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import io
import subprocess
import threading
import logging


def subprocess_with_logger(logger, check=False, *args, **kwargs):
    def log_stream(pipe, logger):
        # by default stderr will be a binary stream
        # we wrap it into a text stream

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Python subprocess.run and argparse nargs (multi-value options) is tricky

If you have a Python program that uses argparse nargs to specify multi-value options then you need to make sure that each value is its own parameter.

import subprocess

subprocess.run(
    [
 'command',

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import os
import itertools
import numpy as np
import keras.utils as utils
from pathlib import Path
from mnist import MNIST


def chunk_gen(size, iterable):
    it = iter(iterable)

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Population Variance/Stddev vs Sample Variance/Stddev

According to: http://vortex.ihrc.fiu.edu/MET4570/members/Lectures/Lect05/m10divideby_nminus1.pdf the reason why we use n - 1 is because that ensures that the average of all sample variances of every combination of sample (with replacement) of a population is equal to the population variance!

import random
import numpy as np

def calculate_variance(population):

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"""
Copyright 2017 Ronald J. Nowling

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software

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"""
This file has an example function, with a documentation string which should
serve as a template for scikit-learn docstrings.
"""

def sklearn_template(X, y, a=1, flag=True, f=None, **kwargs):
    """This is where a short one-line description goes

    This is where a longer, multi-line description goes.  It's not
    required, but might be helpful if more information is needed.

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from lxml import html
import csv, os, json
import requests
from exceptions import ValueError
from time import sleep
 
 
def linkedin_companies_parser(url):
    for i in range(5):
        try:

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import torch
from torch import LongTensor
from torch.nn import Embedding, LSTM
from torch.autograd import Variable
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence

## We want to run LSTM on a batch of 3 character sequences ['long_str', 'tiny', 'medium']
#
#     Step 1: Construct Vocabulary
#     Step 2: Load indexed data (list of instances, where each instance is list of character indices)

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from __future__ import print_function
import sys
import io
import pip
import httplib2
import os
from mimetypes import MimeTypes

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