""" general utility functions for loading, saving, and manipulating data """ import os import logging import pprint as pp import re import shutil # zipfile formats import warnings from datetime import datetime from os.path import basename, getsize, join from pathlib import Path import logging import pandas as pd import requests from natsort import natsorted from symspellpy import SymSpell from tqdm.auto import tqdm import warnings warnings.filterwarnings( action="ignore", message=".*the GPL-licensed package `unidecode` is not installed*" ) # cleantext GPL-licensed package reminder is annoying class DisableLogger: def __enter__(self): logging.disable(logging.CRITICAL) def __exit__(self, exit_type, exit_value, exit_traceback): logging.disable(logging.NOTSET) with DisableLogger(): from cleantext import clean def clear_loggers(): for handler in logging.root.handlers[:]: logging.root.removeHandler(handler) def get_timestamp(): return datetime.now().strftime("%b-%d-%Y_t-%H") def print_spacer(n=1): """print_spacer - print a spacer line""" print("\n -------- " * n) def remove_trailing_punctuation(text: str): """ remove_trailing_punctuation - remove trailing punctuation from a string Args: text (str): [string to be cleaned] Returns: [str]: [cleaned string] """ return text.strip("?!.,;:") def correct_phrase_load(my_string: str): """ correct_phrase_load [basic / unoptimized implementation of SymSpell to correct a string] Args: my_string (str): [text to be corrected] Returns: str: the corrected string """ sym_spell = SymSpell(max_dictionary_edit_distance=2, prefix_length=7) dictionary_path = ( r"symspell_rsc/frequency_dictionary_en_82_765.txt" # from repo root ) bigram_path = ( r"symspell_rsc/frequency_bigramdictionary_en_243_342.txt" # from repo root ) # term_index is the column of the term and count_index is the # column of the term frequency sym_spell.load_dictionary(dictionary_path, term_index=0, count_index=1) sym_spell.load_bigram_dictionary(bigram_path, term_index=0, count_index=2) # max edit distance per lookup (per single word, not per whole input string) suggestions = sym_spell.lookup_compound( clean(my_string), max_edit_distance=2, ignore_non_words=True ) if len(suggestions) < 1: return my_string else: first_result = suggestions[0] return first_result._term def fast_scandir(dirname: str): """ fast_scandir [an os.path-based means to return all subfolders in a given filepath] """ subfolders = [f.path for f in os.scandir(dirname) if f.is_dir()] for dirname in list(subfolders): subfolders.extend(fast_scandir(dirname)) return subfolders # list def create_folder(directory: str): os.makedirs(directory, exist_ok=True) def chunks(lst: list, n: int): """ chunks - Yield successive n-sized chunks from lst Args: lst (list): list to be chunked n (int): size of chunks """ for i in range(0, len(lst), n): yield lst[i : i + n] def shorten_list( list_of_strings: list, max_chars: int = 512, no_blanks=True, verbose=False ): """a helper function that iterates through a list backwards, adding to a new list. When is met, that list entry is not added. Args: list_of_strings (list): list of strings to be shortened max_chars (int, optional): maximum number of characters in a the list in total. Defaults to 512. no_blanks (bool, optional): if True, blank strings are not added to the new list. Defaults to True. verbose (bool, optional): if True, print the list of strings before and after the shorten. Defaults to False. """ list_of_strings = [ str(x) for x in list_of_strings ] # convert to strings if not already shortened_list = [] total_len = 0 for i, string in enumerate(list_of_strings[::-1], start=1): if len(string.strip()) == 0 and no_blanks: continue if len(string) + total_len >= max_chars: logging.info(f"string # {i} puts total over limit, breaking ") break total_len += len(string) shortened_list.insert(0, string) if len(shortened_list) == 0: logging.info(f"shortened list with max_chars={max_chars} has no entries") if verbose: print(f"total length of list is {total_len} chars") return shortened_list def chunky_pandas(my_df, num_chunks: int = 4): """ chunky_pandas [split dataframe into `num_chunks` equal chunks, return each inside a list] Args: my_df (pd.DataFrame) num_chunks (int, optional): Defaults to 4. Returns: list: a list of dataframes """ n = int(len(my_df) // num_chunks) list_df = [my_df[i : i + n] for i in range(0, my_df.shape[0], n)] return list_df def load_dir_files( directory: str, req_extension=".txt", return_type="list", verbose=False ): """ load_dir_files - an os.path based method of returning all files with extension `req_extension` in a given directory and subdirectories Args: Returns: list or dict: an iterable of filepaths or a dict of filepaths and their respective filenames """ appr_files = [] # r=root, d=directories, f = files for r, d, f in os.walk(directory): for prefile in f: if prefile.endswith(req_extension): fullpath = os.path.join(r, prefile) appr_files.append(fullpath) appr_files = natsorted(appr_files) if verbose: print("A list of files in the {} directory are: \n".format(directory)) if len(appr_files) < 10: pp.pprint(appr_files) else: pp.pprint(appr_files[:10]) print("\n and more. There are a total of {} files".format(len(appr_files))) if return_type.lower() == "list": return appr_files else: if verbose: print("returning dictionary") appr_file_dict = {} for this_file in appr_files: appr_file_dict[basename(this_file)] = this_file return appr_file_dict def URL_string_filter(text): """ URL_string_filter - filter out nonstandard "text" characters """ custom_printable = ( "0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ._" ) filtered = "".join((filter(lambda i: i in custom_printable, text))) return filtered def getFilename_fromCd(cd): """getFilename_fromCd - get the filename from a given cd str""" if not cd: return None fname = re.findall("filename=(.