denisrasulev

# Progress Bar 1

import progressbar as pb
widg = ['Processed: ', pb.Counter(), ' lines (', pb.Timer(), ')']
pbar = pb.ProgressBar(widgets=widg)
for i in pbar(content):
    some code


# Progress Bar 2

denisrasulev

import chardet
import pandas as pd


def find_encoding(fname):
    r_file = open(fname, 'rb').read()
    result = chardet.detect(r_file)
    charenc = result['encoding']
    return charenc

denisrasulev

#!/usr/bin/env python3

# Importing all needed modules
import multiprocessing
import random
import time
import timeit

print("Starting Multiprocessing...")

denisrasulev

import torch
from torch import nn

cl_1 = nn.Conv2d(1, 64, 3)
mp_1 = nn.MaxPool2d(2)

cl_2 = nn.Conv2d(64, 128, 3)
mp_2 = nn.MaxPool2d(2)

cl_3 = nn.Conv2d(128, 512, 3)

denisrasulev

# Convolutional neural network (two convolutional layers)
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.network2D = nn.Sequential(
           nn.Conv2d(1, 32, kernel_size=5, stride=1, padding=2),
           nn.ReLU(),
           nn.MaxPool2d(kernel_size=2, stride=2),
           nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2),
           nn.ReLU(),

denisrasulev

def humanize_bytes(bytes, precision=1):
    """Return a humanized string representation of a number of bytes.
    Assumes `from __future__ import division`.
    >>> humanize_bytes(1)
    '1 byte'
    >>> humanize_bytes(1024)
    '1.0 kB'
    >>> humanize_bytes(1024*123)
    '123.0 kB'
    >>> humanize_bytes(1024*12342)

denisrasulev

from tqdm import tqdm

l = ['this', 'is', 'a', 'list']

for word in tqdm(l):
	do_something(word)