find(\"f2\")\n self.trout = self.root.find(\"tree\")\n# self.preferred.set(int(self.sf1.attrib['preferred'] == 'True'))\n self.txtPrefChar.delete(0.0, 9999.9999)\n if self.sf1.text != None:\n self.txtPrefChar.insert(9999.9999, self.sf1.text)","def main():\n in_file_name = \"input.txt\"\n out_file_name = \"output.txt\"\n\n in_file = open(in_file_name, 'r')\n\n out_file = open(out_file_name, 'w')\n out_file.write('IlgizZamaleev' + '\\n')\n\n problems = int(in_file.readline())\n\n for i in range(problems):\n automato = Parser().parse_automata(in_file)\n\n tests = int(in_file.readline())\n\n out_file.write(str(i + 1) + '\\n')\n\n for j in range(tests):\n word = in_file.readline()[:-1]\n out_file.write(automato.check_word(word) + '\\n')\n\n in_file.close()\n out_file.close()","def main(argv):\n argparser = ArgumentParser(\n description=\"Measure agreement of the sentiment corpus.\")\n argparser.add_argument(\"dir1\",\n help=\"directort containing annotations of the\"\n \" first expert\")\n argparser.add_argument(\"dir2\",\n help=\"directort containing annotations of the\"\n \" second expert\")\n args = argparser.parse_args(argv)\n\n def check_arg(arg):\n if not os.path.isdir(args.dir1) or not os.access(args.dir1, os.R_OK):\n print(\"Invalid argument {!r} should be a readable\"\n \" directory\".format(arg), file=sys.stderr)\n\n check_arg(args.dir1)\n check_arg(args.dir2)\n annotations = []\n skip_ids = set()\n for fpath1 in glob.iglob(os.path.join(args.dir1, \"*.tsv\")):\n if not os.access(fpath1, os.R_OK):\n print(\"WARNING: cannot read file {:s}\".format(\n fpath1\n ), file=sys.stderr)\n continue\n base_fpath1 = os.path.basename(fpath1)\n fpath2 = os.path.join(args.dir2, base_fpath1)\n if not os.path.exists(fpath2):\n print(\"WARNING: file {:s} not found\".format(\n fpath1\n ), file=sys.stderr)\n continue\n elif not os.access(fpath2, os.R_OK):\n print(\"WARNING: cannot read file {:s}\".format(\n fpath1\n ), file=sys.stderr)\n continue\n read_annotations(annotations, skip_ids, fpath1, 1)\n read_annotations(annotations, skip_ids, fpath2, 2)\n annotations = [(coder, item, label)\n for (coder, item, label) in annotations\n if item not in skip_ids]\n at = AnnotationTask(annotations, distance=interval_distance)\n # print(at.avg_Ao())\n # print(at.Ae_kappa())\n print(\"Cohen's Kappa: {:.4f}\\nKrippendorff's Alpha: {:4f}\".format(\n at.kappa(), at.alpha()\n ))","def main():\n\n if os.path.exists(os.path.join(PROCESSED_PATH,\n 'all_posts_data.csv')):\n print(\"-- all_posts_data.csv found locally - delete interm files if rerun needed\")\n total_df = pd.read_csv(PROCESSED_PATH / 'all_posts_data.csv')\n else:\n training_post_filenames = glob.glob(os.path.join(DATA_DIR,\n 'raw',\n 'clpsych16-data',\n 'data',\n 'training',\n 'posts', '*.xml'))\n dev_post_filenames = glob.glob(os.path.join(DATA_DIR,\n 'raw',\n 'clpsych16-data',\n 'data',\n 'testing',\n 'posts', '*.xml'))\n\n new_posts2017 = glob.glob(os.path.join(DATA_DIR,\n 'raw',\n 'clpsych17-test',\n 'posts', '*.xml'))\n\n training_labels = os.path.join(DATA_DIR,\n 'raw',\n 'clpsych16-data',\n 'data',\n 'training',\n 'labels.tsv')\n dev_labels = os.path.join(DATA_DIR,\n 'raw',\n 'clpsych16-data',\n 'data',\n 'testing',\n 'labels.tsv')\n\n training_df = create_posts_df(training_post_filenames)\n dev_df = create_posts_df(dev_post_filenames)\n new_df = create_posts_df(new_posts2017)\n\n training_df['corpus_source'] = '2016train_2017train'\n dev_df['corpus_source'] = '2016test_2017train'\n new_df['corpus_source'] = '2017test'\n\n training_df = merge_post_labels(training_df, training_labels)\n dev_df = merge_post_labels(dev_df, dev_labels)\n\n training_df = merge_author_ranks(training_df)\n dev_df = merge_author_ranks(dev_df)\n new_df = merge_author_ranks(new_df)\n\n total_df = pd.concat([training_df, dev_df, new_df])\n\n test_labels = os.path.join(DATA_DIR,\n 'raw',\n 'clpsych17-test',\n 'test_ids.tsv')\n total_df.reset_index(inplace=True)\n total_df = merge_test_ids(total_df, test_labels)\n label_file = os.path.join(DATA_DIR,\n 'raw',\n 'clpsych17-test-labels.tsv')\n merge_ground_truth(total_df, label_file)\n output_path = PROCESSED_PATH / 'all_posts_data.csv'\n \n\n # clean body of text\n total_df['cleaned_body'], total_df['contained_quote'] = zip(*total_df['body'].apply(process_body))\n total_df['images'] = total_df['body'].apply(process_images)\n\n print('--Writing data to {}--'.format(output_path))\n total_df.to_csv(output_path, index=False)\n\n sentences_df = total_df.loc[:, ['post_id', 'cleaned_body', 'label', 'predict_me']]\n # the following will split posts into sentences and write out to a separate csv\n split_to_sentences(sentences_df)","def main(args):\r\n logger.info(\"Starting site generation...\")\r\n logger.info(args)\r\n logger.info(\"Opening syllabus.yml...\")\r\n\r\n## PUT THIS BACK LATER!!!!!####\r\n\r\n with open(\"/home/tofts/git/upskilling/data/syllabus.yml\", \"r\") as syllabus_file:\r\n syllabus = yaml.safe_load(syllabus_file)\r\n\r\n logger.info(\"Found the following modules:\")\r\n for module in syllabus['modules']:\r\n logger.info(f\"...{syllabus['modules'][module]['name']}\")\r\n\r\n logger.info(\"Opening squad.yml...\")\r\n with open(\"/home/tofts/git/upskilling/data/squad.yml\", \"r\") as squad_file:\r\n squad = yaml.safe_load(squad_file)\r\n\r\n for member in squad['members']:\r\n logger.info(f\"Found member {member.upper()}\")\r\n logger.info(f\"...{squad['members'][member]}\")\r\n\r\n logger.info(\"Processing index.html template...\")\r\n with open(\"/home/tofts/git/upskilling/templates/index.html\") as index_template_file:\r\n index_template = index_template_file.read()\r\n\r\n index_template = module_list(index_template, syllabus, squad)\r\n index_template = task_list(index_template, syllabus, squad)\r\n index_template = member_progress_list(index_template, syllabus, squad)\r\n\r\n logger.info(\"Writing completed template out...\")\r\n with open(\"/home/tofts/git/upskilling/docs/index.html\", \"w\") as index_output_file:\r\n index_output_file.write(index_template)\r\n\r\n logger.info(\"Generating member pages...\")\r\n with open(\"/home/tofts/git/upskilling/templates/member-page.html\", \"r\") as member_template_file:\r\n member_page_template = member_template_file.read()\r\n generate_member_pages(member_page_template, syllabus, squad)\r\n\r\n logger.info(\"Generating task detail pages...\")\r\n with open(\"/home/tofts/git/upskilling/templates/task-details.html\", \"r\") as task_details_file:\r\n task_details_template = task_details_file.read()\r\n generate_task_details_pages(task_details_template, syllabus, squad)\r\n\r\n logger.info(\"Generating downloads...\")\r\n generate_downloads(syllabus, squad)","def main ():\n\n\tfio = fileIo('input.txt')\n text = fio.getInput()\n\n\tp = re.compile(r'#?\\d[\\s\\.]?[\\s]?')\n\tout = filter(None, p.split(text))\n\ti = 0\n\tlistOfLists = []\n\t\n\n\tfor s in out:\n\t\ti += 1\n\t\ttext = nltk.word_tokenize(s)\n\t\tpos = nltk.pos_tag(text)\n\t\tpattern = \"NP: {<DT>?<JJ>*<NN>}\"\n\t\tNPChunker = nltk.RegexpParser(pattern)\n\t\tresult = NPChunker.parse(pos)\n\t\tlistOfLists.append( result )\n\n\tprint \"Noun Count:\\n\" + str(countNouns( listOfLists ))\n\tprint \"Verb Count:\\n\" + str(countVerbs( listOfLists ))\n\tprint \"Adjective Count:\\n\" + str(countAdjectives( listOfLists ))","def preprocess_courses_corpus():\n soup = None\n with open('courses_corpus.html', 'r') as infile:\n content = infile.read()\n\n soup = BeautifulSoup(content, 'html.parser')\n\n docid = 0\n data = {}\n data['documents'] = []\n\n main_table = soup.find_all(\"div\", attrs={'class': 'courseblock'})\n for course in main_table:\n docid += 1\n title = course.find_all('p', attrs={'class':'courseblocktitle noindent'})[0].text.lstrip('\\n') if len(course.find_all('p', attrs={'class':'courseblocktitle noindent'}))!=0 else ''\n description = (course.find_all('p', attrs={'class':'courseblockdesc noindent'})[0].text.lstrip('\\n') if len(course.find_all('p', attrs={'class':'courseblockdesc noindent'}))!=0 else '') + ' ' + (course.find_all('p', attrs={'class':'courseblockextra noindent'})[0].text if len(course.find_all('p', attrs={'class':'courseblockextra noindent'}))!=0 else '')\n\n data['documents'].append({\n 'docId' : docid,\n 'title' : title.strip(),\n 'description' : description.strip()\n })\n\n with open('courses_data.json', 'w') as outfile:\n json.dump(data, outfile)","def save_annotated_text_to_xml(self):\n #initialise file to write the output\n outfile = open(('annotated_text_' + self.lang + '_' + \n self.method + '.xml'), 'w')\n #initialise xml\n annotated_doc = etree.Element('Annotated_document')\n main_text = ''\n #counter for the sentences\n counter_sentence = 0\n #counter for the paragraphs\n counter_paragraph = 0\n #open txt file\n with open(self.lang + '.txt') as file:\n for paragraph in file:\n paragraph_string = ''\n sentences = tokenize.sent_tokenize(paragraph)\n for sentence in sentences:\n #build lists with the ends of the tokens with NE and the NEs\n end_list = [0]\n end_list += [i[2] for i in \n self.named_entity_list_total[counter_sentence]]\n ne_list = [i[3] for i in \n self.named_entity_list_total[counter_sentence]]\n counter_sentence += 1\n #build new string\n new_string = ''\n for i in range(len(end_list)-1):\n new_string += (sentence[end_list[i]:end_list[i+1]]+\n '<annotation class=\"'+ne_list[i]+'\"/>')\n new_string += sentence[end_list[-1]:len(sentence)]\n paragraph_string += new_string+'\\n'\n #print title, author, abstract and main text differently to xml\n if counter_paragraph == 0:\n title_text = etree.SubElement(annotated_doc, \"Title\")\n #add text to the node\n init_text = \"<text>{0}</text>\".format(paragraph_string[6:])\n fin_text = etree.fromstring(init_text)\n title_text.append(fin_text)\n elif counter_paragraph == 1:\n author_text = etree.SubElement(annotated_doc, \"Author\")\n #add text to the node\n init_text = \"<text>{0}</text>\".format(paragraph_string[7:])\n fin_text = etree.fromstring(init_text)\n author_text.append(fin_text)\n elif counter_paragraph == 2:\n abstract_text = etree.SubElement(annotated_doc, \"Abstract\")\n #add text to the node\n init_text = \"<text>{0}</text>\".format(paragraph_string[9:])\n fin_text = etree.fromstring(init_text)\n abstract_text.append(fin_text)\n else: \n main_text += paragraph_string\n counter_paragraph += 1\n main_text_xml = etree.SubElement(annotated_doc, \"Main_text\")\n #add text to the node\n init_text = \"<text>{0}</text>\".format(main_text)\n fin_text = etree.fromstring(init_text)\n main_text_xml.append(fin_text)\n #convert and write to outfile\n xml_bytes = etree.tostring(annotated_doc, encoding='UTF-8', \n pretty_print=True, xml_declaration=True)\n xml_str = xml_bytes.decode(\"utf-8\")\n outfile.write(xml_str)\n outfile.close()\n return","def generate_megafile():\n\n print(\"\\nFetching testing dataset…\")\n testing = get_testing()\n\n print(\"\\nFetching ECDC dataset…\")\n ecdc = get_ecdc()\n\n location_mismatch = set(testing.location).difference(set(ecdc.location))\n for loc in location_mismatch:\n print(f\"<!> Location '{loc}' has testing data but is absent from ECDC data\")\n\n print(\"\\nFetching OxCGRT dataset…\")\n cgrt = get_cgrt()\n\n all_covid = (\n ecdc\n .merge(testing, on=[\"date\", \"location\"], how=\"outer\")\n .merge(cgrt, on=[\"date\", \"location\"], how=\"left\")\n .sort_values([\"location\", \"date\"])\n )\n\n # Add ISO codes\n print(\"Adding ISO codes…\")\n iso_codes = pd.read_csv(os.path.join(INPUT_DIR, \"iso/iso3166_1_alpha_3_codes.csv\"))\n\n missing_iso = set(all_covid.location).difference(set(iso_codes.location))\n if len(missing_iso) > 0:\n print(missing_iso)\n raise Exception(\"Missing ISO code for some locations\")\n\n all_covid = iso_codes.merge(all_covid, on=\"location\")\n\n # Add continents\n print(\"Adding continents…\")\n continents = pd.read_csv(\n os.path.join(INPUT_DIR, \"owid/continents.csv\"),\n names=[\"_1\", \"iso_code\", \"_2\", \"continent\"],\n usecols=[\"iso_code\", \"continent\"],\n header=0\n )\n\n all_covid = continents.merge(all_covid, on=\"iso_code\", how=\"right\")\n\n # Add macro variables\n # - the key is the name of the variable of interest\n # - the value is the path to the corresponding file\n macro_variables = {\n \"population\": \"un/population_2020.csv\",\n \"population_density\": \"wb/population_density.csv\",\n \"median_age\": \"un/median_age.csv\",\n \"aged_65_older\": \"wb/aged_65_older.csv\",\n \"aged_70_older\": \"un/aged_70_older.csv\",\n \"gdp_per_capita\": \"wb/gdp_per_capita.csv\",\n \"extreme_poverty\": \"wb/extreme_poverty.csv\",\n \"cardiovasc_death_rate\": \"gbd/cardiovasc_death_rate.csv\",\n \"diabetes_prevalence\": \"wb/diabetes_prevalence.csv\",\n \"female_smokers\": \"wb/female_smokers.csv\",\n \"male_smokers\": \"wb/male_smokers.csv\",\n \"handwashing_facilities\": \"un/handwashing_facilities.csv\",\n \"hospital_beds_per_thousand\": \"owid/hospital_beds.csv\",\n \"life_expectancy\": \"owid/life_expectancy.csv\",\n \"human_development_index\": \"un/human_development_index.csv\",\n }\n all_covid = add_macro_variables(all_covid, macro_variables)\n\n print(\"Writing to CSV…\")\n all_covid.to_csv(os.path.join(DATA_DIR, \"owid-covid-data.csv\"), index=False)\n\n print(\"Writing to XLSX…\")\n all_covid.to_excel(os.path.join(DATA_DIR, \"owid-covid-data.xlsx\"), index=False)\n\n print(\"Writing to JSON…\")\n df_to_json(all_covid, os.path.join(DATA_DIR, \"owid-covid-data.json\"), macro_variables.keys())\n\n # Store the last updated time\n timestamp_filename = os.path.join(DATA_DIR, \"owid-covid-data-last-updated-timestamp.txt\")\n with open(timestamp_filename, \"w\") as timestamp_file:\n timestamp_file.write(datetime.utcnow().replace(microsecond=0).isoformat())\n\n print(\"All done!\")","def preprocess_corpus(train_sents):\n global lookupLexiconDict\n lookupLexiconDict = {}\n \n lexiconDir = getcwd()+'\\\\data\\\\lexicon'\n filesList = [hfile for hfile in listdir(lexiconDir) if path.isfile(lexiconDir+'\\\\'+hfile) ]\n \n decision_tags = ['facility','product','musicartist']\n fileMappingDict = \\\n {\n 'architecture.museum':'facility',\n 'automotive.make':'product',\n 'automotive.model':'product',\n 'award.award':'musicartist',\n 'base.events.festival_series':'geo-loc',\n #'bigdict':'@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@',\n 'book.newspaper':'company',\n 'broadcast.tv_channel':'tvshow',\n 'business.brand':'company',\n 'business.consumer_company':'company',\n 'business.consumer_product':'product',\n 'business.sponsor':'company',\n 'cap.1000':'geo-loc',\n 'cvg.computer_videogame':'product',\n 'cvg.cvg_developer':'company',\n 'cvg.cvg_platform':'product',\n 'education.university':'facility',\n 'english.stop':'O',\n 'firstname.5k':'person',\n 'government.government_agency':'company',\n 'internet.website':'company',\n 'lastname.5000':'person',\n 'location':'geo-loc',\n 'location.country':'geo-loc',\n 'lower.5000':'O',\n 'people.family_name':'person',\n 'people.person':'person',\n 'people.person.lastnames':'person', # <-----------------------------\n 'product':'product',\n 'sports.sports_league':'sportsteam',\n 'sports.sports_team':'sportsteam',\n 'time.holiday':'O',\n 'time.recurring_event':'O',\n 'transportation.road':'geo-loc',\n 'tv.tv_network':'tvshow',\n 'tv.tv_program':'tvshow',\n 'venture_capital.venture_funded_company':'company',\n 'venues':'geo-loc'\n }\n\n for lexFile in filesList:\n if lexFile not in fileMappingDict: continue\n print 'Processing ', lexFile\n \n with open(lexiconDir+'\\\\'+lexFile) as f:\n for line in f:\n line = line.lower().split()\n if len(line) == 1: low=0\n else:low=1\n for i in range(low,len(line)):\n key = tuple(line[:i+1])\n if key not in lookupLexiconDict:\n lookupLexiconDict[key] = [fileMappingDict[lexFile]]\n else:\n lookupLexiconDict[key].append(fileMappingDict[lexFile]) \n\n \n #pass ","def readSources(self):\n for sourceCount, sourceElement in enumerate(self.root.findall(\".sources/source\")):\n # shall we just read the UFO here?\n filename = sourceElement.attrib.get('filename')\n # filename is a path relaive to the documentpath. resolve first.\n sourcePath = os.path.abspath(os.path.join(os.path.dirname(self.path), filename))\n sourceName = sourceElement.attrib.get('name')\n if sourceName is None:\n # if the source element has no name attribute\n # (some authoring tools do not need them)\n # then we should make a temporary one. We still need it for reference.\n sourceName = \"temp_master.%d\"%(sourceCount)\n self.reportProgress(\"prep\", 'load', sourcePath)\n if not os.path.exists(sourcePath):\n raise MutatorError(\"Source not found at %s\"%sourcePath)\n sourceObject = self._instantiateFont(sourcePath)\n # read the locations\n sourceLocationObject = None\n sourceLocationObject = self.locationFromElement(sourceElement)\n\n if sourceLocationObject is None:\n raise MutatorError(\"No location defined for source %s\"%sourceName)\n\n # read lib flag\n for libElement in sourceElement.findall('.lib'):\n if libElement.attrib.get('copy') == '1':\n self.libSource = sourceName\n\n # read the groups flag\n for groupsElement in sourceElement.findall('.groups'):\n if groupsElement.attrib.get('copy') == '1':\n self.groupsSource = sourceName\n\n # read the info flag\n for infoElement in sourceElement.findall(\".info\"):\n if infoElement.attrib.get('copy') == '1':\n self.infoSource = sourceName\n if infoElement.attrib.get('mute') == '1':\n self.muted['info'].append(sourceName)\n\n # read the features flag\n for featuresElement in sourceElement.findall(\".features\"):\n if featuresElement.attrib.get('copy') == '1':\n if self.featuresSource is not None:\n self.featuresSource = None\n else:\n self.featuresSource = sourceName\n\n mutedGlyphs = []\n for glyphElement in sourceElement.findall(\".glyph\"):\n glyphName = glyphElement.attrib.get('name')\n if glyphName is None:\n continue\n if glyphElement.attrib.get('mute') == '1':\n if not sourceName in self.muted['glyphs']:\n self.muted['glyphs'][sourceName] = []\n self.muted['glyphs'][sourceName].append(glyphName)\n\n for kerningElement in sourceElement.findall(\".kerning\"):\n if kerningElement.attrib.get('mute') == '1':\n self.muted['kerning'].append(sourceName)\n\n # store\n self.sources[sourceName] = sourceObject, sourceLocationObject\n self.reportProgress(\"prep\", 'done')","def main():\n parser = argparse.ArgumentParser()\n parser.add_argument('--allData', default='', action='store', nargs='+',\n help=\"add train test validation dataset together\")\n parser.add_argument('--topic', default='', help=\"target topic\")\n parser.add_argument('--contentWordNumber', default='', help=\"threshold for content Word Number\")\n parser.add_argument('--returnNSents', default='', help=\"top N sentences\")\n\n args = parser.parse_args()\n data = loadData(args.allData, args.topic, args.contentWordNumber)\n allDataDic, targetTweets = data.readData()\n \"\"\"\n ##modify at def_processing, re-run below line##\n outputDB = data.processing(allDataDic)\n with open(ROOT.DATA_ROOT+'/'+'allDataDic.txt', 'w') as file:\n file.write(json.dumps(outputDB))\n \"\"\"\n\n targData = data.processing(targetTweets)\n outputDB = json.load(open(ROOT.DATA_ROOT + '/allDataDic.txt'))\n contentWords = data.tfidf(outputDB, targData)\n return targetTweets,targData, contentWords","def main():\n ff = FileForensics()\n # ff.scan_dir(\"/Users/ns/notes\") # FIXME\n ff.scan_dir(\"/Users/ns/work/termination_data\")\n\n print \"\\n--- BIG FILES ---\"\n for (size, mime, filename) in ff.get_big_files():\n print (bcolors.FAIL+\"{:>10} MB\"+bcolors.ENDC+\" {:<20} {:<10}\").\\\n format(size, mime, filename)\n\n print \"\\n--- FOUND KEYWORDS ---\"\n for (file, matches) in ff.get_keyword_files():\n print \"{:<5} {:<20} ({:<10})\".format(\n len(matches), file[\"mime\"], file[\"filename\"])\n for position, match in matches:\n print \"\\t- {:<10} {:<10}\".format(position, match)\n print\n\n print \"\\n--- HIGH ENTROPY FILES ---\"\n for (file, ent) in ff.get_highentropy_files():\n print (bcolors.FAIL+\"\\t {:.2f}\"+bcolors.ENDC+\" ({:<10}) {:<10}\").\\\n format(ent, file[\"mime\"], file[\"filename\"])","def main():\n\n global text_column_name\n # set to false\n # TODO: add RC corpus to non RC corpus\n text_corpus, already_processed_article_list = import_data_and_subset(\n path_metadata, path_rc_metadata, sepr, path_preprocessed_article_list, article_id, text_column_name,\n rc_content_column_name, reset_already_preprocessed_article_list=False)\n\n print 'raw corpus loaded'\n\n # text_corpus = remove_tags_from_corpus(\n # text_corpus, text_column_name)\n\n not_preprocessed_article_list = find_new_articles(\n text_corpus, index_column_name, already_processed_article_list)\n print 'new_article list is ' + str(len(not_preprocessed_article_list))\n\n text_corpus = subset_corpus(\n text_corpus, index_column_name, not_preprocessed_article_list)\n print 'subsetting of unprocessed articles from corpus done'\n print 'articles left after subsetting are ' + str(text_corpus[index_column_name].nunique())\n\n text_corpus = parallelize(text_corpus, remove_tags_from_corpus)\n print 'html tags removed from corpus'\n # text_corpus = convert_corpus_to_list(text_corpus)\n\n # text_corpus = contraction_expansion_on_corpus(\n # text_corpus, text_column_name)\n text_corpus = parallelize(text_corpus, contraction_expansion_on_corpus)\n print 'contractions expansion completed'\n\n # text_corpus = tokenization_on_corpus(text_corpus, text_column_name)\n text_corpus = parallelize(text_corpus, tokenization_on_corpus)\n print 'tokenization completed'\n # text_corpus = punctuation_removal_on_corpus(text_corpus, text_column_name)\n text_corpus = parallelize(text_corpus, punctuation_removal_on_corpus)\n print 'punctuation removal completed'\n # text_corpus = lowercase_on_corpus(text_corpus, text_column_name)\n text_corpus = parallelize(text_corpus, lowercase_on_corpus)\n print 'lower casing done'\n # text_corpus = lemmitization_on_corpus(text_corpus, text_column_name)\n text_corpus = parallelize(text_corpus, lemmitization_on_corpus)\n print 'lemmatization done'\n # text_corpus = stopword_removal_on_corpus(text_corpus, text_column_name)\n text_corpus = parallelize(text_corpus, stopword_removal_on_corpus)\n print 'stopwords removed'\n text_corpus = remove_article_with_little_or_no_content(text_corpus)\n print 'articles with less or no corpus removed'\n store_to_disk(text_corpus, path_preprocessed_files, append_mode=True)\n print 'per-processing done and saved at mentioned path'","def main(nlp, file_path, final_file_path, from_line=0, to_line=None):\n with open(final_file_path, \"w\") as parsed_file:\n with open(file_path) as cnn_dm:\n line = cnn_dm.readline().strip()\n article_idx = 0\n while article_idx < from_line:\n line = cnn_dm.readline().strip()\n article_idx += 1\n if to_line is None:\n while line is not None and line != '':\n process_line(nlp, line, parsed_file)\n article_idx += 1\n print(\"{} articles processed from file {}\".format(article_idx, file_path))\n line = cnn_dm.readline().strip()\n else:\n while article_idx < to_line and line is not None and line != '':\n process_line(nlp, line, parsed_file)\n article_idx += 1\n print(\"{}th article processed from file {}\".format(article_idx, file_path))\n line = cnn_dm.readline().strip()","def DocParser(accession_list):\n\thandle = open(\"prosite.txt\")\n\trecords = Prodoc.parse(handle)\n\trecord_text_list=[]\n\ttry:\n\t\t# Loop to go through prosite.doc entries. \n\t\tfor record in records:\n\t\t\tif record.accession in accession_list: # If an entry is in the list of the already found domains\n\t\t\t\trecord_text_list.append(record.text) # Save it\n\texcept:\n\t\tprint(False)\n\treturn record_text_list","def main():\n with read_std_files(OUT_FILE) as (qrys_file, docs_file, out_file):\n doc_count, token_count, word_map = map_docs(docs_file)\n avg_doc_len = token_count / float(doc_count)\n for doc_id, doc_tokens in tokenize(docs_file):\n doc_len = len(doc_tokens)\n doc_dct = dictify(doc_tokens)\n for query_id, query_tokens in tokenize(qrys_file):\n query_dct = dictify(query_tokens)\n similarity = tfidf(query_dct, doc_dct, doc_len, doc_count, avg_doc_len, word_map)\n log(out_file, query_id, doc_id, similarity)","def test3_advanced_info(self):\n\t\tprint \"\\nTEST 3: Extracting detailed entities info from each ontology in %s folder.\\n=================\" % DATA_FOLDER\n\n\t\tfor f in os.listdir(DATA_FOLDER):\n\t\t\tif not f.startswith('.'):\n\t\t\t\tprint \"Loading... >\", f\n\n\t\t\t\t# divert output to a file temporarily \n\t\t\t\tsaveout = sys.stdout \n\t\t\t\tfsock = open('out.log', 'w') \n\t\t\t\tsys.stdout = fsock \n\t\t\t\t\n\t\t\t\to = ontospy.Ontology(DATA_FOLDER + f)\n\t\t\t\tprintEntitiesInformation(o)\t\t\t\t\n\t\t\t\t\n\t\t\t\tsys.stdout = saveout\n\t\t\t\tfsock.close()\n\t\t\t\tprint \"Success.\"","def build():\n for root, dirs, files in os.walk(IN_PATH):\n for filename in files:\n if filename.endswith('.csv'):\n with open(os.path.join(IN_PATH, filename), encoding='utf-8') as f:\n reader = csv.reader(f)\n next(reader)\n data = nested_dict()\n web_data = nested_dict()\n for row in reader:\n if row[0].startswith('report.') or row[0].startswith('cardset.'):\n d = data\n elif row[0].startswith('web.'):\n d = web_data\n path = row[0].split('.')\n for i in range(len(path)):\n if i == len(path) - 1:\n d[path[i]] = row[1]\n else:\n d = d[path[i]]\n with open (os.path.join(OUT_PATH, filename.replace('.csv', '.json')), 'w', encoding='utf-8') as fout:\n json.dump({**data, **web_data}, fout)\n with open (os.path.join(WEB_PATH, filename.replace('.csv', '.js')), 'w', encoding='utf-8') as fout:\n fout.write('var STRINGS = {};'.format(json.dumps(web_data)))\n\n with open(os.path.join(IN_PATH, 'en_US.csv'), encoding='utf-8') as f:\n reader = csv.reader(f)\n next(reader)\n data = nested_dict()\n web_data = nested_dict()\n for row in reader:\n path = row[0].split('.')\n if row[0].startswith('report.') or row[0].startswith('cardset.'):\n d = data\n elif row[0].startswith('web.'):\n d = web_data\n\n for i in range(len(path)):\n if i == len(path) - 1:\n d[path[i]] = zz_string(row[1], row[0])\n else:\n d = d[path[i]]\n with open(os.path.join(OUT_PATH, 'zz_ZZ.json'), 'w', encoding='utf-8') as fout:\n json.dump({**data, **web_data}, fout)\n with open(os.path.join(WEB_PATH, 'zz_ZZ.js'), 'w', encoding='utf-8') as fout:\n fout.write('var STRINGS = {};'.format(json.dumps(web_data)))"],"string":"[\n \"def main(url, inputFile, directory, rss, opml, output, verbose, debug, relevanceAlgorithm):\\n\\n if (len(argv) < 2):\\n print(\\n \\\"Usage: python3 ctirt.py [options] [target files]\\\\n\\\\n Use --> ctirt.py --help for more details...\\\"\\n )\\n exit(1)\\n\\n if (verbose and url) or (url and debug):\\n print(\\\"URL is mutually exclusive with verbose and debug\\\")\\n exit(1)\\n \\n \\n \\n # INITIALIZE DOCUMENTS LIST\\n documents = [] # list of document objects\\n\\n # OPML FILE INPUT\\n\\n if opml:\\n printLogo()\\n print(\\\"\\\\033[0;34m\\\" + \\\"Parsing provided opml file: \\\" + \\\"\\\\033[0m\\\" + \\\"\\\\033[1m\\\" + opml + \\\"\\\\033[0m\\\")\\n\\n rssList = parser.parseOpml(opml)\\n\\n for rss in rssList:\\n print(\\\"Parsing RSS feed: \\\" + \\\"\\\\033[1m\\\" + rss + \\\"\\\\033[0m\\\")\\n\\n feed = parser.parseRss(rss)\\n \\n if not verbose:\\n # progress bar\\n progressBar = IncrementalBar('\\\\tParsing URLs in RSS feed:', max=len(feed.entries), suffix='%(index)d / %(max)d')\\n\\n for entry in feed.entries:\\n document = Document()\\n\\n document.path = entry.link\\n \\n document.name, document.text = parser.parseUrl(document.path)\\n \\n document.wordCount = parser.countWords(document.text)\\n \\n # Add document object to list, add document wordcount to list\\n documents.append(document)\\n\\n if not verbose:\\n progressBar.next()\\n else:\\n print(\\\"Done.\\\")\\n \\n print(\\\"\\\\n\\\\t\\\" + \\\"\\\\033[0;32m\\\" + u'\\\\u2713' + \\\" Done parsing RSS feed: \\\" + \\\"\\\\033[0m\\\" + \\\"\\\\033[1m\\\" + rss + \\\"\\\\033[0m\\\")\\n # RSS INPUT\\n\\n elif rss:\\n printLogo()\\n print(\\\"Parsing\\\", rss)\\n\\n feed = parser.parseRss(rss)\\n if not verbose:\\n # progress bar\\n progressBar = IncrementalBar('Parsing URLs', max=len(feed.entries), suffix='%(index)d / %(max)d')\\n\\n for entry in feed.entries:\\n document = Document()\\n\\n document.path = entry.link\\n \\n document.name, document.text = parser.parseUrl(document.path)\\n \\n document.wordCount = parser.countWords(document.text)\\n \\n # Add document object to list, add document wordcount to list\\n documents.append(document)\\n\\n if not verbose:\\n progressBar.next()\\n else:\\n print(\\\"Done.\\\")\\n \\n if not verbose:\\n progressBar.finish()\\n\\n print(\\\"Done.\\\")\\n \\n # URL INPUT\\n \\n elif url:\\n printLogo()\\n print(\\\"Parsing...\\\")\\n\\n document = Document()\\n\\n document.path = url\\n \\n document.name, document.text = parser.parseUrl(url)\\n \\n document.wordCount = parser.countWords(document.text)\\n \\n # Add document object to list, add document wordcount to list\\n documents.append(document)\\n\\n print(\\\"Done.\\\")\\n\\n \\n # SINGLE FILE INPUT\\n\\n elif inputFile:\\n printLogo()\\n print(\\\"Parsing...\\\")\\n\\n document = Document()\\n\\n document.name = os.path.splitext(inputFile)[0]\\n document.path = inputFile\\n\\n if inputFile.lower().endswith(\\\".pdf\\\"): # PDF Parsing\\n document.text = parser.parsePdf(inputFile)\\n elif inputFile.lower().endswith(\\\".html\\\"): # HTML Parsing\\n document.text = parser.parseHtml(inputFile)\\n\\n document.wordCount = parser.countWords(document.text) # Document word count\\n\\n # Add document object to list, add document wordcount to list\\n documents.append(document)\\n\\n print(\\\"Done.\\\")\\n\\n\\n # DIRECTORY INPUT\\n\\n elif directory:\\n printLogo()\\n if not verbose:\\n # progress bar\\n progressBar = IncrementalBar('Parsing', max=len(\\n os.listdir(directory)), suffix='%(index)d / %(max)d')\\n\\n # Loop through files in directory\\n for inputFile in os.scandir(directory):\\n beginningTime = time.time()\\n\\n if verbose:\\n timeStamp = time.time()\\n print(\\\"***[\\\" + inputFile.name[0:50] + \\\"]***\\\", \\\"is currently being parsed\\\",\\n \\\"-->\\\", (timeStamp - beginningTime), \\\"seconds have elapsed...\\\")\\n\\n document = Document()\\n\\n document.name = os.path.splitext(inputFile.name)[0]\\n document.path = inputFile.path\\n\\n if verbose:\\n print(inputFile.name)\\n\\n if inputFile.name.lower().endswith(\\\".pdf\\\"): # PDF Parsing\\n document.text = parser.parsePdf(inputFile.path)\\n elif inputFile.name.lower().endswith(\\\".html\\\"): # HTML Parsing\\n document.text = parser.parseHtml(inputFile.path)\\n\\n document.wordCount = parser.countWords(\\n document.text) # Document word count\\n\\n # Add document object to list, add document wordcount to list\\n documents.append(document)\\n\\n if not verbose:\\n progressBar.next()\\n else:\\n print(\\\"Done.\\\")\\n \\n if not verbose:\\n progressBar.finish()\\n\\n\\n # BASIC RELEVANCE CALCULATION\\n\\n for document in documents:\\n document.relevance = relevance.computeBasicRelevance(document.text)\\n\\n\\n # TF-IDF RELEVANCE CALCULATION\\n\\n if directory and (verbose or debug or relevanceAlgorithm == \\\"tfidf\\\"):\\n dirWordCount = parser.countDirectoryWords(documents)\\n\\n wordList = {}\\n with open('./assets/wordlist.json') as f:\\n jsonWordList = load(f)\\n for pair in jsonWordList.items():\\n wordList[pair[0]] = float(pair[1])\\n\\n for document in documents:\\n # TODO Figure out how to run - fix arguments (ex. import wordlist), make debug work better by allowing it to work not in verbose\\n idfs = relevance.computeIDF(documents, dirWordCount)\\n print(\\\"**************** IDFS ****************\\\")\\n print(idfs)\\n tf = relevance.computeTF(wordList, document.wordCount)\\n print(\\\"**************** TF DICT ****************\\\")\\n print(tf)\\n\\n tfidf = relevance.computeTFIDF(tf, idfs)\\n print(\\\"**************** TF-IDF Values ****************\\\")\\n print(tfidf)\\n\\n relevanceScore = 0\\n\\n for word, val in tfidf.items():\\n relevanceScore += val\\n \\n document.tfidf = relevanceScore * 100\\n\\n\\n # OUTPUT SECTION\\n\\n documents.sort(key=lambda document: document.relevance, reverse=True)\\n\\n table = []\\n tableHeaders = []\\n outputData = []\\n # print(\\\"**************** RELEVANCE SCORES ****************\\\")\\n for document in documents:\\n outputData.append({'name': document.name[0:30], 'relevance': document.relevance,'path': document.path, 'topTerms': list(document.wordCount.items())[:10]})\\n if url or rss or opml: \\n table.append([document.name[0:30], document.relevance, document.path])\\n tableHeaders = [\\\"Document\\\",\\\"Relevance Score\\\",\\\"URL\\\"]\\n elif not verbose:\\n table.append([document.name[0:70], document.relevance])\\n tableHeaders=[\\\"Document\\\",\\\"Relevance Score\\\"]\\n elif verbose and directory:\\n table.append([document.name[0:70], document.relevance, document.tfidf, list(document.wordCount.items())[:10]])\\n tableHeaders=[\\\"Document\\\",\\\"Relevance Score\\\", \\\"TF-IDF Score\\\", \\\"Top Terms\\\"]\\n else:\\n table.append([document.name[0:70], document.relevance, list(document.wordCount.items())[:10]])\\n tableHeaders=[\\\"Document\\\",\\\"Relevance Score\\\", \\\"Top Terms\\\"]\\n\\n print(tabulate(table, headers=tableHeaders, tablefmt=\\\"fancy_grid\\\"))\\n\\n # OUTPUT TO FILE\\n\\n with open(output, 'w', encoding='utf-8') as o:\\n dump(outputData, o, indent=3)\",\n \"def main(args):\\n\\tif not os.path.isdir(args.dir):\\n\\t\\tprint \\\"The specified folder is not a directory.\\\"\\n\\t\\tsys.exit(1)\\n\\tNUMBER_OF_FILES = len(os.listdir(args.dir))\\n\\tif args.num_of_files:\\n\\t\\tNUMBER_OF_FILES = args.num_of_files\\n\\tprint \\\"Parsing\\\", NUMBER_OF_FILES, \\\"files\\\"\\n\\tsql = None\\n\\tif not args.stdout:\\n\\t\\tsql = sqlite3.connect(args.database)\\n\\t\\tsql.execute(\\\"\\\"\\\"PRAGMA foreign_keys = ON;\\\"\\\"\\\")\\n\\t\\tsql.execute(\\\"\\\"\\\"CREATE TABLE airdates(\\n\\t\\t\\tgame INTEGER PRIMARY KEY,\\n\\t\\t\\tairdate TEXT\\n\\t\\t);\\\"\\\"\\\")\\n\\t\\tsql.execute(\\\"\\\"\\\"CREATE TABLE documents(\\n\\t\\t\\tid INTEGER PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tclue TEXT,\\n\\t\\t\\tanswer TEXT\\n\\t\\t);\\\"\\\"\\\")\\n\\t\\tsql.execute(\\\"\\\"\\\"CREATE TABLE categories(\\n\\t\\t\\tid INTEGER PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tcategory TEXT UNIQUE\\n\\t\\t);\\\"\\\"\\\")\\n\\t\\tsql.execute(\\\"\\\"\\\"CREATE TABLE clues(\\n\\t\\t\\tid INTEGER PRIMARY KEY AUTOINCREMENT,\\n\\t\\t\\tgame INTEGER,\\n\\t\\t\\tround INTEGER,\\n\\t\\t\\tvalue INTEGER,\\n\\t\\t\\tFOREIGN KEY(id) REFERENCES documents(id),\\n\\t\\t\\tFOREIGN KEY(game) REFERENCES airdates(game)\\n\\t\\t);\\\"\\\"\\\")\\n\\t\\tsql.execute(\\\"\\\"\\\"CREATE TABLE classifications(\\n\\t\\t\\tclue_id INTEGER,\\n\\t\\t\\tcategory_id INTEGER,\\n\\t\\t\\tFOREIGN KEY(clue_id) REFERENCES clues(id),\\n\\t\\t\\tFOREIGN KEY(category_id) REFERENCES categories(id)\\n\\t\\t);\\\"\\\"\\\")\\n\\tfor i, file_name in enumerate(glob(os.path.join(args.dir, \\\"*.html\\\")), 1):\\n\\t\\twith open(os.path.abspath(file_name)) as f:\\n\\t\\t\\tparse_game(f, sql, i)\\n\\tif not args.stdout:\\n\\t\\tsql.commit()\\n\\tprint \\\"All done\\\"\",\n \"def main():\\n LESSONS_PATH = os.path.join(LESSON_LOCATOR_DATA, LESSON_SETS[0])\\n ORIGINAL_LESSONS_PATH = os.path.join(LESSONS_PATH, \\\"original\\\")\\n ANNOTATED_LESSONS_PATH = os.path.join(LESSONS_PATH, \\\"annotated\\\")\\n\\n if not os.path.exists(ANNOTATED_LESSONS_PATH):\\n os.mkdir(ANNOTATED_LESSONS_PATH)\\n\\n print(\\\"Scanning original lessons in %s...\\\" % ORIGINAL_LESSONS_PATH)\\n\\n for item in os.listdir(ORIGINAL_LESSONS_PATH):\\n if item == \\\".DS_Store\\\": continue\\n\\n print(\\\" found: %s\\\" % item)\\n\\n item_path = os.path.join(ORIGINAL_LESSONS_PATH, item)\\n\\n lesson_number = None\\n lesson_description = None\\n mobj = re.search(r'^AY\\\\s+(\\\\d+)\\\\s*-\\\\s*(.