AhmadMoussa

import math
def calculatePadding(L, KSZ, S, D, deconv = False):
    '''

    :param L:       Input length (or width)
    :param KSZ:     Kernel size (or width)
    :param S:       Stride
    :param D:       Dilation Factor
    :return:        Returns padding such that output width is exactly half of input width
    '''

AhmadMoussa

import pandas as pd

file_path = 'path_to_your.csv'
if(file_path[-4:] == '.csv'):
    df = pd.read_csv(file_path, sep=';')
    df.to_excel (file_path[:-4] + '.xlsx', index = None, header=True)
    file_path = file_path[:-4] + '.xlsx'

sheet = pd.read_excel(file_path, sheet_name='Sheet1')

AhmadMoussa

I am attesting that this GitHub handle AhmadMoussa is linked to the Tezos account tz1hXx6tvTV3kzxpBR6E4hDYcxdgcRGHUTFj for tzprofiles

sig:edsigtfS27ix8oe7qtCWtw7X3yPUfFFbcVn9eYXZoaxfv4aMfY6wh1BQaeWVZLsm5UqetiZVhKyJdeQ4x1CAn4XLbfW5jsxty4f

AhmadMoussa

var listOfColors = ["#1c77c3","#39a9db","#40bcd8","#f39237","#d63230","#540d6e","#ee4266","#ffd23f","#f3fcf0","#1f271b"];
["#540d6e","#ee4266","#ffd23f","#f3fcf0","#1f271b"]

class Orbiter{
  constructor(cx,cy){
      this.centerX = cx
      this.centerY = cy
      
      this.positionX = 0
      this.positionY = 0

AhmadMoussa

// Where is the circle
let x, y;
let num_circles = 150;
let circles = [];

class Circle {
  constructor() {
    this.x = random(0, width)
    this.y = random(0, height)
    this.diameter = random(10, 30);

AhmadMoussa

var listOfColors = ["#1c77c3", "#39a9db", "#40bcd8", "#f39237", "#d63230", "#540d6e", "#ee4266", "#ffd23f", "#f3fcf0", "#1f271b"];
["#540d6e", "#ee4266", "#ffd23f", "#f3fcf0", "#1f271b"]

class Orbiter {
  constructor(cx, cy) {
    this.centerX = cx
    this.centerY = cy

    this.positionX = 0
    this.positionY = 0

AhmadMoussa

#ifdef GL_ES
precision mediump float;
#endif

uniform vec2 u_resolution;

float rectshape(vec2 position, vec2 scale){
	scale = vec2(0.5) - scale * 0.5;

	vec2 shaper = vec2(step(scale.x, position.x), step(scale.y, position.y));

AhmadMoussa

import librosa
import threading
import numpy as np
import os

num_splits = 8
data_path = "ProcessedNumpys"
dataset = []

def add_to_arr(i, data_path, filenames, start, end):

AhmadMoussa

import torch
from torch import nn

def convBlock(inc, outc, ksz, conv_or_deconv):
    return nn.Sequential(
        nn.Conv2d(in_channels=inc, out_channels=outc, kernel_size=ksz,
                  stride=2) if conv_or_deconv else nn.ConvTranspose2d(in_channels=inc, out_channels=outc,
                                                                      kernel_size=ksz, stride=2),
        nn.LeakyReLU(),
        nn.BatchNorm2d(num_features=outc)

AhmadMoussa

# ffmpeg -i filenameee.m4a -acodec libmp3lame -ab 256k output.mp3