"""
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 <max_chars> 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