+)\\\\.txt$', item)\\n if mobj:\\n lesson_number = mobj.group(1)\\n lesson_description = mobj.group(2)\\n\\n print(\\\" number: %s\\\" % lesson_number)\\n print(\\\" description: %s\\\" % lesson_description)\\n\\n lesson = dict()\\n lesson['number'] = lesson_number\\n lesson['description'] = lesson_description\\n\\n fh = open(item_path)\\n lesson_raw_text = fh.read()\\n fh.close()\\n lesson_text = re.split(r'\\\\n', lesson_raw_text)\\n# lesson_raw_text_reencoded = lesson_raw_text.decode('mac-roman').encode('utf-8')\\n# lesson_text = re.split(r'\\\\n', lesson_raw_text_reencoded)\\n\\n lesson['text'] = lesson_text\\n lesson['parsed'] = parseLesson(lesson_text)\\n\\n if lesson['parsed']['end_of_lesson'] is None:\\n print(\\\" lesson has no 'end of lesson' marker\\\")\\n\\n lesson_json = json.dumps(lesson, indent=4)\\n annotated_lesson_path = os.path.join(ANNOTATED_LESSONS_PATH, \\\"ay_%04d.json\\\" % int(lesson_number))\\n fh = open(annotated_lesson_path, \\\"w\\\")\\n fh.write(lesson_json)\\n fh.close()\\n\\n else:\\n print(\\\"ERROR: File name not understood: %s\\\" % item)\\n\\n return 0\",\n \"def documents(iati_import, activity, project, activities_globals):\\n imported_docs = []\\n changes = []\\n\\n xml_ns = 'http://www.w3.org/XML/1998/namespace'\\n first_image = True\\n\\n for doc_link in activity.findall('document-link'):\\n url = ''\\n doc_format = ''\\n title = ''\\n title_language = ''\\n category = ''\\n language = ''\\n\\n if 'url' in doc_link.attrib.keys():\\n url = doc_link.attrib['url']\\n\\n # Check if it's the first image\\n if url and url.rsplit('.', 1)[1].lower() in VALID_IMAGE_EXTENSIONS and first_image:\\n first_image = False\\n continue\\n\\n if 'format' in doc_link.attrib.keys():\\n if not len(doc_link.attrib['format']) > 75:\\n doc_format = doc_link.attrib['format']\\n else:\\n add_log(iati_import, 'document_link_format',\\n 'format is too long (75 characters allowed)', project)\\n\\n # Check if the format is 'application/http'\\n if doc_format == 'application/http':\\n continue\\n\\n title_element = doc_link.find('title')\\n if not title_element is None:\\n title = get_text(title_element, activities_globals['version'])\\n if len(title) > 100:\\n add_log(iati_import, 'document_link_title',\\n 'title is too long (100 characters allowed)', project,\\n IatiImportLog.VALUE_PARTLY_SAVED)\\n title = title[:100]\\n\\n if activities_globals['version'][0] == '1' and \\\\\\n '{%s}lang' % xml_ns in title_element.attrib.keys():\\n if not len(title_element.attrib['{%s}lang' % xml_ns]) > 2:\\n title_language = title_element.attrib['{%s}lang' % xml_ns]\\n else:\\n add_log(iati_import, 'document_link_title_language',\\n 'language is too long (2 characters allowed)', project)\\n elif activities_globals['version'][0] == '2':\\n narrative_element = title_element.find('narrative')\\n if not narrative_element is None and \\\\\\n '{%s}lang' % xml_ns in narrative_element.attrib.keys():\\n if not len(narrative_element.attrib['{%s}lang' % xml_ns]) > 2:\\n title_language = narrative_element.attrib['{%s}lang' % xml_ns]\\n else:\\n add_log(iati_import, 'document_link_title_language',\\n 'language is too long (2 characters allowed)', project)\\n\\n category_element = doc_link.find('category')\\n if not category_element is None and 'code' in category_element.attrib.keys():\\n if not len(category_element.attrib['code']) > 3:\\n category = category_element.attrib['code']\\n else:\\n add_log(iati_import, 'document_link_category',\\n 'category is too long (3 characters allowed)', project)\\n\\n language_element = doc_link.find('language')\\n if not language_element is None and 'code' in language_element.attrib.keys():\\n if not len(language_element.attrib['code']) > 2:\\n language = language_element.attrib['code']\\n else:\\n add_log(iati_import, 'document_link_language',\\n 'language is too long (2 characters allowed)', project)\\n\\n doc, created = get_model('rsr', 'projectdocument').objects.get_or_create(\\n project=project,\\n url=url,\\n format=doc_format,\\n title=title,\\n title_language=title_language,\\n category=category,\\n language=language\\n )\\n\\n if created:\\n changes.append(u'added project document (id: %s): %s' % (str(doc.pk), doc))\\n\\n imported_docs.append(doc)\\n\\n for doc_link in project.documents.all():\\n if not doc_link in imported_docs:\\n changes.append(u'deleted project document (id: %s): %s' %\\n (str(doc_link.pk),\\n doc_link.__unicode__()))\\n doc_link.delete()\\n\\n return changes\",\n \"def read_and_clean_files(clueweb_file, ann_file, data_dir, ann_dir):\\n annotation_input = fileinput.FileInput(os.path.join(ann_dir, ann_file), openhook=fileinput.hook_compressed)\\n annotation_list = []\\n for line in annotation_input:\\n\\tannotation_list.append(Annotation.parse_annotation(line))\\n\\n warc_path = os.path.join(data_dir, clueweb_file)\\n warc_file = warc.open(warc_path)\\n print \\\"Replacing entity mentions for \\\", clueweb_file, \\\":\\\", ann_file, \\\"...\\\"\\n start = time.time()\\n warc_entry = WarcEntry(warc_path, warc_file, annotation_list)\\n cleaned_records = warc_entry.replace_entity_mentions()\\n end = time.time()\\n print \\\"Time used: \\\", end - start\\n warc_file.close()\\n return cleaned_records\",\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 main():\\n download_insert_title_basics()\\n download_insert_title_principals()\\n download_insert_name_basics()\\n download_insert_title_ratings()\\n scrap_keywords()\\n create_and_insert_soup()\\n return\",\n \"def load_known_corpora():\\n\\n print (\\\"Loading Corpora\\\")\\n\\n # For debugging purposes, comment out the definition of all but say the first\\n # corpus, to speed the ingest time.\\n shenoute_a22 = Corpus()\\n shenoute_a22.annis_corpus_name = \\\"shenoute.a22\\\"\\n shenoute_a22.title = \\\"Acephalous Work 22\\\"\\n shenoute_a22.slug = \\\"acephalous_work_22\\\"\\n shenoute_a22.urn_code = \\\"shenoute.a22\\\"\\n shenoute_a22.github = \\\"https://github.com/CopticScriptorium/corpora/tree/master/shenoute-a22\\\"\\n\\n patrum = Corpus()\\n patrum.annis_corpus_name = \\\"apophthegmata.patrum\\\"\\n patrum.title = \\\"Apophthegmata Patrum\\\"\\n patrum.slug = \\\"ap\\\"\\n patrum.urn_code = \\\"ap\\\"\\n patrum.github = \\\"https://github.com/CopticScriptorium/corpora/tree/master/AP\\\"\\n\\n saof = Corpus()\\n saof.annis_corpus_name = \\\"shenoute.abraham.our.father\\\"\\n saof.title = \\\"Abraham Our Father\\\"\\n saof.slug = \\\"abraham_our_father\\\"\\n saof.urn_code = \\\"shenoute.abraham\\\"\\n saof.github = \\\"https://github.com/CopticScriptorium/corpora/tree/master/abraham\\\"\\n\\n besa_ap = Corpus()\\n besa_ap.annis_corpus_name = \\\"besa.letters\\\"\\n besa_ap.title = \\\"Letter to Aphthonia\\\"\\n besa_ap.slug = \\\"to_aphthonia\\\"\\n besa_ap.urn_code = \\\"besa.aphthonia\\\"\\n besa_ap.github = \\\"https://github.com/CopticScriptorium/corpora/tree/master/besa-letters\\\"\\n\\n fox = Corpus()\\n fox.annis_corpus_name = \\\"shenoute.fox\\\"\\n fox.title = \\\"Not Because a Fox Barks\\\"\\n fox.slug = \\\"not_because_a_fox_barks\\\"\\n fox.urn_code = \\\"shenoute.fox\\\"\\n fox.github = \\\"https://github.com/CopticScriptorium/corpora/tree/master/shenoute-fox\\\"\\n\\n mark = Corpus()\\n mark.annis_corpus_name = \\\"sahidica.mark\\\"\\n mark.title = \\\"Gospel of Mark\\\"\\n mark.slug = \\\"gospel_of_mark\\\"\\n mark.urn_code = \\\"nt.mark\\\"\\n mark.github = \\\"https://github.com/CopticScriptorium/corpora/tree/master/bible\\\"\\n\\n corinth = Corpus()\\n corinth.annis_corpus_name = \\\"sahidica.1corinthians\\\"\\n corinth.title = \\\"1 Corinthians\\\"\\n corinth.slug = \\\"1st_corinthians\\\"\\n corinth.urn_code = \\\"nt.1cor\\\"\\n corinth.github = \\\"https://github.com/CopticScriptorium/corpora/tree/master/bible\\\"\\n\\n snt = Corpus()\\n snt.annis_corpus_name = \\\"sahidica.nt\\\"\\n snt.title = \\\"New Testament\\\"\\n snt.slug = \\\"new-testament\\\"\\n snt.urn_code = \\\"nt\\\"\\n snt.github = \\\"https://github.com/CopticScriptorium/corpora/tree/master/bible\\\"\\n\\n eager = Corpus()\\n eager.annis_corpus_name = \\\"shenoute.eagerness\\\"\\n eager.title = \\\"I See Your Eagerness\\\"\\n eager.slug = \\\"eagernesss\\\"\\n eager.urn_code = \\\"shenoute.eagerness\\\"\\n eager.github = \\\"https://github.com/CopticScriptorium/corpora/tree/master/shenoute-eagerness\\\"\\n\\n besa_nuns = Corpus()\\n besa_nuns.annis_corpus_name = \\\"besa.letters\\\"\\n besa_nuns.title = \\\"Letter to Thieving Nuns\\\"\\n besa_nuns.slug = \\\"to_thieving_nuns\\\"\\n besa_nuns.urn_code = \\\"besa.thieving\\\"\\n besa_nuns.github = \\\"https://github.com/CopticScriptorium/corpora/tree/master/besa-letters\\\"\\n\\n doc_pap = Corpus()\\n doc_pap.annis_corpus_name = \\\"doc.papyri\\\"\\n doc_pap.title = \\\"Documentary Papyri\\\"\\n doc_pap.slug = \\\"papyri\\\"\\n doc_pap.urn_code = \\\"copticDoc:papyri_info\\\"\\n doc_pap.github = \\\"https://github.com/CopticScriptorium/corpora/tree/master/doc-papyri\\\"\\n\\n known_corpora = [shenoute_a22, patrum, saof, besa_ap, fox, mark, corinth, snt, eager, besa_nuns, doc_pap]\\n# known_corpora = [shenoute_a22]\\n \\n for one in known_corpora:\\n try:\\n Corpus.objects.get(slug__exact=one.slug)\\n except Corpus.DoesNotExist:\\n one.save()\",\n \"def process(self):\\n\\n linelang = defaultdict(int)\\n wordlang = defaultdict(int)\\n\\n linefont = defaultdict(int)\\n wordfont = defaultdict(int)\\n\\n inputfiles = self.input_files\\n for input_file in inputfiles:\\n\\n alignurl = input_file.url\\n pcgts = parse(alignurl, True)\\n page = pcgts.get_Page()\\n regions = page.get_TextRegion()\\n\\n for region in regions:\\n lines = region.get_TextLine()\\n\\n for line in lines:\\n try:\\n llang = line.primaryLanguage\\n linelang[llang] += 1\\n except TypeError:\\n pass\\n\\n try:\\n lfont = line.fontFamily\\n linefont[lfont] += 1\\n except TypeError:\\n pass\\n\\n words = line.get_Word()\\n for word in words:\\n try:\\n wlang = word.language\\n wordlang[wlang] += 1\\n except TypeError:\\n pass\\n\\n try:\\n wfont = word.get_TextStyle().fontFamily\\n wordfont[wfont] += 1\\n except TypeError:\\n pass\\n\\n #predominant language\\n try:\\n lang = max(linelang, key=lambda k: linelang[k])\\n except TypeError:\\n try:\\n lang = max(wordlang, key=lambda k: wordlang[k])\\n except TypeError:\\n lang = 'German'\\n\\n #predominant font\\n try:\\n font = max(linefont, key=lambda k: linefont[k])\\n except TypeError:\\n try:\\n font = max(wordfont, key=lambda k: wordfont[k])\\n except TypeError:\\n font = 'Antiqua'\\n\\n\\n print(lang)\\n print(font)\",\n \"def merge_articles(docs_folder):\\n\\n s = \\\"\\\"\\n \\n for doc in os.listdir(docs_folder):\\n try:\\n with open(docs_folder + doc ,'r') as f:\\n\\n lines = f.readlines()\\n raw_doc = \\\"\\\".join(txt for txt in lines)\\n left_idx_headline = [ m.end(0) for m in re.finditer(r\\\"<HEADLINE>\\\",raw_doc)]\\n right_idx_headline = [ m.start(0) for m in re.finditer(r\\\"</HEADLINE>\\\",raw_doc)]\\n\\n left_idx_text = [ m.end(0) for m in re.finditer(r\\\"<TEXT>\\\",raw_doc)]\\n right_idx_text = [ m.start(0) for m in re.finditer(r\\\"</TEXT>\\\",raw_doc)]\\n\\n raw_headline = raw_doc[left_idx_headline[0]:right_idx_headline[0]]\\n raw_text = raw_doc[left_idx_text[0]:right_idx_text[0]]\\n\\n left_idx_paragraph_headline = [ m.end(0) for m in re.finditer(r\\\"<P>\\\",raw_headline)]\\n right_idx_paragraph_headline = [ m.start(0) for m in re.finditer(r\\\"</P>\\\",raw_headline)]\\n\\n left_idx_paragraph_text = [ m.end(0) for m in re.finditer(r\\\"<P>\\\",raw_text)]\\n right_idx_paragraph_text = [ m.start(0) for m in re.finditer(r\\\"</P>\\\",raw_text)]\\n\\n for i in range(len(left_idx_paragraph_headline)):\\n s += raw_headline[left_idx_paragraph_headline[i]:right_idx_paragraph_headline[i]-2] + \\\".\\\"\\n\\n for i in range(len(left_idx_paragraph_text)):\\n s += raw_text[left_idx_paragraph_text[i]:right_idx_paragraph_text[i]-1]\\n except:\\n pass\\n\\n return s\",\n \"def main():\\n token_dict_dict = {}\\n all_dict = {}\\n pronoun_proportion_list = []\\n tag = 'PRP' # base tag for all pronouns, see 'https://www.clips.uantwerpen.be/pages/MBSP-tags' for more info\\n\\n for text in glob.glob(file_loc):\\n file_title = os.path.basename(text).split('.')[0]\\n\\n with open(text, 'r') as f:\\n speech = f.read()\\n text_dict = {}\\n\\n try:\\n #TextBlob goodness that tags all the words for me\\n speech_blob = TextBlob(clean(speech))\\n speech_blob.tags\\n except:\\n #for some reason Trump's address contained a unicode 128 character that I couldn't find\\n #instead of getting rid of it in a single file, i decided to have an except that could catch that case in\\n #all sitations and handle them accordingly\\n\\n #lets the user know that there was an issue, and that it's been handled\\n print file_title,\\n print \\\"contains unexpected unicode characters. they have been removed and the document has been processed\\\"\\n\\n #gets rid of all unicode characters. i could do this by default, but all the other files ran fine\\n #so i didn't think it was worth it\\n speech_blob = TextBlob(clean(speech.decode('unicode_escape').encode('ascii','ignore')))\\n\\n for token in speech_blob.tags:\\n # builds the inital dictionary of data, only looks at words with a specified tag\\n if tag in token[1]:\\n try:\\n text_dict[token[0]] += 1\\n except:\\n text_dict[token[0]] = 1\\n try:\\n all_dict[token[0]] += 1\\n except:\\n all_dict[token[0]] = 1\\n #breaks the title into 3 pieces: number, president, date\\n token_dict_dict[file_title] = text_dict\\n partial_split, date = string.rsplit(file_title, '_', 1)\\n num_pres, pres = string.split(partial_split, '_', 1)\\n\\n pronoun_proportion_list.append(\\n (pres, date, total_to_proportion(pronoun_breakdown(token_dict_dict[file_title])))\\n )\\n create_pronoun_graph(sort_list_by_president_order(pronoun_proportion_list))\",\n \"def main():\\n\\tfl = '/home/rupesh20/ProjectFinal/IITB/conv.txt'\\n\\tPconv = '/home/rupesh20/ProjectFinal/IITB/prevconv.txt'\\n filename = '/home/rupesh20/ProjectFinal/IITB/NEw.txt'\\n outfile = '/home/rupesh20/ProjectFinal/IITB/Demo.txt'\\n outputfile ='/home/rupesh20/ProjectFinal/IITB/Test.txt'\\n\\n\\t\\\"\\\"\\\" \\n\\t\\tOther comments are for debugging.\\n\\t\\\"\\\"\\\"\\n #filename = '/home/rupesh20/ProjectFinal/final/en_US/en_US.blogs.txt'\\n #newfilename = '/home/rupesh20/ProjectFinal/IITB/NEw.txt'\\n #file = open(filename,'r')\\n #newfile = open(newfilename,'w')\\n\\n #text = file.read()\\n #file.close()\\n #words = text.split()\\n\\t\\n #new = []\\n #new=lowercase(words,0,new)\\n #new=url(new,0)\\n #new=unwanted(new)\\n #new=DictLook(new)\\n\\t\\t\\n\\twith open(Pconv) as f:\\n \\t\\tlines = f.readlines()\\n\\t\\\"\\\"\\\" \\n\\t\\tlines : contains list of strings in english for conversion \\n\\t\\\"\\\"\\\"\\n #Writer(new,newfile)\\n #newfile.close()\\n\\n\\t\\\"\\\"\\\" \\n\\t\\tObjects for files are opened\\n\\t\\\"\\\"\\\"\\n\\ttxt = open(fl,'w')\\n Tobj1=open(outputfile,'w')\\n #Ttext1=Tobj1.read()\\n #Tobj1.close()1\\n Nobj=open(filename,'r')\\n Ntext=Nobj.read()\\n Nobj.close()\\n Nobj1=open(outfile,'r')\\n Ntext1=Nobj1.read()\\n Nobj1.close()\\n\\t\\\"\\\"\\\" \\n\\t\\tSentences are Tokenized \\n\\t\\\"\\\"\\\"\\n sentences=sent_tokenize(Ntext)\\n testSentences=sent_tokenize(Ntext1)\\n \\n Csize=0\\n\\t\\\"\\\"\\\" \\n\\t\\tAfter Tokenize the text in sentences \\n\\t\\tWe have calculated the Ngram \\n\\t\\t1,2 3,4,5 grams\\n\\t\\tGram1 list contains uni-gram\\n\\t\\t||ly Gram2,3,4,5 contains further grams\\n\\t\\t\\n\\t\\\"\\\"\\\"\\n for sent in sentences:\\n for j in xrange(1,6):\\n Csize= Ngram(sent,j,Csize)\\n print Csize \\n FreqCount(Gram)\\n for sent in testSentences:\\n TestNgram(sent,5)\\n #print Gram5\\n #print Sliced(x)\\n \\n\\t\\\"\\\"\\\"\\\" \\n\\t\\tCalcScore : function to ecaluate the score using the \\n\\t\\t\\t stupid backoff algo\\n\\t\\toutput file : generated after the calcscore module , sent : Score\\n\\t\\t\\t\\n\\t\\\"\\\"\\\"\\n\\tCalcScore(Csize,Tobj1)\\n\\n\\t\\\"\\\"\\\" \\n\\t\\tProcesscontent : module is for Alignment, S-O-V \\n\\t\\t\\t\\tdetermization.\\n\\t\\n\\t\\\"\\\"\\\"\\n\\n\\t\\\"\\\"\\\" \\n\\t\\tbelow code translate, del list[:]( delete whole list )\\n\\t\\n\\t\\\"\\\"\\\"\\n\\tfor line in lines:\\n\\t\\ttemp = []\\n\\t\\ttemp.append(line)\\t\\n\\t\\tprocessContent(temp)\\n\\t\\tfor i in Subj:\\n\\t\\t\\tBilingDict(txt,i)\\n\\t\\tfor i in obj:\\n\\t\\t\\tBilingDict(txt,i)\\n\\t\\tfor i in verb:\\n\\t\\t\\tBilingDict(txt,i)\\t\\n\\t\\tdel Subj[:]\\n\\t\\tdel verb[:]\\n\\t\\tdel obj[:]\\n\\t\\t\\n\\t\\ttxt.write('\\\\n')\\n\\n\\t#print Subj[0] \\n\\t#print verb\\n\\t#print obj\\n\\t\\\"\\\"\\\" \\n\\t\\tBilingdict : module contain JSON lookup table.\\n\\t\\t\\t\\tgenrates a file with HINDI text.\\n\\n\\t\\\"\\\"\\\"\\n\\t\\t\\t \\n\\t#print StupidBackoff(x,Sliced(x),Csize)\\n #print CountGram1\\n #print CountGram2\\n #print CountGram3\\n #print CountGram4\\n #freq.plot(50)\",\n \"def main() -> None:\\n\\n # Retrieving the wiki URL\\n url = get_api_url()\\n print(url)\\n\\n # Creates file if it does not exist\\n open(TEXT_FILE, \\\"a\\\")\\n\\n with open(TEXT_FILE, \\\"r\\\") as f:\\n last_title = f.readline().strip()\\n print(\\\"Starting from:\\\", last_title)\\n\\n # Retrieving the pages JSON and extracting page titles\\n pages_json = get_pages_json(url, last_title)\\n pages = pages_json[\\\"query\\\"][\\\"allpages\\\"]\\n print(\\\"Pages to be scanned:\\\", pages)\\n\\n # Tagging operations\\n for page in pages:\\n curr_title = page[\\\"title\\\"]\\n cats_to_add = get_categories(curr_title)\\n if cats_to_add:\\n print(\\\"Adding categories\\\", cats_to_add, \\\"to '%s'\\\" % curr_title)\\n for cat in cats_to_add:\\n add_category(curr_title, \\\"[[Category:\\\" + cat + \\\"]]\\\")\\n\\n # Extracting title to continue iterating from next run\\n if \\\"continue\\\" in pages_json:\\n continue_from_title = pages_json[\\\"continue\\\"][\\\"apcontinue\\\"]\\n print(\\\"Continuing from:\\\", continue_from_title, \\\"next run.\\\")\\n else:\\n continue_from_title = \\\"\\\"\\n\\n with open(TEXT_FILE, \\\"w+\\\") as f:\\n f.write(continue_from_title)\\n print(\\\"Wrote\\\", continue_from_title, \\\"in\\\", TEXT_FILE)\\n\\n print(\\\"No pages left to be tagged\\\")\",\n \"def main():\\n files = init.file_list\\n citations = load_citations.load_files(files)\\n citations_a = citations[0]\\n citations_b = citations[1]\\n\\n common_citations, num_common = compare_citations.common_citations(citations_a, citations_b)\\n save_citations.save_citations(common_citations)\",\n \"def build_training_data():\\r\\n for i in range(len(FILE_NAMES)):\\r\\n input_text = read_file(FILE_NAMES[i])\\r\\n list_of_word_lines = limiting_sentence_length(input_text)\\r\\n data = create_training_data_file(list_of_word_lines, LANGUAGE[i])\\r\\n write_training_data(data, LANGUAGE[i])\\r\\n merge_training_data()\",\n \"def Main(root_directory):\\n filepaths = GetAllFilepaths(root_directory)\\n for filepath in filepaths:\\n parser = fileparser.CreateParser(filepath)\\n if not parser:\\n ReportWarning('cannot find a parser for file %s, skipping...' %\\n filepath)\\n continue\\n old_file_contents = ReadFileIntoString(filepath)\\n comment_blocks = parser.FindAllCommentBlocks(old_file_contents)\\n if not comment_blocks:\\n ReportWarning('cannot find any comment blocks in file %s' %\\n filepath)\\n old_copyright_block = parser.FindCopyrightBlock(comment_blocks)\\n if not old_copyright_block:\\n ReportWarning('cannot find copyright block in file %s' % filepath)\\n (year, holder) = parser.GetCopyrightBlockAttributes(old_copyright_block)\\n if holder and not ConfirmAllowedCopyrightHolder(holder):\\n ReportWarning(\\n 'unrecognized copyright holder \\\"%s\\\" in file %s, skipping...' % (\\n holder, filepath))\\n continue\\n new_copyright_block = parser.CreateCopyrightBlock(year, holder)\\n if old_copyright_block:\\n new_file_contents = old_file_contents.replace(\\n old_copyright_block, new_copyright_block, 1)\\n else:\\n new_file_contents = new_copyright_block + old_file_contents\\n WriteStringToFile(new_file_contents, filepath)\",\n \"def test2_basic_info(self):\\n\\t\\tprint \\\"\\\\nTEST 2: Extracting basic info from each ontology in %s folder.\\\\n=================\\\" % DATA_FOLDER\\n\\n\\t\\tfor f in os.listdir(DATA_FOLDER):\\n\\t\\t\\tif not f.startswith('.'):\\n\\t\\t\\t\\tprint \\\"Loading... >\\\", f\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t# divert output to a file temporarily \\n\\t\\t\\t\\tsaveout = sys.stdout \\n\\t\\t\\t\\tfsock = open('out.log', 'w') \\n\\t\\t\\t\\tsys.stdout = fsock \\n\\t\\t\\t\\t\\n\\t\\t\\t\\to = ontospy.Ontology(DATA_FOLDER + f)\\n\\t\\t\\t\\tprintBasicInfo(o)\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tsys.stdout = saveout\\n\\t\\t\\t\\tfsock.close()\\n\\t\\t\\t\\tprint \\\"Success.\\\"\",\n \"def createStructuredTranscript_Non_Core_Doc():\\n\\n #create a temporary folder that will hold the data transformed from doc to docx\\n os.system('mkdir ' + INPUT_FOLDER+'temp')\\n\\n core_doc_asset = []\\n missing_count = 0\\n missing_files=[]\\n # get all the docx files that are part of the core asset\\n for file in glob.glob(INPUT_FOLDER+\\\"*.doc\\\"):\\n\\n # RG numbers for the core asset\\n if (\\\"RG-50.030\\\" not in file and\\n \\\"RG-50.106\\\" not in file and\\n \\\"RG-50.549\\\" not in file):\\n \\n\\n \\n # convert file to docx, storing it in an untracked folder called temp\\n file_docx = file + 'x'\\n command = 'textutil -convert docx ' + file + ' -output ' + INPUT_FOLDER+'temp/'+ file_docx.split('/')[-1]\\n call(command, shell=True)\\n\\n # append to the array\\n core_doc_asset.append(file_docx)\\n \\n\\n \\n\\n # get the units for each file, store them and update tracker\\n core_doc_asset=create_dictionary_of_file_list(core_doc_asset)\\n \\n not_processed=0\\n processed_doc=0\\n \\n # get the units for each file, store them and update tracker \\n for mongo_rg in core_doc_asset:\\n # get text units for this entry\\n processed=[]\\n result=[]\\n \\n for file in core_doc_asset[mongo_rg]:\\n \\n \\n \\n units = getTextUnits(INPUT_FOLDER+'temp/'+file.split('/')[-1])\\n \\n if units:\\n #replace white spaces\\n for i,element in enumerate(units):\\n units[i]['unit']=' '.join(element['unit'].split())\\n result.extend(units)\\n \\n processed.append(True)\\n else:\\n #check if processed\\n processed.append(False)\\n\\n #set the method used to transform the transcript\\n h.update_field(DB, TRACKER, \\\"rg_number\\\", mongo_rg, \\\"method\\\", \\\"transcribe_non_core_doc\\\")\\n\\n not_processed=not_processed+1\\n\\n if False in processed:\\n\\n h.update_field(DB, TRACKER, \\\"rg_number\\\", mongo_rg, \\\"status\\\", \\\"Unprocessed\\\")\\n not_processed=not_processed+1\\n missing_files.append(' '.join(core_doc_asset[mongo_rg]))\\n else:\\n # insert units on the output collection\\n h.update_field(DB, OUTPUT, \\\"shelfmark\\\", 'USHMM '+mongo_rg, \\\"structured_transcript\\\", result)\\n\\n \\n # update status on the stracker\\n \\n h.update_field(DB, TRACKER, \\\"rg_number\\\", mongo_rg, \\\"status\\\", \\\"Processed\\\")\\n processed_doc=processed_doc+1\\n \\n\\n #delete the temporary folder\\n os.system('rm -r ' + INPUT_FOLDER+'temp')\\n\\n \\n #write the missing files to text file\\n file = open(OUTPUT_FOLDER_USHMM_PROCESSING_LOGS+'transcribe_non_core_doc_failed.txt','w')\\n file.write('\\\\n'.join(missing_files))\\n\\n \\n # success\\n pprint.pprint(\\\"Non-core doc files were successfully processed, but there are \\\" + str(missing_count) + \\\" missing\\\")\",\n \"def main():\\n # %%\\n CFG.profiles_yamls_path.mkdir(parents=True, exist_ok=True)\\n fpaths = list( _Config.raw_profiles_path.glob('*.html') )\\n print( f'{len(fpaths)} htmls found' )\\n # %%\\n fpath = CFG.raw_profiles_path / 'luis-mario-urrea-murillo.html'\\n # %%\\n fpath = CFG.raw_profiles_path / 'cristian-david-montoya-saldarriaga-09638514a.html'\\n # %%\\n fpaths = [ CFG.raw_profiles_path / 'ricardo-alarcon-44079b105.html' ]\\n # %%\\n fpaths = [ Path('/home/teo/_data/talent/linkedin_raw_profiles/israellaguan.html')]\\n # %%\\n dics = {}\\n # %%\\n\\n for i, fpath in enumerate(fpaths):\\n if fpath in dics:\\n continue\\n\\n with fpath.open('rt') as f_in:\\n html = f_in.read()\\n\\n print( f'\\\\n***{i+1}/{len(fpaths)} {fpath.name}:')\\n dic = extract_one( html, fpath )\\n dic['linkedin_url'] = f\\\"https://www.linkedin.com/in/{fpath.name.split('.')[0]}\\\"\\n dic['scraped_at'] = dt.datetime.fromtimestamp( fpath.stat().st_ctime )\\n # pprint(dic['work_stats'])\\n dics[fpath] = dic\\n\\n dics_arr = list(dics.values())\\n # %%\\n del dics\\n # %%\\n\\n with (CFG.profiles_yamls_path / 'all_profiles.json').open('wt') as f_out:\\n json.dump( dics_arr, f_out, cls=DateTimeEncoder, indent=4 )\\n # %%\\n with (CFG.profiles_yamls_path / 'all_profiles.yaml').open('wt') as f_out:\\n yaml.safe_dump( dics_arr, f_out )\\n # %%\\n df = produce_summary_table( dics_arr )\\n df.to_excel( CFG.raw_profiles_path.parent / 'mined_ruby_candidates_sample.xlsx',\\n index=False)\\n # %%\",\n \"def mk_eng_txt_files(self, **rewrite):\\n\\n\\n print \\\"mk_eng_txt_files: RETRIEVING PAGE_LIST..........\\\"\\n page_list = c_m.l_of_l_read(self.page_list_path)\\n\\n if len(page_list) < 1: # handling empty page_list case\\n print \\\"mk_eng_txt_files: PAGE LIST IS NOT POPULATED, RUN HTML_File_Maker AND Text_Extractor MODULES FIRST\\\"\\n else: # handling page_list for partially transalted sites\\n print \\\"mk_eng_txt_files: IN CASE PAGE LIST ALREADY HAD SOME ENG_TEXT ENTRIES SETTING INITIAL new_page_list TO LAST KNOWN PAGE_LIST VERSION\\\"\\n self.new_page_list = copy(page_list)\\n\\n\\n\\n # iterating throug unique text per page txts\\n for data_set in page_list:\\n self.p_text_f_name = data_set[2]\\n print \\\"mk_eng_txt_files: TRANSLATING TEXT FROM FILE %s\\\" % self.p_text_f_name\\n\\n self.eng_p_text_f_name = \\\"eng_\\\" + self.p_text_f_name\\n self.eng_p_text_f_path = self.text_eng_folder_path + self.eng_p_text_f_name\\n\\n self.page_text = c_m.simply_read(self.text_folder_path, self.p_text_f_name)\\n\\n # if page has less than 10 symbols it is not translated\\n if len(self.page_text) < 10:\\n print \\\"mk_eng_txt_files: NOT WORTH TRANSLATING, WRITING AS IS AND SKIPPING...\\\"\\n c_m.simply_write(self.page_text, self.eng_p_text_f_path)\\n\\n elif len(self.page_text) > self.max_page_length:\\n print \\\"mk_eng_txt_files: PAGE TEXT IS TOO LONG DEVIDING TO PARTS, TRANSLATING AND GETTING BACK FULL PAGE TEXT\\\"\\n text_output = self.get_text_parts(**rewrite)\\n\\n else: # 10 < len(page_text) < 2000\\n\\n if rewrite[\\\"rewrite\\\"]:\\n print \\\"mk_eng_txt_files: TRANSLATING IN REWRITE MODE\\\"\\n text_output = self.get_text()\\n\\n\\n elif not os.path.exists(self.eng_p_text_f_path):\\n print \\\"mk_eng_txt_files: TRANSLATING IN ONLY ONCE MODE\\\"\\n text_output = self.get_text()\\n\\n else:\\n print \\\"mk_eng_txt_files: SKIPPING FILE, ALREADY TRANSLATED\\\"\\n # continue\\n\\n # print \\\"WRITING TRANSLATED OUTPUT TO FILE: \\\", self.eng_p_text_f_name\\n # c_m.simply_write(text_output, self.text_eng_folder_path, self.eng_p_text_f_name) \\n data_set.append(self.eng_p_text_f_name) # updating dataset with eng_text file name\\n self.new_page_list.append(data_set) # updating page list with updated entry\\n\\n\\n print \\\"mk_eng_txt_files: DONE TRANSLATING SITE %s \\\" % self.domain\\n print \\\"mk_eng_txt_files: UPDATING PAGE LIST WITH ENG TEXT FILE NAMES\\\"\\n c_m.l_of_l_write(self.new_page_list, self.page_list_path)\\n print \\\"mk_eng_txt_files: SITE TRANSLATION FINISHED, CLOSING CHROME WEBDIRVER\\\"\\n self.loaded_driver.quit()\",\n \"def preMain(dictADT):\\r\\n data_addr = \\\"..\\\\\\\\csv\\\\\\\\normal.csv\\\"\\r\\n\\r\\n userPref = dictADT\\r\\n mainPref(\\\"base.txt\\\", userPref)\\r\\n\\r\\n sample_path = \\\"base.txt\\\"\\r\\n webing(sample_path, data_addr)\\r\\n\\r\\n path_in = \\\"base.txt\\\"\\r\\n path_out = \\\"..\\\\\\\\resultHTML\\\\\\\\result.html\\\"\\r\\n mover(path_in, path_out)\\r\\n\\r\\n baseDel(\\\"base.txt\\\")\\r\\n # baseDel(\\\"resultHTML\\\\\\\\result.html\\\")\\r\\n baseDel(\\\"..\\\\\\\\csv\\\\\\\\normal.csv\\\")\\r\\n return True\",\n \"def main():\\n\\n # Remove the files if existing before creating new ones\\n OUT_VOCAB = \\\"results/vocabs.txt\\\"\\n OUT_SYN = \\\"results/syn.txt\\\"\\n OUT_EXCP = \\\"results/exception.txt\\\"\\n for f in [OUT_SYN, OUT_VOCAB, OUT_EXCP]:\\n if os.path.isfile(f):\\n os.remove(f)\\n\\n \\\"\\\"\\\"\\n Now we do the recursion:\\n Step 1: Find the children link;\\n Step 2: Add to the url_list;\\n Step: Move to next entry of the url_list and Go to Step 1;\\n \\\"\\\"\\\"\\n with open(DATA, \\\"r\\\") as fin:\\n url_list = fin.readlines()\\n\\n # Write down the urls as tuples, 0 as the first level\\n url_list = [((PREFIX_URL + subURL).strip(), 0) for subURL in url_list]\\n\\n with open(OUT_VOCAB, \\\"w\\\") as vocab_file, \\\\\\n open(OUT_SYN, \\\"w\\\") as syn_file, \\\\\\n open(OUT_EXCP, \\\"w\\\") as exp_file:\\n cur_index = 0\\n while cur_index < len(url_list):\\n # Current url of the term, current level number (hierarchy) of the term\\n cur_url, cur_hrc = url_list[cur_index]\\n try:\\n # Get the current sub-url\\n time.sleep(0.1)\\n r = requests.get(cur_url + \\\"?\\\" + API_KEY)\\n source_dict = json.loads(r.text)\\n\\n # Getting the vocabulary and the synonym from this page.\\n vocab, syn = parse_words(source_dict=source_dict)\\n if cur_hrc + 1 < int(level):\\n # Fetch all the children's name and url\\n children_urls = fetch_children_url(source_dict=source_dict, hrc=cur_hrc + 1)\\n # print children_urls\\n url_list = url_list + children_urls # Concatenate the lists\\n\\n if vocab is not None:\\n syn_file.write(json.dumps({vocab: syn}) + \\\"\\\\n\\\")\\n vocab_file.write(vocab + \\\"\\\\n\\\")\\n\\n if cur_index % 100 == 0:\\n print \\\"\\\\tNow at index # {}, {} in total.\\\".format(str(cur_index), str(len(url_list)))\\n except UnicodeEncodeError as unicode_err:\\n exp_file.write(str(unicode_err))\\n except KeyError as key_err:\\n exp_file.write(str(key_err))\\n except:\\n pass\\n cur_index += 1\",\n \"def main():\\n with open(\\\"page_data.yaml\\\", 'r') as inputstr:\\n config_data = yaml.safe_load(inputstr)\\n ointf = OutputInterface('template.txt')\\n table_data = get_song_artist_matches()\\n ofilen = config_data['directory'] + os.sep + 'common_songs.html'\\n title = 'Song Titles and Band Name Overlap'\\n header = ['No.', 'Artist', 'Peak', 'Date', 'Song/Artist', 'Peak',\\n 'Date', 'Song']\\n ointf.build_page(ofilen, title, header, fmt_table(table_data))\\n ointf.inject(XTRAEDIT)\\n ointf.output()\",\n \"def test_project(self, doi_dataset, doi_bib, orcid, metadata_parser):\\n apply_mock(doi_dataset, doi_bib, orcid, metadata_parser)\\n os.chdir(\\\"input/\\\")\\n _set_args(\\\"-i\\\",\\\"yamls/project.yaml\\\",\\\"-o\\\",\\\"../out\\\")\\n with HiddenPrints():\\n ya2ro.main()\\n\\n data = yaml.load(\\\"yamls/project.yaml\\\", Loader=SafeLoader)\\n with open(\\\"../out/project/index-en.html\\\") as f:\\n web = f.read()\\n\\n self.assert_data_in_web(data, web)\",\n \"def index_file(self, file_name):\\n self.contents = []\\n article_text = \\\"\\\"\\n article_annots = [] # for annot-only index\\n\\n f = open(file_name, \\\"r\\\")\\n for line in f:\\n line = line.replace(\\\"#redirect\\\", \\\"\\\")\\n # ------ Reaches the end tag for an article ---------\\n if re.search(r'</doc>', line):\\n # ignores null titles\\n if wiki_uri is None:\\n print \\\"\\\\tINFO: Null Wikipedia title!\\\"\\n # ignores disambiguation pages\\n elif (wiki_uri.endswith(\\\"(disambiguation)>\\\")) or \\\\\\n ((len(article_text) < 200) and (\\\"may refer to:\\\" in article_text)):\\n print \\\"\\\\tINFO: disambiguation page \\\" + wiki_uri + \\\" ignored!\\\"\\n # ignores list pages\\n elif (wiki_uri.startswith(\\\"<wikipedia:List_of\\\")) or (wiki_uri.startswith(\\\"<wikipedia:Table_of\\\")):\\n print \\\"\\\\tINFO: List page \\\" + wiki_uri + \\\" ignored!\\\"\\n # adds the document to the index\\n else:\\n self.__add_to_contents(Lucene.FIELDNAME_ID, wiki_uri, Lucene.FIELDTYPE_ID)\\n if self.annot_only:\\n self.__add_to_contents(Lucene.FIELDNAME_CONTENTS, article_annots, Lucene.FIELDTYPE_ID_TV)\\n else:\\n self.__add_to_contents(Lucene.FIELDNAME_CONTENTS, article_text, Lucene.FIELDTYPE_TEXT_TVP)\\n self.lucene.add_document(self.contents)\\n self.contents = []\\n article_text = \\\"\\\"\\n article_annots = []\\n\\n # ------ Process other lines of article ---------\\n tag_iter = list(self.tagRE.finditer(line))\\n # adds line to content if there is no annotation\\n if len(tag_iter) == 0:\\n article_text += line\\n continue\\n # A tag is detected in the line\\n for t in tag_iter:\\n tag = t.group(3)\\n if tag == \\\"doc\\\":\\n doc_title = self.titleRE.search(t.group(2))\\n wiki_uri = WikipediaUtils.wiki_title_to_uri(doc_title.group(1)) if doc_title else None\\n if tag == \\\"a\\\":\\n article_text += t.group(1) + t.group(4) # resolves annotations and replace them with mention\\n # extracts only annotations\\n if self.annot_only:\\n link_title = self.linkRE.search(t.group(2))\\n link_uri = WikipediaUtils.wiki_title_to_uri(unquote(link_title.group(1))) if link_title else None\\n if link_uri is not None:\\n article_annots.append(link_uri)\\n else:\\n print \\\"\\\\nINFO: link to the annotation not found in \\\" + file_name\\n last_span = tag_iter[-1].span()\\n article_text += line[last_span[1]:]\\n f.close()\",\n \"def main():\\n\\n global APP_NAME\\n\\n # Defines Argument Parser and fefines flags and expected inputs\\n parser = argparse.ArgumentParser(\\n description='Converts specified html files or all html files to \\\\\\n\\t\\t\\tdjango format within a \\\\n specified directory.'\\n )\\n # Defines the -f flag, standing for files, to gey file nameof the HTML\\n # file to convert\\n parser.add_argument(\\n 'files',\\n metavar='f',\\n type=str,\\n nargs='*',\\n help='provide file names to convert'\\n )\\n # Defines the -a flag, for defining the APP_NAME, you want the file\\n # converted to, for.\\n parser.add_argument(\\n '-a',\\n dest='app_name',\\n type=str,\\n nargs='?',\\n help='provide django app name'\\n )\\n # Defines the -d flag, standing for directory, which accepts the path\\n # to a directory containing the files to be translated\\n parser.add_argument(\\n '-d',\\n dest='base_directory',\\n type=str,\\n nargs='?',\\n help='Provide base directory'\\n )\\n\\n # Parse the Arguments from the user\\n args = parser.parse_args()\\n\\n # Deconstruct the arguments from the parser\\n files = args.files\\n directory = args.base_directory\\n app_name = args.app_name\\n\\n # If APP_NAME is not passes in as an argument, leave it as ''(empty)\\n if app_name is not None:\\n APP_NAME = app_name + \\\"/\\\"\\n\\n # If directory is not passed in as an argument, use the current working\\n # directory to fetch files\\n if directory is None:\\n directory = os.getcwd()\\n\\n logging.info(\\\"Directory : \\\" + str(directory))\\n logging.info(\\\"app_name : \\\" + str(app_name))\\n\\n # Check if the directory passed in as argument already has the directory \\n # 'Modified_files', else create it.\\n if not os.path.exists(os.path.join(directory, \\\"Modified_files\\\")):\\n os.mkdir(os.path.join(directory, \\\"Modified_files\\\"))\\n\\n if files != []:\\n for file in files:\\n processFile(directory, directory + \\\"/\\\" + file, file)\\n\\n else:\\n # If no file was passed in as input, then extract all files in the \\n # directory passed in, with extension '.html'\\n for file in os.listdir(directory):\\n if file.endswith(\\\".html\\\"):\\n processFile(directory, directory + \\\"/\\\" + file, file)\",\n \"def main(base_path):\\n current = os.getcwd()\\n try:\\n if not(os.path.exists(base_path)):\\n ans = 'y'\\n if p_out:\\n print(\\\"Do you want to create \\\" + base_path + \\\"?