- Instalaciones:
yarn add --dev jest babel-jest @babel/preset-env @babel/preset-react
yarn add --dev @testing-library/react @types/jest jest-environment-jsdom
- Opcional: Si usamos Fetch API en el proyecto:
Tested on an OpenBSD system using OpenVPN LDAP authentication:
sed -n '/Virtual/,/GLOBAL/{//!p;}' /var/log/openvpn-status.log | awk -F'[,:]' '{print $3}' | while read -r r; do echo -n $r; fgrep $r /var/log/openvpn | fgrep -m 1 username | awk -F\' '{ print " - " $2}' ; done
// Genera los días festivos de colombia dado un año | |
// Basado en los artículos: | |
// https://www.festivos.com.co/calculo y https://elpais.com/elpais/2017/04/12/el_aleph/1492008750_544261.html | |
package main | |
import ( | |
"fmt" | |
"time" | |
) |
// Genera los días festivos de colombia dado un año | |
// Basado en los artículos: | |
// https://www.festivos.com.co/calculo y https://elpais.com/elpais/2017/04/12/el_aleph/1492008750_544261.html | |
package main | |
import ( | |
"fmt" | |
"time" | |
) |
" Specify a directory for plugins | |
call plug#begin('~/.vim/plugged') | |
Plug 'neoclide/coc.nvim', {'branch': 'release'} | |
Plug 'scrooloose/nerdtree' | |
"Plug 'tsony-tsonev/nerdtree-git-plugin' | |
Plug 'Xuyuanp/nerdtree-git-plugin' | |
Plug 'tiagofumo/vim-nerdtree-syntax-highlight' | |
Plug 'ryanoasis/vim-devicons' | |
Plug 'airblade/vim-gitgutter' |
""" | |
PRINT GRAPH WITH LEVEL | |
""" | |
from queue import Queue | |
# Defining vertex class | |
class Vertex(): | |
def __init__(self, value): | |
self.value = value |
var arr = ['Sacha', 'Og', 'Haru']; | |
arr[Symbol.iterator] = function *() { | |
var i = this.length - 1; | |
while (i >= 0) { | |
yield this[i]; | |
i--; | |
} | |
} | |
for (var value of arr) { |
import time | |
print "..." | |
time.sleep(1) | |
print "..." | |
print "..." | |
print "..." | |
print "..." | |
time.sleep(1) | |
print "..." |
""" | |
Train a neural network to implement the discrete Fourier transform | |
""" | |
import matplotlib.pyplot as plt | |
import numpy as np | |
from tensorflow.keras.layers import Dense | |
from tensorflow.keras.models import Sequential | |
N = 32 | |
batch = 10000 |