+)", cd) if len(fname) > 0: output = fname[0] elif cd.find("/"): possible_fname = cd.rsplit("/", 1)[1] output = URL_string_filter(possible_fname) else: output = None return output def get_zip_URL( URLtoget: str, extract_loc: str = None, file_header: str = "dropboxexport_", verbose: bool = False, ): """get_zip_URL - download a zip file from a given URL and extract it to a given location""" r = requests.get(URLtoget, allow_redirects=True) names = getFilename_fromCd(r.headers.get("content-disposition")) fixed_fnames = names.split(";") # split the multiple results this_filename = file_header + URL_string_filter(fixed_fnames[0]) # define paths and save the zip file if extract_loc is None: extract_loc = "dropbox_dl" dl_place = join(os.getcwd(), extract_loc) create_folder(dl_place) save_loc = join(os.getcwd(), this_filename) open(save_loc, "wb").write(r.content) if verbose: print("downloaded file size was {} MB".format(getsize(save_loc) / 1000000)) # unpack the archive shutil.unpack_archive(save_loc, extract_dir=dl_place) if verbose: print("extracted zip file - ", datetime.now()) x = load_dir_files(dl_place, req_extension="", verbose=verbose) # remove original try: os.remove(save_loc) del save_loc except Exception: print("unable to delete original zipfile - check if exists", datetime.now()) print("finished extracting zip - ", datetime.now()) return dl_place def merge_dataframes(data_dir: str, ext=".xlsx", verbose=False): """ merge_dataframes - given a filepath, loads and attempts to merge all files as dataframes Args: data_dir (str): [root directory to search in] ext (str, optional): [anticipate file extension for the dataframes ]. Defaults to '.xlsx'. Returns: pd.DataFrame(): merged dataframe of all files """ src = Path(data_dir) src_str = str(src.resolve()) mrg_df = pd.DataFrame() all_reports = load_dir_files(directory=src_str, req_extension=ext, verbose=verbose) failed = [] for df_path in tqdm(all_reports, total=len(all_reports), desc="joining data..."): try: this_df = pd.read_excel(df_path).convert_dtypes() mrg_df = pd.concat([mrg_df, this_df], axis=0) except Exception: short_p = os.path.basename(df_path) print( f"WARNING - file with extension {ext} and name {short_p} could not be read." ) failed.append(short_p) if len(failed) > 0: print("failed to merge {} files, investigate as needed") if verbose: pp.pprint(mrg_df.info(True)) return mrg_df def download_URL(url: str, file=None, dlpath=None, verbose=False): """ download_URL - download a file from a URL and show progress bar Parameters ---------- url : str, URL to download file : str, optional, default None, name of file to save to. If None, will use the filename from the URL dlpath : str, optional, default None, path to save the file to. If None, will save to the current working directory verbose : bool, optional, default False, print progress bar Returns ------- str - path to the downloaded file """ if file is None: if "?dl=" in url: # is a dropbox link prefile = url.split("/")[-1] filename = str(prefile).split("?dl=")[0] else: filename = url.split("/")[-1] file = clean(filename) if dlpath is None: dlpath = Path.cwd() # save to current working directory else: dlpath = Path(dlpath) # make a path object r = requests.get(url, stream=True, allow_redirects=True) total_size = int(r.headers.get("content-length")) initial_pos = 0 dl_loc = dlpath / file with open(str(dl_loc.resolve()), "wb") as f: with tqdm( total=total_size, unit="B", unit_scale=True, desc=file, initial=initial_pos, ascii=True, ) as pbar: for ch in r.iter_content(chunk_size=1024): if ch: f.write(ch) pbar.update(len(ch)) if verbose: print(f"\ndownloaded {file} to {dlpath}\n") return str(dl_loc.resolve()) def dl_extract_zip( URLtoget: str, extract_loc: str = None, file_header: str = "TEMP_archive_dl_", verbose: bool = False, ): """ dl_extract_zip - generic function to download a zip file and extract it Parameters ---------- URLtoget : str, zip file URL to download extract_loc : str, optional, default None, path to save the zip file to. If None, will save to the current working directory file_header : str, optional, default 'TEMP_archive_dl_', prefix for the zip file name verbose : bool, optional, default False, print progress bar Returns ------- str - path to the downloaded and extracted folder """ extract_loc = Path(extract_loc) extract_loc.mkdir(parents=True, exist_ok=True) save_loc = download_URL( url=URLtoget, file=f"{file_header}.zip", dlpath=None, verbose=verbose ) shutil.unpack_archive(save_loc, extract_dir=extract_loc) if verbose: print("extracted zip file - ", datetime.now()) x = load_dir_files(extract_loc, req_extension="", verbose=verbose) # remove original try: os.remove(save_loc) del save_loc except Exception as e: warnings.warn(message=f"unable to delete original zipfile due to {e}") if verbose: print("finished extracting zip - ", datetime.now()) return extract_loc