(y/n)\\\")\\n ans = sys.stdin.read(1)\\n print(\\\"\\\")\\n if ans in ('y', 'Y'):\\n pass\\n elif ans in ('n', 'N'):\\n raise NoneOutput\\n else:\\n raise InputError\\n else:\\n m_path = os.path.join(base_path, 'nzmath/manual')\\n if os.path.exists(m_path):\\n ans = 'y'\\n if p_out:\\n print(\\\"Do you want to remove \\\" + m_path + \\\"?(y/n)\\\")\\n ans = sys.stdin.read(1)\\n print(\\\"\\\")\\n if ans in ('y', 'Y'):\\n for root, dirs, files in os.walk(m_path, topdown=False):\\n for name in files:\\n os.remove(os.path.join(root, name))\\n for name in dirs:\\n os.rmdir(os.path.join(root, name))\\n elif ans in ('n', 'N'):\\n raise NoneOutput\\n else:\\n raise InputError\\n dirname = os.path.join(base_path, 'nzmath/manual/modules')\\n if not(os.path.exists(dirname)):\\n os.makedirs(dirname)\\n os.chdir(os.path.join(base_path, 'nzmath/manual/'))\\n csspage = convertHPURL('manual/default.css')\\n if p_out:\\n print(\\\"get css from \\\" + csspage)\\n retryConnection(urllib.request.urlretrieve, csspage, 'default.css')\\n while ad_list:\\n files = ad_list.pop()\\n MyWikiParser(files).feeds()\\n if p_out:\\n print(\\\"\\\\n\\\" + \\\"All process is done!\\\" + \\\"\\\\n\\\")\\n print(\\\"Ok, now created nzmath-current manual located to\\\")\\n print(os.path.join(base_path, \\\"nzmath\\\"))\\n print(\\\"if you check difference between nzmath-cvs manual, with GNU diff,\\\")\\n print(\\\"$ diff -ubBr /tmp/nzmath/manual {your-nzmathcvs-repo}/manual\\\")\\n print(\\\"or you check only new version files,\\\")\\n print(\\\"$ diff -r --brief /tmp/nzmath/manual {your-nzmathcvs-repo}/manual .\\\")\\n except NoneOutput:\\n if p_out:\\n print('end.')\\n except InputError:\\n print(\\\"Error: Invalid input!\\\")\\n except LookupError:\\n print(\\\"Error: Maybe, Japanese encodings(ex.euc_jp) is not supported.\\\")\\n except:\\n if p_out:\\n print(\\\"Check \\\" + base_path + \\\" (dir? truly path? and so on.)\\\")\\n print(\\\"Delete \\\" + base_path + \\\" and try again.\\\")\\n print(\\\"(Maybe, caused by problem of network connection)\\\\n\\\")\\n print(sys.exc_info()[0])\\n os.chdir(current)\",\n \"def parse_and_analyse_corenlp_coref(input_dir = 'CoreNLP_coref_anno/dev', gold_annotations_folder = '../../../data/baseline/dev'):\\n\\tmentions = []\\n\\n\\n\\twith open('coref_analyse_output.txt', 'w') as out_file:\\n\\n\\t\\tfor file_name in os.listdir(input_dir):\\n\\t\\t\\tif re.match(r'(.+)\\\\.xml', file_name)!= None:\\n\\t\\t\\t\\tokr_graph = load_graph_from_file(gold_annotations_folder + '/'+ re.match(r'(.+)\\\\.xml', file_name).group(1)[:-4]+'.xml')\\n\\n\\t\\t\\t\\ttree = ET.parse(input_dir + '/' + file_name)\\n\\t\\t\\t\\tdocument = tree.getroot()[0]\\n\\t\\t\\t\\tsentence_wise_predicted_mentions = defaultdict(list)\\n\\t\\t\\t\\tsentence_wise_gold_mentions = defaultdict(list)\\n\\t\\t\\t\\tpredicted_coref_dict = defaultdict(list)\\n\\t\\t\\t\\tgold_coref_dict = defaultdict(list)\\n\\n\\t\\t\\t\\tcoref_node = document.find('coreference')\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tfor coref_id, coref_chain in enumerate(coref_node):\\n\\t\\t\\t\\t\\tfor mention in coref_chain:\\n\\t\\t\\t\\t\\t\\tsent_num = int(mention[0].text)\\n\\t\\t\\t\\t\\t\\tstart = int(mention[1].text)-1\\n\\t\\t\\t\\t\\t\\tend = int(mention[2].text)-1\\n\\t\\t\\t\\t\\t\\ttext = mention[4].text\\n\\t\\t\\t\\t\\t\\tsentence_wise_predicted_mentions[sent_num].append({\\\"indices\\\":range(start, end),\\\"coref\\\":coref_id+1, \\\"text\\\":text})\\n\\t\\t\\t\\t\\t\\tpredicted_coref_dict[coref_id+1].append({\\\"indices\\\":range(start, end), \\\"s_num\\\":sent_num, \\\"text\\\":text })\\n\\n\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\t\\t\\t\\t\\n\\t\\t\\t\\tfor entity in okr_graph.entities.values():\\n\\t\\t\\t\\t\\tfor mention in entity.mentions.values():\\n\\t\\t\\t\\t\\t\\tsentence_wise_gold_mentions[mention.sentence_id].append({\\\"indices\\\":mention.indices,\\\"coref\\\":entity.id, 'text':mention.terms})\\n\\n\\t\\t\\t\\tprint'###'+ file_name + '\\\\n'\\t\\n\\t\\t\\t\\tfor sentence_id, sentence in enumerate(okr_graph.sentences.values()):\\n\\t\\t\\t\\t\\tprint 'Sentence: ', ' '.join(sentence) \\n\\t\\t\\t\\t\\tprint 'Predicted entities: ', [element['text'] for element in sentence_wise_predicted_mentions[sentence_id+1]]\\n\\t\\t\\t\\t\\tprint 'Gold entities: ', [element['text'] for element in sentence_wise_gold_mentions[sentence_id+1]]\\n\\t\\t\\t\\t\\tprint ' '\\n\\t\\t\\t\\n\\t\\t\\t\\tprint \\\"Not printing singletons\\\"\\n\\t\\t\\t\\tprint('\\\\nThe predicted clusters: ')\\n\\t\\t\\t\\tfor cluster_id, cluster in enumerate(predicted_coref_dict.values()):\\n\\t\\t\\t\\t\\tprint('Cluster id: ', cluster_id +1)\\n\\t\\t\\t\\t\\tprint([[okr_graph.sentences[mention['s_num']][index] for index in mention['indices']]for mention in predicted_coref_dict[cluster_id+1]] )\\n\\n\\t\\t\\t\\tprint('\\\\n The Gold clusters:')\\t\\n\\t\\t\\t\\tfor entity in okr_graph.entities.values():\\n\\t\\t\\t\\t\\tprint('cluster_id: ', entity.id )\\n\\t\\t\\t\\t\\tprint([mention.terms for mention in entity.mentions.values()])\\n\\n\\t\\t\\t\\tprint '**********'\",\n \"def main():\\n\\n for lang, family, host, db, footer, summary in projects:\\n wiki = wikipedia.Site(lang, family)\\n conn = MySQLdb.connect(host=host, db=db, read_default_file='~/.my.cnf', use_unicode=True)\\n cursor = conn.cursor()\\n cursor.execute(\\\"SELECT page_title from page where page_namespace=0 and page_title regexp \\\\\\\".*[–-].*\\\\\\\";\\\")\\n row = cursor.fetchone()\\n endashes = sets.Set()\\n hyphens = sets.Set()\\n while row:\\n pagetitle = re.sub(ur\\\"_\\\", ur\\\" \\\", unicode(row[0], \\\"utf-8\\\"))\\n if re.search(ur\\\"–\\\", pagetitle) and not re.search(ur\\\"[-\\\\(\\\\)]\\\", pagetitle): #descartamos las que tienen paréntesis, (cación) (desambiguación)...\\n endashes.add(pagetitle)\\n if not re.search(ur\\\"–\\\", pagetitle) and re.search(ur\\\"-\\\", pagetitle):\\n hyphens.add(pagetitle)\\n row = cursor.fetchone() \\n cursor.close()\\n conn.close()\\n\\n print len(endashes), \\\"endashes\\\"\\n\\n for pagetitle in endashes:\\n footer2=footer\\n pagetitle_ = re.sub(ur\\\"–\\\", ur\\\"-\\\", pagetitle)\\n if pagetitle_ not in hyphens:\\n #creamos\\n redirect = wikipedia.Page(wiki, pagetitle_)\\n if not redirect.exists():\\n time.sleep(3)\\n target = wikipedia.Page(wiki, pagetitle)\\n if target.exists():\\n if target.isRedirectPage():\\n footer2=\\\"\\\"\\n target = target.getRedirectTarget()\\n if target.exists() and not target.isRedirectPage():\\n redirect.put(ur\\\"#REDIRECT [[%s]]%s\\\" % (target.title(), footer2), u\\\"BOT - %s [[%s]]\\\" % (summary, target.title()))\\n print ur\\\"#REDIRECT [[%s]]%s\\\" % (target.title(), footer2)\\n else:\\n pass #demasiadas redirecciones anidadas\\n else:\\n redirect.put(ur\\\"#REDIRECT [[%s]]%s\\\" % (target.title(), footer2), u\\\"BOT - %s [[%s]]\\\" % (summary, target.title()))\\n print ur\\\"#REDIRECT [[%s]]%s\\\" % (target.title(), footer2)\",\n \"def derive_project(\\n session_directory, corpus_filter, session_filter, pre_select=1, post_select=0\\n):\\n # Change to the session directory\\n with change_dir(session_directory):\\n # Get the parent directory of the session directory\\n parent_directory = os.path.dirname(session_directory)\\n\\n # Check if 'project.xml' exists in the parent directory\\n if \\\"project.xml\\\" not in os.listdir(parent_directory):\\n raise FileNotFoundError(\\\"project.xml not found in parent project directory\\\")\\n\\n # Determine excluded 'Pre' files based on the pre-select option\\n if pre_select == 1:\\n # Exclude 'Pre' files with a single digit\\n exclude_pre_list = [\\n e for e in os.listdir(session_directory) if re.search(r\\\"Pre\\\\s?\\\\d\\\", e)\\n ]\\n if pre_select == 2:\\n # Exclude 'Pre' files with a single digit and rename files with ' 2' suffix\\n multi_pre_list = [\\n e for e in os.listdir(session_directory) if re.search(r\\\"Pre\\\\s?\\\\d\\\", e)\\n ]\\n exclude_pre_list = [e.replace(\\\" 2\\\", \\\"\\\") for e in multi_pre_list]\\n\\n # Determine excluded 'Post' files based on the post-select option\\n if post_select == 1:\\n # Exclude 'Post II' files\\n exclude_post_list = [\\n e for e in os.listdir(session_directory) if re.search(r\\\"Post\\\\s?II\\\", e)\\n ]\\n if post_select == 2:\\n # Exclude 'Post II' files and remove ' II' suffix\\n multi_post_list = [\\n e for e in os.listdir(session_directory) if re.search(r\\\"Post\\\\s?II\\\", e)\\n ]\\n exclude_post_list = [e.replace(\\\" II\\\", \\\"\\\") for e in multi_post_list]\\n # Create corpus folders\\n for corpus in corpus_filter:\\n try:\\n os.mkdir(os.path.join(parent_directory, corpus))\\n except FileExistsError:\\n print(f\\\"{corpus} directory already exists. Adding to folder\\\")\\n # Organize all files\\n for e in os.listdir(session_directory):\\n assert os.path.isfile(e), \\\"Error: Subdirectory found.\\\"\\n for corpus in corpus_filter:\\n # Don't include excluded Pre files specified by pre-select option\\n if pre_select > 0:\\n if e in exclude_pre_list:\\n continue\\n # Don't include excluded Post files specified by pre-select option\\n if post_select > 0:\\n if e in exclude_post_list:\\n continue\\n # Copy the file to the appropriate corpus folder if it matches the filters\\n if fnmatch.fnmatch(e, \\\"*\\\" + corpus + \\\"*\\\"):\\n for key in session_filter:\\n if fnmatch.fnmatch(e, \\\"*\\\" + key + \\\"*\\\"):\\n shutil.copy(e, os.path.join(parent_directory, corpus, e))\\n\\n else:\\n continue\\n else:\\n continue\\n\\n return\",\n \"def merge():\\n result = []\\n for f in glob.glob(f\\\"{DATA_DIR}/COP*.json\\\"):\\n with open(f, \\\"r\\\") as infile:\\n result.append(json.load(infile))\\n\\n with open(f\\\"{DATA_DIR}/corpus.json\\\", \\\"w\\\", encoding=\\\"utf-8\\\") as outfile:\\n json.dump(result, outfile)\",\n \"def build_corpus(self):\\n # #############################\\n\\n doc = metapy.index.Document()\\n tok = metapy.analyzers.ICUTokenizer(suppress_tags=True)\\n tok = metapy.analyzers.LowercaseFilter(tok)\\n tok = metapy.analyzers.LengthFilter(tok, min=3, max=1000)\\n tok = metapy.analyzers.Porter2Filter(tok)\\n tok = metapy.analyzers.ListFilter(tok, \\\"lemur-stopwords.txt\\\", metapy.analyzers.ListFilter.Type.Reject)\\n collection = -1\\n\\n with open(self.documents_path) as file:\\n for num, line in enumerate(file):\\n l = line.strip()\\n c = int(l[0])\\n l = l[2:]\\n doc.content(l)\\n tok.set_content(doc.content())\\n if c != collection:\\n self.documents.append([])\\n collection = c\\n self.documents[c].append([token for token in tok])\\n self.number_of_collections = len(self.documents)\\n self.number_of_documents = len(self.documents[0])\\n #print(self.number_of_collections)\\n #print(self.number_of_documents)\\n #print(self.documents[0])\",\n \"def main() -> None:\\n\\n\\t# Retrieving the wiki URL\\n\\turl = get_api_url()\\n\\tprint(url)\\n\\n\\t# Creates file if it does not exist\\n\\topen(TEXT_FILE, \\\"a\\\")\\n\\n\\twith open(TEXT_FILE, \\\"r\\\") as f:\\n\\t\\tlast_title = f.readline().strip()\\n\\t\\tprint(\\\"Starting from:\\\", last_title)\\n\\n\\tmodify_pages(url, last_title)\\n\\n\\tprint(\\\"\\\\nNo pages left to be tagged\\\")\",\n \"def read_news(old=True, recent=True):\\n corpus = \\\"\\\"\\n doc = Doc()\\n if old:\\n # Read news csv files\\n print(\\\"Preparing old news\\\")\\n path = '/data/WorkData/media_and_judging/data/collected/nytimes/'\\n newsfiles = glob.glob(path + '*.csv')\\n print(newsfiles)\\n tf = len(newsfiles)\\n print(\\\"Total old news files: {}\\\".format(tf))\\n\\n # concate all cleaned news text\\n for news in newsfiles:\\n df = pd.read_csv(news)\\n count = 0\\n for index, row in df.iterrows():\\n count += 1\\n # append text\\n try:\\n cleaned_text = normalizeString(row[5].replace(\\\"LEAD: \\\", \\\"\\\"))\\n corpus += cleaned_text\\n\\n # add to lang row[4]: title, row[5]: content\\n date = formatDate(row[0], '%Y/%m/%d')\\n doc.add_news(row[4], cleaned_text, date)\\n except:\\n pass\\n if count % 500 == 0:\\n print(\\\"Progress Report: Old news {}, total {}\\\".\\n format(count, len(news)))\\n tf -= 1\\n print(\\\"Files Remaining: \\\", tf)\\n\\n if recent:\\n # Read json\\n # {title: {content:, ptime:, utime:, }..}\\n print(\\\"Preparing recent news\\\")\\n path = '/data/WorkData/media_and_judging/data/collected/nytimes/'\\n newsfiles = glob.glob(path + '*.json')\\n tf = len(newsfiles)\\n print(\\\"Length of recent news: {}\\\".format(tf))\\n\\n # concate all cleaned news text\\n for news in newsfiles:\\n count = 0\\n n = json_load(news)\\n for title in n:\\n count += 1\\n # append text\\n cleaned_text = normalizeString(clean_text(n[title]['content']\\n .replace(\\\"LEAD: \\\", \\\"\\\")))\\n\\n corpus += cleaned_text\\n # add to lang\\n try:\\n if 'ptime' in n[title]:\\n date = formatDate(n[title]['ptime'], '%Y%m%d%H%M%S%f')\\n else:\\n date = formatDate(n[title]['DATE'], '%m. %d, %Y')\\n except:\\n date = 'UNKNOWN'\\n doc.add_news(title, cleaned_text, date)\\n if count % 500 == 0:\\n print(\\\"Progress Report: Recent news, {}, total {}\\\".\\n format(count, len(news)))\\n tf -= 1\\n print(\\\"Files Remaining: \\\", tf)\\n\\n return corpus, doc\",\n \"def main(path: str):\\n output_dir = Path(path)\\n output_dir.mkdir(exist_ok=True)\\n\\n for lang in LANGS:\\n lang_dir = output_dir / lang\\n lang_dir.mkdir(exist_ok=True)\\n analysis_paths: List[str] = []\\n for datasets, set_name in ((DEV_FILES, \\\"dev\\\"), (TRAIN_FILES, \\\"train\\\")):\\n for year, url in datasets[lang].items():\\n filename = f\\\"{set_name}_analyses.{year}\\\"\\n analysis_path = lang_dir / filename\\n urllib.request.urlretrieve(url, analysis_path)\\n analysis_paths.append(str(analysis_path))\\n\\n # Concatenate all the files and filter for duplicates\\n all_analyses_path = lang_dir / \\\"all_analyses\\\"\\n cmd = f\\\"cat {' '.join(analysis_paths)} | sort | uniq > {all_analyses_path}\\\"\\n subprocess.check_call(cmd, shell=True)\",\n \"def doiparse(newdir):\\n with open(newdir + 'EC.csv', 'r') as ECdata:\\n reader = csv.DictReader(ECdata)\\n data = [x for x in reader]\\n print(\\\"creating ANVL files in \\\" + newdir)\\n for record in data:\\n dirid = record['id']\\n output = open(newdir + dirid + '.txt', 'a')\\n handle = record['dc.identifier.uri']\\n # Target URL field\\n output.write('_target: ' + handle + '\\\\n')\\n # Datacite Schema\\n output.write('_profile: datacite \\\\n')\\n # Handle\\n output.write('datacite.ObjectLocationURL: ' + handle + '\\\\n')\\n\\n # Author\\n if record['dc.contributor.author']:\\n output.write('datacite.creator: ' + record['dc.contributor.author'] + '\\\\n')\\n \\n # Title\\n try:\\n if record['dc.title']:\\n output.write('datacite.title: ' + record['dc.title'] + '\\\\n')\\n else:\\n output.write('datacite.title: None')\\n with open(newdir + 'notitlesETDs.txt', 'a') as fother:\\n fother.write(record['id'])\\n fother.write('\\\\n')\\n except KeyError:\\n pass\\n\\n # Publisher\\n output.write('datacite.publisher: Cornell University Library' + '\\\\n')\\n\\n # Publication Year\\n if record['dc.date.issued']:\\n output.write('datacite.publicationyear: ' + record['dc.date.issued'][:4] + '\\\\n')\\n else:\\n output.write('datacite.publicationyear: None')\\n with open(newdir + 'noyearsETDs.txt', 'a') as fyrother:\\n fyrother.write(record['id'])\\n fyrother.write('\\\\n')\\n\\n # Resource Type\\n output.write('datacite.resourcetype: Text \\\\n')\\n print('ANVL txt files created.')\\n return(data)\",\n \"def parseArnetminerDataset():\\n\\n inputFile = open(os.path.join(projectRoot, 'data','DBLP-citation-Feb21.txt'))\\n graph = MultiDiGraph()\\n\\n # Sets for authors, papers, conferences, and terms found so far\\n indexToPaperIdMap = {}\\n citationCountMap = {}\\n indexSet = set()\\n\\n beginning = inputFile.tell()\\n\\n print \\\"Parsing nodes for graph...\\\"\\n\\n # Counts for statistics\\n VALID_PAPERS = 1566322 # 99.62% of total papers in DBLP dataset\\n papersProcessed = 0\\n skippedPaperIndices = set()\\n invalidPaperIndices = set()\\n\\n # Add each paper to graph (adding missing associated terms, authors, and conferences)\\n for title, authors, conference, terms, citationCount, index in __papersFromFile(inputFile, skippedPaperIndices, invalidPaperIndices):\\n\\n # Check that index is unique, and record it\\n assert index not in indexSet\\n indexSet.add(index)\\n\\n # Create unique identifier with paper index & title\\n paperId = '%d----%s' % (index, title)\\n citationCountMap[paperId] = citationCount\\n indexToPaperIdMap[index] = paperId\\n\\n # Add symmetric edges & nodes (if they don't already exist in the network)\\n for author in authors:\\n graph.add_edges_from([(author, paperId), (paperId, author)])\\n graph.add_edges_from([(conference, paperId), (paperId, conference)])\\n for term in terms:\\n graph.add_edges_from([(term, paperId), (paperId, term)])\\n\\n # Output progress\\n papersProcessed += 1\\n sys.stdout.write(\\\"\\\\r Processed %d / %d papers...\\\" % (papersProcessed, VALID_PAPERS))\\n\\n # Rewind file\\n inputFile.seek(beginning)\\n\\n print \\\"Parsing citations for graph...\\\"\\n\\n # Counts for statistics\\n papersProcessed = 0\\n successfulCitations = 0\\n omittedPaperCitations = 0\\n invalidPaperCitations = 0\\n invalidCitations = 0\\n\\n # Add citations to the graph\\n for title, index, citations in __citationsFromFile(inputFile):\\n citingId = '%d----%s' % (index, title)\\n for citationIndex in citations:\\n\\n # Add citation edge if it was found\\n if citationIndex in indexToPaperIdMap:\\n successfulCitations += 1\\n graph.add_edge(citingId, indexToPaperIdMap[citationIndex])\\n\\n # Tally missing citation appropriately\\n elif citationIndex in skippedPaperIndices:\\n omittedPaperCitations += 1\\n elif citationIndex in invalidPaperIndices:\\n invalidPaperCitations += 1\\n else:\\n print \\\"\\\\nCitation '%d' not found for '%s'\\\" % (citationIndex, title)\\n invalidCitations += 1\\n\\n # Output progress\\n papersProcessed += 1\\n sys.stdout.write(\\\"\\\\r Processed Citations for %d / %d papers...\\\" % (papersProcessed, VALID_PAPERS))\\n\\n # Basic statistics about cleanliness of citations\\n totalCitations = invalidCitations + successfulCitations\\n successfulCitationsPercent = 100 * float(successfulCitations) / totalCitations\\n omittedPaperCitationsPercent = 100 * float(omittedPaperCitations) / totalCitations\\n invalidPaperCitationsPercent = 100 * float(invalidPaperCitations) / totalCitations\\n invalidCitationsPercent = 100 * float(invalidCitations) / totalCitations\\n print \\\"\\\\n\\\\nTotal Citations: %d\\\" % totalCitations\\n print \\\" Citations Added (Successful): %d (%2.2f%%)\\\" % (successfulCitations, successfulCitationsPercent)\\n print \\\" Citations Skipped (Skipped Paper): %d (%2.2f%%)\\\" % (omittedPaperCitations, omittedPaperCitationsPercent)\\n print \\\" Citations Skipped (Invalid Paper): %d (%2.2f%%)\\\" % (invalidPaperCitations, invalidPaperCitationsPercent)\\n print \\\" Citations Invalid (Unknown): %d (%2.2f%%)\\\" % (invalidCitations, invalidCitationsPercent)\\n\\n return graph\",\n \"def openie_prepare_files(document_file, no_entity_filter=False, consider_sections=False):\\n temp_dir = tempfile.mkdtemp()\\n temp_in_dir = os.path.join(temp_dir, \\\"input\\\")\\n filelist_fn = os.path.join(temp_dir, \\\"filelist.txt\\\")\\n out_fn = os.path.join(temp_dir, \\\"output.txt\\\")\\n os.mkdir(temp_in_dir)\\n input_files = []\\n\\n amount_skipped_files = 0\\n doc_count = count_documents(document_file)\\n logging.info('counting files to process....')\\n if no_entity_filter:\\n for document_content in read_pubtator_documents(document_file):\\n doc = TaggedDocument(from_str=document_content)\\n if not doc or not doc.title or not doc.abstract:\\n amount_skipped_files += 1\\n else:\\n doc_count += 1\\n # TODO: Not beautiful but join sections via a '.' to ensure sentence splitting in CoreNLP\\n content = '. '.join([te for te, _ in doc.iterate_over_text_elements(sections=consider_sections)])\\n input_file = os.path.join(temp_in_dir, \\\"{}.txt\\\".format(doc.id))\\n input_files.append(input_file)\\n with open(input_file, \\\"w\\\") as f:\\n f.write(content)\\n else:\\n logging.info('Init spacy nlp...')\\n spacy_nlp = English() # just the language with no model\\n spacy_nlp.add_pipe(\\\"sentencizer\\\")\\n\\n doc2sentences, doc2tags = filter_document_sentences_without_tags(doc_count, document_file, spacy_nlp,\\n consider_sections=consider_sections)\\n doc_count = len(doc2tags)\\n for doc_id, sentences in doc2sentences.items():\\n if sentences:\\n input_file = os.path.join(temp_in_dir, \\\"{}.txt\\\".format(doc_id))\\n input_files.append(input_file)\\n with open(input_file, 'wt') as f:\\n f.write(' '.join(sentences))\\n\\n logging.info('{} files need to be processed. {} files skipped.'.format(doc_count, amount_skipped_files))\\n with open(filelist_fn, \\\"w\\\") as f:\\n f.write(\\\"\\\\n\\\".join(input_files))\\n return filelist_fn, out_fn, doc_count\",\n \"def index_file(self, anns_file):\\n print \\\"Indexing \\\" + anns_file + \\\"... \\\",\\n\\n with open(anns_file, 'rb') as tsvfile:\\n reader = csv.reader(tsvfile, delimiter=\\\"\\\\t\\\", quoting=csv.QUOTE_NONE)\\n file_dict = defaultdict(list)\\n # Reads tsv lines\\n for line in reader:\\n doc_id, en = line[0], line[7]\\n file_dict[doc_id].append(en)\\n\\n for doc_id, en_list in file_dict.iteritems():\\n contents = self.__get_lucene_contents(doc_id, en_list)\\n self.lucene.add_document(contents)\\n\\n print \\\"done\\\"\",\n \"def html_reader(input_dir):\\r\\n #read data from the html file\\r\\n with open(input_dir,'r') as html_file:\\r\\n content = html_file.read()\\r\\n content = (content.split('\\\\n'))[4:-4]\\r\\n num = re.compile(\\\"(.*\\\\t\\\\d.*)|(\\\\d*\\\\d\\\\.\\\\d*)\\\")\\r\\n information = []\\r\\n for i in range(len(content)):\\r\\n if num.match(content[i])==None:\\r\\n information.append(content[i])\\r\\n information = information[:-1]\\r\\n #data parsing\\r\\n Date = re.compile('( ?CACM|June)')\\r\\n Meta = re.compile(\\\"(CA\\\\d\\\\d\\\\d\\\\d\\\\d\\\\d|June)\\\")\\r\\n #get date and meta index\\r\\n for i in range(len(information)):\\r\\n if Date.match(information[i])!=None:\\r\\n index_date = i\\r\\n if Meta.match(information[i])!=None:\\r\\n index_meta =i\\r\\n content = information[:index_date]\\r\\n others = information[index_date+2:index_meta]\\r\\n for i in range(len(content)):\\r\\n if content[i]==\\\"\\\":\\r\\n title = content[:i]\\r\\n abstract = content[i+1:]\\r\\n break\\r\\n #get author and other\\r\\n author = []\\r\\n other = []\\r\\n for i in range(len(others)):\\r\\n if others[i]==\\\"\\\":\\r\\n if re.match(\\\"[A-Z].*, ?[A-Z].*\\\\..*\\\",others[0]) != None:\\r\\n author = others[:i]\\r\\n other = others[i+1:]\\r\\n else:\\r\\n other = others\\r\\n break\\r\\n for i in range(len(author)):\\r\\n if re.match(\\\"[A-Z].*, ?[A-Z].*\\\\..*\\\",author[i]) != None:\\r\\n name = author[i].split(\\\",\\\")\\r\\n author[i] = (name[1]+name[0])\\r\\n author[i] = author[i].replace(\\\" \\\",\\\"\\\")\\r\\n author[i] = author[i].replace(\\\"\\\\t\\\",\\\"\\\")\\r\\n author[i] = author[i].lower()\\r\\n\\r\\n #parse date\\r\\n date = []\\r\\n date.append(re.search(\\\"19\\\\d\\\\d\\\", information[index_date]).group())\\r\\n date.append(re.search(\\\"(January|February|March|April|May|June|JUly|July|August|September|October|November|December)\\\",information[index_date]).group().lower())\\r\\n\\r\\n #parse meta data\\r\\n meta = []\\r\\n meta.append(re.search(\\\"CA\\\\d\\\\d\\\\d\\\\d\\\\d\\\\d\\\\w?\\\",information[index_meta]).group().lower())#0\\r\\n meta.append(re.search(\\\"[a-z0-9] [A-Z]{2}[A-Z]?\\\",information[index_meta]).group()[2:].lower())#1\\r\\n meta.append(re.search(\\\"(January|February|March|April|May|June|JUly|July|August|September|October|November|December)\\\",information[index_meta]).group().lower())#2\\r\\n meta.append(re.search(\\\"\\\\w \\\\d\\\\d?\\\",information[index_meta]).group()[2:])#3\\r\\n meta.append(re.search(\\\"\\\\d?\\\\d:\\\\d\\\\d\\\",information[index_meta]).group())#4\\r\\n meta.append(re.search(\\\"(AM|PM)\\\",information[index_meta]).group().lower())#5\\r\\n meta.append(re.search(\\\"19\\\\d\\\\d\\\",information[index_meta]).group())#6\\r\\n\\r\\n #build corpus\\r\\n corpus = set()\\r\\n lemmatizer = WordNetLemmatizer()\\r\\n for i in range(len(title)):\\r\\n title[i] = re.sub(\\\"\\\\(|\\\\)|-|\\\\d\\\\d?\\\\d?|:|/|\\\\.|`|\\\\?\\\",\\\" \\\",title[i])\\r\\n words = word_tokenize(title[i])\\r\\n for word in words:\\r\\n normal_word = word.lower()\\r\\n if normal_word not in stopwords.words(\\\"english\\\"):\\r\\n corpus.add(lemmatizer.lemmatize(normal_word))\\r\\n\\r\\n for i in range(len(abstract)):\\r\\n abstract[i] = re.sub(\\\"\\\\(|\\\\)|-|\\\\d\\\\d?\\\\d?|:|/|\\\\.|`|\\\\?|,\\\",\\\" \\\",abstract[i])\\r\\n words = word_tokenize(abstract[i])\\r\\n for word in words:\\r\\n normal_word = word.lower()\\r\\n if normal_word not in stopwords.words(\\\"english\\\"):\\r\\n corpus.add(lemmatizer.lemmatize(normal_word))\\r\\n\\r\\n for i in range(len(other)):\\r\\n other[i] = re.sub(\\\"\\\\(|\\\\)|-|\\\\d\\\\d?\\\\d?|:|/|\\\\.|`|\\\\?|,\\\",\\\" \\\",other[i])\\r\\n words = word_tokenize(other[i])\\r\\n for word in words:\\r\\n normal_word = word.lower()\\r\\n if normal_word not in stopwords.words(\\\"english\\\"):\\r\\n corpus.add(lemmatizer.lemmatize(normal_word))\\r\\n\\r\\n corpus = list(corpus)\\r\\n\\r\\n return paper(author= author, other= other, metadata= meta,date = date,title = title,abstract = abstract,id=int(input_dir[-9:-5]),corpus = corpus)\",\n \"def collect_data(self, data_folder):\\n print(\\\"Start collecting\\\")\\n input_file_path = os.path.join(data_folder, \\\"dblp.xml\\\")\\n with codecs.open(input_file_path, \\\"r\\\", encoding=\\\"iso-8859-1\\\") as file:\\n start = time.time()\\n tmp = start\\n\\n title = \\\"\\\"\\n key_prefix = \\\"\\\"\\n authors = []\\n personal_information = False\\n publications_type = [\\\"article\\\", \\\"inproceedings\\\", \\\"proceedings\\\", \\\"book\\\",\\n \\\"incollection\\\", \\\"phdthesis\\\", \\\"mastersthesis\\\"]\\n start_publication_regex = r\\\"<([^\\\\/]*?) (.*)>\\\" # </article><article mdate=\\\"2017-05-28\\\" key=\\\"journals/acta/Simon83\\\">\\n end_publication_regex = r\\\"<\\\\/(.*?)>\\\" # </article><article mdate=\\\"2017-05-28\\\" key=\\\"journals/acta/Simon83\\\">\\n author_regex = r\\\"<author>(.*)</author>\\\" # <author>Katsuyuki Tateishi</author>\\n title_regex = r\\\"<title>(.*)</title>\\\" # <title>A quadratic speedup theorem ...</title>\\n start_person_regex = r'<www.*key=\\\"(.*)\\\">' #<www mdate=\\\"2009-06-10\\\" key=\\\"homepages/32/3977\\\">\\n end_person_regex = r\\\"<\\\\/www>\\\"\\n note_url_regex = r\\\"<note.*?>|<url.*?>\\\"\\n inside_publication = False\\n inside_person = False\\n for i, line in tqdm(enumerate(file)):\\n # Test end of publication, if true add publication\\n result = regex.search(end_publication_regex, line)\\n if result:\\n if result.group(1) in publications_type:\\n if inside_publication and authors and title:\\n # This a a publication, lets add it to the dictionnay author -> title\\n self.__add_publication(authors, title)\\n title = \\\"\\\"\\n authors = []\\n inside_publication = False\\n personal_information = False\\n key_prefix = \\\"\\\"\\n\\n #Test beginning of a publication\\n result = regex.search(start_publication_regex, line)\\n if result and result.group(1) in publications_type:\\n inside_publication = True\\n title = \\\"\\\"\\n authors = []\\n personal_information = False\\n key_prefix = \\\"\\\"\\n\\n #Check if author\\n result = regex.search(author_regex, line)\\n if result and (inside_publication or inside_person):\\n authors.append(result.group(1))\\n #Check if title\\n result = regex.search(title_regex, line)\\n if result and (inside_publication or inside_person):\\n title = result.group(1)\\n #Check if containt url or note\\n result = regex.search(note_url_regex, line)\\n if result:\\n #if inside_person and (\\\"<note\\\" in line or \\\"<url\\\" in line):\\n personal_information = True\\n # Check if end of person, if true add author\\n result = regex.search(end_person_regex, line)\\n if result and inside_person:\\n if authors and \\\"homepages/\\\" in key_prefix and title == \\\"Home Page\\\":\\n self.__add_author(key_prefix, authors, personal_information)\\n key_prefix = \\\"\\\"\\n authors = []\\n inside_person = False\\n personal_information = False\\n title = \\\"\\\"\\n #Check start person\\n result = regex.search(start_person_regex, line)\\n if result:\\n inside_person = True\\n key_prefix = result.group(1)\\n\\n\\n self.__save_everything(data_folder)\\n self.__merge_data()\\n self.__save_everything(data_folder)\",\n \"def main(db=\\\"dbs/readme.db\\\"):\\r\\n\\r\\n firefox_profile_folder = read_firefox_cookies.get_profile_folder()\\r\\n cjar = read_firefox_cookies.get_cookie_jar(firefox_profile_folder)\\r\\n\\r\\n if cjar is False:\\r\\n sys.exit()\\r\\n\\r\\n # Try to make connections less noisy here.\\r\\n logging.getLogger(\\r\\n 'urllib3.connectionpool').setLevel(logging.ERROR)\\r\\n\\r\\n try:\\r\\n works_page = \\\"https://archiveofourown.org/users/RockSunner/works\\\"\\r\\n with PageGetter(cookie_jar=cjar) as pgetter:\\r\\n # Note: will need to be able to plug in a name.\\r\\n tree = pgetter.get_page(works_page)\\r\\n find_navigation = \\\\\\r\\n etree.XPath('//ol[@class = \\\"pagination actions\\\"]/li/a')\\r\\n page_nums = find_navigation(tree)\\r\\n keys = []\\r\\n for res in page_nums:\\r\\n res_t = res.text_content()\\r\\n if RepresentsInt(res_t):\\r\\n keys.append(int(res_t))\\r\\n else:\\r\\n break\\r\\n\\r\\n scraper = Ao3Scraper()\\r\\n recs = []\\r\\n\\r\\n scraper.parse_tree(tree, recs)\\r\\n\\r\\n for key in keys:\\r\\n payload = {\\\"page\\\": key}\\r\\n tree = pgetter.get_page(works_page, payload)\\r\\n scraper.parse_tree(tree, recs)\\r\\n\\r\\n report_gen = ReportGen(\\\"A03\\\")\\r\\n with ReadMeDb(db, echo=False) as read_db:\\r\\n for rec in recs:\\r\\n story = read_db.get_or_create_ao3_story(rec[\\\"title\\\"])\\r\\n changed = compare_ao3_rec(story, rec, report_gen)\\r\\n if \\\"kudos\\\" in changed:\\r\\n eprint(\\\"Could look up kudos here\\\")\\r\\n report_gen.print_report()\\r\\n read_db.set_commit_flag()\\r\\n\\r\\n pgetter.stop_sleep()\\r\\n\\r\\n except ConnectionRefusedError as exc:\\r\\n eprint(\\\"ConnectionRefused to archiveofourown.org\\\", exc)\\r\\n except ConnectionAbortedError as exc:\\r\\n eprint(\\\"Connection problem\\\", exc)\\r\\n except Exception as exc:\\r\\n eprint(\\\"Cannot connect to archiveofourown.org\\\", exc)\",\n \"def main():\\r\\n # Create clean output files\\r\\n open(args.outdir2+\\\"/\\\"+args.outfile1, 'w').close()\\r\\n open(args.outdir2+\\\"/\\\"+args.outfile2, 'w').close()\\r\\n\\r\\n # Run functions\\r\\n taxonList = nsrTaxonomy()\\r\\n synonymList, synonymDict = nsrSynonyms()\\r\\n generaList = nsrGenera(taxonList, synonymList)\\r\\n boldExtract(generaList)\\r\\n zipDirectory(args.outdir1, zip_path)\\r\\n boldNSR(taxonList, synonymList, synonymDict, zip_path)\\r\\n print(\\\"Done\\\")\",\n \"def main():\\n papers_with_references = add_references_to_papers(PAPERS_FILE, TEXT_DIR)\\n citations = match_citations_with_papers(papers_with_references)\\n insert_into_citation_table(citations)\",\n \"def doTheJob(input_file):\\n\\n Parse.parseFile(input_file)\\n Write.writeFile(input_file.replace(\\\".txt\\\", \\\".html\\\"))\",\n \"def parse_PGR(kb_data):\\n\\n print(\\\"Parsing corpus...\\\")\\n output_PGR = dict()\\n filepaths = [\\\"./retrieved_data/corpora/PGR/train.tsv\\\", \\\"./retrieved_data/corpora/PGR/test.tsv\\\"]\\n\\n for filepath in filepaths:\\n \\n with open(filepath, encoding=\\\"utf-8\\\") as corpus_file:\\n reader = csv.reader(corpus_file, delimiter=\\\"\\\\t\\\")\\n row_count = int()\\n\\n for row in reader:\\n row_count += 1\\n \\n if row_count > 1: # Skip the header\\n doc_id = row[0]\\n annotation_str = row[3]\\n hp_id = row[5].replace(\\\"_\\\", \\\":\\\")\\n start_pos, end_pos = row[8], row[9]\\n\\n if hp_id in kb_data.child_to_parent.keys(): # Only consider concept ids with ONE direct ancestor\\n direct_ancestor = kb_data.child_to_parent[hp_id]\\n annotation = (annotation_str, start_pos, end_pos, hp_id, direct_ancestor)\\n output_PGR = add_annotation_to_output_dict(doc_id, annotation, output_PGR)\\n \\n print(\\\"...Done!\\\") \\n return output_PGR\",\n \"def clean_documents():\\n start = datetime.now()\\n for i, raw_filename in enumerate(os.listdir(RAW_DIR)):\\n fullpath = os.path.join(RAW_DIR, raw_filename)\\n if os.path.isfile(fullpath):\\n print(\\\"Cleaning {0} {1}\\\".format(i, fullpath), file=stderr)\\n try:\\n with open(fullpath, \\\"r\\\") as f:\\n text = f.read()\\n text = clean(text)\\n soup = BeautifulSoup(text, \\\"html.parser\\\")\\n cleaned = visible_text(soup)\\n score = germanwings_score(cleaned)\\n if not score:\\n print(\\\"not germanwings: {0}\\\".format(raw_filename))\\n else:\\n clean_filename = os.path.join(CLEAN_DIR, raw_filename)\\n with open(clean_filename, \\\"w\\\") as f:\\n f.write(cleaned.encode(\\\"ascii\\\", \\\"ignore\\\"))\\n except Exception as exc:\\n print(\\\"{0}: {1}\\\".format(fullpath, exc), file=stderr)\\n end = datetime.now()\\n print(\\\"Elapsed time to clean: {0}\\\".format(end - start), file=stderr)\",\n \"def main(file: str, db: WikiDB, filter: str = \\\"births\\\") -> None:\\n pageCount = 0\\n for event, elem in etree.iterparse(file, events=('end',)):\\n elem.tag = strip_tag_name(elem.tag)\\n if elem.tag != \\\"page\\\": continue # Only parses a page once you finish getting all the elem attrib\\n # print(elem.find(\\\"title\\\").text) # For debugging\\n pageCount += 1\\n try:\\n parsePage(elem, db, filter)\\n # Some articles throw an exception\\n # Other articles run forever and hit the time limit, which throws a keyboard interrupt\\n except BaseException as e:\\n print(e)\\n elem.clear() # Clear elem for next use\\n if not pageCount % 100000: # Prints out periodic progress statements\\n elapsed_time = time.time() - start_time\\n print(\\\"{} articles parsed\\\".format((pageCount / 100000) * 100000), end=\\\" \\\")\\n print(\\\"Elapsed time: {}\\\".format(hms_string(elapsed_time)))\\n db.commit()\\n\\n elapsed_time = time.time() - start_time\\n\\n print(\\\"Elapsed time: {}\\\".format(hms_string(elapsed_time)))\",\n \"def make_well_project(laspath='data/las/', stripath='data/tops/'):\\n wells = {}\\n lasfiles = glob(laspath + '*.LAS')\\n stripfiles = glob(stripath + '*.csv')\\n for fname, sname in zip(lasfiles, stripfiles):\\n name = Path(fname).stem\\n wells[name] = Well.from_las(fname)\\n wells[name].data['tops'] = Striplog.from_csv(sname)\\n proj = Project(list(wells.values()))\\n return proj\",\n \"def loadFiles():\\n all_chapters = []\\n for name in names:\\n f = open(file_path + name, \\\"r\\\", encoding=\\\"utf-8\\\")\\n html_file = f.read()\\n f.close()\\n chap_text = extractText(html_file)\\n new_text = cleanText(chap_text)\\n all_chapters.append(new_text)\\n concatenated_chapters = \\\" \\\".join(all_chapters)\\n return concatenated_chapters\",\n \"def prepare_anno(infolder, outfolder, mode=\\\"split\\\"):\\n\\tprint(\\\"Starting...