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from PIL import Image
from torchvision import datasets, transforms
import torch
import torch.utils.data as data
import urllib.request
import scipy.io
import os
import imageio
import numpy as np
from os import listdir

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# Based on implementations
# - vae core https://github.com/pytorch/examples/blob/master/vae/main.py
# - miwae https://github.com/yoonholee/pytorch-vae
# - notes on VAE from the article at https://iopscience.iop.org/article/10.3847/PSJ/ab9a52 (but can be taken from elsewhere too)

from __future__ import print_function
import argparse
import torch
import torch.utils.data
from torch import nn, optim

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# https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_gui/py_trackbar/py_trackbar.html#trackbar

import cv2
import numpy as np

class GUIHandler(object):

    def onChangeSend(self,x):

        self.A = cv2.getTrackbarPos('A', self.window_name)

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# v1

import mock
settings = mock.Mock()
settings.foo = 42


# v2
# works even when checking for non-existing entries...

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Safe combination for Windows 10 (crazy, eh?)

GPU: GeForce 1050 Ti

Working:
Cuda 10 -> cuda_10.0.130_411.31_win10
Cudnn for it -> cudnn-10.0-windows10-x64-v7.6.5.32(for CUDA 10.0).zip
Python 3.7.7
Tensorflow-gpu 1.14.0 -> pip3 install --upgrade tensorflow-gpu==1.14.0

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import numpy as np
good_colors = [ #qualitative - http://colorbrewer2.org/#type=qualitative&scheme=Set1&n=6
               (228, 26, 28),  #red
               (55, 126, 184), #blue
               (77, 175, 74),  #lightgreen
               (152, 78, 163), #violet
               (255, 127, 0),  #orange
               (255, 255, 51), #yellow
               (166, 86, 40),  #brown orange
               (247, 129, 191) #pink

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find SOURCEDIR/ -type f | shuf -n $(shuf -i30000-30000 -n1) | tr '\n' '\0' | xargs -0 -n1 cp -t TARGETDIR/

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# add padding to the image to be at 360x360 px
ffmpeg -i image.jpg -qscale 0 -vf "pad=360:360:360:360:color=black" imagePad.jpg

# auto overwrite
ffmpeg -y -i image.jpg -qscale 0 -vf "pad=360:360:360:360:color=black" image.jpg

# for all files in a folder
for file in *.jpg; do ffmpeg -y -i $file -qscale 0 -vf "pad=360:360:360:360:color=black" $file; done

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import librosa
import numpy as np

# Variables setup:
fft_size=2048
window_size=2048 # window size
hop_size=512     # window shift size
sample_rate=44100
lws_L = 5

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# New remote

git remote add newremote https://github.com/user/repo.git
git push newremote master