- 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 |