\\\")\\n\\t\\n\\tinpath = os.path.join(infolder, \\\"*.xml\\\")\\n\\tfilecounter = 0\\n\\t\\n\\t# check output folders\\n\\tif not os.path.exists(outfolder):\\n\\t\\tos.makedirs(outfolder)\\n\\t\\t\\n\\tout_tei = os.path.join(outfolder, \\\"temp\\\")\\n\\tout_txt = os.path.join(outfolder, \\\"txt\\\")\\n\\t\\n\\tif not os.path.exists(out_tei):\\n\\t\\tos.makedirs(out_tei)\\n\\tif not os.path.exists(out_txt):\\n\\t\\tos.makedirs(out_txt)\\n\\t\\t\\n\\t\\n\\tfor filepath in glob.glob(inpath):\\n\\t\\tfilecounter+= 1\\n\\t\\tfn = os.path.basename(filepath)[:-4]\\n\\t\\toutfile_x = fn + \\\".xml\\\"\\n\\t\\t\\n\\t\\tdoc = etree.parse(filepath)\\n\\t\\t\\n\\t\\tif mode == \\\"split-1\\\":\\n\\t\\t\\ttransform = etree.XSLT(xslt_TEIwrapper_1)\\n\\t\\telse:\\n\\t\\t\\ttransform = etree.XSLT(xslt_TEIwrapper)\\n\\n\\t\\tresult_tree = transform(doc)\\n\\t\\tresult = str(result_tree)\\n\\t\\t\\n\\t\\t# create TEI wrapper for future annotation results\\n\\t\\twith open(os.path.join(outfolder, \\\"temp\\\", outfile_x), \\\"w\\\") as output:\\n\\t\\t\\toutput.write(result)\\n\\t\\t\\t\\n\\t\\t# create one full text file per chapter (or for the whole text)\\n\\t\\ttei = {'tei':'http://www.tei-c.org/ns/1.0'}\\n\\t\\tcligs_id = doc.xpath(\\\"//tei:idno[@type='cligs']/text()\\\", namespaces=tei)\\n\\t\\tif mode == \\\"split-1\\\":\\n\\t\\t\\tresults = doc.xpath(\\\"//tei:text/tei:body\\\", namespaces=tei)\\n\\t\\telse:\\n\\t\\t\\tresults = doc.xpath(\\\"//tei:div[ancestor::tei:body][not(descendant::tei:div[not(ancestor::tei:floatingText)])][not(ancestor::tei:floatingText)]\\\", namespaces=tei)\\n\\t\\t\\n\\t\\tif isinstance(cligs_id, list):\\n\\t\\t\\tcligs_id = cligs_id[0]\\n\\t\\telif isinstance(cligs_id, str) == False:\\n\\t\\t\\traise ValueError(\\\"This type (\\\" + str(type(cligs_id)) + \\\") is not supported for cligs_id. Must be list or string.\\\")\\n\\t\\t\\n\\t\\tfor i,r in enumerate(results):\\n\\t\\t\\ttransform = etree.XSLT(xslt_extractDIVs)\\n\\t\\t\\tresult_tree = transform(r)\\n\\t\\t\\tresult = str(result_tree)\\n\\t\\t\\t\\n\\t\\t\\toutfile = cligs_id + \\\"_d\\\" + str(i + 1) + \\\".txt\\\"\\n\\t\\t\\t\\n\\t\\t\\twith open(os.path.join(outfolder, \\\"txt\\\", outfile), \\\"w\\\") as output:\\n\\t\\t\\t\\toutput.write(result)\\n\\t\\n\\tprint(\\\"Done. \\\" + str(filecounter) + \\\" files treated.\\\")\",\n \"def combine_corpus(include_whole_abstracts=False):\\r\\n\\tif load_combined_corpus_maps():\\r\\n\\t\\treturn\\r\\n\\t\\r\\n\\tglobal gensim_dictionary\\r\\n\\tglobal search_criteria_dict, solution_dict, linked_abstracts_dict, abstracts_dict\\r\\n\\tglobal common_corpus_list, question_map, abstract_map, abstract_whole_map\\r\\n\\tglobal corpus_layout\\r\\n\\t\\r\\n\\tprint('\\\\nbuilding common corpus')\\r\\n\\t\\r\\n\\tcommon_corpus_list = list()\\r\\n\\tquestion_map = dict()\\r\\n\\tabstract_map = dict()\\r\\n\\tabstract_whole_map = dict()\\r\\n\\t\\r\\n\\tsentence_count = 0\\r\\n\\t\\r\\n\\tcorpus_layout = []\\r\\n\\t\\r\\n\\tfor qi,qv in search_criteria_dict.items():\\r\\n\\t\\tquestions = qv\\r\\n\\t\\t\\r\\n\\t\\tsentence_incr = len(questions)\\r\\n\\t\\tquestion_map[qi] = (sentence_count,sentence_count + sentence_incr)\\r\\n\\t\\tsentence_count += sentence_incr\\r\\n\\t\\t\\r\\n\\t\\tfor st in questions:\\r\\n\\t\\t\\tcommon_corpus_list.append(st)\\r\\n\\t\\r\\n\\tcorpus_layout.append(('Q',sentence_count))\\r\\n\\t\\r\\n\\tfor ai,av in abstracts_dict.items():\\r\\n\\t\\tabstract = av\\r\\n\\t\\t\\r\\n\\t\\tsentence_incr = len(abstract)\\r\\n\\t\\tabstract_map[ai] = (sentence_count,sentence_count + sentence_incr)\\r\\n\\t\\tsentence_count += sentence_incr\\r\\n\\r\\n\\t\\tfor st in abstract:\\r\\n\\t\\t\\tcommon_corpus_list.append(st)\\r\\n\\t\\r\\n\\tcorpus_layout.append(('A',sentence_count))\\r\\n\\r\\n\\tif include_whole_abstracts:\\r\\n\\t\\tfor ai,av in abstracts_dict.items():\\r\\n\\t\\t\\tabstract = av\\r\\n\\t\\t\\t\\r\\n\\t\\t\\tabstract_whole_map[ai] = (sentence_count,sentence_count + 1)\\r\\n\\t\\t\\tsentence_count += 1\\r\\n\\r\\n\\t\\t\\tst = tuple('\\\\n'.join(s) for s in zip(*abstract))\\r\\n\\t\\t\\tif len(st) < 2:\\r\\n\\t\\t\\t\\tst = ('','')\\r\\n\\r\\n\\t\\t\\tcommon_corpus_list.append(st)\\r\\n\\t\\t\\r\\n\\t\\tcorpus_layout.append(('W',sentence_count))\\r\\n\\t\\r\\n\\tpickle.dump(question_map,open(paths.path_data_map_questions_pickle,\\\"wb\\\"))\\r\\n\\tpickle.dump(abstract_map,open(paths.path_data_map_abstracts_pickle,\\\"wb\\\"))\\r\\n\\tpickle.dump(abstract_whole_map,open(paths.path_data_map_abstracts_whole_pickle,\\\"wb\\\"))\\t\\r\\n\\tpickle.dump(common_corpus_list,open(paths.path_data_corpus_pickle,\\\"wb\\\"))\\r\\n\\tpickle.dump(corpus_layout,open(paths.path_data_corpus_layout_pickle,\\\"wb\\\"))\",\n \"def setup():\\n\\n p4_slackbots = model.Project(\\\"Merch Website\\\", \\\"http://76.176.109.127:6969/\\\", \\\"/static/img/p4_slackbots.png\\\",\\n \\\"P4Slackbots\\\",\\n [\\\"Abhijay Deevi\\\", \\\"Kevin Do\\\", \\\"Travis Medley\\\", \\\"Paul Bokelman\\\", \\\"Gavin Theriault\\\"],\\n \\\"This project is a merch website that we created for our Youtube channels, \\\"\\n \\\"GodlyGoats and \\\"\\n \\\"Albertpani Compani. We have a lot of merch you can buy and other information.\\\")\\n\\n p4_hangman = model.Project(\\\"Music Website\\\", url_for('p4_hangman_bp.index'), \\\"/static/img/p4hangman.png\\\",\\n \\\"P4 Hangman\\\",\\n [\\\"Charlie Zhu\\\", \\\"Rohan Nallapati\\\", \\\"Rivan Nayak\\\", \\\"Sarah Xie\\\", \\\"Noah Pidding\\\"],\\n \\\"This website includes a portfolio of our projects we worked on this trimester as well \\\"\\n \\\"as a music section including three different genres of music with multiple examples \\\"\\n \\\"and descriptions of each.\\\")\\n\\n p4_fruitycoders = model.Project(\\\"Photography Website\\\", \\\"google.com\\\", \\\"/static/img/p4_fruitycoders.png\\\",\\n \\\"P4 fruitycoders\\\",\\n [\\\"Sophie Lee\\\", \\\"Linda Long\\\", \\\"Maggie Killada\\\", \\\"Adam Holbel\\\", \\\"Wenshi Bao\\\"],\\n \\\"Our website (Fruity Photos) features the history of photography, as well as the \\\"\\n \\\"works \\\"\\n \\\"and biographies of several famous photographers, such as Ansel Adams and Annie \\\"\\n \\\"Leibovitz.\\\")\\n\\n \\\"\\\"\\\"p4_coderjoes = model.Project(\\\"CoderJoes Store\\\", url_for('p4_coderjoes_bp.index'), \\\"/static/img/p4_coderjoes.png\\\",\\n \\\"P4 Guessers\\\",\\n [\\\"Lola Bulkin\\\", \\\"Grace Le\\\", \\\"Ryan Moghaddas\\\", \\\"William Cherres\\\", \\\"Brayden Basinger\\\"],\\n \\\"CoderJoes is a virtual store where you can find recipes, ideas, and descriptions, \\\"\\n \\\"as well as a group portfolio of our work over the trimester.\\\")\\\"\\\"\\\"\\n\\n projects = [p4_slackbots, p4_hangman, p4_fruitycoders]#, p4_coderjoes\\n period = model.Period(\\\"Period 4\\\", \\\"AP Principles of Computer Science - Python\\\", projects)\\n return period\",\n \"def loadLinks(self):\\n\\t\\tmainPageFile = open(self.mainPageFilePath, 'r')\\n\\t\\ttry:\\n\\t\\t\\tlines = mainPageFile.readlines()\\n\\t\\t\\tif len(lines) > 0:\\n\\t\\t\\t\\tself.mainPage = lines[0]\\n\\t\\t\\telse:\\n\\t\\t\\t\\tprint(\\\"No link in : \\\", self.mainPageFilePath)\\n\\t\\tfinally:\\n\\t\\t mainPageFile.close()\\n\\n\\t\\tlinkFile = open(self.linkFilePath, 'r')\\n\\t\\ttry:\\n\\t\\t\\t# add all links to a list\\n\\t\\t\\tfor line in linkFile.readlines():\\n\\t\\t\\t\\tif \\\"http\\\" in line:\\n\\t\\t\\t\\t\\tsubPageName = line.split(\\\".de\\\")[1].replace(\\\"/\\\", \\\"\\\")\\n\\t\\t\\t\\t\\tself.subpages.append( Subpage(line.strip(' \\\\t\\\\n\\\\r'),subPageName,self.mainPage) )\\n\\t\\tfinally:\\n\\t\\t linkFile.close()\",\n \"def main():\\r\\n\\tlang = get_arguments()\\r\\n\\twiki_analyzer(lang)\",\n \"def run():\\n assert os.path.exists(args.input_path), \\\"input_path doesn't exist\\\"\\n assert os.path.exists(args.output_path), \\\"output_path doesn't exist\\\"\\n\\n # read all the paths to the input documents\\n doc_files = []\\n for root, dirs, files in os.walk(args.input_path):\\n for file in files:\\n if not file.endswith('gz') and not file.endswith('xml'):\\n continue\\n doc_files.append(os.path.join(root, file))\\n print('{} medline files found from {}'\\n ''.format(len(doc_files), args.input_path))\\n\\n print('converting...')\\n pool = Pool(processes=args.num_workers)\\n total_doc = 0\\n total_batch = 0\\n total_empty = 0\\n for d, b, n in tqdm(pool.imap_unordered(partial(convert), doc_files),\\n total=len(doc_files)):\\n total_doc += d\\n total_batch += b\\n total_empty += n\\n\\n print('total docs: {}, total batches: {} created (empty doc {})'\\n ''.format(total_doc, total_batch, total_empty))\",\n \"def organize_bib():\\n\\n parser = argparse.ArgumentParser(description='Organize papers')\\n parser.add_argument('bibfile', help='The bibtex file to be processed.')\\n subparsers = parser.add_subparsers(help='Available commands')\\n\\n help_tmpl = 'Create a YAML file describing metadata for the organization tasks.'\\n parser_tmpl = subparsers.add_parser('genmetadata', help=help_tmpl)\\n parser_tmpl.add_argument('--PI', default='Qi, Fei',\\n help='The name of the PI.')\\n parser_tmpl.add_argument('--metadata', '-m',\\n help='Output metadata in a YAML file.')\\n parser_tmpl.set_defaults(func=gen_metadata)\\n\\n help_patch = 'Create attachements for uploading to NSFC website.'\\n parser_attach = subparsers.add_parser('genattach', help=help_patch)\\n parser_attach.add_argument('--metadata', '-m',\\n help='Metadata in YAML format specifying the task.')\\n parser_attach.add_argument('--script', '-s',\\n help='Bash script for creating tasks.')\\n parser_attach.set_defaults(func=gen_attachments) \\n\\n help_extract = 'Extract bib items based on a given aux file.'\\n parser_extract = subparsers.add_parser('extract', help=help_extract)\\n parser_extract.add_argument('--auxfile', '-x',\\n help='AUX file created by LaTeX.')\\n parser_extract.add_argument('--output', '-o',\\n help='Output of cited references in BibTeX format.')\\n parser_extract.set_defaults(func=extract_bibtex)\\n\\n help_simplify = 'Simplify a BibTeX file.'\\n parser_simplify = subparsers.add_parser('simplify', help=help_simplify)\\n parser_simplify.add_argument('--bib-journal', '-b',\\n help='BibTeX of canonical journal/conference titles.')\\n parser_simplify.add_argument('--output', '-o',\\n help='Output of simplified BibTeX.')\\n parser_simplify.set_defaults(func=simplify_bibtex)\\n\\n args = parser.parse_args()\\n # print(args)\\n args.func(args)\",\n \"def parse(input_path, output_dir, func_validate = None, target_tag = None, size_limit_KB = None):\\n if not os.path.exists(output_dir): os.makedirs(output_dir)\\n\\n if os.path.isfile(input_path):\\n print (\\\"The input file is\\\", input_path)\\n print (\\\"The output directory is\\\", output_dir)\\n files = [[input_path, os.path.basename(input_path)]]\\n else:\\n input_dir = input_path\\n print (\\\"The input directory is\\\", input_dir)\\n print (\\\"The output directory is\\\", output_dir)\\n print (\\\"Searching HTML files in the input directory...\\\")\\n files = file_utils.get_filelist_in_path(\\\"html\\\", input_dir, True)\\n\\n def process(files):\\n corpus_store = corpus_utils.CorpusStore(func_validate = func_validate)\\n\\n for idx, file in enumerate(files):\\n f_abst = file[0] # absolute path\\n f_rel = file[1] # relative path\\n print (\\\"(\\\", idx, \\\"of\\\", len(files), \\\") file\\\", f_rel)\\n file_content = file_utils.read_file_any_encoding(f_abst)\\n if (len(file_content) == 0):\\n continue\\n\\n # 1st, process the data with Atomic Parser\\n parser = pat.Parser(corpus_store)\\n parser.parse(file_content)\\n\\n # Process the same data with Atomic HeaderBody Parser\\n # parser = pah.Parser(corpus_store)\\n # parser.parse(file_content)\\n\\n # Process the same data with HeaderBody Parser\\n parser = phb.Parser(corpus_store, target_tag)\\n parser.parse(file_content)\\n\\n # Export the parsed data into file\\n print (\\\"Exporting the result...\\\")\\n return corpus_store.export_corpus(output_dir, size_limit_KB = size_limit_KB)\\n\\n print (\\\"Total\\\", len(files), \\\"files to process.\\\")\\n exported_files = process(files)\\n print (\\\"Exported:\\\", exported_files)\",\n \"def get_parses(corpus_sents, corpus_name, target_dir):\\n print('Generating the parse files for the {:s} corpus ...'.format(corpus_name))\\n # Define POS tag inventory separated along the open/ closed class axis;\\n # exclude tags not associated with either class (such as 'filler' words), due to their relatively low frequency\\n # and low relevance for the contrastive analysis of the two ID-variant corpora\\n open_class_tags = ['FW', 'GW', 'JJ', 'JJR', 'JJS', 'NN', 'NNP', 'NNPS', 'NNS', 'PDT', 'RB', 'RBR', 'RBS', 'UH',\\n 'VB', 'VBD', 'VBG', 'VBN', 'VBP', 'VBZ', 'WRB']\\n closed_class_tags = ['AFX', 'BES', 'CC', 'CD', 'DT', 'EX', 'HVS', 'IN', 'MD', 'POS', 'PRP', 'PRP$', 'RP', 'SYM',\\n 'TO', 'WDT', 'WP', 'WP$']\\n all_tags = open_class_tags + closed_class_tags\\n\\n # Parse corpus contents with SpaCy\\n model = sc.load('en')\\n parses = [model(sent) for sent in corpus_sents]\\n # Obtain tag counts for the specified tag inventory\\n flat_tags = [parse.tag_ for parsed_sent in parses for parse in parsed_sent]\\n unique_tags = set(flat_tags)\\n tag_counts = sorted([(tag, flat_tags.count(tag)) for tag in unique_tags if tag in all_tags],\\n reverse=True, key=lambda x: x[1])\\n\\n # Calculate open class fraction (total and top 50%), to determine whether open classes are distributed differently\\n # in sentences of varying ID; intuitively one may expect high-ID sentences to contain a greater portion of open\\n # class words, as they exhibit greater variation and are therefore less predictable in sentential context\\n top_open = [tag_tpl[1] for tag_tpl in tag_counts[: len(tag_counts) // 2] if tag_tpl[0] in open_class_tags]\\n top_closed = [tag_tpl[1] for tag_tpl in tag_counts[: len(tag_counts) // 2] if tag_tpl[0] in closed_class_tags]\\n top_all = [tag_tpl[1] for tag_tpl in tag_counts[: len(tag_counts) // 2]]\\n top_open_fraction = sum(top_open) / sum(top_all)\\n top_closed_fraction = sum(top_closed) / sum(top_all)\\n\\n full_open = [tag_tpl[1] for tag_tpl in tag_counts if tag_tpl[0] in open_class_tags]\\n full_closed = [tag_tpl[1] for tag_tpl in tag_counts if tag_tpl[0] in closed_class_tags]\\n full_all = [tag_tpl[1] for tag_tpl in tag_counts]\\n full_open_fraction = sum(full_open) / sum(full_all)\\n full_closed_fraction = sum(full_closed) / sum(full_all)\\n\\n # Write tag counts to file\\n tag_log_path = os.path.join(target_dir, '{:s}_tag_counts.txt'.format(corpus_name))\\n with codecs.open(tag_log_path, 'w', encoding='utf8') as tag_file:\\n for i in range(len(tag_counts)):\\n tag_file.write('{:s}\\\\t{:d}\\\\n'.format(tag_counts[i][0], tag_counts[i][1]))\\n # Calculate corpus statistics\\n tag_file.write('=' * 10 + '\\\\n')\\n tag_file.write('Open class fraction of most frequent 50% POS tags: {:.4f}\\\\n'.format(top_open_fraction))\\n tag_file.write('Closed class fraction of most frequent 50% POS tags: {:.4f}\\\\n'.format(top_closed_fraction))\\n tag_file.write('Open class fraction of all identified POS tags: {:.4f}\\\\n'.format(full_open_fraction))\\n tag_file.write('Closed class fraction of all identified POS tags: {:.4f}'.format(full_closed_fraction))\\n print('Done with POS-tagging.')\\n\\n # Perform dependency parsing related analysis\\n def _get_dlt(_parent, _children):\\n \\\"\\\"\\\" Computes the integration cost at the head of dependency relations identified within the input sentence,\\n according to the Dependency Locality Theory. \\\"\\\"\\\"\\n dlt_cost = 0\\n for child in _children:\\n # Determine the span length between the child and parent node\\n left = min(_parent.i, child.i)\\n right = max(_parent.i, child.i)\\n for j in range(left + 1, right):\\n # Identify discourse referents present within the determined span\\n if 'NN' in parse[j].tag_ or 'VB' in parse[j].tag_:\\n dlt_cost += 1\\n # Check if the parent node is also occupied by a new discourse referent\\n if 'NN' in _parent.tag_ or 'VB' in _parent.tag_:\\n dlt_cost += 1\\n return dlt_cost\\n\\n corpus_spans = list()\\n corpus_costs = list()\\n # Compute the mean dependency arc length and DLT integration cost for each sentence within the corpus\\n for parse in parses:\\n sent_spans = list()\\n sent_costs = list()\\n for parent in parse:\\n children = [w for w in parent.lefts] + [w for w in parent.rights]\\n if len(children) == 0:\\n continue\\n parent_spans = [abs(parent.i - child.i) for child in children]\\n sent_spans += parent_spans\\n sent_costs += [_get_dlt(parent, children)]\\n # Collect means\\n corpus_spans += [np.mean(sent_spans)]\\n corpus_costs += [np.mean(sent_costs)]\\n\\n # Calculate SVO fraction (ultimately did not yield any interesting insights)\\n clause_triples = list()\\n svo_count = 0\\n other_count = 0\\n for parse in parses:\\n # Identify subjects, predicates, and objects\\n subjects = [[word.i, word.head.i] for word in parse if 'subj' in word.dep_ and word.head.pos_ == 'VERB']\\n objects = [[word.head.i, word.i] for word in parse if 'obj' in word.dep_]\\n for subj_list in subjects:\\n for obj_list in objects:\\n if subj_list[-1] == obj_list[0]:\\n clause_triple = subj_list + obj_list[-1:]\\n clause_triples.append(clause_triple)\\n # Check if isolated triples are in the SVO order, increment counter if so\\n if clause_triple[0] < clause_triple[1] < clause_triple[2]:\\n svo_count += 1\\n else:\\n other_count += 1\\n # Compute word order fractions\\n svo_fraction = svo_count / len(clause_triples)\\n other_fraction = other_count / len(clause_triples)\\n\\n # Write mean sentence-wise dependency arc lengths and DLT integration costs to file\\n parse_log_path = os.path.join(target_dir, '{:s}_parse_stats.txt'.format(corpus_name))\\n with codecs.open(parse_log_path, 'w', encoding='utf8') as parse_file:\\n # Document mean sentence dependency arc length and mean sentence DLT integration cost\\n for i in range(len(corpus_spans)):\\n parse_file.write('{:.4f}\\\\t{:.4f}\\\\n'.format(corpus_spans[i], corpus_costs[i]))\\n # Calculate corpus statistics\\n parse_file.write('=' * 10 + '\\\\n')\\n parse_file.write('Span length max: {:.4f}\\\\n'.format(np.max(corpus_spans)))\\n parse_file.write('Span length min: {:.4f}\\\\n'.format(np.min(corpus_spans)))\\n parse_file.write('Span length mean: {:.4f}\\\\n'.format(np.mean(corpus_spans)))\\n parse_file.write('Span length standard deviation: {:.4f}\\\\n'.format(np.std(corpus_spans)))\\n parse_file.write('=' * 10 + '\\\\n')\\n parse_file.write('DLT cost max: {:.4f}\\\\n'.format(np.max(corpus_costs)))\\n parse_file.write('DLT cost min: {:.4f}\\\\n'.format(np.min(corpus_costs)))\\n parse_file.write('DLT cost mean: {:.4f}\\\\n'.format(np.mean(corpus_costs)))\\n parse_file.write('DLT cost standard deviation: {:.4f}\\\\n'.format(np.std(corpus_costs)))\\n # Document word order distribution\\n parse_file.write('=' * 10 + '\\\\n')\\n parse_file.write('SVO clauses count: {:d}\\\\n'.format(svo_count))\\n parse_file.write('SVO clauses fraction: {:.4f}\\\\n'.format(svo_fraction))\\n parse_file.write('Other clauses count: {:d}\\\\n'.format(other_count))\\n parse_file.write('Other clauses fraction: {:.4f}'.format(other_fraction))\\n print('Done with dependency parsing.')\",\n \"def build_corpus(self):\\n print(\\\"Inside the build_corpus >>>>>\\\")\\n documentsCount = 0\\n documents = self.documents\\n\\t\\t\\n with open(self.documents_path) as file:\\n for documents in file.readlines():\\n documents = documents.rstrip('}\\\\n ').strip('0\\\\t').strip('1\\\\t').split(' ')\\n documentsCount = documentsCount +1\\n self.documents.append(documents)\\n\\t\\t\\t\\n self.number_of_documents = documentsCount\",\n \"def overall_annotation_function(path_to_species_trees, path_to_gene_trees, project_name = \\\"myproject\\\", path_to_ranger_outputs = \\\"\\\", ):\\r\\n\\r\\n #initially gather the names of the datasets from the species_trees folder\\r\\n dataset_names = gather_dataset_names(path_to_species_trees, project_name, \\\"_CC\\\")\\r\\n #create an object of class PhyloData for each unique dataset\\r\\n phylodata_objects = []\\r\\n for name in dataset_names:\\r\\n phylodata_objects.append(annotate_classes.PhyloData(name))\\r\\n #for each object, have it try and assign itself the correct files\\r\\n print(\\\"populating phylodata objects\\\")\\r\\n populate_objects(phylodata_objects, project_name, path_to_species_trees, path_to_gene_trees, path_to_ranger_outputs)\\r\\n #run the visualizer for each object\\r\\n parse_and_visualize(phylodata_objects, project_name)\",\n \"def parse(file_name, user, agenda_type, db):\\n db.execute('''select section from roll where onyen=%(onyen)s''', dict(onyen=user))\\n row = db.fetchone()\\n section = None if row is None else row.section\\n\\n # Get Recitation zoom\\n db.execute(\\\"\\\"\\\"select url from zoom where type='recitation'\\\"\\\"\\\")\\n row = db.fetchone()\\n recitation_zoom_url = row.url if row else None\\n\\n # Get lecture zoom\\n lecture_zoom_urls = []\\n if section in ['001', '003']:\\n db.execute(\\\"\\\"\\\"select url from zoom where type='lecture' and section='001'\\\"\\\"\\\")\\n lecture_zoom_urls.append(db.fetchone().url)\\n if section in ['002', '003']:\\n db.execute(\\\"\\\"\\\"select url from zoom where type='lecture' and section='002'\\\"\\\"\\\")\\n lecture_zoom_urls.append(db.fetchone().url)\\n\\n # Get checklist information\\n checklist_info = get_checklist_info(db, user, agenda_type)\\n\\n if agenda_type == 'la':\\n first_day_of_class = date(2021, 1, 12)\\n else:\\n first_day_of_class = date(2021, 1, 19)\\n last_day_of_classes = date(2021, 5, 5)\\n today = date.today()\\n with open(file_name, \\\"rt\\\") as fp:\\n agenda = fp.read().split(\\\"\\\\n| \\\")\\n day = first_day_of_class\\n result = []\\n for one_days_details in agenda:\\n lines = one_days_details.split(\\\"\\\\n\\\")\\n title = lines[0]\\n output_lines = []\\n for line in lines[1:]:\\n if line.startswith(\\\"S \\\"):\\n line = slide_line(line)\\n elif line.startswith(\\\"#\\\"):\\n line = comment_line(line, user)\\n elif line.startswith(\\\"Z\\\"):\\n line = zoom_line(line, day, section, lecture_zoom_urls, recitation_zoom_url)\\n elif line.startswith(\\\"CL\\\"):\\n line = checklist_line(line, day, checklist_info)\\n output_lines.append(line)\\n when = set_when(day, today)\\n\\n result.append(\\n {\\\"date\\\": day, \\\"title\\\": title, \\\"when\\\": when,\\n \\\"body\\\": renderMarkdown(renderTemplate(\\\"\\\\n\\\".join(output_lines)))})\\n day = increment_day(day, last_day_of_classes, result, agenda_type)\\n return result\",\n \"def _prepare_analysis_input(self, documents):\\n subdoc_to_doc_map = {}\\n wordtype_to_number = {}\\n number_to_wordtype = []\\n wordtypes = {}\\n \\n # prevent duplicating work\\n if os.path.exists(self.wordtype_file):\\n return\\n \\n try:\\n # First find singletons\\n if self.remove_singletons:\\n word_type_count_threshold = max(1, int(math.log(documents.count(), 10)) - 2)\\n temp_word_type_counts = {}\\n for doc_index, doc in enumerate(documents):\\n tokens = self.tokenize(doc.get_content())\\n for token, token_start in tokens:\\n temp_word_type_counts[token] = temp_word_type_counts.setdefault(token, 0) + 1\\n for word_type, count in temp_word_type_counts.iteritems(): # add singletons to stopword list\\n if count <= word_type_count_threshold:\\n self._excluded_words[word_type] = True\\n with io.open(self.excluded_words_file, 'w', encoding='utf-8') as ex_f:\\n ex_f.write(unicode(json.dumps(self._excluded_words)))\\n \\n haltwords = dict(self.stopwords)\\n haltwords.update(self._excluded_words)\\n # Second find bigrams, iterate through documents and train.\\n if self.find_bigrams:\\n from import_tool.analysis.bigram_finder import BigramFinder\\n bigram_finder = BigramFinder(stopwords=haltwords)\\n for doc_index, doc in enumerate(documents):\\n bigram_finder.train(doc_index, self.tokenize(doc.get_content()))\\n bigram_finder.print()\\n \\n # Third, we're going to stem words\\n if self.stem_words:\\n from import_tool.analysis.stemmer import Stemmer\\n stemmer = Stemmer(self._working_dir, self.base_dir)\\n \\n # for each document tokenize and map tokens to numbers to avoid regex problems before passing data to Mallet\\n with io.open(self.mallet_input_file, 'w', encoding='utf-8') as w:\\n with io.open(self.start_index_file, 'w', encoding='utf-8') as w2:\\n count = 0\\n subcount = 0\\n for doc_index, doc in enumerate(documents):\\n doc_content = unicode(doc.get_content())\\n count += 1\\n subdocuments = self.create_subdocuments(doc_index, doc_content)\\n token_start_index_offset = 0 # needed to make sure the start index remains correct once the document is re-merged\\n for subdoc_name, subdoc_content in subdocuments:\\n if subcount > 0:\\n w2.write(u'\\\\n')\\n subcount += 1\\n subdoc_to_doc_map[subdoc_name] = doc_index\\n tokens = self.tokenize(subdoc_content)\\n \\n if self.find_bigrams:\\n tokens = bigram_finder.combine(tokens, subdoc_content)\\n \\n token_numbers = []\\n token_start_indices = []\\n only_tokens = []\\n tokens_temp = []\\n for tok, tok_start in tokens:\\n only_tokens.append(tok)\\n tokens_temp.append([tok, tok_start + token_start_index_offset])\\n tokens = tokens_temp\\n tokens_temp = None\\n if self.stem_words:\\n stemmed_tokens = stemmer.stem(only_tokens)\\n else:\\n stemmed_tokens = only_tokens\\n for tup, tok_stem in zip(tokens, stemmed_tokens):\\n tok, tok_start = tup\\n wordtypes[tok] = True\\n wordtypes[tok_stem] = True\\n try:\\n tok_num = wordtype_to_number[tok_stem]\\n except:\\n tok_num = len(wordtype_to_number)\\n number_to_wordtype.append(tok_stem)\\n wordtype_to_number[tok_stem] = tok_num\\n token_numbers.append(unicode(tok_num))\\n token_start_indices.append([tok, tok_start])\\n text = u' '.join(token_numbers)\\n w.write(u'{0} all {1}\\\\n'.format(subdoc_name, text))\\n w2.write(unicode(json.dumps(token_start_indices)))\\n token_start_index_offset += len(subdoc_content)\\n for tok, tok_start in tokens:\\n try:\\n assert doc_content[tok_start:tok_start+len(tok)].lower() == tok.lower()\\n except:\\n print(tok_start)\\n print(len(tok))\\n print('\\\"'+doc_content[tok_start:tok_start+len(tok)].lower()+'\\\"')\\n print('\\\"'+tok.lower()+'\\\"')\\n raise\\n if not count:\\n raise Exception('No files processed.')\\n # record which subdocuments belong to which documents\\n with io.open(self.subdoc_to_doc_map_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(subdoc_to_doc_map)))\\n with io.open(self.wordtype_to_number_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(wordtype_to_number)))\\n with io.open(self.number_to_wordtype_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(number_to_wordtype)))\\n with io.open(self.wordtype_file, 'w', encoding='utf-8') as w:\\n w.write(unicode(json.dumps(wordtypes)))\\n except: # cleanup\\n self._cleanup(self.mallet_input_file)\\n self._cleanup(self.subdoc_to_doc_map_file)\\n self._cleanup(self.wordtype_to_number_file)\\n self._cleanup(self.number_to_wordtype_file)\\n self._cleanup(self.wordtype_file)\\n self._cleanup(self.excluded_words_file)\\n raise\",\n \"def makeHTMLIndexFile(self):\\n part1 = \\\"\\\"\\\"<html>\\n <body>\\n <title>Index</title>\\n <div id=\\\"pg_body\\\">\\n <div id=\\\"testSuitesTitle\\\">TestSuites</div>\\n <div id=\\\"resultsTitle\\\">Results</div>\\n <div id=\\\"testSuites\\\">\\n \\\"\\\"\\\"\\n part2 = self.makeLinks()\\n part3 = \\\"\\\"\\\"</div>\\n <div id=\\\"results\\\">\\n <iframe id=\\\"loadHTMLResults\\\" name=\\\"loadHTMLResults\\\" frameborder=\\\"0\\\" src=\\\"statistics.html\\\" style=\\\"height:100%;width:100%;\\\"></iframe>\\n </div>\\n <div id=\\\"footer\\\">Test Engineer(s) :\\\"\\\"\\\"+testEngineers+\\\"\\\"\\\"</div>\\n </div>\\n </body>\\n </html>\\n\\t\\t<style>\\n\\t\\tbody{\\n margin:0;\\n }\\n\\t\\t#pg_body{\\n\\t\\twidth=100%;\\n\\t\\ttext-align:center;\\n\\t\\t}\\n\\t\\t#testSuitesTitle{\\n\\t\\twidth:25%;\\n\\t\\tfloat:left;\\n\\t\\tbackground-color:#6495ED;\\n\\t\\tfont-weight:bold;\\n\\t\\tcolor:white;\\n\\t\\t}\\n\\t\\t#resultsTitle{\\n\\t\\twidth:75%;\\n\\t\\tfloat:right;\\n\\t\\tbackground-color:#6495ED;\\n\\t\\tfont-weight:bold;\\n\\t\\tcolor:white;\\n\\t\\t}\\n\\t\\t#testSuites{\\n\\t\\twidth:25%;\\n\\t\\tfloat:left;\\n\\t\\tbackground-color:lightgrey;\\n\\t\\tfont-weight:bold;\\n\\t\\ttext-align:left;\\n\\t\\theight:94%;\\n\\t\\toverflow:scroll;\\n\\t\\t}\\n\\t\\t#results{\\n\\t\\twidth:75%;\\n\\t\\tbackground-color:white;\\n\\t\\tfloat:right;\\n\\t\\ttext-align:left;\\n\\t\\theight:94%;\\n\\t\\toverflow:scroll;\\n\\t\\t}\\n\\t\\t#footer{\\n\\t\\twidth:100%;\\n\\t\\ttext-align:left;\\n\\t\\tcolor:lightgrey;\\n\\t\\tbackground-color:#6495ED;\\n\\t\\t}\\n\\t\\t</style>\\n \\\"\\\"\\\"\\n \\n page = (part1+part2+part3)\\n f = open(self.dir+'/index.html','w')\\n f.write(page)\\n f.close\",\n \"def append_corpus(output):\\n files = []\\n output_path = output + \\\"/ig/\\\" + \\\"ig_corpus.txt\\\"\\n for root, directories, filenames in os.walk(output + \\\"/ig/\\\"):\\n for filename in filenames:\\n files.append(os.path.join(root, filename))\\n corpusfiles = filter(lambda x: \\\".txt\\\" in x, files)\\n if not os.path.exists(os.path.dirname(output_path)):\\n os.makedirs(os.path.dirname(output_path))\\n with open(output_path, \\\"w+\\\") as corpusFile:\\n for file in corpusfiles:\\n fileH = open(file, \\\"r\\\")\\n corpusFile.write(fileH.read())\",\n \"def summarize_corpus():\\n\\t\\n\\t# get metadata\\n\\t#get_metadata.from_TEIP5(wdir, corpus_inpath, \\\"metadata\\\", md_mode)\\n\\t\\n\\t# visualize some metadata\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"author-continent\\\")\\n\\tvisualize_metadata.describe_corpus(wdir, md_csv, \\\"author-country\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"language\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre_hist\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre_x\\\")\\n\\tvisualize_metadata.plot_pie(wdir, md_csv, \\\"subgenre\\\")\\n\\n\\tvisualize_metadata.describe_corpus(wdir, md_csv, \\\"subgenre\\\")\\n\\t#visualize_metadata.describe_corpus(wdir, md_csv, \\\"gender\\\")\\n\\t\\n\\t# make some counts\\n\\tmd_table = pd.DataFrame.from_csv(os.path.join(wdir, md_csv), header=0)\\n\\tnum_texts = len(md_table)\\n\\t#num_language = len(md_table.groupby([\\\"language\\\"]))\\n\\t#num_continent = len(md_table.groupby([\\\"author-continent\\\"]))\\n\\t#num_countries = len(md_table.groupby([\\\"author-country\\\"]))\\n\\t#num_authors = len(md_table.groupby([\\\"author-name\\\"]))\\n\\tnum_authors = len(md_table.groupby([\\\"author-name\\\"]))\\n\\tnum_subgenre = len(md_table.groupby([\\\"subgenre\\\"]))\\n\\t#num_subgenre_x = len(md_table.groupby([\\\"subgenre_x\\\"]))\\n\\t#fr_subgenre_hist = md_table.groupby([\\\"subgenre_hist\\\"]).count()\\n\\t#num_historical = fr_subgenre_hist[\\\"idno\\\"][\\\"historical\\\"]\\n\\t#num_not_historical = fr_subgenre_hist[\\\"idno\\\"][\\\"not_historical\\\"]\\n\\t\\n\\t\\n\\td = {\\\"texts\\\":[num_texts], \\n\\t#\\\"languages\\\":[num_language],\\n\\t#\\\"continents\\\":[num_continent],\\n\\t#\\\"countries\\\":[num_countries],\\n\\t\\\"authors\\\":[num_authors],\\n\\t#\\\"subgenre_x\\\":[num_subgenre_x],\\n\\t\\\"subgenre\\\":[num_subgenre]}\\n\\t#\\\"num_historical\\\":[num_historical],\\n\\t#\\\"num_not_historical\\\":[num_not_historical]}\\n\\t\\n\\t\\n\\t\\n\\tcount_fr = pd.DataFrame(d)\\n\\tcount_fr.to_csv(os.path.join(wdir, \\\"corpus-description.csv\\\"), sep=\\\",\\\", header=True)\\n\\tprint(\\\"Done: summarize corpus\\\")\",\n \"def fileparser(file_path,graph_path,keyword_path,nkeyword_path,anchor_path):\\n\\n\\tf = open(file_path, 'r')\\n\\n\\t#TODO perhaps a sanity check, see if file exists and is well formated, also checking dirs\\n\\n\\t#main event\\n\\t#feed the file contents to our HTML parser, to process it\\n\\thtml_code = f.read().decode('utf-8')\\n\\thtml_parser = OurHTMLParser()\\n\\thtml_parser.feed(html_code)\\n\\n\\t#keep the results\\n\\tanchors = html_parser.anchors\\n\\tkeyword_idx = html_parser.keyword_idx\\n\\tnkeyword_idx = html_parser.nkeyword_idx\\n\\n\\t#get the main file name: /home/user/fileX.html -> fileX\\n\\tfullname = os.path.basename(file_path)\\n\\tname = os.path.splitext(fullname)[0]\\n\\n\\t#converting graphs and indices to save\\n\\tgraph = gengraph(keyword_idx)\\n\\tkeyword_dic = idx2json(keyword_idx)\\n\\tnkeyword_dic = idx2json(nkeyword_idx)\\n\\n\\tgraph_filepath = os.path.join(graph_path,name) + '_graph' + '.gml'\\n\\tkeyword_filepath = os.path.join(keyword_path,name) + '_keywords' + '.json'\\n\\tnkeyword_filepath = os.path.join(nkeyword_path,name) + '_non_keywords' + '.json'\\n\\tanchor_filepath = os.path.join(anchor_path,name) + '_anchors' + '.json'\\n\\n\\n\\tnx.write_gml(graph,graph_filepath)\\n\\t\\t\\n\\twith open(keyword_filepath, 'w') as keyword_f:\\n\\t\\tjson.dump(keyword_dic,keyword_f)\\n\\n\\twith open(nkeyword_filepath, 'w') as nkeyword_f:\\n\\t\\tjson.dump(nkeyword_dic,nkeyword_f)\\n\\n\\twith open(anchor_filepath, 'w') as anchor_f:\\n\\t\\tjson.dump(anchors,anchor_f)\",\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 main():\\n args = utils.read_arguments(__doc__)\\n documents = []\\n filenames = list(traverse_directory(args[\\\"input_dirpath\\\"],'*clean*.txt'))\\n labels_dirname = args[\\\"labels_dirpath\\\"]\\n labels_from_json = get_all_labels_from_json(labels_dirname)\\n for filename in tqdm(filenames):\\n with AnnotatedIBMFactory(filename) as instance_extractor:\\n filename_key = filename.split(\\\"/\\\")[-1]\\n document = instance_extractor.build_document(\\n labels_from_json[filename_key])\\n documents.append(document)\\n utils.pickle_to_file(documents, args['output_file'])\",\n \"def load_enron():\\n connection=apsw.Connection(\\\"enron.db\\\")\\n cursor=connection.cursor()\\n\\n \\n cursor.execute(\\\"CREATE VIRTUAL TABLE corpus USING fts4(rowid, title, body);\\\")\\n\\n\\n enrondir = \\\"/Users/jonas/Downloads/enron_mail_20110402/maildir\\\"\\n\\n pi = 0\\n \\n for dirpath, dirnames, filenames in os.walk(enrondir):\\n for filename in filenames:\\n try:\\n subject, body = enron.parse_file(os.path.join(dirpath, filename))\\n \\n cursor.execute(\\\"insert into corpus values(?,?,?)\\\", (pi, subject, body))\\n except:\\n pass\\n \\n if pi % 100 == 0:\\n print pi\\n pi += 1\",\n \"def boldExtract(genera):\\r\\n # Prepare Web Service Endpoint for BOLD's Public Data Portal API\\r\\n # Appending BOLD's base URL to each genera from the NSR list\\r\\n base_url = 'http://v4.boldsystems.org/index.php/API_Public/combined?taxon='\\r\\n source_urls = list(map(lambda x: \\\"{}{}{}\\\".\\r\\n format(base_url, x, '&format=tsv'), genera))\\r\\n\\r\\n # Download sequence data from BOLD using list of url's\\r\\n print('Beginning sequence data retrieval...')\\r\\n counter = 0\\r\\n for url in source_urls:\\r\\n r = http.request('GET', url)\\r\\n name = genera[counter]\\r\\n counter += 1\\r\\n with open(args.outdir1+\\\"/\\\"+name+\\\".tsv\\\", \\\"wb\\\") as fcont:\\r\\n fcont.write(r.data)\",\n \"def processFiles(self, testDataFiles, sourceDirectory, sourceFilename, outputFile, language, IncludeSourceInOutput, maxRunTime, interpreter, maxOutputLines, AutoGraderVersion):\\n \\n print (\\\"***Start***\\\")\\n self.sourceDirectory = sourceDirectory\\n #self.TopLevelFilesFound = []\\n #self.subdirs = {} #dictionary of sbudirectories\\n \\n\\n #delete the output file\\n self._removeFile(outputFile)\\n\\n #create the html header. Use the name of the source directory as the header text.\\n self._MakeHtmlHeader(outputFile, language, \\\"AutoGrader\\\", os.path.split(sourceDirectory)[-1])\\n\\n #--------- C++ ---------\\n if language == 'C++':\\n #call _recursivelyFindFiles to generate the self.TopLevelFilesFound and self.SubDirsFound lists\\n (self.TopLevelFilesFound, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, \\\".cpp\\\")\\n (tlf, sd) = self._recursivelyFindFiles(sourceDirectory, \\\".cc\\\")\\n\\n self.TopLevelFilesFound += tlf\\n self.SubDirsFound += sd\\n\\n exeFile = sourceDirectory + '/' + 'AG.out'\\n\\n\\n def doInnerCppProcessing(sourceFiles, gradingTextLabel): #sourceFiles is a list of filenames\\n #get/report the analytics on the source files\\n self._reportFileAnalytics(sourceFiles, outputFile, language)\\n \\n #include source code here if selected\\n if IncludeSourceInOutput == True:\\n self._formatSource(sourceFiles, outputFile, language)\\n\\n #compile the file\\n self._removeFile(exeFile)\\n self._compileCppFiles(interpreter, sourceFiles, outputFile, exeFile, maxOutputLines)\\n\\n if os.path.isfile(exeFile): #did the compilation succeed?\\n print(\\\"Compilation succeeded.\\\")\\n self._reportErrorMsg(\\\"Compilation succeeded.<br>\\\", outputFile) \\n if len(testDataFiles) == 0: #no input data required\\n exec_time = self._shellExec('\\\"'+exeFile+'\\\"', '', '', outputFile, maxRunTime, maxOutputLines, sourceDirectory)\\n print (format(\\\"%0.4f\\\" % exec_time) + \\\" secs.\\\")\\n else:\\n for dataFile in testDataFiles:\\n self._reportDataFile(dataFile, outputFile)\\n \\n #print the name of the datafile to indicate progress. This is a temporary solution to allow us to identify\\n #programs that don't end. Ultimately, we will want to use a fork()/wait() pair and be able to set a max run time.\\n _, filename = os.path.split(dataFile)\\n print (\\\"processing '\\\" + filename + \\\"'...\\\")\\n exec_time = self._shellExec('\\\"'+exeFile+'\\\"', '', dataFile, outputFile, maxRunTime, maxOutputLines, sourceDirectory)\\n\\n print (format(\\\"%0.4f\\\" % exec_time) + \\\" secs.\\\")\\n self._reportExecTime(exec_time, outputFile)\\n print ()\\n else:\\n print(\\\"Executable not found. Check compiler output.\\\")\\n self._reportErrorMsg(\\\"Executable not found. Check compiler output.<br>\\\", outputFile)\\n\\n self._removeFile(exeFile)\\n self._gradingBox(sourceDirectory, sourceFiles[0], outputFile, gradingTextLabel)\\n return\\n\\n\\n #compile the names of single-file programs and multi-file programs into a single list to enable sorting.\\n #note that this is a list of tuples. The first element is the name of the file or directory.\\n #the second element specifies if the first is a filename or directory name.\\n filesAndDirs = []\\n for x in self.TopLevelFilesFound:\\n filesAndDirs.append((x,'file')) #add files\\n\\n for x in self.SubDirsFound:\\n filesAndDirs.append((x,'dir')) #add directories\\n\\n filesAndDirs.sort() #sort the list\\n\\n print (\\\"**********************************\\\")\\n for x in filesAndDirs:\\n print (x)\\n print (\\\"**********************************\\\")\\n\\n #now, process the sorted list depending on whether it is a single-file or directory.\\n for n, x in enumerate(filesAndDirs):\\n if x[1] == 'file': #this is a file\\n #we will process source code in the top-level directory as single-file programs \\n print ('=======================================================')\\n print (x[0])\\n print ('=======================================================')\\n #doInnerCppProcessing([x[0]], 'student'+str(n))\\n doInnerCppProcessing([x[0]], 'student')\\n elif x[1] == 'dir': #this is a directory\\n #we will process source code in sub-directories of the top-level directory as multi-file programs\\n print ('=======================================================')\\n print (x[0])\\n print ('=======================================================')\\n sourceFiles = []\\n self._findFilesInDir(x[0], \\\".cpp\\\", sourceFiles) #add .h files to the files list\\n self._findFilesInDir(x[0], \\\".cc\\\", sourceFiles) #add .h files to the files list\\n self._findFilesInDir(x[0], \\\".h\\\", sourceFiles) #add .h files to the files list\\n self._findFilesInDir(x[0], \\\".hpp\\\", sourceFiles) #add .hpp files to the files list\\n #doInnerCppProcessing(sourceFiles, 'student'+str(n))\\n doInnerCppProcessing(sourceFiles, 'student')\\n else:\\n print (\\\"***** EXCEPTION: Entity for processing is neither a file nor a directory. *****\\\")\\n \\n \\n self._reportErrorMsg('<br><br><b>**** ' + str(len(self.TopLevelFilesFound) + len(self.SubDirsFound)) + ' project(s) processed. ****</b>', outputFile)\\n\\n #--------- Python ---------\\n elif language == 'Python':\\n print ('Find sub dirs')\\n #first, find all the subdirectories that contain a python source file that matches <sourceFilename>\\n #(_, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, sourceFilename)\\n #(_, self.SubDirsFound) = self._recursivelyFindFiles(sourceDirectory, '/' + sourceFilename)\\n self.SubDirsFound = self._recursivelyFindFile(sourceDirectory, sourceFilename)\\n\\n print ('Find TLFs')\\n #now, find all .py files in the top level directory.\\n self.TopLevelFilesFound = self._findFilesInDir(sourceDirectory, \\\".py\\\", [])\\n #(self.TopLevelFilesFound, _) = self._recursivelyFindFiles(sourceDirectory, \\\".py\\\")\\n\\n\\n #does this block do anything??? 9/25/16\\n #if sourceFilename == \\\"\\\" or sourceFilename == None:\\n #self._recursivelyFindFiles(sourceDirectory, \\\".py\\\")\\n #else:\\n #self._recursivelyFindFiles(sourceDirectory, '/' + sourceFilename)\\n\\n\\n def doInnerPythonProcessing(sourceFiles, topLevelModule, gradingTextLabel): #sourceFiles is a list of filenames\\n #get/report the analytics on the source files\\n self._reportFileAnalytics(sourceFiles, outputFile, language)\\n \\n #include source code here if selected\\n if IncludeSourceInOutput == True:\\n self._formatSource(sourceFiles, outputFile, language)\\n\\n\\n if len(testDataFiles) == 0: #no input data required\\n exec_time = self._shellExec('\\\"'+interpreter+'\\\"', topLevelModule, '', outputFile, maxRunTime, maxOutputLines, sourceDirectory)\\n print (format(\\\"%0.4f\\\" % exec_time) + \\\" secs.\\\")\\n self._reportExecTime(exec_time, outputFile)\\n else:\\n for dataFile in testDataFiles:\\n self._reportDataFile(dataFile, outputFile)\\n \\n #print the name of the datafile to indicate progress. This is a temporary solution to allow us to identify\\n #programs that don't end. Ultimately, we will want to use a fork()/wait() pair and be able to set a max run time.\\n _, filename = os.path.split(dataFile)\\n print (\\\"processing '\\\" + filename + \\\"'...\\\")\\n exec_time = self._shellExec('\\\"'+interpreter+'\\\"', topLevelModule, dataFile, outputFile, maxRunTime, maxOutputLines, sourceDirectory)\\n\\n print (format(\\\"%0.4f\\\" % exec_time) + \\\" secs.\\\")\\n self._reportExecTime(exec_time, outputFile)\\n print ()\\n \\n self._gradingBox(sourceDirectory, sourceFiles[0], outputFile, gradingTextLabel)\\n return\\n\\n\\n #compile the names of single-file programs and multi-file programs into a single list to enable sorting.\\n #note that this is a list of tuples. The first element is the name of the file or directory.\\n #the second element specifies if the first is a filename or directory name.\\n filesAndDirs = []\\n for x in self.TopLevelFilesFound:\\n filesAndDirs.append((x,'file')) #add files\\n\\n for x in self.SubDirsFound:\\n filesAndDirs.append((x,'dir')) #add directories\\n\\n filesAndDirs.sort() #sort the list\\n\\n print (\\\"**********************************\\\")\\n for x in filesAndDirs:\\n print (x)\\n print (\\\"**********************************\\\")\\n\\n\\n #now, process the sorted list depending on whether it is a single-file or directory\\n for n, x in enumerate(filesAndDirs):\\n if x[1] == 'file': #this is a file\\n #we will process source code in the top-level directory as single-file programs \\n print ('=======================================================')\\n print (x[0])\\n print ('=======================================================')\\n #doInnerPythonProcessing([x[0]], x[0], 'student'+str(n))\\n doInnerPythonProcessing([x[0]], x[0], 'student')\\n elif x[1] == 'dir': #this is a directory\\n #we will process source code in sub-directories of the top-level directory as multi-file programs\\n print ('=======================================================')\\n print (x[0])\\n print ('=======================================================')\\n #doInnerPythonProcessing(self._findFilesInDir(x[0],\\\".py\\\", []), x[0]+'/'+sourceFilename, 'student'+str(n))\\n doInnerPythonProcessing(self._findFilesInDir(x[0],\\\".py\\\", []), x[0]+'/'+sourceFilename, 'student')\\n else:\\n print (\\\"***** EXCEPTION: Entity for processing is neither a file nor a directory. *****\\\")\\n \\n \\n self._reportErrorMsg('<br><br><b>**** ' + str(len(self.TopLevelFilesFound) + len(self.SubDirsFound)) + ' project(s) processed. ****</b>', outputFile)\\n\\n\\n\\n #--------- Unknown language ---------\\n else:\\n print ('Unknown language choice: ' + language)\\n return\\n\\n \\n self._reportErrorMsg('<br><font face=\\\"verdana\\\">', outputFile)\\n self._reportErrorMsg('Report Generator: AutoGrader v' + AutoGraderVersion + '<br>', outputFile)\\n if language == 'C++':\\n self._reportErrorMsg('C++ Compiler: ' + interpreter + '<br>', outputFile)\\n elif language == 'Python':\\n self._reportErrorMsg('Python Interpreter: ' + interpreter + '<br>', outputFile)\\n else:\\n self._reportErrorMsg('Compiler/Interpreter: Not Specified <br>', outputFile)\\n self._reportErrorMsg('<br></font>', outputFile)\\n\\n self._writeOutput('''<font size='+6'><input type=\\\"button\\\" style=\\\"font-size:20px;width:250px\\\" value=\\\"Download Feedback\\\" OnClick=\\\"download_feedback_file()\\\">\\n <br><br></font></form>''', outputFile)\\n\\n\\n feedback_filename = 'feedback.txt'\\n download_script = '''\\n <script type=\\\"text/javascript\\\">\\n\\n function download_feedback_file()\\n {\\n \\n x = document.getElementsByName(\\\"student\\\").length\\n msg = \\\"\\\"\\n for (i=0; i<x; i++)\\n {\\n msg = msg + document.getElementsByName(\\\"student\\\")[i].value\\n msg = msg + '\\\\\\\\n\\\\\\\\n-------------------------------------------------------\\\\\\\\n'\\n }\\n\\n var element = document.createElement('a');\\n element.setAttribute('href', 'data:text/plain;charset=utf-8,' + encodeURIComponent(msg));\\n element.setAttribute('download', \\\"''' + feedback_filename + '''\\\");\\n\\n element.style.display = 'none';\\n document.body.appendChild(element);\\n\\n element.click();\\n\\n document.body.removeChild(element);\\n }\\n\\n </script>\\n </body></html>\\n\\n '''\\n self._writeOutput(download_script, outputFile)\\n\\n self._writeOutput('</body></html>', outputFile)\\n\\n \\n #open the output file using the default application\\n cmd = 'open \\\"' + outputFile + '\\\"'\\n os.system(cmd)\\n \\n print (\\\"***End***\\\\n\\\")\\n\\n\\n\\n #--------- ---------\",\n \"def copy_project_docs(srctree):\\n docdir = os.path.join(srctree, 'Doc')\\n\\n # This block shouldn't be here, but I do not yet know how to\\n # embed this in ReST files.\\n extra_info = {}\\n if os.path.exists(os.path.join(docdir, 'website.lst')):\\n fd = open(os.path.join(docdir, 'website.lst'))\\n for ln in fd.readlines():\\n if ln.startswith('#'): continue\\n fields = ln.split(',')\\n extra_info[fields[0].strip()] = {\\n 'section': fields[1].strip(),\\n 'priority': int(fields[2].strip()),\\n }\\n\\n docs = [ os.path.join(docdir, fn)\\n for fn in os.listdir(docdir) if fn.endswith('.txt') ]\\n docs.append(os.path.join(srctree, 'Install.txt'))\\n docs.append(os.path.join(srctree, 'NEWS.txt'))\\n docs.append(os.path.join(docdir, 'tutorial', 'tutorial.txt'))\\n docs.append(os.path.join(docdir, 'tutorial_embed', 'extending_objc_with_python.txt'))\\n NAMES = {\\n os.path.join(srctree, 'Examples', '00ReadMe.txt') : 'Examples.txt',\\n }\\n docs.extend(NAMES)\\n\\n alldocs = {}\\n\\n for fname in docs:\\n print \\\"-\\\", fname\\n docinfo = {}\\n\\n bn = NAMES.get(fname)\\n if bn is None:\\n bn = os.path.split(fname)[-1]\\n if bn in ('index.txt', 'announcement.txt'):\\n continue\\n if extra_info.has_key(bn):\\n docinfo.update(extra_info[bn])\\n\\n if bn.endswith('.txt'):\\n bn = bn[:-3].lower() + \\\"php\\\"\\n else:\\n bn = bn.lower() + '.php'\\n fd = open(fname)\\n input = fd.read()\\n fd.close()\\n output = docutils.core.publish_string(\\n source = input,\\n source_path = fname,\\n destination_path = bn,\\n writer_name = 'hthtml')\\n \\n output_lines = output.split('\\\\n')\\n for i in range(len(output_lines)):\\n if output_lines[i] == '':\\n break\\n idx = output_lines[i].find(':')\\n if idx == -1:\\n break\\n\\n key = output_lines[i][:idx].strip()\\n value = output_lines[i][idx+1:].strip()\\n docinfo[key] = value\\n\\n output = '\\\\n'.join(output_lines[i:])\\n if not docinfo.has_key('title'):\\n docinfo['title'] = bn\\n alldocs[bn] = docinfo\\n \\n fd = open(os.path.join('docroot', 'doc', bn), 'w')\\n fd.write(PHP_HEADER%docinfo)\\n\\n fd.write(output);\\n\\n fd.write(PHP_FOOTER)\\n\\n # Calculate indices for user and developer documentation\\n docs = alldocs.keys()\\n developer_docs = []\\n user_docs = []\\n\\n for doc in alldocs:\\n if not alldocs[doc].has_key('section'):\\n print \\\"Skipping\\\", doc\\n continue\\n\\n if alldocs[doc]['section'] == 'user':\\n user_docs.append([alldocs[doc]['title'], doc])\\n elif alldocs[doc]['section'] == 'developer':\\n developer_docs.append([alldocs[doc]['title'], doc])\\n\\n def doccmp(a, b):\\n r = cmp(alldocs[a[1]]['priority'], alldocs[b[1]]['priority'])\\n if r != 0: return r\\n\\n return cmp(a[1], b[1])\\n user_docs.sort(doccmp)\\n developer_docs.sort(doccmp)\\n \\n # Rewrite the indices (substitute the current document lists)\\n for fname in ('index.php', 'usage.php', 'developer.php'):\\n fd = open(os.path.join('docroot', 'doc', fname), 'r')\\n index_php = fd.readlines()\\n fd.close()\\n\\n fd = open(os.path.join('docroot', 'doc', fname), 'w')\\n skip = 0\\n for ln in index_php:\\n if not skip:\\n fd.write(ln)\\n if ln.find('/USERDOC') != -1:\\n skip = 0\\n fd.write(ln)\\n elif ln.find('USERDOC') != -1:\\n skip = 1\\n for title, link in user_docs:\\n fd.write('<LI><A HREF=\\\"%s\\\">%s</A>\\\\n'%(link, title))\\n if ln.find('/DEVDOC') != -1:\\n skip = 0\\n fd.write(ln)\\n elif ln.find('DEVDOC') != -1:\\n skip = 1\\n for title, link in developer_docs:\\n fd.write('<LI><A HREF=\\\"%s\\\">%s</A>\\\\n'%(link, title))\\n\\n EXAMPLES = os.path.join('docroot', 'doc', 'examples.php')\\n OUTEXAMPLES = os.path.join('docroot', 'examples', 'index.php')\\n replace_examples_svn(EXAMPLES)\\n if os.path.exists(OUTEXAMPLES):\\n os.unlink(OUTEXAMPLES)\\n shutil.copyfile(EXAMPLES, OUTEXAMPLES)\\n\\n # Copy tutorial files\\n TUTORIAL_ENDINGS = ['.nib', '.py', '-src', '.h', '.m']\\n tutdir = os.path.join(docdir, 'tutorial')\\n files = os.listdir(tutdir)\\n replacements = []\\n for fn in files:\\n for ext in TUTORIAL_ENDINGS:\\n if fn.endswith(ext):\\n dstname = os.path.join('docroot', 'doc', fn)\\n replacements.append(copy_tutorial_file(fn, tutdir, dstname))\\n break\\n replace_tutorial_zips(os.path.join('docroot', 'doc', 'tutorial.php'), replacements)\\n \\n tutdir = os.path.join(docdir, 'tutorial_embed', 'src')\\n files = os.listdir(tutdir)\\n if not os.path.exists(os.path.join('docroot', 'doc', 'src')):\\n os.mkdir(os.path.join('docroot', 'doc', 'src'))\\n for fn in files:\\n for ext in TUTORIAL_ENDINGS:\\n if fn.endswith(ext):\\n dstname = os.path.join('docroot', 'doc', 'src', fn)\\n replacements.append(copy_tutorial_file(fn, tutdir, dstname))\\n break\\n replace_tutorial_zips(os.path.join('docroot', 'doc', 'tutorial.php'), replacements)\\n\\n #print \\\"Don't forget to update docroot/doc/tutorial.php: it's references to\\\"\\n #print \\\"'step3-MainMenu.nib' and 'step12-src' should be changed to ZIP files\\\"\",\n \"def load_gold_standard():\\n\\tstandard = {}\\n\\n\\tfor dirpath, dirnames, filenames in os.walk(PATH_TO_DATA):\\n\\t\\tfor basename in filenames:\\n\\t\\t\\tif basename.endswith(\\\".apf.xml\\\"):\\n\\t\\t\\t\\tprint(os.path.join(dirpath, basename))\\n\\t\\t\\t\\tcounts = numpy.array(numpy.zeros(len(EVENT_TYPES)))\\n\\t\\t\\t\\tcounts, all_events = count_events(os.path.join(dirpath, basename), counts)\\n\\t\\t\\t\\t\\n\\t\\t\\t\\t\\\"\\\"\\\" Excluse all documents with less than 2 event types \\\"\\\"\\\"\\n\\t\\t\\t\\tif numpy.sum(counts) < MIN_EVENT_TYPES:\\n\\t\\t\\t\\t\\tcontinue\\n\\n\\t\\t\\t\\tname = os.path.splitext(os.path.splitext(basename)[0])[0]\\n\\t\\t\\t\\tstandard[name] = (counts, all_events)\\n\\n\\tprint(\\\"Loaded documents: \\\" + str(len(standard)))\\n\\treturn standard\",\n \"def build_index():\\n for site in get_sites():\\n text = read_site(site)\\n while text == False:\\n text = read_site(site) # keep attempting to read until successful\\n index_site(site, text)\",\n \"def build_index():\\n for site in get_sites():\\n text = read_site(site)\\n while text == False:\\n text = read_site(site) # keep attempting to read until successful\\n index_site(site, text)\",\n \"def read_doc(self):\\n self.data_read[:] = []\\n for para in self.document.paragraphs:\\n text = para.text\\n # skip blank lines\\n if text.strip():\\n # remove duplicated spaces\\n text = ' '.join(text.split())\\n # for older versions of final CAPA's\\n self.fill_project_info(text, new_format=False)\\n self.data_read.append(text)\\n\\n # Constant in old & new report format\\n # Batch/Project name\\n # Lead(s)'s name\\n # Reported date\\n for i in range(0, len(self.data_read)):\\n if next((x for x in self.leads if x in self.data_read[i]), None):\\n self.project_info.update({'Project Name': self.data_read[i - 1]})\\n self.project_info.update({'Lead(s)': self.data_read[i]})\\n self.project_info.update({'Date Reported': self.data_read[i + 1]})\\n break\",\n \"def main(argv):\\n\\n defaults = {\\n 'uspto_url': 'http://www.google.com/googlebooks/uspto-patents-applications-biblio.html',\\n 'html_page': None,\\n 'requested_years': [],\\n 'write_to_stdout': False,\\n 'print_to_file': False,\\n 'download': False,\\n 'output_directory': '.'\\n }\\n\\n validate_input(argv, defaults);\\n\\n if not defaults['html_page']:\\n defaults['html_page'] = load_url(uspto_url)\\n soup = BeautifulSoup(html_page)\\n\\n links_per_year = defaultdict(list)\\n links = soup.findAll('a', attrs={ 'href': re.compile('zip$') })\\n logging.info(' found ' + str(len(links)) + ' links')\\n for link in links:\\n logging.debug(' . ' + link['href'])\\n matched_year = re.search( '/([0-9]{4})/', link['href'])\\n if matched_year:\\n links_per_year[matched_year.group(1)].append(link['href'])\\n filtered_dict = links_per_year\\n if requested_years:\\n filtered_dict = { year : links_per_year[year] for year in requested_years }\\n if write_to_stdout:\\n for links in sorted(filtered_dict.itervalues()):\\n for link in links:\\n print link\\n if print_to_file:\\n makedirs(output_directory)\\n for k,v in filtered_dict.iteritems():\\n basename = k + '.txt'\\n filename = output_directory + '/' + basename\\n if os.path.isfile(filename):\\n os.remove(filename)\\n with open(filename, 'a') as text_file:\\n for link in sorted(v):\\n text_file.write(link + '\\\\n')\\n logging.debug(' file ' + basename + ' written to disk')\\n logging.info(' all files written to disk')\\n if download:\\n for year, links in filtered_dict.iteritems():\\n makedirs(os.path.join(output_directory, year))\\n for link in links:\\n try:\\n filename = os.path.join(output_directory, year, link.split('/')[-1])\\n prog = urlgrabber.progress.text_progress_meter()\\n urlgrabber.urlgrab(str(link), filename, progress_obj=prog)\\n except Exception, e:\\n logging.warn(' error while downloading %s: %s' % (link, e))\",\n \"def gather_documents(self):\\n self.document_gatherer.gather_and_save_everything(Constants.path_cord, \\n Constants.path_metadata, \\n Constants.path_linked_documents,\\n Constants.path_unlinked_documents,\\n Constants.path_parsed_documents,\\n Constants.path_all_documents)\\n \\n print(\\\"Done gathering documents.\\\")\",\n \"def readcorpus(index):\\n for docid in range(0, 1001):\\n file = open(PATH + \\\"Document-\\\" + str(docid) + \\\".txt\\\", 'r', encoding=\\\"utf-8\\\")\\n allcontent = file.readlines()\\n stoplist = getstopwords()\\n corpus = []\\n stemmer = PorterStemmer()\\n getatitle(allcontent, corpus)\\n getmetakeywords(allcontent, corpus)\\n getcontent(allcontent, corpus)\\n flagfordate = 0\\n for i in range(0, len(corpus)):\\n if flagfordate == 1:\\n flagfordate = 0\\n continue\\n word = corpus[i]\\n if word in MONTH:\\n if expressionfordateb(corpus, i) is True or expressionfordatef(corpus, i) is True:\\n word = constructdate_expression(corpus, i, index)\\n increasecount(index[0], word, docid)\\n if word in stoplist:\\n continue\\n increasecount(index[1], word, docid)\\n increasecount(index[2], processword(stemmer.stem(word)), docid)\",\n \"def parse_data(self, file_data):\\n if self.data:\\n all_api_reference = self.data.findAll([\\\"h4\\\",\\\"h3\\\"])\\n for api in all_api_reference: \\n title = api.text.replace(' #', '')\\n\\n href = self.parse_link(file_data, api)\\n\\n content = self.parse_content(api)\\n\\n example = self.parse_example(api)\\n\\n section = {\\n 'title': title,\\n 'href': href,\\n 'content': content,\\n 'example': example\\n }\\n self.parsed_data.append(section)\",\n \"def _load_project(self, _thefile='', _lang=''):\\n# messagebox.showwarning('_load_project', 'Entered')\\n# def _load_project(self, thefile):\\n# \\\"\\\"\\\"loads an existing project (.prj) file,adapting it's contents\\n# to the current Simple/Advanced choice\\\"\\\"\\\"\\n #set current project label\\n if len(_lang) > 0:\\n lang = _lang\\n else:\\n lang = self.selected_lang.get()\\n if lang == '':\\n lang = 'en-US'\\n# messagebox.showwarning('_load_project', \\\\\\n# '_lang=>{}<, lang=>{}<'.format(_lang, lang))\\n self.lblProject['text'] = '{} {}'.\\\\\\n format(LOCALIZED_TEXT[lang]['Current Project>'], \\\\\\n self.current_project.get())\\n self.update()\\n\\n linesin = list()\\n #thefile comes from current project label or test\\n if _thefile:\\n thefile = _thefile\\n else:\\n thefile = ospath.normpath(\\\\\\n self.BibTerm + '/' + self.current_project + '.prj')\\n if os.path.exists(thefile):\\n filein = codecs.open(thefile, mode='r', encoding='utf-8')\\n for aline in filein.readlines():\\n # if len(aline.strip()) > 0:\\n if aline.strip():\\n linesin.extend([aline.strip()])\\n filein.close()\\n lines = ''.join(linesin)\\n self.root = etree.fromstring(lines)\\n self.settings = self.root.find(\\\"settings\\\")\\n self.sf0 = self.settings.find(\\\"f0\\\")\\n if self.sf0.get('fallback') != self.dict_in.get():\\n self.dict_in.set(self.sf0.get('fallback'))\\n self.dict_in_changed.set(1)\\n else:\\n self.dict_in_changed.set(0)\\n if self.sf0.get('terms') != self.terms_in.get():\\n self.terms_in.set(self.sf0.get('terms'))\\n self.terms_in_changed.set(1)\\n else:\\n self.terms_in_changed.set(0)\\n if self.sf0.get('old') != self.terms_in.get():\\n self.old_dict.set(self.sf0.get('old'))\\n self.old_dict_changed.set(1)\\n else:\\n self.old_dict_changed.set(0)\\n self.sf1 = self.settings.find(\\\"f1\\\")\\n self.sf2 = self.settings.find(\\\"f2\\\")\\n self.trout = self.root.find(\\\"tree\\\")\\n# self.preferred.set(int(self.sf1.attrib['preferred'] == 'True'))\\n self.txtPrefChar.delete(0.0, 9999.9999)\\n if self.sf1.text != None:\\n self.txtPrefChar.insert(9999.9999, self.sf1.text)\",\n \"def main():\\n in_file_name = \\\"input.txt\\\"\\n out_file_name = \\\"output.txt\\\"\\n\\n in_file = open(in_file_name, 'r')\\n\\n out_file = open(out_file_name, 'w')\\n out_file.write('IlgizZamaleev' + '\\\\n')\\n\\n problems = int(in_file.readline())\\n\\n for i in range(problems):\\n automato = Parser().parse_automata(in_file)\\n\\n tests = int(in_file.readline())\\n\\n out_file.write(str(i + 1) + '\\\\n')\\n\\n for j in range(tests):\\n word = in_file.readline()[:-1]\\n out_file.write(automato.check_word(word) + '\\\\n')\\n\\n in_file.close()\\n out_file.close()\",\n \"def main(argv):\\n argparser = ArgumentParser(\\n description=\\\"Measure agreement of the sentiment corpus.\\\")\\n argparser.add_argument(\\\"dir1\\\",\\n help=\\\"directort containing annotations of the\\\"\\n \\\" first expert\\\")\\n argparser.add_argument(\\\"dir2\\\",\\n help=\\\"directort containing annotations of the\\\"\\n \\\" second expert\\\")\\n args = argparser.parse_args(argv)\\n\\n def check_arg(arg):\\n if not os.path.isdir(args.dir1) or not os.access(args.dir1, os.R_OK):\\n print(\\\"Invalid argument {!r} should be a readable\\\"\\n \\\" directory\\\".format(arg), file=sys.stderr)\\n\\n check_arg(args.dir1)\\n check_arg(args.dir2)\\n annotations = []\\n skip_ids = set()\\n for fpath1 in glob.iglob(os.path.join(args.dir1, \\\"*.tsv\\\")):\\n if not os.access(fpath1, os.R_OK):\\n print(\\\"WARNING: cannot read file {:s}\\\".format(\\n fpath1\\n ), file=sys.stderr)\\n continue\\n base_fpath1 = os.path.basename(fpath1)\\n fpath2 = os.path.join(args.dir2, base_fpath1)\\n if not os.path.exists(fpath2):\\n print(\\\"WARNING: file {:s} not found\\\".format(\\n fpath1\\n ), file=sys.stderr)\\n continue\\n elif not os.access(fpath2, os.R_OK):\\n print(\\\"WARNING: cannot read file {:s}\\\".format(\\n fpath1\\n ), file=sys.stderr)\\n continue\\n read_annotations(annotations, skip_ids, fpath1, 1)\\n read_annotations(annotations, skip_ids, fpath2, 2)\\n annotations = [(coder, item, label)\\n for (coder, item, label) in annotations\\n if item not in skip_ids]\\n at = AnnotationTask(annotations, distance=interval_distance)\\n # print(at.avg_Ao())\\n # print(at.Ae_kappa())\\n print(\\\"Cohen's Kappa: {:.4f}\\\\nKrippendorff's Alpha: {:4f}\\\".format(\\n at.kappa(), at.alpha()\\n ))\",\n \"def main():\\n\\n if os.path.exists(os.path.join(PROCESSED_PATH,\\n 'all_posts_data.csv')):\\n print(\\\"-- all_posts_data.csv found locally - delete interm files if rerun needed\\\")\\n total_df = pd.read_csv(PROCESSED_PATH / 'all_posts_data.csv')\\n else:\\n training_post_filenames = glob.glob(os.path.join(DATA_DIR,\\n 'raw',\\n 'clpsych16-data',\\n 'data',\\n 'training',\\n 'posts', '*.xml'))\\n dev_post_filenames = glob.glob(os.path.join(DATA_DIR,\\n 'raw',\\n 'clpsych16-data',\\n 'data',\\n 'testing',\\n 'posts', '*.xml'))\\n\\n new_posts2017 = glob.glob(os.path.join(DATA_DIR,\\n 'raw',\\n 'clpsych17-test',\\n 'posts', '*.xml'))\\n\\n training_labels = os.path.join(DATA_DIR,\\n 'raw',\\n 'clpsych16-data',\\n 'data',\\n 'training',\\n 'labels.tsv')\\n dev_labels = os.path.join(DATA_DIR,\\n 'raw',\\n 'clpsych16-data',\\n 'data',\\n 'testing',\\n 'labels.tsv')\\n\\n training_df = create_posts_df(training_post_filenames)\\n dev_df = create_posts_df(dev_post_filenames)\\n new_df = create_posts_df(new_posts2017)\\n\\n training_df['corpus_source'] = '2016train_2017train'\\n dev_df['corpus_source'] = '2016test_2017train'\\n new_df['corpus_source'] = '2017test'\\n\\n training_df = merge_post_labels(training_df, training_labels)\\n dev_df = merge_post_labels(dev_df, dev_labels)\\n\\n training_df = merge_author_ranks(training_df)\\n dev_df = merge_author_ranks(dev_df)\\n new_df = merge_author_ranks(new_df)\\n\\n total_df = pd.concat([training_df, dev_df, new_df])\\n\\n test_labels = os.path.join(DATA_DIR,\\n 'raw',\\n 'clpsych17-test',\\n 'test_ids.tsv')\\n total_df.reset_index(inplace=True)\\n total_df = merge_test_ids(total_df, test_labels)\\n label_file = os.path.join(DATA_DIR,\\n 'raw',\\n 'clpsych17-test-labels.tsv')\\n merge_ground_truth(total_df, label_file)\\n output_path = PROCESSED_PATH / 'all_posts_data.csv'\\n \\n\\n # clean body of text\\n total_df['cleaned_body'], total_df['contained_quote'] = zip(*total_df['body'].apply(process_body))\\n total_df['images'] = total_df['body'].apply(process_images)\\n\\n print('--Writing data to {}--'.format(output_path))\\n total_df.to_csv(output_path, index=False)\\n\\n sentences_df = total_df.loc[:, ['post_id', 'cleaned_body', 'label', 'predict_me']]\\n # the following will split posts into sentences and write out to a separate csv\\n split_to_sentences(sentences_df)\",\n \"def main(args):\\r\\n logger.info(\\\"Starting site generation...\\\")\\r\\n logger.info(args)\\r\\n logger.info(\\\"Opening syllabus.yml...\\\")\\r\\n\\r\\n## PUT THIS BACK LATER!!!!!####\\r\\n\\r\\n with open(\\\"/home/tofts/git/upskilling/data/syllabus.yml\\\", \\\"r\\\") as syllabus_file:\\r\\n syllabus = yaml.safe_load(syllabus_file)\\r\\n\\r\\n logger.info(\\\"Found the following modules:\\\")\\r\\n for module in syllabus['modules']:\\r\\n logger.info(f\\\"...{syllabus['modules'][module]['name']}\\\")\\r\\n\\r\\n logger.info(\\\"Opening squad.yml...\\\")\\r\\n with open(\\\"/home/tofts/git/upskilling/data/squad.yml\\\", \\\"r\\\") as squad_file:\\r\\n squad = yaml.safe_load(squad_file)\\r\\n\\r\\n for member in squad['members']:\\r\\n logger.info(f\\\"Found member {member.upper()}\\\")\\r\\n logger.info(f\\\"...{squad['members'][member]}\\\")\\r\\n\\r\\n logger.info(\\\"Processing index.html template...\\\")\\r\\n with open(\\\"/home/tofts/git/upskilling/templates/index.html\\\") as index_template_file:\\r\\n index_template = index_template_file.read()\\r\\n\\r\\n index_template = module_list(index_template, syllabus, squad)\\r\\n index_template = task_list(index_template, syllabus, squad)\\r\\n index_template = member_progress_list(index_template, syllabus, squad)\\r\\n\\r\\n logger.info(\\\"Writing completed template out...\\\")\\r\\n with open(\\\"/home/tofts/git/upskilling/docs/index.html\\\", \\\"w\\\") as index_output_file:\\r\\n index_output_file.write(index_template)\\r\\n\\r\\n logger.info(\\\"Generating member pages...\\\")\\r\\n with open(\\\"/home/tofts/git/upskilling/templates/member-page.html\\\", \\\"r\\\") as member_template_file:\\r\\n member_page_template = member_template_file.read()\\r\\n generate_member_pages(member_page_template, syllabus, squad)\\r\\n\\r\\n logger.info(\\\"Generating task detail pages...\\\")\\r\\n with open(\\\"/home/tofts/git/upskilling/templates/task-details.html\\\", \\\"r\\\") as task_details_file:\\r\\n task_details_template = task_details_file.read()\\r\\n generate_task_details_pages(task_details_template, syllabus, squad)\\r\\n\\r\\n logger.info(\\\"Generating downloads...\\\")\\r\\n generate_downloads(syllabus, squad)\",\n \"def main ():\\n\\n\\tfio = fileIo('input.txt')\\n text = fio.getInput()\\n\\n\\tp = re.compile(r'#?\\\\d[\\\\s\\\\.]?[\\\\s]?')\\n\\tout = filter(None, p.split(text))\\n\\ti = 0\\n\\tlistOfLists = []\\n\\t\\n\\n\\tfor s in out:\\n\\t\\ti += 1\\n\\t\\ttext = nltk.word_tokenize(s)\\n\\t\\tpos = nltk.pos_tag(text)\\n\\t\\tpattern = \\\"NP: {<DT>?<JJ>*<NN>}\\\"\\n\\t\\tNPChunker = nltk.RegexpParser(pattern)\\n\\t\\tresult = NPChunker.parse(pos)\\n\\t\\tlistOfLists.append( result )\\n\\n\\tprint \\\"Noun Count:\\\\n\\\" + str(countNouns( listOfLists ))\\n\\tprint \\\"Verb Count:\\\\n\\\" + str(countVerbs( listOfLists ))\\n\\tprint \\\"Adjective Count:\\\\n\\\" + str(countAdjectives( listOfLists ))\",\n \"def preprocess_courses_corpus():\\n soup = None\\n with open('courses_corpus.html', 'r') as infile:\\n content = infile.read()\\n\\n soup = BeautifulSoup(content, 'html.parser')\\n\\n docid = 0\\n data = {}\\n data['documents'] = []\\n\\n main_table = soup.find_all(\\\"div\\\", attrs={'class': 'courseblock'})\\n for course in main_table:\\n docid += 1\\n title = course.find_all('p', attrs={'class':'courseblocktitle noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblocktitle noindent'}))!=0 else ''\\n description = (course.find_all('p', attrs={'class':'courseblockdesc noindent'})[0].text.lstrip('\\\\n') if len(course.find_all('p', attrs={'class':'courseblockdesc noindent'}))!=0 else '') + ' ' + (course.find_all('p', attrs={'class':'courseblockextra noindent'})[0].text if len(course.find_all('p', attrs={'class':'courseblockextra noindent'}))!=0 else '')\\n\\n data['documents'].append({\\n 'docId' : docid,\\n 'title' : title.strip(),\\n 'description' : description.strip()\\n })\\n\\n with open('courses_data.json', 'w') as outfile:\\n json.dump(data, outfile)\",\n \"def save_annotated_text_to_xml(self):\\n #initialise file to write the output\\n outfile = open(('annotated_text_' + self.lang + '_' + \\n self.method + '.xml'), 'w')\\n #initialise xml\\n annotated_doc = etree.Element('Annotated_document')\\n main_text = ''\\n #counter for the sentences\\n counter_sentence = 0\\n #counter for the paragraphs\\n counter_paragraph = 0\\n #open txt file\\n with open(self.lang + '.txt') as file:\\n for paragraph in file:\\n paragraph_string = ''\\n sentences = tokenize.sent_tokenize(paragraph)\\n for sentence in sentences:\\n #build lists with the ends of the tokens with NE and the NEs\\n end_list = [0]\\n end_list += [i[2] for i in \\n self.named_entity_list_total[counter_sentence]]\\n ne_list = [i[3] for i in \\n self.named_entity_list_total[counter_sentence]]\\n counter_sentence += 1\\n #build new string\\n new_string = ''\\n for i in range(len(end_list)-1):\\n new_string += (sentence[end_list[i]:end_list[i+1]]+\\n '<annotation class=\\\"'+ne_list[i]+'\\\"/>')\\n new_string += sentence[end_list[-1]:len(sentence)]\\n paragraph_string += new_string+'\\\\n'\\n #print title, author, abstract and main text differently to xml\\n if counter_paragraph == 0:\\n title_text = etree.SubElement(annotated_doc, \\\"Title\\\")\\n #add text to the node\\n init_text = \\\"<text>{0}</text>\\\".format(paragraph_string[6:])\\n fin_text = etree.fromstring(init_text)\\n title_text.append(fin_text)\\n elif counter_paragraph == 1:\\n author_text = etree.SubElement(annotated_doc, \\\"Author\\\")\\n #add text to the node\\n init_text = \\\"<text>{0}</text>\\\".format(paragraph_string[7:])\\n fin_text = etree.fromstring(init_text)\\n author_text.append(fin_text)\\n elif counter_paragraph == 2:\\n abstract_text = etree.SubElement(annotated_doc, \\\"Abstract\\\")\\n #add text to the node\\n init_text = \\\"<text>{0}</text>\\\".format(paragraph_string[9:])\\n fin_text = etree.fromstring(init_text)\\n abstract_text.append(fin_text)\\n else: \\n main_text += paragraph_string\\n counter_paragraph += 1\\n main_text_xml = etree.SubElement(annotated_doc, \\\"Main_text\\\")\\n #add text to the node\\n init_text = \\\"<text>{0}</text>\\\".format(main_text)\\n fin_text = etree.fromstring(init_text)\\n main_text_xml.append(fin_text)\\n #convert and write to outfile\\n xml_bytes = etree.tostring(annotated_doc, encoding='UTF-8', \\n pretty_print=True, xml_declaration=True)\\n xml_str = xml_bytes.decode(\\\"utf-8\\\")\\n outfile.write(xml_str)\\n outfile.close()\\n return\",\n \"def generate_megafile():\\n\\n print(\\\"\\\\nFetching testing dataset…\\\")\\n testing = get_testing()\\n\\n print(\\\"\\\\nFetching ECDC dataset…\\\")\\n ecdc = get_ecdc()\\n\\n location_mismatch = set(testing.location).difference(set(ecdc.location))\\n for loc in location_mismatch:\\n print(f\\\"<!> Location '{loc}' has testing data but is absent from ECDC data\\\")\\n\\n print(\\\"\\\\nFetching OxCGRT dataset…\\\")\\n cgrt = get_cgrt()\\n\\n all_covid = (\\n ecdc\\n .merge(testing, on=[\\\"date\\\", \\\"location\\\"], how=\\\"outer\\\")\\n .merge(cgrt, on=[\\\"date\\\", \\\"location\\\"], how=\\\"left\\\")\\n .sort_values([\\\"location\\\", \\\"date\\\"])\\n )\\n\\n # Add ISO codes\\n print(\\\"Adding ISO codes…\\\")\\n iso_codes = pd.read_csv(os.path.join(INPUT_DIR, \\\"iso/iso3166_1_alpha_3_codes.csv\\\"))\\n\\n missing_iso = set(all_covid.location).difference(set(iso_codes.location))\\n if len(missing_iso) > 0:\\n print(missing_iso)\\n raise Exception(\\\"Missing ISO code for some locations\\\")\\n\\n all_covid = iso_codes.merge(all_covid, on=\\\"location\\\")\\n\\n # Add continents\\n print(\\\"Adding continents…\\\")\\n continents = pd.read_csv(\\n os.path.join(INPUT_DIR, \\\"owid/continents.csv\\\"),\\n names=[\\\"_1\\\", \\\"iso_code\\\", \\\"_2\\\", \\\"continent\\\"],\\n usecols=[\\\"iso_code\\\", \\\"continent\\\"],\\n header=0\\n )\\n\\n all_covid = continents.merge(all_covid, on=\\\"iso_code\\\", how=\\\"right\\\")\\n\\n # Add macro variables\\n # - the key is the name of the variable of interest\\n # - the value is the path to the corresponding file\\n macro_variables = {\\n \\\"population\\\": \\\"un/population_2020.csv\\\",\\n \\\"population_density\\\": \\\"wb/population_density.csv\\\",\\n \\\"median_age\\\": \\\"un/median_age.csv\\\",\\n \\\"aged_65_older\\\": \\\"wb/aged_65_older.csv\\\",\\n \\\"aged_70_older\\\": \\\"un/aged_70_older.csv\\\",\\n \\\"gdp_per_capita\\\": \\\"wb/gdp_per_capita.csv\\\",\\n \\\"extreme_poverty\\\": \\\"wb/extreme_poverty.csv\\\",\\n \\\"cardiovasc_death_rate\\\": \\\"gbd/cardiovasc_death_rate.csv\\\",\\n \\\"diabetes_prevalence\\\": \\\"wb/diabetes_prevalence.csv\\\",\\n \\\"female_smokers\\\": \\\"wb/female_smokers.csv\\\",\\n \\\"male_smokers\\\": \\\"wb/male_smokers.csv\\\",\\n \\\"handwashing_facilities\\\": \\\"un/handwashing_facilities.csv\\\",\\n \\\"hospital_beds_per_thousand\\\": \\\"owid/hospital_beds.csv\\\",\\n \\\"life_expectancy\\\": \\\"owid/life_expectancy.csv\\\",\\n \\\"human_development_index\\\": \\\"un/human_development_index.csv\\\",\\n }\\n all_covid = add_macro_variables(all_covid, macro_variables)\\n\\n print(\\\"Writing to CSV…\\\")\\n all_covid.to_csv(os.path.join(DATA_DIR, \\\"owid-covid-data.csv\\\"), index=False)\\n\\n print(\\\"Writing to XLSX…\\\")\\n all_covid.to_excel(os.path.join(DATA_DIR, \\\"owid-covid-data.xlsx\\\"), index=False)\\n\\n print(\\\"Writing to JSON…\\\")\\n df_to_json(all_covid, os.path.join(DATA_DIR, \\\"owid-covid-data.json\\\"), macro_variables.keys())\\n\\n # Store the last updated time\\n timestamp_filename = os.path.join(DATA_DIR, \\\"owid-covid-data-last-updated-timestamp.txt\\\")\\n with open(timestamp_filename, \\\"w\\\") as timestamp_file:\\n timestamp_file.write(datetime.utcnow().replace(microsecond=0).isoformat())\\n\\n print(\\\"All done!\\\")\",\n \"def preprocess_corpus(train_sents):\\n global lookupLexiconDict\\n lookupLexiconDict = {}\\n \\n lexiconDir = getcwd()+'\\\\\\\\data\\\\\\\\lexicon'\\n filesList = [hfile for hfile in listdir(lexiconDir) if path.isfile(lexiconDir+'\\\\\\\\'+hfile) ]\\n \\n decision_tags = ['facility','product','musicartist']\\n fileMappingDict = \\\\\\n {\\n 'architecture.museum':'facility',\\n 'automotive.make':'product',\\n 'automotive.model':'product',\\n 'award.award':'musicartist',\\n 'base.events.festival_series':'geo-loc',\\n #'bigdict':'@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@',\\n 'book.newspaper':'company',\\n 'broadcast.tv_channel':'tvshow',\\n 'business.brand':'company',\\n 'business.consumer_company':'company',\\n 'business.consumer_product':'product',\\n 'business.sponsor':'company',\\n 'cap.1000':'geo-loc',\\n 'cvg.computer_videogame':'product',\\n 'cvg.cvg_developer':'company',\\n 'cvg.cvg_platform':'product',\\n 'education.university':'facility',\\n 'english.stop':'O',\\n 'firstname.5k':'person',\\n 'government.government_agency':'company',\\n 'internet.website':'company',\\n 'lastname.5000':'person',\\n 'location':'geo-loc',\\n 'location.country':'geo-loc',\\n 'lower.5000':'O',\\n 'people.family_name':'person',\\n 'people.person':'person',\\n 'people.person.lastnames':'person', # <-----------------------------\\n 'product':'product',\\n 'sports.sports_league':'sportsteam',\\n 'sports.sports_team':'sportsteam',\\n 'time.holiday':'O',\\n 'time.recurring_event':'O',\\n 'transportation.road':'geo-loc',\\n 'tv.tv_network':'tvshow',\\n 'tv.tv_program':'tvshow',\\n 'venture_capital.venture_funded_company':'company',\\n 'venues':'geo-loc'\\n }\\n\\n for lexFile in filesList:\\n if lexFile not in fileMappingDict: continue\\n print 'Processing ', lexFile\\n \\n with open(lexiconDir+'\\\\\\\\'+lexFile) as f:\\n for line in f:\\n line = line.lower().split()\\n if len(line) == 1: low=0\\n else:low=1\\n for i in range(low,len(line)):\\n key = tuple(line[:i+1])\\n if key not in lookupLexiconDict:\\n lookupLexiconDict[key] = [fileMappingDict[lexFile]]\\n else:\\n lookupLexiconDict[key].append(fileMappingDict[lexFile]) \\n\\n \\n #pass \",\n \"def readSources(self):\\n for sourceCount, sourceElement in enumerate(self.root.findall(\\\".sources/source\\\")):\\n # shall we just read the UFO here?\\n filename = sourceElement.attrib.get('filename')\\n # filename is a path relaive to the documentpath. resolve first.\\n sourcePath = os.path.abspath(os.path.join(os.path.dirname(self.path), filename))\\n sourceName = sourceElement.attrib.get('name')\\n if sourceName is None:\\n # if the source element has no name attribute\\n # (some authoring tools do not need them)\\n # then we should make a temporary one. We still need it for reference.\\n sourceName = \\\"temp_master.%d\\\"%(sourceCount)\\n self.reportProgress(\\\"prep\\\", 'load', sourcePath)\\n if not os.path.exists(sourcePath):\\n raise MutatorError(\\\"Source not found at %s\\\"%sourcePath)\\n sourceObject = self._instantiateFont(sourcePath)\\n # read the locations\\n sourceLocationObject = None\\n sourceLocationObject = self.locationFromElement(sourceElement)\\n\\n if sourceLocationObject is None:\\n raise MutatorError(\\\"No location defined for source %s\\\"%sourceName)\\n\\n # read lib flag\\n for libElement in sourceElement.findall('.lib'):\\n if libElement.attrib.get('copy') == '1':\\n self.libSource = sourceName\\n\\n # read the groups flag\\n for groupsElement in sourceElement.findall('.groups'):\\n if groupsElement.attrib.get('copy') == '1':\\n self.groupsSource = sourceName\\n\\n # read the info flag\\n for infoElement in sourceElement.findall(\\\".info\\\"):\\n if infoElement.attrib.get('copy') == '1':\\n self.infoSource = sourceName\\n if infoElement.attrib.get('mute') == '1':\\n self.muted['info'].append(sourceName)\\n\\n # read the features flag\\n for featuresElement in sourceElement.findall(\\\".features\\\"):\\n if featuresElement.attrib.get('copy') == '1':\\n if self.featuresSource is not None:\\n self.featuresSource = None\\n else:\\n self.featuresSource = sourceName\\n\\n mutedGlyphs = []\\n for glyphElement in sourceElement.findall(\\\".glyph\\\"):\\n glyphName = glyphElement.attrib.get('name')\\n if glyphName is None:\\n continue\\n if glyphElement.attrib.get('mute') == '1':\\n if not sourceName in self.muted['glyphs']:\\n self.muted['glyphs'][sourceName] = []\\n self.muted['glyphs'][sourceName].append(glyphName)\\n\\n for kerningElement in sourceElement.findall(\\\".kerning\\\"):\\n if kerningElement.attrib.get('mute') == '1':\\n self.muted['kerning'].append(sourceName)\\n\\n # store\\n self.sources[sourceName] = sourceObject, sourceLocationObject\\n self.reportProgress(\\\"prep\\\", 'done')\",\n \"def main():\\n parser = argparse.ArgumentParser()\\n parser.add_argument('--allData', default='', action='store', nargs='+',\\n help=\\\"add train test validation dataset together\\\")\\n parser.add_argument('--topic', default='', help=\\\"target topic\\\")\\n parser.add_argument('--contentWordNumber', default='', help=\\\"threshold for content Word Number\\\")\\n parser.add_argument('--returnNSents', default='', help=\\\"top N sentences\\\")\\n\\n args = parser.parse_args()\\n data = loadData(args.allData, args.topic, args.contentWordNumber)\\n allDataDic, targetTweets = data.readData()\\n \\\"\\\"\\\"\\n ##modify at def_processing, re-run below line##\\n outputDB = data.processing(allDataDic)\\n with open(ROOT.DATA_ROOT+'/'+'allDataDic.txt', 'w') as file:\\n file.write(json.dumps(outputDB))\\n \\\"\\\"\\\"\\n\\n targData = data.processing(targetTweets)\\n outputDB = json.load(open(ROOT.DATA_ROOT + '/allDataDic.txt'))\\n contentWords = data.tfidf(outputDB, targData)\\n return targetTweets,targData, contentWords\",\n \"def main():\\n ff = FileForensics()\\n # ff.scan_dir(\\\"/Users/ns/notes\\\") # FIXME\\n ff.scan_dir(\\\"/Users/ns/work/termination_data\\\")\\n\\n print \\\"\\\\n--- BIG FILES ---\\\"\\n for (size, mime, filename) in ff.get_big_files():\\n print (bcolors.FAIL+\\\"{:>10} MB\\\"+bcolors.ENDC+\\\" {:<20} {:<10}\\\").\\\\\\n format(size, mime, filename)\\n\\n print \\\"\\\\n--- FOUND KEYWORDS ---\\\"\\n for (file, matches) in ff.get_keyword_files():\\n print \\\"{:<5} {:<20} ({:<10})\\\".format(\\n len(matches), file[\\\"mime\\\"], file[\\\"filename\\\"])\\n for position, match in matches:\\n print \\\"\\\\t- {:<10} {:<10}\\\".format(position, match)\\n print\\n\\n print \\\"\\\\n--- HIGH ENTROPY FILES ---\\\"\\n for (file, ent) in ff.get_highentropy_files():\\n print (bcolors.FAIL+\\\"\\\\t {:.2f}\\\"+bcolors.ENDC+\\\" ({:<10}) {:<10}\\\").\\\\\\n format(ent, file[\\\"mime\\\"], file[\\\"filename\\\"])\",\n \"def main():\\n\\n global text_column_name\\n # set to false\\n # TODO: add RC corpus to non RC corpus\\n text_corpus, already_processed_article_list = import_data_and_subset(\\n path_metadata, path_rc_metadata, sepr, path_preprocessed_article_list, article_id, text_column_name,\\n rc_content_column_name, reset_already_preprocessed_article_list=False)\\n\\n print 'raw corpus loaded'\\n\\n # text_corpus = remove_tags_from_corpus(\\n # text_corpus, text_column_name)\\n\\n not_preprocessed_article_list = find_new_articles(\\n text_corpus, index_column_name, already_processed_article_list)\\n print 'new_article list is ' + str(len(not_preprocessed_article_list))\\n\\n text_corpus = subset_corpus(\\n text_corpus, index_column_name, not_preprocessed_article_list)\\n print 'subsetting of unprocessed articles from corpus done'\\n print 'articles left after subsetting are ' + str(text_corpus[index_column_name].nunique())\\n\\n text_corpus = parallelize(text_corpus, remove_tags_from_corpus)\\n print 'html tags removed from corpus'\\n # text_corpus = convert_corpus_to_list(text_corpus)\\n\\n # text_corpus = contraction_expansion_on_corpus(\\n # text_corpus, text_column_name)\\n text_corpus = parallelize(text_corpus, contraction_expansion_on_corpus)\\n print 'contractions expansion completed'\\n\\n # text_corpus = tokenization_on_corpus(text_corpus, text_column_name)\\n text_corpus = parallelize(text_corpus, tokenization_on_corpus)\\n print 'tokenization completed'\\n # text_corpus = punctuation_removal_on_corpus(text_corpus, text_column_name)\\n text_corpus = parallelize(text_corpus, punctuation_removal_on_corpus)\\n print 'punctuation removal completed'\\n # text_corpus = lowercase_on_corpus(text_corpus, text_column_name)\\n text_corpus = parallelize(text_corpus, lowercase_on_corpus)\\n print 'lower casing done'\\n # text_corpus = lemmitization_on_corpus(text_corpus, text_column_name)\\n text_corpus = parallelize(text_corpus, lemmitization_on_corpus)\\n print 'lemmatization done'\\n # text_corpus = stopword_removal_on_corpus(text_corpus, text_column_name)\\n text_corpus = parallelize(text_corpus, stopword_removal_on_corpus)\\n print 'stopwords removed'\\n text_corpus = remove_article_with_little_or_no_content(text_corpus)\\n print 'articles with less or no corpus removed'\\n store_to_disk(text_corpus, path_preprocessed_files, append_mode=True)\\n print 'per-processing done and saved at mentioned path'\",\n \"def main(nlp, file_path, final_file_path, from_line=0, to_line=None):\\n with open(final_file_path, \\\"w\\\") as parsed_file:\\n with open(file_path) as cnn_dm:\\n line = cnn_dm.readline().strip()\\n article_idx = 0\\n while article_idx < from_line:\\n line = cnn_dm.readline().strip()\\n article_idx += 1\\n if to_line is None:\\n while line is not None and line != '':\\n process_line(nlp, line, parsed_file)\\n article_idx += 1\\n print(\\\"{} articles processed from file {}\\\".format(article_idx, file_path))\\n line = cnn_dm.readline().strip()\\n else:\\n while article_idx < to_line and line is not None and line != '':\\n process_line(nlp, line, parsed_file)\\n article_idx += 1\\n print(\\\"{}th article processed from file {}\\\".format(article_idx, file_path))\\n line = cnn_dm.readline().strip()\",\n \"def DocParser(accession_list):\\n\\thandle = open(\\\"prosite.txt\\\")\\n\\trecords = Prodoc.parse(handle)\\n\\trecord_text_list=[]\\n\\ttry:\\n\\t\\t# Loop to go through prosite.doc entries. \\n\\t\\tfor record in records:\\n\\t\\t\\tif record.accession in accession_list: # If an entry is in the list of the already found domains\\n\\t\\t\\t\\trecord_text_list.append(record.text) # Save it\\n\\texcept:\\n\\t\\tprint(False)\\n\\treturn record_text_list\",\n \"def main():\\n with read_std_files(OUT_FILE) as (qrys_file, docs_file, out_file):\\n doc_count, token_count, word_map = map_docs(docs_file)\\n avg_doc_len = token_count / float(doc_count)\\n for doc_id, doc_tokens in tokenize(docs_file):\\n doc_len = len(doc_tokens)\\n doc_dct = dictify(doc_tokens)\\n for query_id, query_tokens in tokenize(qrys_file):\\n query_dct = dictify(query_tokens)\\n similarity = tfidf(query_dct, doc_dct, doc_len, doc_count, avg_doc_len, word_map)\\n log(out_file, query_id, doc_id, similarity)\",\n \"def test3_advanced_info(self):\\n\\t\\tprint \\\"\\\\nTEST 3: Extracting detailed entities info from each ontology in %s folder.\\\\n=================\\\" % DATA_FOLDER\\n\\n\\t\\tfor f in os.listdir(DATA_FOLDER):\\n\\t\\t\\tif not f.startswith('.'):\\n\\t\\t\\t\\tprint \\\"Loading... >\\\", f\\n\\n\\t\\t\\t\\t# divert output to a file temporarily \\n\\t\\t\\t\\tsaveout = sys.stdout \\n\\t\\t\\t\\tfsock = open('out.log', 'w') \\n\\t\\t\\t\\tsys.stdout = fsock \\n\\t\\t\\t\\t\\n\\t\\t\\t\\to = ontospy.Ontology(DATA_FOLDER + f)\\n\\t\\t\\t\\tprintEntitiesInformation(o)\\t\\t\\t\\t\\n\\t\\t\\t\\t\\n\\t\\t\\t\\tsys.stdout = saveout\\n\\t\\t\\t\\tfsock.close()\\n\\t\\t\\t\\tprint \\\"Success.\\\"\",\n \"def build():\\n for root, dirs, files in os.walk(IN_PATH):\\n for filename in files:\\n if filename.endswith('.csv'):\\n with open(os.path.join(IN_PATH, filename), encoding='utf-8') as f:\\n reader = csv.reader(f)\\n next(reader)\\n data = nested_dict()\\n web_data = nested_dict()\\n for row in reader:\\n if row[0].startswith('report.') or row[0].startswith('cardset.'):\\n d = data\\n elif row[0].startswith('web.'):\\n d = web_data\\n path = row[0].split('.')\\n for i in range(len(path)):\\n if i == len(path) - 1:\\n d[path[i]] = row[1]\\n else:\\n d = d[path[i]]\\n with open (os.path.join(OUT_PATH, filename.replace('.csv', '.json')), 'w', encoding='utf-8') as fout:\\n json.dump({**data, **web_data}, fout)\\n with open (os.path.join(WEB_PATH, filename.replace('.csv', '.js')), 'w', encoding='utf-8') as fout:\\n fout.write('var STRINGS = {};'.format(json.dumps(web_data)))\\n\\n with open(os.path.join(IN_PATH, 'en_US.csv'), encoding='utf-8') as f:\\n reader = csv.reader(f)\\n next(reader)\\n data = nested_dict()\\n web_data = nested_dict()\\n for row in reader:\\n path = row[0].split('.')\\n if row[0].startswith('report.') or row[0].startswith('cardset.'):\\n d = data\\n elif row[0].startswith('web.'):\\n d = web_data\\n\\n for i in range(len(path)):\\n if i == len(path) - 1:\\n d[path[i]] = zz_string(row[1], row[0])\\n else:\\n d = d[path[i]]\\n with open(os.path.join(OUT_PATH, 'zz_ZZ.json'), 'w', encoding='utf-8') as fout:\\n json.dump({**data, **web_data}, fout)\\n with open(os.path.join(WEB_PATH, 'zz_ZZ.js'), 'w', encoding='utf-8') as fout:\\n fout.write('var STRINGS = {};'.format(json.dumps(web_data)))\"\n]","isConversational":false},"negative_scores":{"kind":"list like","value":["0.56196046","0.5577734","0.5525874","0.5449749","0.53875864","0.53493977","0.53235525","0.5311306","0.5266425","0.5258258","0.52553433","0.5223228","0.5213364","0.51755184","0.51462036","0.5135373","0.513461","0.512963","0.5127664","0.51267636","0.51189923","0.5118192","0.5117418","0.5115215","0.5092166","0.5076811","0.50628084","0.5062126","0.5060258","0.50514185","0.50455827","0.50428545","0.5041864","0.50402516","0.50330806","0.50307447","0.50266093","0.50216144","0.50012517","0.5001179","0.4993681","0.49901187","0.4980225","0.4966064","0.4961019","0.49577516","0.49486983","0.494459","0.49441555","0.49395996","0.49312103","0.4924975","0.49242726","0.4921476","0.49187776","0.49109644","0.49071476","0.4905785","0.49034762","0.4902646","0.4899989","0.48966566","0.48899132","0.4889822","0.4886812","0.4882157","0.48699135","0.48687124","0.4860529","0.4857714","0.48539346","0.4850137","0.48490897","0.48473945","0.4846913","0.4846913","0.4829217","0.48284116","0.48276258","0.48245552","0.48128787","0.4811066","0.48060367","0.4802369","0.48022234","0.48020205","0.4799807","0.4796059","0.47940123","0.47909945","0.47774523","0.47755167","0.4774632","0.47735655","0.477071","0.47691455","0.47652993","0.4763265","0.47612008","0.47602087"],"string":"[\n \"0.56196046\",\n \"0.5577734\",\n \"0.5525874\",\n \"0.5449749\",\n \"0.53875864\",\n \"0.53493977\",\n \"0.53235525\",\n \"0.5311306\",\n \"0.5266425\",\n \"0.5258258\",\n \"0.52553433\",\n \"0.5223228\",\n \"0.5213364\",\n \"0.51755184\",\n \"0.51462036\",\n \"0.5135373\",\n \"0.513461\",\n \"0.512963\",\n \"0.5127664\",\n \"0.51267636\",\n \"0.51189923\",\n \"0.5118192\",\n \"0.5117418\",\n \"0.5115215\",\n \"0.5092166\",\n \"0.5076811\",\n \"0.50628084\",\n \"0.5062126\",\n \"0.5060258\",\n \"0.50514185\",\n \"0.50455827\",\n \"0.50428545\",\n \"0.5041864\",\n \"0.50402516\",\n \"0.50330806\",\n \"0.50307447\",\n \"0.50266093\",\n \"0.50216144\",\n \"0.50012517\",\n \"0.5001179\",\n \"0.4993681\",\n \"0.49901187\",\n \"0.4980225\",\n \"0.4966064\",\n \"0.4961019\",\n \"0.49577516\",\n \"0.49486983\",\n \"0.494459\",\n \"0.49441555\",\n \"0.49395996\",\n \"0.49312103\",\n \"0.4924975\",\n \"0.49242726\",\n \"0.4921476\",\n \"0.49187776\",\n \"0.49109644\",\n \"0.49071476\",\n \"0.4905785\",\n \"0.49034762\",\n \"0.4902646\",\n \"0.4899989\",\n \"0.48966566\",\n \"0.48899132\",\n \"0.4889822\",\n \"0.4886812\",\n \"0.4882157\",\n \"0.48699135\",\n \"0.48687124\",\n \"0.4860529\",\n \"0.4857714\",\n \"0.48539346\",\n \"0.4850137\",\n \"0.48490897\",\n \"0.48473945\",\n \"0.4846913\",\n \"0.4846913\",\n \"0.4829217\",\n \"0.48284116\",\n \"0.48276258\",\n \"0.48245552\",\n \"0.48128787\",\n \"0.4811066\",\n \"0.48060367\",\n \"0.4802369\",\n \"0.48022234\",\n \"0.48020205\",\n \"0.4799807\",\n \"0.4796059\",\n \"0.47940123\",\n \"0.47909945\",\n \"0.47774523\",\n \"0.47755167\",\n \"0.4774632\",\n \"0.47735655\",\n \"0.477071\",\n \"0.47691455\",\n \"0.47652993\",\n \"0.4763265\",\n \"0.47612008\",\n \"0.47602087\"\n]","isConversational":false},"document_score":{"kind":"string","value":"0.6385642"},"document_rank":{"kind":"string","value":"0"}}}],"truncated":false,"partial":true},"paginationData":{"pageIndex":218,"numItemsPerPage":100,"numTotalItems":95772,"offset":21800,"length":100}},"jwt":"eyJhbGciOiJFZERTQSIsImtpZCI6IjVHZDBvd0g5MTM2eDZjc1FvbE1zcktNWUZoRVFoUm5PVVVybEpjOUhaUEEifQ.eyJyZWFkIjp0cnVlLCJwZXJtaXNzaW9ucyI6eyJyZXBvLmNvbnRlbnQucmVhZCI6dHJ1ZX0sImlhdCI6MTc4NjY0MTU3OSwianRpIjoiZWY1ZDM0MjYtNTA3YS00ZTE2LWFkOTMtMDc3YTQwMGQ1ZmVhIiwic3ViIjoiL2RhdGFzZXRzL25vbWljLWFpL2Nvcm5zdGFjay1weXRob24tdjEiLCJleHAiOjE3ODY2NDUxNzksImlzcyI6Imh0dHBzOi8vaHVnZ2luZ2ZhY2UuY28ifQ.sdY5Ka52ZFt3oyyrOlTpg7_lM_0Nd3aBPXQl_-NOqzkE7Bl8Qe2-W0rmaUMLwr8fxecEUZ-oYB3FWmecITJlBw","displayUrls":true,"splitSizeSummaries":[{"config":"default","split":"train","numRows":95772,"numBytesParquet":1928375891}]},"discussionsStats":{"closed":1,"open":1,"total":2},"fullWidth":true,"hasGatedAccess":true,"hasFullAccess":true,"isEmbedded":false,"savedQueries":{"community":[],"user":[]}}">
query stringlengths 9 3.4k | document stringlengths 9 87.4k | metadata dict | negatives listlengths 4 101 | negative_scores listlengths 4 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
A posteriori SNR estimate. | def gamma_hat(self, xi_gamma_bar_hat):
_, gamma_bar_hat = tf.split(xi_gamma_bar_hat, num_or_size_splits=2, axis=-1)
gamma_hat = self.gamma_map.inverse(gamma_bar_hat)
return gamma_hat | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SNR(S, S0):\n return np.var(S) / np.var(S - S0)",
"def calculateSNR(self):\n pass",
"def get_S_r(self):\n\n S_r = np.sum((self.eta_model - self.eta_exp) ** 2.)\n\n return S_r",
"def rsr(self) -> float:\n return float(self.rmse() / np.std(self.true))",
"def psnr(y_true, y_pred):\n... | [
"0.6921239",
"0.6244635",
"0.6144825",
"0.6120464",
"0.6095354",
"0.60743237",
"0.5972495",
"0.59635097",
"0.59581226",
"0.5924699",
"0.59187704",
"0.5913058",
"0.5862083",
"0.5802056",
"0.5787848",
"0.5745068",
"0.5743066",
"0.5743066",
"0.5688942",
"0.5688405",
"0.56845427"... | 0.0 | -1 |
Compute example for Deep Xi, i.e. observation (noisyspeech STMS) and target (gain). | def example(self, s, d, s_len, d_len, snr):
s, d, x, n_frames = self.mix(s, d, s_len, d_len, snr)
s_STMS, _ = self.polar_analysis(s)
d_STMS, _ = self.polar_analysis(d)
x_STMS, _ = self.polar_analysis(x)
xi = self.xi(s_STMS, d_STMS) # instantaneous a priori SNR.
gamma = self.gamma(x_STMS, d_STMS) # instantan... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inner(base_example: int = 0):\n EX = Xy_raw.iloc[base_example]\n print(\"Base Example IDs:\")\n print((Xy_raw.index[[base_example]]).to_frame().reset_index(drop=True).iloc[0].rename(index=base_example))\n\n def get_widget(col: str):\n V = EX[col]\n ... | [
"0.561758",
"0.5532706",
"0.5529303",
"0.5527711",
"0.5519285",
"0.550442",
"0.5463318",
"0.54252416",
"0.54042125",
"0.53651315",
"0.53214335",
"0.53111446",
"0.53067505",
"0.5293779",
"0.5253569",
"0.5244779",
"0.5231657",
"0.52291465",
"0.52013093",
"0.519316",
"0.5190529"... | 0.4949127 | 50 |
Compute statistics for map class. | def stats(self, s_sample, d_sample, x_sample, wav_len):
s_STMS_sample, d_STMS_sample, x_STMS_sample = self.transfrom_stats(s_sample,
d_sample, x_sample, wav_len)
self.mag_map.stats(s_STMS_sample) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_statistics(self):",
"def _get_stats(self):\n self.stats = set()\n self._bstats = set()\n self._h_bstats = set()\n self._tstats = set()\n self._ftstats = set()\n for cl in self.data_classes:\n for stat in cl._bstats:\n self.stats.add(... | [
"0.65744215",
"0.62907714",
"0.6119016",
"0.5945289",
"0.59396243",
"0.5904752",
"0.5866978",
"0.5799329",
"0.5752798",
"0.5724647",
"0.5713357",
"0.5663257",
"0.5654346",
"0.56468195",
"0.5640491",
"0.56287575",
"0.5622677",
"0.5621717",
"0.56140465",
"0.5601541",
"0.5593295... | 0.0 | -1 |
Compute example for Deep Xi, i.e. observation (noisyspeech STMS) and target (mapped cleanspeech STMS). | def example(self, s, d, s_len, d_len, snr):
s, d, x, n_frames = self.mix(s, d, s_len, d_len, snr)
s_STMS, _ = self.polar_analysis(s)
x_STMS, _ = self.polar_analysis(x)
s_STMS_bar = self.mag_map.map(s_STMS)
return x_STMS, s_STMS_bar, n_frames | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def run_examples():\n\n for example in examples:\n\n print(str(example) + \" : \", end=\" \")\n try:\n t, smush = analyse(example, my_env)\n print(lookup(t, smush))\n # print(\"Smush\")\n # for k,v in smush.items():\n # print(f\"\\t{k} : {... | [
"0.5824792",
"0.5691818",
"0.5683374",
"0.5670519",
"0.5620943",
"0.5595104",
"0.5562455",
"0.5492571",
"0.5483506",
"0.546794",
"0.53820264",
"0.53769636",
"0.5361784",
"0.53402865",
"0.5306678",
"0.5282997",
"0.52611387",
"0.5255092",
"0.525358",
"0.52528375",
"0.52450264",... | 0.0 | -1 |
Cleanspeech magnitude spectrum estimate. | def mag_hat(self, s_STMS_bar_hat):
s_STMS_hat = self.mag_map.inverse(s_STMS_bar_hat)
return s_STMS_hat | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def calc_fft_mag(self, ch_id: int, func_id: int) -> None:\n self.write(':function{0}:fftmagnitude channel{1}'.format(func_id, ch_id))",
"def testCalspecMags(self):\n std = MKIDStd.MKIDStd()\n bFilter = std.filters['B']\n vFilter = std.filters['V']\n\n # BD17\n bd17Flux =... | [
"0.6712164",
"0.65049195",
"0.64892966",
"0.6451894",
"0.643721",
"0.64305013",
"0.63324183",
"0.6301175",
"0.62896734",
"0.6238569",
"0.62249374",
"0.61692566",
"0.61671096",
"0.61482257",
"0.61449903",
"0.6119389",
"0.6116",
"0.6094681",
"0.605757",
"0.60449857",
"0.6025843... | 0.0 | -1 |
Compute statistics for map class. | def stats(self, s_sample, d_sample, x_sample, wav_len):
pass
# s_STMS_sample, d_STMS_sample, x_STMS_sample = self.transfrom_stats(s_sample,
# d_sample, x_sample, wav_len)
# smm_sample = tf.math.truediv(s_STMS_sample, x_STMS_sample)
# self.smm_map.stats(smm_sample) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_statistics(self):",
"def _get_stats(self):\n self.stats = set()\n self._bstats = set()\n self._h_bstats = set()\n self._tstats = set()\n self._ftstats = set()\n for cl in self.data_classes:\n for stat in cl._bstats:\n self.stats.add(... | [
"0.65744215",
"0.62907714",
"0.6119016",
"0.5945289",
"0.59396243",
"0.5904752",
"0.5866978",
"0.5799329",
"0.5752798",
"0.5724647",
"0.5713357",
"0.5663257",
"0.5654346",
"0.56468195",
"0.5640491",
"0.56287575",
"0.5622677",
"0.5621717",
"0.56140465",
"0.5601541",
"0.5593295... | 0.0 | -1 |
Compute example for Deep Xi, i.e. observation (noisyspeech STMS) and target (mapped SMM). | def example(self, s, d, s_len, d_len, snr):
s, d, x, n_frames = self.mix(s, d, s_len, d_len, snr)
s_STMS, _ = self.polar_analysis(s)
x_STMS, _ = self.polar_analysis(x)
smm = tf.math.truediv(s_STMS, x_STMS)
smm_bar = tf.clip_by_value(smm, 0.0, 5.0)
return x_STMS, smm_bar, n_frames | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def example(self, s, d, s_len, d_len, snr):\n\t\ts, d, x, n_frames = self.mix(s, d, s_len, d_len, snr)\n\t\ts_STDCT = self.stdct_analysis(s)\n\t\td_STDCT = self.stdct_analysis(d)\n\t\tx_STDCT = self.stdct_analysis(x)\n\t\txi = self.xi(s_STDCT, d_STDCT)\n\t\txi_bar = self.xi_map.map(xi)\n\t\tcd = self.cd(s_STDCT, d... | [
"0.5828444",
"0.5814742",
"0.57863617",
"0.5774569",
"0.5745876",
"0.5639922",
"0.56284",
"0.5524214",
"0.5508927",
"0.54629517",
"0.5452237",
"0.5401009",
"0.5391395",
"0.5337255",
"0.5312473",
"0.5297813",
"0.5287868",
"0.52411807",
"0.52124095",
"0.52074903",
"0.5198711",
... | 0.51034695 | 32 |
An observation for Deep Xi (noisyspeech STMS and STPS). | def observation(self, x):
x = self.normalise(x)
x_STMS, x_STPS = self.polar_analysis(x)
x_STMS_STPS = tf.concat([x_STMS, x_STPS], axis=-1)
return x_STMS_STPS, x_STMS_STPS | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def observation(self, x):\n\t\tx = self.normalise(x)\n\t\tx_STDCT = self.stdct_analysis(x)\n\t\treturn x_STDCT, None",
"def observation(self, x):\n\t\tx = self.normalise(x)\n\t\tx_STMS, x_STPS = self.polar_analysis(x)\n\t\treturn x_STMS, x_STPS",
"def get_observable_description(stix_obj, log):\n desc = \"\"... | [
"0.551871",
"0.5380339",
"0.5367954",
"0.52299434",
"0.5219479",
"0.50972205",
"0.50261205",
"0.4998411",
"0.4998411",
"0.49744838",
"0.49730173",
"0.49615476",
"0.49530286",
"0.49497813",
"0.49444118",
"0.49292263",
"0.49218303",
"0.49119952",
"0.48962384",
"0.48684102",
"0.... | 0.5608419 | 0 |
Compute statistics for map class. | def stats(self, s_sample, d_sample, x_sample, wav_len):
s_STMS_sample, d_STMS_sample, x_STMS_sample = self.transfrom_stats(s_sample,
d_sample, x_sample, wav_len)
xi_sample = self.xi(s_STMS_sample, d_STMS_sample)
self.xi_map.stats(xi_sample) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_statistics(self):",
"def _get_stats(self):\n self.stats = set()\n self._bstats = set()\n self._h_bstats = set()\n self._tstats = set()\n self._ftstats = set()\n for cl in self.data_classes:\n for stat in cl._bstats:\n self.stats.add(... | [
"0.65744215",
"0.62907714",
"0.6119016",
"0.5945289",
"0.59396243",
"0.5904752",
"0.5866978",
"0.5799329",
"0.5752798",
"0.5724647",
"0.5713357",
"0.5663257",
"0.5654346",
"0.56468195",
"0.5640491",
"0.56287575",
"0.5622677",
"0.5621717",
"0.56140465",
"0.5601541",
"0.5593295... | 0.0 | -1 |
Compute example for Deep Xi, i.e. observation (noisyspeech STMS) and target (mapped a priori SNR and cleanspeech STPS). | def example(self, s, d, s_len, d_len, snr):
s, d, x, n_frames = self.mix(s, d, s_len, d_len, snr)
s_STMS, s_STPS = self.polar_analysis(s)
d_STMS, _ = self.polar_analysis(d)
x_STMS, x_STPS = self.polar_analysis(x)
x_STMS_STPS = tf.concat([x_STMS, x_STPS], axis=-1)
s_stps_bar = self.s_stps_map.map(s_STPS)
x... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def example(self, s, d, s_len, d_len, snr):\n\t\ts, d, x, n_frames = self.mix(s, d, s_len, d_len, snr)\n\t\ts_STDCT = self.stdct_analysis(s)\n\t\td_STDCT = self.stdct_analysis(d)\n\t\tx_STDCT = self.stdct_analysis(x)\n\t\txi = self.xi(s_STDCT, d_STDCT)\n\t\txi_bar = self.xi_map.map(xi)\n\t\tcd = self.cd(s_STDCT, d... | [
"0.5912997",
"0.5688673",
"0.56836474",
"0.5642693",
"0.56281203",
"0.55855364",
"0.55046743",
"0.54866296",
"0.543769",
"0.5418274",
"0.53932106",
"0.5371007",
"0.53687495",
"0.5326887",
"0.52985626",
"0.52625364",
"0.52527297",
"0.52423537",
"0.5238088",
"0.5227399",
"0.520... | 0.5223498 | 20 |
A priori SNR estimate. | def xi_hat(self, xi_s_stps_bar_hat):
xi_bar_hat, _ = tf.split(xi_s_stps_bar_hat, num_or_size_splits=2, axis=-1)
xi_hat = self.xi_map.inverse(xi_bar_hat)
return xi_hat | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SNR(S, S0):\n return np.var(S) / np.var(S - S0)",
"def calculateSNR(self):\n pass",
"def observed_snr(psrsnr, psrra, psrdec):\n global beam_profile\n return lambda obsra,obsdec: psrsnr*beam_profile.gain_at_angular_offset(angsep_arcmin(psrra, psrdec, obsra, obsdec))",
"def get_S_r(self):\n... | [
"0.73832285",
"0.6766759",
"0.650804",
"0.64868206",
"0.6477918",
"0.6270175",
"0.62219375",
"0.60975206",
"0.60889465",
"0.5873745",
"0.58645684",
"0.5822557",
"0.57586515",
"0.57262117",
"0.56956685",
"0.56705034",
"0.56653404",
"0.56461066",
"0.5641833",
"0.56205493",
"0.5... | 0.0 | -1 |
An observation for Deep Xi (noisyspeech __________). | def observation(self, x):
x = self.normalise(x)
x_STDCT = self.stdct_analysis(x)
return x_STDCT, None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _get_observation(self, observation):",
"def observe(self, observation):\n # shallow copy observation (deep copy can be expensive)\n obs = observation.copy()\n is_training = 'labels' in observation\n\n if not obs.get('preprocessed', False) or 'text2vec' not in obs:\n # p... | [
"0.5956446",
"0.5778979",
"0.56497204",
"0.5631569",
"0.5631569",
"0.56061625",
"0.54807776",
"0.533764",
"0.5285102",
"0.52759814",
"0.5189152",
"0.5159776",
"0.51573926",
"0.51573926",
"0.51565295",
"0.51266104",
"0.5122549",
"0.5074193",
"0.5045252",
"0.5032521",
"0.502552... | 0.4674456 | 61 |
Compute statistics for map class. | def stats(self, s_sample, d_sample, x_sample, wav_len):
s_STDCT_sample, d_STDCT_sample, x_STDCT_sample = self.transfrom_stats(s_sample,
d_sample, x_sample, wav_len)
xi_sample = self.xi(s_STDCT_sample, d_STDCT_sample)
self.xi_map.stats(xi_sample)
cd_sample = self.cd(s_STDCT_sample, d_STDCT_sample)
self.cd_m... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def compute_statistics(self):",
"def _get_stats(self):\n self.stats = set()\n self._bstats = set()\n self._h_bstats = set()\n self._tstats = set()\n self._ftstats = set()\n for cl in self.data_classes:\n for stat in cl._bstats:\n self.stats.add(... | [
"0.65744215",
"0.62907714",
"0.6119016",
"0.5945289",
"0.59396243",
"0.5904752",
"0.5866978",
"0.5799329",
"0.5752798",
"0.5724647",
"0.5713357",
"0.5663257",
"0.5654346",
"0.56468195",
"0.5640491",
"0.56287575",
"0.5622677",
"0.5621717",
"0.56140465",
"0.5601541",
"0.5593295... | 0.0 | -1 |
Transforms timedomain sample to shorttime discrete cosine transform sample. | def transfrom_stats(self, s_sample, d_sample, x_sample, wav_len):
s_STDCT_sample = []
d_STDCT_sample = []
x_STDCT_sample = []
for i in tqdm(range(s_sample.shape[0])):
s_STDCT = self.stdct_analysis(s_sample[i,0:wav_len[i]])
d_STDCT = self.stdct_analysis(d_sample[i,0:wav_len[i]])
x_STDCT = self.stdct_ana... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def discrete_cosine_tranform(self, matrix):\n\n\n x = matrix.shape[0]\n y = matrix.shape[1]\n N = x\n\n #Fourier Transform matrix:\n dct = np.zeros([x,y])\n\n for u in range(0, x):\n for v in range(0, y):\n sum_ft = 0\n for i in ran... | [
"0.6027315",
"0.5937768",
"0.58317256",
"0.5667302",
"0.56169987",
"0.55450565",
"0.55325866",
"0.5453489",
"0.544513",
"0.5440955",
"0.53548825",
"0.5332746",
"0.5305275",
"0.52922577",
"0.5248724",
"0.5182515",
"0.5168746",
"0.51626503",
"0.51584834",
"0.5127825",
"0.511244... | 0.0 | -1 |
Compute example for Deep Xi, i.e. observation (noisyspeech STDCT) and target (________). | def example(self, s, d, s_len, d_len, snr):
s, d, x, n_frames = self.mix(s, d, s_len, d_len, snr)
s_STDCT = self.stdct_analysis(s)
d_STDCT = self.stdct_analysis(d)
x_STDCT = self.stdct_analysis(x)
xi = self.xi(s_STDCT, d_STDCT)
xi_bar = self.xi_map.map(xi)
cd = self.cd(s_STDCT, d_STDCT)
cd_bar = self.cd... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def inner(base_example: int = 0):\n EX = Xy_raw.iloc[base_example]\n print(\"Base Example IDs:\")\n print((Xy_raw.index[[base_example]]).to_frame().reset_index(drop=True).iloc[0].rename(index=base_example))\n\n def get_widget(col: str):\n V = EX[col]\n ... | [
"0.5948067",
"0.5827459",
"0.57894105",
"0.5788213",
"0.5717672",
"0.56456167",
"0.56404275",
"0.56256986",
"0.56024945",
"0.56013584",
"0.55033076",
"0.54619145",
"0.54451805",
"0.5437617",
"0.5351964",
"0.53468955",
"0.5345055",
"0.53102803",
"0.5282422",
"0.52763444",
"0.5... | 0.5453168 | 12 |
A priori SNR estimate. | def xi_hat(self, xi_cd_bar_hat):
xi_bar_hat, _ = tf.split(xi_cd_bar_hat, num_or_size_splits=2, axis=-1)
xi_hat = self.xi_map.inverse(xi_bar_hat)
return xi_hat | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def SNR(S, S0):\n return np.var(S) / np.var(S - S0)",
"def calculateSNR(self):\n pass",
"def observed_snr(psrsnr, psrra, psrdec):\n global beam_profile\n return lambda obsra,obsdec: psrsnr*beam_profile.gain_at_angular_offset(angsep_arcmin(psrra, psrdec, obsra, obsdec))",
"def get_S_r(self):\n... | [
"0.7378384",
"0.6761361",
"0.6504994",
"0.64850724",
"0.6474928",
"0.6267786",
"0.6218195",
"0.6095376",
"0.6088404",
"0.5869324",
"0.58604723",
"0.58211243",
"0.5758803",
"0.5722649",
"0.5691067",
"0.56659824",
"0.56636083",
"0.5644464",
"0.56381714",
"0.56173897",
"0.561616... | 0.0 | -1 |
Trigger Test Execution and dump logs at appropriate location | def start_test_exec(cls):
time_str = cls.get_current_time()
os.system("robot -l ./logs/log_{0}.html -r ./logs/report_{0}.html -o ./logs/output_{0}.xml \
./test_suite/{1}".format(time_str, test_suite)) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def on_test_begin(self, logs=None):",
"def execute(self):\n for test in self.tests:\n test.execute()\n self.logger.dump()\n print(\"Finished!\")",
"def on_test_end(self, logs=None):",
"def main(): \n suite = unittest.TestLoader().discover(unitTestDirectory) \n os.chdir... | [
"0.72355634",
"0.7038262",
"0.6995081",
"0.6811806",
"0.6726337",
"0.6570318",
"0.64170235",
"0.64038604",
"0.63951",
"0.6389814",
"0.63837314",
"0.63769907",
"0.6355394",
"0.63484395",
"0.6343703",
"0.629082",
"0.6271052",
"0.62661713",
"0.62363887",
"0.622673",
"0.6221592",... | 0.6903039 | 3 |
Change the setting according to different languages | def config(language):
global k # Set The window size
global ignore_U_activated # Set True to ignore the training data with Unknown senseid
global vector_0_1 # Set True to convert vectors to "binary form" e.g.: [0,3,0,4]->[0,1,0,1]
global remove_punctuations_activated # Set True to ignore all the pun... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_language(self, lang):\n self.lang = lang",
"def do_lang(self, lang):\n\n self.lang = lang\n print(\"Set language to %s\" % lang)",
"def use_en(self):\n pass",
"def _change_lang(self):\n lang = self.ddnGuiLanguage.get()\n self.lblProject['text'] = LOCALIZED_TE... | [
"0.69599074",
"0.69340986",
"0.68247986",
"0.6628936",
"0.6613911",
"0.6550377",
"0.6531084",
"0.65080315",
"0.64949137",
"0.64905906",
"0.6372799",
"0.6351079",
"0.6329504",
"0.6323646",
"0.63018024",
"0.6256488",
"0.6245913",
"0.62027204",
"0.6172503",
"0.6155253",
"0.61533... | 0.0 | -1 |
replace the accented char to unicode | def replace_accented(input_str):
nkfd_form = unicodedata.normalize('NFKD', input_str)
return u"".join([c for c in nkfd_form if not unicodedata.combining(c)]) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _replace_accented(text: str) -> str:\n return unidecode.unidecode(text)",
"def removeAccent(v):\n\tif v == u'á':\n\t\treturn u'a'\n\telif v == u'é':\n\t\treturn u'é'\n\telif v == u'í':\n\t\treturn u'í'\n\telif v == u'ó':\n\t\treturn u'ó'\n\telif v == u'ú':\n\t\treturn u'ú'\n\telse:\n\t\treturn v",
"def ... | [
"0.7634012",
"0.7428132",
"0.730271",
"0.6957801",
"0.6929055",
"0.69152033",
"0.6757189",
"0.6592186",
"0.658624",
"0.65804595",
"0.65484446",
"0.6519656",
"0.64930964",
"0.6430187",
"0.64014155",
"0.6372255",
"0.6342636",
"0.6339085",
"0.63120615",
"0.6309369",
"0.6292421",... | 0.7028545 | 3 |
Method to remove the punctuations. used in get_left_right_lists() | def remove_punctuations(tokens):
if not remove_punctuations_activated:
return tokens
output = []
for token in tokens:
if token not in string.punctuation:
output.append(token)
return output | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_punct(self,text):",
"def remove_punctuation(self):\n new_words = []\n # new_words = \"\"\n for word in self.words:\n new_word = re.sub(r'[^\\w\\s]', '', word)\n if new_word != '':\n new_words.append(new_word)\n # new_words += f\"... | [
"0.7727744",
"0.7568999",
"0.7444831",
"0.7415628",
"0.7375689",
"0.7340214",
"0.7339326",
"0.73319995",
"0.71979105",
"0.71084756",
"0.71058077",
"0.7086695",
"0.70844007",
"0.7029791",
"0.70217824",
"0.69636303",
"0.6910627",
"0.6901472",
"0.68913054",
"0.6882913",
"0.68826... | 0.7303184 | 8 |
Method to remove the stop words used in get_left_right_lists() This method does not work for Catalan language. | def remove_stop_words(tokens, language):
if not remove_stop_words_activated or language.__eq__("Catalan"):
return tokens
output = []
stop = stopwords.words(language.lower())
for token in tokens:
if token not in stop:
output.append(token)
return output | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def remove_stopwords_fun(self):\n tokens = str(self.doc).split()\n cleaned_tokens = [token for token in tokens\n if token.lower() not in self.stopword_list]\n self.doc = ' '.join(cleaned_tokens)",
"def remove_stop_words(self):\n self.word_list = [word for word... | [
"0.8411666",
"0.79770213",
"0.7972711",
"0.7953195",
"0.7869722",
"0.78230315",
"0.7814691",
"0.7796424",
"0.773496",
"0.77157074",
"0.7703494",
"0.7695574",
"0.76948166",
"0.76899654",
"0.76872563",
"0.76872563",
"0.76872563",
"0.76872563",
"0.7681711",
"0.76450783",
"0.7597... | 0.7611969 | 20 |
Method to perform stemming used in ge_left_right_lists() This method does not work for Catalan language | def stemming(tokens, language):
if not stemming_activated or language.__eq__("Catalan"):
return tokens
output = []
stemmer = SnowballStemmer(language.lower())
for token in tokens:
output.append(stemmer.stem(token))
return output | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def stemming(self,sentence):",
"def do(text):\n return freeling_stemming(text)",
"def stem(s):\n special = {'appall', 'kill', 'stroll', 'kiss', 'thrill', 'chugg', 'dress', 'err', 'express', 'fall', 'free', 'gall', 'add','cross', 'impress', 'inn', 'call', 'ball', 'bill', 'buzz'} \n ie_words = {'vyin... | [
"0.75466335",
"0.70338714",
"0.688869",
"0.6862174",
"0.681996",
"0.6696101",
"0.6692604",
"0.6598883",
"0.65930766",
"0.6570551",
"0.65544206",
"0.65350866",
"0.65114987",
"0.6464337",
"0.63825566",
"0.6353275",
"0.63480026",
"0.63435036",
"0.6331262",
"0.6287844",
"0.628566... | 0.66490656 | 7 |
Method to convert words to lowercase used in ge_left_right_lists() | def lowercase(tokens):
if not lowercase_activated:
return tokens
output = [token.lower() for token in tokens]
return output | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _to_lowercase(words):\n new_words = []\n for word in words:\n new_word = word.lower()\n new_words.append(new_word)\n return new_words",
"def toLowercase(self, words):\n\t\tnewWords = [word.lower() for word in words]\n\t\treturn newWords",
"def to_lower(self, word_list):\n retu... | [
"0.79666525",
"0.7932478",
"0.777571",
"0.7759111",
"0.7755746",
"0.772872",
"0.772872",
"0.772872",
"0.772872",
"0.772872",
"0.772872",
"0.772872",
"0.76889205",
"0.759694",
"0.7588431",
"0.724481",
"0.7243115",
"0.70872545",
"0.70810866",
"0.70804024",
"0.7060341",
"0.702... | 0.730405 | 15 |
Method to expand the neighbor_word_list with synsets, hyponyms and hypernyms. used in get_neighbor_words_set() | def expand_synset(neighbor_word_list, language):
if not expand_synset_activated or not language.__eq__("English"):
return neighbor_word_list
new_neighbor_word_set = set()
for word in neighbor_word_list:
new_neighbor_word_set.add(word)
synsets = wn.synsets(word)
synonyms_lis... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_neighbor_words_list(sentence, language):\n if language.__eq__(\"Spanish\") or language.__eq__(\"Catalan\"):\n sentence = sentence.getElementsByTagName('target')[0]\n\n left_list, right_list = get_left_right_lists(sentence, language)\n neighbor_word_list = []\n\n for item in left_list[-k:... | [
"0.6233785",
"0.54712224",
"0.5385365",
"0.53399545",
"0.5255301",
"0.5233051",
"0.5192063",
"0.51569444",
"0.5123623",
"0.50904995",
"0.5077879",
"0.50651765",
"0.50616145",
"0.5047993",
"0.5031947",
"0.5022816",
"0.4951013",
"0.4943876",
"0.49117774",
"0.49117774",
"0.48616... | 0.806715 | 0 |
Method to get the tokens on the left and right of the target word. The result list of tokens will be filtered according to different settings. | def get_left_right_lists(sentence, language):
left_list = nltk.word_tokenize(replace_accented(sentence.childNodes[0].nodeValue.replace('\n', '')))
right_list = nltk.word_tokenize(replace_accented(sentence.childNodes[2].nodeValue.replace('\n', '')))
left_list = remove_stop_words(left_list, language)
rig... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_tokens(self):\n\t\treturn self.get_starttokens() + self.get_endtokens()",
"def tokens(self):\n\t\tlabels_and_synonyms = list(itertools.chain.from_iterable(list(self.term_to_tokens.values())))\n\t\ttokens = set(list(itertools.chain.from_iterable([word_tokenize(x) for x in labels_and_synonyms])))\n\t\tretu... | [
"0.6641464",
"0.65268147",
"0.64090097",
"0.62245643",
"0.62148035",
"0.6099981",
"0.6064908",
"0.60643786",
"0.6055512",
"0.60490125",
"0.60379094",
"0.6024344",
"0.6016069",
"0.59767205",
"0.5975851",
"0.59644526",
"0.58826864",
"0.58402985",
"0.5834286",
"0.581225",
"0.580... | 0.58912784 | 16 |
Method to get a list of words that are within k distance of the target word in the sentece. The result might add the synsets, hyponyms, hypernyms according to the setting. | def get_neighbor_words_list(sentence, language):
if language.__eq__("Spanish") or language.__eq__("Catalan"):
sentence = sentence.getElementsByTagName('target')[0]
left_list, right_list = get_left_right_lists(sentence, language)
neighbor_word_list = []
for item in left_list[-k:]:
neigh... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_closest(target_word: str, word_to_idx: Dict, embeddings: torch.Tensor, n: int = 5) -> List[Tuple[str, torch.Tensor]]:\n\n # Calculate distances to all other words\n\n word_embedding = embeddings[word_to_idx[target_word.lower()]]\n distances = []\n for word, index in word_to_idx.items():\n ... | [
"0.66739005",
"0.6587198",
"0.6474009",
"0.63638276",
"0.62140906",
"0.59948486",
"0.5934092",
"0.5922862",
"0.58830327",
"0.5794155",
"0.5770745",
"0.5758911",
"0.57467514",
"0.5735627",
"0.57349575",
"0.5718581",
"0.5696987",
"0.5696338",
"0.5691832",
"0.56733865",
"0.56412... | 0.6351839 | 4 |
Map the target word instance into the vector space | def extract_vector(inst, neighbor_word_list, _4c_4d_feature, language):
if language.__eq__("English"):
sentence = inst.getElementsByTagName('context')[0]
else:
sentence = inst.getElementsByTagName('context')[0].getElementsByTagName('target')[0]
x = []
neighbors = {}
left_list, right... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def word2vec_mapping_func():\n return {\"belonging to\": \"belonging\", \"parked on\": \"parked\", \"growing on\": \"growing\", \"standing on\": \"standing\",\n \"made of\": \"made\", \"attached to\": \"attached\", \"hanging from\": \"hanging\", \"in front of\": \"front\",\n \"lying on\": ... | [
"0.6583183",
"0.6582477",
"0.63796186",
"0.6378741",
"0.63744193",
"0.6344551",
"0.62345594",
"0.6214528",
"0.6144772",
"0.60987633",
"0.60512793",
"0.600911",
"0.5989764",
"0.59577274",
"0.59557563",
"0.5949267",
"0.5930038",
"0.5929878",
"0.5927253",
"0.5925144",
"0.5886749... | 0.5646689 | 42 |
Method to extract the features defined in 4c and 4d of the requirement | def extract_4c_4d_feature(neighbor_word_list, senseid_list, inst_list, language):
if not extract_4c_feature_activated and not extract_chi_square_activated and not extract_pmi_activated:
return []
output = []
# count_map usage: count_map[neighbor_index][sense_index]
# each cell represent how ma... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def extract_features(self):\n self.extract_features_static()\n self.extract_features_dynamic()",
"def extract_features(self, inputs):\n pass",
"def find_features(pyr):\n\n feature_lst = spread_out_corners(pyr[0], 3, 3, 3)\n descriptors = sample_descriptor(pyr[2],feature_lst/4,3)\n ... | [
"0.666153",
"0.65839595",
"0.6532236",
"0.64225817",
"0.64024997",
"0.63473165",
"0.63437116",
"0.6284789",
"0.62694913",
"0.6235019",
"0.61966836",
"0.6129801",
"0.60978454",
"0.60606545",
"0.6060298",
"0.60424834",
"0.6025452",
"0.6024328",
"0.5987201",
"0.5986229",
"0.5950... | 0.58417434 | 29 |
Method to read the training set, parse the data, and train the classifier models. | def parse_train_data(training_set, language):
print "Reading training set: " + training_set
xmldoc = minidom.parse(training_set)
lex_list = xmldoc.getElementsByTagName('lexelt')
training_output = {}
print "Processing training set and training models..."
for node in lex_list:
lexelt = no... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def train(self):\n # 1. Extracting details of attributes\n\n self.get_attribute_data()\n if self.train_data is None and self.train_data_file is None:\n raise ValueError(\"Neither training data not training file provided\")\n\n self.get_train_data()\n self.classifie... | [
"0.77862453",
"0.7440359",
"0.7225737",
"0.71957695",
"0.71397114",
"0.7089807",
"0.70832306",
"0.7017925",
"0.70015496",
"0.6981088",
"0.6969763",
"0.6942313",
"0.6940365",
"0.6899603",
"0.6886717",
"0.6883019",
"0.6876929",
"0.6874251",
"0.6859709",
"0.6850023",
"0.6836232"... | 0.7331279 | 2 |
Method to read the test set, parse the test data, and map each instance into a vector space | def parse_test_data(test_set, training_output, language):
print "Reading test set: " + test_set
xmldoc = minidom.parse(test_set)
data = {}
lex_list = xmldoc.getElementsByTagName('lexelt')
for node in lex_list:
lexelt = node.getAttribute('item') # item "active.v"
data[lexelt] = []
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test(self, test_data):\n with open(test_data, 'r') as test_data:\n results = {}\n for type in self.label_type_map:\n results[self.label_type_map[type]] = []\n while True:\n tokens = test_data.readline().split()\n pos = test_da... | [
"0.66019076",
"0.62800115",
"0.6211686",
"0.6174027",
"0.60878825",
"0.60251486",
"0.60153896",
"0.599982",
"0.59790295",
"0.5974165",
"0.5945519",
"0.59419864",
"0.59393996",
"0.5916594",
"0.59055924",
"0.58963406",
"0.587455",
"0.5853635",
"0.5831062",
"0.5817713",
"0.58151... | 0.66560996 | 0 |
This runs a command on the remote host. This returns a pexpect.spawn object. This handles the case when you try to connect to a new host and ssh asks you if you want to accept the public key fingerprint and continue connecting. | def ssh_command (user, host, password, command):
ssh_newkey = 'Are you sure you want to continue connecting (yes/no)?'
child = pexpect.spawn('ssh -l %s %s %s'%(user, host, command))
i = child.expect([ssh_newkey, PASSWORD, pexpect.TIMEOUT])
if i == 0: # First Time access - send yes to connect.
chi... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def execute_remote(ipaddr, command):\n\n\treturn execute(['ssh', '-f', '-t', '-oPasswordAuthentication=no',\n\t\t'-l', 'alt', ipaddr, command])",
"def ssh(pi):\n command = \"ssh {0}\".format(pi)\n subprocess.Popen(command, shell=True)",
"def _remote_cmd(self, cmd, block=True):\n s = remote_cmd(hos... | [
"0.69595283",
"0.6643719",
"0.6521727",
"0.6504823",
"0.6419268",
"0.6395431",
"0.6382922",
"0.6355576",
"0.63307595",
"0.63192797",
"0.63166046",
"0.62774366",
"0.6273903",
"0.62710875",
"0.6231371",
"0.6216641",
"0.62067455",
"0.6205677",
"0.6203712",
"0.61976033",
"0.61973... | 0.74644256 | 0 |
The constructor for reviewsMenuClass. Attributes | def __init__(self):
super().__init__()
self.status = True
self.token = tokensClass()
self.layout = layout.reviewsLayoutClass(self)
self.title = "LMS Reviews GUI"
self.location = (50, 125) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __init__(self, menu_name):\n self.menu_name = menu_name",
"def __init__(self, parent_menu, label):\n self._parent_menu = parent_menu\n self._options = {}\n self._label = label\n self._menu = Menu(parent_menu)\n self._key_underline = 0",
"def __init__(self, name, ti... | [
"0.7024792",
"0.6597017",
"0.6345977",
"0.6256314",
"0.62525284",
"0.62365556",
"0.614118",
"0.60794574",
"0.60352033",
"0.59544903",
"0.59349453",
"0.59194565",
"0.5849597",
"0.5827087",
"0.58053714",
"0.57678133",
"0.57440007",
"0.57258856",
"0.5698869",
"0.56835824",
"0.56... | 0.68060464 | 1 |
Check if the windows data has changed. | def checkChanges(self):
results = [
self.values[1],
self.values[f"-{self.values[1]}-"],
self.values["-TOGGLE-ALL-"],
self.values["-INVITED-"],
self.values["-ASSIGNED-"],
self.values["-GRADED-"],
self.values["-BLOCKED-"] ]
if results == self.oldRes... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def isDataChanged(self):\n return bool(self._dirty or self.ui.channelEditor.getQModel().isDataChanged() or self._dirtyMntGrps)",
"def has_changed(self):\n return bool(self.changed_data)",
"def has_changed(self):\n return self.get_old_value() != self.get_current_value()",
"def is_map_upda... | [
"0.7027425",
"0.6961861",
"0.66968995",
"0.6653497",
"0.64225286",
"0.6422174",
"0.62375546",
"0.62212265",
"0.61942613",
"0.6186411",
"0.6186323",
"0.6186147",
"0.6186073",
"0.6154169",
"0.61373127",
"0.61164427",
"0.6112394",
"0.610498",
"0.6104862",
"0.6104862",
"0.6092413... | 0.6343356 | 6 |
Return a Document object representing the content of the document from the given input. | def load_asterix_category_format(k):
global filenames
try:
__basePath__ = os.path.abspath(os.path.join(os.getcwd(), '../../../..'))
# Look for file in current executing directory
path_filename1 = filenames[k]
# On default directory (absolute)
path_filename2 = __basePath... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def create_document(file_name):\n path = INPUT_DIR+file_name # assemble the file descriptor\n file = open(path) # open in read mode\n doc = Document() # create a new document\n # add the title field\n doc.add(StringField(\"title\", input_file, Field.Store.YES))\n # add the whole book\n doc.... | [
"0.64854324",
"0.6355642",
"0.6201583",
"0.60756934",
"0.59871686",
"0.59677255",
"0.5932967",
"0.5922465",
"0.5922113",
"0.5915639",
"0.5866021",
"0.5860824",
"0.5852239",
"0.5846324",
"0.58160543",
"0.58058417",
"0.58058417",
"0.58058417",
"0.58058417",
"0.58058417",
"0.580... | 0.0 | -1 |
Encodes a dictionary (asterix) in the EUROCONTROL ASTERIX category. | def encode(asterix):
assert type(asterix) is dict
asterix_record = 0
#priority_asterix_cat = [21, 34]
for k, v in asterix.iteritems():
#for k in priority_asterix_cat:
v = asterix[k]
record = 0
n_octets_data_record = 0
cat = 0
ctf = load_asterix_category_for... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encode(category_main : ):",
"def latin1_to_ascii(self, unicrap):\n xlate = {0xc0: 'A', 0xc1: 'A', 0xc2: 'A', 0xc3: 'A', 0xc4: 'A', 0xc5: 'A',\n 0xc6: 'Ae', 0xc7: 'C',\n 0xc8: 'E', 0xc9: 'E', 0xca: 'E', 0xcb: 'E',\n 0xcc: 'I', 0xcd: 'I', 0xce: 'I', 0xcf: ... | [
"0.5903913",
"0.49887666",
"0.49819857",
"0.49383077",
"0.49203125",
"0.4909551",
"0.4891353",
"0.47965437",
"0.47933576",
"0.4793037",
"0.47612906",
"0.47022176",
"0.4689794",
"0.46841627",
"0.46771052",
"0.46750674",
"0.4664644",
"0.4662446",
"0.4597144",
"0.4587141",
"0.45... | 0.6899768 | 0 |
Encodes the record from the given category (cat). | def encode_category(cat, did, tree):
if did == {}:
return 0, 0
mdi = {}
for c in tree.getElementsByTagName('DataItem'):
di = c.getAttribute('id')
if di.isdigit():
di = int(di)
rule = c.getAttribute('rule')
if di in did:
if verbose >= 1:
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def encode(category_main : ):",
"def add_category(self, category: str) -> None:\n for letter in self.data:\n if not self.data[letter].get(category):\n self.data[letter][category] = []\n print(f'Categoria: {category} adicionada ao dicionário.')\n self.save()\n self.beautify_json()",
"def... | [
"0.67229545",
"0.561765",
"0.5484989",
"0.5475443",
"0.5324302",
"0.5320905",
"0.53020734",
"0.524053",
"0.51852304",
"0.50320554",
"0.50262564",
"0.5022167",
"0.49936855",
"0.49604708",
"0.4958038",
"0.4957628",
"0.49234056",
"0.49139455",
"0.48985356",
"0.48847762",
"0.4876... | 0.6056459 | 1 |
Returns the encoded Data Item. Encodes the Data Item in the data field of record according to the rules defined in the XML file. | def encode_dataitem(dfd, tree):
assert type(dfd) is dict or type(dfd) is list
for c in tree.getElementsByTagName('DataItemFormat'):
for d in c.childNodes:
if d.nodeName == 'Fixed':
return encode_fixed(dfd, d)
else:
if d.nodeName == 'Variable':
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def prep_posting_data(cls, items: List[dict]) -> bytes:\n\n def my_item_func(x):\n if x == \"contributors\":\n return \"contributor\"\n elif x == \"records\":\n return \"record\"\n\n xml = dicttoxml(\n items, custom_root=\"records\", attr... | [
"0.5869862",
"0.5869558",
"0.5837552",
"0.5814655",
"0.56730103",
"0.5657856",
"0.56101656",
"0.5610163",
"0.55314076",
"0.544302",
"0.5434582",
"0.54142344",
"0.54142344",
"0.53586537",
"0.53413665",
"0.53378576",
"0.5311251",
"0.53084904",
"0.529813",
"0.52737993",
"0.52665... | 0.5936579 | 0 |
Returns the encoded Data Item as a fixed length Data Field. | def encode_fixed(bd, tree):
length = int(tree.getAttribute('length'))
value = 0
has_encoded = False
for cn in tree.childNodes:
if cn.nodeName != 'Bits':
continue
key = cn.getElementsByTagName('BitsShortName')[0].firstChild.nodeValue
bits_unit = cn.getElementsByTagNam... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getDataField(self):\n if not self.checkPacketLength():\n raise AttributeError(\"inconsistent packetLength\")\n dataFieldLength = self.packetLength + 1\n return self.getBytes(PRIMARY_HEADER_BYTE_SIZE, dataFieldLength)",
"def _make_data(self, approximate_length):\n fragments = []\n ... | [
"0.63875425",
"0.59056175",
"0.5892664",
"0.5751579",
"0.5726015",
"0.5717459",
"0.56408036",
"0.5604431",
"0.55373055",
"0.5520101",
"0.5478277",
"0.54433966",
"0.5440741",
"0.5440741",
"0.5440741",
"0.54311866",
"0.54259306",
"0.5384928",
"0.5370295",
"0.53652525",
"0.53558... | 0.0 | -1 |
Returns the encoded Data Item as a variable length Data Field. | def encode_variable(db, tree):
variable = None
length = 0
for cn in tree.childNodes:
if cn.nodeName == 'Fixed':
l, v = encode_fixed(db, cn)
assert l <= 1
if l > 0:
if v % 2 == 1: # remove FX
v -= 1
length += 1
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getDataField(self):\n if not self.checkPacketLength():\n raise AttributeError(\"inconsistent packetLength\")\n dataFieldLength = self.packetLength + 1\n return self.getBytes(PRIMARY_HEADER_BYTE_SIZE, dataFieldLength)",
"def _make_data(self, approximate_length):\n fragments = []\n ... | [
"0.61314625",
"0.5894277",
"0.58546203",
"0.5693786",
"0.5666211",
"0.5647324",
"0.5623186",
"0.56118745",
"0.55618566",
"0.55471104",
"0.5499954",
"0.5487959",
"0.54705465",
"0.5446327",
"0.5446327",
"0.5446327",
"0.543052",
"0.53857625",
"0.53433186",
"0.5292977",
"0.528206... | 0.49473855 | 73 |
Returns the encoded Data Item as a repetitive Data Field. | def encode_repetitive(db, tree):
found = False
cn = None
for cn in tree.childNodes:
if cn.nodeName == 'Fixed':
found = True
break # found
if found is False:
if verbose >= 1:
print 'Repetitive node not found'
return 0, 0
assert type(db) i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def format_data(self, _item_fields, special=None):\n\n if special:\n _item_fields[\"special\"] = special\n\n return _item_fields",
"def parseDataField(self):\r\n devId = str(self.deviceId)\r\n datamap = self._datamaps[devId]\r\n work = ''\r\n dataIndex = 0\r\n... | [
"0.6104918",
"0.58814096",
"0.5728895",
"0.57196414",
"0.57100666",
"0.56371856",
"0.5614055",
"0.55791503",
"0.547264",
"0.5462666",
"0.54213923",
"0.5414014",
"0.5412282",
"0.5395359",
"0.5324551",
"0.52903366",
"0.52721554",
"0.5272136",
"0.52712506",
"0.5270539",
"0.52601... | 0.0 | -1 |
Returns the encoded Data Item as a compound Data Field. | def encode_compound(db, tree):
length = 0
data = 0
sf = 0
subfields = []
for cn in tree.childNodes:
l = 0
if cn.nodeName == 'Variable':
l, v = encode_variable(db, cn)
else:
if cn.nodeName == 'Fixed':
l, v = encode_fixed(db, cn)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def GetPyData(self, item):\r\n\r\n return item.GetData()",
"def packed_data_from_item(self, item):\n\n # Pack the header parts into a struct with the order:\n # (inUse, previousBlock, length, nextBlock)\n header_struct = struct.Struct(self.HEADER_STRUCT_FORMAT_STR)\n packed_dat... | [
"0.6337071",
"0.6224041",
"0.6077705",
"0.59297544",
"0.5877927",
"0.57838255",
"0.57687294",
"0.5726019",
"0.5706959",
"0.5676793",
"0.5676793",
"0.5676793",
"0.56754076",
"0.56752783",
"0.56488013",
"0.5550367",
"0.5550367",
"0.5540494",
"0.5540494",
"0.55401415",
"0.551202... | 0.5412696 | 29 |
Board Path Cloner This function is used to clone the BoardPath object. | def clone(self):
# Run the constructor.
other = BoardPath()
# Copy the object variables
other._current_cost = self._current_cost
other._path = self._path[:]
other._current_loc = self._current_loc
return other | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clone(self):\n joined_function = lambda: dot_joiner(self.path, self.path_type)\n return self.__class__(self.path, self.configuration, self.converters, self.ignore_converters, joined_function=joined_function)",
"def clone(self) -> 'BoardGraph':\n return self.__class__(self.board_class)",
... | [
"0.69156903",
"0.6680933",
"0.6565345",
"0.64692",
"0.63603795",
"0.63189757",
"0.6306653",
"0.62988687",
"0.6227066",
"0.6202716",
"0.6115038",
"0.6103501",
"0.6099532",
"0.60984504",
"0.6085155",
"0.6061067",
"0.60572535",
"0.6047523",
"0.5992621",
"0.59245497",
"0.59100866... | 0.81588864 | 0 |
Current Location Accessor Function to get the current location for this path. | def get_current_location(self):
return self._current_loc | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_current_location(self):\n return self.enu_2_local()",
"def get_current_location():\n global current_location\n return current_location",
"def get_location(self):\r\n return self.__location",
"def get_location(self):\n\t\treturn self.location",
"def get_location(self):\n r... | [
"0.82560873",
"0.8195256",
"0.8011756",
"0.79347134",
"0.7920117",
"0.78825885",
"0.7742611",
"0.7653603",
"0.7653603",
"0.7628534",
"0.7536863",
"0.75195265",
"0.75195265",
"0.75195265",
"0.75195265",
"0.75195265",
"0.75195265",
"0.75195265",
"0.75195265",
"0.75195265",
"0.7... | 0.8912243 | 0 |
Distance Calculator Flexible function for calculating the distance. Depending on the specified heuristic (either explicit in call or implicit with class), different distances can be returned for the same functions. | def get_distance(self, heuristic=""):
# If no heuristic is specified, used the default
if(heuristic == ""):
heuristic = BoardPath._heuristic
if(heuristic == "manhattan"):
return self.calculate_manhattan_dist()
elif(heuristic == "euclidean"):
return se... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def heuristic(current, goal):\r\n distance = getDistance(current, goal)\r\n return distance",
"def test_distance(self):\n\n def f(a, b):\n if a == b:\n return 0\n if (a in \"UC\" and b in \"UC\") or (a in \"AG\" and b in \"AG\"):\n return 1\n ... | [
"0.639866",
"0.6343634",
"0.61924297",
"0.59297323",
"0.59297323",
"0.5853613",
"0.5760782",
"0.57106924",
"0.56957674",
"0.56786555",
"0.56701374",
"0.565588",
"0.5652829",
"0.56316733",
"0.56216407",
"0.5561049",
"0.5556251",
"0.55511826",
"0.5511873",
"0.550787",
"0.549677... | 0.7659271 | 0 |
Manhattan Distance Calculator Calculates difference between current location and\ the goal location using Manhattan distance. | def calculate_manhattan_dist(self):
return self._current_cost + abs(self._current_loc.get_row() - self._goal_loc.get_row()) +\
abs(self._current_loc.get_column() - self._goal_loc.get_column()) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __manhattan(self, x_state, y_state, x_goal, y_goal):\n distance = (abs(x_state - x_goal) + abs(y_state - y_goal))\n return distance",
"def manhattan_distance(self):\n return calculate_manhattan_distance(self.location, self.target_location)",
"def manhattan_distance(self):\n x, y... | [
"0.8165803",
"0.8088598",
"0.7407255",
"0.73463696",
"0.73238844",
"0.7292226",
"0.7271559",
"0.7247168",
"0.7209934",
"0.71817213",
"0.7162157",
"0.71465975",
"0.7138987",
"0.71370137",
"0.70733595",
"0.7065889",
"0.70469683",
"0.70367557",
"0.69594985",
"0.69328445",
"0.689... | 0.816172 | 1 |
Euclidean Distance Calculator Calculates difference between current location and\ the goal location using Euclidean distance. | def calculate_euclidean_dist(self):
x_dist = self._current_loc.get_column() - self._goal_loc.get_column()
y_dist = self._current_loc.get_row() - self._goal_loc.get_row()
# Note ** is power operator in Python
return self._current_cost + sqrt(x_dist**2 + y_dist**2) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def euclidean_distance(self):\n return sqrt(pow((self.goal_pose.x - self.ground_truth_pose.x), 2) +\n pow((self.goal_pose.y - self.ground_truth_pose.y), 2))",
"def getEuclideanDistance():\r\n global euclideanDistance\r\n return euclideanDistance",
"def euclideanDistance(loc1, lo... | [
"0.7016859",
"0.696656",
"0.6942588",
"0.6918102",
"0.6652748",
"0.66375005",
"0.65335757",
"0.64920557",
"0.6466708",
"0.6416474",
"0.63655174",
"0.63655174",
"0.63360476",
"0.6332252",
"0.6328849",
"0.632643",
"0.63199997",
"0.63198346",
"0.63112885",
"0.62728566",
"0.62400... | 0.78475267 | 0 |
Custom Zayd Special Distance Calculator Calculates difference between current location and\ the goal location using Zayd's distance heuristic. | def calculate_made_up_dist(self):
# Ensure if current state equals goal, cost is only the current cost
if self._goal_loc == self._current_loc:
return self._current_cost
# Distance is at least the Manhattan distance as cannot move diagonal
estimated_distance = self.calculate... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def heuristic(current, goal):\r\n distance = getDistance(current, goal)\r\n return distance",
"def heuristic(current, goal):\r\n # First tried manhattan distance but wasn't good enough so did direct distance which makes sense since the robot came move diagonally \r\n #return abs(current[0]-goal[0])... | [
"0.65399253",
"0.64060277",
"0.6318687",
"0.618697",
"0.6142503",
"0.6033802",
"0.6030698",
"0.59373665",
"0.589785",
"0.58269316",
"0.5824655",
"0.57994634",
"0.5755225",
"0.5716154",
"0.5683554",
"0.56740296",
"0.56517506",
"0.5640818",
"0.5628238",
"0.5612277",
"0.56118983... | 0.54997057 | 35 |
Direct Blocked Path Checker This function checks whether if all direct next moves from the current location are blocked by an unpassable object or the edge of the board. This can be used to determine a penalty factor when calculating the heuristic. This function is used in the made_up heuristics function. | def _is_all_direct_next_moves_blocked(self, reference_board=None):
# Use untraversed board if none is specified
if reference_board is None:
reference_board = BoardPath._untraversed_board
# Case #1 - Goal and Current Location in the Same Row
if self._current_loc.get_row() == ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def test_move_knight_legally_blocked(self):\n for piece in [('N', True), ('N', False)]:\n self.c.board = \\\n [[('K', piece[1]) for i in range(8)] for i in range(8)]\n self.c.turn = piece[1]\n self.c.board[4][4] = piece\n for dest in ['d6', 'f6', 'c... | [
"0.61759573",
"0.61158156",
"0.5978466",
"0.5958256",
"0.59319913",
"0.5661901",
"0.56494457",
"0.56460166",
"0.5641585",
"0.5622185",
"0.5597397",
"0.5537868",
"0.54507625",
"0.5450352",
"0.5431192",
"0.54221904",
"0.5419678",
"0.5415468",
"0.5415228",
"0.541518",
"0.5394872... | 0.7079593 | 0 |
Mover Checker Verifies whether a move is valid for a given path. | def is_move_valid(self, direction, reference_board=None):
# Verify a left move does not take you off the board.
if (direction == "l"):
if (self._current_loc.get_column() == 0):
return False
# Verify an up move does not take you off the board.
elif (direction =... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check_move(self, move):\n\n if str(move) in self.moves_made:\n return False\n return True",
"def validate_move(self, move_from, move_to, board):\n\n pass",
"def move_valid(move):\n return True",
"def validate_move(move):\n if move[0] in cc.VALID_RANKS and move[1] in ... | [
"0.71684873",
"0.7160028",
"0.7147075",
"0.7132495",
"0.70763445",
"0.7041763",
"0.68818665",
"0.6880778",
"0.685912",
"0.68394464",
"0.67674154",
"0.6734097",
"0.6732282",
"0.67246544",
"0.6705072",
"0.6702802",
"0.6683885",
"0.6683341",
"0.66618806",
"0.66553384",
"0.660803... | 0.71321493 | 4 |
Move Location Calculator Calculates the new location for a move in the specified direction. | def _calculate_move_location(self, direction):
current_row = self._current_loc.get_row()
current_column = self._current_loc.get_column()
# Calculate the new location for a left move
if (direction == "l"):
return Location(current_row, current_column - 1)
# Calculate t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def getNewLocation(self, currentLocation, directionalMovement):\n x = currentLocation[0] + directionalMovement[0]\n y = currentLocation[1] + directionalMovement[1]\n return (x, y)",
"def get_location(self, currentlocation, action):\n\t\t# Retrieve movement tuple from dictionary\n\t\tmovement... | [
"0.6761584",
"0.65838546",
"0.6551763",
"0.6501945",
"0.6485612",
"0.6441349",
"0.6333201",
"0.623619",
"0.62274575",
"0.6121013",
"0.6087272",
"0.60715115",
"0.60713834",
"0.6050827",
"0.6002279",
"0.59576184",
"0.5944363",
"0.59366095",
"0.5923641",
"0.59176326",
"0.5860694... | 0.8537776 | 0 |
Mover Function Moves the path to a new location. | def move(self, direction):
# Ensure the move is valid
assert self.is_move_valid(direction), "Tried to make an invalid move"
# Calculate the move location.
self._current_loc = self._calculate_move_location(direction)
# Update the path.
self._path.append(self._current_loc... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def move(self, path):\n self.current_location = (path[1][1], path[1][0])",
"def move_by(self, path, env=None):\n env = self._find_env(env)\n old_pos = self.position(env)\n new_pos = [p + c for p, c in zip(old_pos, path)]\n env.move_agent(self, new_pos)",
"def move(self, new_p... | [
"0.7614908",
"0.6925959",
"0.69229007",
"0.6762728",
"0.665183",
"0.6649759",
"0.65716606",
"0.65481114",
"0.6428406",
"0.64077437",
"0.6395367",
"0.63458943",
"0.63458943",
"0.6343252",
"0.63418186",
"0.6317858",
"0.6307312",
"0.6253887",
"0.62428087",
"0.621114",
"0.6158344... | 0.6371334 | 11 |
Goal Checker This function checks if the goal has been reached. | def is_at_goal(self):
return self._current_loc.get_row() == BoardPath._goal_loc.get_row() and \
self._current_loc.get_column() == BoardPath._goal_loc.get_column() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def checkGoal(self):\n # -- It is not included for simplifity --#\n if self.reward_cumulative != None:\n x = round((abs(self.reward_cumulative) - abs(round(self.reward_cumulative))) * 100);\n rem_goal = x % 25\n rem_timeout = x % 20\n if rem_goal == 0 and x... | [
"0.8073732",
"0.8071885",
"0.77985656",
"0.7331105",
"0.7092723",
"0.70926803",
"0.7066472",
"0.69500685",
"0.6914559",
"0.6880784",
"0.68365633",
"0.6739718",
"0.6722361",
"0.6709348",
"0.6690268",
"0.66785365",
"0.666108",
"0.666108",
"0.6649259",
"0.66438985",
"0.6612663",... | 0.6985418 | 7 |
Goal Setter This function sets the goal for the board. | def set_goal(goal_loc):
BoardPath._goal_loc = goal_loc | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_goal(self, goal: GoalType) -> None:\n self.goal = goal",
"def goal(self, goal):\n\n self._goal = goal",
"def set_goal(self, goal):\r\n self.goal = goal\r\n self.start_time = self.get_current_time()",
"def set_goal(self, **kwargs):\n return self.env.set_goal(**kwargs... | [
"0.7807485",
"0.7740968",
"0.75928867",
"0.753877",
"0.7105755",
"0.69478315",
"0.6941798",
"0.66204673",
"0.6585629",
"0.64913666",
"0.63631916",
"0.6287369",
"0.62222",
"0.6091264",
"0.6044519",
"0.59357023",
"0.59132695",
"0.5908332",
"0.5907529",
"0.58134097",
"0.5791385"... | 0.7828492 | 0 |
Untraversed Board Setter This function stores the untraversed board configuration. | def set_untraversed_board(board):
BoardPath._untraversed_board = board | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def resetBoard(self):\n pass",
"def reset(self, board):",
"def reset_board(self):\n\n self.board = np.array(self.initial_board)",
"def resetBoard(self):\n\t\tself.board = np.zeros((self.boardSize,self.boardSize))",
"def resetBoard(self):\n\t\tself.board = np.zeros((self.boardSize,self.boardSi... | [
"0.65023744",
"0.6442172",
"0.63471204",
"0.63091016",
"0.63091016",
"0.6104631",
"0.6043555",
"0.5902821",
"0.58919376",
"0.5850664",
"0.58014584",
"0.57706535",
"0.57347137",
"0.5697121",
"0.56899226",
"0.5621945",
"0.5598106",
"0.5597589",
"0.5568787",
"0.5554146",
"0.5552... | 0.7495511 | 0 |
Traversed Board Setter This function stores the traversed board configuration. | def set_traversed_board(board):
BoardPath._traversed_board = board
# Extract the size of the traversed board
# This is stored and used in "made_up" heuristic analysis
BoardPath._traversed_board_size = 0
for board_row in board:
BoardPath._traversed_board_size += len(bo... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def set_board(board):",
"def setBoard(self, board):\n\t\tself.gameBoard = board",
"def set_board(self, configuration):\n self._config = list(configuration)\n self._houses_num = len(configuration)\n \n tmp_houses = list(configuration)\n tmp_houses.reverse()\n \n ... | [
"0.6318433",
"0.6227784",
"0.6074598",
"0.59444225",
"0.5924237",
"0.57436174",
"0.5697527",
"0.5650028",
"0.5642821",
"0.564118",
"0.5608471",
"0.5591121",
"0.5589941",
"0.5576632",
"0.54089105",
"0.53993165",
"0.5385703",
"0.536131",
"0.53228533",
"0.532268",
"0.5311601",
... | 0.6119311 | 2 |
Heuristic Setter This function stores the board path heuristic | def set_heuristic(heuristic):
BoardPath._heuristic = heuristic | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def heuristic(self):\r\n # 1.\r\n blacks, whites = 0, 0\r\n weights = [0 for _ in range(6)]\r\n directions = [[-1, -1], [-1, 1], [1, 1], [1, -1]]\r\n user_dir = directions[:2] if self.current_player == 'n' else directions[2:]\r\n for i in range(8):\r\n for j in ... | [
"0.72173494",
"0.7166709",
"0.68784773",
"0.68497974",
"0.6678026",
"0.6647084",
"0.6623912",
"0.6621537",
"0.6619162",
"0.65451026",
"0.6475537",
"0.6408203",
"0.6355258",
"0.631753",
"0.62351453",
"0.6221724",
"0.6215816",
"0.6163034",
"0.61629605",
"0.61505026",
"0.6150489... | 0.77581763 | 0 |
Path Printer This function prints the object's path through the path to the console | def print_path(self):
temp_board = self._untraversed_board
step_numb = 0
prev_row = -1
prev_col = -1
# Iterate through each board configuration.
for loc in self._path:
# Depending on if this is the initial setup or not
# Process the next print out.... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __repr__(self):\n\n return self.print_path([])",
"def printPath(path):\r\n result = ''\r\n for i in range(len(path)):\r\n result = result + str(path[i])\r\n if i != len(path) - 1:\r\n result = result + '->'\r\n return result",
"def __str__(self):\n return \"\... | [
"0.7480165",
"0.7081566",
"0.7079112",
"0.70077777",
"0.69893605",
"0.6977167",
"0.69615716",
"0.68941194",
"0.6790297",
"0.6766931",
"0.6659395",
"0.65597045",
"0.6550416",
"0.6519578",
"0.6499361",
"0.6494377",
"0.637156",
"0.63513625",
"0.63493174",
"0.6345297",
"0.6338126... | 0.6851385 | 8 |
Less Than Operator Less than operator used for the distance of two BoardPath objects. | def __lt__(self, other):
return self.get_distance() < other.get_distance() | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __lt__(self, other):\n return self.lessThan(other)",
"def less(lhs, rhs):\n return _make.less(lhs, rhs)",
"def __lt__(self, other):\n return self.dist_from_source < other.dist_from_source",
"def __lt__(self, other):\n return self.abs2phy.__lt__(other)",
"def __lt__(self, other):... | [
"0.70282334",
"0.69592035",
"0.6899089",
"0.68775755",
"0.68775755",
"0.6856517",
"0.6844349",
"0.6821911",
"0.67643285",
"0.6722422",
"0.6687183",
"0.6684212",
"0.6676479",
"0.6667317",
"0.6630886",
"0.66158414",
"0.6570471",
"0.65577924",
"0.65521175",
"0.65098625",
"0.6481... | 0.7412242 | 0 |
Row Number Accessor Accessor to get the row number of this location. | def get_row(self):
return self._row_number | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def row_counter(self) -> int:\n return self.writer.row_counter",
"def row(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"row\")",
"def row(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"row\")",
"def row(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"row... | [
"0.75195545",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74487907",
"0.74372005",
"0.74104637",
"0.74104637",
"0.7353418",... | 0.81669563 | 0 |
Column Number Accessor Accessor to get the column number of this location. | def get_column(self):
return self._column_number | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def column(self) -> int:\n return self._column",
"def get_column(self, pos, **opts):\n lnum, cnum = self._get_column(pos, **opts)\n return lnum + self.LINE_NUM_BASE, cnum + self.COLUMN_NUM_BASE",
"def column(self) -> pulumi.Input[int]:\n return pulumi.get(self, \"column\")",
"def ... | [
"0.784214",
"0.7419771",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.7389335",
"0.71282494",
"0.6932579",
"0.6846213",
"0.68336886",
... | 0.82080346 | 0 |
Valid Location Checker Checks whether this location is valid. | def is_valid(self):
if self.get_row() != -1 and self.get_column() != -1:
return True
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def is_valid(self):\n for location in self.locations.values():\n if not location.is_valid:\n return False\n return True",
"def clean_location(self):\n location = self.cleaned_data['location']\n\n matching_ids = get_all_matching_location_ids(location)\n ... | [
"0.6865433",
"0.65096635",
"0.64228725",
"0.6274094",
"0.6223627",
"0.61751413",
"0.61403114",
"0.6098187",
"0.6028773",
"0.5820583",
"0.58124954",
"0.58024067",
"0.5796383",
"0.57874674",
"0.57832456",
"0.57574135",
"0.57457554",
"0.57377124",
"0.5665434",
"0.56494164",
"0.5... | 0.0 | -1 |
Special function for == operator | def __eq__(self, other):
# Ensure same class and values match
if isinstance(other, self.__class__):
return self.__dict__ == other.__dict__
else:
return False | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def equals(x, y):\n return x == y",
"def is_equal(self, a, b):\n return a == b",
"def __eq__(self,*args):\r\n pass",
"def __eq__(self,*args):\r\n pass",
"def __eq__(self,*args):\r\n pass",
"def __eq__(self,*args):\r\n pass",
"def __eq__(self,*args):\r\n pass",
"def __eq__(self,*args):\... | [
"0.77434087",
"0.7511841",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",
"0.75031555",... | 0.0 | -1 |
Special function for != operator | def __ne__(self, other):
return not (self == other) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __ne__(self, rhs):\n return not self.__eq__(rhs)",
"def __ne__(left, right):\n return (not (left == right))",
"def __ne__(self, other):\n return not self.__eq__(other)",
"def __ne__(self, other):\n return not (self == other) # opposite of __eq__",
"def __ne__(self, othe... | [
"0.8012074",
"0.7888594",
"0.7855215",
"0.7828879",
"0.779572",
"0.7794179",
"0.7726372",
"0.7711095",
"0.76889086",
"0.76813257",
"0.7669499",
"0.7669306",
"0.76685494",
"0.76685494",
"0.76685494",
"0.76685494",
"0.7649886",
"0.76432693",
"0.7642552",
"0.7642552",
"0.762906"... | 0.0 | -1 |
Lists the member teams. | def teams(request):
if request.method == 'GET':
# Validates if the member exists. If members logon successfully they are created on the teams-app.
email = request.session.get('email', None)
full_name = request.session.get('full_name', None)
try:
member = Member.objects.g... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def teams(self):\r\n url = '{0}/{1}'.format(self.get_url(), 'teams')\r\n\r\n return http.Request('GET', url), parsers.parse_json",
"def getAllTeams(self):\n return []",
"def team_list(request):\n template = loader.get_template('team/team_list.html')\n teams_list = Team.objects.all().... | [
"0.759812",
"0.75281477",
"0.74621797",
"0.74324244",
"0.74222827",
"0.7367157",
"0.725578",
"0.7211295",
"0.71726364",
"0.71253264",
"0.70860684",
"0.7013575",
"0.6984913",
"0.6915943",
"0.6838619",
"0.68201035",
"0.6818231",
"0.67570263",
"0.6731368",
"0.6731368",
"0.669519... | 0.72662145 | 6 |
Adds a selected team to the member teams list. | def update_member_teams(request, team_name):
if request.method == 'GET':
email = request.session.get('email', None)
member = Member.objects.get(email=email)
all_teams = Team.objects.all()
for team in all_teams:
if team.name == team_name:
member.teams.add(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"async def add_to_team(self, player : Player, team):\r\n if player in self.remaining:\r\n self.teams[team].append(player)\r\n self.remaining.remove(player)\r\n return discord.Embed(title=\"Valorant 10 Man Bot\",\r\n description=\"{} has been drafted to team {}\... | [
"0.6469067",
"0.6444061",
"0.6429615",
"0.64136",
"0.6323522",
"0.62817746",
"0.62376755",
"0.62157303",
"0.6175544",
"0.61600906",
"0.61417055",
"0.613774",
"0.6088733",
"0.6083329",
"0.6033315",
"0.6011394",
"0.5988793",
"0.59338915",
"0.592007",
"0.5912118",
"0.5909751",
... | 0.6332764 | 4 |
Creates a team. It doesn't relates member and teams, so users have to use the really cool search feature. | def create_team(request):
if request.method == 'POST':
email = request.session.get('email', None)
team_name = request.POST.get('team_name', None)
team = Team(name=team_name)
team.save()
message = "Team created, please use the cool search feature and assign yourself to the te... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def post(self):\n req = team_req.parse_args(strict=True)\n curr_user = api.user.get_user()\n if curr_user[\"teacher\"]:\n raise PicoException(\"Teachers may not create teams\", 403)\n req[\"team_name\"] = req[\"team_name\"].strip()\n if not all(\n [\n ... | [
"0.8057657",
"0.77950835",
"0.7587349",
"0.7408502",
"0.7399746",
"0.7387658",
"0.7241044",
"0.72111934",
"0.71348476",
"0.71306115",
"0.7128486",
"0.70897",
"0.7060156",
"0.70001376",
"0.6890768",
"0.6879242",
"0.68079567",
"0.674662",
"0.6741851",
"0.6687012",
"0.66641074",... | 0.7286078 | 6 |
Function to return user_song_list_count which has data of all users and the count of songs the user has listened to. | def preprocessing():
track_metadata = pd.read_csv('/home/sachet/Artificial Intelligence/song_data.csv')
count_play = pd.read_csv('/home/sachet/Artificial Intelligence/10000.txt', sep='\t', header=None, names=['user','song','play_count'])
unique_track_metadata = track_metadata.groupby('song_id').max().reset_... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_count(username):\n return get_contributor(username)[\"count\"]",
"def user_counts(user_id):\n return _fetch_records(f\"SELECT rating_type, count FROM counts_by_rating_type WHERE user_id = {user_id} AND count > 0\")",
"def get_counts(self, obj: User):\n uploader = obj.id\n public_cou... | [
"0.6450388",
"0.62384784",
"0.621782",
"0.62003124",
"0.6174985",
"0.6170376",
"0.61630744",
"0.6100274",
"0.60886586",
"0.6022709",
"0.59969884",
"0.5996942",
"0.5968442",
"0.5961288",
"0.5941371",
"0.5929401",
"0.5906927",
"0.5895797",
"0.5879137",
"0.58469474",
"0.5841158"... | 0.5567753 | 39 |
Rotates the point around the X axis by the given angle in degrees. | def rotateX(self, angle):
rad = math.radians(angle)
cosa = math.cos(rad)
sina = math.sin(rad)
y = self.y * cosa - self.z * sina
z = self.y * sina + self.z * cosa
return Point3D(self.x, y, z) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotate_x(self, angle):\n angle *= np.pi / 180\n return self.transform(np.matrix([[1, 0, 0],\n [0, np.cos(angle), -np.sin(angle)],\n [0, np.sin(angle), np.cos(angle)]]))",
"def rotate_x(self, angle:... | [
"0.7946597",
"0.75407946",
"0.7309387",
"0.7100913",
"0.70926636",
"0.70239174",
"0.6984318",
"0.6948638",
"0.6939483",
"0.68799853",
"0.687543",
"0.6835535",
"0.6781335",
"0.6731041",
"0.6605782",
"0.65896446",
"0.6499886",
"0.64935017",
"0.6486113",
"0.64761364",
"0.6472282... | 0.64040524 | 27 |
Rotates the point around the Y axis by the given angle in degrees. | def rotateY(self, angle):
rad = math.radians(angle)
cosa = math.cos(rad)
sina = math.sin(rad)
z = self.z * cosa - self.x * sina
x = self.z * sina + self.x * cosa
return Point3D(x, self.y, z) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotate_y(self, angle):\n angle *= np.pi / 180\n return self.transform(np.matrix([[np.cos(angle), 0, np.sin(angle)],\n [0, 1, 0],\n [-np.sin(angle), 0, np.cos(angle)]]))",
"def rotate_y... | [
"0.78093827",
"0.75357753",
"0.7415357",
"0.72273874",
"0.6876136",
"0.6802425",
"0.6794168",
"0.6675276",
"0.66429347",
"0.6481192",
"0.6430551",
"0.63396794",
"0.6271535",
"0.62343675",
"0.61944264",
"0.6171163",
"0.6093572",
"0.60700744",
"0.6050427",
"0.604153",
"0.602756... | 0.59002674 | 25 |
Rotates the point around the Z axis by the given angle in degrees. | def rotateZ(self, angle):
rad = math.radians(angle)
cosa = math.cos(rad)
sina = math.sin(rad)
x = self.x * cosa - self.y * sina
y = self.x * sina + self.y * cosa
return Point3D(x, y, self.z) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def rotateZ(self, angle):\r\n rad = angle * math.pi / 180\r\n cosa = math.cos(rad)\r\n sina = math.sin(rad)\r\n x = self.x * cosa - self.y * sina\r\n y = self.x * sina + self.y * cosa\r\n return Point3D(x, y, self.z)",
"def rotateZ(self, angle):\n rad = angle * ma... | [
"0.82258207",
"0.8223098",
"0.8223098",
"0.8223098",
"0.8223098",
"0.8217796",
"0.80511457",
"0.7841674",
"0.7492067",
"0.7388943",
"0.72343695",
"0.72204983",
"0.70974624",
"0.70917773",
"0.7034674",
"0.7019151",
"0.6976446",
"0.69459265",
"0.6892008",
"0.6891864",
"0.674605... | 0.8195645 | 6 |
Transforms this 3D point to 2D using a perspective projection. | def project(self, win_width, win_height, fov, viewer_distance):
factor = fov / (viewer_distance + self.z)
x = self.x * factor + win_width / 2
y = -self.y * factor + win_height / 2
return Point3D(x, y, 1) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def project_point(self, point: Point3D) -> Point3D:\n x, y, z = point\n cam_x, cam_y, cam_z = self._pos\n x -= cam_x\n y -= cam_y\n z -= cam_z\n dx = self._cy*(self._sz*y + self._cz*x) - self._sy*z\n dy = self._sx*(self._sy*(self._sz*y + self._cz*x) + self._cy*z) + ... | [
"0.7485761",
"0.7218691",
"0.6950993",
"0.6886399",
"0.68223923",
"0.66571516",
"0.6603911",
"0.65672183",
"0.6495761",
"0.64801496",
"0.6474243",
"0.6377783",
"0.6376653",
"0.6376052",
"0.6375711",
"0.63709044",
"0.6364304",
"0.63624424",
"0.63512695",
"0.63367707",
"0.63009... | 0.62003535 | 30 |
Get coordinates from input mol, and generate/format input text for Psi4 calculation. | def make_psi_input(mol, label, method, basisset, calctype='opt', mem=None):
# check that specified calctype is valid
check_calc(calctype)
inputstring = ""
# specify memory requirements, if defined
if mem != None:
inputstring += "memory %s\n" % mem
inputstring += ('molecule %s {\n' % l... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def molToPsi4(self):\n mol = self.molfile\n mol = Chem.AddHs(mol)\n AllChem.EmbedMolecule(mol, useExpTorsionAnglePrefs=True, useBasicKnowledge=True)\n AllChem.UFFOptimizeMolecule(mol)\n atoms = mol.GetAtoms()\n string = string = \"\\n\"\n for i, atom in enumerate(at... | [
"0.6875006",
"0.62146163",
"0.61228514",
"0.54665756",
"0.53889376",
"0.5295396",
"0.5280031",
"0.5270912",
"0.51930785",
"0.51587594",
"0.5158178",
"0.5153392",
"0.5149018",
"0.5137124",
"0.5098608",
"0.5091383",
"0.50637925",
"0.5058507",
"0.50482875",
"0.50428116",
"0.5032... | 0.50096345 | 22 |
THIS FUNCTION IS A WORK IN PROGRESS. Get coordinates from input mol, and generate/format input text for Psi4 calculation via JSON wrapper. | def make_psi_json(mol, label, method, basisset, calctype='opt', mem=None):
# check that specified calctype is valid
check_calc(calctype)
inputdict = {}
moldict = {}
modeldict = {}
keydict = {}
inputdict["schema_name"] = "qc_schema_input"
inputdict["schema_version"] = 1
# specify me... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def molToPsi4(self):\n mol = self.molfile\n mol = Chem.AddHs(mol)\n AllChem.EmbedMolecule(mol, useExpTorsionAnglePrefs=True, useBasicKnowledge=True)\n AllChem.UFFOptimizeMolecule(mol)\n atoms = mol.GetAtoms()\n string = string = \"\\n\"\n for i, atom in enumerate(at... | [
"0.6123903",
"0.55045384",
"0.5481912",
"0.54539114",
"0.5443194",
"0.54166734",
"0.54024506",
"0.5377121",
"0.53512585",
"0.53508705",
"0.5325877",
"0.5307904",
"0.5301415",
"0.5284678",
"0.5265575",
"0.52480125",
"0.52115595",
"0.5199945",
"0.5173829",
"0.5150477",
"0.51422... | 0.5171564 | 19 |
Read in molecule(s) (and conformers, if present) in insdf file. Create Psi4 input calculations for each structure. | def confs_to_psi(insdf,
method,
basis,
calctype='opt',
memory=None,
via_json=False):
wdir = os.getcwd()
# open molecules
molecules = reader.read_mols(insdf)
### For each molecule: for each conf, generate input
for... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def convert_single_sdf_to_pdb(pdb_subfolder_path, sdf_file_path):\n\n if os.path.exists(sdf_file_path) is True:\n\n file_basename = basename(sdf_file_path)\n file_basename = file_basename.split(\"__input1\")[0]\n\n file_output_name = \"{}{}_\".format(pdb_subfolder_path, file_basename)\n\n ... | [
"0.6264046",
"0.59976745",
"0.58319986",
"0.5608725",
"0.56042486",
"0.55735916",
"0.5559321",
"0.5514504",
"0.5502829",
"0.5502089",
"0.54991895",
"0.5464917",
"0.5451997",
"0.5418302",
"0.54164004",
"0.54018366",
"0.53971356",
"0.5395792",
"0.53391236",
"0.5336778",
"0.5336... | 0.6674122 | 0 |
Extract a date from the JSON filing, at the provided path. | def get_date(filing: Dict, path: str) -> date:
try:
raw = dpath.util.get(filing, path)
return date.fromisoformat(raw)
except (IndexError, KeyError, TypeError, ValueError):
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fromJSON(self, path='') -> dict:\n try:\n return(importJSON(path))\n except Exception as error:\n print(f\"Error: self.fromJSON({path}) -> {error}\")",
"def parse_data_from_file(path):\n print(path.stem)\n \n raw = path.stem.split('-')\n\n rawdate = raw[0][2:]\... | [
"0.6075106",
"0.5673903",
"0.56059843",
"0.5590737",
"0.55821204",
"0.5564711",
"0.55302495",
"0.54868776",
"0.54759276",
"0.54637516",
"0.5429487",
"0.5429347",
"0.5408953",
"0.5400296",
"0.53059995",
"0.530356",
"0.52951413",
"0.5294175",
"0.5283827",
"0.5268045",
"0.523605... | 0.6837925 | 0 |
Extract a str from the JSON filing, at the provided path. | def get_str(filing: Dict, path: str) -> str:
try:
raw = dpath.util.get(filing, path)
return str(raw)
except (IndexError, KeyError, TypeError, ValueError):
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_json_str(self):\n\n with open(self.path, mode='r', encoding='utf-8') as file:\n return file.read()",
"def actual(self, path: str):\n nodes = path.split('.')\n resp_json = self.json\n for node in nodes:\n resp_json = resp_json.get(node)\n return res... | [
"0.6552176",
"0.6368723",
"0.62871057",
"0.62073576",
"0.6198753",
"0.61034834",
"0.5978531",
"0.5907652",
"0.59014565",
"0.5865438",
"0.58326775",
"0.58097404",
"0.5789452",
"0.5682722",
"0.56802696",
"0.5669492",
"0.5638627",
"0.5630336",
"0.56209874",
"0.5619586",
"0.56013... | 0.6647993 | 0 |
Extract a boolean from the JSON filing, at the provided path. | def get_bool(filing: Dict, path: str) -> str:
try:
raw = dpath.util.get(filing, path)
return bool(raw)
except (IndexError, KeyError, TypeError, ValueError):
return None | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def boolean_value(cls, json_field: str, value: bool) -> \"JsonPattern\":\n return jsii.sinvoke(cls, \"booleanValue\", [json_field, value])",
"def getBool( self, par, path ):\n\n return self.db.getBoolPar( par, path )",
"def is_json_path(location):\n if filetype.is_f... | [
"0.6071287",
"0.57850856",
"0.5647551",
"0.55946714",
"0.5420584",
"0.5406428",
"0.5285786",
"0.5227173",
"0.52162",
"0.51716995",
"0.5158244",
"0.5113517",
"0.51068",
"0.50923777",
"0.509074",
"0.5047108",
"0.50103277",
"0.49863604",
"0.49533832",
"0.4951799",
"0.49441293",
... | 0.7121972 | 0 |
quote src to string | def _quote(src, encoding="utf-8"):
if isinstance(src, unicode):
src = src.encode(encoding)
return urllib.quote(src) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def replacement(self):\n assert (self.src or self.inline) and not (self.src and self.inline)\n if self.src:\n return '<script async type=\"text/javascript\" src=\"%s\"></script>' % urllib.quote(self.src)\n else:\n return '<script>\\n%s\\n</script>' % self.inline",
"def Sourceify(path):\n retu... | [
"0.5974841",
"0.58601075",
"0.5774958",
"0.5739309",
"0.561575",
"0.5580705",
"0.5553651",
"0.5544228",
"0.5541506",
"0.548759",
"0.5487165",
"0.54729",
"0.5467639",
"0.54428",
"0.54321027",
"0.5386833",
"0.53533804",
"0.53520805",
"0.5338085",
"0.53334767",
"0.5330983",
"0... | 0.6619573 | 0 |
unquote str to url | def _unquote(src, encoding="utf-8"):
return urllib.unquote(src).decode(encoding) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def urldecode(val):\n return urllib2.unquote(val)",
"def urldecode(val):\n return urllib.unquote_plus(val)",
"def quote_uri(uri):\n import urlparse\n import urllib\n\n up=urlparse.urlparse(uri)\n np=urllib.quote(up[2])\n return urlparse.urlunparse((up[0],up[1],np,up[3],up[4],up[5]))",
"def... | [
"0.7064307",
"0.7051374",
"0.6763194",
"0.6752534",
"0.66290367",
"0.65754646",
"0.6573373",
"0.6551047",
"0.65141356",
"0.6489508",
"0.6452029",
"0.6445967",
"0.6365435",
"0.6339722",
"0.62543184",
"0.6241144",
"0.620201",
"0.61938894",
"0.6175648",
"0.61493933",
"0.612075",... | 0.65834916 | 5 |
decorator for get method register | def get(url):
url = add_slash(url)
def _(func):
re_url = re.compile("^%s$" % url)
REQUEST_MAPPINGS['GET'].append((re_url, url, func))
return func
return _ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get(self, *args, **kwargs):",
"def get(self, *args):",
"def __get__(*args, **kwargs):\n \n pass",
"def __get__(*args, **kwargs):\n \n pass",
"def handle_get(cls, **kwargs):\n raise NotImplementedError",
"def __call__(self, *args, **kwargs):\n return self.get(*args, *... | [
"0.68309295",
"0.6675848",
"0.6626815",
"0.6626815",
"0.66126513",
"0.6610192",
"0.6610192",
"0.6586184",
"0.6586184",
"0.6536064",
"0.65258396",
"0.645564",
"0.6337337",
"0.63317025",
"0.63232625",
"0.62869775",
"0.62707186",
"0.6268726",
"0.62609226",
"0.62609226",
"0.62550... | 0.5364231 | 97 |
decorator for post method register | def post(url):
url = add_slash(url)
def _(func):
re_url = re.compile("^%s$" % url)
REQUEST_MAPPINGS['POST'].append((re_url, url, func))
return func
return _ | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def post(self):\n self.finish(self.register())",
"def _post_dispatch(self, request, *args, **kwargs):\n pass",
"def after_request(self, func: typing.Callable):\n return self.add_hook(type_=\"post\", hook=func)",
"def post(self):",
"def post():\n raise NotImplementedError",
"def ha... | [
"0.6869386",
"0.67186683",
"0.6587866",
"0.65795",
"0.6578321",
"0.65417224",
"0.6537671",
"0.642135",
"0.642135",
"0.642135",
"0.642135",
"0.642135",
"0.642135",
"0.642135",
"0.642135",
"0.642135",
"0.642135",
"0.642135",
"0.642135",
"0.642135",
"0.642135",
"0.63676924",
... | 0.0 | -1 |
Parses the content of a file. | def parse(self, path, is_test_instance=False):
assert os.path.exists(path)
samples = []
with open(path, 'r') as f:
for line in f:
instance = Instance()
if is_test_instance:
instance.add_sentence(line, self.dictionary, is_test_insta... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def parse_file(self, file):\n return self.parse(file.read())",
"def parse_file(self, file_name: str):\n if not os.path.exists(file_name):\n log.error('File {} does not exist'.format(file_name))\n return None\n try:\n with open(file_name) as file:\n ... | [
"0.73819876",
"0.71874154",
"0.7125175",
"0.70569646",
"0.69479567",
"0.6932132",
"0.6914554",
"0.6875423",
"0.6741799",
"0.67292744",
"0.670316",
"0.6673647",
"0.6659528",
"0.6657381",
"0.66160166",
"0.66102195",
"0.660509",
"0.6532565",
"0.65184915",
"0.65127826",
"0.649885... | 0.0 | -1 |
Tokenizes a text file. | def tokenize(self, path):
assert os.path.exists(path)
# Add words to the dictionary
with open(path, 'r') as f:
tokens = 0
lines_c = 0
for line in f:
words = ['<start>'] + line.split() + ['<eos>']
len_ = len(words)
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def tokenize_file(filename):\n filename, text = read_file(filename)\n token_text = word_tokenize(text)\n return filename, token_text",
"def tokenize_text_file(path, sanitize=True, remove_duplicates=False, stopwords=None):\n with open(path, 'r') as f:\n text_string = f.read()\n tokens = ... | [
"0.7719976",
"0.75545526",
"0.7165831",
"0.7099891",
"0.69710827",
"0.6951215",
"0.6950511",
"0.69196737",
"0.6892084",
"0.6843397",
"0.68261045",
"0.68261045",
"0.68049693",
"0.6728787",
"0.6702082",
"0.66849124",
"0.66235036",
"0.6609853",
"0.6596192",
"0.6591185",
"0.65899... | 0.6815363 | 12 |
Return the expected keys for the log entry. | def expected_log_keys(learner: adaptive.BaseLearner) -> list[str]:
# Check if the result contains the expected keys
expected_keys = [
"elapsed_time",
"overhead",
"npoints",
"cpu_usage",
"mem_usage",
]
if not _at_least_adaptive_version("0.16.0", raises=False) and n... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def check(self, entry_type:str, x:str):\n keys = set()\n x = self._decode(x)\n\n for log in self.logs:\n for datum in log[entry_type]:\n res = self._follow(datum, x)\n\n if type(res) == dict:\n for key in res.keys():\n ... | [
"0.6501111",
"0.62000287",
"0.61273104",
"0.61169225",
"0.6086626",
"0.60766816",
"0.60219604",
"0.6018274",
"0.5984301",
"0.5967556",
"0.5930063",
"0.5905795",
"0.5868908",
"0.586158",
"0.5827901",
"0.57728463",
"0.5768729",
"0.5760981",
"0.5749968",
"0.5745742",
"0.5743827"... | 0.71414775 | 0 |
Make a representation of object that is easy to find in corpus. | def friendly_id(self):
id = f"{self.annotator_id}_{self.document_title.split('_')[0]}"
try: # try making an sentence identifier if there is an in_sentence attrib
sen_id = ",".join(str(se.element_id + 1) for se in self.in_sentence)
id += f"_s{sen_id}"
except Exception as... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __repr__(self):\n return ''.joint(\"A corpus made of \", len(self._corpus),\n \" with types: \", [type(item) for item in self._corpus])",
"def build(self, obj):\n if isinstance(obj, self.art_type):\n return obj\n elif isinstance(obj, (tuple, list, dict, ... | [
"0.6311109",
"0.6053932",
"0.5645673",
"0.56127447",
"0.5482682",
"0.54713523",
"0.5400021",
"0.53901446",
"0.5380758",
"0.5339382",
"0.5327804",
"0.5323678",
"0.53051144",
"0.53021866",
"0.5297798",
"0.52929544",
"0.52819115",
"0.52755284",
"0.5261796",
"0.5260795",
"0.52493... | 0.0 | -1 |
Return the Spacy processed sentences in which the element is positioned. | def get_processed_sentence(self):
if not hasattr(self, "in_sentence"):
raise AttributeError(f"{self} does not have attribute 'in_sentence'.")
try:
sen_ixs = [sen.element_id for sen in self.in_sentence]
except TypeError as e:
sen_ixs = [self.in_sentence.eleme... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sentences(self) -> List[str]:\n\t\treturn [self.text[start:end] for start, end in self.tokenizations]",
"def sentences(self) -> List[str]:\n\t\treturn [self.text[start:end] for start, end in self.tokenizations]",
"def get_sentences(self):\n return [s for s in self.text.split('\\n')]",
"def get_pro... | [
"0.69350046",
"0.69350046",
"0.6880929",
"0.68049884",
"0.6687255",
"0.6577726",
"0.64769137",
"0.6395686",
"0.6395686",
"0.6374306",
"0.63575345",
"0.6353244",
"0.6340651",
"0.62586695",
"0.6229776",
"0.622054",
"0.6184957",
"0.61654425",
"0.6101179",
"0.6096108",
"0.6073652... | 0.7071078 | 0 |
Check if the annotation is pronominal by full parsed PoS tag. All tokens should be pronominal (works best for anaphoric pronominal mentions as intended). | def check_pronominal(self):
pronom_tags = ["PRP", "PRP$", "WDT", "WP", "WP$"]
token_procs = self.get_processed_tokens()
all_pronom = all(
t.tag_ in pronom_tags for t in token_procs
) # True if all tokens are pronom_tags
# print(f"{' '.join(t.text + '.' + t.tag_ for t... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def contains_pronoun(cluster):\n for mention in cluster:\n if any([w.tag_.startswith(\"PRP\") for w in mention]):\n # Found a mention with a pronoun\n return True\n return False",
"def find_pronoun(sent):\n pronoun = None\n\n for word, part_of_speech in sent.pos_tags:\n ... | [
"0.61451167",
"0.58428335",
"0.5761659",
"0.55204415",
"0.542974",
"0.53121424",
"0.52965194",
"0.5273935",
"0.5249292",
"0.5238592",
"0.52133167",
"0.5151928",
"0.5150011",
"0.5145963",
"0.5134433",
"0.5119308",
"0.5116591",
"0.50993526",
"0.5094933",
"0.5078213",
"0.5075910... | 0.72758985 | 0 |
Replace annotation unit by its Canonical referent. | def replace_by_canonical_referent(self, cross_sentence=True):
replaced_count = 0
# replace the participant in their containers
def replace_in_containers(myobj, replacing):
containers = {
"event": "participants",
"sentiment_expression": "targets",
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def sanitized(self) -> AnnotationWrapper:\n res = self.replace('typing.', ''). \\\n replace('telegrambotapiwrapper.typelib.', ''). \\\n replace(\"<class '\", \"\"). \\\n replace(\"'>\", \"\")\n return AnnotationWrapper(res)",
"def copy_annotations(source: str, targe... | [
"0.5260051",
"0.5162772",
"0.5024999",
"0.50209576",
"0.50172675",
"0.49961436",
"0.49337363",
"0.49098545",
"0.4908495",
"0.48858345",
"0.48811057",
"0.48475933",
"0.48307392",
"0.48301882",
"0.4786552",
"0.47759408",
"0.4762112",
"0.4751336",
"0.4724744",
"0.47108185",
"0.4... | 0.4454109 | 49 |
Custom repr for easier debugging. | def __repr__(self):
text = str(self)
text_ellips = (text[:31] + ".." + text[-31:]) if len(text) > 64 else text
return f"{self.element_id}. {text_ellips}..." | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\r\n return repr(self)",
"def __repr__(self) -> str:\n items = (\"{}={}\".format(k, repr(v)) for k, v in self.__dict__.items())\n return \"<{}({})>\".format(self.__class__.__name__, \", \".join(items))",
"def __str__(self):\n return repr(self)",
"def __repr__(sel... | [
"0.82584816",
"0.8253082",
"0.82329077",
"0.8191106",
"0.81365615",
"0.81327766",
"0.81327766",
"0.81327766",
"0.8118914",
"0.8113123",
"0.80449194",
"0.80449194",
"0.80449194",
"0.80449194",
"0.80425906",
"0.801727",
"0.7994082",
"0.79937655",
"0.798417",
"0.79790634",
"0.79... | 0.0 | -1 |
Get a list of token objects for the event extent. The extent can be set to include discontiguous_triggers, participants, and/or fillers. Setting this to an empty list will only return the original In the definition of an event nugget we include all of these. | def get_extent_tokens(self, extent=["discontiguous_triggers"], source_order=True):
all_tokens = self.tokens.copy()
core_sen_idx = all_tokens[0].in_sentence.element_id # to ensure discont is in same sentence
for ext in extent:
if getattr(self, ext):
all_tokens.extend(... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_extent_tokens(self, extent=[], source_order=True):\n all_tokens = self.tokens.copy()\n\n for ext in extent:\n if getattr(self, ext):\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens)\n\n all_tokens = list(\n set(all_tokens)\n ... | [
"0.77589923",
"0.6758597",
"0.64047307",
"0.6147929",
"0.60916317",
"0.6056437",
"0.5989617",
"0.58163434",
"0.58126074",
"0.56831574",
"0.567847",
"0.56537056",
"0.55833375",
"0.54851276",
"0.54851276",
"0.54851276",
"0.5463045",
"0.5441857",
"0.54370433",
"0.53718114",
"0.5... | 0.7712019 | 1 |
Get a list of token ids for the event extent. Relies on Event.get_extent_tokens() for fetching the tokens. | def get_extent_token_ids(self, **kwargs):
token_span = self.get_extent_tokens(**kwargs)
return [t.index for t in token_span] | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_extent_tokens(self, extent=[], source_order=True):\n all_tokens = self.tokens.copy()\n\n for ext in extent:\n if getattr(self, ext):\n all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens)\n\n all_tokens = list(\n set(all_tokens)\n ... | [
"0.7306065",
"0.7179015",
"0.67308676",
"0.66366893",
"0.6510626",
"0.63861024",
"0.6332538",
"0.6213533",
"0.6002818",
"0.59769213",
"0.59459466",
"0.58939326",
"0.58939326",
"0.58939326",
"0.5877605",
"0.58751804",
"0.58647084",
"0.5804137",
"0.5802045",
"0.5801702",
"0.578... | 0.85507065 | 0 |
Get a list of token objects for the sentiment expression extent. The extent can be set to include targets. Setting this to an empty list will only return the original tokens. | def get_extent_tokens(self, extent=[], source_order=True):
all_tokens = self.tokens.copy()
for ext in extent:
if getattr(self, ext):
all_tokens.extend(t for x in getattr(self, ext) for t in x.tokens)
all_tokens = list(
set(all_tokens)
) # this i... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_extent_tokens(self, extent=[\"discontiguous_triggers\"], source_order=True):\n all_tokens = self.tokens.copy()\n core_sen_idx = all_tokens[0].in_sentence.element_id # to ensure discont is in same sentence\n\n for ext in extent:\n if getattr(self, ext):\n all_t... | [
"0.79144996",
"0.66847426",
"0.64567703",
"0.64525014",
"0.6422023",
"0.63470286",
"0.6025481",
"0.5879312",
"0.5853642",
"0.58203405",
"0.58203405",
"0.55929494",
"0.5589577",
"0.55816835",
"0.5545209",
"0.553422",
"0.54197466",
"0.54197466",
"0.54197466",
"0.5392735",
"0.53... | 0.82137984 | 0 |
Set token id based on document id + token position in text | def get_token_id(self):
return f"{self.document_title}_{self.index}" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def correct_token_begin_position(self, tokens, text):\n beginning = 0\n for token in tokens:\n token.text_begin = text.find(token.text_content, beginning) \n beginning += len(token.text_content)\n return tokens",
"def doc2id(self, doc):\n doc = map(self.process_t... | [
"0.61441404",
"0.6103226",
"0.60713357",
"0.58848673",
"0.58489716",
"0.5830993",
"0.5819702",
"0.5808241",
"0.5795522",
"0.57590806",
"0.5744294",
"0.5744294",
"0.5727401",
"0.57221353",
"0.57152474",
"0.56934476",
"0.568862",
"0.56846684",
"0.5665206",
"0.5659678",
"0.56587... | 0.7065767 | 0 |
Append value to attribute list, create the attribute list if does not exist. | def append_attribute(myobj, attrib_k, val):
vals = getattr(myobj, attrib_k, [])
if val not in vals:
vals.append(val)
setattr(myobj, attrib_k, vals) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _setAttribute(self, attribute, value):\n\n # if multiple values found\n if hasattr(self, attribute):\n\n # make sure attribute is a list\n values = getattr(self, attribute)\n if not isinstance(values, list):\n setattr(self, attribute, [values])\n\n ... | [
"0.72828615",
"0.7063005",
"0.70154303",
"0.6982224",
"0.6949298",
"0.6837699",
"0.66487557",
"0.6644792",
"0.660447",
"0.6597281",
"0.65214103",
"0.64085853",
"0.639992",
"0.6395541",
"0.63902277",
"0.6352462",
"0.6338568",
"0.63299024",
"0.63299024",
"0.63299024",
"0.632369... | 0.74247986 | 0 |
Function to converts XML attributes into a dictionary | def __convertAttributes__(xml_source):
attributes = {}
for attrName, attrValue in xml_source.attributes.items():
attributes[attrName] = attrValue
return attributes | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def get_attrs_dict(self, root_element):\n attr_elements = root_element.findall(\"attribute\")\n attrs_dict = {}\n for el in attr_elements:\n attrs_dict[el.attrib[\"name\"]] = {\n \"value\": el.attrib[\"value\"],\n \"type\": el.attrib.get(\"type\", None)... | [
"0.7548967",
"0.722563",
"0.71245855",
"0.711149",
"0.7095163",
"0.7094138",
"0.70292187",
"0.6932637",
"0.69197536",
"0.68689096",
"0.6855458",
"0.68021226",
"0.68001693",
"0.6756407",
"0.6698822",
"0.6619554",
"0.6612907",
"0.6576497",
"0.6554866",
"0.6512518",
"0.6490863",... | 0.7341555 | 1 |
Custom repr for easier debugging. | def __repr__(self):
return f"{self.document_id} {self.annotator_id}" | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def __str__(self):\r\n return repr(self)",
"def __repr__(self) -> str:\n items = (\"{}={}\".format(k, repr(v)) for k, v in self.__dict__.items())\n return \"<{}({})>\".format(self.__class__.__name__, \", \".join(items))",
"def __str__(self):\n return repr(self)",
"def __repr__(sel... | [
"0.8258345",
"0.8253225",
"0.82328606",
"0.81909287",
"0.8136453",
"0.8132754",
"0.8132754",
"0.8132754",
"0.81184274",
"0.81119144",
"0.8045108",
"0.8045108",
"0.8045108",
"0.8045108",
"0.80425274",
"0.801632",
"0.79938865",
"0.7993399",
"0.79841006",
"0.79778564",
"0.797201... | 0.0 | -1 |
Clean duplicate docs that are a consequence of opening them in WebAnno. | def clean_duplicate_documents(self):
title_k = lambda x: x.title
for k, g in groupby(sorted(self.annotation_documents, key=title_k), title_k):
g = list(g)
if len(g) > 1:
# check first if one is in test set
to_remove = [x for x in g if x not in self... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def clean_docs(self, docs):\n cleaned = [self.cleaning(doc) for doc in docs]\n print(cleaned[0])\n return cleaned",
"def clean_docs(c):\n c.run(f\"rm -fr {DOCS_BUILD_DIR}\")",
"def clean_all_documents(cls):\n for index, text in enumerate(cls.documents):\n text_processe... | [
"0.71175075",
"0.682035",
"0.64427346",
"0.64351165",
"0.64301306",
"0.6388901",
"0.6304049",
"0.6276406",
"0.6156239",
"0.60863686",
"0.60153323",
"0.5972687",
"0.59360844",
"0.5934813",
"0.5888154",
"0.58511215",
"0.58008873",
"0.5787599",
"0.5778761",
"0.5765141",
"0.57586... | 0.67015225 | 2 |
Run the Spacy processing pipeline on the annotation documents. Add processed docs so they can be accessed. | def process_spacy(self):
def prevent_sentence_boundary_detection(doc):
for token in doc:
# This will entirely disable spaCy's sentence detection
token.is_sent_start = False
return doc
def process_sentence(sen_tokens):
doc = spacy.toke... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def process(self, doc):\n # don't try to process null notes\n if not doc[1]:\n if self.verbose:\n print(\"Error segmenting doc\",doc[0])\n return []\n # odd notes may throw an error. Just continue rather than stopping the entire process\n try:\n ... | [
"0.6669254",
"0.6452818",
"0.6272669",
"0.6263057",
"0.6151627",
"0.6133174",
"0.6116166",
"0.60921097",
"0.6062387",
"0.6043837",
"0.5952495",
"0.5947582",
"0.5938026",
"0.5930227",
"0.58644366",
"0.5852183",
"0.58298236",
"0.5801322",
"0.57992077",
"0.57630277",
"0.57474416... | 0.8031649 | 0 |
Replaces all participant arguments in project with canonical referent links. | def replace_canonical_referents(self, **kwargs):
replaced_count = 0
for doc in self.annotation_documents:
for sen in doc.sentences:
for part in sen.participants:
replaced_count += part.replace_by_canonical_referent(**kwargs) #this will replace in-place in ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def fix_links():\n pass",
"def replace_by_canonical_referent(self, cross_sentence=True):\n replaced_count = 0\n\n # replace the participant in their containers\n def replace_in_containers(myobj, replacing):\n containers = {\n \"event\": \"participants\",\n ... | [
"0.56018543",
"0.5512885",
"0.5474612",
"0.53858924",
"0.52240896",
"0.5085354",
"0.49265683",
"0.49210376",
"0.49045438",
"0.4873174",
"0.48181435",
"0.47943506",
"0.4749334",
"0.46768937",
"0.46495956",
"0.46395046",
"0.45719907",
"0.45718217",
"0.45712867",
"0.45704007",
"... | 0.5472057 | 3 |
Parses the main corpus and IAA gold standard files and joins them in one WebAnnoProject. | def parse_main_iaa(main_dirp, iaa_dirp, opt_fp):
# moderator_id = "gilles"
# exclude_moderator = lambda x: moderator_id not in Path(x.path).stem
# include_moderator = lambda x: moderator_id in Path(x.path).stem
main_project = WebannoProject(main_dirp)
# # exclude moderator and trial files which st... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def main(url, inputFile, directory, rss, opml, output, verbose, debug, relevanceAlgorithm):\n\n if (len(argv) < 2):\n print(\n \"Usage: python3 ctirt.py [options] [target files]\\n\\n Use --> ctirt.py --help for more details...\"\n )\n exit(1)\n\n if (verbose and url) or (url ... | [
"0.56196046",
"0.5577734",
"0.5525874",
"0.5449749",
"0.53875864",
"0.53493977",
"0.53235525",
"0.5311306",
"0.5266425",
"0.5258258",
"0.52553433",
"0.5223228",
"0.5213364",
"0.51755184",
"0.51462036",
"0.5135373",
"0.513461",
"0.512963",
"0.5127664",
"0.51267636",
"0.5118992... | 0.6385642 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.